Payam Yazdan-Ashoori1, Shun Fu Lee2, Quazi Ibrahim2, Harriette G C Van Spall3. 1. Department of Medicine, McMaster University, Hamilton, Ontario, Canada. 2. Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada. 3. Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. Electronic address: Harriette.VanSpall@phri.ca.
Abstract
UNLABELLED: The Length of stay, Acuity, Comorbidities, Emergency department visits in prior 6 months (LACE) index threshold of 10 predicts readmission or death in general medical patients in administrative databases. We assessed whether the unadjusted LACE index, computed at the bedside, can predict 30-day outcomes in patients hospitalized for heart failure. METHODS: We used logistic regression with LACE as the continuous predictor and 30-day readmissions and 30-day readmission or death as outcomes. We determined a suitable LACE threshold using logistic regression and the closest-to-(0,1) criterion for dichotomized LACE scores. We assessed model discrimination with C statistics and 95% CI. RESULTS: Of 378 patients, a majority (91%) had LACE scores ≥10. Incremental LACE scores increased the odds of 30-day readmissions (odds ratio [OR] 1.13, 95% CI 1.02-1.24) and 30-day readmissions or death (OR 1.11, 95% CI 1.01-1.22). C statistics for 30-day readmissions (0.59, 95% CI 0.52-0.65) and 30-day readmission or death (0.57, 95% CI 0.51-0.64) were nonsignificantly lower than the Centers for Medicare/Medicaid Services-endorsed readmission risk score (0.61, 95% CI 0.55-0.67 and 0.62, 95% CI 0.55-0.68, respectively). LACE ≥13 predicted 30-day readmissions (OR 1.91, 95% CI 1.17-3.09) and 30-day readmission or death (OR 1.59, 95% CI 1.00-2.54), and met the closest-to-(0,1) criterion for optimal threshold. CONCLUSIONS: LACE calculated at the bedside predicts 30-day clinical outcomes in hospitalized heart failure patients. While there is a continuum of risk, a threshold of ≥13 is more suitable than ≥10 to identify high-risk patients. Given its modest discrimination, however, we do not recommend its preferential use over validated risk prediction tools such as readmission risk score.
UNLABELLED: The Length of stay, Acuity, Comorbidities, Emergency department visits in prior 6 months (LACE) index threshold of 10 predicts readmission or death in general medical patients in administrative databases. We assessed whether the unadjusted LACE index, computed at the bedside, can predict 30-day outcomes in patients hospitalized for heart failure. METHODS: We used logistic regression with LACE as the continuous predictor and 30-day readmissions and 30-day readmission or death as outcomes. We determined a suitable LACE threshold using logistic regression and the closest-to-(0,1) criterion for dichotomized LACE scores. We assessed model discrimination with C statistics and 95% CI. RESULTS: Of 378 patients, a majority (91%) had LACE scores ≥10. Incremental LACE scores increased the odds of 30-day readmissions (odds ratio [OR] 1.13, 95% CI 1.02-1.24) and 30-day readmissions or death (OR 1.11, 95% CI 1.01-1.22). C statistics for 30-day readmissions (0.59, 95% CI 0.52-0.65) and 30-day readmission or death (0.57, 95% CI 0.51-0.64) were nonsignificantly lower than the Centers for Medicare/Medicaid Services-endorsed readmission risk score (0.61, 95% CI 0.55-0.67 and 0.62, 95% CI 0.55-0.68, respectively). LACE ≥13 predicted 30-day readmissions (OR 1.91, 95% CI 1.17-3.09) and 30-day readmission or death (OR 1.59, 95% CI 1.00-2.54), and met the closest-to-(0,1) criterion for optimal threshold. CONCLUSIONS: LACE calculated at the bedside predicts 30-day clinical outcomes in hospitalized heart failurepatients. While there is a continuum of risk, a threshold of ≥13 is more suitable than ≥10 to identify high-risk patients. Given its modest discrimination, however, we do not recommend its preferential use over validated risk prediction tools such as readmission risk score.
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