Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: retrospective cohort study

Sarah Damery, Gill Combes

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)
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Abstract

Objective: To assess how well the LACE index and its constituent elements predicts 30-day hospital readmission and to determine whether other combinations of clinical or sociodemographic variables may enhance prognostic capability.Design: Retrospective cohort study with split sample design for model validation.Setting: One large hospital Trust in the West Midlands.Participants: All alive-discharge adult inpatient episodes between 1st January 2013 and 31st December 2014. Data sources: Anonymised data for each inpatient episode were obtained from the hospital information system. These included age at index admission, gender, ethnicity, admission/discharge date, length of stay, treatment specialty, admission type and source, discharge destination, comorbidities, number of accident and emergency (A&E) visits in the six months before the index admission, and whether a patient was readmitted within 30 days of index discharge. Outcome measures: Clinical and patient characteristics of readmission vs. non-readmission episodes, proportion of readmission episodes at each LACE score, regression modelling of variables associated with readmission to assess the effectiveness of LACE and other variable combinations to predict 30-day readmission.Results: The training cohort included data on 91,922 patient episodes. Increasing LACE score and each of its individual components were independent predictors of readmission (AUC 0.773; 95% CI: 0.768 to 0.779 for LACE; AUC 0.806; 95% CI: 0.801 to 0.812 for the four LACE components). A LACE score of 11 was most effective at distinguishing between higher and lower risk patients. However, only 25% of readmission episodes occurred in the higher scoring group. A model combining A&E visits and hospital episodes per patient in the previous year was more effective at predicting readmission (AUC 0.815; 95% CI: 0.810 to 0.819). Conclusions: Although LACE shows good discriminatory power in statistical terms, it may have little added value over and above clinical judgement in predicting a patient’s risk of hospital readmission.
Original languageEnglish
Article numbere016921
JournalBMJ open
Volume7
Issue number7
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • LACE
  • readmissions
  • case finding
  • risk stratification
  • hospital

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