Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Adiyeke, Esra Ren, Yuanfang Shickel, Benjamin Ruppert, Matthew M. Guan, Ziyuan Kane-Gill, Sandra L. Murugan, Raghavan Amatullah, Nabihah Stottlemyer, Britney A. Tran, Tiffany L. Ricketts, Dan Horvat, Christopher M Rashidi, Parisa Bihorac, Azra Ozrazgat-Baslanti, Tezcan |
| author_facet | Adiyeke, Esra Ren, Yuanfang Shickel, Benjamin Ruppert, Matthew M. Guan, Ziyuan Kane-Gill, Sandra L. Murugan, Raghavan Amatullah, Nabihah Stottlemyer, Britney A. Tran, Tiffany L. Ricketts, Dan Horvat, Christopher M Rashidi, Parisa Bihorac, Azra Ozrazgat-Baslanti, Tezcan |
| contents | Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes adult patients admitted to a non-intensive care unit at the University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of Florida Health (UFH) (n = 127,202). We developed and compared deep learning and conventional machine learning models to predict progression to Stage 2 or higher AKI within the next 48 hours. We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model). We internally and externally validated the models on each site and performed subgroup analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating curve values (AUROC) for the UFH test cohort ranged between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80, 0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06]) for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the top three features with the highest influence across the models and health centers. Conclusion: Locally developed models displayed marginally reduced discrimination when tested on another institution, while the top set of influencing features remained the same across the models and sites. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_04209 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study Adiyeke, Esra Ren, Yuanfang Shickel, Benjamin Ruppert, Matthew M. Guan, Ziyuan Kane-Gill, Sandra L. Murugan, Raghavan Amatullah, Nabihah Stottlemyer, Britney A. Tran, Tiffany L. Ricketts, Dan Horvat, Christopher M Rashidi, Parisa Bihorac, Azra Ozrazgat-Baslanti, Tezcan Machine Learning Artificial Intelligence Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes adult patients admitted to a non-intensive care unit at the University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of Florida Health (UFH) (n = 127,202). We developed and compared deep learning and conventional machine learning models to predict progression to Stage 2 or higher AKI within the next 48 hours. We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model). We internally and externally validated the models on each site and performed subgroup analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating curve values (AUROC) for the UFH test cohort ranged between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80, 0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06]) for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the top three features with the highest influence across the models and health centers. Conclusion: Locally developed models displayed marginally reduced discrimination when tested on another institution, while the top set of influencing features remained the same across the models and sites. |
| title | Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.04209 |