The development and validation of a “5A” severity scale for predicting in-hospital mortality after accidental hypothermia from J-point registry data

Okada, Yohei; Matsuyama, Tasuku; Morita, Sachiko; Ehara, Naoki; Miyamae, Nobuhiro; Jo, Takaaki; Sumida, Yorihisa; Okada, Nobunaga; Watanabe, Makoto; Nozawa, Masahiro; Tsuruoka, Ayumu; Fujimoto, Yoshihiro; Okumura, Yasuko; Kitamura, Tetsuhisa; Yamamoto, Shungo; Iiduka, Ryoji; Koike, Kazuhiko

doi:10.1186/s40560-019-0384-2

Cited by 14 publications

(28 citation statements)

References 23 publications

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“…Based on reports in the previous literature [ 1 , 2 , 7 – 9 , 13 , 19 – 21 ], we selected three potential predictors: serum pH, lactate, and potassium values. These values were defined as the measurements from the initial blood test or blood biochemistry tests conducted on hospital arrival in the emergency department.…”

Section: Methodsmentioning

confidence: 99%

Predictive accuracy of biomarkers for survival among cardiac arrest patients with hypothermia: a prospective observational cohort study in Japan

Okada

Irisawa

Yoshiya

et al. 2020

Scand J Trauma Resusc Emerg Med

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Background There is limited information on the predictive accuracy of commonly used predictors, such as lactate, pH or serum potassium for the survival among out-of-hospital cardiac arrest (OHCA) patients with hypothermia. This study aimed to identify the predictive accuracy of these biomarkers for survival among OHCA patients with hypothermia. Methods In this retrospective analysis, we analyzed the data from a multicenter, prospective nationwide registry among OHCA patients transported to emergency departments in Japan (the JAAM-OHCA Registry). We included all adult (≥18 years) OHCA patients with hypothermia (≤32.0 °C) who were registered from June 2014 to December 2017 and whose blood test results on hospital arrival were recorded. We calculated the predictive accuracy of pH, lactate, and potassium for 1-month survival. Results Of the 34,754 patients in the JAAM-OHCA database, we included 754 patients from 66 hospitals. The 1-month survival was 5.8% (44/754). The areas under the curve of the predictors and 95% confidence interval were as follows: pH 0.829 [0.767–0.877] and lactate 0.843 [0.793–0.882]. On setting the cutoff points of 6.9 in pH and 120 mg/dL (13.3 mmol/L) in lactate, the predictors had a high sensitivity (lactate: 0.91; pH 0.91) and a low negative likelihood ratio (lactate: 0.14; pH 0.13), which are suitable to exclude survival to 1 month. Furthermore, in additional analysis that included only the patients with potassium values available, a cutoff point of 7.0 (mmol/L) for serum potassium had high sensitivity (0.96) and a low negative likelihood ratio (0.09). Conclusion This study indicated the predictive accuracy of serum lactate, pH, and potassium for 1-month survival among adult OHCA patients with hypothermia. These biomarkers may help define a more appropriate resuscitation strategy.

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Section: Methodsmentioning

confidence: 99%

Predictive accuracy of biomarkers for survival among cardiac arrest patients with hypothermia: a prospective observational cohort study in Japan

Okada

Irisawa

Yoshiya

et al. 2020

Scand J Trauma Resusc Emerg Med

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“…This multicenter retrospective study aimed to clarify the relationship between the level of consciousness and core BT and to determine whether the level of consciousness could be used to predict severe hypothermia and in-hospital mortality among patients with AH. We used data from the J-point registry database [2,8], which has been previously described [2,[8][9][10]. Brie y, the registry includes information regarding patients with BT unknown or ≤35°C who were treated for AH in emergency departments between April 1, 2011 and March 31, 2016.…”

Section: Study Design and Settingmentioning

confidence: 99%

“…The JCS is widely accepted by paramedics and nurses working in the emergency department and is correlated with the GCS [14]. Therefore, when GCS data were missing, we imputed values for mildly impaired consciousness (JCS level 1, 1-3), moderately impaired consciousness (JCS level 2, 10-30), and severely impaired consciousness (JCS level 3, 100-300) based on previous reports [8,9]. sex, ADL, consciousness, systolic blood pressure, and heart rate.…”

Section: Exposurementioning

confidence: 99%

Clinical Relevance of Impaired Consciousness in Accidental Hypothermia: A Japanese Multicenter Retrospective Study

Fukuda¹,

Nozawa²,

Okada³

et al. 2020

Preprint

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Background: Severe accidental hypothermia (AH) is a life-threatening condition, and early identification can enable transport to an appropriate medical facility. The Swiss staging model has been used to classify patients with AH, but little is known regarding the relationship between the degree of impaired consciousness and core body temperature (BT) in AH. This study aimed to clarify the relationship between the level of consciousness and core BT and determine whether the level of consciousness could be used to predict severe hypothermia and in-hospital mortality among patients with AH.Methods: We retrospectively investigated the clinical relevance of impaired consciousness in AH. We included adult patients with AH and excluded patients with out-of-hospital cardiac arrest. The patients were identified from the J-point registry, which contains information regarding patients treated for AH between April 1, 2011 and March 31, 2016 in any of the 12 participating institutions in Japan. The primary exposure of interest was the level of consciousness at hospital arrival. Odds ratios were calculated for severe hypothermia and in-hospital mortality.Results: Overall, 505 of the 572 patients in the registry were included. Compared to mildly impaired consciousness, the adjusted odds ratio for severe hypothermia was 3.3 (95% confidence interval [CI]: 1.7–6.3) for moderately impaired consciousness and 4.7 (95% CI: 2.4–9.1) for severely impaired consciousness. Severely impaired consciousness as a predictor severe hypothermia had a sensitivity of 0.44 (95% CI: 0.34–0.54), specificity of 0.78 (95% CI: 0.74–0.82), positive likelihood ratio of 2.04, and negative likelihood ratio of 0.71. Compared to mildly impaired consciousness, the adjusted odds ratio for in-hospital mortality was 1.7 (95% CI: 0.95–2.9) for moderately impaired consciousness and 2.1 (95% CI: 1.2–3.8) for severely impaired consciousness.Conclusions: Severely impaired consciousness was a reliable predictor of severe hypothermia and in-hospital mortality in patients with AH. Thus, in an urban out-of-hospital emergency setting, the level of impaired consciousness may be helpful for triaging patients to the appropriate hospital.

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“…However, accidental hypothermia is relatively rare (approximately 5–10 cases of annual emergency visits in each emergency department) [ 2 ]; thus, it is challenging for inexperienced medical staff to accurately estimate the prognosis. Although few prediction models or scales have been suggested earlier to predict mortality [ 5 – 8 ], there is no established model.…”

Section: Introductionmentioning

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 development and validation of a “5A” severity scale for predicting in-hospital mortality after accidental hypothermia from J-point registry data

Cited by 14 publications

References 23 publications

Predictive accuracy of biomarkers for survival among cardiac arrest patients with hypothermia: a prospective observational cohort study in Japan

Predictive accuracy of biomarkers for survival among cardiac arrest patients with hypothermia: a prospective observational cohort study in Japan

Clinical Relevance of Impaired Consciousness in Accidental Hypothermia: A Japanese Multicenter Retrospective Study

Machine learning-based prediction models for accidental hypothermia patients

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