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Economic Evaluation|Articles in Press
Performance of the UK Prospective Diabetes Study Outcomes Model 2 in a Contemporary UK Type 2 Diabetes Trial Cohort
Mi Jun Keng, MSc
Mi Jun Keng
Correspondence: Mi Jun Keng, MSc, Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Richard Doll Bldg, Old Road Campus, Oxford, United Kingdom OX3 7LF.
Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, England, UK
British Heart Foundation Centre of Research Excellence, Oxford, England, UK
Jose Leal, DPhil
Jose Leal
Marion Mafham, MD
Marion Mafham
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, England, UK
Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, England, UK
Louise Bowman, BA, MBBS, MD
Louise Bowman
Jane Armitage, MBBS
Borislava Mihaylova, DPhil
Borislava Mihaylova
Wolfson Institute of Population Health, Queen Mary University of London, London, England, UK
Open AccessPublished:October 20, 2021DOI:https://doi.org/10.1016/j.jval.2021.09.005
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There is uncertainty over the accuracy of predictions by the UK Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2), developed using patient data collected from 1977 to 2007, in contemporary diabetes cohort.
This study demonstrates that the UKPDS-OM2 overpredicts the risks of myocardial infarction, stroke, heart failure, and deaths over 10 years of follow up in A Study of Cardiovascular Events in Diabetes (ASCEND), one of the largest trials in people with diabetes in the United Kingdom that followed participants from 2005 to 2017. In particular, the performance of the UKPDS-OM2 was found to be poorer in older patients who received a diagnosis of diabetes at an older age.
Calibration of risk equations in the UKPDS-OM2 or estimation of new risk equations is needed to predict long-term outcomes for clinical or economic analyses in contemporary cohorts such as in ASCEND.
The UK Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS-OM) developed using 30-year (1977-2007) data from the UKPDS is widely used for health outcomes' projections and economic evaluations of therapies for patients with type 2 diabetes (T2D). Nevertheless, its reliability for contemporary UK T2D populations is unclear. We assessed the performance of version 2 of the model (UKPDS-OM2) using data from A Study of Cardiovascular Events in Diabetes (ASCEND), which followed participants with diabetes in the UK between 2005 and 2017.
The UKPDS-OM2 was used to predict the occurrence of myocardial infarction (MI), other ischemic heart disease, stroke, cardiovascular (CV) death, and other death among the 14 569 participants with T2D in ASCEND, all without previous CV disease at study entry. Calibration (comparison of predicted and observed year-on-year cumulative incidence over 10 years) and discrimination (c-statistics) of the model were assessed for each endpoint. The percentage error in event rates at year 7 (mean duration of follow up) was used to quantify model bias.
The UKPDS-OM2 substantially overpredicted MI, stroke, CV death, and other death over the 10-year follow-up period (by 149%, 42%, 269%, and 52%, respectively, at year 7). Discrimination of the model for MI and other ischemic heart disease (c-statistics 0.58 and 0.60, respectively) was poorer than that for other outcomes (c-statistics ranging from 0.66 to 0.72).
The UKPDS-OM2 substantially overpredicted risks of key CV outcomes and death in people with T2D in ASCEND. Appropriate adjustments or a new model may be required for assessments of long-term effects of treatments in contemporary T2D cohorts.
UK Prospective Diabetes Study outcomes model
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The UK Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS-OM) is a stochastic patient-level simulation model that uses the risk factor profiles of patients with T2D to predict the occurrence of diabetes-related macrovascular and microvascular complications over patients' lifetimes and at the same time to quantify their (quality-adjusted) life-years and healthcare costs.
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Version 1 of the model was developed using data collected in the UKPDS, which randomized participants between 1977 and 1991, and followed them until 1997 (median follow-up 10.3 years).
Temporal validation of the model using data collected during the 10-year post-trial monitoring period found that the model overpredicted the 10-year risks of heart failure and mortality and underpredicted the 10-year risks of stroke and amputation.
Temporal validation of the UKPDS outcomes model using 10-year posttrial monitoring data.
Risk equations in the original model were subsequently updated including also the post-trial follow-up data (overall median follow-up 17.6 years) and version 2 of the model (UKPDS-OM2) was released.
Internal validation of the UKPDS-OM2 demonstrated good agreement between simulated and observed outcomes among UKPDS patients over a period of 25 years. External validations of the UKPDS-OM2 have been performed in several more recent North American cohorts,
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All studies report overprediction of mortality. This is consistent with observations in the report from the latest Mount Hood Diabetes Challenge, a forum for diabetes modelers to compare model performance, which found the UKPDS-OM2 and other diabetes model based on risk equations of the UKPDS-OM2 to overestimate cardiovascular (CV) mortality, although the results were generated using simulated patient cohorts instead of individual patient-level data.
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Overprediction of myocardial infarction (MI) was also observed in the North American, Italian, and Dutch cohorts, but not in the German cohort. The performance of the model can vary among countries because of differences in healthcare systems and health of the population in general (reflected by differences in life expectancy, mortality rates, and rates of T2D-related complications).
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Findings from a previous study using The Health Improvement Network database in the United Kingdom suggest that the UKPDS-OM2 overpredicts MI, but no direct comparison between predicted and observed risk was presented and no results for mortality were reported.
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It remains unclear whether the UKPDS-OM2 can predict well in a contemporary UK cohort. Hence, we took the opportunity of the large long-term randomized trial A Study of Cardiovascular Events in Diabetes (ASCEND),
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which included 15 480 participants with diabetes recruited in the United Kingdom followed for an average of 7.4 years to assess the performance of the UKPDS-OM2.
Validation Cohort
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Briefly, ASCEND was a 2 × 2 factorial design trial that randomized 15 480 participants with established diabetes mellitus (both type 1 and type 2) but without diagnosed CV disease (CVD) to 100 mg aspirin daily or matching placebo and, separately, to 1 g capsule containing omega-3 fatty acids daily or placebo. Participants were recruited between 2005 and 2011 and followed for an average of 7.4 years until 2017. Among ASCEND participants, only those with T2D formed the validation cohort because UKPDS-OM2 was developed for predicting outcomes in patients with T2D.
UKPDS Outcomes Model Version 2
The UKPDS-OM2 is a patient-level simulation that takes patient demographics (eg, age, sex, ethnicity), diabetes duration, history of T2D-related complications, and risk factor progression over time (all inputs required listed in Appendix Table 1 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005) to predict the occurrence of macrovascular events (MI, other ischemic heart disease [IHD], heart failure, and stroke), microvascular events (lower limb amputation, blindness, end-stage renal failure, and lower extremity ulcer), and deaths (all-cause death and CV death [defined as death from MI, other IHD, heart failure, or stroke]) (definition of each endpoint in Appendix Table 2 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). The UKPDS-OM2 is an improved update from the UKPDS-OM1,
with risk equations re-estimated using an additional 38 000 patient-years of observational data and including several additional outcomes.
Low-density lipoprotein cholesterol, hemoglobin, and white blood cell count measurements were not available in ASCEND. External data available to the authors were used to establish the relationships among these characteristics and other patient characteristics at baseline, which were used to impute these missing values.
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Additionally, the UKPDS-OM2 requires the specification of risk factor progression over time. Nevertheless, biomarkers were collected only at baseline for most participants in ASCEND. Hence, the risk factor progression equations in the UKPDS-OM2
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were used to project risk factors in subsequent years.
The key endpoints included were deaths (CV death as defined in UKPDS-OM2 and other death), MI, other IHD (includes angina, death from coronary heart disease not because of MI and coronary revascularization) and stroke, because these endpoints were rigorously sought, recorded, and adjudicated in ASCEND. Heart failure and amputation were patient reported and not adjudicated, so these were presented for exploratory purposes. Blindness, end-stage renal failure, and lower extremity ulcer were excluded in the validation because these outcomes were not routinely sought or recorded in ASCEND. A total of 99% of participants had complete follow-up information, with only 1% of participants lost to follow up and censored. For outcomes included in the analysis, we matched the endpoints as closely as possible to the respective endpoint definitions in the UKPDS-OM2 (see Appendix Table 2 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005).
The UKPDS-OM2 was used to estimate the annual predicted probability of a first occurrence of each endpoint for each patient with T2D in the ASCEND cohort. A total of 10 000 Monte-Carlo replications were used for each patient to minimize stochastic uncertainty. The predicted cumulative incidence of each endpoint from the model was compared with the observed cumulative incidence of the corresponding endpoint in ASCEND accounting for competing risk (from deaths for nonfatal events and from deaths from other causes for cause-specific deaths) and controlling for and excluding effects of allocation to aspirin and omega-3 fatty acids treatments.
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Performance of the UKPDS-OM2 was assessed based on its calibration (ie, agreement between predicted and observed cumulative incidence) and discrimination (ie, the ability of the model to differentiate individuals who experience events from those who do not).
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Calibration was assessed graphically for each endpoint by comparing the year-on-year (up to 10 years) cumulative incidence observed in ASCEND with that predicted by the UKPDS-OM2. The percentage error and the absolute difference between the predicted and observed 7-year cumulative incidence were used to quantify and compare the magnitude of discordance for each endpoint. The mean absolute percentage error (MAPE) over 7 years was used to compare the overall accuracy across endpoints. Calibration was also assessed for each endpoint by dividing the cohort into risk deciles based on 7-year cumulative incidence predicted by the UKPDS-OM2 and plotting the observed 7-year cumulative incidence against that predicted for each decile. We further assessed model calibration in subgroups (sex, age, duration of diabetes, glycated hemoglobin [HbA1c], and body mass index [BMI] at baseline) for mortality outcomes. Uno's c-statistic estimated at 7 years of follow up was used to assess model's discrimination of risk for different endpoints.
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To assess the impact of using the risk factor progression equations in the UKPDS-OM2 to impute missing risk factor trajectories over time, we considered the alternative scenario whereby all risk factors were held constant at their baseline value over years in the model. That is, if a patient had baseline HbA1c of 6.0%, their HbA1c was assumed to be 6.0% for the entire duration of simulation. Additionally, we excluded participants with missing baseline information (for variables collected in ASCEND) in the ASCEND validation cohort to assess if the results are robust to imputation of baseline information. Finally, to assess the impact of aspirin and omega-3 fatty acid treatment adjustment on the endpoints, we excluded participants who were assigned to receive either one or both treatments in ASCEND and present a comparison of predicted and observed cumulative incidence of each endpoint only among participants allocated to placebo aspirin and placebo omega-3 fatty acid.
All analyses were performed in R 3.5.1.
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The reporting of this validation study is in accordance with the TRIPOD checklist
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(Appendix Section 2 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005).
Of 15 480 participants in ASCEND, all participants with T2D (N = 14 569 [94%]) formed the validation cohort (Table 1). Participants in this cohort were mostly white (98%). At baseline, they were on average 64 years old, with a median duration of diabetes of 6 years and no previous CVD. They were generally well managed with average HbA1c 7.1% (54 mmol/mol), total cholesterol 4.1 mmol/liter, and systolic blood pressure 136 mm Hg. A summary of the baseline characteristics, after imputing missing data required by the UKPDS-OM2, can be found in Appendix Table 3 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005. A comparison of baseline characteristics of participants with T2D in ASCEND and in the UKPDS can be found in Appendix Table 4 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005.
Table 1Baseline characteristics of the participants with type 2 diabetes in ASCEND.
Type 2 diabetes cohort in ASCEND
Missing values are excluded from tabulation; percentages are calculated excluding participants with missing information for each variable.
(N = 14 569), n (%)
Missing data, n (%)
Male 9166 (63) 0 (0)
Age (years) 63.8 (8.9) 0 (0)
<60 4890 (34)
60 to <70 6078 (42)
≥70 3601 (25)
Ethnicity 43 (<1)
White 14 037 (96)
South Asian 138 (1)
Afro-Caribbean 181 (1)
Other 170 (1)
Diabetes duration (years) 6 (3-11) 856 (6)
Current smoker 1195 (8) 162 (1)
HbA1c (%) 7.1 (1.2) 5303 (36)
HbA1c (mmol/mol) 54 (13.1) 5303 (36)
Systolic blood pressure (mm Hg) 136.4 (15.2) 4141 (28)
Diastolic blood pressure (mm Hg) 77.3 (9.4) 4148 (28)
BMI (kg/m2) 31.4 (6.5) 109 (1)
eGFR (mL/min/1.73 m2) 84.5 (20.9) 5304 (36)
Total cholesterol (mmol/liter) 4.1 (0.9) 5300 (36)
HDL cholesterol (mmol/liter) 1.2 (0.3) 5316 (36)
Urinary albumin ≥50 mg/liter 681 (7) 5328 (37)
Diabetes management 0 (0%)
Diet only 2529 (17)
Any hypoglycemic agent but not insulin 9020 (62)
Insulin ± other hypoglycemic agent 3020 (21)
Self-reported hypertension 9141 (63) 105 (1)
Self-reported diabetic retinopathy 2523 (17) 140 (1)
Use of cardiovascular treatments 0
Concomitant medication use was self-reported.
ACE inhibitor or ARB 8634 (59)
Beta-blocker 1989 (14)
Calcium channel blocker 3659 (25)
Thiazide or related diuretic 2866 (20)
Statin 11 078 (76)
NSAID 1280 (9)
Aspirin (treatment allocation in study) 7282 (50)
Note. Values are mean (SD) or median (interquartile range) for continuous factors and n (%) for categorical factors.
ACE indicates angiotensin converting enzyme; ARB, angiotensin II receptor blocker; ASCEND, A Study of Cardiovascular Events in Diabetes; BMI, body mass index; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; HDL, high density lipoprotein; NSAID, nonsteroidal anti-inflammatory drug.
∗ Missing values are excluded from tabulation; percentages are calculated excluding participants with missing information for each variable.
† Concomitant medication use was self-reported.
At 7 years of follow up, cumulative incidence of all-cause death predicted by UKPDS-OM2 was overestimated by 98% (absolute difference 9.3%), which is due to the model overpredicting both CV death by 269% (absolute difference 5.5%) and deaths from other causes by 52% (absolute difference 3.9%) (Fig. 1 and Table 2). The cumulative incidences of MI and stroke were also overpredicted by 149% (absolute difference 4.1%) and 42% (absolute difference 1.2%), respectively. By contrast, the observed cumulative incidence of other IHD was similar to that predicted by the UKPDS-OM2. This was driven by the much higher rate of coronary revascularizations in ASCEND (accounting for 40% of events contributing to the other IHD endpoint) than the UKPDS study period. Excluding coronary revascularization from the other IHD endpoint, we see similar overprediction as for MI (see Appendix Fig. 1 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). For exploratory purposes, we further looked at calibration of the model for heart failure and amputation (see Appendix Table 5 and Appendix Fig. 2 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). Heart failure was overpredicted by 197% (absolute difference, 2.4%). Amputation was well predicted for the first 7 years. Nevertheless, in contrast to the gradual decrease in the rate of amputation observed in ASCEND, the amputation rate was predicted to steadily increase, resulting in the divergence between observed and predicted cumulative incidence in later years. After stratifying by decile of predicted 7-year cumulative incidence, greater deviations were noted between the observed and predicted risks for participants at higher predicted risks for all endpoints, except for other IHD and amputation (see Appendix Fig. 3 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). Overall, over 7 years of follow up, the MAPE of the UKPDS-OM2 was worst for CV death (302%) and heart failure (283%).
Figure 1Comparison of cumulative incidence of complications predicted by the UKPDS-OM2 over 10 years with that observed in ASCEND. CV death is defined as death from MI, other IHD, heart failure, or stroke (as in UKPDS-OM2). 1UKPDS-OM2 risk factor progression equations used to project risk factor values during follow up (base case). 2Values of risk factors during follow up fixed to baseline values (sensitivity analysis).
CI indicates confidence interval; CV, cardiovascular; IHD, ischemic heart disease; MI, myocardial infarction; UKPDS-OM2, UK Prospective Diabetes Study Outcomes Model 2.
Table 2Comparison of cumulative incidence of complications predicted by the UKPDS-OM2 with observed rates at 7 years of follow up for participants in ASCEND.
Observed cumulative incidence, % (95% CI)
UKPDS-OM2 risk factor progression equations used to project risk factor values during follow-up (base case).
Values of risk factors during follow-up fixed to baseline values (sensitivity analysis).
Cumulative incidence, %
% error
MAPE, %
c-statistic
All-cause death 9.4 (8.5-10.3) 18.7 98 158 0.72 17.0 79 146 0.73
CV death 2.0 (1.6-2.5) 7.5 269 302 0.70 5.6 178 246 0.71
Other death 7.4 (6.6-8.2) 11.3 52 115 0.72 11.3 53 116 0.73
MI 2.8 (2.3-3.3) 6.9 149 151 0.58 5.5 100 122 0.58
Other IHD 5.8 (5.1-6.5) 5.1 −12 13 0.60 4.2 −28 23 0.60
Stroke 2.8 (2.3-3.3) 4.0 42 47 0.66 3.1 12 29 0.66
Note. % error defined as percentage error; CV death is defined as death from MI, other IHD, heart failure, or stroke (as in UKPDS-OM2). % error is the degree of overprediction as percentage of observed (negative values represent underprediction) calculated using the cumulative incidences at year 7. MAPE is the absolute percentage error averaged across the 7 years of follow-up.
ASCEND indicates A Study of Cardiovascular Events in Diabetes; CI, confidence interval; CV, cardiovascular; IHD, ischemic heart disease; MAPE, mean absolute percentage error; MI, myocardial infarction; UKPDS-OM2, UK Prospective Diabetes Study Outcomes Model 2.
∗ UKPDS-OM2 risk factor progression equations used to project risk factor values during follow-up (base case).
† Values of risk factors during follow-up fixed to baseline values (sensitivity analysis).
Discrimination of the model for MI and other IHD (c-statistics of 0.58 and 0.60, respectively) was poorer than that for other endpoints (c-statistics ranging from 0.66 to 0.75) (Table 2 and see Appendix Table 5 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). This is also reflected in the plot comparing predicted with observed 7-year cumulative incidence stratified by predicted risk (see Appendix Fig. 3 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005), where although clear gradients of increasing risks were observed across deciles of predicted risk for other endpoints, these gradients were much less distinct for MI and other IHD across the risk deciles.
Overall, the model overpredicted mortality across all risk factor subgroups explored (sex, age, HbA1c, duration of diabetes, BMI) (see Appendix Figs. 4-8 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). Lower levels of bias were observed in the following subgroups (see Appendix Table 6 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005): for CV deaths, participants below 60 years old (MAPE 99% compared with 350% for participants between 60 and 70 years old and 403% for participants at least 70 years old) and participants with a duration of diabetes of at least 5 years (MAPE 256% for participants with >10 years of diabetes, 197% for participants with 5-10 years of diabetes, and 658% for participants with <5 years of diabetes), and for death from other causes, participants with HbA1c of at least 7.5% (MAPE 97% compared with 192% for participants with HbA1c <7.5%) and participants with BMI of at least 35 kg/m2 (MAPE 24%, compared with at least 100% for participants with BMI <35 kg/m2).
In the sensitivity analyses in which all risk factors were held constant at their baseline values, the predicted cumulative incidences were smaller than in the main analyses, with somewhat smaller discrepancies than the observed cumulative incidences. Nevertheless, the model still substantially overpredicted death, MI, and heart failure rates (percentage error at year 7, 79%, 100%, and 134%, respectively). Amputation is well predicted over 10 years.
In the subgroup of participants with complete baseline information (n = 7578), with the exception of heart failure, the percentage error at year 7 and the MAPE across 7 years are both higher in this subgroup of participants across all endpoints, particularly for CV death and MI (see Appendix Table 7 and Appendix Fig. 9 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). Discrimination and absolute differences between the predicted and observed cumulative incidences across all endpoints were similar to results in the main analyses with missing data imputed. In the subgroup of participants with allocated placebo aspirin and placebo omega-3 fatty acid (n = 3651), the performance of the UKPDS-OM2 is similar to the main analyses with adjustments for effects of allocation to aspirin and omega-3 fatty acids treatments (see Appendix Table 8 and Appendix Fig. 10 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005).
The UKPDS-OM2 substantially overpredicted the risks of MI, heart failure, and death (particularly CV death) over 10 years of follow up in ASCEND. Stroke was also overpredicted, although to a lesser extent. In terms of discrimination, the model was positively predictive for all endpoints, but discriminated risks of MI and other IHD less well than other endpoints.
Despite MI being overpredicted, other IHD (which includes coronary revascularizations) appears well predicted. Nevertheless, the number of coronary revascularizations performed each year in the United Kingdom has increased over the last decades
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with percutaneous coronary interventions increasing 3-fold between 2000 and 2010. Patients are now more likely to receive preventive coronary revascularization possibly contributing to the decline in the risk of MI and the apparent increase in the risk of other IHD (which includes coronary revascularization). Excluding coronary revascularization from the other IHD endpoint leads to a similar pattern of overprediction (see Appendix Fig. 1 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). Furthermore, the observed cumulative incidence of a combined endpoint of MI or coronary revascularization (assuming that coronary revascularizations prevented some MIs) was also lower than the cumulative incidence of MI predicted by the UKPDS-OM2. These exploratory analyses suggest that the UKPDS-OM2 is overpredicting the risk of IHD in general and that other IHD seems well predicted partly because of the higher rate of coronary revascularizations in ASCEND.
The UKPDS is a significantly older trial in which participants were followed between 1977 and 2007. Compared with UKPDS participants, ASCEND participants had better managed lipids (75% of ASCEND participants took a statin vs 0.3% on lipid-lowering drugs in the UKPDS), blood pressure (67% of ASCEND participants on antihypertensive drugs vs 12% in the UKPDS), and blood glucose (similar HbA1c levels despite ASCEND participants having an average of 6 years of diabetes vs newly diagnosed in UKPDS); higher use of coronary revascularization; and reduced likelihood of smoking (8% in ASCEND vs 31% in UKPDS) (see Appendix Table 4 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005).
UK Prospective Diabetes Study (UKPDS) Group
Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) [published correction appears in Lancet. 1999;354(9178):602].
This may reflect earlier diagnosis of diabetes in ASCEND participants compared with participants in UKPDS and improved risk factor control in recent years. Given that the overprediction for death and MI is consistently observed in other validation studies,
this suggest that perhaps improvements in diabetes and CVD diagnosis and treatment and dietary and lifestyle changes have an impact on CV risks beyond the impact from risk factors accounted for by the UKPDS-OM2. Previous studies have also observed a decline in age-adjusted CVD incidence and case-fatality rates in the last 2 decades,
Read S.H.
Fischbacher C.M.
Colhoun H.M.
Trends in incidence and case fatality of acute myocardial infarction, angina and coronary revascularisation in people with and without type 2 diabetes in Scotland between 2006 and 2015.
Diabetologia. 2019; 62: 418-425
Gregg E.W.
Sattar N.
Ali M.K.
The changing face of diabetes complications.
Smolina K.
Wright F.L.
Rayner M.
Goldacre M.J.
Determinants of the decline in mortality from acute myocardial infarction in England between 2002 and 2010: linked national database study [published correction appears in BMJ. 2013;347:f7379].
BMJ. 2012; 344: d8059
Conrad N.
Judge A.
Tran J.
Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals.
over and above the effects explained by improvement in risk factors.
Unal B.
Critchley J.A.
Capewell S.
Explaining the decline in coronary heart disease mortality in England and Wales between 1981 and 2000.
Circulation. 2004; 109: 1101-1107
Hardoon S.L.
Whincup P.H.
Lennon L.T.
Wannamethee S.G.
Morris R.W.
How much of the recent decline in the incidence of myocardial infarction in British men can be explained by changes in cardiovascular risk factors? Evidence from a prospective population-based study.
Circulation. 2008; 117: 598-604
Ford E.S.
Proportion of the decline in cardiovascular mortality disease due to prevention versus treatment: public health versus clinical care.
Annu Rev Public Health. 2011; 32: 5-22
Hotchkiss J.W.
Davies C.A.
Dundas R.
Explaining trends in Scottish coronary heart disease mortality between 2000 and 2010 using IMPACTSEC model: retrospective analysis using routine data.
BMJ. 2014; 348: g1088
Therefore, the UKPDS-OM2 requires calibration or de novo estimation of at least mortality and CV event risks using contemporary patient data.
Additionally, the performance of the UKPDS-OM2 was found to be poorer in patients who were older or had a shorter duration of diabetes at baseline. In particular, poorer performance in older patients was also observed in a Dutch and an Italian cohort from an earlier patient-level validation study.
In the UKPDS, participants received a diagnosis of diabetes at a mean age of 53 years.
In contrast, patients in ASCEND (mean age 64; mean duration of diabetes 6), the Dutch cohort (mean age 65, mean duration of diabetes 7), and the Italian cohort (mean age 68; mean duration of diabetes 11) were diagnosed on average approximately 5 years later. Diagnosis of diabetes at an older age compared with a younger age has been found to be associated with a slower rate of glycemic deterioration
Donnelly L.A.
Zhou K.
Doney A.S.F.
Jennison C.
Franks P.W.
Pearson E.R.
Rates of glycaemic deterioration in a real-world population with type 2 diabetes.
and lower CV and mortality risks.
Rawshani A.
Franzén S.
Age at diagnosis of type 2 diabetes mellitus and associations with cardiovascular and mortality risks.
The UKPDS data may not generalize well to older patients who received a diagnosis of diabetes at an older age, which may explain the poorer performance of the model in this patient category. Hence, new equations based on more contemporary cohorts would be helpful in better capturing disease risks in these patient categories.
The 2 major strengths of the present analysis are the large sample size of ASCEND and its high-quality individual patient data, with complete follow-up data for 99% of participants and adjudicated endpoints (for MI, other IHD, stroke, and deaths). The model validation was also performed on a UK patient cohort, which eliminates the uncertainty because of geographic and ethnic differences present in previous external validations of the UKPDS-OM2 that were performed in North American or other European cohorts.
Limitations of the analyses should also be acknowledged. First, data on some baseline risk factors such as low-density lipoprotein cholesterol, hemoglobin, and white blood cell count were not available in ASCEND and were imputed based on their relationships with other markers (eg, total cholesterol) derived in external data sets. This assumes the generalizability of such relationships to participants in the ASCEND cohort. For other risk factors with missing data, the missing at random assumption was made in imputing these values. In the subset of participants with no missing baseline data (for variables collected in ASCEND), similar magnitudes of overprediction and discrimination were found. MAPE of the UKPDS-OM2 was generally higher suggesting that the bias reported may be an underestimation, but our conclusion is still robust. Second, follow-up blood and urine samples were only collected in a random sample of about 1200 ASCEND participants, so risk factors were projected over time using the risk factor progression equations in the UKPDS-OM2. Exploratory tabulations (see Appendix Fig. 11 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005) suggest that the rate of deterioration of these cardiometabolic markers was slightly overpredicted at 2.5 years into the follow up. We do not have sufficient data to reliably assess the validity of these equations for the ASCEND participants. Nevertheless, the use of these equations to project missing risk factor progression over time reflects how other users are likely to use the model in absence of risk factor progression data. Moreover, we present results with risk factors held constant at their baseline values to provide a benchmark for the range within which the actual predictions could lie. Third, although adverse events in ASCEND were chosen to match the definitions of endpoints in the UKPDS-OM2, we acknowledge that differences may exist. Nevertheless, these are unlikely to materially affect the degree of discrepancy in event rates reported here. Additionally, most microvascular endpoints in the UKPDS-OM2 (blindness, end-stage renal failure, and lower extremity ulcer) were excluded from the analysis because they were not routinely sought or recorded in ASCEND and incidence of occurrence of these endpoints could not be reliably estimated. Fourth, the inclusion/exclusion criteria in ASCEND may restrict the generalizability of results to other T2D cohorts. Participants in ASCEND had no previous CVD (exclusion criteria in ASCEND) despite a median duration of diabetes of 6 years,
UK prospective diabetes study (UKPDS). VIII. Study design, progress and performance.
in contrast to the 6% and 4% of participants with MI and other IHD, respectively, 6 years after diabetes diagnosis in the UKPDS.
There are also further exclusion criteria in ASCEND (eg, patients with liver disease and blindness excluded) that make the ASCEND cohort more selective. Additionally, participants in ASCEND were predominantly white. Nevertheless, many of these factors are covariates in the UKPDS-OM2 risk equations, and it is expected that the model is able to account for these differences. There may be other systematic differences between the ASCEND and the UKPDS cohorts that we do not observe and are unaccounted for, but these differences are unlikely to account for the degree of discrepancy in event rates reported here. Nevertheless, caution needs to be taken when generalizing these results to other T2D cohorts.
Previous validation studies have demonstrated that, despite overestimating absolute risks, models can predict the relative risks of treatment well.
Palmer A.J.
Clarke P.
Mount Hood 5 Modeling Group
Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting.
Although estimation of relative effects is important, accurate estimates of absolute effects are necessary if the aim is to evaluate the magnitude of impact of the treatment on number of adverse events, costs, and (quality-adjusted) life-years. Overestimating disease risks will directly result in overestimating health benefit, which is likely to affect cost-effectiveness.
Raikou M.
McGuire A.
Cost-effectiveness of primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes: results from the Collaborative Atorvastatin Diabetes Study (CARDS).
Reifsnider O.
Kansal A.
Pimple P.
Aponte-Ribero V.
Brand S.
Shetty S.
Cost-effectiveness analysis of empagliflozin versus sitagliptin as second-line therapy for treatment in patients with type 2 diabetes in the United States.
Diabetes Obes Metab. 2021; 23: 791-799
As an illustration, for the ASCEND T2D cohort included in this study, a 15% risk reduction in CV death results in 0.058 increase in quality-adjusted life-years (QALYs) based on the 7-year CV death risk observed in ASCEND, in contrast to 0.218 QALYs based on the overestimated 7-year CV death risk predicted by the UKPDS-OM2 (see Appendix Table 9 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005). The difference in incremental lifetime cost is less pronounced (£6137 vs £5579), which overall translates into substantial difference in the incremental cost-effectiveness ratio (£105 540 per QALY vs £25 585 per QALY). Furthermore, the magnitude of bias in these incremental impacts can vary across different risk groups, as illustrated by the separate comparisons among participants in the lowest and highest CV death risk deciles (see Appendix Table 9 and Appendix Section 3 in Supplemental Materials found at https://doi.org/10.1016/j.jval.2021.09.005).
The results from our analyses, together with findings from previous external validations of the UKPDS-OM2,
indicate that the UKPDS-OM2 may not provide reliable estimates of absolute event rates for contemporary T2D cohorts such as ASCEND. The model would need to be recalibrated or new risk equations need to be estimated to predict long-term outcomes in contemporary T2D cohorts.
To confidently use a prediction model, its ability to reliably predict outcomes in cohorts of patients beyond those used in model development needs to be confirmed. In this study, the UKPDS-OM2 substantially overpredicted risks of CV death, other death, and MI in a large cohort of patients with T2D. Appropriate adjustments would be needed when using the model for clinical or economic analyses in contemporary cohorts such as ASCEND particularly when absolute risk or total cost and (quality-adjusted) life expectancy is of interest, or perhaps new models for predicting long-term outcomes in T2D may be required.
Author Contributions: Concept and design: Keng, Leal, Mihaylova
Acquisition of data: Mafham, Bowman, Armitage
Analysis and interpretation of data: Keng, Leal, Mihaylova
Drafting of the manuscript: Keng
Critical revision of the paper for important intellectual content: Keng, Leal, Mafham, Armitage, Mihaylova
Statistical analysis: Keng, Leal, Mihaylova
Provision of study materials or patients: Mafham, Bowman, Armitage
Obtaining funding: Bowman, Armitage, Mihaylova
Supervision: Leal, Bowman, Armitage, Mihaylova
Conflict of Interest Disclosures: Ms Keng reported receiving a DPhil stipend from the British Heart Foundation Centre of Research Excellence, Oxford, England, United Kingdom (grant code: RE/13/1/30181). Dr Leal reported a research grant paid to his institution from Innovative Medicines Initiative 2 Joint Undertaking under grant agreement number 115 881 (RHAPSODY). Dr Mafham reported her institution receiving research grants from the British Heart Foundation; reported core departmental funding from the Medical Research Council and Cancer Research UK paid to her institution; reported that Bayer Pharma AG provided the aspirin and placebo and funding for drug packaging to her institution and Solvay, Abbott, and Mylan provided the omega-3 and placebo and funding for drug packaging to her institution; reported a grant paid to her institution from The Medicines Company/Novartis; and reported being a board member (unpaid) of NHS DigiTrials, a member (unpaid) of the steering committees for ORION-4 trial, ASCEND trial, RECOVERY trial, and a member (unpaid) of the funding committee for Kidney Research UK. Dr Bowman reported receiving grants paid to her institution from the British Heart Foundation, Medical Research Council, Cancer Research UK, Novartis, and the National Institute for Health Research Oxford Biomedical Research Centre; reported Bayer Pharma AG provided the study aspirin and placebo and funding for drug packaging; and reported that Solvay, Abbott, and Mylan provided the study omega-3 and placebo and funding for drug packaging. Dr Armitage reported grants paid to Oxford University for the trial from British Heart Foundation; reported core grants paid to Oxford University from the Medical Research Council and Cancer Research UK; reported grants, aspirin, and placebo tablets from Bayer; reported grants, fish oil capsules, and placebo from Mylan, Abbott, and Solvay; reported grants paid to Oxford University for the trial from The Medicines Company and Novartis; reported a grant paid to Oxford University from the National Institute for Health Research Health Technology Assessment; reported reimbursement for travel costs from Bayer; and reported being a participant (unpaid) on a data safety monitoring board or advisory board. Dr Mihaylova reported grants paid to the University of Oxford from the National Institute for Health Research Oxford Biomedical Research Centre and is an editor of Value in Health and had no role in the peer review process of this article.
Funding/Support: This study was supported by a grant from British Heart Foundation Centre of Research Excellence , Oxford, England, United Kingdom (grant code: RE/13/1/30181 ).
Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
ASCEND was supported by grants to the University of Oxford from the British Heart Foundation and by Bayer (Germany and the United States), Solvay, Abbott, and Mylan. The Clinical Trial Service Unit at the University of Oxford receives support from the UK Medical Research Council (which funds the MRC Population Health Research Unit in a strategic partnership with the University of Oxford), the British Heart Foundation, and Cancer Research UK. We thank all ASCEND participants, the collaborating doctors and general practitioners, the members of the steering committee and the data monitoring committee, and all the administrative and clinical staff who helped to conduct the trial.
Download .docx (3.61 MB)
(Accessed May 18, 2021)
Published online: October 20, 2021
Accepted: September 6, 2021
Publication stage
In Press Corrected Proof
© 2021 ISPOR-The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc.


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The photo was shot using a Apple iPhone 4S camera at an aperture of f/2.4, 1/1175 sec. shutter speed and ISO 50. Flash did not fire, compulsory flash mode. The original image file has a resolution of 3264 x 2448 pixels, or in other words 8.0 megapixels.The photo has a resolution of 72 DPI and occupies a disk space of 3.40 megabytes.
According to the image metadata, the photo was shot on Sunday 1st of September 2013. The local time was 11:53:18. The timezone was Europe / Amsterdam, which is GMT +02:00. Please note that timezone was guessed using the GPS coordinates and may not be accurate. The EXIF timestamp may also be wrong if the date and time weren't set correctly in the digital camera.
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Michigan governor Rick Snyder has proposed a change in Michigan's auto insurance benefits that would change the current unlimited benefit to one having a cap of $1,000,000 for an individual. Today's system requires a $175 per vehicle annual fee be paid to the Michigan Catastrophic Claims Association (MCCA) to pay for those most seriously injured in automobile accidents.
Today's Detroit News story on Governor Snyder's proposal explains the trade-offs in the proposal. Rates are expected to fall $125 per vehicle – for the first year only – as part of the plan. After that, rates would again be subject to market pricing by the insurance companies. The governor's plan also proposes a fee schedule for benefits similar to that of Worker's Compensation. At the current time, there is no fee scheduled for auto accident related benefits, so services performed can cost 2-3 times more if done in relation to an auto claim.
So what happens under the new plan if you are seriously injured and $1,000,000 is not enough to cover the costs? The answer from this plan is that you would be covered by Medicaid. Opponents of the plan say that this is not a good approach as you would be essentially causing people to go bankrupt so that they could obtain the ongoing Medicaid benefits.
So for those of you with 2 cars, is it worth $250 per year cash in your pocket for these benefit changes? Or are you more comfortable with the current system, even though Michigan's rates are the highest in the country?


text:
package com.amazonaws.auth;
import static com.amazonaws.util.StringUtils.UTF8;

import java.io.IOException;
import java.io.InputStream;
import java.security.InvalidKeyException;
import java.security.MessageDigest;
import java.security.NoSuchAlgorithmException;
import java.util.Arrays;

import javax.crypto.Mac;
import javax.crypto.spec.SecretKeySpec;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;

import com.amazonaws.SdkClientException;
import com.amazonaws.internal.SdkInputStream;
import com.amazonaws.util.BinaryUtils;

/**
 * A wrapper class of InputStream that implements chunked-encoding.
 */
public final class AwsChunkedEncodingInputStream extends SdkInputStream {
    protected static final String DEFAULT_ENCODING = "UTF-8";

    private static final int DEFAULT_CHUNK_SIZE = 128 * 1024;
    private static final int DEFAULT_BUFFER_SIZE = 256 * 1024;

    private static final String CRLF = "\r\n";
    private static final String CHUNK_STRING_TO_SIGN_PREFIX = "AWS4-HMAC-SHA256-PAYLOAD";
    private static final String CHUNK_SIGNATURE_HEADER = ";chunk-signature=";
    private static final int SIGNATURE_LENGTH = 64;
    private static final byte[] FINAL_CHUNK = new byte[0];

    private InputStream is = null;
    private final int maxBufferSize;
    private final String dateTime;
    private final String keyPath;
    private final String headerSignature;
    private String priorChunkSignature;
    private final AWS4Signer aws4Signer;

    private final MessageDigest sha256;
    private final Mac hmacSha256;

    /** Iterator on the current chunk that has been signed */
    private ChunkContentIterator currentChunkIterator;

    /**
     * Iterator on the buffer of the decoded stream,
     * Null if the wrapped stream is marksupported,
     * otherwise it will be initialized when this wrapper is marked.
     */
    private DecodedStreamBuffer decodedStreamBuffer;

    private boolean isAtStart = true;
    private boolean isTerminating = false;

    private static final Log log = LogFactory.getLog(AwsChunkedEncodingInputStream.class);

    public AwsChunkedEncodingInputStream(InputStream in, byte[] kSigning,
            String datetime, String keyPath, String headerSignature,
            AWS4Signer aws4Signer) {
        this(in, DEFAULT_BUFFER_SIZE, kSigning, datetime, keyPath, headerSignature, aws4Signer);
    }

    /**
     * A wrapper of InputStream that implements pseudo-chunked-encoding.
     * Each chunk will be buffered for the calculation of the chunk signature
     * which is added at the head of each chunk.

     * The default chunk size cannot be customized, since we need to calculate
     * the expected encoded stream length before reading the wrapped stream.

     * This class will use the mark() & reset() of the wrapped InputStream if they
     * are supported, otherwise it will create a buffer for bytes read from
     * the wrapped stream.
     * @param in
     *             The original InputStream.
     * @param maxBufferSize
     *             Maximum number of bytes buffered by this class.
     * @param kSigning
     *             Signing key.
     * @param datetime
     *             Datetime, as used in SigV4.
     * @param keyPath
     *             Keypath/Scope, as used in SigV4.
     * @param headerSignature
     *             The signature of the signed headers. This will be used for
     *             calculating the signature of the first chunk.
     * @param aws4Signer
     *             The AWS4Signer used for hashing and signing.
     */
    public AwsChunkedEncodingInputStream(InputStream in, int maxBufferSize,
            byte[] kSigning, String datetime, String keyPath,
            String headerSignature, AWS4Signer aws4Signer) {
        if (in instanceof AwsChunkedEncodingInputStream) {
            // This could happen when the request is retried, and we need to re-calculate the signatures.
            AwsChunkedEncodingInputStream originalChunkedStream = (AwsChunkedEncodingInputStream)in;
            maxBufferSize = Math.max(originalChunkedStream.maxBufferSize, maxBufferSize);
            is = originalChunkedStream.is;
            decodedStreamBuffer = originalChunkedStream.decodedStreamBuffer;
        }
        else {
            is = in;
            decodedStreamBuffer = null;
        }

        if (maxBufferSize < DEFAULT_CHUNK_SIZE)
            throw new IllegalArgumentException("Max buffer size should not be less than chunk size");
        try {
            this.sha256 = MessageDigest.getInstance("SHA-256");
            final String signingAlgo = SigningAlgorithm.HmacSHA256.toString();
            this.hmacSha256 = Mac.getInstance(signingAlgo);
            hmacSha256.init(new SecretKeySpec(kSigning, signingAlgo));
        } catch (NoSuchAlgorithmException e) {
            throw new IllegalStateException(e);
        } catch (InvalidKeyException e) {
            throw new IllegalArgumentException(e);
        }
        this.maxBufferSize = maxBufferSize;
        this.dateTime = datetime;
        this.keyPath = keyPath;
        this.headerSignature = headerSignature;
        this.priorChunkSignature = headerSignature;
        this.aws4Signer = aws4Signer;
    }

    @Override
    public int read() throws IOException {
        byte[] tmp = new byte[1];
        int count = read(tmp, 0, 1);
        if (count != -1) {
            if (log.isDebugEnabled())
                log.debug("One byte read from the stream.");
            int unsignedByte = (int) tmp[0] & 0xFF;
            return unsignedByte;
        } else {
            return count;
        }
    }

    @Override
    public int read(byte[] b, int off, int len) throws IOException {
        abortIfNeeded();
        if (b == null) {
            throw new NullPointerException();
        } else if (off < 0 || len < 0 || len > b.length - off) {
            throw new IndexOutOfBoundsException();
        } else if (len == 0) {
            return 0;
        }

        if (null == currentChunkIterator
                || !currentChunkIterator.hasNext()) {
            if (isTerminating)
                return -1;
            else {
                isTerminating = setUpNextChunk();
            }
        }

        int count = currentChunkIterator.read(b, off, len);
        if (count > 0) {
            isAtStart = false;
            if (log.isTraceEnabled())
                log.trace(count + " byte read from the stream.");
        }
        return count;
    }

    @Override
    public long skip(long n) throws IOException {
        if (n <= 0) {
            return 0;
        }
        long remaining = n;
        int toskip = (int) Math.min(DEFAULT_BUFFER_SIZE, n);
        byte[] temp = new byte[toskip];
        while (remaining > 0) {
            int count;
            if ( (count = read(temp, 0, toskip)) < 0) {
                break;
            }
            remaining -= count;
        }
        return n - remaining;
    }

    /**
     * @see java.io.InputStream#markSupported()
     */
    @Override
    public boolean markSupported() {
        return true;
    }

    /**
     * The readlimit parameter is ignored.
     */
    @Override
    public void mark(int readlimit) {
        abortIfNeeded();
        if ( !isAtStart )
            throw new UnsupportedOperationException("Chunk-encoded stream only supports mark() at the start of the stream.");
        if (is.markSupported()) {
            if (log.isDebugEnabled()) {
                log.debug("AwsChunkedEncodingInputStream marked at the start of the stream "
                        + "(will directly mark the wrapped stream since it's mark-supported).");
            }
            is.mark(readlimit);
        }
        else {
            if (log.isDebugEnabled()) {
                log.debug("AwsChunkedEncodingInputStream marked at the start of the stream "
                        + "(initializing the buffer since the wrapped stream is not mark-supported).");
            }
            decodedStreamBuffer = new DecodedStreamBuffer(maxBufferSize);
        }
    }

    /**
     * Reset the stream, either by resetting the wrapped stream or using the
     * buffer created by this class.
     */
    @Override
    public void reset() throws IOException {
        abortIfNeeded();
        // Clear up any encoded data
        currentChunkIterator = null;
        priorChunkSignature = headerSignature;
        // Reset the wrapped stream if it is mark-supported,
        // otherwise use our buffered data.
        if (is.markSupported()) {
            if (log.isDebugEnabled())
                log.debug("AwsChunkedEncodingInputStream reset "
                        + "(will reset the wrapped stream because it is mark-supported).");
            is.reset();
        }
        else {
            if (log.isDebugEnabled())
                log.debug("AwsChunkedEncodingInputStream reset "
                        + "(will use the buffer of the decoded stream).");
            if (null == decodedStreamBuffer) {
                throw new IOException("Cannot reset the stream because the mark is not set.");
            }
            decodedStreamBuffer.startReadBuffer();
        }

        currentChunkIterator = null;
        isAtStart = true;
        isTerminating = false;
    }

    public static long calculateStreamContentLength(long originalLength) {
        if (originalLength < 0) {
            throw new IllegalArgumentException("Nonnegative content length expected.");
        }

        long maxSizeChunks = originalLength / DEFAULT_CHUNK_SIZE;
        long remainingBytes =  originalLength % DEFAULT_CHUNK_SIZE;
        return maxSizeChunks * calculateSignedChunkLength(DEFAULT_CHUNK_SIZE)
                + (remainingBytes > 0? calculateSignedChunkLength(remainingBytes) : 0)
                + calculateSignedChunkLength(0);
    }

    private static long calculateSignedChunkLength(long chunkDataSize) {
        return Long.toHexString(chunkDataSize).length()
                + CHUNK_SIGNATURE_HEADER.length()
                + SIGNATURE_LENGTH
                + CRLF.length()
                + chunkDataSize
                + CRLF.length();
    }

    /**
     * Read in the next chunk of data, and create the necessary chunk extensions.
     * @return
     *         Returns true if next chunk is the last empty chunk.
     */
    private boolean setUpNextChunk() throws IOException {
        byte[] chunkData = new byte[DEFAULT_CHUNK_SIZE];
        int chunkSizeInBytes = 0;
        while (chunkSizeInBytes < DEFAULT_CHUNK_SIZE) {
            /** Read from the buffer of the decoded stream */
            if (null != decodedStreamBuffer
                    && decodedStreamBuffer.hasNext()) {
                chunkData[chunkSizeInBytes++] = decodedStreamBuffer.next();
            }
            /** Read from the wrapped stream */
            else {
                int bytesToRead = DEFAULT_CHUNK_SIZE - chunkSizeInBytes;
                int count = is.read(chunkData, chunkSizeInBytes, bytesToRead);
                if (count != -1) {
                    if (null != decodedStreamBuffer)
                        decodedStreamBuffer.buffer(chunkData, chunkSizeInBytes, count);
                    chunkSizeInBytes += count;
                }
                else
                    break;
            }
        }
        if (chunkSizeInBytes == 0){
            byte[] signedFinalChunk = createSignedChunk(FINAL_CHUNK);
            currentChunkIterator = new ChunkContentIterator(signedFinalChunk);
            return true;
        }
        else {
            if (chunkSizeInBytes < chunkData.length) {
                chunkData = Arrays.copyOf(chunkData, chunkSizeInBytes);
            }
            byte[] signedChunkContent = createSignedChunk(chunkData);
            currentChunkIterator = new ChunkContentIterator(signedChunkContent);
            return false;
        }
    }

    private byte[] createSignedChunk(byte[] chunkData) {
        StringBuilder chunkHeader = new StringBuilder();
        // chunk-size
        chunkHeader.append(Integer.toHexString(chunkData.length));
        // sig-extension
        final String chunkStringToSign =
                CHUNK_STRING_TO_SIGN_PREFIX + "\n" +
                dateTime + "\n" +
                keyPath + "\n" +
                priorChunkSignature + "\n" +
                AbstractAWSSigner.EMPTY_STRING_SHA256_HEX + "\n" +
                BinaryUtils.toHex(sha256.digest(chunkData));
        final String chunkSignature =
            BinaryUtils.toHex(aws4Signer.signWithMac(chunkStringToSign, hmacSha256));
        priorChunkSignature = chunkSignature;
        chunkHeader.append(CHUNK_SIGNATURE_HEADER)
                   .append(chunkSignature)
                   .append(CRLF)
                   ;
        try {
            byte[] header = chunkHeader.toString().getBytes(UTF8);
            byte[] trailer = CRLF.getBytes(UTF8);
            byte[] signedChunk = new byte[header.length + chunkData.length + trailer.length];
            System.arraycopy(header, 0, signedChunk, 0, header.length);
            System.arraycopy(chunkData, 0, signedChunk, header.length, chunkData.length);
            System.arraycopy(trailer, 0,
                    signedChunk, header.length + chunkData.length,
                    trailer.length);
            return signedChunk;
        } catch (Exception e) {
            throw new SdkClientException("Unable to sign the chunked data. " + e.getMessage(), e);
        }
    }

    @Override
    protected InputStream getWrappedInputStream() {
        return is;
    }
}

redpajama_set_name: RedPajamaGithub


text:
Is Their an Overabundance of Video Game Awards?
And the Award for Best Video Game Awards Goes To No One
Movies have the Oscars, TV the Emmys, theatre the Tonys, and music the Grammys. What do video games have? Thinking about game awards reveals how different from older entertainment media today's game industry really is.
The first movie
theaters appeared in the U.S. in 1896. The Academy Awards (Oscars) began 33 years later in 1929. Pong, the first commercially successful arcade game, was released in 1972. 48 years later, no Academy Awards of Video Games yet dominate the gaming awards scene.
The issue is not a lack of awards, but rather an overabundance. Video games have a wide and messy field of awards, none of which have achieved the stature of the Oscars (etc.). There are quite a few awards handed out by panels of expert judges: DICE (Design, Innovate, Communicate, Entertain) Awards (previously the Interactive Achievement Awards or IAAs), Game Developers Choice Awards, and Inside Gaming Awards in the States, as well as the BAFTA (British Academy of Film and Television Arts) Games Awards (previously the BAFTA Interactive Entertainment Awards) in the UK. Competing with these in popular awareness are awards determined by popular vote, such as the Spike Video Game Awards (VGAs) and the Golden Joystick Awards (the only awards listed here to predate 1998, having been around since 1983). Many individual gaming websites and publications like IGN, GameSpot, and Game Informer also present their own awards.
Alongside the kinds of awards other media also have, games have another category of awards that has no counterpart in any other medium (to the best of my knowledge): preview or demo awards, best represented by the Game Critics "Best of E3" Awards. Can you imagine awards for Best Film Previews or Best Single Released Well Before the Album being as present in the public consciousness as "Best of E3" Awards are in the gamer world? For that matter, can you imagine a successful film festival focused on films that weren't finished yet? Gamers relate to games in some fundamentally different ways than filmgoers to films or listeners to music, as these examples suggest.
On the one hand, I'm a bit sad games don't have a once-a-year event like the Oscars to raise their profile as a respectable medium with the larger public. There are still a lot of people who write off the entire medium as mindless entertainment for kids or nerdy adults living in their parents' basements. Seeing games become an institution
in a way they recognize – by having a fancy award ceremony – would help speed up the process of those people figuring out their stereotype is wrong. This would be good in that I think people who write off games are missing out.
On the other hand, people who write off games are wrong, and society is increasingly realizing that anyway, Gaming Oscars or no, so in the end I think it's a good thing that games don't have a dominant awards organization. We don't need one. I enjoy hearing others' opinions on movies and games and such, but I tend to ignore the Oscars because there's something ridiculous about a small group of people deciding what the Best Film (or Director, or Actor) of the year is in an artistic medium, and when something gets as big as the Oscars, it's sometimes easy to forget that it's still just one group's opinion.
Such opinions are subjective, and the more institutionalized and established the nomination and judging process gets, the more safe and mainstream it tends to become, to the detriment of recognition of independent and artistically risky productions. So I think it's a good thing that there's not one go-to source for Best Game of the Year. Instead, we have a bunch of different organizations presenting awards chosen by both critics and audiences, in delightful disagreement with each other (at least some of the time). This dissensus helps us remember that there is, and will never be, an objectively Best Game – that's not how creative media work. Ours is a medium that makes use of contemporary technology and connectivity like the internet, and so I think it's fitting that our awards mirror the chaos of the web – there's a million different awards, they're mostly visible online, and there's no single frontrunner. Excellent work gets recognized, but no one group has the power to convince the public that their selections are truly the best.
Trust Me! Play Video Games Anywhere, Tailored Game...
One Piece Unlimited World Red Nintendo 3DS Review

redpajama_set_name: RedPajamaCommonCrawl


This beautifully light and luxe dress is an essential piece beach coverup in the modern woman's wardrobe. The luxury details of this design feature a high neckline, thick shoulder straps, a removable sash, and crystal embellished buttons. The sheer finish allows the soft fabric to drape the body, while the side tie sash cinches the waist offering full versatility for a tailored look and feel. Our Audrey Dress is a part of the Amaiò Après Collection. Each of these pieces are intended to be worn over our swimsuits, ready-to-wear styles, or on their own with pieces from your already established wardrobe.


text:
Q: jQuery Ajax with async: false and return data Im building a browser based application.
On window.load, i call a jquery AJAX GET function which is supposed to get me some gamedata from the server.
Once the data is there, i parse this data into my javascript and build some stuff up.
For that reason, i set async to false.
Now looking at this function, i want to call another function on ajax.success, but i want to use the freshly returned data as well as the data that was sent to this very function as a parameter (from another async ajax call).
How can i do this ?
thanks
window.onload = function(){
    ajax.getFleets();
    ajax.getShips(fleets);
    manager.draw() // can only draw if both ajax functions have received data !)
}

getShips: function(fleets){

    $.ajax({
        type: "GET",
        url: "getGameData.php",
        datatype: "json",
        data: {
            type: "ships"
            },
        error: ajax.error,
        success: ajax.mergeData, //want to pass fleets and returnData to this function
        async: false
    });
},

Also, on a general note, using async: false is the correct approach for this, right ?
thanks,

A: $.ajax will pass your callback function the result of the AJAX request, so you can use a simple anonymous function to get both parts in. Let's say you have your callback function defined like this:
ajax = {};
ajax.mergeData = function(fleets, ajaxResult) {
    console.log(fleets);
    console.log(ajaxResult);
};

You can define your callback function as:
function(result) { ajax.mergeData(fleets, result); }

Putting that all together, your getShips function will look like this:
ajax.getShips = function(fleets) {
    $.ajax({
        type: "GET",
        url: "getGameData.php",
        datatype: "json",
        data: {
            type: "ships"
            },
        error: ajax.error,
        success: function(result) { ajax.mergeData(fleets, result); }
    });
};

To answer your second question: async: false is a deprecated option, so you can't rely on it to work. It will also lock up the browser until the server responds, which makes for a really bad user experience. It's far better to use callback functions or promises to carry out an action when your AJAX requests complete. Here's a short summary of the promises concept that might be useful.

Which leaves the last, big question: how do we chain these calls together so getShips() depends on the result of getFleets(), and manager.draw() depends on both? With just two calls, you could get away with just using callbacks; getShips' success callback invokes getFleets, and getFleets' callback invokes manager.draw. This is the easiest way to get an A → B → C dependency chain working – but we could also use jQuery's promises, which might make our code easier to follow.
First, let's change getFleets to return the result of calling $.ajax:
ajax = {};
ajax.getFleets = function() {
    return $.ajax({ /* Details elided here */ });
}

Next, let's update getShips to make an AJAX request for that data, and return a promise that returns the combined data:
ajax.getShips = function(fleets) {
    return $.ajax({ /* Details elided again */ })
            .done(function(ajaxResult) { return mergeData(fleets, ajaxResult); });
 };

This is a bit trickier; $.ajax returns a promise that will eventually resolve to our ship data. When it does, we'll call the function in .done that returns the merged data. And that .done also returns a promise – so getShips is now returning a promise that will eventually return the merged data.
So now we can write our window.onload function to chain all these together, and only call manager.draw when all the data's available:
window.onload = function() {
    ajax.getFleets()
        .done(ajax.getShips)
        .done(manager.draw);
}

redpajama_set_name: RedPajamaStackExchange


text:
Q: Is "Conditional Rendering" possible with Pure JS? I use Pure JS directives for rendering:
http://beebole.com/pure/documentation/rendering-with-directives/
If a node in the template is missing, the default behaviour of Pure JS is to crash due to:

The node "XXX" was not found in the template

This default behaviour is totally comprehensible, because it ensures there are no inconsistencies in the template. In same cases, though, one would like to skip a failing assignment and to proceed with the rest of the assignments (possibly logging the error), to avoid the whole rendering to fail because of a single error / typo.
Is there any way to obtain this behaviour with Pure JS? Can I tell Pure JS to render an element just "if it exists"?

A: Unfortunately current stable version of Pure JS (revision: 2.79) does not allow to render an element "if it exists"?
The following Pure code snippet shows how the error is thrown:

if(selector === '.' || ( !selector && attr ) ){
    target[0] = dom;
}else{
    target = plugins.find(dom, selector);
}
if(!target || target.length === 0){
    return error('The node "' + sel + '" was not found in the template:\n' + outerHTML(dom).replace(/\t/g,'  '));
}

As you can see, if target has not been found (by one of libraries such as jQuery, dojo etc.) then the mentioned error is thrown.

Workaround I use in that kind of situations is as follows:

*

*I use template with all possible elements so that Pure does not throw errors

*Some elements (e.g. displaying error messages etc.) are hidden using CSS classes

*I change CSS classes using Pure and JavaScript functions with directives to hide / show elements depending on input data.


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Eight-time PGA Tour Champions winner Scott McCarron demonstrates a variety of fitness exercises for mobility training to add distance to your game.
I knew Bill Shean at the University of Michigan, where we were caddie-scholars. Bill was the best golfer in the house, mostly self-taught. Blue-eyed and with a big, toothy smile, he reminded me of a Kennedy.


text:
Robert De Niro To Be Honored With Brass Ring Award at 2018 Carousel of Hope Ball
Event chair and host Barbara Davis announced today that Academy award-winning actor, producer and director Robert De Niro will be honored with the "Brass Ring Award" at The 2018 Carousel of Hope Ball on Saturday, October 6.
The biennial event is dedicated to increasing awareness for diabetes, and raising funds for clinical care and diabetes research at the Barbara Davis Center For Diabetes. Longtime supporter, Robert De Niro, will receive the award at this year's ball for his humanitarian efforts and continued support of the cause. The "Brass Ring Award" was named after the elusive brass ring that carousel riders would try to catch when the ride first became popular. Now universally recognized as a symbol of achievement, the brass ring represents the ultimate goal of finding a cure for diabetes. "Brass Ring Award" recipients exemplify the dedication and strength that brings the Children's Diabetes Foundation and the Barbara Davis Center one step closer to finding a cure. Past "Brass Ring Award" recipients include Frank Sinatra, Hillary Clinton, Stevie Wonder, Whitney Houston, George Clooney, Jane Fonda, Sir Sidney Poitier, David Foster, Halle Berry, Sherry Lansing and more.
Speaking on the award, Chairman and Host of The Carousel of Hope Ball Barbara Davis, said, "We are so delighted to announce that the multi-talented, two-time Oscar-winning actor, director and producer, Robert De Niro will receive the Brass Ring Award at the 2018 Carousel of Hope Ball. He is one of the kindest, most caring human beings I have ever known. He goes above and beyond to help others and has supported the Carousel of Hope from the very start."
De Niro has been recognized around the world for his outstanding work in film. In 2009, De Niro received the coveted Kennedy Center Honor, and President Obama presented De Niro with the Presidential Medal of Freedom in 2016, recognizing him for his outstanding contributions to the U.S. He served as the jury president of the 64th Cannes Film Festival, and in 2009, received the Stanley Kubrick Award from the BAFTA Britannia Awards. AARP The Magazine gave De Niro the 2010 Movies for Grownups Lifetime Achievement Award, and the following year, he was honored with the Cecil B. DeMille Award at the 2011 Golden Globe Awards. He was honored by AFI with their Life Achievement Award in 2003, and two years prior, received the Bravo Lifetime Achievement Award at the Independent Feature Projects' Gotham Awards. De Niro and the cast of "The Good Shepherd" were awarded the Silver Berlin Bear at the 2007 Berlin International Film Festival. He is the recipient of two National Board of Review Awards, four New York Film Critics Circle Awards, two Los Angeles Film Critics Association Awards, and GLAAD's 2016 Excellent in Media Award.
Robert De Niro launched his prolific motion picture career in Brian De Palma's "The Wedding Party" in 1969. By 1974 he had won the New York Film Critics Award for Best Supporting Actor in recognition of his critically acclaimed performance in "Bang the Drum Slowly" and from the National Society of Film Critic for Martin Scorsese's "Mean Streets." In 1974 De Niro won the Academy Award for Best Supporting Actor for his portrayal of the young Vito Corleone in "The Godfather, Part II." In 1980 he won his second Oscar, as Best Actor, for his extraordinary portrayal of Jake La Motta in Scorsese's "Raging Bull." De Niro has earned Academy Award nominations for his work in five additional films: as Travis Bickle in Scorsese's acclaimed "Taxi Driver;" as a Vietnam vet in Michael Cimino's "The Deer Hunter," as a catatonic patient brought to life in Penny Marshall's "Awakenings," in 1992 as Max Cady, an ex-con looking for revenge, in Scorsese's remake of the 1962 classic "Cape Fear" and as a father to a bi-polar son in David O. Russell's "Silver Linings Playbook."
He was most recently seen in Tribeca Productions' and HBO's "Wizard of Lies" starring as Bernie Madoff, for which he received both an Emmy Award nomination for Lead Actor in a Limited Series or Movie and a Screen Actors Guild nomination for Outstanding Performance by a Male Actor in a Miniseries or Television Movie. His next project will be Netflix's "The Irishman", in which he stars and is producing with Martin Scorsese, for their ninth collaboration.
De Niro takes pride in the development of his production company, Tribeca Productions, the Tribeca Film Center, which he co-founded with Jane Rosenthal in 1988, and in the Tribeca Film Festival, which he founded with Rosenthal and Craig Hatkoff in 2001 as a response to the attacks on the World Trade Center. The festival, which just completed its 17th run, was conceived to foster the economic and cultural revitalization of Lower Manhattan through an annual celebration of film, music, and culture; the festival's mission is to promote New York City as a major filmmaking center and help filmmakers reach the broadest possible audiences. Through Tribeca Productions, De Niro develops projects on which he serves in a combination of capacities, including producer, director and actor.
This year marks the 32nd Carousel Ball, where guests will enjoy cocktails, silent and live auctions, dinner, exciting entertainment, and musical performances. Since its inception in 1978, the iconic event has been a Hollywood favorite, uniting entertainment, sports, and business industry leaders from all areas for a night dedicated to raising awareness and, ultimately, finding a cure for diabetes. Diabetes, which afflicts over 30 million people in the United States alone, is rapidly on the rise. It is estimated that one in three babies born today will have diabetes in their lifetime and each year, approximately 50,000 Americans are diagnosed with type 1 diabetes. Of those, 25,000 are children. The Carousel of Hope Balls have raised more than $100 million to date, with proceeds benefitting the Children's Diabetes Foundation and the Barbara Davis Center for Diabetes (BDC). Funds are raised through sponsorships, dinner tickets, a live and silent auction, and contributions. Rated #1 Benefit in Southern California by Bizbash for multiple years, this star-studded evening is not to be missed.
The 2018 Carousel of Hope Ball is generously sponsored by American Airlines, Tina & Rick Caruso, Marlyn and Robert Day, and Dexcom.
Mrs. Davis will co-host a kickoff cocktail party with Bruce Bozzi, Jr. at The Palm on May 21.
More info can be found here.
Barbara Davis
SAG-AFTRA Rememebers Actor and SAG Life Achievement Recipient Sidney Poitier Jan 11, 2022
SAG-AFTRA mourns the loss of SAG Life Achievement Award recipient Sidney Poitier, who died last week at the age of 94. More →
The Hollywood Reporter Hosted Annual Women In Entertainment Breakfast Honoring Jennifer Aniston, Selma Blair Dec 16, 2021
Women's Media Center Announces First WMC Exceptional Journalism Awards Recognizing Outstanding Journalism by Diverse Women Storytellers Dec 15, 2021
Andrea Bocelli, David Foster and Katharine McPhee Perform at Prostate Cancer Foundation's Annual New York Dinner Dec 7, 2021
George Clooney: Famine & drought in South Sudan, Somalia, Nigeria, Yemen
What Do You Care About? #VoteYourFuture
UN Messenger of Peace Stevie Wonder calls on helping those in need
Stop the Hate: 49 Celebrities Honor 49 Victims of Orlando Tragedy in Ryan Murphy-Produced Tribute


UPDATE 5/25: Two-day passes are now $10 off at Amoeba Hollywood! Weekend passes are now $25 (plus $2 service fee).
The schedule for the 2012 Silver Lake Jubilee has been announced! The festival takes place on Memorial Day weekend in Los Angeles and features two days of music, comedy, food trucks, vendors, beer gardens, and more.
On 15 October, 2011 (this Saturday if you're reading this in a timely fashion), the very cool indie toy store & gallery, Monkeyhouse Toys and Gallery (for kids whose sensibility is more Roald Dahl/Tim Burton than Bratz/Hannah Montana) is opening an art exhibit, The Spooky Show.
If you find yourself free this Sunday, DJs Larry G (Supercrass) and Timothy L (Modernbrit) of Club Underground (and Amoeba, in the latter's case) are going to DJ a set of post-punk, indie and more at Brightwell between the hours of 3:00 and 7:00.
Club Underground bills itself as "LA's Premiere indie/britpop/new wave/electropop/twee/60s/soul party since 2001. It occurs most Fridays at the Grand Star Jazz Club in Chinatown.


Thank you to everyone who attended our meeting last night with Tim Boon of Good Shepherd Community Hospice. Even though our meetings now officially end at 7:45, I think we could have continued talking until 10pm!
Please join us on Wednesday, December 16th for our second annual BBC – Boston Bridge Conversations. We will have small group discussions for the first part, and then extended networking time with home-cooked food by the board.
On December 2nd from 7-9pm, we will have our first Boston Bridge Book Club where we will be discussing Dr. Atul Gawande's book Being Mortal, which could not be more relevant based on last night's discussion. We will meet at the Panera in Coolidge Corner. Please email Sylvia Lewinstein to RSVP – otherwise she won't be able to save you a seat!
You can send an updated membership form and check (payable to Boston Bridge) by mail to the treasurer Kelly Marcella at 12 Cottage Street, Apt 3, Boston, MA 02128 or you can submit your form and payment to Kelly at the next Boston Bridge Meeting.
If you believe in the mission of Boston Bridge, please consider supporting the organization by renewing your membership or joining today!
Here is information about the latest round of the Jordan Liebhaber Scholarship Fund.
Per our discussion last night and the recent Veteran's Day holiday, this is an op-ed piece by a palliative care physician on how we can improve the end-of-life experience for hospital patients.
We touched on the theme of this piece in our discussion last night, about the unequal utilization of palliative and hospice care between different racial groups in the US.
This is an article in the Wall Street Journal that explores how negative stereotypes about aging can become a self-fulfilling prophecy, and how the flip side of positive thinking around aging can have mental and physical benefits.
This is a piece on the ever-widening doughnut hole of seniors who make too much money to qualify for many public benefits, but not nearly enough to pay for all of their expenses.
The title of this radio piece on NPR speaks for itself: "Gray-Haired Granny? An 85-Year-Old Writer Goes Punk Rock Instead".
And now, for the moment you did not realize you've been waiting for, this year's top 50 national Influencers in Aging!
Have a lovely Thanksgiving weekend, and we will see many of you in December.


text:
Rosokhrankultura (), full name: Federal Service for monitoring compliance with cultural heritage protection law (), is a state agency of Russia responsible for keeping the national register of cultural heritage, enforcing preservation of listed properties through monitoring compliance with preservation law and enforcing compliance with copyright law, including licensing of copyright management agencies.

As at May, 2009, the agency is subordinated to the federal Ministry of Culture. For a brief period in 2008 Rosokhrankultura was merged into the Russian Federal Surveillance Service for Mass Communications, Communications but was eventually reorganized back into an independent agency. It is chaired, since June 2008, by Alexander Kibovsky (born 1973), historian of Imperial Russian Army and a lecturer on military costume at the Moscow Art Theatre college.

In September 2008 Rosokhrankultura performed a survey of damages of the 2008 South Ossetia War in South Ossetia.

External links
 Federal Service for monitoring compliance with cultural heritage protection law

References

Politics of Russia

redpajama_set_name: RedPajamaWikipedia


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text:
12:10 +04, April 19, 2019
Unai Emery: I am very proud of my players
Arsenal boss Unai Emery commented on the away victory over the Italian Napoli in the Europa League second quarter finals, Football Italia reported.
"I am very proud of my players. We started very well. The first half was good because after we scored, we continued not to give them opportunities. We defended well second half. In the two matches we can be happy to win against Napoli," he said adding: "Valencia are a very good team with good players. It will be a difficult match for us."
As reported earlier, in the return game held in Naples, the midfielder of the Armenian national team Henrikh Mkhitaryan came on as a substitute in the 34th minute instead of the injured Welsh winger Aaron Ramsey.
Arsenal defeated Napoli 2-0 in the first clash.
"We are very well prepared for the return match with Macedonia…
Milan excluded from Europa League campaign
UEFA announced Milan did not fulfill the requirement for reporting financial loss for the 2016, 2017, 2018 periods…
Eden Hazard recognized as best player of the week
Three other members of Chelsea were...
Giroud: Arsenal, Chelsea changed my life
'It was a good final and we have been better, more efficient in the second half…
Chelsea win Europa League title for 2nd time
They beat Arsenal, which—due to security concerns for the final in Baku—were without their Armenian international Henrikh Mkhitaryan…
last news on "Football – Europa League"