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Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients.

ABSTRACT: BACKGROUND:Identifying patients at high risk of hospital preventable readmission is an essential step towards selecting those who might benefit from specific transitional interventions. OBJECTIVE:Derive and validate a predictive risk score for potentially avoidable readmission (PAR) based on analysis of readmissions, with a focus on medication. DESIGN/SETTING/PARTICIPANTS:Retrospective analysis of all hospital admissions to internal medicine wards between 2011 and 2014. Comparison between patients readmitted within 30 days and non-readmitted patients, as identified using a specially designed algorithm. Univariate and multivariate regression analyses of demographic data, clinical diagnoses, laboratory results, and the medication data of patients admitted during the first period (2011-2013), to identify factors associated with PAR. Using these, derive a predictive score with a regression coefficient-based scoring method. Subsequently, validate this score with a second cohort of patients admitted in 2013-2014. Variables were identified at hospital discharge. RESULTS:The derivation cohort included 7,317 hospital stays. Multivariate logistic regressions found significant associations with PAR for: [adjusted OR (95% CI)] hospital length of stay > 4 days [1.3 (1.1-1.7)], admission in previous 6 months [2.3 (1.9-2.8)], heart failure [1.3 (1.0-1.7)], chronic ischemic heart disease [1.7 (1.2-2.3)], diabetes with organ damage [2.2 (1.3-3.8)], cancer [1.4 (1.0-1.9)], metastatic carcinoma [1.9 (1.3-3.0)], anemia [1.2 (1.0-1.5)], hypertension [1.3 (1.1-1.7)], arrhythmia [1.3 (1.0-1.6)], hyperkalemia [1.4 (1.0-1.7)], opioid drug prescription [1.3 (1.1-1.6)], and acute myocardial infarction [0.6 (0.4-0.9)]. The PAR-Risk Score, derived from these results, demonstrated fair discriminatory and calibration power (C-statistic = 0.699; Brier Score = 0.069). The results for the validation cohort's operating characteristics were similar (C-statistic = 0.687; Brier Score = 0.064). CONCLUSION:This study identified routinely-available factors that were significantly associated with PAR. A predictive score was derived and internally validated.

SUBMITTER: Blanc AL

PROVIDER: S-EPMC6629067 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients.

Blanc Anne-Laure AL Fumeaux Thierry T Stirnemann Jérôme J Dupuis Lozeron Elise E Ourhamoune Aimad A Desmeules Jules J Chopard Pierre P Perrier Arnaud A Schaad Nicolas N Bonnabry Pascal P

PloS one 20190715 7

<h4>Background</h4>Identifying patients at high risk of hospital preventable readmission is an essential step towards selecting those who might benefit from specific transitional interventions.<h4>Objective</h4>Derive and validate a predictive risk score for potentially avoidable readmission (PAR) based on analysis of readmissions, with a focus on medication.<h4>Design/setting/participants</h4>Retrospective analysis of all hospital admissions to internal medicine wards between 2011 and 2014. Com ...[more]

PMID: 31306461

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Project description:BackgroundIdentifying potentially avoidable admissions to Canadian hospitals is an important health system goal. With general internal medicine (GIM) accounting for 40% of hospital admissions, we sought to develop a method to identify potentially avoidable admissions and characterize patient, provider and health system factors.MethodsWe conducted an observational study of GIM admissions at our institution from August 2019 to February 2020. We defined potentially avoidable admissions as admissions that could be managed in an appropriate and safe manner in the emergency department or ambulatory setting and asked staff physicians to screen admissions daily and flag candidates as potentially avoidable admissions. For each candidate, we prepared a case review and debriefed with members of the admitting team. We then reviewed each candidate with our research team, assigned an avoidability score (1 [low] to 4 [high]) and identified contributing factors for those with scores of 3 or more.ResultsWe screened 601 total admissions and staff physicians flagged 117 (19.5%) of these as candidate potential avoidable admissions. Consensus review identified 67 candidates as potentially avoidable admissions (11.1%, 95% confidence interval 8.8%-13.9%); these patients were younger (mean age 65 yr v. 72 yr), had fewer comorbidities (Canadian Institute for Health Information Case Mix Group+ 0.42 v. 1.14), had lower resource-intensity weighting scores (0.72 v. 1.50) and shorter hospital lengths of stay (29 h v. 105 h) (p < 0.01). Common factors included diagnostic and therapeutic uncertainty, perceived need for short-term monitoring, government directive of a 4-hour limit for admission decision-making and subspecialist request to admit.InterpretationOur prospective method of screening, flagging and case review showed that 1 in 9 GIM admissions were potentially avoidable. Other institutions could consider adapting this methodology to ascertain their rate of potentially avoidable admissions and to understand contributing factors to inform improvement endeavours.

Project description:Identification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit.To externally validate the HOSPITAL score in an international multicenter study to assess its generalizability.International retrospective cohort study of 117?065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded.The HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay.30-day potentially avoidable readmission to the index hospital using the SQLape algorithm.Overall, 117?065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16?992 of 117?065 (14.5%) were followed by a 30-day readmission, and 11?307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n?=?73?031 [62.4%]), intermediate (n?=?27?612 [23.6%]), and high risk (n?=?16?422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson ?2 test P?=?.89).The HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.

Project description:BackgroundUrgent, unplanned hospital readmissions are increasingly being used to gauge the quality of care. We reviewed urgent readmissions to determine which were potentially avoidable and compared rates of all-cause and avoidable readmissions.MethodsIn a multicentre, prospective cohort study, we reviewed all urgent readmissions that occurred within six months among patients discharged to the community from 11 teaching and community hospitals between October 2002 and July 2006. Summaries of the readmissions were reviewed by at least four practising physicians using standardized methods to judge whether the readmission was an adverse event (poor clinical outcome due to medical care) and whether the adverse event could have been avoided. We used a latent class model to determine whether the probability that each readmission was truly avoidable exceeded 50%.ResultsOf the 4812 patients included in the study, 649 (13.5%, 95% confidence interval [CI] 12.5%-14.5%) had an urgent readmission within six months after discharge. We considered 104 of them (16.0% of those readmitted, 95% CI 13.3%-19.1%; 2.2% of those discharged, 95% CI 1.8%-2.6%) to have had a potentially avoidable readmission. The proportion of patients who had an urgent readmission varied significantly by hospital (range 7.5%-22.5%; χ(2) = 92.9, p < 0.001); the proportion of readmissions deemed avoidable did not show significant variation by hospital (range 1.2%-3.7%; χ(2) = 12.5, p < 0.25). We found no association between the proportion of patients who had an urgent readmission and the proportion of patients who had an avoidable readmission (Pearson correlation 0.294; p = 0.38). In addition, we found no association between hospital rankings by proportion of patients readmitted and rankings by proportion of patients with an avoidable readmission (Spearman correlation coefficient 0.28, p = 0.41).InterpretationUrgent readmissions deemed potentially avoidable were relatively uncommon, comprising less than 20% of all urgent readmissions following hospital discharge. Hospital-specific proportions of patients who were readmitted were not related to proportions with a potentially avoidable readmission.

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Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients.

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Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients.

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