Developing a Tool for Selection for Medical School : A search for academic and non-academic parameters to predict future medical school performance

Worldwide places in medical school are scarce and medical education and training are expensive for providers and learners. Therefore, medical schools aim to offer the places available only to those applicants with the highest probability of successful medical training and subsequent career. To reach this goal, medical schools have developed several selection procedures, including interviews, admission tests and other measures of personal competencies. Uniquely in the Netherlands, selection was organised nationally based on a lottery that is weighted for academic attainment. However, both the lottery and the unproven selection procedures have been described as unfair to medical school applicants, as neither includes any truly objective criteria for predicting future performance. The Dutch situation in which access to medical school was granted by lottery and the possibility to select up to 50% of the students by a selection procedure provided a unique opportunity to form a control group of randomly admitted students to compare with those selected. We developed an evidence-based selection procedure addressing non-academic (i.e. motivation) as well as academic skills. The former evaluated motivation through the determination of the candidate’s active involvement in extracurricular activities, the latter by tests concerning the study skills of candidates in a medical school context. The main outcome was that the relative risk for dropping out of medical school was significantly lower in selected students than in controls admitted by lottery. Those selected obtained a higher mean grade than the lottery admitted students on their clerkships. Thereby selected students participated more often in extracurricular activities, which was also associated with higher clerkship grades

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. Logistic regression was used to measure the effect of admission group on participation in any msECA, adjusted for pre-university GPA. Two-way ANCOVA was used to examine the inter-relationships between admission group, participation in msECAs and clerkship grade, with pre-university GPA as covariate. Significantly more selected students compared to lottery-admitted students participated in any msECA. Participation in msECAs was associated with a higher pre-university GPA for lottery-admitted students only, whereas participation in msECAs was associated with higher clerkship grades for selected students only. These results suggest that persistent participation in extracurricular activities of selected students favours better clinical achievement, supporting the inclusion of ECAs in the selection procedure. More insight in the rationale behind participation in extracurricular activities during medical school may explain differences found between lottery-admitted and selected students

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. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. Results Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. Conclusions In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Comparison of outcome and characteristics between 6343 COVID-19 patients and 2256 other community-acquired viral pneumonia patients admitted to Dutch ICUs

Purpose: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs. Materials and methods: Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared. Results: 6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58. Conclusion: Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/)

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. METHODS A prospective cohort study was performed to compare the performance of 389 medical students admitted by selection with that of 938 students admitted by weighted lottery between 2001 and 2004. Follow-up of these cohorts lasted 5.5-8.5 years. The main outcome measures were the mean grade obtained on the first five discipline-specific clerkships by all cohorts and the mean grade achieved on all 10 clerkships by the cohorts of 2001 and 2002. RESULTS Selected students obtained a significantly higher mean grade during their first five clerkships than lottery-admitted students (mean +/- standard error [SE] 7.95 +/- 0.03, 95% confidence interval [CI] 7.90-8.00 versus mean +/- SE 7.84 +/- 0.02, 95% CI 7.81-7.87; p = 8.0 1.5 times more often than lottery-admitted students. An analysis of all mean grades awarded on 10 clerkships revealed the same results. Moreover, the longer follow-up period over the clerkships showed that the relative risk for dropout was twice as low in the selected student group as in the lottery-admitted student group. CONCLUSIONS The selected group received significantly higher mean grades on their first five clerkships, which could not be attributed to factors other than the selection procedure. Although the risk for dropout before the clinical phase increased somewhat in both groups, the actual dropout rate proved to be twice as low in the selected group.Development and application of statistical models for medical scientific researc

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

Development and application of statistical models for medical scientific researc

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. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. RESULTS: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). CONCLUSIONS: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

BACKGROUND: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. METHODS: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. RESULTS: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. DISCUSSION: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research

INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY

Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection

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). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. Results: A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. Conclusion: In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.</p