Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.

BACKGROUND: Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce errors to save time and cost. However, the accuracy and practicality of such models are questionable. This review compares the predictive power of multiple ML models and examines the effects of multiple research factors on these models' performance in predicting URVs to EDs. METHODS: We conducted the present scoping review by searching eight databases for data from 2010 to 2023. The criteria focused on eligible articles that used ML to predict ED return visits. The primary outcome was the predictive performances of the ML models, and results were analyzed on the basis of intervals of return visits, patient population, and research scale. RESULTS: A total of 582 articles were identified through the database search, with 14 articles selected for detailed analysis. Logistic regression was the most widely used method; however, eXtreme Gradient Boosting generally exhibited superior performance. Variations in visit interval, target group, and research scale did not significantly affect the predictive power of the models. CONCLUSION: This is the first study to summarize the use of ML for predicting URVs in ED patients. The development of practical ML prediction models for ED URVs is feasible, but improving the accuracy of predicting ED URVs to beyond 0.75 remains a challenge. Including multiple data sources and dimensions is key for enabling ML models to achieve high accuracy; however, such inclusion could be challenging within a limited timeframe. The application of ML models for predicting ED URVs may improve patient safety and reduce medical costs by decreasing the frequency of URVs. Further research is necessary to explore the real-world efficacy of ML models.

ICU admission following an unscheduled return visit to the pediatric emergency department within 72 hours.

INTRODUCTION: The purpose of this study was to describe the demographic characteristics and prognosis of children admitted to the intensive care unit (ICU) after a pediatric emergency department (PED) return visit within 72 h. METHOD: We conducted this retrospective study from 2010 to 2016 in the PED of a tertiary medical center in Taiwan and included patients under the age of 18 years old admitted to the ICU after a PED return visit within 72 h. Clinical characteristics were collected to perform demographic analysis. Pediatric patients who were admitted to the ICU on an initial visit were also enrolled as a comparison group for outcome analysis, including mortality, ventilator use, and length of hospital stay. RESULTS: We included a total of 136 patients in this study. Their mean age was 3.3 years old, 65.4% were male, and 36.0% had Chronic Health Condition (CHC). Disease-related return (73.5%) was by far the most common reason for return. Compared to those admitted on an initial PED visit, clinical characteristics, including vital signs at triage and laboratory tests on return visit with ICU admission, demonstrated no significant differences. Regarding prognosis, ICU admission on return visit has a higher likelihood of ventilator use (aOR:2.117, 95%CI 1.021~4.387), but was not associated with increased mortality (aOR:0.658, 95%CI 0.150~2.882) or LOHS (OR:-1.853, 95%CI -4.045~0.339). CONCLUSION: Patients who were admitted to the ICU on return PED visits were associated with an increased risk of ventilator use but not mortality or LOHS compared to those admitted on an initial visit.

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69-0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69-0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

Is frailty associated with short-term outcomes for elderly patients with acute coronary syndrome?

BACKGROUND: Frailty is a new prognostic factor in cardiovascular medicine due to the aging and increasingly complex nature of elderly patients. It is useful and meaningful to prospectively analyze the manner in which frailty predicts short-term outcomes for elderly patients with acute coronary syndrome (ACS). METHODS: Patients aged ≥ 65 years, with diagnosis of ACS from cardiology department and geriatrics department were included from single-center. Clinical data including geriatrics syndromes were collected using Comprehensive Geriatrics Assessment. Frailty was defined according to the Clinical Frailty Scale and the impact of the co-morbidities on risk was quantified by the coronary artery disease (CAD)-specific index. Patients were followed up by clinical visit or telephone consultation and the median follow-up time is 120 days. Following-up items included all-cause mortality, unscheduled return visit, in-hospital and recurrent major adverse cardiovascular events. Multivariable regression survival analysis was performed using Cox regression. RESULTS: Of the 352 patients, 152 (43.18%) were considered frail according to the study instrument (5-7 on the scale), and 93 (26.42%) were considered moderately or severely frail (6-7 on the scale). Geriatrics syndromes including incontinence, fall history, visual impairment, hearing impairment, constipation, chronic pain, sleeping disorder, dental problems, anxiety or depression, and delirium were more frequently in frail patients than in non-frail patients (P = 0.000, 0.031, 0.009, 0.014, 0.000, 0.003, 0.022, 0.000, 0.074, and 0.432, respectively). Adjusted for sex, age, severity of coronary artery diseases (left main coronary artery lesion or not) and co-morbidities (CAD specific index) by Cox survival analysis, frailty was found to be strongly and independently associated with risk for the primary composite outcomes: all-cause mortality [Hazard Ratio (HR) = 5.393; 95% CI: 1.477-19.692, P = 0.011] and unscheduled return visit (HR = 2.832; 95% CI: 1.140-7.037, P = 0.025). CONCLUSIONS: Comprehensive Geriatrics Assessment and Clinical Frail Scale were useful in evaluation of elderly patients with ACS. Frailty was strongly and independently associated with short-term outcomes for elderly patients with ACS.