Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis

AbstractDespite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day readmission prediction model predicts 7-day readmissions. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We compared model performance and compared differences in strength of model factors between the 7-day model to the 30-day model. While there was no substantial change in model performance between the original 30-day and the re-derived 7-day model, there was significant change in strength of predictors. Characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to the day of discharge.

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Psychosocial Factors and 30-Day Hospital Readmission among Individuals Receiving Maintenance Dialysis: A Prospective Study

American Journal of Nephrology ◽

10.1159/000470917 ◽

2017 ◽

Vol 45 (5) ◽

pp. 400-408 ◽

Cited By ~ 20

Author(s):

Jennifer E. Flythe ◽

Johnathan Hilbert ◽

Abhijit V. Kshirsagar ◽

Constance A. Gilet

Keyword(s):

Risk Factors ◽

Social Support ◽

Depressive Symptoms ◽

Health Literacy ◽

Hospital Readmission ◽

Day Hospital ◽

Multivariable Model ◽

Dialysis Patients ◽

Self Rated Health ◽

Readmission Risk

Background: Thirty-day hospital readmissions are common among maintenance dialysis patients. Prior studies have evaluated easily measurable readmission risk factors such as comorbid conditions, laboratory results, and hospital discharge day. We undertook this prospective study to investigate the associations between hospital-assessed depression, health literacy, social support, and self-rated health (separately) and 30-day hospital readmission among dialysis patients. Methods: Participants were recruited from the University of North Carolina Hospitals, 2014-2016. Validated depression, health literacy, social support, and self-rated health screening instruments were administered during index hospitalizations. Multivariable logistic regression models with 30-day readmission as the dependent outcome were used to examine readmission risk factors. Results: Of the 154 participants, 58 (37.7%) had a 30-day hospital readmission. In unadjusted analyses, individuals with positive screening for depression, lower health literacy, and poorer social support were more likely to have a 30-day readmission (vs. negative screening). Positive depression screening and poorer social support remained significantly associated with 30-day readmission in models adjusted for race, heart failure, admitting service, weekend discharge day, and serum albumin: adjusted OR (95% CI) 2.33 (1.02-5.15) for positive depressive symptoms and 2.57 (1.10-5.91) for poorer social support. The area under the receiver operating characteristic curve (AUC) of the multivariable model adjusted for social support status was significantly greater than the AUC of the multivariable model without social support status (test for equality; p value = 0.04). Conclusion: Poor social support and depressive symptoms identified during hospitalizations may represent targetable readmission risk factors among dialysis patients. Our findings suggest that hospital-based assessments of select psychosocial factors may improve readmission risk prediction.

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Association of Geriatric-Specific Variables with 30-Day Hospital Readmission Risk of Elderly Surgical Patients: A NSQIP Analysis

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2019.12.032 ◽

2020 ◽

Vol 230 (4) ◽

pp. 527-533.e1

Author(s):

Florence E. Turrentine ◽

Victor M. Zaydfudim ◽

Allison N. Martin ◽

R Scott Jones

Keyword(s):

Hospital Readmission ◽

Surgical Patients ◽

Day Hospital ◽

Readmission Risk

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Patient-Related Risk Factors for Unplanned 30-Day Hospital Readmission Following Primary and Revision Total Knee Arthroplasty: A Systematic Review and Meta-Analysis

Journal of Clinical Medicine ◽

10.3390/jcm10010134 ◽

2021 ◽

Vol 10 (1) ◽

pp. 134

Author(s):

Daniel Gould ◽

Michelle M Dowsey ◽

Tim Spelman ◽

Olivia Jo ◽

Wassif Kabir ◽

...

Keyword(s):

Risk Factors ◽

Total Knee Arthroplasty ◽

Knee Arthroplasty ◽

Hospital Readmission ◽

Day Hospital ◽

Post Discharge ◽

Comorbidity Burden ◽

Total Knee ◽

The Impact ◽

Readmission Risk

Total knee arthroplasty (TKA) is a highly effective procedure for advanced osteoarthritis of the knee. Thirty-day hospital readmission is an adverse outcome related to complications, which can be mitigated by identifying associated risk factors. We aimed to identify patient-related characteristics associated with unplanned 30-day readmission following TKA, and to determine the effect size of the association between these risk factors and unplanned 30-day readmission. We searched MEDLINE and EMBASE from inception to 8 September 2020 for English language articles. Reference lists of included articles were searched for additional literature. Patients of interest were TKA recipients (primary and revision) compared for 30-day readmission to any institution, due to any cause, based on patient risk factors; case series were excluded. Two reviewers independently extracted data and carried out critical appraisal. In-hospital complications during the index admission were the strongest risk factors for 30-day readmission in both primary and revision TKA patients, suggesting discharge planning to include closer post-discharge monitoring to prevent avoidable readmission may be warranted. Further research could determine whether closer monitoring post-discharge would prevent unplanned but avoidable readmissions. Increased comorbidity burden correlated with increased risk, as did specific comorbidities. Body mass index was not strongly correlated with readmission risk. Demographic risk factors included low socioeconomic status, but the impact of age on readmission risk was less clear. These risk factors can also be included in predictive models for 30-day readmission in TKA patients to identify high-risk patients as part of risk reduction programs.

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Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

JMIR Medical Informatics ◽

10.2196/16306 ◽

2021 ◽

Vol 9 (3) ◽

pp. e16306

Author(s):

Peng Zhao ◽

Illhoi Yoo ◽

Syed H Naqvi

Keyword(s):

Risk Factors ◽

Protective Factors ◽

Hospital Readmission ◽

Inpatient Care ◽

Early Stage ◽

Multivariate Logistic Regression Analysis ◽

Day Hospital ◽

Multivariate Logistic Regression ◽

Data Set ◽

Readmission Risk

Background Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. Objective The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors. Methods We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors. Results We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported. Conclusions The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

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Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01248-1 ◽

2020 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Sameh N. Saleh ◽

Anil N. Makam ◽

Ethan A. Halm ◽

Oanh Kieu Nguyen

Keyword(s):

Prediction Model ◽

Hospital Readmission ◽

Model Performance ◽

Day Hospital ◽

North Texas ◽

Point Estimates ◽

C Statistic ◽

Split Sample ◽

Baseline Characteristics ◽

Readmission Risk

Abstract Background Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8–30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors. Methods We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50–50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients. Results Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive. Conclusion A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.

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Comparison of Inpatient Standard-of-Care to Outpatient Oritavancin Therapy for Patients With Acute Uncomplicated Cellulitis

Journal of Pharmacy Practice ◽

10.1177/08971900211021258 ◽

2021 ◽

pp. 089719002110212

Author(s):

Brandy Williams ◽

Justin Muklewicz ◽

Taylor D. Steuber ◽

April Williams ◽

Jonathan Edwards

Keyword(s):

Hospital Readmission ◽

Average Length ◽

Hospital Readmissions ◽

Standard Of Care ◽

Economic Outcomes ◽

Day Hospital ◽

Outpatient Parenteral Antimicrobial Therapy ◽

Average Length Of Stay ◽

Observational Cohort ◽

The Difference

Background: Shifting inpatient antibiotic treatment to outpatient parenteral antimicrobial therapy may minimize treatment for acute bacterial skin and skin structure infections, including cellulitis. The purpose of this evaluation was to compare 30-day hospital readmission or admission due to cellulitis and economic outcomes of inpatient standard-of-care (SoC) management of acute uncomplicated cellulitis to outpatient oritavancin therapy. Methods: This retrospective, observational cohort study was conducted at a 941-bed community teaching hospital. Adult patients 18 years and older treated for acute uncomplicated cellulitis between February 2015 to December 2018 were eligible for inclusion. Information was obtained from hospital and billing department records. Patients were assigned to either inpatient SoC or outpatient oritavancin cohorts for comparison. Results: 1,549 patients were included in the study (1,348 in the inpatient SoC cohort and 201 in the outpatient oritavancin cohort). The average length of stay for patients admitted was 3.6 ± 1.5 days. The primary outcome of 30-day hospital readmission or admission due to cellulitis occurred in 49/1348 (3.6%) patients in the inpatient SoC cohort versus 1/201 (0.5%) in the outpatient oritavancin cohort (p = 0.02). The difference between costs and reimbursement was improved in the outpatient oritavancin group (p < 0.001). Conclusion: Outpatient oritavancin for acute uncomplicated cellulitis was associated with reduction in 30-day hospital readmissions or admissions compared to inpatient SoC. Beneficial economic outcomes for the outpatient oritavancin cohort were observed. Additional studies are required to confirm these findings.

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