Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19
Abstract COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algo...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-20724-4 |
| _version_ | 1811226595952689152 |
|---|---|
| author | Yixi Xu Anusua Trivedi Nicholas Becker Marian Blazes Juan Lavista Ferres Aaron Lee W. Conrad Liles Pavan K. Bhatraju |
| author_facet | Yixi Xu Anusua Trivedi Nicholas Becker Marian Blazes Juan Lavista Ferres Aaron Lee W. Conrad Liles Pavan K. Bhatraju |
| author_sort | Yixi Xu |
| collection | DOAJ |
| description | Abstract COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/− 21.5 (mean +/− SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment. |
| first_indexed | 2024-04-12T09:27:50Z |
| format | Article |
| id | doaj.art-40b07ce51df24bc690725b63e7e38abb |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T09:27:50Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-40b07ce51df24bc690725b63e7e38abb2022-12-22T03:38:26ZengNature PortfolioScientific Reports2045-23222022-10-0112111110.1038/s41598-022-20724-4Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19Yixi Xu0Anusua Trivedi1Nicholas Becker2Marian Blazes3Juan Lavista Ferres4Aaron Lee5W. Conrad Liles6Pavan K. Bhatraju7School of Medicine, University of WashingtonSchool of Medicine, University of WashingtonSchool of Medicine, University of WashingtonSchool of Medicine, University of WashingtonSchool of Medicine, University of WashingtonSchool of Medicine, University of WashingtonSchool of Medicine, University of WashingtonSchool of Medicine, University of WashingtonAbstract COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/− 21.5 (mean +/− SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.https://doi.org/10.1038/s41598-022-20724-4 |
| spellingShingle | Yixi Xu Anusua Trivedi Nicholas Becker Marian Blazes Juan Lavista Ferres Aaron Lee W. Conrad Liles Pavan K. Bhatraju Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19 Scientific Reports |
| title | Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19 |
| title_full | Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19 |
| title_fullStr | Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19 |
| title_full_unstemmed | Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19 |
| title_short | Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19 |
| title_sort | machine learning based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with covid 19 |
| url | https://doi.org/10.1038/s41598-022-20724-4 |
| work_keys_str_mv | AT yixixu machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 AT anusuatrivedi machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 AT nicholasbecker machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 AT marianblazes machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 AT juanlavistaferres machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 AT aaronlee machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 AT wconradliles machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 AT pavankbhatraju machinelearningbasedderivationandexternalvalidationofatooltopredictdeathanddevelopmentoforganfailureinhospitalizedpatientswithcovid19 |