Assessing the Performance of a Modified LACE Index (LACE-rt) to Predict Unplanned Readmission After Discharge in a Community Teaching Hospital

Evaluation of the Retrospective LACE Index in Predicting the Risk of Readmission in Patients with Hereditary Angioedema in an Emergency Department

This study aimed to calculate the LACE index in patients who admitted to the emergency department (ED) with hereditary angioedema (HA) diagnosed and to predict recurrent admissions of patients. In this single-center study, patients aged 18 or higher who were admitted to the ED diagnosed with HA were included over a 12-year period. 35 patients diagnosed with code E88.0 were evaluated according to electronic file records. The number of admissions to the ED in the last 6 months was 2. The LACE index was 4, and risk was 71.4%. The patients admitted to the hospital in the last 30 days had a higher rate of admission to the hospital in the last 6 months (p < 0.001). The LACE index at admission predicted 30 days admission with (AUC = 0.75, 95% CI (0.56-0.91)) acceptable discrimination. The LACE index and the number of admissions in the last 6 months included in the evaluation can be considered predictive in recurrent ED admissions of HA patients. However, the distribution of LACE-risk groups is no priority. Therefore, the low-, medium-, or high-risk level of LACE index values should be not taken into consideration in readmission of such patients.

Prediction of Adverse Outcomes Within 90 Days of Surgery in a Heterogeneous Orthopedic Surgery Population

INTRODUCTION

The LACE+ index has been shown to predict readmissions; however, LACE+ has not been validated for extended postoperative outcomes in an orthopedic surgery population. The purpose of this study is to examine whether LACE+ scores predict unplanned readmissions and adverse outcomes following orthopedic surgery. Use of the LACE1 index to proactively identify at-risk patients may enable actions to reduce preventable readmissions.

METHODS

LACE+ scores were retrospectively calculated at the time of discharge for all consecutive orthopedic surgery patients (n = 18,893) at a multicenter health system over 3 years (2016-2018). Coarsened exact matching was used to match patients based on characteristics not assessed in the LACE+ index. Outcome differences between matched patients in different LACE quartiles (i.e. Q4 vs. Q3, Q2, and Q1) were analyzed.

RESULTS

Higher LACE+ scores significantly predicted readmission and emergency department visits within 90 days of discharge and for 30-90 days after discharge for all studied quartiles. Higher LACE+ scores also significantly predicted reoperations, but only between Q4 and Q3 quartiles.

CONCLUSIONS

The results suggest that the LACE+ risk-prediction tool may accurately predict patients with a high likelihood of adverse outcomes after a broad array of orthopedic procedures.

Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

Background

Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.

Objective

The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors.

Methods

We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors.

Results

We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported.

Conclusions

The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

Composite score for prediction of 30-day orthopedic surgery outcomes

The LACE+ (Length of stay, Acuity of admission, Charlson Comorbidity Index score, and Emergency department visits in the past 6 months) risk-prediction tool has never been tested in an orthopedic surgery population. LACE+ may help physicians more effectively identify and support high-risk orthopedics patients after hospital discharge. LACE+ scores were retrospectively calculated for all consecutive orthopedic surgery patients (n = 18 893) at a multi-center health system over 3 years (2016-2018). Coarsened exact matching was employed to create "matched" study groups with different LACE+ score quartiles (Q1, Q2, Q3, Q4). Outcomes were compared between quartiles. In all, 1444 patients were matched between Q1 and Q4 (n = 2888); 2079 patients between Q2 and Q4 (n = 4158); 3032 patients between Q3 and Q4 (n = 6064). Higher LACE+ scores significantly predicted 30D readmission risk for Q4 vs Q1 and Q4 vs Q3 (P < .001). Larger LACE+ scores also significantly predicted 30D risk of ED visits for Q4 vs Q1, Q4 vs Q2, and Q4 vs Q3 (P < .001). Increased LACE+ score also significantly predicted 30D risk of reoperation for Q4 vs Q1 (P = .018), Q4 vs Q2 (P < .001), and Q4 vs Q3 (P < .001).

Effectiveness of ICT-based intimate partner violence interventions: a systematic review

BACKGROUND

Intimate Partner Violence is a "global pandemic". Meanwhile, information and communication technologies (ICT), such as the internet, mobile phones, and smartphones, are spreading worldwide, including in low- and middle-income countries. We reviewed the available evidence on the use of ICT-based interventions to address intimate partner violence (IPV), evaluating the effectiveness, acceptability, and suitability of ICT for addressing different aspects of the problem (e.g., awareness, screening, prevention, treatment, mental health).

METHODS

We conducted a systematic review, following PRISMA guidelines, using the following databases: PubMed, PsycINFO, and Web of Science. Key search terms included women, violence, domestic violence, intimate partner violence, information, communication technology, ICT, technology, email, mobile, phone, digital, ehealth, web, computer, online, and computerized. Only articles written in English were included.

RESULTS

Twenty-five studies addressing screening and disclosure, IPV prevention, ICT suitability, support and women's mental health were identified. The evidence reviewed suggests that ICT-based interventions were effective mainly in screening, disclosure, and prevention. However, there is a lack of homogeneity among the studies' outcome measurements and the sample sizes, the control groups used (if any), the type of interventions, and the study recruitment space. Questions addressing safety, equity, and the unintended consequences of the use of ICT in IPV programming are virtually non-existent.

CONCLUSIONS

There is a clear need to develop women-centered ICT design when programming for IPV. Our study showed only one study that formally addressed software usability. The need for more research to address safety, equity, and the unintended consequences of the use of ICT in IPV programming is paramount. Studies addressing long term effects are also needed.

Validation of the LACE Index (Length of Stay, Acuity of Admission, Comorbidities, Emergency Department Use) in the Adult Neurosurgical Patient Population

BACKGROUND

The LACE index (Length of stay, Acuity of admission, Comorbidities, Emergency department use) quantifies the risk of mortality or unplanned readmission within 30 d after hospital discharge. The index was validated originally in a large, general population and, subsequently, in several specialties, not including neurosurgery.

OBJECTIVE

To determine if the LACE index accurately predicts mortality and unplanned readmission of neurosurgery patients within 30 d of discharge.

METHODS

We performed a retrospective, cohort study of consecutive neurosurgical procedures between January 1 and September 29, 2017 at our institution. The LACE index and other clinical data were abstracted. Data analysis included univariate and multivariate logistic regressions.

RESULTS

Of the 1,054 procedures on 974 patients, 52.7% were performed on females. Mean age was 54.2 ± 15.4 yr. At time of discharge, the LACE index was low (1-4) in 58.3% of patients, moderate (5-9) in 32.4%, and high (10-19) in 9.3%. Rates of readmission and mortality within 30 d were 7.0, 11.4, and 14.3% in the low-, moderate-, and high-risk groups, respectively. Moderate-risk (odds ratio [OR] 1.62, 95% CI 1.02-2.56, P = .04) and high-risk LACE indexes (OR 2.20, 95% CI 1.15-4.19, P = .02) were associated with greater odds of readmission or mortality, adjusting for all variables. Additionally, longer operations (OR 1.11, 95% CI 1.02-1.21, P = .02) had greater odds of readmission. Specificity of the high-risk score to predict 30-d readmission or mortality was 91.2%.

CONCLUSION

A moderate- or high-risk LACE index can be applied to neurosurgical populations to predict 30-d readmission and mortality. Longer operations are potential predictors of readmission or mortality.