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BMC Gastroenterol ; 24(1): 172, 2024 May 17.
Article En | MEDLINE | ID: mdl-38760679

BACKGROUND: Hospital re-admission for persons with Crohn's disease (CD) is a significant contributor to morbidity and healthcare costs. We derived prediction models of risk of 90-day re-hospitalization among persons with CD that could be applied at hospital discharge to target outpatient interventions mitigating this risk. METHODS: We performed a retrospective study in persons with CD admitted between 2009 and 2016 for an acute CD-related indication. Demographic, clinical, and health services predictor variables were ascertained through chart review and linkage to administrative health databases. We derived and internally validated a multivariable logistic regression model of 90-day CD-related re-hospitalization. We selected the optimal probability cut-point to maximize Youden's index. RESULTS: There were 524 CD hospitalizations and 57 (10.9%) CD re-hospitalizations within 90 days of discharge. Our final model included hospitalization within the prior year (adjusted odds ratio [aOR] 3.27, 95% confidence interval [CI] 1.76-6.08), gastroenterologist consultation within the prior year (aOR 0.185, 95% CI 0.0950-0.360), intra-abdominal surgery during index hospitalization (aOR 0.216, 95% CI 0.0500-0.934), and new diagnosis of CD during index hospitalization (aOR 0.327, 95% CI 0.0950-1.13). The model demonstrated good discrimination (optimism-corrected c-statistic value 0.726) and excellent calibration (Hosmer-Lemeshow goodness-of-fit p-value 0.990). The optimal model probability cut point allowed for a sensitivity of 71.9% and specificity of 70.9% for identifying 90-day re-hospitalization, at a false positivity rate of 29.1% and false negativity rate of 28.1%. CONCLUSIONS: Demographic, clinical, and health services variables can help discriminate persons with CD at risk of early re-hospitalization, which could permit targeted post-discharge intervention.


Crohn Disease , Patient Readmission , Humans , Crohn Disease/therapy , Crohn Disease/diagnosis , Patient Readmission/statistics & numerical data , Female , Male , Retrospective Studies , Adult , Risk Assessment , Middle Aged , Patient Acceptance of Health Care/statistics & numerical data , Logistic Models , Young Adult
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