Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Hsu CC

PROVIDER: S-EPMC8775134 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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

Hsu Chun-Chuan CC Chu Cheng-C J CJ Lin Ching-Heng CH Huang Chien-Hsiung CH Ng Chip-Jin CJ Lin Guan-Yu GY Chiou Meng-Jiun MJ Lo Hsiang-Yun HY Chen Shou-Yen SY

Diagnostics (Basel, Switzerland) 20211230 1

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 ...[more]

PMID: 35054249

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Project description:BackgroundUnscheduled emergency department return visits (EDRVs) are key indicators for monitoring the quality of emergency medical care. A high return rate implies that the medical services provided by the emergency department (ED) failed to achieve the expected results of accurate diagnosis and effective treatment. Older adults are more susceptible to diseases and comorbidities than younger adults, and they exhibit unique and complex clinical characteristics that increase the difficulty of clinical diagnosis and treatment. Older adults also use more emergency medical resources than people in other age groups. Many studies have reviewed the causes of EDRVs among general ED patients; however, few have focused on older adults, although this is the age group with the highest rate of EDRVs.ObjectiveThis aim of this study is to establish a model for predicting unscheduled EDRVs within a 72-hour period among patients aged 65 years and older. In addition, we aim to investigate the effects of the influencing factors on their unscheduled EDRVs.MethodsWe used stratified and randomized data from Taiwan's National Health Insurance Research Database and applied data mining techniques to construct a prediction model consisting of patient, disease, hospital, and physician characteristics. Records of ED visits by patients aged 65 years and older from 1996 to 2010 in the National Health Insurance Research Database were selected, and the final sample size was 49,252 records.ResultsThe decision tree of the prediction model achieved an acceptable overall accuracy of 76.80%. Economic status, chronic illness, and length of stay in the ED were the top three variables influencing unscheduled EDRVs. Those who stayed in the ED overnight or longer on their first visit were less likely to return. This study confirms the results of prior studies, which found that economically underprivileged older adults with chronic illness and comorbidities were more likely to return to the ED.ConclusionsMedical institutions can use our prediction model as a reference to improve medical management and clinical services by understanding the reasons for 72-hour unscheduled EDRVs in older adult patients. A possible solution is to create mechanisms that incorporate our prediction model and develop a support system with customized medical education for older patients and their family members before discharge. Meanwhile, a reasonably longer length of stay in the ED may help evaluate treatments and guide prognosis for older adult patients, and it may further reduce the rate of their unscheduled EDRVs.

Project description:ObjectivesTo predict 72-h and 9-day emergency department (ED) return by using gradient boosting on an expansive set of clinical variables from the electronic health record.MethodsThis retrospective study included all adult discharges from a level 1 trauma center ED and a community hospital ED covering the period of March 2013 to July 2017. A total of 1500 variables were extracted for each visit, and samples split randomly into training, validation, and test sets (80%, 10%, and 10%). Gradient boosting models were fit on 3 selections of the data: administrative data (demographics, prior hospital usage, and comorbidity categories), data available at triage, and the full set of data available at discharge. A logistic regression (LR) model built on administrative data was used for baseline comparison. Finally, the top 20 most informative variables identified from the full gradient boosting models were used to build a reduced model for each outcome.ResultsA total of 330 631 discharges were available for analysis, with 29 058 discharges (8.8%) resulting in 72-h return and 52 748 discharges (16.0%) resulting in 9-day return to either ED. LR models using administrative data yielded test AUCs of 0.69 (95% confidence interval [CI] 0.68-0.70) and 0.71(95% CI 0.70-0.72), while gradient boosting models using administrative data yielded test AUCs of 0.73 (95% CI 0.72-0.74) and 0.74 (95% CI 0.73-0.74) for 72-h and 9-day return, respectively. Gradient boosting models using variables available at triage yielded test AUCs of 0.75 (95% CI 0.74-0.76) and 0.75 (95% CI 0.74-0.75), while those using the full set of variables yielded test AUCs of 0.76 (95% CI 0.75-0.77) and 0.75 (95% CI 0.75-0.76). Reduced models using the top 20 variables yielded test AUCs of 0.73 (95% CI 0.71-0.74) and 0.73 (95% CI 0.72-0.74).Discussion and conclusionGradient boosting models leveraging clinical data are superior to LR models built on administrative data at predicting 72-h and 9-day returns.

Project description:BackgroundAcute abdominal pain (AAP) is a major driver for capacity-use in emergency departments (EDs) worldwide. Yet, the health care utilization of patients with AAP before and after the ED remains unclear. The primary objective of this study was to describe adult patients presenting to the ED with AAP and their outpatient care (OC) use before and after the ED. Secondary objectives included description of hospitalization rates, in-hospital mortality, ED re-visits, and exploration of potential risk factors for hospitalization and ED re-visits.MethodsFor the analysis, we combined routine hospital data from patients who visited 15 EDs in Germany in 2016 with their statutory health insurance OC claims data from 2014 to 2017. Adult patients were included based on a chief complaint or an ED diagnosis indicating unspecific AAP or the Manchester Triage System indicator "Abdominal pain in adults". Baseline characteristics, ED diagnosis, frequency and reason of hospitalization, frequency and type of prior-OC (prOC) use up to 3 days before and of post-OC use up to 30 days after the ED visit.Main resultsWe identified 28,085 adults aged ≥ 20 years with AAP. 39.8% were hospitalized, 33.9% sought prOC before the ED visit (48.6% of them were hospitalized) and 62.7% sought post-OC up to 30 days after the ED visit. Hospitalization was significantly more likely for elderly patients (aged 65 and above vs. younger; adjusted OR 3.05 [95% CI 2.87; 3.25]), prOC users (1.71 [1.61; 1.90]) and men (1.44 [1.37; 1.52]). In-hospital mortality rate was 3.1% overall. Re-visiting the ED within 30 days was more likely for elderly patients (1.32 [1.13; 1.55) and less likely for those with prOC use (0.37 [0.31; 0.44]).ConclusionsprOC use was associated with more frequent hospitalizations but fewer ED re-visits. ED visits by prOC patients without subsequent hospitalization may indicate difficulties of OC resources to meet the complex diagnostic requirements and expectations of this patient population. Fewer ED re-visits in prOC users indicate effective care in this subgroup.

Dataset Information

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

Publications

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

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