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Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

BACKGROUND: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. OBJECTIVE: We aimed to derive models for hospital mortality, 180-day mortality and 30...

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Autores principales: Shah, Nirav, Konchak, Chad, Chertok, Daniel, Au, Loretta, Kozlov, Alex, Ravichandran, Urmila, McNulty, Patrick, Liao, Linning, Steele, Kate, Kharasch, Maureen, Boyle, Chris, Hensing, Tom, Lovinger, David, Birnberg, Jonathan, Solomonides, Anthony, Halasyamani, Lakshmi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451512/
https://www.ncbi.nlm.nih.gov/pubmed/32853223
http://dx.doi.org/10.1371/journal.pone.0238065
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author Shah, Nirav
Konchak, Chad
Chertok, Daniel
Au, Loretta
Kozlov, Alex
Ravichandran, Urmila
McNulty, Patrick
Liao, Linning
Steele, Kate
Kharasch, Maureen
Boyle, Chris
Hensing, Tom
Lovinger, David
Birnberg, Jonathan
Solomonides, Anthony
Halasyamani, Lakshmi
author_facet Shah, Nirav
Konchak, Chad
Chertok, Daniel
Au, Loretta
Kozlov, Alex
Ravichandran, Urmila
McNulty, Patrick
Liao, Linning
Steele, Kate
Kharasch, Maureen
Boyle, Chris
Hensing, Tom
Lovinger, David
Birnberg, Jonathan
Solomonides, Anthony
Halasyamani, Lakshmi
author_sort Shah, Nirav
collection PubMed
description BACKGROUND: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. OBJECTIVE: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. MATERIALS & METHODS: We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. RESULTS: Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85. DISCUSSION: We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.
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spelling pubmed-74515122020-09-02 Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models Shah, Nirav Konchak, Chad Chertok, Daniel Au, Loretta Kozlov, Alex Ravichandran, Urmila McNulty, Patrick Liao, Linning Steele, Kate Kharasch, Maureen Boyle, Chris Hensing, Tom Lovinger, David Birnberg, Jonathan Solomonides, Anthony Halasyamani, Lakshmi PLoS One Research Article BACKGROUND: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. OBJECTIVE: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. MATERIALS & METHODS: We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. RESULTS: Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85. DISCUSSION: We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation. Public Library of Science 2020-08-27 /pmc/articles/PMC7451512/ /pubmed/32853223 http://dx.doi.org/10.1371/journal.pone.0238065 Text en © 2020 Shah et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Shah, Nirav
Konchak, Chad
Chertok, Daniel
Au, Loretta
Kozlov, Alex
Ravichandran, Urmila
McNulty, Patrick
Liao, Linning
Steele, Kate
Kharasch, Maureen
Boyle, Chris
Hensing, Tom
Lovinger, David
Birnberg, Jonathan
Solomonides, Anthony
Halasyamani, Lakshmi
Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models
title Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models
title_full Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models
title_fullStr Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models
title_full_unstemmed Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models
title_short Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models
title_sort clinical analytics prediction engine (cape): development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451512/
https://www.ncbi.nlm.nih.gov/pubmed/32853223
http://dx.doi.org/10.1371/journal.pone.0238065
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