Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization.
Estimated glomerular filtration rate (eGFR) decline is associated with negative health outcomes, but the use of decision tree algorithms to predict eGFR decline is underreported. Among patients hospitalized during the first year of the COVID-19 pandemic, it remains unclear which individuals are at t...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317558 |
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| author | Weihao Wang Wei Zhu Janos Hajagos Laura Fochtmann Farrukh M Koraishy |
| author_facet | Weihao Wang Wei Zhu Janos Hajagos Laura Fochtmann Farrukh M Koraishy |
| author_sort | Weihao Wang |
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| description | Estimated glomerular filtration rate (eGFR) decline is associated with negative health outcomes, but the use of decision tree algorithms to predict eGFR decline is underreported. Among patients hospitalized during the first year of the COVID-19 pandemic, it remains unclear which individuals are at the greatest risk of eGFR decline after discharge. We conducted a retrospective cohort study on patients hospitalized at Stony Brook University Hospital in 2020 who were followed for 36 months post discharge. Random Forest (RF) identified the top ten features associated with fast eGFR decline. Logistic regression (LR) and Classification and Regression Trees (CART) were then employed to uncover the relative importance of these top features and identify the highest risk patients. In the cohort of 1,747 hospital survivors, 61.6% experienced fast eGFR decline, which was associated with younger age, higher baseline eGFR, and acute kidney injury (AKI). Multivariate LR analysis showed that older age was associated with lower odds of fast eGFR decline whereas length of hospitalization and vasopressor use with greater odds. CART analysis identified length of hospitalization as the most important factor and that patients with AKI and hospitalization of 27 days or more were at highest risk. After grouping by ICU and COVID-19 status and propensity score matching for demographics, these risk factors of fast eGFR decline remained consistent. CART analysis can help identify patient subgroups with the highest risk of post-discharge eGFR decline. Clinicians should consider the length of hospitalization in post-discharge monitoring of kidney function. |
| format | Article |
| id | doaj-art-62ea253b170246f5a6b70aa3d7f04db8 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj-art-62ea253b170246f5a6b70aa3d7f04db82025-02-07T05:30:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031755810.1371/journal.pone.0317558Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization.Weihao WangWei ZhuJanos HajagosLaura FochtmannFarrukh M KoraishyEstimated glomerular filtration rate (eGFR) decline is associated with negative health outcomes, but the use of decision tree algorithms to predict eGFR decline is underreported. Among patients hospitalized during the first year of the COVID-19 pandemic, it remains unclear which individuals are at the greatest risk of eGFR decline after discharge. We conducted a retrospective cohort study on patients hospitalized at Stony Brook University Hospital in 2020 who were followed for 36 months post discharge. Random Forest (RF) identified the top ten features associated with fast eGFR decline. Logistic regression (LR) and Classification and Regression Trees (CART) were then employed to uncover the relative importance of these top features and identify the highest risk patients. In the cohort of 1,747 hospital survivors, 61.6% experienced fast eGFR decline, which was associated with younger age, higher baseline eGFR, and acute kidney injury (AKI). Multivariate LR analysis showed that older age was associated with lower odds of fast eGFR decline whereas length of hospitalization and vasopressor use with greater odds. CART analysis identified length of hospitalization as the most important factor and that patients with AKI and hospitalization of 27 days or more were at highest risk. After grouping by ICU and COVID-19 status and propensity score matching for demographics, these risk factors of fast eGFR decline remained consistent. CART analysis can help identify patient subgroups with the highest risk of post-discharge eGFR decline. Clinicians should consider the length of hospitalization in post-discharge monitoring of kidney function.https://doi.org/10.1371/journal.pone.0317558 |
| spellingShingle | Weihao Wang Wei Zhu Janos Hajagos Laura Fochtmann Farrukh M Koraishy Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. PLoS ONE |
| title | Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. |
| title_full | Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. |
| title_fullStr | Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. |
| title_full_unstemmed | Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. |
| title_short | Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization. |
| title_sort | classification and regression trees analysis identifies patients at high risk for kidney function decline following hospitalization |
| url | https://doi.org/10.1371/journal.pone.0317558 |
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