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Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study

BACKGROUND: Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models...

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Autores principales: Mohammadi, Ramin, Jain, Sarthak, Namin, Amir T, Scholem Heller, Melissa, Palacholla, Ramya, Kamarthi, Sagar, Wallace, Byron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732713/
https://www.ncbi.nlm.nih.gov/pubmed/33245283
http://dx.doi.org/10.2196/19761
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author Mohammadi, Ramin
Jain, Sarthak
Namin, Amir T
Scholem Heller, Melissa
Palacholla, Ramya
Kamarthi, Sagar
Wallace, Byron
author_facet Mohammadi, Ramin
Jain, Sarthak
Namin, Amir T
Scholem Heller, Melissa
Palacholla, Ramya
Kamarthi, Sagar
Wallace, Byron
author_sort Mohammadi, Ramin
collection PubMed
description BACKGROUND: Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models. OBJECTIVE: This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task. METHODS: We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded. RESULTS: The proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability. CONCLUSIONS: Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.
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spelling pubmed-77327132020-12-22 Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study Mohammadi, Ramin Jain, Sarthak Namin, Amir T Scholem Heller, Melissa Palacholla, Ramya Kamarthi, Sagar Wallace, Byron JMIR Med Inform Original Paper BACKGROUND: Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models. OBJECTIVE: This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task. METHODS: We performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded. RESULTS: The proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability. CONCLUSIONS: Machine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients. JMIR Publications 2020-11-27 /pmc/articles/PMC7732713/ /pubmed/33245283 http://dx.doi.org/10.2196/19761 Text en ©Ramin Mohammadi, Sarthak Jain, Amir T Namin, Melissa Scholem Heller, Ramya Palacholla, Sagar Kamarthi, Byron Wallace. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 27.11.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Mohammadi, Ramin
Jain, Sarthak
Namin, Amir T
Scholem Heller, Melissa
Palacholla, Ramya
Kamarthi, Sagar
Wallace, Byron
Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study
title Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study
title_full Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study
title_fullStr Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study
title_full_unstemmed Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study
title_short Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study
title_sort predicting unplanned readmissions following a hip or knee arthroplasty: retrospective observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732713/
https://www.ncbi.nlm.nih.gov/pubmed/33245283
http://dx.doi.org/10.2196/19761
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