Risk Factors for Emergency Department Short Time Readmission in Stratified Population

Reducing 48-h emergency department revisits and subsequent admissions: a retrospective study of increased emergency medicine resident floor coverage

BACKGROUND

Early unexpected hospital admission after emergency department (ED) discharge is an important topic regarding effective preventive measures. Reducing avoidable return visits can improve ED effectiveness and emergency care. This study evaluated the effects of an increase in the number of physicians and the 24-h coverage of emergency physicians on 48-h ED revisits with subsequent hospital admission. The characteristics and risk factors of the patients were also investigated.

RESULTS

This was a retrospective analysis performed 2 years before and 2 years after the implementation of an intervention in a tertiary care hospital in Thailand. The medical records of adult patients who revisited the ED within 48 h for related complaints were reviewed. The effect of the intervention was analyzed, and a prediction model was developed based on logistic regression. After implementing the intervention, the hospital admission rate at the second ED visit decreased from 44.5 to 41.1%; no significant difference was found (95% confidence interval (CI) - 5.05 to 11.78). Patients who required hospital admission had a significantly higher comorbidity score, more ED visits, and more hospitalizations within the past 12 months. A significantly higher hospital admission rate was also observed among patients older than 60 years, those who had an initial infectious diagnosis, and those who had a higher triage severity level (ESI II) at their first visit. The odds ratio (OR) showed lower odds of hospital admission at the second visit in the postintervention period; this difference was not significant (OR 0.87; 95% CI 0.61 to 1.23).

CONCLUSION

Our intervention did not significantly decrease the incidence of admission at an ED revisit. However, some factors identified in this study seem to have some benefits and might be helpful for preventing errors and constructing a standard discharge care plan for patients with these risk factors.

Survival of Frail Elderly with Delirium

This study aims to determine when frailty increases the risks of delirium mortality. Hospital patients falling into the elderly frail or pre-frail category were recruited, some without delirium, some with delirium at admission, and some who developed delirium during admission. We screened for frailty, cognitive status, and co-morbidities whenever possible and extracted drug information and mortality data from electronic health records. Kaplan–Meier estimates of survival probability functions were computed at four times, comparing delirium versus non delirium patients. Differences in survival were assessed by a log-rank test. Independent Cox’s regression was carried out to identify significant hazard risks (HR) at 1 month, 6 months, 1 year, and 2 years. Delirium predicted mortality (log-rank test, p < 0.0001) at all four censoring points. Variables with significant HRs were frailty indicators, comorbidities, polypharmacy, and the use of specific drugs. For the delirium cohort, variables with the most significant 2-year hazard risks (HR(95%CI)) were: male gender (0.43 20 (0.26,0.69)), weight loss (0.45 (0.26,0.74)), sit and stand up test (0.67 (0.49,0.92)), readmission within 30 days of discharge (0.50 (0.30,0.80)), cerebrovascular disease (0.45 (0.27,0.76)), head trauma (0.54 22 (0.29,0.98)), number of prescribed drugs (1.10 (1.03,1.18)), and the use of diuretics (0.57 (0.34,0.96)). These results suggest that polypharmacy and the use of diuretics increase mortality in frail elderly patients with delirium.

Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript, we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosting decision tree model trained to identify patients at risk of reattendance within 72 h of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8847 attendances by 7237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI 0.722–0.773) and an average precision of 0.233 (95% CI 0.194–0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques (the UMAP algorithm) can be used to investigate the different sub-groups of explanations present in our patient cohort.

Machine learning analysis to predict health outcomes among emergency department users in Southern Brazil: a protocol study

OBJECTIVE

Emergency services are essential to the organization of the health care system. Nevertheless, they face different operational difficulties, including overcrowded services, largely explained by their inappropriate use and the repeated visits from users. Although a known situation, information on the theme is scarce in Brazil, particularly regarding longitudinal user monitoring. Thus, this project aims to evaluate the predictive performance of different machine learning algorithms to estimate the inappropriate and repeated use of emergency services and mortality.

METHODS

To that end, a study will be conducted in the municipality of Pelotas, Rio Grande do Sul, with around five thousand users of the municipal emergency department.

RESULTS

If the study is successful, we will provide an algorithm that could be used in clinical practice to assist health professionals in decision-making within hospitals. Different knowledge dissemination strategies will be used to increase the capacity of the study to produce innovations for the organization of the health system and services.

CONCLUSION

A high performance predictive model may be able to help decisionmaking in the emergency services, improving quality of care.

High Rate of Adherence to Urate-Lowering Treatment in Patients with Gout: Who's to Blame?

Introduction

Gout is commonly associated with low adherence rates, thus limiting the effectiveness of treatment. Nevertheless, informed and empowered patients may be more likely to achieve high adherence. We intend to demonstrate that adherence in clinical practice may reach that achieved in clinical trials.

Methods

This was a transversal study within an inception cohort of patients with gout prospectively followed up. Patients were informed at entrance in the cohort of outcomes, targets, and means to implement for successful treatment. Adherence was evaluated through electronic medication possession ratio (MPR) for urate-lowering medication and oral medications for hypertension, diabetes, and hyperlipidemia for comparison. Factors associated with nonadherence, and the relation between nonadherence and serum urate levels while on treatment were analyzed.

Results

Data were retrieved from 336 patients, who showed a mean MPR of 87.5%, with 82.1% of patients showing MPR ≥ 0.8. Rates of adherence for hypertension, hyperlipidemia, and diabetes were quite similar (88%, 87%, and 83%, respectively), although MPR > 0.8 was significantly lower for oral medications for diabetes. Adherence was lower, but nevertheless quite fair, during the first year of follow-up, and increasing over time. Active follow-up and comorbidity were associated with good adherence, and adherence and long-term follow-up were associated with higher rates of achieving serum urate within therapeutic target.

Conclusion

Patients with gout show high rates of adherence if empowered. Active follow-up and comorbidity are associated with high rates of adherence. Adherence is strongly associated with higher rates of achievement of therapeutic serum urate target.

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

BACKGROUND

Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.

OBJECTIVE

To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.

MATERIALS

An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost.

METHODS

Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.

RESULTS

Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.

CONCLUSIONS

We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.