Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort
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Abstract
Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.
Design
Retrospective cohort study.
Setting
A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.
Participants
SARS-CoV-2 positive patients (age ≥18) admitted to the hospital.
Main outcome measures
21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.
Results
2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).
Conclusion
Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.
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SciScore for 10.1101/2020.10.10.20210591: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The study protocol was reviewed by the medical ethics committees of the Amsterdam University Medical Centers (Amsterdam UMC; 20.131) and Maastricht University Medical Center (MUMC; 2020-1323).
Consent: Given the exceptional circumstances related to the COVID-19 crisis and in accordance with national guidelines and European privacy law, the need for informed consent was waived and opt out procedure was communicated by press release.Randomization After feature selection, both models were fitted and parameters optimized by a 50-iteration randomized grid search using a stratified shuffle split cross-validation. Blinding not detected. Power Analysis not detected. Sex … SciScore for 10.1101/2020.10.10.20210591: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: The study protocol was reviewed by the medical ethics committees of the Amsterdam University Medical Centers (Amsterdam UMC; 20.131) and Maastricht University Medical Center (MUMC; 2020-1323).
Consent: Given the exceptional circumstances related to the COVID-19 crisis and in accordance with national guidelines and European privacy law, the need for informed consent was waived and opt out procedure was communicated by press release.Randomization After feature selection, both models were fitted and parameters optimized by a 50-iteration randomized grid search using a stratified shuffle split cross-validation. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All code in the pipeline was implemented using the Scikit-learn python package.[18] To adhere to guidelines on transparent reporting of multivariable prediction models, the TRIPOD checklist is included in the supplementary table 3.[22] All code used in this paper is available at DOI:10.5281/zenodo. Scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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