Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients
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Abstract
Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management.
Methods
We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity.
Results
A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression.
Conclusions
This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.
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SciScore for 10.1101/2020.07.13.20150177: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The code to develop the models was written in Python and open source libraries scikit-learn,11 xgboost and eli5 were used for the implementation of the machine-learning classifiers and cross-validation schemes. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
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 did …
SciScore for 10.1101/2020.07.13.20150177: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The code to develop the models was written in Python and open source libraries scikit-learn,11 xgboost and eli5 were used for the implementation of the machine-learning classifiers and cross-validation schemes. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
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 did not find any issues relating to the usage of bar graphs.
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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SciScore for 10.1101/2020.07.13.20150177: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Institutional Salamanca Barcelona approval was (2020/03/470) provided and the by the Ethics Committee Comité Ètic d’Investigació (HCB/2020/0273), which waived the need for of the Clínica informed of University the Hospital Hospital consent. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources The code to develop the models was written in Python and open source libraries scikit-learn, 11 xgboost and eli5 were used for the implementation of the machine-learning classifiers and cross- validation schemes. Py…SciScore for 10.1101/2020.07.13.20150177: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Institutional Salamanca Barcelona approval was (2020/03/470) provided and the by the Ethics Committee Comité Ètic d’Investigació (HCB/2020/0273), which waived the need for of the Clínica informed of University the Hospital Hospital consent. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources The code to develop the models was written in Python and open source libraries scikit-learn, 11 xgboost and eli5 were used for the implementation of the machine-learning classifiers and cross- validation schemes. Pythonsuggested: (IPython, SCR_001658)About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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