Derivation and Internal Validation of a Mortality Prognostication Machine Learning Model in Ebola Virus Disease Based on Iterative Point-of-Care Biomarkers.

Autor: Bearnot CJ; Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA., Mbong EN; International Medical Corps, Goma, Democratic Republic of Congo., Muhayangabo RF; International Medical Corps, Goma, Democratic Republic of Congo., Laghari R; International Medical Corps, Goma, Democratic Republic of Congo., Butler K; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA., Gainey M; Rhode Island Hospital, Providence, Rhode Island, USA., Perera SM; International Medical Corps, Washington, DC, USA., Michelow IC; Division of Infectious Diseases and Immunology, Department of Pediatrics, School of Medicine, University of Connecticut, Farmington, Connecticut, USA., Tang OY; Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.; Department of Neurosurgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA., Levine AC; Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA., Colubri A; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA., Aluisio AR; Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Jazyk: angličtina
Zdroj: Open forum infectious diseases [Open Forum Infect Dis] 2024 Jan 05; Vol. 11 (2), pp. ofad689. Date of Electronic Publication: 2024 Jan 05 (Print Publication: 2024).
DOI: 10.1093/ofid/ofad689
Abstrakt: Background: Although multiple prognostic models exist for Ebola virus disease mortality, few incorporate biomarkers, and none has used longitudinal point-of-care serum testing throughout Ebola treatment center care.
Methods: This retrospective study evaluated adult patients with Ebola virus disease during the 10th outbreak in the Democratic Republic of Congo. Ebola virus cycle threshold (Ct; based on reverse transcriptase polymerase chain reaction) and point-of-care serum biomarker values were collected throughout Ebola treatment center care. Four iterative machine learning models were created for prognosis of mortality. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days 1 and 2, days 3 and 4, and days 5 and 6 associated with mortality were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided period-specific areas under curve with 95% CIs.
Results: Of 310 cases positive for Ebola virus disease, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium; low albumin during days 1 and 2; elevated C-reactive protein, BUN, and potassium during days 3 and 4; and elevated C-reactive protein and BUN during days 5 and 6. The area under curve substantially improved with each iteration: base model, 0.74 (95% CI, .69-.80); days 1 and 2, 0.84 (95% CI, .73-.94); days 3 and 4, 0.94 (95% CI, .88-1.0); and days 5 and 6, 0.96 (95% CI, .90-1.0).
Conclusions: This is the first study to utilize iterative point-of-care biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to 6 days into patient care.
Competing Interests: Potential conflicts of interest. All authors: No reported conflicts.
(© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)
Databáze: MEDLINE
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