Validation of chief complaints, medical history, medications, and physician diagnoses structured with an integrated emergency department information system in Japan: the Next Stage ER system

Goto, Tadahiro; Hara, Kozo; Hashimoto, Katsuhiko; Soeno, Shoko; Shirakawa, Toru; Sonoo, Tomohiro; Nakamura, Kensuke

doi:10.1002/ams2.554

Cited by 19 publications

(15 citation statements)

References 26 publications

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“…In addition, we extracted 28-day mortality, the length of ICU and hospital stay, and patient disposition. All of these data were extracted from electronic health records by using information system produced by TXP Medical Co., Ltd., which is linked to an electronic medical record system [ 14 ]. In addition, we have also reviewed medical records manually to confirm the extracted information.…”

Section: Methodsmentioning

confidence: 99%

Time in blood glucose range 70 to 180 mg/dL and survival rate in critically ill patients: A retrospective cohort study

et al. 2021

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Background While time in targeted blood glucose range (TIR) 70–140 mg/dL is a known factor associated with mortality in critically ill patients, it remains unclear whether TIR is associated with 28-day mortality under the glycemic control with a less tight target glucose range of 70–180 mg/dL. We aimed to examine whether TIR 70–180 mg/dL was associated with 28-day mortality. Methods This is a retrospective cohort study using data from a tertiary care center in Japan collected from January 2016 through October 2019. We included adult patients (aged ≥20 years) admitted to the ICU. We excluded patients 1) with diabetic ketoacidosis patients, 2) discharged within 48 hours, 3) with repeated ICU admissions. We calculated TIR 70–180 mg/dL using the measured blood glucose values (≥3 times per day). The primary outcome was 28-day mortality. We examined the association between TIR and 28-day mortality using a logistic regression and Cox proportional hazard models with a stratification by glycosylated hemoglobin (HbA1c) level of 6.5%. Additionally, we repeated the analyses using the TIR category to assess the optimal TIR. For the sensitivity analysis, we repeated the primary analysis using TIR during the first three days from ICU admission. Results Of 1,230 patients, the median age was 72 years, 65% were male, and 250 patients (20%) had HbA1c ≥6.5% on admission. In patients with HbA1c <6.5%, TIR <80% was associated with an increased risk of 28-day mortality, with an adjusted odds ratio (OR) of 1.88 (95%CI: 1.36–2.61). Likewise, when using 10% incremental TIR as a categorical variable, lower TIR was associated with a worse 28-day mortality compared with TIR ≥90% (e.g., adjusted OR of TIR <60%, 3.62 [95%CI 2.36–5.53]). Similar associations were found in the analyses using Cox proportional hazards model and using TIR during the first three days. By contrast, in patients with HbA1c ≥6.5%, there was no consistent association of TIR with 28-day mortality. Conclusions We found that lower TIR 70–180 mg/dL was associated with a higher 28-day mortality in critically ill patients with HbA1c <6.5%, whereas there was no consistent association in patients with HbA1c ≥6.5%.

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Section: Methodsmentioning

confidence: 99%

Time in blood glucose range 70 to 180 mg/dL and survival rate in critically ill patients: A retrospective cohort study

et al. 2021

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“…Further, to enable the use of machine learning techniques in a timely manner, a standardized format to extract clinical data would be essential. Although some systems have been used to collect data structurally in the emergency and critical medicine fields, they are not normally dedicated for use in such fields in most institutions in Japan [ 54 , 55 ]. Therefore, when ease and speed of prediction without special software are considered, traditional prediction models such as the 5A score or SOFA score may be valuable.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction models for accidental hypothermia patients

et al. 2021

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Background Accidental hypothermia is a critical condition with high risks of fatal arrhythmia, multiple organ failure, and mortality; however, there is no established model to predict the mortality. The present study aimed to develop and validate machine learning-based models for predicting in-hospital mortality using easily available data at hospital admission among the patients with accidental hypothermia. Method This study was secondary analysis of multi-center retrospective cohort study (J-point registry) including patients with accidental hypothermia. Adult patients with body temperature 35.0 °C or less at emergency department were included. Prediction models for in-hospital mortality using machine learning (lasso, random forest, and gradient boosting tree) were made in development cohort from six hospitals, and the predictive performance were assessed in validation cohort from other six hospitals. As a reference, we compared the SOFA score and 5A score. Results We included total 532 patients in the development cohort [N = 288, six hospitals, in-hospital mortality: 22.0% (64/288)], and the validation cohort [N = 244, six hospitals, in-hospital mortality 27.0% (66/244)]. The C-statistics [95% CI] of the models in validation cohorts were as follows: lasso 0.784 [0.717–0.851] , random forest 0.794[0.735–0.853], gradient boosting tree 0.780 [0.714–0.847], SOFA 0.787 [0.722–0.851], and 5A score 0.750[0.681–0.820]. The calibration plot showed that these models were well calibrated to observed in-hospital mortality. Decision curve analysis indicated that these models obtained clinical net-benefit. Conclusion This multi-center retrospective cohort study indicated that machine learning-based prediction models could accurately predict in-hospital mortality in validation cohort among the accidental hypothermia patients. These models might be able to support physicians and patient’s decision-making. However, the applicability to clinical settings, and the actual clinical utility is still unclear; thus, further prospective study is warranted to evaluate the clinical usefulness.

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“…The medical charts were structured using the Next Stage ER system (TXP Medical Co., Ltd.) 8 . Using it, physicians can input data such as vital signs, chief complaints, 9 past medical history, physical assessments, and clinical diagnoses using prespecified forms. Consequently, reliably structured data are obtainable more efficiently than by retrospective extraction of structured information by the application of natural language processing techniques to unstructured electronic medical records 8 .…”

Section: Methodsmentioning

confidence: 99%

Initial assessment in emergency departments by chief complaint and respiratory rate

Soeno

Hara

Fujimori

et al. 2021

J of Gen and Family Med

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Background Understanding heterogeneity of the respiratory rate (RR) as a risk stratification marker across chief complaints is important to reduce misinterpretation of the risk posed by outcome events and to build accurate risk stratification tools. This study was conducted to investigate the associations between RR and clinical outcomes according to the five most frequent chief complaints in an emergency department (ED): fever, shortness of breath, altered mental status, chest pain, and abdominal pain. Methods This retrospective cohort study examined ED data of all adult patients who visited the ED of a tertiary medical center during April 2018‐September 2019. The primary exposure was RR at the ED visit. Outcome measures were hospitalization and mechanical ventilation use. We used restrictive cubic spline and logistic regression models to assess the association of interest. Results Of 16 956 eligible ED patients, 4926 (29%) required hospitalization; 448 (3%) required mechanical ventilation. Overall, U‐shaped associations were found between RR and the risk of hospitalization (eg, using RR = 16 as the reference, the odds ratio [OR] of RR = 32, 6.57 [95% CI 5.87‐7.37]) and between RR and the risk of mechanical ventilation. This U‐shaped association was driven by patients' association with altered mental status (eg, OR of RR = 12, 2.63 [95% CI 1.25‐5.53]). For patients who have fever or shortness of breath, the risk of hospitalization increased monotonously with increased RR. Conclusions U‐shaped associations of RR with the risk of overall clinical outcomes were found. These associations varied across chief complaints.

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Validation of chief complaints, medical history, medications, and physician diagnoses structured with an integrated emergency department information system in Japan: the Next Stage ER system

Cited by 19 publications

References 26 publications

Time in blood glucose range 70 to 180 mg/dL and survival rate in critically ill patients: A retrospective cohort study

Time in blood glucose range 70 to 180 mg/dL and survival rate in critically ill patients: A retrospective cohort study

Machine learning-based prediction models for accidental hypothermia patients

Initial assessment in emergency departments by chief complaint and respiratory rate

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