***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: Background S100A12 protein is an endogenous receptor lig and for advanced glycation end products . In this study , the plasma S100A12 level was assessed as an independent predictor of mortality , and its utility in clinical setting s was examined . Methods In a previous cross-sectional study , plasma S100A12 levels were measured in 550 maintenance hemodialysis patients to determine the association between S100A12 and the prevalence of cardiovascular diseases ( CVD ) . In this prospect i ve study , the risk of mortality within a two-year period was determined . An integer scoring system was developed to predict mortality on the basis of the plasma S100A12 levels . Results Higher plasma S100A12 levels ( ≥18.79 ng/mL ) were more closely associated with higher all-cause mortality than lower plasma S100A12 levels ( < 18.79 ng/mL ; P = 0.001 ) . Multivariate Cox proportional hazards analysis revealed higher plasma S100A12 levels [ hazard ratio ( HR ) , 2.267 ; 95 % confidence interval ( CI ) , 1.195–4.302 ; P = 0.012 ] , age ≥65 years ( HR , 1.961 ; 95%CI , 1.017–3.781 ; P = 0.044 ) , serum albumin levels < 3.5 g/dL ( HR , 2.198 ; 95%CI , 1.218–3.968 ; P = 0.012 ) , and history of CVD ( HR , 2.068 ; 95%CI , 1.146–3.732 ; P = 0.016 ) to be independent predictors of two-year all-cause mortality . The integer score was derived by assigning points to these factors and determining total scores . The scoring system revealed trends across increasing scores for predicting the all-cause mortality [ c-statistic = 0.730 ( 0.656–0.804 ) ] . The result ing model demonstrated good discriminative power for distinguishing the validation population of 303 hemodialysis patients [ c-statistic = 0.721 ( 0.627–0.815 ) ] . Conclusion The results indicate that plasma S100A12 level is an independent predictor for two-year all-cause mortality . A simple integer scoring system was therefore established for predicting mortality on the basis of plasma S100A12 levels BACKGROUND AND OBJECTIVES Prognostic information is rarely conveyed by nephrologists because of clinical uncertainty about accuracy . The objective of this study was to develop an integrated prognostic model of 6-mo survival for patients who receive hemodialysis ( HD ) .  DESIGN , SETTING , PARTICIPANTS , & MEASUREMENTS A short-term prognostic model was developed using prospect i ve data from a derivation cohort of 512 patients who were receiving HD at five dialysis clinics . Patient charts were review ed for actuarial predictors ( e.g. , Charlson Comorbidity ) , and nephrologists answered the " surprise " question ( SQ ) , " Would I be surprised if this patient died within the next 6 mo ? " Survival was monitored for up to 24 mo . The prognostic model was tested with a validation cohort of 514 patients from eight clinics . RESULTS In a Cox multivariate analysis of the derivation cohort , five variables were independently associated with early mortality : Older age ( hazard ratio [ HR ] for a 10-yr increase 1.36 ; 95 % confidence interval [ CI ] 1.17 to 1.57 ) , dementia ( HR 2.24 ; 95 % CI 1.11 to 4.48 ) , peripheral vascular disease ( HR 1.88 ; 95 % CI 1.24 to 2.84 ) , decreased albumin ( HR for a 1-U increase 0.27 ; 95 % CI 0.15 to 0.50 ) , and SQ ( HR 2.71 ; 95 % CI 1.76 to 4.17 ) . Area under the curve for the result ing prognostic model predictions of 6-mo mortality were 0.87 ( 95 % CI 0.82 to 0.92 ) in the derivation cohort and 0.80 ( 95 % CI 0.73 to 0.88 ) in the validation cohort . CONCLUSIONS An integrated 6-mo prognostic tool was developed and vali date d for the HD population . The instrument may be of value for research ers and clinicians to improve end-of-life care by providing more accurate prognostic information BACKGROUND Recent guidelines from the US National Kidney Foundation Dialysis Outcomes Quality Initiative recommend an earlier start of dialysis treatment than has been common practice . Their implementation would have a substantial effect on patients ' daily lives and would increase costs . The guidelines are largely opinion-based , because evidence is still lacking . METHODS As part of a prospect i ve multicentre study in the Netherl and s , we included , between January , 1997 , and May , 1999 , all new patients with end-stage renal disease , for whom data were available on residual renal function 0 - 4 weeks before the start of dialysis .  We recorded date of death or censoring until August , 2000 . FINDINGS 94 ( 37 % ) of 253 patients started  dialysis treatment  later than recommended by the US guideline . There was an increased mortality risk for these patients compared with those who started dialysis on time , although it was not significant ( adjusted hazard ratio 1.66 [ 95 % CI 0.95 - 2.89 ] ) . The adjusted difference in estimated survival time after 3 years on dialysis treatment was 2.5 months ( 1.1 - 4.0 ) in favour of timely starters . Conversely , the average delay in dialysis initiation for late starters , the extra time free of dialysis , was at least 4.1 months . INTERPRETATION Although we observed a gain in survival time with a timely start of dialysis , it is probably a reflection of initiating dialysis earlier in the disease . We question the benefit of putting this guideline into daily practice , given the current clinical evidence and the effects it would have on patients and dialysis re sources Objective To identify existing prediction models for the risk of development of type 2 diabetes and to externally vali date them in a large independent cohort . Data sources Systematic search of English , German , and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes . Design Performance of the models was assessed in terms of discrimination ( C statistic ) and calibration ( calibration plots and Hosmer-Lemeshow test).The validation study was a prospect i ve cohort study , with a case cohort study in a r and om subcohort . Setting Models were applied to the Dutch cohort of the European Prospect i ve Investigation into Cancer and Nutrition cohort study ( EPIC-NL ) . Participants 38 379 people aged 20 - 70 with no diabetes at baseline , 2506 of whom made up the r and om subcohort . Outcome measure Incident type 2 diabetes . Results The review identified 16 studies containing 25 prediction models . We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration . During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the r and om subcohort . The C statistic for the basic models ranged from 0.74 ( 95 % confidence interval 0.73 to 0.75 ) to 0.84 ( 0.82 to 0.85 ) for risk at 7.5 years . For prediction models including biomarkers the C statistic ranged from 0.81 ( 0.80 to 0.83 ) to 0.93 ( 0.92 to 0.94 ) . Most prediction models overestimated the observed risk of diabetes , particularly at higher observed risks . After adjustment for differences in incidence of diabetes , calibration improved considerably . Conclusions Most basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years . Models including biomarkers classified cases slightly better than basic ones . Most models overestimated the actual risk of diabetes . Existing prediction models therefore perform well to identify those at high risk , but can not sufficiently quantify actual risk of future diabetes BACKGROUND The risk of death in  dialysis patients is high , but varies significantly among patients . No prediction tool is used widely in current clinical practice . We aim ed to predict long-term mortality in incident dialysis patients using easily obtainable variables . STUDY DESIGN Prospect i ve nationwide multicenter cohort study in the United Kingdom ( UK Renal Registry ) ; models were developed using Cox proportional hazards . SETTING & PARTICIPANTS Patients initiating hemodialysis or peritoneal dialysis therapy in 2002 - 2004 who survived at least 3 months on dialysis treatment were followed up for 3 years . Analyses were restricted to participants for whom information for comorbid conditions and laboratory measurements were available ( n = 5,447 ) . The data set was divided into data sets for model development ( n = 3,631 ; training ) and validation ( n = 1,816 ) using r and om selection . PREDICTORS Basic patient characteristics , comorbid conditions , and laboratory variables . OUTCOMES All-cause mortality censored for kidney transplant , recovery of kidney function , and loss to follow-up . RESULTS In the training data set , 1,078 patients ( 29.7 % )  died within the observation period . The final model for the training data set included patient characteristics ( age , race , primary kidney disease , and treatment modality ) , comorbid conditions ( diabetes , history of cardiovascular disease , and smoking ) , and laboratory variables ( hemoglobin , serum albumin , creatinine , and calcium levels ) ; reached a C statistic of 0.75 ( 95 % CI , 0.73 - 0.77 ) ; and could discriminate accurately among patients with low ( 6 % ) , intermediate ( 19 % ) , high ( 33 % ) , and very high ( 59 % ) mortality risk . The model was applied further to the validation data set and achieved a C statistic of 0.73 ( 95 % CI , 0.71 - 0.76 ) . LIMITATIONS Number of missing comorbidity data and lack of an external validation data set . CONCLUSIONS Basic patient characteristics , comorbid conditions , and laboratory variables can predict 3-year mortality in incident  dialysis patients with sufficient accuracy . Identification of subgroups of patients according to mortality risk can guide future research and subsequently target treatment decisions in individual patients In many fields , including the field of nephrology , missing data are unfortunately an unavoidable problem in clinical /epidemiological research . The most common methods for dealing with missing data are complete case analysis -excluding patients with missing data --mean substitution -- replacing missing values of a variable with the average of known values for that variable- and last observation carried forward . However , these methods have severe drawbacks potentially result ing in biased estimates and /or st and ard errors . In recent years , a new method has arisen for dealing with missing data called multiple imputation . This method predicts missing values based on other data present in the same patient . This procedure is repeated several times , result ing in multiple imputed data sets . Thereafter , estimates and st and ard errors are calculated in each imputation set and pooled into one overall estimate and st and ard error . The main advantage of this method is that missing data uncertainty is taken into account . Another advantage is that the method of multiple imputation gives unbiased results when data are missing at r and om , which is the most common type of missing data in clinical practice , whereas conventional methods do not . However , the method of multiple imputation has scarcely been used in medical literature . We , therefore , encourage authors to do so in the future when possible BACKGROUND Mortality and morbidity rates are higher in patients receiving haemodialysis therapy  than in the general population . Detection of risk factors related to early death in these patients could be of aid for clinical and administrative decision making . Objectives . The aims of this study were ( 1 ) to identify risk factors ( comorbidity and variables specific to haemodialysis ) associated with death in the first year following the start of haemodialysis and ( 2 ) to design and vali date a prognostic model to quantify the probability of death for each patient . METHODS An analysis was carried out on all patients starting haemodialysis treatment in Catalonia during the period 1997 - 2003 ( n = 5738 ) . The data source was the Renal Registry of Catalonia , a m and atory population registry . Patients were r and omly divided into two sample s : 60 % ( n = 3455 ) of the total were used to develop the prognostic model and the remaining 40 % ( n = 2283 ) to vali date the model . Logistic regression analysis was used to construct the model . RESULTS One-year mortality in the total study population was 16.5 % . The predictive model included the following variables : age , sex , primary renal disease , grade of functional autonomy , chronic obstructive pulmonary disease , malignant processes , chronic liver disease , cardiovascular disease , initial vascular access and malnutrition . The analyses showed adequate calibration for both the sample to develop the model and the validation sample ( Hosmer-Lemeshow statistic 0.97 and P = 0. 
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According to the calibration , most models overestimated the probability of death .
Conclusion Overall , the performance of the models was poorer in the external validation than in the original population , affirming the importance of external validation .
Floege et al ’s models showed the highest predictive performance .