Prognostic models that aim to improve the prediction of clinical events, individualized treatment and decision-making are increasingly being developed and published. However, relatively few models are externally validated and validation by independent researchers is rare. External validation is necessary to determine a prediction model’s reproducibility and generalizability to new and different patients. Various methodological considerations are important when assessing or designing an external validation study. In this article, an overview is provided of these considerations, starting with what external validation is, what types of external validation can be distinguished and why such studies are a crucial step towards the clinical implementation of accurate prediction models. Statistical analyses and interpretation of external validation results are reviewed in an intuitive manner and considerations for selecting an appropriate existing prediction model and external validation population are discussed. This study enables clinicians and researchers to gain a deeper understanding of how to interpret model validation results and how to translate these results to their own patient population.

Study queStionWhat is the predicted risk of acute kidney injury after orthopaedic surgery and does it affect short term and long term survival? MethodSThe cohort comprised adults resident in the National Health Service Tayside region of Scotland who underwent orthopaedic surgery from 1 January 2005 to 31 December 2011. The model was developed in 6220 patients (two hospitals) and externally validated in 4395 patients from a third hospital. Several preoperative variables were selected for candidate predictors, based on literature, clinical expertise, and availability in the orthopaedic surgery setting. The main outcomes were the development of any severity of acute kidney injury (stages 1-3) within the first postoperative week, and 90 day, one year, and longer term survival. Study anSwer and liMitationSUsing logistic regression analysis, independent predictors of acute kidney injury were older age, male sex, diabetes, number of prescribed drugs, lower estimated glomerular filtration rate, use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers, and American Society of Anesthesiologists grade. The model's predictive performance for discrimination was good (C statistic 0.74 in development cohort, 0.70 in validation cohort). Calibration was good in the development cohort and after recalibration in the validation cohort. Only the highest risks were over-predicted. Survival was worse in patients with acute kidney injury compared with those without (adjusted hazard ratio 1.53, 95% confidence interval 1.38 to 1.70). This was most noticeable in the short term (adjusted hazard ratio: 90 day 2.36, 1.94 to 2.87) and diminished over time (90 day-one year 1.40, 1.10 to 1.79; >1 year 1.28, 1.10 to 1.48). The model used routinely collected data in the orthopaedic surgery setting therefore some variables that could potentially improve predictive performance were not available. However, the readily available predictors make the model easily applicable.what thiS Study addS A preoperative risk prediction model consisting of seven predictors for acute kidney injury was developed, with good predictive performance in patients undergoing orthopaedic surgery. Survival was significantly poorer in patients even with mild (stage 1) postoperative acute kidney injury.

Cardiovascular mortality is high in ESRD, partly driven by sudden cardiac death and recurrent heart failure due to uremic cardiomyopathy. We investigated whether speckle-tracking echocardiography is superior to routine echocardiography in early detection of uremic cardiomyopathy in animal models and whether it predicts cardiovascular mortality in patients undergoing dialysis. Using speckle-tracking echocardiography in two rat models of uremic cardiomyopathy soon (4-6 weeks) after induction of kidney disease, we observed that global radial and circumferential strain parameters decreased significantly in both models compared with controls, whereas standard echocardiographic readouts, including fractional shortening and cardiac output, remained unchanged. Furthermore, strain parameters showed better correlations with histologic hallmarks of uremic cardiomyopathy. We then assessed echocardiographic and clinical characteristics in 171 dialysis patients. During the 2.5-year follow-up period, ejection fraction and various strain parameters were significant risk factors for cardiovascular mortality (primary end point) in a multivariate Cox model (ejection fraction hazard ratio [HR], 0.97 [95% confidence interval (95% CI), 0.95 to 0.99; P=0.012]; peak global longitudinal strain HR, 1.17 [95% CI, 1.07 to 1.28; P,0.001]; peak systolic and late diastolic longitudinal strain rates HRs, 4.7 [95% CI, 1.23 to 17.64; P=0.023] and 0.25 [95% CI, 0.08 to 0.79; P=0.02], respectively). Multivariate Cox regression analysis revealed circumferential early diastolic strain rate, among others, as an independent risk factor for all-cause mortality (secondary end point; HR, 0.43; 95% CI, 0.25 to 0.74; P=0.002). Together, these data support speckle tracking as a postprocessing echocardiographic technique to detect uremic cardiomyopathy and predict cardiovascular mortality in ESRD.

Background Acute kidney injury (AKI) can affect hospitalized patients with coronavirus disease 2019 (COVID-19), with estimates ranging between 0.5% and 40%. We performed a systematic review and meta-analysis of studies reporting incidence, mortality and risk factors for AKI in hospitalized COVID-19 patients. Methods We systematically searched 11 electronic databases until 29 May 2020 for studies in English reporting original data on AKI and kidney replacement therapy (KRT) in hospitalized COVID-19 patients. Incidences of AKI and KRT and risk ratios for mortality associated with AKI were pooled using generalized linear mixed and random-effects models. Potential risk factors for AKI were assessed using meta-regression. Incidences were stratified by geographic location and disease severity. Results A total of 3042 articles were identified, of which 142 studies were included, with 49 048 hospitalized COVID-19 patients including 5152 AKI events. The risk of bias of included studies was generally low. The pooled incidence of AKI was 28.6% [95% confidence interval (CI) 19.8–39.5] among hospitalized COVID-19 patients from the USA and Europe (20 studies) and 5.5% (95% CI 4.1–7.4) among patients from China (62 studies), whereas the pooled incidence of KRT was 7.7% (95% CI 5.1–11.4; 18 studies) and 2.2% (95% CI 1.5–3.3; 52 studies), respectively. Among patients admitted to the intensive care unit, the incidence of KRT was 20.6% (95% CI 15.7–26.7; 38 studies). Meta-regression analyses showed that age, male sex, cardiovascular disease, diabetes mellitus, hypertension and chronic kidney disease were associated with the occurrence of AKI; in itself, AKI was associated with an increased risk of mortality, with a pooled risk ratio of 4.6 (95% CI 3.3–6.5). Conclusions AKI and KRT are common events in hospitalized COVID-19 patients, with estimates varying across geographic locations. Additional studies are needed to better understand the underlying mechanisms and optimal treatment of AKI in these patients.

BackgroundIt is unknown whether stopping renin-angiotensin system (RAS) inhibitor therapy in patients with advanced CKD affects outcomes.MethodsWe studied patients referred to nephrologist care, listed on the Swedish Renal Registry during 2007–2017, who developed advanced CKD (eGFR<30 ml/min per 1.73 m2) while on RAS inhibitor therapy. Using target trial emulation techniques on the basis of cloning, censoring, and weighting, we compared the risks of stopping within 6 months and remaining off treatment versus continuing RAS inhibitor therapy. These included risks of subsequent 5-year all-cause mortality, major adverse cardiovascular events, and initiation of kidney replacement therapy (KRT).ResultsOf 10,254 prevalent RAS inhibitor users (median age 72 years, 36% female) with new-onset eGFR <30 ml/min per 1.73 m2, 1553 (15%) stopped RAS inhibitor therapy within 6 months. Median eGFR was 23 ml/min per 1.73 m2. Compared with continuing RAS inhibition, stopping this therapy was associated with a higher absolute 5-year risk of death (40.9% versus 54.5%) and major adverse cardiovascular events (47.6% versus 59.5%), but with a lower risk of KRT (36.1% versus 27.9%); these corresponded to absolute risk differences of 13.6 events per 100 patients, 11.9 events per 100 patients, and −8.3 events per 100 patients, respectively. Results were consistent whether patients stopped RAS inhibition at higher or lower eGFR, across prespecified subgroups, after adjustment and stratification for albuminuria and potassium, and when modeling RAS inhibition as a time-dependent exposure using a marginal structural model.ConclusionsIn this nationwide observational study of people with advanced CKD, stopping RAS inhibition was associated with higher absolute risks of mortality and major adverse cardiovascular events, but also with a lower absolute risk of initiating KRT.

For the presentation of risk, both relative and absolute measures can be used. The relative risk is most often used, especially in studies showing the effects of a treatment. Relative risks have the appealing feature of summarizing two numbers (the risk in one group and the risk in the other) into one. However, this feature also represents their major weakness, that the underlying absolute risks are concealed and readers tend to overestimate the effect when it is presented in relative terms. In many situations, the absolute risk gives a better representation of the actual situation and also from the patient's point of view absolute risks often give more relevant information. In this article, we explain the concepts of both relative and absolute risk measures. Using examples from nephrology literature we illustrate that unless ratio measures are reported with the underlying absolute risks, readers cannot judge the clinical relevance of the effect. We therefore recommend to report both the relative risk and the absolute risk with their 95% confidence intervals, as together they provide a complete picture of the effect and its implications.

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 resulting in biased estimates and/or standard 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, resulting in multiple imputed data sets. Thereafter, estimates and standard errors are calculated in each imputation set and pooled into one overall estimate and standard 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 random, 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 Prediction tools that identify chronic kidney disease (CKD) patients at a high risk of developing kidney failure have the potential for great clinical value, but limited uptake. The aim of the current study is to systematically review all available models predicting kidney failure in CKD patients, organize empirical evidence on their validity and ultimately provide guidance in the interpretation and uptake of these tools. Methods PubMed and EMBASE were searched for relevant articles. Titles, abstracts and full-text articles were sequentially screened for inclusion by two independent researchers. Data on study design, model development and performance were extracted. The risk of bias and clinical usefulness were assessed and combined in order to provide recommendations on which models to use. Results Of 2183 screened studies, a total of 42 studies were included in the current review. Most studies showed high discriminatory capacity and the included predictors had large overlap. Overall, the risk of bias was high. Slightly less than half the studies (48%) presented enough detail for the use of their prediction tool in practice and few models were externally validated. Conclusions The current systematic review may be used as a tool to select the most appropriate and robust prognostic model for various settings. Although some models showed great potential, many lacked clinical relevance due to being developed in a prevalent patient population with a wide range of disease severity. Future research efforts should focus on external validation and impact assessment in clinically relevant patient populations.