The Etiology of Advanced Chronic Kidney Disease in Southeast Asia: A Meta-analysis.

INTRODUCTION: Chronic kidney disease (CKD) etiology varies greatly between developed and developing countries. In addition, differences in underlying pathogenesis and therapeutic options affect the progression towards advanced-CKD. This meta-analysis aims to identify the etiology of advanced-CKD in Southeast Asia. METHODS: A systematic search in four electronic-databases and complementary search on national kidney registries and repository libraries was conducted until July 20, 2023. The risk of bias was assessed using Newcastle-Ottawa Scale for observational studies and Version-2 of Cochrane for intervention studies. A random-effects model was used to estimate pooled prevalence. The protocol is registered in the International Prospective Register of Systematic Reviews PROSPERO; Registration ID:CRD42022300786. RESULTS: We analyzed 81 studies involving 32,834 subjects. The pooled prevalence of advanced-CKD etiologies are diabetic kidney disease (DKD) 29.2% (95%CI 23.88-34.78), glomerulonephritis 20.0% (95%CI 16.84-23.38), hypertension 16.8% (95%CI 14.05-19.70), other 8.6% (95%CI 6.97-10.47), unknown 7.5% (95%CI 4.32-11.50), and polycystic kidney disease 0.7% (95%CI 0.40-1.16). We found a significant increase in DKD prevalence from 21% (9.2%, 95%CI 0.00-33.01) to 30% (95%CI 24.59-35.97) before and after the year 2000. Among upper-middle-income and high-income countries, DKD is the most prevalent (26.8%, 95%CI 21.42-32.60 and 38.9%, 95%CI 29.33-48.79, respectively), while glomerulonephritis is common in lower-middle-income countries (33.8%, 95%CI 15.62-54.81). CONCLUSION: The leading cause of advanced-CKD in Southeast Asia is DKD, with a substantial proportion of glomerulonephritis. An efficient screening program targeting high-risk populations (diabetes mellitus and glomerulonephritis) is needed, with the aim to delay CKD progression.

Clinical impact of the Kidney Failure Risk Equation for vascular access planning.

BACKGROUND: Risk-based thresholds for arteriovenous (AV) access creation has been proposed to aid vascular access planning. We aimed to assess the clinical impact of implementing the kidney failure risk equation (KFRE) for vascular access referral. METHODS: 16,102 nephrology-referred chronic kidney disease (CKD) patients from the Swedish Renal Registry 2008-2018 were included. The KFRE was calculated repeatedly, and the timing was identified for when the KFRE risk exceeded several pre-defined thresholds and/or the estimated glomerular filtration rate <15 ml/min/1.73m2 (eGFR15). To assess the utility of the KFRE/eGFR thresholds, cumulative incidence curves of kidney replacement therapy (KRT) or death, and decision-curve analyses were computed at 6, 12 months, and 2 years. The potential impact of using the different thresholds was illustrated by an example from the Swedish access registry. RESULTS: The 12-month specificity for KRT initiation was highest for KFRE>50% 94.5 (95% Confidence interval [CI] 94.3-94.7), followed by KFRE>40% 90.0 (95% CI 89.7-90.3), while sensitivity was highest for KFRE>30% 79.3 (95% CI 78.2-80.3) and eGFR<15 ml/min/1.73m2 81.2 (95% CI 80.2-82.2). The 2-year positive predictive value was 71.5 (95% CI 70.2-72.8), 61.7 (95% CI 60.4-63.0) and 47.2 (95% CI 46.1-48.3) for KFRE>50%, KFRE>40%, and eGFR<15 respectively. Decision curve analyses suggested the largest net benefit for KFRE>40% over two years and KFRE>50% over 12 months when it is important to avoid the harm of possibly unnecessary surgery. In Sweden, 54% of nephrology-referred patients started hemodialysis in a central venous catheter (CVC) of which only 5% had AV access surgery >6 months before initiation. 60% of the CVC patients exceeded KFRE>40% a median of 0.8 years (interquartile range 0.4-1.5) before KRT initiation. CONCLUSIONS: The utility of using KFRE>40% and KFRE>50% is higher compared to the more traditionally used eGFR threshold <15 ml/min/1.73m2 for vascular access planning.

A tool to predict the risk of lower extremity amputation in patients starting dialysis.

BACKGROUND AND HYPOTHESIS: Non-traumatic lower extremity amputation (LEA) is a severe complication during dialysis. To inform decision-making for physicians, we developed a multivariable prediction model for LEA after starting dialysis. METHODS: Data from the Swedish Renal Registry (SNR) between 2010 and 2020 were geographically split into a development and validation cohort. Data from NECOSAD between 1997 and 2009 were used for validation targeted at Dutch patients. Inclusion criteria were no previous LEA and kidney transplant and age ≥ 40 years at baseline. A Fine-Gray model was developed with LEA within 3 years after starting dialysis as outcome of interest. Death and kidney transplant were treated as competing events. One coefficient, ordered by expected relevance, per 20 events was estimated. Performance was assessed with calibration and discrimination. RESULTS: SNR was split into an urban development cohort with 4 771 individuals experiencing 201 (4.8%) events and a rural validation cohort with 4.876 individuals experiencing 155 (3.2%) events. NECOSAD contained 1 658 individuals experiencing 61 (3.7%) events. Ten predictors were included: female sex, age, diabetes mellitus, peripheral artery disease, cardiovascular disease, congestive heart failure, obesity, albumin, haemoglobin and diabetic retinopathy. In SNR, calibration intercept and slope were -0.003 and 0.912 respectively. The C-index was estimated as 0.813 (0.783-0.843). In NECOSAD, calibration intercept and slope were 0.001 and 1.142 respectively. The C-index was estimated as 0.760 (0.697-0.824). Calibration plots showed good calibration. CONCLUSION: A newly developed model to predict LEA after starting dialysis showed good discriminatory performance and calibration. By identifying high-risk individuals this model could help select patients for preventive measures.

Prognostic Models in Nephrology: Where Do We Stand and Where Do We Go from Here? Mapping Out the Evidence in a Scoping Review.

Prognostic models can strongly support individualized care provision and well-informed shared decision making. There has been an upsurge of prognostic research in the field of nephrology, but the uptake of prognostic models in clinical practice remains limited. Therefore, we map out the research field of prognostic models for kidney patients and provide directions on how to proceed from here. We performed a scoping review of studies developing, validating, or updating a prognostic model for patients with CKD. We searched all published models in PubMed and Embase and report predicted outcomes, methodological quality, and validation and/or updating efforts. We found 602 studies, of which 30.1% concerned CKD populations, 31.6% dialysis populations, and 38.4% kidney transplantation populations. The most frequently predicted outcomes were mortality ( n =129), kidney disease progression ( n =75), and kidney graft survival ( n =54). Most studies provided discrimination measures (80.4%), but much less showed calibration results (43.4%). Of the 415 development studies, 28.0% did not perform any validation and 57.6% performed only internal validation. Moreover, only 111 models (26.7%) were externally validated either in the development study itself or in an independent external validation study. Finally, in 45.8% of development studies no useable version of the model was reported. To conclude, many prognostic models have been developed for patients with CKD, mainly for outcomes related to kidney disease progression and patient/graft survival. To bridge the gap between prediction research and kidney patient care, patient-reported outcomes, methodological rigor, complete reporting of prognostic models, external validation, updating, and impact assessment urgently need more attention.

Considerable Variability Among Transplant Nephrologists in Judging Deceased Donor Kidney Offers.

Introduction: Transplant clinicians may disagree on whether or not to accept a deceased donor kidney offer. We investigated the interobserver variability between transplant nephrologists regarding organ acceptance and whether the use of a prediction model impacted their decisions. Methods: We developed an observational online survey with 6 real-life cases of deceased donor kidneys offered to a waitlisted recipient. Per case, nephrologists were asked to estimate the risk of adverse outcome and whether they would accept the offer for this patient, or for a patient of their own choice, and how certain they felt. These questions were repeated after revealing the risk of adverse outcome, calculated by a validated prediction model. Results: Sixty Dutch nephrologists completed the survey. The intraclass correlation coefficient of their estimated risk of adverse outcome was poor (0.20, 95% confidence interval [CI] 0.08-0.62). Interobserver agreement of the decision on whether or not to accept the kidney offer was also poor (Fleiss kappa 0.13, 95% CI 0.129-0.130). The acceptance rate before and after providing the outcome of the prediction model was significantly influenced in 2 of 6 cases. Acceptance rates varied considerably among transplant centers. Conclusion: In this study, the estimated risk of adverse outcome and subsequent decision to accept a suboptimal donor kidney varied greatly among transplant nephrologists. The use of a prediction model could influence this decision and may enhance nephrologists' certainty about their decision.

The Etiology of Kidney Failure in Indonesia: A Multicenter Study in Tertiary-Care Centers in Jakarta.

Background: Despite a large number of patients requiring dialysis, the etiology of kidney failure is poorly documented in Indonesia. With the aim to reduce the disease burden, it is essential to obtain more insight in the etiology of chronic kidney disease (CKD). Objectives: In the present study, we attempted to investigate the primary renal disease of kidney failure patients from five tertiary-care centers in Jakarta. Methods: This is a multicenter, cross-sectional study of kidney failure patients receiving kidney replacement therapy (KRT), from December 2021 to July 2022. We recruited patients aged ≥18 years, had been receiving dialysis for at least three months or a kidney transplantation. Findings: This study included 1,152 patients treated with hemodialysis (68.1%), peritoneal dialysis (7.5%), and kidney transplantation (24.4%). At the start of KRT, the median (interquartile-range [IQR]) age was 48 [37-58] years with low eGFR (median [IQR]: 5.9 [4.0-8.34] ml/minute/1.73 m2). Hypertension was the main comorbidity (74.2%), followed by diabetes mellitus (30.1%). The major primary kidney disease was diabetic kidney disease (27.2%), followed by glomerulonephritis (13.0%), hypertension (11.5%), and urolithiasis (10.3%). Lupus nephritis was the common underlying etiology of secondary glomerulonephritis (91%). A high rate of unknown cause (31.1%) was also observed. Conclusions: Our results suggest that diabetic kidney disease is the leading cause of kidney failure in Jakarta, followed by glomerulonephritis. This study highlights the need for a better approach on primary prevention of diabetes mellitus as well as to better recognize glomerulonephritis at earlier stage might have a significant impact on reduction of the rate of kidney failure in Indonesia.

Level of Overestimation Among Dutch Recreational Skiers: Unskilled Tourists in the Mountains.

OBJECTIVE: To examine the level of overestimation (LO), associated factors, and identify the group of severe overestimators, among recreational skiers. DESIGN: Cross-sectional observational study. SETTING: An intermediate difficulty slope in an artificial snow indoor ski hall, and one in the mountains (Flachau, Austria). PARTICIPANTS: Dutch recreational skiers. INDEPENDENT VARIABLES: Participants were asked to rate themselves (SRSS, self-reported skill score). While skiing downhill they were objectively evaluated by 2 expert assessors (OSS, observed skill score). Potential associated factors and predictors for severe overestimation were assessed by a questionnaire. MAIN OUTCOME MEASURES: The LO, calculated by subtracting the OSS from the SRSS, was categorized into "no," "mild," and "severe." Potential differences between these groups were analyzed, and regression analyses were performed to identify the factors associated with severe overestimation. To construct a profile of severe overestimators, the dataset was stratified based on 3 variables. RESULTS: Overestimation was largely present (79.8%), and was severe in 32%. The LO decreased toward the more skilled skiers. Severe overestimators were mainly male, skied the least hours per day, were more avoidant, and showed the highest proportions of beginners and slightly advanced skiers. The profile of "severe overestimator" is characterized by physically unprepared males, avoidant for certain weather circumstances. CONCLUSIONS: Overestimation among recreational Dutch skiers is largely present, particularly among physically unprepared males, avoidant of certain snow and weather conditions. These features may function as a proxy to identify "severe overestimators" in comparable populations. Preventive strategies should focus to increase awareness particularly among these subjects.

Systematic metareview of prediction studies demonstrates stable trends in bias and low PROBAST inter-rater agreement.

OBJECTIVES: To (1) explore trends of risk of bias (ROB) in prediction research over time following key methodological publications, using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and (2) assess the inter-rater agreement of the PROBAST. STUDY DESIGN AND SETTING: PubMed and Web of Science were searched for reviews with extractable PROBAST scores on domain and signaling question (SQ) level. ROB trends were visually correlated with yearly citations of key publications. Inter-rater agreement was assessed using Cohen's Kappa. RESULTS: One hundred and thirty nine systematic reviews were included, of which 85 reviews (containing 2,477 single studies) on domain level and 54 reviews (containing 2,458 single studies) on SQ level. High ROB was prevalent, especially in the Analysis domain, and overall trends of ROB remained relatively stable over time. The inter-rater agreement was low, both on domain (Kappa 0.04-0.26) and SQ level (Kappa -0.14 to 0.49). CONCLUSION: Prediction model studies are at high ROB and time trends in ROB as assessed with the PROBAST remain relatively stable. These results might be explained by key publications having no influence on ROB or recency of key publications. Moreover, the trend may suffer from the low inter-rater agreement and ceiling effect of the PROBAST. The inter-rater agreement could potentially be improved by altering the PROBAST or providing training on how to apply the PROBAST.

Predicting mortality after start of long-term dialysis-International validation of one- and two-year prediction models.

BACKGROUND: Mortality prediction is critical on long-term kidney replacement therapy (KRT), both for individual treatment decisions and resource planning. Many mortality prediction models already exist, but as a major shortcoming most of them have only been validated internally. This leaves reliability and usefulness of these models in other KRT populations, especially foreign, unknown. Previously two models were constructed for one- and two-year mortality prediction of Finnish patients starting long-term dialysis. These models are here internationally validated in KRT populations of the Dutch NECOSAD Study and the UK Renal Registry (UKRR). METHODS: We validated the models externally on 2051 NECOSAD patients and on two UKRR patient cohorts (5328 and 45493 patients). We performed multiple imputation for missing data, used c-statistic (AUC) to assess discrimination, and evaluated calibration by plotting average estimated probability of death against observed risk of death. RESULTS: Both prediction models performed well in the NECOSAD population (AUC 0.79 for the one-year model and 0.78 for the two-year model). In the UKRR populations, performance was slightly weaker (AUCs: 0.73 and 0.74). These are to be compared to the earlier external validation in a Finnish cohort (AUCs: 0.77 and 0.74). In all tested populations, our models performed better for PD than HD patients. Level of death risk (i.e., calibration) was well estimated by the one-year model in all cohorts but was somewhat overestimated by the two-year model. CONCLUSIONS: Our prediction models showed good performance not only in the Finnish but in foreign KRT populations as well. Compared to the other existing models, the current models have equal or better performance and fewer variables, thus increasing models' usability. The models are easily accessible on the web. These results encourage implementing the models into clinical decision-making widely among European KRT populations.

Estimating skills level in recreational skiing: Development and validation of a practical multidimensional instrument.

Skiing and snowboarding are both popular recreational alpine sports, with substantial injury risk of variable severity. Although skills level has repeatedly been associated with injury risk, a validated measure to accurately estimate the actual skills level without objective assessment is missing. This study aimed to develop a practical validated instrument, to better estimate the actual skills level of recreational skiers, based on the criteria of the Dutch Skiing Federation (DSF), and covering five different skill domains. A sample of Dutch recreational skiers (n = 84) was asked to fill in a questionnaire reflecting seven, a priori chosen predictors by expert opinion, to ski downhill and to be objectively evaluated by expert assessors. The instrument was developed to have a multidimensional character and was validated according to the TRIPOD guideline (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). The sample reported an overall incorrect self-reported estimation of their skills, compared with the observed skill score. The instrument showed good calibration and underwent multiple validation methods. The estimated skills score showed to be closer to the observed scores, than self-reportage. Our study provides a practical, multidimensional, and validated instrument to estimate the actual skills level. It proved to better reflect the actual skills levels compared with self-reportage among recreational skiers.

Predicting Kidney Failure, Cardiovascular Disease and Death in Advanced CKD Patients.

Introduction: Predicting the timing and occurrence of kidney replacement therapy (KRT), cardiovascular events, and death among patients with advanced chronic kidney disease (CKD) is clinically useful and relevant. We aimed to externally validate a recently developed CKD G4+ risk calculator for these outcomes and to assess its potential clinical impact in guiding vascular access placement. Methods: We included 1517 patients from the European Quality (EQUAL) study, a European multicentre prospective cohort study of nephrology-referred advanced CKD patients aged ≥65 years. Model performance was assessed based on discrimination and calibration. Potential clinical utility for timing of referral for vascular access placement was studied with diagnostic measures and decision curve analysis (DCA). Results: The model showed a good discrimination for KRT and "death after KRT," with 2-year concordance (C) statistics of 0.74 and 0.76, respectively. Discrimination for cardiovascular events (2-year C-statistic: 0.70) and overall death (2-year C-statistic: 0.61) was poorer. Calibration was fairly accurate. Decision curves illustrated that using the model to guide vascular access referral would generally lead to less unused arteriovenous fistulas (AVFs) than following estimated glomerular filtration rate (eGFR) thresholds. Conclusion: This study shows moderate to good predictive performance of the model in an older cohort of nephrology-referred patients with advanced CKD. Using the model to guide referral for vascular access placement has potential in combating unnecessary vascular surgeries.

Comprehensive comparison of stroke risk score performance: a systematic review and meta-analysis among 6 267 728 patients with atrial fibrillation.

AIMS: Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations and updates, assess their methodological quality, and calculate pooled estimates of the predictive performance. METHODS AND RESULTS: We searched PubMed and Web of Science for studies developing, validating, or updating risk scores for IS in AF patients. Methodological quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). To assess discrimination, pooled c-statistics were calculated using random-effects meta-analysis. We identified 19 scores, which were validated and updated once or more in 70 and 40 studies, respectively, including 329 validations and 76 updates-nearly all on the CHA2DS2-VASc and CHADS2. Pooled c-statistics were calculated among 6 267 728 patients and 359 373 events of IS. For the CHA2DS2-VASc and CHADS2, pooled c-statistics were 0.644 [95% confidence interval (CI) 0.635-0.653] and 0.658 (0.644-0.672), respectively. Better discriminatory abilities were found in the newer risk scores, with the modified-CHADS2 demonstrating the best discrimination [c-statistic 0.715 (0.674-0.754)]. Updates were found for the CHA2DS2-VASc and CHADS2 only, showing improved discrimination. Calibration was reasonable but available for only 17 studies. The PROBAST indicated a risk of methodological bias in all studies. CONCLUSION: Nineteen risk scores and 76 updates are available to predict IS in patients with AF. The guideline-endorsed CHA2DS2-VASc shows inferior discriminative abilities compared with newer scores. Additional external validations and data on calibration are required before considering the newer scores in clinical practice. CLINICAL TRIAL REGISTRATION: ID CRD4202161247 (PROSPERO).

Use of guideline-recommended medical therapy in patients with heart failure and chronic kidney disease: from physician's prescriptions to patient's dispensations, medication adherence and persistence.

AIM: Half of heart failure (HF) patients have chronic kidney disease (CKD) complicating their pharmacological management. We evaluated physicians' and patients' patterns of use of evidence-based medical therapies in HF across CKD stages. METHODS AND RESULTS: We studied HF patients with reduced (HFrEF) and mildly reduced (HFmrEF) ejection fraction enrolled in the Swedish Heart Failure Registry in 2009-2018. We investigated the likelihood of physicians to prescribe guideline-recommended therapies to patients with CKD, and of patients to fill the prescriptions within 90 days of incident HF (initiating therapy), to adhere (proportion of days covered ≥80%) and persist (continued use) on these treatments during the first year of therapy. We identified 31 668 patients with HFrEF (median age 74 years, 46% CKD). The proportions receiving a prescription for angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEi/ARB/ARNi) were 96%, 92%, 86%, and 68%, for estimated glomerular filtration rate (eGFR) ≥60, 45-59, 30-44, and <30 ml/min/1.73 m2 , respectively; for beta-blockers 94%, 93%, 92%, and 92%, for mineralocorticoid receptor antagonists (MRAs) 45%, 44%, 37%, 24%; and for triple therapy (combination of ACEi/ARB/ARNi + beta-blockers + MRA) 38%, 35%, 28%, and 15%. Patients with CKD were less likely to initiate these medications, and less likely to adhere to and persist on ACEi/ARB/ARNi, MRA, and triple therapy. Among stoppers, CKD patients were less likely to restart these medications. Results were consistent after multivariable adjustment and in patients with HFmrEF (n = 15 114). CONCLUSIONS: Patients with HF and CKD are less likely to be prescribed and to fill prescriptions for evidence-based therapies, showing lower adherence and persistence, even at eGFR categories where these therapies are recommended and have shown efficacy in clinical trials.

Stopping versus continuing renin-angiotensin-system inhibitors after acute kidney injury and adverse clinical outcomes: an observational study from routine care data.

Background: The risk-benefit ratio of continuing with renin-angiotensin system inhibitors (RASi) after an episode of acute kidney injury (AKI) is unclear. While stopping RASi may prevent recurrent AKI or hyperkalaemia, it may deprive patients of the cardiovascular benefits of using RASi. Methods: We analysed outcomes of long-term RASi users experiencing AKI (stage 2 or 3, or clinically coded) during hospitalization in Stockholm and Sweden during 2007-18. We compared stopping RASi within 3 months after discharge with continuing RASi. The primary study outcome was the composite of all-cause mortality, myocardial infarction (MI) and stroke. Recurrent AKI was our secondary outcome and we considered hyperkalaemia as a positive control outcome. Propensity score overlap weighted Cox models were used to estimate hazard ratios (HRs), balancing 75 confounders. Weighted absolute risk differences (ARDs) were also determined. Results: We included 10 165 individuals, of whom 4429 stopped and 5736 continued RASi, with a median follow-up of 2.3 years. The median age was 78 years; 45% were women and median kidney function before the index episode of AKI was 55 mL/min/1.73 m2. After weighting, those who stopped had an increased risk [HR, 95% confidence interval (CI)] of the composite of death, MI and stroke [1.13, 1.07-1.19; ARD 3.7, 95% CI 2.6-4.8] compared with those who continued, a similar risk of recurrent AKI (0.94, 0.84-1.05) and a decreased risk of hyperkalaemia (0.79, 0.71-0.88). Discussion: Stopping RASi use among survivors of moderate-to-severe AKI was associated with a similar risk of recurrent AKI, but higher risk of the composite of death, MI and stroke.

External validation of risk scores to predict in-hospital mortality in patients hospitalized due to coronavirus disease 2019.

BACKGROUND: The coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Prediction models that accurately estimate mortality risk in hospitalized patients could assist medical staff in treatment and allocating limited resources. AIMS: To externally validate two promising previously published risk scores that predict in-hospital mortality among hospitalized COVID-19 patients. METHODS: Two prospective cohorts were available; a cohort of 1028 patients admitted to one of nine hospitals in Lombardy, Italy (the Lombardy cohort) and a cohort of 432 patients admitted to a hospital in Leiden, the Netherlands (the Leiden cohort). The endpoint was in-hospital mortality. All patients were adult and tested COVID-19 PCR-positive. Model discrimination and calibration were assessed. RESULTS: The C-statistic of the 4C mortality score was good in the Lombardy cohort (0.85, 95CI: 0.82-0.89) and in the Leiden cohort (0.87, 95CI: 0.80-0.94). Model calibration was acceptable in the Lombardy cohort but poor in the Leiden cohort due to the model systematically overpredicting the mortality risk for all patients. The C-statistic of the CURB-65 score was good in the Lombardy cohort (0.80, 95CI: 0.75-0.85) and in the Leiden cohort (0.82, 95CI: 0.76-0.88). The mortality rate in the CURB-65 development cohort was much lower than the mortality rate in the Lombardy cohort. A similar but less pronounced trend was found for patients in the Leiden cohort. CONCLUSION: Although performances did not differ greatly, the 4C mortality score showed the best performance. However, because of quickly changing circumstances, model recalibration may be necessary before using the 4C mortality score.

Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models.

BACKGROUND: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounted for when comparing predicted risks to observed outcomes. METHODS: We discuss existing measures of calibration and discrimination that incorporate competing events for time-to-event models. These methods are illustrated using a clinical-data example concerning the prediction of kidney failure in a population with advanced chronic kidney disease (CKD), using the guideline-recommended Kidney Failure Risk Equation (KFRE). The KFRE was developed using Cox regression in a diverse population of CKD patients and has been proposed for use in patients with advanced CKD in whom death is a frequent competing event. RESULTS: When validating the 5-year KFRE with methods that account for competing events, it becomes apparent that the 5-year KFRE considerably overestimates the real-world risk of kidney failure. The absolute overestimation was 10%age points on average and 29%age points in older high-risk patients. CONCLUSIONS: It is crucial that competing events are accounted for during external validation to provide a more reliable assessment the performance of a model in clinical settings in which competing risks occur.

The importance of motivation in selecting undergraduate medical students for extracurricular research programmes.

INTRODUCTION: Extracurricular research programmes (ERPs) may contribute to reducing the current shortage in physician-scientists, but usually select students based on grades only. The question arises if students should be selected based on their motivation, regardless of their previous academic performance. Focusing on grades and lacking to take motivation into account when selecting students for ERPs might exclude an important target group when aiming to cultivate future physician-scientists. Therefore, this study compared ERP students with lower and higher previous academic performance on subsequent academic performance, ERP performance, and motivational factors. METHODS: Prospective cohort study with undergraduate medical students who filled in a yearly questionnaire on motivational factors. Two student groups participating in an ERP were compared: students with first-year grade point average (GPA) ≥7 versus <7 on a 10-point grading scale. Linear and logistic regressions analyses were used to compare groups on subsequent academic performance (i.e. third-year GPA, in-time bachelor completion), ERP performance (i.e. drop-out, number of credits), and motivational factors (i.e. intrinsic motivation for research, research self-efficacy beliefs, perceptions of research, curiosity), while adjusting for gender and motivational factors at baseline. RESULTS: The <7 group had significantly lower third-year GPA, and significantly higher odds for ERP drop-out than the ≥7 group. However, there was no significant between-group difference on in-time bachelor completion and the <7 group was not inferior to the ≥7 group in terms of intrinsic motivation for research, perceptions of research, and curiosity. CONCLUSIONS: Since intrinsic motivation for research, perceptions of research, and curiosity are prerequisites of future research involvement, it seems beneficial to focus on motivation when selecting students for ERPS, allowing students with lower current academic performance to participate in ERPs as well.