Dataset Information

Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community.

ABSTRACT:

Background

Readmissions to hospital are common, costly and often preventable. An easy-to-use index to quantify the risk of readmission or death after discharge from hospital would help clinicians identify patients who might benefit from more intensive post-discharge care. We sought to derive and validate an index to predict the risk of death or unplanned readmission within 30 days after discharge from hospital to the community.

Methods

In a prospective cohort study, 48 patient-level and admission-level variables were collected for 4812 medical and surgical patients who were discharged to the community from 11 hospitals in Ontario. We used a split-sample design to derive and validate an index to predict the risk of death or nonelective readmission within 30 days after discharge. This index was externally validated using administrative data in a random selection of 1,000,000 Ontarians discharged from hospital between 2004 and 2008.

Results

Of the 4812 participating patients, 385 (8.0%) died or were readmitted on an unplanned basis within 30 days after discharge. Variables independently associated with this outcome (from which we derived the mnemonic "LACE") included length of stay ("L"); acuity of the admission ("A"); comorbidity of the patient (measured with the Charlson comorbidity index score) ("C"); and emergency department use (measured as the number of visits in the six months before admission) ("E"). Scores using the LACE index ranged from 0 (2.0% expected risk of death or urgent readmission within 30 days) to 19 (43.7% expected risk). The LACE index was discriminative (C statistic 0.684) and very accurate (Hosmer-Lemeshow goodness-of-fit statistic 14.1, p=0.59) at predicting outcome risk.

Interpretation

The LACE index can be used to quantify risk of death or unplanned readmission within 30 days after discharge from hospital. This index can be used with both primary and administrative data. Further research is required to determine whether such quantification changes patient care or outcomes.

SUBMITTER: van Walraven C

PROVIDER: S-EPMC2845681 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Publications

Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community.

van Walraven Carl C Dhalla Irfan A IA Bell Chaim C Etchells Edward E Stiell Ian G IG Zarnke Kelly K Austin Peter C PC Forster Alan J AJ

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne 20100301 6

<h4>Background</h4>Readmissions to hospital are common, costly and often preventable. An easy-to-use index to quantify the risk of readmission or death after discharge from hospital would help clinicians identify patients who might benefit from more intensive post-discharge care. We sought to derive and validate an index to predict the risk of death or unplanned readmission within 30 days after discharge from hospital to the community.<h4>Methods</h4>In a prospective cohort study, 48 patient-lev ...[more]

PMID: 20194559

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Project description:OBJECTIVES:To understand from a patient and carer perspective: (1) what features of the discharge process could be improved to avoid early unplanned hospital readmission (within 72?hours of acute care discharge) and (2) what elements of discharge planning could have enhanced the discharge experience. DESIGN:A qualitative descriptive design was used. Study data were collected using semi-structured interviews that were transcribed verbatim and analysed using inductive thematic analysis. Data related to participant characteristic were collected by medical record audit and summarised using descriptive statistics. SETTING:Three acute care hospitals from one health service in Australia. PARTICIPANTS:Patients who had an early unplanned hospital readmission and/or their carers, if present during the interviews and willing to participate, with patient permission. FINDINGS:Thirty interviews were conducted (23 patients only; 6 patient and carer dyads; 1 carer only). Five themes were constructed: 'experiences of care', 'hearing and being heard', 'what's wrong with me', 'not just about me' and 'all about going home'. There was considerable variability in patients' and carers' experiences of hospital care, discharge processes and early unplanned hospital readmission. Features of the discharge process that could be improved to potentially avoid early unplanned hospital readmission were better communication, optimal clinical care including ensuring readiness for discharge and shared decision-making regarding discharge timing and goals on returning home. The discharge experience could have been enhanced by improved communication between patients (and carers) and the healthcare team, not rushing the discharge process and a more coordinated approach to patient transport home from hospital. CONCLUSIONS:The study findings highlight the complexities of the discharge process and the importance of effective communication, shared decision-making and carer engagement in optimising hospital discharge and reducing early unplanned hospital readmissions.

Project description:IntroductionReadmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC's Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC's Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator.MethodsA monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1st of January 2018 and the 31st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X2 goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models.ResultsAt least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676-0.708), 0.703 (95% CI 0.687-0.719) and 0.705 (95% CI 0.690-0.720). The Hosmer-Lemeshow X2 tests had values of p<0.001.ConclusionIn summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability-model updating is warranted.

Project description:BackgroundReducing the 30-day unplanned hospital readmission rate is a goal for physicians and policymakers in order to improve quality of care. However, data on the readmission rate of critically ill patients in Japan and knowledge of the predictors associated with readmission are lacking. We investigated predictors associated with 30-day rehospitalization for medical and surgical adult patients separately.MethodsPatient data from 502 acute care hospitals with intensive care unit (ICU) facilities in Japan were retrospectively extracted from the Japanese Diagnosis Procedure Combination (DPC) database between April 2012 and February 2014. Factors associated with unplanned hospital readmission within 30 days of hospital discharge among medical and surgical ICU survivors were identified using multivariable logistic regression analysis.ResultsOf 486,651 ICU survivors, we identified 5583 unplanned hospital readmissions within 30 days of discharge following 147,423 medical hospitalizations (3.8% readmitted) and 11,142 unplanned readmissions after 339,228 surgical hospitalizations (3.3% readmitted). The majority of unplanned hospital readmissions, 60.9% of medical and 63.1% of surgical case readmissions, occurred within 15 days of discharge. For both medical and surgical patients, the Charlson comorbidity index score; category of primary diagnosis during the index admission (respiratory, gastrointestinal, and metabolic and renal); hospital length of stay; discharge to skilled nursing facilities; and having received a packed red blood cell transfusion, low-dose steroids, or renal replacement therapy were significantly associated with higher unplanned hospital readmission rates.ConclusionsFrom patient data extracted from a large Japanese national database, the 30-day unplanned hospital readmission rate after ICU stay was 3.4%. Further studies are required to improve readmission prediction models and to develop targeted interventions for high-risk patients.

Project description:Aims/hypothesisHospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia.MethodsWe used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score ('base' model). In the second model, we added to the 'base' model the 20 most common medical conditions and applied a stepwise backward selection of variables ('disease' model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics.ResultsIn derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models.Conclusions/interpretationThis simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia.

Project description:BackgroundAdmitted patients with chronic disease are at high risk of an unplanned hospital readmission, however, little research has examined unplanned readmission among Aboriginal people in Australia. This study aimed to examine whether rates of unplanned 28?day hospital readmission, or death, significantly differ between Aboriginal and non-Aboriginal patients in New South Wales, Australia, over a nine-year period.MethodsA retrospective cohort analysis of a sample of de-identified linked hospital administrative data was conducted. Eligible patients were: 1) aged ?18?years old, 2) admitted to an acute facility in a NSW public hospital between 30th June 2005 and 1st July 2014, and 3) admitted with either cardiovascular disease, chronic respiratory disease, diabetes or renal disease. The primary composite outcome was unplanned readmission or death within 28?days of discharge. Generalized linear models and a test for trend were used to assess rates of unplanned readmission or death over time in Aboriginal and non-Aboriginal patients with chronic disease, accounting for sociodemographic variables.ResultsThe final study cohort included 122,145 separations corresponding to 48,252 patients (Aboriginal?=?57.2%, n =?27,601; non-Aboriginal?=?42.8%, n =?20,651). 13.9% (n =?16,999) of all separations experienced an unplanned readmission or death within 28?days of discharge. Death within 28?days of discharge alone accounted for only a small number of separations (1.4%; n =?1767). Over the nine-year period, Aboriginal separations had a significantly higher relative risk of an unplanned readmission or death (Relative risk?=?1.34 (1.29, 1.40); p-value <?0.0001) compared with non-Aboriginal separations once adjusted for sociodemographic, disease variables and restricted to <?75?years of age. A test for trend, including an interaction between year and Aboriginal status, showed there was no statistically significant change in proportions over the nine-year period for Aboriginal and non-Aboriginal separations (p-value for trend?=?0.176).ConclusionAboriginal people with chronic disease had a significantly higher risk of unplanned readmission or death 28?days post discharge from hospital compared with non-Aboriginal people, and there has been no significant change over the nine year period. It is critical that effective interventions to reduce unplanned readmissions for Aboriginal people are identified.

Dataset Information

Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community.

Background

Methods

Results

Interpretation

Publications

Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community.

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