A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.
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ABSTRACT:
SUBMITTER: Hsu CC
PROVIDER: S-EPMC8775134 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundUnscheduled return visits (URVs) to the emergency department (ED) constitute a crucial indicator of patient care quality.ObjectiveWe aimed to analyze the clinical characteristics of patients who visited the ED with abdominal pain and to identify the risk of URVs with admission (URVAs) from URVs without admission (URVNAs).MethodsThis retrospective study included adult patients who visited the ED of Taipei Medical University Hospital because of abdominal pain and revisited in 72 h over a 5-year period (January 1, 2014, to December 31, 2018). Multivariable logistic regression analysis was employed to identify risk factors for URVAs and receiver operating characteristic (ROC) curve analysis was performed to determine the efficacy of variables predicting URVAs and the optimal cut-off points for the variables. In addition, a classification and regression tree (CART)-based scoring system was used for predicting risk of URVA.ResultsOf 702 eligible patients with URVs related to abdominal pain, 249 had URVAs (35.5%). In multivariable analysis, risk factors for URVAs during the index visit included execution of laboratory tests (yes vs no: adjusted odds ratio [AOR], 4.32; 95% CI 2.99-6.23), older age (≥ 40 vs < 40 years: AOR, 2.10; 95% CI 1.10-1.34), Level 1-2 triage scores (Levels 1-2 vs Levels 3-5: AOR, 2.30; 95% CI 1.26-4.19), and use of ≥ 2 analgesics (≥ 2 vs < 2: AOR, 2.90; 95% CI 1.58-5.30). ROC curve analysis results revealed the combination of these 4 above variables resulted in acceptable performance (area under curve: 0.716). The above 4 variables were used in the CART model to evaluate URVA propensity.ConclusionsElder patients with abdominal pain who needed laboratory workup, had Level 1-2 triage scores, and received ≥ 2 doses of analgesics during their index visits to the ED had higher risk of URVAs.
| S-EPMC10433659 | biostudies-literature
Project description: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.
| S-EPMC11309716 | biostudies-literature
Project description:This study aims to identify reasons for unscheduled return visits (URVs), and risk factors for diagnostic errors leading to URVs, with comparisons to data from a similar study conducted in the same institution 9 years ago. This retrospective study included adult patients who attended the emergency department (ED) of a tertiary hospital in Singapore between January 2014 and June 2014, with re-attendance within 72 h for the same or similar complaint. The primary outcome was wrong or delayed diagnoses. Secondary outcomes include admission to the ED observation unit or ward on return visit. Findings were compared with the previous study performed in 2005 to identify trends. Of 67,422 attendances, there were 1298 (1.93%) URVs from 1207 patients (median age 34, interquartile range 24 to 52 years; 59.7% male). The most common presenting complaint was abdominal pain (22.2%). One hundred ninety-one (15.8%) patients received an initial wrong or delayed diagnosis. Factors (adjusted odds ratio; 95% CI) associated with this were: presenting complaints of abdominal pain (2.99; 2.12-4.23), fever (1.60; 1.1-2.33), neurological deficit (4.26; 1.94-9.35), and discharge without follow-up (1.61; 1.1-2.26). Among re-attendances, 459 (38.0%) required admission. Factors (adjusted odds ratio; 95% CI) associated with admission were: male gender (1.88; 1.42 to 2.48); comorbidities of diabetes mellitus (2.07; 1.29-3.31), asthma (5.23; 1.59-17.26), and renal disease (7.48; 2.00-28.05); presenting complaints of abdominal pain (1.83; 1.32-2.55), fever (3.05; 2.10-4.44), and giddiness or vertigo (2.17; 1.26-3.73). There was a reduction in URV rate compared to the previous study in 2005 (1.93% versus 2.19%). Abdominal pain at the index visit remains a significant cause of URVs (22.2% versus 25.1%). Presenting complaints of neurological deficits, abdominal pain, fever, and discharge without follow-up were associated with wrong or delayed diagnoses among URVs.
| S-EPMC6723936 | biostudies-literature
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.
| S-EPMC8367131 | biostudies-literature
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.
| S-EPMC6951979 | biostudies-literature
Project description:ObjectiveA common emergency department (ED) patient care outcome metric is 72-hour ED return visits (EDRVs). Risks predictive of EDRV vary in different studies. However, risk differences associated with related versus unrelated EDRV and subsequent EDRV disposition deviations (EDRVDD) are rarely addressed. We aim to compare the potential risk patterns predictive of related and unrelated EDRV and further determine those potential risks predictive of EDRVDD.MethodsWe conducted a large retrospective observational study from September 1, 2015 through June 30, 2016. ED Patient demographic characteristics and clinical metrics were compared among patients of 1) related; 2) unrelated; and 3) no EDRVs. EDRVDD was defined as obvious disposition differences between initial ED visit and return visits. A multivariate multinomial logistic regression was performed to determine the independent risks predictive of EDRV and EDRVDD after adjusting for all confounders.ResultsA total of 63,990 patients were enrolled; 4.65% were considered related EDRV, and 1.80% were unrelated. The top risks predictive of EDRV were homeless, patient left without being seen, eloped, or left against medical advice. The top risks predictive of EDRVDD were geriatric and whether patients had primary care physicians regardless as to whether patient returns were related or unrelated to their initial ED visits.ConclusionOver 6% of patients experienced ED return visits within 72 hours. Though risks predicting such revisits were multifactorial, similar risks were identified not only for ED return visits, but also for return ED visit disposition deviations.
| S-EPMC6614047 | biostudies-literature
Project description:ObjectivesThis study describes the proportion of emergency department (ED) returns within 7 days due to adverse events, defined as adverse outcomes related to healthcare received.DesignProspective cohort study.SettingWe used an electronically triggered adverse event surveillance system at a tertiary care ED from May to June 2010 to examine ED returns within 7 days of index visit.ParticipantsOne of three trained nurses determined whether the visit was related to index emergency care. For such records, one of three trained emergency physicians conducted adverse event determinations.Main outcome measureWe determined adverse event type and severity and analysed the data with descriptive statistics, χ(2) tests and logistic regression.ResultsOf 13,495 index ED visits, 923 (6.8%) were followed by ED returns within 7 days. The median age of all patients was 47 years and 52.8% were women. After nursing review, 211 cases required physician review. Of these, 53 visits were adverse events (positive predictive value (PPV)=5.7%, 95% CI 4.4% to 7.4%) and 30 (56.6%) were preventable. Common adverse event types involved management, diagnostic or medication issues. We observed one potentially preventable death and 58.5% of adverse events resulting in transient disability. The PPV of a modified trigger with a cut-off of return within 72 h, resulting in admission was 11.9% (95% CI 6.8% to 18.9%).ConclusionsOur electronic trigger efficiently identified adverse events among 12% of patients with ED returns within 72 h, requiring hospital admission. Given the high degree of preventability of the identified adverse events, this trigger also holds promise as a performance measurement tool.
| S-EPMC4316869 | biostudies-literature
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.
| S-EPMC11320862 | biostudies-literature
Project description:This study was conducted to determine whether overcrowding in the emergency department (ED) affects the occurrence of a return visit (RV) within 72 hours. The crowding indicator of index visit was the average number of total patients, patients under observation, and boarding patients during the first 1 and 4 hours from ED arrival time and the last 1 h before ED departure. Logistic regression analysis was conducted to determine whether each indicator affects the occurrence of RV and post-RV admission. Of the 87,360 discharged patients, 3743 (4.3%) returned to the ED within 72 hours. Of the crowding indicators pertaining to total patients, the last 1 h significantly affected decrease in RV (p = 0.0046). Boarding patients were found to increase RV occurrence during the first 1 h (p = 0.0146) and 4 hours (p = 0.0326). Crowding indicators that increased the likelihood of admission post-RV were total number of patients during the first 1 h (p = 0.0166) and 4 hours (p = 0.0335) and evaluating patients during the first 1 h (p = 0.0059). Overcrowding in the ED increased the incidence of RV and likelihood of post-RV admission. However, overcrowding at the time of ED departure was related to reduced RV.
| S-EPMC7290478 | biostudies-literature
Project description:BackgroundEmergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors.MethodsData on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016-2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital's catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit.ResultsThe number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE's were 8.7%).ConclusionsBecause of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.
| S-EPMC8196936 | biostudies-literature