Assessing the accuracy of the LACE index to predict 30-day readmissions in regional Victoria, Australia

Background and objectiveHospital readmissions within 30 days are used as an indicator of quality of hospital care. We aimed to evaluate the ability of the LACE (Length of stay, Acuity of admission, Comorbidities based on Charlson comorbidity score and number of Emergency visits in the last 6 months) index to predict the risk of 30-day readmissions in patients hospitalised for community-acquired pneumonia (CAP).MethodsIn this retrospective cohort study a LACE index score was calculated for patients with a principal diagnosis of CAP admitted to a tertiary hospital in Sydney, Australia. The predictive ability of the LACE score for 30-day readmissions was assessed using receiver operator characteristic curves with C-statistic.ResultsOf 3996 patients admitted to hospital for CAP at least once, 8.0% (n=327) died in hospital and 14.6% (n=584) were readmitted within 30 days. 17.8% (113 of 636) of all 30-day readmissions were again due to CAP, followed by readmissions for chronic obstructive pulmonary disease, heart failure and chest pain. The LACE index had moderate discriminative ability to predict 30-day readmission (C-statistic=0.6395) but performed poorly for the prediction of 30-day readmissions due to CAP (C-statistic=0.5760).ConclusionsThe ability of the LACE index to predict all-cause 30-day hospital readmissions is comparable to more complex pneumonia-specific indices with moderate discrimination. For the prediction of 30-day readmissions due to CAP, the performance of the LACE index and modified risk prediction models using readily available variables (sex, age, specific comorbidities, after-hours, weekend, winter or summer admission) is insufficient.

Background and objectivePatients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (COPD) have a high 30-day hospital readmission rate, which has a large impact on the health care system and patients’ quality of life. The use of a prediction model to quantify a patient’s risk of readmission may assist in directing interventions to patients who will benefit most. The objective of this study was to calculate the rate of 30-day readmissions and evaluate the accuracy of the LACE index (length of stay, acuity of admission, co-morbidities, and emergency department visits within the last 6 months) for 30-day readmissions in a general hospital population of COPD patients.MethodsAll patients admitted with a principal diagnosis of COPD to Liverpool Hospital, a tertiary hospital in Sydney, Australia, between 2006 and 2016 were included in the study. A LACE index score was calculated for each patient and assessed using receiver operator characteristic curves.ResultsDuring the study period, 2,662 patients had 5,979 hospitalizations for COPD. Four percent of patients died in hospital and 25% were readmitted within 30 days; 56% of all 30-day readmissions were again due to COPD. The most common reasons for readmission, following COPD, were heart failure, pneumonia, and chest pain. The LACE index had moderate discriminative ability to predict 30-day readmission (C-statistic =0.63).ConclusionThe 30-day hospital readmission rate was 25% following hospitalization for COPD in an Australian tertiary hospital and as such comparable to international published rates. The LACE index only had moderate discriminative ability to predict 30-day readmission in patients hospitalized for COPD.

This study aimed to evaluate the accuracy of the HOSPITAL Score (Haemoglobin level at discharge, Oncology at discharge, Sodium level at discharge, Procedure during hospitalization, Index admission, number of hospital...

The Predictive Value of the HOSPITAL Score and LACE Index for an Adult Neurosurgical Population: A Prospective Analysis

Improving patient outcomes after acute myocardial infarction (AMI) may be facilitated by identifying patients at a high risk of adverse events before hospital discharge. We aimed to determine the accuracy of the LACE (Length of stay, Acuity, Comorbidities, Emergency presentations within prior 6 months) index score (a prediction tool) for predicting 30-day all-cause mortality and readmission rates (independently and combined) in South Australian AMI patients who had an angiogram. All consecutive AMI patients enrolled in the Coronary Angiogram Database of South Australia Registry at two major tertiary hospitals and discharged alive between July 2016 to June 2017. A LACE score was calculated for each patient, and receiver operating characteristic curve analysis was performed. Analysis of registry patients found a 30-day unplanned readmission rate of 11.8% and mortality rate of 0.7%. Moreover, the LACE index was a moderate predictor (C-statistic = 0.62) of readmissions in this cohort, and a score ≥10 indicated moderate discriminatory capacity to predict 30-day readmissions. The LACE index shows moderate discriminatory capacity to predict 30-day readmissions and mortality. A cut-off score of nine to optimize sensitivity may assist clinicians in identifying patients at a high risk of adverse outcomes.

Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of hospitalization, Acuity, Comorbidities, Emergency department visits) index. All patients aged ≥ 65years discharged alive between 2010 and 2019 after a hospitalization for acute HF were included in this retrospective cohort study. We applied MICE (Multivariate Imputation via Chained Equations) method to obtain a balanced, fully valued dataset and LASSO (Least Absolute Shrinkage and Selection Operator) algorithm to get the most significant features. Training (80% of records) and test (20%) cohorts were randomly selected. Study population: 3079 patients, 394 (12.8%) presented at least one readmission within 30days, and 2685 (87.2%) did not. In the test cohort AUCs (IC95%) of XGBoost, Ada Boost Classifier, Random forest, and Gradient Boosting, and LACE Index were: 0.803 (0.734-0.872), 0.782 (0.711-0.854), 0.776 (0.703-0.848), 0.786 (0.715-0.857), and 0.504 (0.414-0.594), respectively, for predicting readmissions. A SHAP analysis was performed to offer a breakdown of the ML variables associated with readmission. Positive and negative predicting values estimates of the different ML models and LACE index were also provided, for several values of readmission rate prevalence. Among elderly patients, the rate of all-cause unplanned 30-day readmissions after hospitalization due to an acute HF was high. ML models performed better than the conventional LACE index for predicting readmissions. ML models can be proposed as promising tools for the identification of subjects at high risk of hospitalization in this clinical setting, enabling care teams to target interventions for improving overall clinical outcomes.

(1) Background: Elders have higher rates of rehospitalization, especially those with functional decline. We aimed to investigate potential predictors of 30-day readmission risk by comprehensive geriatric assessment (CGA) in hospitalized patients aged 65 years or older and to examine the predictive ability of the LACE index and HOSPITAL score in older patients with a combination of malnutrition and physical dysfunction. (2) Methods: We included patients admitted to a geriatric ward in a tertiary hospital from July 2012 to August 2018. CGA components including cognitive, functional, nutritional, and social parameters were assessed at admission and recorded, as well as clinical information. The association factors with 30-day hospital readmission were analyzed by multivariate logistic regression analysis. The predictive ability of the LACE and HOSPITAL score was assessed using receiver operator characteristic curve analysis. (3) Results: During the study period, 1509 patients admitted to a ward were recorded. Of these patients, 233 (15.4%) were readmitted within 30 days. Those who were readmitted presented with higher comorbidity numbers and poorer performance of CGA, including gait ability, activities of daily living (ADL), and nutritional status. Multivariate regression analysis showed that male gender and moderately impaired gait ability were independently correlated with 30-day hospital readmissions, while other components such as functional impairment (as ADL) and nutritional status were not associated with 30-day rehospitalization. The receiver operating characteristics for the LACE index and HOSPITAL score showed that both predicting scores performed poorly at predicting 30-day hospital readmission (C-statistic = 0.59) and did not perform better in any of the subgroups. (4) Conclusions: Our study showed that only some components of CGA, mobile disability, and gender were independently associated with increased risk of readmission. However, the LACE index and HOSPITAL score had a poor discriminating ability for predicting 30-day hospitalization in all and subgroup patients. Further identifiers are required to better estimate the 30-day readmission rates in this patient population.

To examine whether the EQ-5D-3L at the time of discharge from hospital provides additional prognostic information above the LACE index for 30-day post-discharge hospital readmission and to explore the association of EQ-5D-3L with readmissions, emergency department (ED) visits, and death within the same period. Using data (n = 495; mean age 62.9years (SD 18.6), 50.5% female) from a prospective cohort study of patients discharged from medical wards at two university hospitals, the prognostic ability of EQ-5D-3L was examined using C-statistic, Integrated Discrimination Improvement (IDI) Index, and Akaike's Information Criterion (AIC). The associations between EQ-5D-3L dimensions, total sum, index and VAS scores at the time of discharge and 30-day post-discharge ED visits, readmission, and readmission/death were examined using multivariate logistic regression. At the time of discharge, 58.6% of participants reported problems in mobility, 28.3% in self-care, 62.1% in usual activities, 62.7% in pain/discomfort, and 42.4% in anxiety/depression. Mean (SD) total sum score was 7.9 (2.0), index score was 0.69 (0.21), and VAS score was 63.7 (18.4). In adjusted analyses, mobility, self-care, usual activities, and the total sum score were significantly associated with 30-day readmission and readmission/death. Differences in C-statistic for LACE readmission prediction models with and without EQ-5D-3L were small. AIC analysis suggests that readmission prediction models containing EQ-5D-3L dimensions or scores were more often preferred to those with the LACE index only. IDI analysis indicates that the discrimination slope of readmission prediction models is significantly improved with the addition of mobility, self-care, or the total sum score of the EQ-5D-3L. The EQ-5D-3L, especially the mobility and self-care dimensions as well as the total sum score, improves 30-day readmission prediction of the LACE index and is associated with 30-day readmissions or readmissions/death.

Factors contributing to and causes of hospital readmissions have been investigated worldwide, but very few studies have been performed in South Africa (SA) and none in the Western Cape Province. To investigate possible preventable and non-preventable factors contributing to readmissions to the Department of Internal Medicine at Tygerberg Hospital (TBH), Cape Town, within 30 days of hospital discharge. The researchers tested a risk-stratification tool (the LACE index) to evaluate the tool's performance in the TBH system. A retrospective analysis was conducted of all 30-day readmissions (initial hospitalisation and rehospitalisation within 30 days) to the Department of Internal Medicine at TBH for the period 1 January 2014 - 31 March 2015. Potential risk factors leading to readmission were recorded. A total of 11 826 admissions were recorded. Of these patients, 1 242 were readmitted within 30 days, representing a readmission rate of 10.5%. The majority of patients (66%) were readmitted within 14 days after discharge. The most important risk factor for readmission was the number of comorbidities, assessed using the Charlston score. The study also identified a large burden of potentially avoidable causes (35% of readmissions) due to system-related issues, premature discharge being the most common. Other reasons for 30-day readmission were nosocomial infection, adverse drug reactions, especially warfarin toxicity, inadequate discharge planning and physician error. Despite TBH being a low-resource, high-turnover system, the 30-day readmission rate was calculated at 10.5%. Global readmission rates vary from 10% to 25%, depending on the reference article/source used. We found that 35% of 30-day readmissions were potentially avoidable. Venous thromboembolism was a minor contributor to readmission but was associated with a very high mortality rate. A secondary outcome evaluated was the utility of the LACE and modified LACE (mLACE) index in the TBH environment. The risk tool performed well in the TBH population, and a high LACE and mLACE score correlated with an increased risk of 30-day readmission (p<0.001).

Several studies identify heart failure (HF) as a potential risk for hospital readmission; however, studies on predictability of heart failure readmission is limited. The objective of this work was to investigate whether a specific type of heart failure (HFpEF or HFrEF) has a higher association to the rate of 30-day hospital readmission and compare their predictability with the two risk scores: HOSPITAL score and LACE index. Retrospective study from single academic center. Sample size included adult patients from an academic hospital in a two-year period (2015 - 2017). Exclusion criteria included death, transfer to another hospital, and unadvised leave from hospital. Baseline characteristics, diagnosis-related group, and ICD diagnosis codes were obtained. Variables affecting HOSPITAL score and LACE index and types of heart failure present were also extracted. Qualitative variables were compared using Pearson chi2 or Fisher's exact test (reported as frequency) and quantitative variables using non-parametric Mann-Whitney U test (reported as mean ± standard deviation). Variables from univariate analysis with P values of 0.05 or less were further analyzed using multivariate logistic regression. Odds ratio was used to measure potential risk. The sample size of adult patients in the study period was 1,916. All eligible cohort of patients who were readmitted were analyzed. Cumulative score indicators of HOSPITAL Score, LACE index (including the Charlson Comorbidity Index) predicted 30-day readmissions with P values of <0.001. The P value of HFpEF was found to be significant in the readmitted group (P < 0.001) compared to HFrEF (P = 0.141). Multivariate logistic regression further demonstrated the association of HFpEF with higher risk of readmission with odds ratio of 1.77 (95% CI: 1.25 - 2.50) and P value of 0.001. Our data from an academic tertiary care center supports HFpEF as an independent risk factor for readmission. Multidisciplinary management of HFpEF may be an important target for interventions to reduce hospital readmissions.

Background/Objectives: Hospital readmissions are a key quality metric impacting both patient outcomes and healthcare costs. Traditional logistic regression models, including the LACE index (Length of stay, Admission type, Comorbidity index, and recent Emergency department visits), are commonly used for readmission risk stratification, though their accuracy may be limited by non-linear interactions with other clinical variables. This study compared the predictive performance of non-linear machine learning (ML) models with stepwise logistic regression (LR) and the LACE index for predicting 30-day general medicine readmissions. Methods: We retrospectively analysed adult general medical admissions at a tertiary hospital in Australia from 1 July 2022 to 30 June 2023. Thirty-two variables were extracted from electronic medical records, including demographics, comorbidities, prior healthcare use, socioeconomic status (SES), laboratory data, and frailty (measured by the Hospital Frailty Risk Score). Predictive models included stepwise LR and four ML algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), random forest, Extreme Gradient Boosting (XGBoost), and artificial neural networks (ANNs). Performance was assessed using the area under the curve (AUC), with comparisons made using DeLong's test. Results: Of 5371 admissions, 1024 (19.1%) resulted in 30-day readmissions. Readmitted patients were older and frailer and had more comorbidities and lower SES. Logistic regression (LR) identified the key predictors of outcomes, including heart failure, alcoholism, nursing home residency, and prior admissions, achieving an AUC of 0.62. LR's performance was comparable to that of the LACE index (AUC = 0.61) and machine learning models: LASSO (AUC = 0.63), random forest (AUC = 0.60), and artificial neural networks (ANNs) (AUC = 0.60) (p > 0.05). However, LR significantly outperformed XGBoost (AUC = 0.55) (p < 0.05). Conclusions: About one in five general medicine patients are readmitted within 30 days. Traditional LR performed as well as or better than ML models for readmission risk prediction.

Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%-25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors. The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004-2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation. This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index. Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.

Aims: This study aimed to utilize the existing LACE index (length of stay, acuity of admission, comorbidity index and emergency room visit in the past six months) to predict the risk of 30-day readmission and to find the associated factors in patients with AMI. Methods: This was a retrospective study and LACE index scores were calculated for patients admitted with AMI between 2015 and 2019. Data were utilized from the hospital’s electronic medical record. Multivariate logistic regression was performed to find the association between covariates and 30-day readmission. The risk prediction ability of the LACE index for 30-day readmission was analyzed by receiver operating characteristic curves with the C statistic. Results: A total of 205 (5.7%) patients were readmitted within 30 days. The odds ratio of older age group (OR = 1.78, 95% CI: 1.54–2.05), admission via emergency ward (OR = 1.45; 95% CI: 1.42–1.54) and LACE score ≥10 (OR = 2.71; 95% CI: 1.03–4.37) were highly associated with 30-day readmissions and statistically significant. The receiver operating characteristic curve C statistic of the LACE index for AMI patients was 0.78 (95% CI: 0.75–0.80) and showed favorable discrimination in the prediction of 30-day readmission. Conclusion: The LACE index showed a good discrimination to predict the risk of 30-day readmission for hospitalized patients with AMI. Further study would be recommended to focus on additional factors that can be used to predict the risk of 30-day readmission; this should be considered to improve the model performance of the LACE index for other acute conditions by using the national-based administrative data.

BackgroundThe 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).MethodsThis 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.ResultsOf 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.ConclusionThe 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.

Background and AimsReadmission within 30 days in inflammatory bowel disease (IBD) patients increases treatment costs and serves as a quality indicator. The LACE (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency Department visits in past 6 months) index is used to predict the risk of unplanned readmission within 30 days. The aim of this study was to evaluate the accuracy of using the LACE index in IBD.MethodsCalculation of LACE index was done prospectively for IBD patients admitted to a single tertiary care center. Patient, disease, and treatment characteristics, as well as index hospitalization characteristics including indication for admission and disease activity measures were retrospectively recorded. Descriptive statistics and univariable exact logistic regression analyses were performed.ResultsIn total, 64 IBD patients were admitted during the study period. The 30-day readmission rate of IBD patients was 19% and overall median LACE index was 6, with IQR 6–7. LACE index categorized 16% of IBD patients in low-risk group, 82% in moderate risk group, and 2% in high-risk group. LACE index did not predict 30-day readmission (OR 1.35, CI: 0.88–2.18, P = 0.19). There was no significant difference in 30-day readmission rates with inpatient antibiotic or narcotic use, admission C-reactive protein (CRP), anemia, IBD duration, maintenance therapy, or prior IBD operation. For every 1 day increase in length of stay (LOS), patients were 8% more likely (OR: 1.08, 95% CI: 1.00–1.16) to be readmitted within 30 days (P = .05).ConclusionsLACE index does not accurately identify 30-day readmission risk in the IBD population. As increased LOS is associated with higher risk, there may be benefit for targeted strategic resource allocation via specialized services.