Dataset Information

LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital.

ABSTRACT:

Background

The LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).

Methods

This was a retrospective study. Data were extracted from the hospital's electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.

Results

Of the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75-0.81). The Hosmer-Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.

Conclusion

The LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be considered to improve the model performance of the LACE index with other acute conditions by using administrative data.

SUBMITTER: Rajaguru V

PROVIDER: S-EPMC9309494 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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:IntroductionHospital readmissions are common, expensive, and a key target of the Medicare Value Based Purchasing (VBP) program. Risk assessment tools 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. One such tool is the HOSPITAL score that uses seven readily available clinical variables to predict the risk of readmission within 30 days of discharge. The HOSPITAL score has been internationally validated in large academic medical centers. This study aims to determine if the HOSPITAL score is similarly useful in a moderate sized university affiliated hospital in the midwestern United States.Materials and methodsAll adult medical patients 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 was a significant predictor of hospital readmission within 30 days.ResultsDuring the study period, 998 discharges were recorded for the hospitalist service. The analysis includes data for the 931 discharges. Patients who died during the hospital stay, were transferred to another hospital, or left against medical advice were excluded. Of these patients, 109 (12%) were readmitted to the same hospital within 30 days. The patients who were readmitted were more likely to have a length of stay greater than or equal to 5 days (55% vs. 41%, p = 0.005) and were more likely to have been admitted more than once to the hospital within the last year (100% vs. 49%, p < 0.001). A receiver operating characteristic evaluation of the HOSPITAL score for this patient population shows a C statistic of 0.77 (95% CI [0.73-0.81]), indicating good discrimination for hospital readmission. The Brier score for the HOSPITAL score in this setting was 0.10, indicating good overall performance. The Hosmer-Lemeshow goodness of fit test shows a ? (2) value of 1.63 with a p value of 0.20.DiscussionThis single center retrospective study indicates that the HOSPITAL score has good discriminatory ability to predict hospital readmissions within 30 days for a medical hospitalist service at a university-affiliated hospital. This data for all causes of hospital readmission is comparable to the discriminatory ability of the HOSPITAL score in the international validation study (C statistics of 0.72 vs. 0.77) conducted at considerably larger hospitals (975 average beds vs. 507 at MMC) for potentially avoidable hospital readmissions.ConclusionsThe internationally validated HOSPITAL score may be a useful tool in moderate sized community hospitals to identify patients at high risk of hospital readmission within 30 days. This easy to use scoring system using readily available data can be used as part of interventional strategies to reduce the rate of hospital readmission.

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Project description:ImportanceThe association of the Hospital Readmission Reduction Program (HRRP) with reductions in racial disparities in 30-day outcomes for myocardial infarction (MI), is unknown, including whether this varies by HRRP hospital penalty status.ObjectiveTo assess temporal trends in 30-day readmission and mortality rates among black and nonblack patients discharged after hospitalization for acute MI at low-performing and high-performing hospitals, as defined by readmission penalty status after HRRP implementation.Design, setting, and participantsThis observational cohort analysis used data from the multicenter National Cardiovascular Data Registry Chest Pain-MI Registry centers that were subject to the first cycle of HRRP, between January 1, 2008, and November 30, 2016. All patients hospitalized with MI who were included in National Cardiovascular Data Registry Chest Pain-MI Registry were included in the analysis. Data were analyzed from April 2018 to September 2019.ExposuresHospital performance category and race (black compared with nonblack patients). Centers were classified as high performing or low performing based on the excess readmission ratio (predicted to expected 30-day risk adjusted readmission rate) for MI during the first HRRP cycle (in October 2012).Main outcomes and measuresThirty-day all-cause readmission and mortality rates.ResultsAmong 753 hospitals that treated 155 397 patients with acute MI (of whom 11 280 [7.3%] were black), 399 hospitals (53.0%) were high performing. Thirty-day readmission rates declined over time in both black and nonblack patients (annualized 30-day readmission rate: 17.9% vs 20.8%). Black (compared with nonblack) race was associated with higher unadjusted odds of 30-day readmission in both low-performing and high-performing centers (odds ratios: before HRRP: low-performing hospitals, 1.14 [95% CI, 1.03-1.26]; P = .01; high-performing hospitals, 1.17 [95% CI, 1.04-1.32]; P = .01; after HRRP: low-performing hospitals, 1.23 [95% CI, 1.13-1.34]; P < .001; high-performing hospitals, 1.25 [95% CI, 1.12-1.39]; P < .001). However, these racial differences were not significant after adjustment for patient characteristics. The 30-day mortality rates declined significantly over time in nonblack patients, with stable (nonsignificant) temporal trends among black patients. Adjusted associations between race and 30-day mortality showed that 30-day mortality rates were significantly lower among black (compared with nonblack) patients in the low-performing hospitals (odds ratios: pre-HRRP, 0.79 [95% CI, 0.63-0.97]; P = .03; post-HRRP, 0.80 [95% CI, 0.68-0.95]; P = .01) but not in high-performing hospitals. Finally, the association between race and 30-day outcomes did not vary after the HRRP period began in either high-performing or low-performing hospitals.Conclusions and relevanceIn this analysis, 30-day readmission rates among patients with MI declined over time for both black and nonblack patients. Differences in race-specific 30-day readmission rates persisted but appeared to be attributable to patient-level factors. The 30-day mortality rates have declined for nonblack patients and remained stable among black patients. Implementation of the HRRP was not associated with improvement or worsening of racial disparities in readmission and mortality rates.

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Project description:ImportanceIn the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.ObjectiveTo compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.Design, setting, and participantsThis was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020.ExposuresAcute myocardial infarction that required hospital admission.Main outcomes and measuresThe main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts.ResultsThe final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models.Conclusions and relevanceIn this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.