Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries
Abstract Background We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas. Methods We used data from adult HD patients treated at regional institu...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | BMC Nephrology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12882-022-02961-x |
| _version_ | 1811249823954763776 |
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| author | Adrián M. Guinsburg Yue Jiao María Inés Díaz Bessone Caitlin K. Monaghan Beatriz Magalhães Michael A. Kraus Peter Kotanko Jeffrey L. Hymes Robert J. Kossmann Juan Carlos Berbessi Franklin W. Maddux Len A. Usvyat John W. Larkin |
| author_facet | Adrián M. Guinsburg Yue Jiao María Inés Díaz Bessone Caitlin K. Monaghan Beatriz Magalhães Michael A. Kraus Peter Kotanko Jeffrey L. Hymes Robert J. Kossmann Juan Carlos Berbessi Franklin W. Maddux Len A. Usvyat John W. Larkin |
| author_sort | Adrián M. Guinsburg |
| collection | DOAJ |
| description | Abstract Background We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas. Methods We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0–14, 15–30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries. Results Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0–14 days, 7.9% and 4.6% of patients died within 15–30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0–14 and 15–30 days after COVID-19, yet not mortality > 30 days after presentation. Conclusions Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0–14 and 15–30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods. |
| first_indexed | 2024-04-12T15:54:28Z |
| format | Article |
| id | doaj.art-d0ee193e582e47619020a68cc6da9ace |
| institution | Directory Open Access Journal |
| issn | 1471-2369 |
| language | English |
| last_indexed | 2024-04-12T15:54:28Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nephrology |
| spelling | doaj.art-d0ee193e582e47619020a68cc6da9ace2022-12-22T03:26:25ZengBMCBMC Nephrology1471-23692022-10-0123112310.1186/s12882-022-02961-xPredictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countriesAdrián M. Guinsburg0Yue Jiao1María Inés Díaz Bessone2Caitlin K. Monaghan3Beatriz Magalhães4Michael A. Kraus5Peter Kotanko6Jeffrey L. Hymes7Robert J. Kossmann8Juan Carlos Berbessi9Franklin W. Maddux10Len A. Usvyat11John W. Larkin12Fresenius Medical Care Latin AmericaFresenius Medical Care, Global Medical OfficeFresenius Medical Care Latin AmericaFresenius Medical Care, Global Medical OfficeFresenius Medical Care Latin AmericaFresenius Medical Care North AmericaRenal Research InstituteFresenius Medical Care, Global Medical OfficeFresenius Medical Care North AmericaFresenius Medical Care Latin AmericaFresenius Medical Care AG & Co. KGaA, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeAbstract Background We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas. Methods We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0–14, 15–30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries. Results Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0–14 days, 7.9% and 4.6% of patients died within 15–30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0–14 and 15–30 days after COVID-19, yet not mortality > 30 days after presentation. Conclusions Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0–14 and 15–30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.https://doi.org/10.1186/s12882-022-02961-xCOVID-19HemodialysisMortality RiskMachine LearningPrediction ModelMultinational |
| spellingShingle | Adrián M. Guinsburg Yue Jiao María Inés Díaz Bessone Caitlin K. Monaghan Beatriz Magalhães Michael A. Kraus Peter Kotanko Jeffrey L. Hymes Robert J. Kossmann Juan Carlos Berbessi Franklin W. Maddux Len A. Usvyat John W. Larkin Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries BMC Nephrology COVID-19 Hemodialysis Mortality Risk Machine Learning Prediction Model Multinational |
| title | Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries |
| title_full | Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries |
| title_fullStr | Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries |
| title_full_unstemmed | Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries |
| title_short | Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries |
| title_sort | predictors of shorter and longer term mortality after covid 19 presentation among dialysis patients parallel use of machine learning models in latin and north american countries |
| topic | COVID-19 Hemodialysis Mortality Risk Machine Learning Prediction Model Multinational |
| url | https://doi.org/10.1186/s12882-022-02961-x |
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