***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Objective To develop and vali date a risk algorithm ( QStroke ) to estimate risk of stroke or transient ischaemic attack in patients without prior stroke or transient ischaemic attack at baseline ; to compare ( a ) QStroke with CHADS2 and CHA2DS2VASc scores in patients with atrial fibrillation and ( b ) the performance of QStroke with the Framingham stroke score in the full population free of stroke or transient ischaemic attack . Design Prospect i ve open cohort study using routinely collected data from general practice during the study period 1 January 1998 to 1 August 2012 . Setting 451 general practice s in Engl and and Wales contributing to the national Q Research data base to develop the algorithm and 225 different Q Research practice s to vali date the algorithm . Participants 3.5 million patients aged 25 - 84 years with 24.8 million person years in the derivation cohort who experienced 77 578 stroke events . For the validation cohort , we identified 1.9 million patients aged 25 - 84 years with 12.7 million person years who experienced 38 404 stroke events . We excluded patients with a prior diagnosis of stroke or transient ischaemic attack and those prescribed oral anticoagulants at study entry . Main outcome measures Incident diagnosis of stroke or transient ischaemic attack recorded in general practice records or linked death certificates during follow-up . Risk factors Self assigned ethnicity , age , sex , smoking status , systolic blood pressure , ratio of total serum cholesterol to high density lipoprotein cholesterol concentrations , body mass index , family history of coronary heart disease in first degree relative under 60 years , Townsend deprivation score , treated hypertension , type 1 diabetes , type 2 diabetes , renal disease , rheumatoid arthritis , coronary heart disease , congestive cardiac failure , valvular heart disease , and atrial fibrillation Results The QStroke algorithm explained 57 % of the variation in women and 55 % in men without a prior stroke . The D statistic for QStroke was 2.4 in women and 2.3 in men . QStroke had improved performance on all measures of discrimination and calibration compared with the Framingham score in patients without a prior stroke . Among patients with atrial fibrillation , levels of discrimination were lower , but QStroke had some improved performance on all measures of discrimination compared with CHADS2 and CHA2DS2VASc . Conclusion QStroke provides a valid measure of absolute stroke risk in the general population of patients free of stroke or transient ischaemic attack as shown by its performance in a separate validation cohort . QStroke also shows some improvement on current risk scoring methods , CHADS2 and CHA2DS2VASc , for the subset of patients with atrial fibrillation for whom anticoagulation may be required . Further research is needed to evaluate the cost effectiveness of using these algorithms in primary care Objective To derive a new cardiovascular disease risk score ( QRISK ) for the United Kingdom and to vali date its performance against the established Framingham cardiovascular disease algorithm and a newly developed Scottish score ( ASSIGN ) . Design Prospect i ve open cohort study using routinely collected data from general practice . Setting UK practice s contributing to the Q RESEARCH data base . Participants The derivation cohort consisted of 1.28 million patients , aged 35 - 74 years , registered at 318 practice s between 1 January 1995 and 1 April 2007 and who were free of diabetes and existing cardiovascular disease . The validation cohort consisted of 0.61 million patients from 160 practice s. Main outcome measures First recorded diagnosis of cardiovascular disease ( incident diagnosis between 1 January 1995 and 1 April 2007 ) : myocardial infa rct ion , coronary heart disease , stroke , and transient ischaemic attacks . Risk factors were age , sex , smoking status , systolic blood pressure , ratio of total serum cholesterol to high density lipoprotein , body mass index , family history of coronary heart disease in first degree relative aged less than 60 , area measure of deprivation , and existing treatment with antihypertensive agent . Results A cardiovascular disease risk algorithm ( QRISK ) was developed in the derivation cohort . In the validation cohort the observed 10 year risk of a cardiovascular event was 6.60 % ( 95 % confidence interval 6.48 % to 6.72 % ) in women and 9.28 % ( 9.14 % to 9.43 % ) in men . Overall the Framingham algorithm over-predicted cardiovascular disease risk at 10 years by 35 % , ASSIGN by 36 % , and QRISK by 0.4 % . Measures of discrimination tended to be higher for QRISK than for the Framingham algorithm and it was better calibrated to the UK population than either the Framingham or ASSIGN models . Using QRISK 8.5 % of patients aged 35 - 74 are at high risk ( 20 % risk or higher over 10 years ) compared with 13 % when using the Framingham algorithm and 14 % when using ASSIGN . Using QRISK 34 % of women and 73 % of men aged 64 - 75 would be at high risk compared with 24 % and 86 % according to the Framingham algorithm . UK estimates for 2005 based on QRISK give 3.2 million patients aged 35 - 74 at high risk , with the Framingham algorithm predicting 4.7 million and ASSIGN 5.1 million . Overall , 53 668 patients in the validation data set ( 9 % of the total ) would be reclassified from high to low risk or vice versa using QRISK compared with the Framingham algorithm . Conclusion QRISK performed at least as well as the Framingham model for discrimination and was better calibrated to the UK population than either the Framingham model or ASSIGN . QRISK is likely to provide more appropriate risk estimates to help identify high risk patients on the basis of age , sex , and social deprivation . It is therefore likely to be a more equitable tool to inform management decisions and help ensure treatments are directed towards those most likely to benefit . It includes additional variables which improve risk estimates for patients with a positive family history or those on antihypertensive treatment . However , since the validation was performed in a similar population to the population from which the algorithm was derived , it potentially has a “ home advantage . ” Further validation in other population s is therefore required Background Chronic Kidney Disease is a major cause of morbidity and interventions now exist which can reduce risk . We sought to develop and vali date two new risk algorithms ( the QKidney ® Scores ) for estimating ( a ) the individual 5 year risk of moderate-severe CKD and ( b ) the individual 5 year risk of developing End Stage Kidney Failure in a primary care population . Methods We conducted a prospect i ve open cohort study using data from 368 Q Research ® general practice s to develop the scores . We vali date d the scores using two separate sets of practice s - 188 separate Q Research ® practice s and 364 practice s contributing to the THIN data base . We studied 775,091 women and 799,658 men aged 35 - 74 years in the Q Research ® derivation cohort , who contributed 4,068,643 and 4,121,926 person-years of observation respectively . We had two main outcomes ( a ) moderate-severe CKD ( defined as the first evidence of CKD based on the earliest of any of the following : kidney transplant ; kidney dialysis ; diagnosis of nephropathy ; persistent proteinuria ; or glomerular filtration rate of < 45 mL/min ) and ( b ) End Stage Kidney Failure . We derived separate risk equations for men and women . We calculated measures of calibration and discrimination using the two separate validation cohorts . Results Our final model for moderate-severe CKD included : age , ethnicity , deprivation , smoking , BMI , systolic blood pressure , diabetes , rheumatoid arthritis , cardiovascular disease , treated hypertension , congestive cardiac failure ; peripheral vascular disease , NSAID use and family history of kidney disease . In addition , it included SLE and kidney stones in women . The final model for End Stage Kidney Failure was similar except it did not include NSAID use . Each risk prediction algorithms performed well across all measures in both validation cohorts . For the THIN cohort , the model to predict moderate-severe CKD explained 56.38 % of the total variation in women and 57.49 % for men . The D statistic values were high with values of 2.33 for women and 2.38 for men . The ROC statistic was 0.875 for women and 0.876 for men . Conclusions These new algorithms have the potential to identify high risk patients who might benefit from more detailed assessment , closer monitoring or interventions to reduce their risk Objective To develop and externally vali date a risk algorithm ( QAdmissions ) to estimate the risk of emergency hospital admission for patients aged 18–100 years in primary care . Design Prospect i ve open cohort study using routinely collected data from general practice linked to hospital episode data during the 2-year study period 1 January 2010 to 31 December 2011 . Setting 405 general practice s in Engl and contributing to the national Q Research data base to develop the algorithm . Two validation cohorts to vali date the algorithm ( 1 ) 202 different Q Research practice s and ( 2 ) 343 practice s in Engl and contributing to the Clinical Practice Research Data Link ( CPRD ) . All general practice s had data linked to hospital episode statistics at the individual patient level . Participants We studied 2 849 381 patients aged 18–100 years in the derivation cohort with over 4.6 million person-years of follow-up . 265 573 of these patients had one or more emergency admissions during follow-up . For the Q Research validation cohort , we identified 1 340 622 patients aged 18–100 years with over 2.2 million person-years of follow-up . Of these patients , 132 723 had one or more emergency admissions during follow-up . The CPRD cohort included 2 475 360 patients aged 18–100 years with over 3.8 million person-years of follow-up . 234 204 of these patients had one or more emergency admissions during follow-up . We excluded patients without a valid NHS number and a valid Townsend score . Endpoint First ( ie , incident ) emergency admission to hospital in the next 2 years as recorded on the linked hospital episodes records . Risk factors C and i date variables recorded on the general practitioner computer system including ( 1 ) demographic variables ( age , sex , strategic health authority , Townsend deprivation score , ethnicity ) ; ( 2 ) lifestyle variables ( smoking , alcohol intake ) ; ( 3 ) chronic diseases ; ( 4 ) prescribed medication ; ( 5 ) clinical values ( body mass index , systolic blood pressure ) ; ( 6 ) laboratory test results ( haemoglobin , platelets , erythrocyte sedimentation rate , ratio of total serum cholesterol to high density lipoprotein cholesterol concentrations , liver function tests ) . We also included the number of emergency admissions in the preceding year based on information recorded on the linked hospital episodes records . Results The final QAdmissions algorithm incorporated 30 variables . When applied to the Q Research validation cohort , it explained 41 % of the variation in women and 43 % of that in men . The D statistic for QAdmissions was 1.7 in women and 1.8 in men . The receiver operating curve statistic was 0.78 for men and 0.77 for women . QAdmissions had good performance on all measures of discrimination and calibration . The positive predictive value for emergency admissions for the top tenth of patients at highest risk was 42 % and the sensitivity was 39 % . The results for the CPRD validation cohort were similar . Conclusions The QAdmissions model provided a valid measure of absolute risk of emergency admission to hospital in the general population as shown by its performance in a separate validation cohort . Further research is needed to evaluate the cost-effectiveness of using these algorithms in primary care OBJECTIVE To identify prospect ively the possible risk factors for  total hip replacement ( THR ) due to primary osteoarthritis in a large cohort . METHODS Data from a cardiovascular screening were matched with 9 years of national data on THR . Mean age at the start of followup was 54.9 years , and the 50,034 participants were followed up for an average of 9 years . During followup , 672 persons had a first THR due to primary osteoarthritis . RESULTS We found dose-response associations between body mass index ( BMI ) , body weight , and the level of physical activity at work and THR for primary osteoarthritis . The highest versus the lowest quarter of BMI had a relative risk of 2.0 ( 95 % confidence interval [ 95 % CI ] 1.4 - 2.9 ) among men and 3.0 ( 95 % CI 2.1 - 4.1 ) among women . The highest versus the lowest quarter of body weight had a relative risk of 2.1 ( 95 % CI 1.4 - 3.2 ) among men and 3.4 ( 95 % CI 2.4 - 4.9 ) among women . Intensive versus sedentary physical activity at work had a relative risk of 2.1 ( 95 % CI 1.5 - 3.0 ) among men and 2.1 ( 95 % CI 1.3 - 3.3 ) among women . No association was found between physical activity in leisure and THR for primary osteoarthritis . CONCLUSION Intensive physical activity at work  and a high BMI each contribute 
    Output:
    
Conclusions Two prediction models with good discrimination and calibration that estimate individuals ’ risk of THR and TKR have been developed and vali date d in large-scale , nationally representative data , and are readily automated in electronic patient records