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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

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 o...

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Autores principales: Guinsburg, Adrián M., Jiao, Yue, Bessone, María Inés Díaz, Monaghan, Caitlin K., Magalhães, Beatriz, Kraus, Michael A., Kotanko, Peter, Hymes, Jeffrey L., Kossmann, Robert J., Berbessi, Juan Carlos, Maddux, Franklin W., Usvyat, Len A., Larkin, John W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587666/
https://www.ncbi.nlm.nih.gov/pubmed/36273142
http://dx.doi.org/10.1186/s12882-022-02961-x
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author Guinsburg, Adrián M.
Jiao, Yue
Bessone, María Inés Díaz
Monaghan, Caitlin K.
Magalhães, Beatriz
Kraus, Michael A.
Kotanko, Peter
Hymes, Jeffrey L.
Kossmann, Robert J.
Berbessi, Juan Carlos
Maddux, Franklin W.
Usvyat, Len A.
Larkin, John W.
author_facet Guinsburg, Adrián M.
Jiao, Yue
Bessone, María Inés Díaz
Monaghan, Caitlin K.
Magalhães, Beatriz
Kraus, Michael A.
Kotanko, Peter
Hymes, Jeffrey L.
Kossmann, Robert J.
Berbessi, Juan Carlos
Maddux, Franklin W.
Usvyat, Len A.
Larkin, John W.
author_sort Guinsburg, Adrián M.
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02961-x.
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spelling pubmed-95876662022-10-23 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 Guinsburg, Adrián M. Jiao, Yue Bessone, María Inés Díaz Monaghan, Caitlin K. Magalhães, Beatriz Kraus, Michael A. Kotanko, Peter Hymes, Jeffrey L. Kossmann, Robert J. Berbessi, Juan Carlos Maddux, Franklin W. Usvyat, Len A. Larkin, John W. BMC Nephrol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02961-x. BioMed Central 2022-10-22 /pmc/articles/PMC9587666/ /pubmed/36273142 http://dx.doi.org/10.1186/s12882-022-02961-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Guinsburg, Adrián M.
Jiao, Yue
Bessone, María Inés Díaz
Monaghan, Caitlin K.
Magalhães, Beatriz
Kraus, Michael A.
Kotanko, Peter
Hymes, Jeffrey L.
Kossmann, Robert J.
Berbessi, Juan Carlos
Maddux, Franklin W.
Usvyat, Len A.
Larkin, John W.
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 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 Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587666/
https://www.ncbi.nlm.nih.gov/pubmed/36273142
http://dx.doi.org/10.1186/s12882-022-02961-x
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