Characteristics and outcomes of patients with an unscheduled return visit within 72 hours to the Paediatric Emergency Centre at a Private Tertiary Referral Hospital in Kenya

Chief complaints and computed tomography results in the emergency department: a three-year retrospective cohort study

BACKGROUND

Computed tomography (CT) is frequently performed in the patients who admitted to the emergency department (ED), discharged but returned to ED within 72 h. It is unknown whether the main complaints of patients assist physicians to use CT effectively. This study aimed to find the association between chief complaints and the CT results.

METHODS

This three-year retrospective cohort study was conducted in the ED of a tertiary medical center. Adult patients who returned to the ED after the index visit were included from 2019 to 2021. Demographics, pre-existing diseases, chief complaints, and CT region were recorded by independent ED physicians. A logistic regression model with an odds ratio (OR) and 95% confidence interval (CI) was used to determine the relationship between chief complaints and positive CT results.

RESULTS

In total, 7,699 patients revisited ED after the index visit; 1,202 (15.6%) received CT. The top chief complaints in patients who received CT were abdominal pain, dizziness, and muscle weakness. Patients with abdominal pain or gastrointestinal symptoms had a significantly higher rate of positive abdominopelvic CT than those without it (OR 2.83, 95% CI 1.98-4.05, p < 0.001), while the central nervous system and cardiopulmonary chief complaints were not associated (or negatively associated) with new positive CT findings.

CONCLUSION

Chief complaints of patients on revisit to the ED are associated with different yields of new findings when CT scans of the chest, abdomen and head are performed. Physicians should consider these differential likelihoods of new positive findings based on these data.

Machine learning models for predicting unscheduled return visits of patients with abdominal pain at emergency department and validation during COVID-19 pandemic: A retrospective cohort study

Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. We used electronic health records from Chang Gung Memorial Hospital from 2018 to 2019 as a training dataset, and various machine learning models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC) were developed and subsequently used to validate against the 2020 to 2021 data. The models highlighted several determinants for 72-hour URVs, including patient age, prior ER visits, specific vital signs, and medical interventions. The LR, XGB, and VC models exhibited the same AUC of 0.71 in the testing set, whereas the VC model displayed a higher F1 score (0.21). The XGB model demonstrated the highest specificity (0.99) and precision (0.64) but the lowest sensitivity (0.01). Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions.

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.