Predicting readmissions: poor performance of the LACE index in an older UK population

Cotter, Paul; Bhalla, Vikas; Wallis, Stephen; Biram, Richard

doi:10.1093/ageing/afs073

Cited by 118 publications

(116 citation statements)

References 22 publications

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“…19 The LACE index did not perform well when applied to older patients in the United Kingdom. 43 Accurately predicting high risk for readmission with a simple tool is important because clinicians are not accurate predictors of unplanned readmission. 16 A prediction tool that is independent of the discharge diagnosis is useful for a general medical population because the majority of such patients are not admitted for the high-cost conditions that are the focus of current readmission reduction initiatives.…”

Section: Discussionmentioning

confidence: 99%

Admission Data Predict High Hospital Readmission Risk

Logue

Smucker

Regan

2016

The Journal of the American Board of Family Medicine

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Purpose: The purpose of this study was to identify data available at the time of hospital admission that predict readmission risk.Methods: We performed a retrospective multiple regression analysis of 958 adult, nonpregnant patients admitted to the Family Medicine Service between June 2012 and October 2013. Data were abstracted from hospital administrative sources and electronic medical records. The outcome was 30-day hospital readmission. Candidate readmission predictors included polypharmacy (>6 medicines), Charlson comorbidity index, age, sex, insurance status, emergency department use, smoking, nursing report of cognitive issues, patient report of social support or financial issues, and a history of heart failure, pneumonia, or chronic obstructive pulmonary disease.Results: Patients at the Family Medicine Service had a 14% readmission risk. Bivariate analysis showed that high Charlson scores (>5), polypharmacy, heart failure, pneumonia, or chronic obstructive pulmonary disease each increased readmission risk (P < .05). A logistic model showed an estimated odds ratio for readmission for high Charlson scores of 1.7 (95% confidence interval, 1.1-2.6) and of 2.1 for polypharmacy (95% confidence interval,

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Section: Discussionmentioning

confidence: 99%

Admission Data Predict High Hospital Readmission Risk

Logue

Smucker

Regan

2016

The Journal of the American Board of Family Medicine

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“…Although many studies have attempted to identify patients at highest risk of readmission, neither experienced clinicians nor experienced researchers using rigorously developed administrative data-rich algorithms can accurately predict which patients will not successfully transition back into the community. [1][2][3][4][5][6] This suggests that currently unrecognized factors likely play a major role in readmission risk. Identification of these factors would be important for future initiatives to reduce readmission rates by targeting resources to those at highest risk.…”

mentioning

confidence: 99%

Association between frailty and 30-day outcomes after discharge from hospital

Kahlon

Pederson

Majumdar

et al. 2015

CMAJ

183

136

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“…Generally, implementing HNLM models involves iterative numerical optimizations, which is a potentially high dimensional and computationally expensive thanks to intractable integrations and analytical approximations and heuristics methods [11,12]. Different approaches, by the way, have been proposed and applied in the literature that may be classified into three main groups.…”

Section: Optimizationmentioning

confidence: 99%

Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs

Shams

Ajorlou

Yang

2014

2014 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-Reducing potentially preventable readmissions has been identified as an important issue for decreasing Medicare costs and improving quality of care provided by hospitals. Based on previous research by medical professionals, preventable readmissions are caused by such factors as flawed patient discharging process, inadequate follow-ups after discharging, and noncompliance of patients on discharging and follow up instructions. It is also found that the risk of preventable readmission also may relate to some patient's characteristics, such as age, health condition, diagnosis, and even treatment specialty. In this study, using both general demographic information and individual past history of readmission records, we develop a risk prediction model based on hierarchical nonlinear mixed effect framework to extract significant prognostic factors associated with patient risk of 30-day readmission. The effectiveness of our proposed approach is validated based on a real dataset from four VA facilities in the State of Michigan. Simultaneously explaining both patient and population based variations of readmission process, such an accurate model can be used to recognize patients with high likelihood of discharging non-compliances, and then targeted post-care actions can be designed to reduce further rehospitalization.

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Predicting readmissions: poor performance of the LACE index in an older UK population

Abstract: the LACE index is a poor tool for predicting 30-day readmission in older UK inpatients. The absence of a simple predictive model may limit the benefit of readmission avoidance strategies.

Cited by 118 publications

References 22 publications

Admission Data Predict High Hospital Readmission Risk

Admission Data Predict High Hospital Readmission Risk

Association between frailty and 30-day outcomes after discharge from hospital

Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs

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