Hospital Readmissions in Patients With Inflammatory Bowel Disease

Hazratjee, Nyla; Agito, Markus; López, Rocío; Lashner, Bret A.; Rizk, Maged

doi:10.1038/ajg.2012.343

Cited by 42 publications

(39 citation statements)

References 19 publications

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“…This study found that reasons for 30-day readmission were related to documented flare, infections, or need for pain control. 17 Our study corroborates these previous data. In our cohort, patients with IBD readmitted within 90 days of an index IBD admission were more likely to have underlying depression or chronic pain regardless of the reason for readmission, as opposed to disease type, recent steroid use, or biological use.…”

Section: Discussionsupporting

confidence: 93%

Risk Factors for Rehospitalization Within 90 Days in Patients with Inflammatory Bowel Disease

Allegretti

Borges

Lucci

et al. 2015

Inflammatory Bowel Diseases

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Background Care of patients with inflammatory bowel disease (IBD) poses a significant burden to the health-care system. Repeat hospitalization in subgroups of IBD patients seems to be a large part of this issue; however, there are limited data examining the characteristics of these patients. The aim of this study was to characterize admission patterns in patients with IBD at a tertiary-care center and to identify preventable risk factors of 90-day readmission after an index IBD admission. Methods Retrospective analysis was performed extracting data from an electronic medical record over a 2-year period. Results Three hundred fifty-six patients were admitted at least once during the 2-year study period for an unplanned IBD-related reason. Of these, 48.9% were admitted once, 38.2% were admitted 2 to 4 times, and 12.9% were admitted 5 or more times during the study period. Patients with any admission within 90 days before index were excluded; n = 33. One hundred two patients had experienced a readmission by 90 days after index admission. Numerous demographic and medical factors were examined for association with readmission. The final Cox model included 3 variables: depression (HR = 1.99, 1.33–3.00), chronic pain (HR = 1.88, 1.14–3.10), and steroid use in the previous 6 months (HR = 1.33, 0.92–2.04). Conclusions Our findings suggest that patients with depression and chronic pain are at greatest risk for a readmission within 90 days after an initial IBD admission. Disease activity, represented by steroid use in the previous 6 months, was not related to readmission. Addressing these problems in the outpatient setting may reduce future hospitalizations.

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Section: Discussionsupporting

confidence: 93%

Risk Factors for Rehospitalization Within 90 Days in Patients with Inflammatory Bowel Disease

Allegretti

Borges

Lucci

et al. 2015

Inflammatory Bowel Diseases

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“…Early predictive models of readmissions focused on all-cause readmission and the most common diagnoses including congestive heart failure (CHF), acute myocardial infarction (acute MI), and chronic obstructive pulmonary disease (COPD), but the literature now spans multiple diagnoses and disciplines. [5–15] However, no studies have studied systematically the effects of changing readmission diagnoses being modeled while holding all else equal. This latter understanding will help interpret and compare studies of different diseases.…”

Section: Introduction1mentioning

confidence: 99%

The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions

Walsh

Hripcsak

2014

Journal of Biomedical Informatics

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Background Hospital readmission risk prediction remains a motivated area of investigation and operations in light of the Hospital Readmissions Reduction Program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance. Objectives To study the effects of varying three factors of model development in the prediction of risk based on health record data: 1) Reason for readmission (primary readmission diagnosis); 2) Available data and data types (e.g. visit history, laboratory results, etc); 3) Cohort selection. Methods Regularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison in cohort selection testing. Calibration by model refitting to outcome prevalence. Results Predicting readmission risk across multiple reasons for readmission resulted in ROC areas ranging from 0.92 for readmission for congestive heart failure to 0.71 for syncope and 0.68 for all-cause readmission. Visit history and laboratory tests contributed the most predictive value; contributions varied by readmission diagnosis. Cohort definition affected performance for both parametric and nonparametric algorithms. Compared to all patients, limiting the cohort to patients whose index admission and readmission diagnoses matched resulted in a decrease in average ROC from 0.78 to 0.55 (difference in ROC 0.23, p value 0.01). Calibration plots demonstrate good calibration with low mean squared error. Conclusion Targeting reason for readmission in risk prediction impacted discriminatory performance. In general, laboratory data and visit history data contributed the most to prediction; data source contributions varied by reason for readmission. Cohort selection had a large impact on model performance, and these results demonstrate the difficulty of comparing results across different studies of predictive risk modeling.

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“…These novel observations add to the literature regarding readmission risk factors previously established for other common gastrointestinal conditions, such as inflammatory bowel disease and acute pancreatitis. 14,15 Future study is necessary to design strategies to mitigate early readmission and rebleeding risk, as have been undertaken in other fields. 16–19 Given that common causes of hospital readmission and death in the present cohort of patients with LGIB included infection, volume overload, renal failure, and thromboembolic events, potential strategies to mitigate readmission risk could include aggressive monitoring of volume status and infection control as an outpatient, as well as ensuring that patients are restarted on antiplatelets and anticoagulants when possible to decrease postbleeding thrombotic events.…”

Section: Discussionmentioning

confidence: 99%

Risk Factors for Adverse Outcomes in Patients Hospitalized With Lower Gastrointestinal Bleeding

Sengupta

Tapper

Patwardhan

et al. 2015

Mayo Clinic Proceedings

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Objective To determine which risk factors and subtypes of lower gastrointestinal bleeding (LGIB) are associated with adverse outcomes after hospital discharge (30-day readmissions, recurrent LGIB, and death). Patients and Methods We conducted a prospective observational study of consecutive patients admitted with LGIB to Beth Israel Deaconess Medical Center from April 1, 2013, through March 30, 2014. Patients were contacted 30 days after discharge to determine hospital readmissions, recurrent LGIB, and death. Multivariable Cox proportional hazards regression models were used to describe associations of variables with 30-day readmissions or recurrent LGIB. Logistic regression was used to determine association with mortality. Results There were 277 patients hospitalized with LGIB. Of the 271 patients surviving to discharge, 21% (n=57) were readmitted within 30 days, 21 of whom were admitted for recurrent LGIB. The following factors were associated with 30-day readmissions: developing in-hospital LGIB (hazard ratio [HR], 2.26; 95% CI, 1.08–4.28), anticoagulation (HR, 1.82; 95% CI, 1.05–3.10), and active malignancy (HR, 2.33; 95% CI, 1.11–4.42). Patients discharged while taking anticoagulants had higher rates of recurrent bleeding (HR, 2.93; 95% CI, 1.15–6.95). Patients with higher Charlson Comorbidity Index scores (odds ratio [OR], 1.57; 95% CI, 1.25–2.08), active malignancy (OR, 6.57; 95% CI, 1.28–28.7), and in-hospital LGIB (OR, 11.5; 95% CI, 2.56–52.0) had increased 30-day mortality risk. Conclusion In-hospital LGIB, anticoagulation, and active malignancy are risk factors for 30-day readmissions in patients hospitalized with LGIB. In-hospital LGIB, Charlson Comorbidity Index scores, and active malignancy are risk factors for 30-day mortality.

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Hospital Readmissions in Patients With Inflammatory Bowel Disease

Abstract: Predicting readmission is complex. A predictive model developed to be used at discharge yielded an area under the curve of 0.757.

Cited by 42 publications

References 19 publications

Risk Factors for Rehospitalization Within 90 Days in Patients with Inflammatory Bowel Disease

Risk Factors for Rehospitalization Within 90 Days in Patients with Inflammatory Bowel Disease

The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions

Risk Factors for Adverse Outcomes in Patients Hospitalized With Lower Gastrointestinal Bleeding

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