Development of the DONOR prediction model on the risk of hypertensive complications in oocyte donation pregnancy: study protocol for a multicentre cohort study in the Netherlands.

INTRODUCTION: Oocyte donation (OD) pregnancy is accompanied by a high incidence of hypertensive complications, with serious consequences for mother and child. Optimal care management, involving early recognition, optimisation of suitable treatment options and possibly eventually also prevention, is in high demand. Prediction of patient-specific risk factors for hypertensive complications in OD can provide the basis for this. The current project aims to establish the first prediction model on the risk of hypertensive complications in OD pregnancy. METHODS AND ANALYSIS: The present study is conducted within the DONation of Oocytes in Reproduction project. For this multicentre cohort study, at least 541 OD pregnancies will be recruited. Baseline characteristics and obstetric data will be collected. Additionally, one sample of maternal peripheral blood and umbilical cord blood after delivery or a saliva sample from the child will be obtained, in order to determine the number of fetal-maternal human leucocyte antigen mismatches. Following data collection, a multivariate logistic regression model will be developed for the binary outcome hypertensive complication 'yes' and 'no'. The Prediction model Risk Of Bias ASsessment Tool will be used as guide to minimise the risk of bias. The study will be reported in line with the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' guideline. Discrimination and calibration will be determined to assess model performance. Internal validation will be performed using the bootstrapping method. External validation will be performed with the 'DONation of Oocytes in Reproduction individual participant data' dataset. ETHICS AND DISSEMINATION: This study is approved by the Medical Ethics Committee LDD (Leiden, Den Haag, Delft), with protocol number P16.048 and general assessment registration (ABR) number NL56308.058.16. Further results will be shared through peer-reviewed journals and international conferences.

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).

Acute kidney injury and kidney replacement therapy in COVID-19: a systematic review and meta-analysis.

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.

A systematic review and external validation of stroke prediction models demonstrates poor performance in dialysis patients.

OBJECTIVES: The objective of this study was to systematically review and externally assess the predictive performance of models for ischemic stroke in incident dialysis patients. STUDY DESIGN AND SETTING: Two reviewers systematically searched and selected ischemic stroke models. Risk of bias was assessed with the PROBAST. Predictive performance was evaluated within The Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a large prospective multicenter cohort of incident dialysis patients. For discrimination, c-statistics were calculated; calibration was assessed by plotting predicted and observed probabilities for stroke, and calibration-in-the-large. RESULTS: Seventy-seven prediction models for stroke were identified, of which 15 were validated. Risk of bias was high, with all of these models scoring high risk in one or more domains. In NECOSAD, of the 1,955 patients, 127 (6.5%) suffered an ischemic stroke during the follow-up of 2.5 years. Compared with the original studies, most models performed worse with all models showing poor calibration and discriminative abilities (c-statistics ranging from 0.49 to 0.66). The Framingham showed reasonable calibration; however, with a c-statistic of 0.57 (95% CI 0.50-0.63), the discrimination was poor. CONCLUSION: This external validation demonstrates the weak predictive performance of ischemic stroke models in incident dialysis patients. Instead of using these models in this fragile population, either existing models should be updated, or novel models should be developed and validated.