Cargando…

A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS

BACKGROUND: Hospitalization for persons with inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a significant contributor to morbidity and health care costs in Canada. Recognition of individuals at high risk of re-hospitalization could help inform target...

Descripción completa

Detalles Bibliográficos
Autores principales: Dziegielewski, C, Gupta, S, Lombardi, J, Kelly, E, McCurdy, J, Sy, R, Ramsay, T, Begum, J, Murthy, S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9991272/
http://dx.doi.org/10.1093/jcag/gwac036.166
_version_ 1784902116044701696
author Dziegielewski, C
Gupta, S
Lombardi, J
Kelly, E
McCurdy, J
Sy, R
Ramsay, T
Begum, J
Murthy, S
author_facet Dziegielewski, C
Gupta, S
Lombardi, J
Kelly, E
McCurdy, J
Sy, R
Ramsay, T
Begum, J
Murthy, S
author_sort Dziegielewski, C
collection PubMed
description BACKGROUND: Hospitalization for persons with inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a significant contributor to morbidity and health care costs in Canada. Recognition of individuals at high risk of re-hospitalization could help inform targeted outpatient interventions that mitigate this risk. PURPOSE: The aim of our study is to derive prediction models of risk of early (90-day) re-hospitalization among persons with IBD. METHOD: We conducted a retrospective cohort study of all adult persons with IBD admitted to The Ottawa Hospital, Canada, for an acute IBD-related indication between April 2009 - March 2016. Demographic, clinical, and health services variables were obtained through chart review. Persons were linked to population-based health administrative datasets to identify historical and future IBD-related hospitalizations across the greater Ottawa region. Multivariable logistic regression models of 90-day re-hospitalization in persons with CD and UC were derived, and candidate predictors that demonstrated an independent association with the outcome at a p-value of 0.1 were retained. Bootstrap internal validation (200 iterations) was performed on the final models. Model performance and calibration were evaluated using the optimism-corrected c-statistic value and Hosmer-Lemeshow goodness of fit test, respectively. Adjusted odds ratios are reported with 95% confidence intervals (CI). Optimal probability cut points for re-hospitalization were selected to optimize sensitivity, specificity, and the J (Youden’s) index. RESULT(S): There were 524 CD and 248 UC hospitalizations during the study period. Of these, 57 (10.9%) CD and 27 (10.9%) UC hospitalizations were associated with re-hospitalization within 90 days of discharge. Forty-two candidate predictors were tested among CD hospitalizations, and 35 were tested among UC hospitalizations. Four variables were retained in each of the final models. Model performance and calibration for each variable are described in Table 1. The optimal range of probability cut points allowed for a sensitivity/positive predictive value (PPV)/false positive rate (FPR) of 0.72/0.23/0.29 (maximum J-index of 0.43) in the model for CD, and 0.78/0.33/0.19 (maximum J-index of 0.59) in the model for UC, respectively. IMAGE: [Image: see text] CONCLUSION(S): Demographic, clinical, and health services variables at the time of discharge have the potential to help identify persons with IBD at risk of early re-hospitalization, thereby permitting targeted outpatient intervention. Application of the models to our reference cohorts would earmark 1/3 or less of patients for early post-discharge intervention, with the potential to benefit more than 70% of patients destined for early re-hospitalization. Although the PPVs of our models were low, the models incorrectly predicted early re-hospitalization in less than 30% of patients. We are in process of externally validating these models in other jurisdictions across Ontario to test their generalizability. PLEASE ACKNOWLEDGE ALL FUNDING AGENCIES BY CHECKING THE APPLICABLE BOXES BELOW: None DISCLOSURE OF INTEREST: None Declared
format Online
Article
Text
id pubmed-9991272
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-99912722023-03-08 A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS Dziegielewski, C Gupta, S Lombardi, J Kelly, E McCurdy, J Sy, R Ramsay, T Begum, J Murthy, S J Can Assoc Gastroenterol Poster Presentations BACKGROUND: Hospitalization for persons with inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a significant contributor to morbidity and health care costs in Canada. Recognition of individuals at high risk of re-hospitalization could help inform targeted outpatient interventions that mitigate this risk. PURPOSE: The aim of our study is to derive prediction models of risk of early (90-day) re-hospitalization among persons with IBD. METHOD: We conducted a retrospective cohort study of all adult persons with IBD admitted to The Ottawa Hospital, Canada, for an acute IBD-related indication between April 2009 - March 2016. Demographic, clinical, and health services variables were obtained through chart review. Persons were linked to population-based health administrative datasets to identify historical and future IBD-related hospitalizations across the greater Ottawa region. Multivariable logistic regression models of 90-day re-hospitalization in persons with CD and UC were derived, and candidate predictors that demonstrated an independent association with the outcome at a p-value of 0.1 were retained. Bootstrap internal validation (200 iterations) was performed on the final models. Model performance and calibration were evaluated using the optimism-corrected c-statistic value and Hosmer-Lemeshow goodness of fit test, respectively. Adjusted odds ratios are reported with 95% confidence intervals (CI). Optimal probability cut points for re-hospitalization were selected to optimize sensitivity, specificity, and the J (Youden’s) index. RESULT(S): There were 524 CD and 248 UC hospitalizations during the study period. Of these, 57 (10.9%) CD and 27 (10.9%) UC hospitalizations were associated with re-hospitalization within 90 days of discharge. Forty-two candidate predictors were tested among CD hospitalizations, and 35 were tested among UC hospitalizations. Four variables were retained in each of the final models. Model performance and calibration for each variable are described in Table 1. The optimal range of probability cut points allowed for a sensitivity/positive predictive value (PPV)/false positive rate (FPR) of 0.72/0.23/0.29 (maximum J-index of 0.43) in the model for CD, and 0.78/0.33/0.19 (maximum J-index of 0.59) in the model for UC, respectively. IMAGE: [Image: see text] CONCLUSION(S): Demographic, clinical, and health services variables at the time of discharge have the potential to help identify persons with IBD at risk of early re-hospitalization, thereby permitting targeted outpatient intervention. Application of the models to our reference cohorts would earmark 1/3 or less of patients for early post-discharge intervention, with the potential to benefit more than 70% of patients destined for early re-hospitalization. Although the PPVs of our models were low, the models incorrectly predicted early re-hospitalization in less than 30% of patients. We are in process of externally validating these models in other jurisdictions across Ontario to test their generalizability. PLEASE ACKNOWLEDGE ALL FUNDING AGENCIES BY CHECKING THE APPLICABLE BOXES BELOW: None DISCLOSURE OF INTEREST: None Declared Oxford University Press 2023-03-07 /pmc/articles/PMC9991272/ http://dx.doi.org/10.1093/jcag/gwac036.166 Text en ڣ The Author(s) 2023. Published by Oxford University Press on behalf of the Canadian Association of Gastroenterology. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Poster Presentations
Dziegielewski, C
Gupta, S
Lombardi, J
Kelly, E
McCurdy, J
Sy, R
Ramsay, T
Begum, J
Murthy, S
A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS
title A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS
title_full A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS
title_fullStr A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS
title_full_unstemmed A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS
title_short A166 RISK STRATIFICATION OF EARLY RE-HOSPITALIZATION IN PERSONS WITH INFLAMMATORY BOWEL DISEASES USING MULTIVARIABLE MODELS
title_sort a166 risk stratification of early re-hospitalization in persons with inflammatory bowel diseases using multivariable models
topic Poster Presentations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9991272/
http://dx.doi.org/10.1093/jcag/gwac036.166
work_keys_str_mv AT dziegielewskic a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT guptas a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT lombardij a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT kellye a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT mccurdyj a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT syr a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT ramsayt a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT begumj a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels
AT murthys a166riskstratificationofearlyrehospitalizationinpersonswithinflammatoryboweldiseasesusingmultivariablemodels