Prognostic ability of the sequential organ failure assessment score in accidental hypothermia: a multi-institutional retrospective cohort study

Effects of rewarming therapies on outcomes in accidental hypothermia: A secondary analysis of a multicenter prospective study

BACKGROUND

Rewarming therapies for accidental hypothermia (AH) include extracorporeal membrane oxygenation (ECMO) and non-ECMO related (conventional) therapies. However, there are limited data available to inform the selection of conventional rewarming therapy. The aim of the present study was to explore what patients' factors and which rewarming therapy predicted favorable prognosis.

METHODS

This study is a secondary analysis of the Intensive Care with Extra Corporeal membrane oxygenation Rewarming in Accidentally Severe Hypothermia (ICE-CRASH) study, a multicenter prospective, observational study conducted in Japan. Enrolled in the ICE-CRASH study were patients aged ≥18 years with a core temperature of ≤32 °C who were transported to the emergency departments of 36 tertiary care hospitals in Japan between 1 December 2019 and 31 March 2022, among whom those who were rewarmed with conventional rewarming therapy were included in the present study. Logistic regression analysis was performed with 28-day survival as the objective variable; and seven factors including age, activities of daily living (ADL) independence, sequential organ failure assessment (SOFA) score, and each rewarming technique as explanatory variables. We performed linear regression analysis to identify whether each rewarming technique was associated with rewarming rate.

RESULTS

Of the 499 patients enrolled in the ICE-CRASH study, 371 were eligible for this secondary analysis. The median age was 81 years, 50.9% were male, and the median initial body temperature was 28.8 °C. Age (odds ratio [OR]: 0.97, 95% confidence interval [CI]: 0.94-1.00) and SOFA score (OR: 0.73, 95% CI: 0.67-0.81) were associated with lower survival, whereas ADL independence (OR: 2.31, 95% CI: 1.15-4.63) was associated with higher survival. No conventional rewarming therapy was associated with 28-day survival. Hot bath was associated with a high rewarming rate (regression coefficient: 1.14, 95% CI: 0.75-1.53).

CONCLUSION

No conventional rewarming therapy was associated with improved 28-day survival, which suggests that background factors such as age, ADL, and severity of condition contribute more to prognosis than does the selection of rewarming technique.

External validation of 5A score model for predicting in-hospital mortality among the accidental hypothermia patients: JAAM-Hypothermia study 2018-2019 secondary analysis

BACKGROUND

The 5A score including five components "Age, Activities of daily living, Arrest, Acidemia and Albumin" was developed as an easy-to-use screening tool for predicting in-hospital mortality among patients with accidental hypothermia. However, the external validity of the 5A score has not yet been evaluated. We aimed to perform an external validation of the 5A score model.

METHOD

This secondary analysis of the multicenter, prospective cohort Japanese Association for Acute Medicine-Hypothermia Study (2018-2019), which was conducted at 87 and 89 institutions throughout Japan, collected data from December 2018 to February 2019 and from December 2019 to February 2020. Adult accidental hypothermia patients whose body temperature was 35 °C or less were included in this analysis. The probability of in-hospital mortality was calculated using a logistic regression model of the 5A score. The albumin was not recorded in this database; thus, it was imputed by estimation. Predictions were compared with actual observations to evaluate the calibration of the model. Furthermore, decision-curve analysis was used to evaluate the clinical usefulness.

RESULTS

Of the 1363 patients registered in the database, data of 1139 accidental hypothermia patients were included for analysis. The median [interquartile range] age was 79 [68-87] years, and there were 625 men (54.9%) in the study cohort. The predicted probability and actual observation by risk groups produced the following results: low 7% (5.4-8.6), mild 19.1% (17.4-20.8), moderate 33.2% (29.9-36.5), and high 61.9% (55.9-67.9) predicted risks, and the low 12.4% (60/483), mild 17.7% (59/334), moderate 32.6% (63/193), and high 69% (89/129) observed mortality. These results indicated that the model was well calibrated. Decision-curve analysis visually indicated the clinical utility of the 5A score model.

CONCLUSION

This study indicated that the 5A score model using estimated albumin value has external validity in a completely different dataset from that used for the 5A model development. The 5A score is potentially helpful to predict the mortality risk and may be one of the valuable information for discussing the treatment strategy with patients and their family members.

Clinical relevance of impaired consciousness in accidental hypothermia: a Japanese multicenter retrospective study

Aim

This study aimed to investigate the association between level of impaired consciousness and severe hypothermia (<28°C) and to evaluate the association between level of impaired consciousness and inhospital mortality among accidental hypothermia patients.

Methods

This was a multicenter retrospective study using the J‐Point registry database, which includes data regarding patients whose core body temperature was 35.0°C or less and who were treated as accidental hypothermia in emergency departments between April 1, 2011 and March 31, 2016. We estimated adjusted odds ratios of the level of impaired consciousness for severe hypothermia less than 28°C and inhospital mortality using a logistic regression model.

Results

The study included 505 of 572 patients in the J‐Point registry. Relative to mildly impaired consciousness (Glasgow Coma Scale [GCS] 13–15), the adjusted odds ratios for severe hypothermia less than 28°C were: moderate (GCS 9–12), 3.26 (95% confidence interval [CI], 1.69–6.25); and severe (GCS < 9), 4.68 (95% CI, 2.40–9.14). Relative to mildly impaired consciousness (GCS 13–15), the adjusted odds ratios for inhospital mortality were: moderate (GCS9–12), 1.65 (95% CI, 0.95–2.88); and severe (GCS < 9), 2.10 (95% CI, 1.17–3.78).

Conclusion

The level of impaired consciousness in patients with accidental hypothermia was associated with severe hypothermia and inhospital mortality.

Time for a prehospital-modified sequential organ failure assessment score: An ambulance-Based cohort study

BACKGROUND

To adapt the Sequential Organ Failure Assessment (SOFA) score to fit the prehospital care needs; to do that, the SOFA was modified by replacing platelets and bilirubin, by lactate, and tested this modified SOFA (mSOFA) score in its prognostic capacity to assess the mortality-risk at 2 days since the first Emergency Medical Service (EMS) contact.

METHODS

Prospective, multicentric, EMS-delivery, ambulance-based, pragmatic cohort study of adults with acute diseases, referred to two tertiary care hospitals (Spain), between January 1st and December 31st, 2020. The discriminative power of the predictive variable was assessed through a prediction model trained using the derivation cohort and evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) on the validation cohort.

RESULTS

A total of 1114 participants comprised two separated cohorts recruited from 15 ambulance stations. The 2-day mortality rate (from any cause) was 5.9% (66 cases). The predictive validity of the mSOFA score was assessed by the calculation of the AUC of ROC in the validation cohort, resulting in an AUC of 0.946 (95% CI, 0.913-0.978, p < .001), with a positive likelihood ratio was 23.3 (95% CI, 0.32-46.2).

CONCLUSIONS

Scoring systems are now a reality in prehospital care, and the mSOFA score assesses multiorgan dysfunction in a simple and agile manner either bedside or en route. Patients with acute disease and an mSOFA score greater than 6 points transferred with high priority by EMS represent a high early mortality group.

TRIAL REGISTRATION

ISRCTN48326533, Registered Octuber 312,019, Prospectively registered (doi:https://doi.org/10.1186/ISRCTN48326533).

Machine learning-based prediction models for accidental hypothermia patients

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-021-00525-z.

Hypothermia in a Japanese subtropical climate: Retrospective validation study of severity score and mortality prediction

INTRODUCTION

This study aimed to clarify the accuracy of an in-hospital mortality prediction score for patients with hypothermia. The score consists of five variables (age ≥70 years, mean arterial pressure <90 mm Hg, pH < 7.35, creatinine >1.5 mg/dL, and confusion). In contrast to the previously reported population in southern Israel, a desert climate, we apply the score system to a Japanese humid subtropical climate.

METHODS

The study included patients with a principal diagnosis of hypothermia who were admitted to our community hospital between January 2008 and January 2019. Using the medical records from initial visits, we retrospectively calculated in-hospital mortality prediction scores along with sensitivity and specificity.

RESULTS

We recruited 69 patients, 67 of which had analyzable data. Among them, the in-hospital mortality rate was 25.4%. Hypothermia was defined as mild (32-35°C) in 34 cases (50.7%), moderate (28-32°C) in 23 cases (34.3%), and severe (<28°C) in 10 cases (14.9%). The C-statistics of the in-hospital mortality prediction score was 0.703 (95% confidence interval, 0.55-0.84) for thirty-day survival prediction. After adjustment of the cutoff point of each item with ROC analysis and selection of the variants, the C-statistics of the in-hospital mortality prediction score rose to 0.81 (95% confidence interval, 0.69-0.92).

CONCLUSION

The in-hospital mortality prediction scores showed slightly less predictive value than those in the previous report. With some modification, however, the score system could still be applied efficiently in the humid Japanese subtropical climate. An appropriate management strategy could be established based on the predicted mortality risk.