Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19

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  • dc.contributor.author Garcia-Gutiérrez, Susana
  • dc.contributor.author Esteban-Aizpiri, Cristobal
  • dc.contributor.author Lafuente, Iratxe
  • dc.contributor.author Barrio, Irantzu
  • dc.contributor.author Quiros, Raul
  • dc.contributor.author Quintana, José María
  • dc.contributor.author Uranga, Ane
  • dc.contributor.author COVID-REDISSEC Working Group
  • dc.date.accessioned 2023-02-15T07:33:03Z
  • dc.date.available 2023-02-15T07:33:03Z
  • dc.date.issued 2022
  • dc.description.abstract Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer-Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer-Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.Registration: ClinicalTrials.gov Identifier: NCT04463706.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Garcia-Gutiérrez S, Esteban-Aizpiri C, Lafuente I, Barrio I, Quiros R, Quintana JM, Uranga A; COVID-REDISSEC Working Group. Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19. Sci Rep. 2022 May 2;12(1):7097. DOI: 10.1038/s41598-022-09771-z
  • dc.identifier.doi http://dx.doi.org/10.1038/s41598-022-09771-z
  • dc.identifier.issn 2045-2322
  • dc.identifier.uri http://hdl.handle.net/10230/55782
  • dc.language.iso eng
  • dc.publisher Nature Research
  • dc.relation.ispartof Sci Rep. 2022 May 2;12(1):7097
  • dc.rights © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword Health care
  • dc.subject.keyword Risk factors
  • dc.title Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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