Your browser doesn't support javascript.
loading
Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.
Hien, Nguyen Thi Kim; Tsai, Feng-Jen; Chang, Yu-Hui; Burton, Whitney; Phuc, Phan Thanh; Nguyen, Phung-Anh; Harnod, Dorji; Lam, Carlos Shu-Kei; Lu, Tsung-Chien; Chen, Chang-I; Hsu, Min-Huei; Lu, Christine Y; Huang, Chih-Wei; Yang, Hsuan-Chia; Hsu, Jason C.
Afiliación
  • Hien NTK; Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan.
  • Tsai FJ; Master Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan.
  • Chang YH; Ph.D. Program in Global Health and Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan.
  • Burton W; PharmD Program, Division of Clinical Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
  • Phuc PT; International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan.
  • Nguyen PA; International Ph.D. Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan.
  • Harnod D; Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan.
  • Lam CS; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.
  • Lu TC; Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
  • Chen CI; Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Hsu MH; Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
  • Lu CY; Department of Emergency, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Huang CW; Division of Emergency, Department of Emergency and Critical Care Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
  • Yang HC; Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan.
  • Hsu JC; Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Front Med (Lausanne) ; 10: 1289968, 2023.
Article en En | MEDLINE | ID: mdl-38249981
ABSTRACT

Background:

Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.

Objective:

This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.

Methods:

Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.

Results:

The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.

Conclusion:

This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: Front Med (Lausanne) Año: 2023 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: Front Med (Lausanne) Año: 2023 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Suiza