Your browser doesn't support javascript.
Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.
Rozenbaum, Daniel; Shreve, Jacob; Radakovich, Nathan; Duggal, Abhijit; Jehi, Lara; Nazha, Aziz.
  • Rozenbaum D; Department of Hematology and Medical Oncology, Cleveland Clinic, Cleveland, OH.
  • Shreve J; Department of Hematology and Medical Oncology, Cleveland Clinic, Cleveland, OH.
  • Radakovich N; Lerner College of Medicine, Cleveland, OH.
  • Duggal A; Department of Critical Care, Cleveland Clinic, Cleveland, OH.
  • Jehi L; Neurological Institute and Lerner College of Medicine, Cleveland, OH.
  • Nazha A; Department of Hematology and Medical Oncology, Cleveland Clinic, Cleveland, OH.
Mayo Clin Proc Innov Qual Outcomes ; 5(4): 795-801, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1225334
ABSTRACT

OBJECTIVE:

To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients. PATIENTS AND

METHODS:

We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system.

RESULTS:

The median LOS was 5 (range, 1-44) days for patients admitted to the regular nursing floor and 10 (range, 1-38) days for patients admitted to the intensive care unit. Patients who died during hospitalization were older, initially admitted to the intensive care unit, and more likely to be white and have worse organ dysfunction compared with patients who survived their hospitalization. Using the 10 most important variables only, the final model's area under the receiver operating characteristics curve was 0.86 for 7-day, 0.88 for 14-day, and 0.85 for 30-day mortality in the validation cohort.

CONCLUSION:

We developed a decision tool that can provide explainable and patient-specific prediction of in-hospital mortality and LOS for COVID-19-positive patients. The model can aid health care systems in bed allocation and distribution of vital resources.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Mayo Clin Proc Innov Qual Outcomes Year: 2021 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Mayo Clin Proc Innov Qual Outcomes Year: 2021 Document Type: Article