Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records.

Purpose: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data.

Methods: Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR).

Delirium in older COVID-19 patients: Evaluating risk factors and outcomes.

Objectives: A high incidence of delirium has been reported in older patients with Coronavirus disease 2019 (COVID-19). We aimed to identify determinants of delirium, including the Clinical Frailty Scale, in hospitalized older patients with COVID-19. Furthermore, we aimed to study the association of delirium independent of frailty with in-hospital outcomes in older COVID-19 patients.

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

Introduction: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19.

Methods: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices.

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

Objectives: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients.

Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.

Background: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction.

Methods: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

Background: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients.

Methods: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020.

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients.

The relationship between extracurricular activities assessed during selection and during medical school and performance.

Several medical schools include candidates' extracurricular activities in their selection procedure, with promising results regarding their predictive value for achievement during the clinical years of medical school. This study aims to reveal whether the better achievement in clinical training of students selected on the basis of their extracurricular activities could be explained by persistent participation in extracurricular activities during medical school (msECAs). Lottery-admitted and selected student admission groups were compared on their participation in three types of msECAs: (1) research master, (2) important board positions or (3) additional degree programme.

Academic and non-academic selection criteria in predicting medical school performance.

Background: A two-step selection procedure, consisting of a non-academic and an academic step, was recently shown to select students with a 2.6 times lower risk of early dropout and a higher clerkship Grade Point Average (GPA) than lottery-admitted controls.

Aim: To determine the relative contribution of the non-academic and academic steps to differences found in student performance.

Selected medical students achieve better than lottery-admitted students during clerkships.

Objectives: A recent controlled study by our group showed that the dropout rate in the first 2 years of study of medical students selected for entry by the assessment of a combination of non-cognitive and cognitive abilities was 2.6 times lower than that of a control group of students admitted by lottery. The aim of the present study was to compare the performance of these two groups in the clinical phase.

Selection of medical students: a controlled experiment.

Objectives: We aimed to discover, through a controlled experiment, whether cognitive and non-cognitive assessment would select higher-achieving applicants to medical school than selection by lottery.

Methods: We carried out a prospective cohort study to compare 389 medical students who had been admitted by selection and 938 students who had been admitted by weighted lottery, between 2001 and 2004. Main outcome measures were dropout rates, study rate (credits per year) and mean grade per first examination attempt per year.