Soyun Shim, Jae Yong Yu, Seyong Jekal, Yee Jun Song, Ki Tae Moon, Ju Hee Lee, Kyung Mi Yeom, Sook Hyun Park, In Sook Cho, Mi Ra Song, Sejin Heo, Jeong Hee Hong
Clin Exp Emerg Med 2022;9(4):345-353. Published online September 21, 2022
Objective Falls are one of the most frequently occurring adverse events among hospitalized patients. The Morse Fall Scale, which has been widely used for fall risk assessment, has the two limitations of low specificity and difficulty in practical implementation. The aim of this study was to develop and validate an interpretable machine learning model for prediction of falls to be integrated in an electronic medical record (EMR) system.
Methods This was a retrospective study involving a tertiary teaching hospital in Seoul, Korea. Based on the literature, 83 known predictors were grouped into seven categories. Interpretable fall event prediction models were developed using multiple machine learning models including gradient boosting and Shapley values.
Results Overall, 191,778 cases with 272 fall events (0.1%) were included in the analysis. With the validation cohort of 2020, the area under the receiver operating curve (AUROC) of the gradient boosting model was 0.817 (95% confidence interval [CI], 0.720–0.904), better performance than random forest (AUROC, 0.801; 95% CI, 0.708–0.890), logistic regression (AUROC, 0.802; 95% CI, 0.721–0.878), artificial neural net (AUROC, 0.736; 95% CI, 0.650–0.821), and conventional Morse fall score (AUROC, 0.652; 95% CI, 0.570–0.715). The model’s interpretability was enhanced at both the population and patient levels. The algorithm was later integrated into the current EMR system.
Conclusion We developed an interpretable machine learning prediction model for inpatient fall events using EMR integration formats.
Citations
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Dohyung Kim, Weon Jung, Jae Yong Yu, Hansol Chang, Se Uk Lee, Taerim Kim, Sung Yeon Hwang, Hee Yoon, Tae Gun Shin, Min Seob Sim, Ik Joon Jo, Won Chul Cha
Clin Exp Emerg Med 2022;9(1):1-9. Published online March 31, 2022
Objective Coronavirus disease 2019 (COVID-19) has notably altered the emergency department isolation protocol, imposing stricter requirements on probable infectious disease patients that enter the department. This has caused adverse effects, such as an increased rate of leave without being seen (LWBS). This study describes the effect of fever/respiratory symptoms as the main cause of isolation regarding LWBS after the COVID-19 pandemic.
Methods We retrospectively analyzed emergency department visits before (March to July 2019) and after (March to July 2020) the COVID-19 pandemic. Patients were grouped based on existing fever or respiratory symptoms, with the LWBS rate as the primary outcome. Logistic regression analysis was used to identify the risk factors of LWBS. Logistic regression was performed using interaction terminology (fever/respiratory symptom patient [FRP]×post–COVID-19) to determine the interaction between patients with FRPs and the COVID-19 pandemic period.
Results A total of 60,290 patients were included (34,492 in the pre–COVID-19, and 25,298 in the post–COVID-19 group). The proportion of FRPs decreased significantly after the pandemic (P<0.001), while the LWBS rate in FRPs significantly increased from 2.8% to 19.2% (P<0.001). Both FRPs (odds ratio, 1.76; 95% confidence interval, 1.59–1.84 (P<0.001) and the COVID-19 period (odds ratio, 2.29; 95% confidence interval, 2.15–2.44; P<0.001) were significantly associated with increased LWBS. Additionally, there was a significant interaction between the incidence of LWBS in FRPs and the COVID-19 pandemic period (P<0.001).
Conclusion The LWBS rate has increased in FRPs after the COVID-19 pandemic; additionally, the effect observed was disproportionate compared with that of nonfever/respiratory symptom patients.
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