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Derivation of age-adjusted LACE index thresholds in the prediction of mortality and frequent hospital readmissions in adults.

ABSTRACT: The LACE index has been shown to predict hospital readmissions and death with variable accuracy. A LACE index???10 is considered as high risk in the existing literature. We aimed to derive age-specific LACE index thresholds in the prediction of mortality and frequent readmissions. Analysis of prospectively collected data of consecutive alive-discharge episodes between 01/04/2017 and 31/03/2019 to a single hospital was conducted. The derivation of LACE index thresholds for predicting all-cause mortality within 6 months of hospital discharge or frequent readmissions (??2 times within 28 days) was examined by receiver operating characteristics (ROC) in 32270 patients (14878 men, 17392 women) aged 18-107 year (mean?=?64.0 years, SD?=?20.5). For all patients with a LACE index???10, the area under the curve (AUC) for predicting mortality was 80.5% (95% CI 79.7-81.3) and for frequent readmissions was 84.0% (83.0-85.1). Two-graph ROC plots showed that the LACE index threshold where sensitivity equates specificity was 9.5 (95% intermediate range?=?5.6-13.5) for predicting mortality and 10.3 (95% intermediate range?=?6.6-13.6) for frequent readmissions. These thresholds were lowest among youngest individuals and rose progressively with age for mortality prediction: 18-49 years?=?5.0, 50-59 years?=?6.5, 60-69 years?=?8.0, 70-79 years?=?9.8 and???80 years?=?11.6, and similarly for frequent readmissions: 18-49 years?=?5.1, 50-59 years?=?7.5, 60-69 years?=?9.1, 70-79 years?=?10.6 and???80 years?=?12.0. Positive and negative likelihood ratios (LRs) ranged 1.5-3.3 and 0.4-0.6 for predicting mortality, and 2.5-4.4 and 0.3-0.6 for frequent readmissions, respectively, with stronger evidence in younger than in older individuals (LRs further from unity). In conclusion, the LACE index predicts mortality and frequent readmissions at lower thresholds and stronger in younger than in older individuals. Age should be taken into account when using the LACE index for identifying patients at high risk.

SUBMITTER: Fry CH

PROVIDER: S-EPMC7511461 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Derivation of age-adjusted LACE index thresholds in the prediction of mortality and frequent hospital readmissions in adults.

Fry Christopher Henry CH Heppleston Erica E Fluck David D Han Thang Sieu TS

Internal and emergency medicine 20200728 7

The LACE index has been shown to predict hospital readmissions and death with variable accuracy. A LACE index ≥ 10 is considered as high risk in the existing literature. We aimed to derive age-specific LACE index thresholds in the prediction of mortality and frequent readmissions. Analysis of prospectively collected data of consecutive alive-discharge episodes between 01/04/2017 and 31/03/2019 to a single hospital was conducted. The derivation of LACE index thresholds for predicting all-cause mo ...[more]

PMID: 32725518

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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:OBJECTIVES:To develop a clinical prediction score for predicting mortality in children following return of spontaneous circulation after in-hospital cardiac arrest. DESIGN:Observational study using prospectively collected data. SETTING:This was an analysis using data from the Get With The Guidelines-Resuscitation registry between January 2000 and December 2015. PATIENTS:Pediatric patients (< 18 yr old) who achieved return of spontaneous circulation. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The primary outcome was in-hospital mortality. Patients were divided into a derivation (3/4) and validation (1/4) cohort. A prediction score was developed using a multivariable logistic regression model with backward selection. Patient and event characteristics for the derivation cohort (n = 3,893) and validation cohort (n = 1,297) were similar. Seventeen variables associated with the outcome remained in the final reduced model after backward elimination. Predictors of in-hospital mortality included age, illness category, pre-event characteristics, arrest location, day of the week, nonshockable pulseless rhythm, duration of chest compressions, and interventions in place at time of arrest. The C-statistic for the final score was 0.77 (95% CI, 0.75-0.78) in the derivation cohort and 0.77 (95% CI, 0.74-0.79) in the validation cohort. The expected versus observed mortality plot indicated good calibration in both the derivation and validation cohorts. The score showed a stepwise increase in mortality with an observed mortality of less than 15% for scores 0-9 and greater than 80% for scores greater than or equal to 25. The model also performed well for neurologic outcome and in sensitivity analyses for events within the past 5 years and for patients with or without a pulse at the onset of chest compressions. CONCLUSIONS:We developed and internally validated a prediction score for initial survivors of pediatric in-hospital cardiac arrest. This prediction score may be useful for prognostication following cardiac arrest, stratifying patients for research, and guiding quality improvement initiatives.

Project description:IntroductionHospital readmissions are common, expensive, and a key target of the Medicare Value Based Purchasing (VBP) program. Validated risk assessment tools such as the HOSPITAL score and LACE index have been developed to identify patients at high risk of hospital readmission so they can be targeted for interventions aimed at reducing the rate of readmission. This study aims to evaluate the utility of HOSPITAL score and LACE index for predicting hospital readmission within 30 days in a moderate-sized university affiliated hospital in the midwestern United States.Materials and methodsAll adult medical patients who underwent one or more ICD-10 defined procedures discharged from the SIU-SOM Hospitalist service from Memorial Medical Center (MMC) from October 15, 2015 to March 16, 2016, were studied retrospectively to determine if the HOSPITAL score and LACE index were a significant predictors of hospital readmission within 30 days.ResultsDuring the study period, 463 discharges were recorded for the hospitalist service. The analysis includes data for the 432 discharges. Patients who died during the hospital stay, were transferred to another hospital, or left against medical advice were excluded. Of these patients, 35 (8%) were readmitted to the same hospital within 30 days. A receiver operating characteristic evaluation of the HOSPITAL score for this patient population shows a C statistic of 0.75 (95% CI [0.67-0.83]), indicating good discrimination for hospital readmission. The Brier score for the HOSPITAL score in this setting was 0.069, indicating good overall performance. The Hosmer-Lemeshow goodness of fit test shows a χ2 value of 3.71 with a p value of 0.59. A receiver operating characteristic evaluation of the LACE index for this patient population shows a C statistic of 0.58 (95% CI [0.48-0.68]), indicating poor discrimination for hospital readmission. The Brier score for the LACE index in this setting was 0.082, indicating good overall performance. The Hosmer-Lemeshow goodness of fit test shows a χ2 value of 4.97 with a p value of 0.66.DiscussionThis single center retrospective study indicates that the HOSPITAL score has superior discriminatory ability when compared to the LACE index as a predictor of hospital readmission within 30 days at a medium-sized university-affiliated teaching hospital.ConclusionsThe internationally validated HOSPITAL score may be superior to the LACE index in moderate-sized community hospitals to identify patients at high risk of hospital readmission within 30 days.

Project description:ImportanceHospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores.ObjectiveTo identify the type of score that best predicts hospital readmissions.Design, setting, and participantsThis prognostic study included 14?062 consecutive adult hospital patients with 16?649 discharges from a tertiary care center, suburban community hospital, and urban critical access hospital in Maryland from September 1, 2016, through December 31, 2016. Patients not included as eligible discharges by the Centers for Medicare & Medicaid Services or the Chesapeake Regional Information System for Our Patients were excluded. A machine learning rank score, the Baltimore score (B score) developed using a machine learning technique, for each individual hospital using data from the 2 years before September 1, 2016, was compared with standard readmission risk assessment scores to predict 30-day unplanned readmissions.Main outcomes and measuresThe 30-day readmission rate evaluated using various readmission scores: B score, HOSPITAL score, modified LACE score, and Maxim/RightCare score.ResultsOf the 10?732 patients (5605 [52.2%] male; mean [SD] age, 54.56 [22.42] years) deemed to be eligible for the study, 1422 were readmitted. The area under the receiver operating characteristic curve (AUROC) for individual rules was 0.63 (95% CI, 0.61-0.65) for the HOSPITAL score, which was significantly lower than the 0.66 for modified LACE score (95% CI, 0.64-0.68; P?<?.001). The B score machine learning score was significantly better than all other scores; 48 hours after admission, the AUROC of the B score was 0.72 (95% CI, 0.70-0.73), which increased to 0.78 (95% CI, 0.77-0.79) at discharge (all P?<?.001). At the hospital using Maxim/RightCare score, the AUROC was 0.63 (95% CI, 0.59-0.69) for HOSPITAL, 0.64 (95% CI, 0.61-0.68) for Maxim/RightCare, and 0.66 (95% CI, 0.62-0.69) for modified LACE score. The B score was 0.72 (95% CI, 0.69-0.75) 48 hours after admission and 0.81 (95% CI, 0.79-0.84) at discharge. In directly comparing the B score with the sensitivity at cutoff values for modified LACE, HOSPITAL, and Maxim/RightCare scores, the B score was able to identify the same number of readmitted patients while flagging 25.5% to 54.9% fewer patients.Conclusions and relevanceAmong 3 hospitals in different settings, an automated machine learning score better predicted readmissions than commonly used readmission scores. More efficiently targeting patients at higher risk of readmission may be the first step toward potentially preventing readmissions.

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Derivation of age-adjusted LACE index thresholds in the prediction of mortality and frequent hospital readmissions in adults.

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Derivation of age-adjusted LACE index thresholds in the prediction of mortality and frequent hospital readmissions in adults.

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