Hypothermia in Emergency Care: Longitudinal Demographic Trends and Predictors of Critical Outcomes in Hungary

Hypothermia in Emergency Care: Longitudinal Demographic Trends and Predictors of Critical Outcomes in Hungary | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hypothermia in Emergency Care: Longitudinal Demographic Trends and Predictors of Critical Outcomes in Hungary Kornél Ádám, Anna Stelkovics, Barbara Zadravecz-Heider, Dóra Melicher, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6202034/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accidental hypothermia, defined as a core temperature below 35°C, can cause metabolic, respiratory, and circulatory disturbances; fatal arrhythmias; or cardiac arrest. Our objective was to analyze the profile of patients presenting at a Hungarian emergency department and to identify predictors of critical outcomes. Methods We conducted a retrospective cohort analysis from 2020–2024 at the Department of Emergency Medicine, Semmelweis University. Patients whose core temperature was less than 35°C were included, and their demographics and triage categories were documented. Hypothermia severity was assessed via the Swiss staging model and the Wilderness Medical Society classification. The primary outcome was a composite of admissions to the intensive care unit and mortality in the emergency department. We tested the ability of hypothermia-specific scales and triage categories, admission temperature, and their combined models to predict the primary outcome. Predictive accuracy was evaluated via receiver operating characteristic (ROC) analysis. The strength of the correlations was quantified via logistic regression. Results A total of 131 patients met the inclusion criteria. The median age was 67.5 years (IQR: 59.0–75.0). Eighty-eight patients (67.2%) were male. The median admission core temperature was 29.3°C (IQR: 26.1–31.4°C). The median length of stay was 13.7 hours (IQR: 9.5–18.9 hours). Severe hypothermia (< 30°C) was present in 47 patients (34.6%). Intensive care unit admission was required for 16 patients (12.2%), and 28 patients (21.4%) died during emergency care. Ambient temperature seasonally affected the incidence of hypothermia but had no influence on the probability of critical outcomes. The triage category outperformed hypothermia-specific stratification tools and was the strongest single predictor of critical outcomes (AUC = 0.683). The combination of triage category and admission core temperature had the highest predictive accuracy (AUC = 0.740, 95% CI: 0.650–0.831) for the primary outcome. Conclusions Accidental hypothermia is a serious and potentially lethal emergency despite milder winters associated with climate change. The admission core temperature improves the predictive performance of general triage systems for critical outcomes. To identify and manage high-risk hypothermic patients in environments with sudden temperature fluctuations, comprehensive, integrated risk assessment methods are essential. accidental hypothermia triage emergency service hospital risk assessment logistic models ROC curve mortality global warming Figures Figure 1 Figure 2 Figure 3 Background Accidental hypothermia is defined as a drop in core body temperature below 35°C. If untreated, it impairs metabolic processes, induces respiratory and circulatory depression, and may lead to life-threatening arrhythmias and cardiac arrest. It may develop despite intact thermoregulation due to cold exposure or pathological thermoregulatory failure [ 1 ]. In winter and early spring, the incidence increases [ 2 , 3 ]. As global climate change reduces extended cold periods, strong, brief cold spells may increase, placing older, chronically ill, and socially marginalized people in danger [ 4 , 5 ]. In Hungary, data on the epidemiology of accidental hypothermia and its proportion among emergency department visits are limited and not well documented. Two tools are generally used for scaling hypothermia (Table 1 ). The Swiss staging model combines physiological responses and symptomatology with predefined core temperature ranges [ 6 ]. A revised version estimates circulatory arrest risk on the basis of responsiveness to overcome the unreliability of core temperature monitoring [ 7 ]. The field-use Wilderness Medical Society classification even offers guidance on rewarming techniques for each severity level [ 8 ]. Table 1 Hypothermia severity classification systems [ 6 – 8 ] Swiss staging model for hypothermia Wilderness Medical Society classification Original Revised Clinical findings Estimated core temperature (°C) AVPU Risk of hypothermic cardiac arrest Category and clinical findings Estimated core temperature (°C) Clear consciousness with shivering 35–32 "Alert" Low Mild: Normal mental status, shivering, but not functioning normally and unable to care for self 35–32 Impaired consciousness without shivering < 32–28 “Verbal” from AVPU Moderate Moderate: Abnormal mental status with shivering, or abnormal mental status without shivering, but conscious 32–28 Unconsciousness < 28–24 “Painful’’ or “Unconscious” from AVPU and vital signs High Severe/profound: Unconscious < 28°C Apparent death < 24 − 13.7 “Unconscious”’ from AVPU and no detectable vital signs Hypothermic cardiac arrest Death due to irreversible hypothermia < 13.7? (< 9?) Emergency triage systems assess hypothermia in different ways [ 9 ] (Table 2 ). Table 2 Five-level emergency triage systems [ 9 – 13 ] Levels System Category Color Time to treat Hypothermia modifying threshold Clinical characteristics Level I ATS Resuscitation 🔴 Immediate - Apparent, immediate life-threatening condition. CTAS MTS Immediate Level II ATS Emergency 🟠 ≤ 10–15 min. - Impending life threat or condition requiring time critical intervention. CTAS Emergent < 32°C MTS Very Urgent ≤ 15 min. < 35°C Level III ATS Urgent 🟡 ≤ 30 min. - Potential life threat or situational emergency requiring symptom management. CTAS 35 − 32°C MTS ≤ 60 min. - Level IV ATS Semi-Urgent 🟢 ≤ 60 min. - Potentially severe conditions with complexity and urgency. CTAS Less Urgent MTS Standard 🔵 ≤ 120 min. Level V ATS Non-Urgent ⚪ ≤ 120 min. - Less urgent conditions, generally not life-threatening. CTAS 🔵 MTS 🟢 ≤ 240 min. In the Manchester Triage System, a core temperature below 35°C is a modifier for category 2, but the final decision is made with additional symptoms and clinical factors considered. [ 10 ]. Hungary used a modified version of the widely used, multidimensional Canadian Triage and Acuity Scale [ 11 ]. In the CTAS-based Hungarian Emergency Triage System (MSTR), a core temperature between 32°C and 35°C is sufficient for classification into category 3, whereas a core temperature less than 32°C is an independent modifier for assigning patients to category 2, indicating the need for immediate intervention [ 12 ]. Croatia is among the countries that have implemented the Australasian Triage Scale, despite its rarity in Europe. It places life-threatening conditions above the severity of hypothermia on its own. [ 13 – 15 ]. When body temperature is a unique modifier, triage system criteria may affect categorization and even treatment [ 9 ]. Demographics and critical outcomes may serve as a foundation for local procedure adaptation [ 16 ]. In real-world emergency care, the predictive accuracy and clinical relevance of specialized hypothermia classification systems—along with conventional triage protocols applied to hypothermic patients—remain insufficiently documented. Furthermore, epidemiological data on hypothermia in Hungary, including seasonal variation, patient demographics, and emergency department outcomes, are notably scarce. To address these gaps, we conducted a retrospective study of hypothermic patients treated at the Department of Emergency Medicine, Semmelweis University, between 2020 and 2024. Our objectives were as follows: The demographic characteristics and seasonal trends of hypothermia cases were analyzed. The predictive performance of the MSTR triage system and two international hypothermia classification tools (the Swiss staging model and the Wilderness Medical Society guidelines) in identifying patients at risk of critical outcomes was evaluated. Assessing whether incorporating the admission core temperature into the MSTR improves risk stratification. Investigate the impact of outdoor temperature on hypothermic patient outcomes. Methods A retrospective cohort analysis was conducted to identify patients presenting with hypothermia to the Department of Emergency Medicine, Semmelweis University, between January 1, 2020, and December 31, 2024. The inclusion criteria were ICD-10 code T68 and a core body temperature below 35.0°C at ED admission, which was manually retrieved from the EHR on January 5, 2025 (eMedSolution system database version 2024/Q4/1). All patients whose core temperature was elevated upon emergency admission were included. Patients with a core temperature of 35.0°C or higher and those with incomplete data were excluded. (Fig. 2 ). Demographic data (age, sex, residence) and clinical characteristics (mode of arrival, triage category, hypothermia severity) were recorded. Categorical variables are reported as frequencies and percentages, age as the median (IQR), and core temperature as the median (IQR). Hypothermia severity was categorized via the Swiss staging model and the Wilderness Medical Society classification. The core temperature was determined via tympanic measurement, and the ambient temperature was exported from the monthly mean temperature data from the local meteorological station on the basis of the registered residence of the patients [ 17 ]. Data normality was evaluated via the Kolmogorov‒Smirnov and Shapiro‒Wilk tests. Critical outcomes were defined as mortality during emergency care or admission to the ICU. Relationships among categorical variables were assessed via the chi-square test. The effect size was quantified via Cramer's V. The Mann‒Whitney U test and rank‒biserial correlation coefficients were implemented for continuous data that were not normally distributed. Univariate logistic regression models were implemented to evaluate the correlations of the triage category, admission core temperature, WMS, Swiss staging, and ambient temperature with the primary outcome. A multivariate logistic regression model was employed to calculate odds ratios with 95% confidence intervals, p values, and Wald statistics for components with low collinearity, as determined by the variance inflation factor (VIF). In the multivariate models, we evaluated the predictive accuracy and clinical relevance of combinations of predictors that were independently significant for the critical outcomes. We used multiple model validity measures to conduct a thorough assessment of the predictive performance and model fit. Wald statistics, Cox-Snell R², Nagelkerke R², the Hosmer–Lemeshow test, and − 2 log-likelihood were used to calculate goodness-of-fit and explanatory power. The predictive performance was evaluated via positive/negative predictive values, sensitivity, specificity, likelihood ratios, and the area under the ROC curve to assess robustness across validation metrics. The optimal cutoff values were obtained by balancing specificity and sensitivity. SPSS 28.0 and R 4.2.0 were used to analyze the results, with a significance level of p < 0.05. Results Study population and demographics Among the 131 patients, n = 88 (67.2%) were male. The median age was 67.5 years (IQR: 59.0–75.0). A total of 74 (56.5%) of the patients were 65 years or older, and another n = 53 (40.5%) were 45–64 years old. Only n = 4 (3.0%) were in the 18–44 years age group (Table 3 ). Age and triage classification Among the hypothermic patients, 129 (98.5%) arrived by ambulance. n = 61 (46.6%) were classified as the most urgent triage category I (resuscitation), n = 58 (44.3%) as category II (emergent), and n = 12 (9.1%) as less urgent categories III–IV (urgent and less urgent), respectively, on the basis of the severity of their condition upon arrival. No patient was classified as MSTR category V. Via tympanic thermometry, the median core temperature upon arrival was 29.3°C (IQR: 26.1–31.4°C), suggesting severe hypothermia in a substantial number of patients (Table 3 ). Distribution of hypothermia by severity According to the Swiss staging model, 30 (22.9%) patients had stage I hypothermia, 48 (36.6%) had stage II hypothermia, 29 (22.1%) had stage III hypothermia, and 24 (18.4%) had stage IV hypothermia. According to the Wilderness Medical Society classification, n = 29 (23.3%) participants presented with mild hypothermia, n = 55 (42.1%) with moderate hypothermia, and n = 47 (34.6%) with severe hypothermia, which is largely consistent with the distribution according to the Swiss counterpart (Table 3 ). Seasonal variation When the monthly mean temperature dropped below 7°C in winter, the incidence of hypothermia increased across all severity stages. All Swiss staging categories (I–IV) occurred at temperatures below 10°C, with more than half of Stage II–IV cases occurring in months with a mean temperature less than 7°C. Stage IV hypothermia (< 24°C) was the most temperature dependent, with 91.7% of the cases occurring below 10°C and none occurring above 18°C (Fig. 1 ). Length of stay and outcomes The median length of ED stay was 13.7 hours (IQR = 9.4). A total of 16 (12.2%) patients were admitted to the intensive care unit, and 81 (61.8%) were transferred to inpatient wards. The discharge rate was n = 6 (4.6%), and the mortality rate was n = 28 (21.4%) (Table 3 ). Table 3 Demographics, clinical characteristics, and outcomes of emergency care 2020 2021 2022 2023 2024 2020–24 Gender Male % (n) 59.1 (13) 70.0 (14) 63.6 (14) 69.2 (18) 70.7 (29) 67.2 (88) Female % (n) 40.9 (9) 30.0 (6) 36.4 (8) 30.8 (8) 29.3 (12) 32.8 (43) Total (n) 22 20 22 26 41 131 Age Median (IQR) 69.0 (60.5- 76.8) 70.0 (62.5- 75.0) 65.0 (57.0- 68.0) 72.0 (63.5- 79.0) 64.0 (57.0- 74.0) 67.5 (59.0- 75.0) 18–44 years % (n) 0.0 (0) 0.0 (0) 4.5 (1) 3.8 (1) 4.8 (2) 3.0 (4) 45–64 years % (n) 40.9 (9) 35.0 (7) 40.9 (9) 23.1 (6) 53.7 (22) 40.5 (53) ≥ 65 years % (n) 59.1 (13) 65.0 (13) 54.6 (12) 73.1 (19) 41.5 (17) 56.5 (74) Form of arrival By ambulance % (n) 100.0 (22) 100.0 (20) 100.0 (22) 92.3 (24) 100.0 (41) 98.5 (129) Triage category MSTR I % (n) 45.5 (10) 65.0 (13) 54.5 (12) 23.1 (6) 48.8 (20) 46.6 (61) MSTR II % (n) 54.5 (12) 35.0 (7) 45.5 (10) 57.7 (15) 34.1 (14) 44.3 (58) MSTR III % (n) 0.0 (0) 0.0 (0) 0.0 (0) 15.4 (4) 9.8 (4) 6.1 (8) MSTR IV % (n) 0.0 (0) 0.0 (0) 0.0 (0) 3.8 (1) 7.3 (3) 3.0 (4) MSTR V % (n) - - - - - - Core temperature at arrival Tymp. °C median (IQR) 29.5 (25.4- 32.3) 29.1 (25.9- 31.3) 29.4 (27.8- 31.5) 30.1 (27.6- 32.1) 29.1 (27.0- 30.0) 29.3 (26.1 31.4) Swiss staging model for hypothermia Stage I % (n) 27.3 (6) 25.0 (5) 27.3 (6) 23.1 (6) 17.1 (7) 22.9 (30) Stage II % (n) 31.8 (7) 40.0 (8) 36.4 (8) 34.6 (9) 39.0 (16) 36.6 (48) Stage III % (n) 18.2 (4) 20.0 (4) 18.1 (4) 26.9 (7) 24.4 (10) 22.1 (29) Stage IV % (n) 22.7 (5) 15.0 (3) 18.2 (4) 15.4 (4) 19.5 (8) 18.4 (24) Stage V % (n) - - - - - - Wilderness Medical Society classification Mild % (n) 31.8 (7) 20.0 (4) 22.7 (5) 26.9 (7) 14.6 (6) 23.3 (29) Moderate % (n) 22.7 (5) 45.0 (9) 50.0 (11) 42.3 (11) 46.3 (19) 42.1 (55) Severe % (n) 45.5 (10) 35.0 (7) 27.3 (6) 30.8 (8) 39.1 (16) 34.6 (47) Care and outcome Median length of stay (IQR) 10.2 (7.3- 23.6) 16.3 (12.4- 20.1) 16.1 (11.6- 17.9) 15.8 (11.1- 20.6) 11.8 (8.93- 15.00) 13.7 (9.5- 18.9) Discharged % (n) 0.0 (0) 5.0 (1) 4.5 (1) 15.4 (4) 0.0 (0) 4.6 (6) Wards % (n) 59.1 (13) 70.0 (14) 54.6 (12) 50.0 (13) 70.8 (29) 61.8 (81) ICU % (n) 13.6 (3) 5.0 (1) 13.6 (3) 11.5 (3) 14.6 (6) 12.2 (16) Died in ED % (n) 27.3 (6) 20.0 (4) 27.3 (6) 23.1 (6) 14.6 (6) 21.4 (28) Predictors and univariate correlations with severe outcomes The triage category was the strongest predictor (p < 0.001), indicating a medium–strong association (Cramer's V = 0.349) with the primary outcome. The Swiss staging system also showed a significant but moderate-strength association with the primary outcome (p = 0.032; Cramer's V = 0.259). There was also a moderate but significant association for the WMS classification (p = 0.017; Cramer's V = 0.249). The initial core temperature was negatively correlated (p = 0.002; Pearson's r=-0.270) with ICU admission and ED death. In contrast, ambient external temperature was not significantly associated with our outcome measure (p = 0.694; r=-0.034) (Supplementary Table 1). Multivariate logistic regression analysis The results of multivariate logistic regression indicate that a more severe triage category is significantly associated with a greater likelihood of progressive deterioration (ICU admission or ED mortality) (OR = 0.310, 95% CI: 0.157–0.610; p < 0.001). A critical outcome is 17.2% less likely to occur with each 1°C increase in core temperature (OR = 0.828, 95% CI: 0.738–0.928, p < 0.001). The risk significantly increased with increasing disease stage, as indicated by Swiss hypothermia staging (OR = 1.877, 95% CI: 1.208–2.919, p = 0.005). Moreover, the WMS classification (OR = 2.035, 95% CI: 1.203–3.442, p = 0.008) indicated a more than twofold increase in the likelihood of ICU admission or ED mortality (Supplementary Table 2). Combined models Improved predictive accuracy was observed in the bivariate models. Compared with any of the univariate models, the combination of core temperature and triage category (OR = 0.390, 95% CI: 0.197–0.774, p = 0.007; Nagelkerke R² = 0.199) offered significantly more precise predictions. Similarly, the results of the combination of Swiss classification and triage category (OR = 0.372, 95% CI: 0.189–0.733, p = 0.004; Nagelkerke R² = 0.183) were comparable (Supplementary Table 2). Predictive performance The diagnostic accuracy of predicting critical resolution was assessed via receiver operating characteristic (ROC) curves. Among the triage category (AUC = 0.683, 95% CI: 0.587–0.779), initial core temperature (AUC = 0.666, 95% CI: 0.566–0.766), Swiss- (AUC = 0.644, 95% CI: 0.543–0.745) and WMS classification (AUC = 0.637, 95% CI: 0.536–0.738), the triage category was found to be the strongest independent predictor (Fig. 3 A), but the combination of triage classification and core temperature (AUC = 0.740, 95% CI: 0.650–0.831) provided the best predictive performance for critical outcomes (Fig. 3 B). For this combination, the positive and negative likelihood ratios (LR + = 6.920, LR-=0.052) were also very good (Supplementary Table 3). Discussion Consistent with earlier epidemiological findings, our data confirm that hypothermia cases in emergency care follow a seasonal trend, reaching their peak in winter [ 4 ]. The greater proportion of male patients is also consistent with prior research, which links this trend to differences in exposure and behavioral factors [ 16 ]. The age distribution of the patients indicated that middle-aged individuals were also prominently represented, whereas hypothermia primarily affected the older age group, as previously reported in large-scale studies [ 18 ]. Although comorbidities and age-related decreases in thermoregulatory ability are significant variables, hypothermia may also be caused by nonphysiological factors [ 5 ]. The significance of prehospital detection and timely intervention is well documented, particularly in the context of hypothermia, where rapid recognition and management can enhance outcomes [ 19 ]. Most of our patients arrived via ambulances, underscoring the critical importance of emergency medical services in these circumstances. The predictive function of decision-making systems in emergency treatment was substantiated by the strong association between critical outcomes and triage categorization. The MSTR exhibited greater accuracy for the primary outcome than hypothermia-specific categorization methods did. Although their predictive power was not as strong as that of the triage classification, the Wilderness Medical Society classification and the Swiss staging model for hypothermia both correlated with critical outcomes. Although the Swiss staging model and other hypothermia classification systems are good for determining severity, the involvement of additional clinical parameters within triage systems may aid in risk stratification and guide treatment in emergency environments [ 8 ]. Critical outcomes were associated with lower core temperatures. In isolation, the core temperature only had a negligible predictive value. The absence of a significant correlation between the primary outcome and outside temperature aligns with the hypothesis that the severity of hypothermia is influenced by exposure duration, comorbidities, and other environmental variables, in addition to ambient temperature. Combining triage with the initial core temperature results in a more accurate prediction than does depending on only a few factors. Our results imply that advanced risk assessment models in emergency care perform better than single clinical indicators do and may improve patient management and resource allocation [ 20 ]. Limitations The retrospective, single-center design may limit generalizability. Our reliance on routinely documented ED indicators potentially introduced inconsistency and residual confounding, in addition to the lack of comorbidity profiles and psychosocial variables. We only assessed short-term outcomes, and the limited sample size may also affect the statistical reliability. The triage categorization of patients with a core temperature of less than 32°C may have introduced collinearity, regardless of our statistical effort. The use of monthly average temperatures may have resulted in oversimplified weather patterns, potentially resulting in the absence of finer meteorological effects. We did not assess additional ED workflow factors, such as waiting times or resource utilization. Conclusion Accidental hypothermia continues to pose a substantial clinical challenge in Hungary and other temperate regions, necessitating the implementation of specialized prehospital and in-hospital care. Although the Swiss staging model and the Wilderness Medical Society guidelines can be beneficial in the assessment of severity, our results suggest that triage alone has significant discriminatory power. For critical outcomes, risk stratification is enhanced by further integrating the core temperature into the CTAS-based MSTR classification. Future research should focus on sophisticating severity scales by including more clinical parameters and dynamic thresholds for core temperature. Abbreviations AUC – area under the ROC curve AVPU – alert, verbal, pain, unresponsive (neurological responsiveness scale) ATS – Australasian Triage Scale CI – confidence interval Cramer’s V – measure of association for nominal variables CTAS – Canadian Triage and Acuity Scale ED – emergency department ICD-10 – International Classification of Diseases, Tenth Revision ICU – intensive care unit IQR – interquartile range LR+ – positive likelihood ratio LR- – negative likelihood ratio MSTR – Hungarian Emergency Triage System (Magyar Sürgősségi Triage Rendszer) MTS – Manchester Triage System n – number (of patients or observations) OR – odds ratio p value (p) – probability value (statistical significance indicator) R² (Cox-Snell, Nagelkerke) – coefficient(s) of determination in logistic regression ROC – receiver operating characteristic (curve) SD – standard deviation SPSS – Statistical Package for the Social Sciences T68 – ICD-10 code for hypothermia Tymp. – tympanic (temperature measurement) VIF – variance inflation factor WMS – Wilderness Medical Society Declarations Data availability The dataset analyzed is available from the corresponding author upon reasonable request. Acknowledgments Not applicable. Funding This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author information Authors and affiliations Department of Emergency Medicine, Semmelweis University, 1082 Budapest, Üllői út 78/A, Budapest, Hungary Kornél Ádám, Anna Stelkovics, Barbara Zadravecz-Heider, Dóra Melicher, Bánk G. Fenyves, Szabolcs Gaál-Marschal & Csaba Varga Department of Emergency Medicine, St. John’s Hospital, Kútvölgyi Hospital. 1125 Budapest, Kútvölgyi út 4., Hungary Szabolcs Gaál-Marschal Department of Emergency Medicine Kaposi Mór Teaching Hospital, 7400 Kaposvár, Tallián Gyula u. 20-32, Hungary Csaba Varga Contributions K. Á. executed the statistical analyses, conceptualized and designed the study, and composed the manuscript. A. S. and B. Z-H. assisted in patient and meteorological data retrieval. B. G. F. and D. M. contributed to the manuscript's revision and conducted a critical review. Sz. G-M. cleaned the interpretation of the data in the finalization phase. Cs. V. provided expert guidance on hypothermia classification systems and supervised the study to ensure alignment with clinical and methodological standards. All the authors have read, reviewed, and approved the final manuscript. Corresponding author Correspondence to Kornél Ádám. Ethics declarations Ethics approval and consent to participate Ethical approval was provided by the Semmelweis University Regional and Institutional Review Board (SE RKEB 274/2024. ). Clinical trial number Not applicable. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form (including individual details, images, or videos) that would require consent for publication. Conflict of interest The authors declare that they have no conflicts of interest. References Brown DJ, Brugger H, Boyd J, Paal P. Accidental hypothermia. N Engl J Med. 2012;367(20):1930–8. 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Takauji S, Hifumi T, Saijo Y, Yokobori S, Kanda J, Kondo Y, et al. Association between frailty and mortality among patients with accidental hypothermia: a nationwide observational study in Japan. BMC Geriatr. 2021;21(1):507. Haverkamp FJC, Giesbrecht GG, Tan E. The prehospital management of hypothermia – An up-to-date overview. Injury. 2018;49(2):149–64. Rösli D, Schnüriger B, Candinas D, Haltmeier T. The impact of accidental hypothermia on mortality in trauma patients overall and patients with traumatic brain injury specifically: a systematic review and meta-analysis. World J Surg. 2020;44(12):4106–17. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterialadametal.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6202034","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427758992,"identity":"9f13476e-281b-43bc-bd97-90b9d8d93d64","order_by":0,"name":"Kornél Ádám","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYFCCBCA+AGIwNz4AC7BDBBOI0MLYbAAWYCZBS5sEXAsDHi387clPNzCcsck3Z29sq/xRsS2an5nH8MGDPwx5Bgewa5E488zsBsONNMudPQfbbvOcuZ07s5nH2CCxjaEYlxaGGwlALR8OGxjcSGy7zdh2O3fDYd5tEokNDIkbcGiRv5H+DaLl/sO2wp//bufuP8y7/UfCH9xaDG7kgBwGsoWxjYG3AWgLM+82hgQ23FoMz7wpu5FwJs3A4ExiszTPsdu5Mw7zf5ZIbJNInIlDi9zx9G03PhyzMTA4fvjgxx81t3P729sSP/74Y5PYh8v7IJCARUwCj/pRMApGwSgYBYQAADyybcLX5tkKAAAAAElFTkSuQmCC","orcid":"","institution":"Semmelweis University","correspondingAuthor":true,"prefix":"","firstName":"Kornél","middleName":"","lastName":"Ádám","suffix":""},{"id":427758994,"identity":"9e2c0b80-6327-4376-b4b6-18890c99e4dc","order_by":1,"name":"Anna Stelkovics","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Stelkovics","suffix":""},{"id":427758995,"identity":"418b38fa-35c8-4876-b44e-2646a1789ff1","order_by":2,"name":"Barbara Zadravecz-Heider","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Zadravecz-Heider","suffix":""},{"id":427758996,"identity":"2736f754-8360-41ed-9319-f157f41700c6","order_by":3,"name":"Dóra Melicher","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Dóra","middleName":"","lastName":"Melicher","suffix":""},{"id":427758997,"identity":"6c03f1df-eaee-40d9-bce5-94a36936d6b8","order_by":4,"name":"Bánk G. Fenyves","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Bánk","middleName":"G.","lastName":"Fenyves","suffix":""},{"id":427758998,"identity":"c132ff04-0761-402e-b1d5-b124d635560e","order_by":5,"name":"Szabolcs Gaál-Marschal","email":"","orcid":"","institution":"North-Buda St. John Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Szabolcs","middleName":"","lastName":"Gaál-Marschal","suffix":""},{"id":427759005,"identity":"f1faf675-5232-414e-b75c-bfae27546abe","order_by":6,"name":"Csaba Varga","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Csaba","middleName":"","lastName":"Varga","suffix":""}],"badges":[],"createdAt":"2025-03-11 10:23:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6202034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6202034/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78691191,"identity":"24987ca8-1139-433f-b115-b13a7e56f359","added_by":"auto","created_at":"2025-03-17 16:17:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":495948,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in hypothermia cases over time based on the Swiss staging model and average ambient temperatures (2020–2024)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6202034/v1/4b6e6905736153de2802b4ca.png"},{"id":78692501,"identity":"252de70f-c7cc-4cd7-9870-dec8ab55428d","added_by":"auto","created_at":"2025-03-17 16:25:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":165194,"visible":true,"origin":"","legend":"\u003cp\u003ePatient selection flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6202034/v1/c2900961cc2aeddaf7541342.png"},{"id":78691192,"identity":"46859401-d85d-4ef4-a17a-6e2f172abce3","added_by":"auto","created_at":"2025-03-17 16:17:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197149,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves for individual and combined predictors of critical outcomes\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6202034/v1/4f249a16d52ffb2fc944c526.png"},{"id":84531669,"identity":"49410020-a56a-4a6a-9a20-475ea30f10da","added_by":"auto","created_at":"2025-06-13 06:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2025760,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6202034/v1/931e9cac-3683-4410-a344-c35b418dfa22.pdf"},{"id":78692978,"identity":"6d5579c0-f082-45bc-8107-d99943c32136","added_by":"auto","created_at":"2025-03-17 16:33:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21975,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterialadametal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6202034/v1/a4f9f9881c09acea738da00c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hypothermia in Emergency Care: Longitudinal Demographic Trends and Predictors of Critical Outcomes in Hungary","fulltext":[{"header":"Background","content":"\u003cp\u003eAccidental hypothermia is defined as a drop in core body temperature below 35\u0026deg;C. If untreated, it impairs metabolic processes, induces respiratory and circulatory depression, and may lead to life-threatening arrhythmias and cardiac arrest. It may develop despite intact thermoregulation due to cold exposure or pathological thermoregulatory failure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In winter and early spring, the incidence increases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As global climate change reduces extended cold periods, strong, brief cold spells may increase, placing older, chronically ill, and socially marginalized people in danger [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Hungary, data on the epidemiology of accidental hypothermia and its proportion among emergency department visits are limited and not well documented.\u003c/p\u003e \u003cp\u003eTwo tools are generally used for scaling hypothermia (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Swiss staging model combines physiological responses and symptomatology with predefined core temperature ranges [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A revised version estimates circulatory arrest risk on the basis of responsiveness to overcome the unreliability of core temperature monitoring [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The field-use Wilderness Medical Society classification even offers guidance on rewarming techniques for each severity level [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypothermia severity classification systems [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSwiss staging model for hypothermia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003eWilderness Medical Society classification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOriginal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRevised\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimated core temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRisk of hypothermic cardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCategory and clinical findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimated core temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear consciousness with shivering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"Alert\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMild: Normal mental status, shivering, but not functioning normally and unable to care for self\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u0026ndash;32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpaired consciousness without shivering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;32\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Verbal\u0026rdquo; from AVPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate: Abnormal mental status with shivering, or abnormal mental status without shivering, but conscious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnconsciousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Painful\u0026rsquo;\u0026rsquo; or \u0026ldquo;Unconscious\u0026rdquo; from AVPU and vital signs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere/profound: Unconscious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApparent death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24 \u0026minus;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Unconscious\u0026rdquo;\u0026rsquo; from AVPU and no detectable vital signs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHypothermic cardiac arrest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath due to irreversible hypothermia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;13.7? (\u0026lt;\u0026thinsp;9?)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEmergency triage systems assess hypothermia in different ways [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFive-level emergency triage systems [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eColor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTime to treat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHypothermia\u003c/p\u003e \u003cp\u003emodifying threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLevel I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResuscitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026#128308;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eImmediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eApparent, immediate\u003c/p\u003e \u003cp\u003elife-threatening condition.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmediate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLevel II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026#128992;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10\u0026ndash;15 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eImpending life threat or condition requiring time critical\u003c/p\u003e \u003cp\u003eintervention.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmergent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;32\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Urgent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;15 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLevel III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUrgent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026#128993;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePotential life threat or situational emergency\u003c/p\u003e \u003cp\u003erequiring symptom management.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u0026thinsp;\u0026minus;\u0026thinsp;32\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLevel IV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-Urgent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026#128994;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePotentially severe conditions with complexity and urgency.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLess Urgent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026#128309;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;120 min.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLevel V\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNon-Urgent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⚪\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;120 min.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLess urgent conditions, generally not life-threatening.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026#128309;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026#128994;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;240 min.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the Manchester Triage System, a core temperature below 35\u0026deg;C is a modifier for category 2, but the final decision is made with additional symptoms and clinical factors considered. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hungary used a modified version of the widely used, multidimensional Canadian Triage and Acuity Scale [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the CTAS-based Hungarian Emergency Triage System (MSTR), a core temperature between 32\u0026deg;C and 35\u0026deg;C is sufficient for classification into category 3, whereas a core temperature less than 32\u0026deg;C is an independent modifier for assigning patients to category 2, indicating the need for immediate intervention [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Croatia is among the countries that have implemented the Australasian Triage Scale, despite its rarity in Europe. It places life-threatening conditions above the severity of hypothermia on its own. [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. When body temperature is a unique modifier, triage system criteria may affect categorization and even treatment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Demographics and critical outcomes may serve as a foundation for local procedure adaptation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn real-world emergency care, the predictive accuracy and clinical relevance of specialized hypothermia classification systems\u0026mdash;along with conventional triage protocols applied to hypothermic patients\u0026mdash;remain insufficiently documented. Furthermore, epidemiological data on hypothermia in Hungary, including seasonal variation, patient demographics, and emergency department outcomes, are notably scarce. To address these gaps, we conducted a retrospective study of hypothermic patients treated at the Department of Emergency Medicine, Semmelweis University, between 2020 and 2024.\u003c/p\u003e \u003cp\u003eOur objectives were as follows:\u003c/p\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe demographic characteristics and seasonal trends of hypothermia cases were analyzed.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e The predictive performance of the MSTR triage system and two international hypothermia classification tools (the Swiss staging model and the Wilderness Medical Society guidelines) in identifying patients at risk of critical outcomes was evaluated.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAssessing whether incorporating the admission core temperature into the MSTR improves risk stratification.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eInvestigate the impact of outdoor temperature on hypothermic patient outcomes.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA retrospective cohort analysis was conducted to identify patients presenting with hypothermia to the Department of Emergency Medicine, Semmelweis University, between January 1, 2020, and December 31, 2024. The inclusion criteria were ICD-10 code T68 and a core body temperature below 35.0\u0026deg;C at ED admission, which was manually retrieved from the EHR on January 5, 2025 (eMedSolution system database version 2024/Q4/1). All patients whose core temperature was elevated upon emergency admission were included. Patients with a core temperature of 35.0\u0026deg;C or higher and those with incomplete data were excluded. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDemographic data (age, sex, residence) and clinical characteristics (mode of arrival, triage category, hypothermia severity) were recorded. Categorical variables are reported as frequencies and percentages, age as the median (IQR), and core temperature as the median (IQR). Hypothermia severity was categorized via the Swiss staging model and the Wilderness Medical Society classification. The core temperature was determined via tympanic measurement, and the ambient temperature was exported from the monthly mean temperature data from the local meteorological station on the basis of the registered residence of the patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Data normality was evaluated via the Kolmogorov‒Smirnov and Shapiro‒Wilk tests.\u003c/p\u003e \u003cp\u003eCritical outcomes were defined as mortality during emergency care or admission to the ICU. Relationships among categorical variables were assessed via the chi-square test. The effect size was quantified via Cramer's V. The Mann‒Whitney U test and rank‒biserial correlation coefficients were implemented for continuous data that were not normally distributed. Univariate logistic regression models were implemented to evaluate the correlations of the triage category, admission core temperature, WMS, Swiss staging, and ambient temperature with the primary outcome. A multivariate logistic regression model was employed to calculate odds ratios with 95% confidence intervals, p values, and Wald statistics for components with low collinearity, as determined by the variance inflation factor (VIF). In the multivariate models, we evaluated the predictive accuracy and clinical relevance of combinations of predictors that were independently significant for the critical outcomes. We used multiple model validity measures to conduct a thorough assessment of the predictive performance and model fit. Wald statistics, Cox-Snell R\u0026sup2;, Nagelkerke R\u0026sup2;, the Hosmer\u0026ndash;Lemeshow test, and \u0026minus;\u0026thinsp;2 log-likelihood were used to calculate goodness-of-fit and explanatory power. The predictive performance was evaluated via positive/negative predictive values, sensitivity, specificity, likelihood ratios, and the area under the ROC curve to assess robustness across validation metrics. The optimal cutoff values were obtained by balancing specificity and sensitivity. SPSS 28.0 and R 4.2.0 were used to analyze the results, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and demographics\u003c/h2\u003e \u003cp\u003eAmong the 131 patients, n\u0026thinsp;=\u0026thinsp;88 (67.2%) were male. The median age was 67.5 years (IQR: 59.0\u0026ndash;75.0). A total of 74 (56.5%) of the patients were 65 years or older, and another n\u0026thinsp;=\u0026thinsp;53 (40.5%) were 45\u0026ndash;64 years old. Only n\u0026thinsp;=\u0026thinsp;4 (3.0%) were in the 18\u0026ndash;44 years age group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAge and triage classification\u003c/h3\u003e\n\u003cp\u003eAmong the hypothermic patients, 129 (98.5%) arrived by ambulance. n\u0026thinsp;=\u0026thinsp;61 (46.6%) were classified as the most urgent triage category I (resuscitation), n\u0026thinsp;=\u0026thinsp;58 (44.3%) as category II (emergent), and n\u0026thinsp;=\u0026thinsp;12 (9.1%) as less urgent categories III\u0026ndash;IV (urgent and less urgent), respectively, on the basis of the severity of their condition upon arrival. No patient was classified as MSTR category V. Via tympanic thermometry, the median core temperature upon arrival was 29.3\u0026deg;C (IQR: 26.1\u0026ndash;31.4\u0026deg;C), suggesting severe hypothermia in a substantial number of patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDistribution of hypothermia by severity\u003c/h3\u003e\n\u003cp\u003eAccording to the Swiss staging model, 30 (22.9%) patients had stage I hypothermia, 48 (36.6%) had stage II hypothermia, 29 (22.1%) had stage III hypothermia, and 24 (18.4%) had stage IV hypothermia. According to the Wilderness Medical Society classification, n\u0026thinsp;=\u0026thinsp;29 (23.3%) participants presented with mild hypothermia, n\u0026thinsp;=\u0026thinsp;55 (42.1%) with moderate hypothermia, and n\u0026thinsp;=\u0026thinsp;47 (34.6%) with severe hypothermia, which is largely consistent with the distribution according to the Swiss counterpart (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSeasonal variation\u003c/h3\u003e\n\u003cp\u003eWhen the monthly mean temperature dropped below 7\u0026deg;C in winter, the incidence of hypothermia increased across all severity stages. All Swiss staging categories (I\u0026ndash;IV) occurred at temperatures below 10\u0026deg;C, with more than half of Stage II\u0026ndash;IV cases occurring in months with a mean temperature less than 7\u0026deg;C. Stage IV hypothermia (\u0026lt;\u0026thinsp;24\u0026deg;C) was the most temperature dependent, with 91.7% of the cases occurring below 10\u0026deg;C and none occurring above 18\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLength of stay and outcomes\u003c/h2\u003e \u003cp\u003eThe median length of ED stay was 13.7 hours (IQR\u0026thinsp;=\u0026thinsp;9.4). A total of 16 (12.2%) patients were admitted to the intensive care unit, and 81 (61.8%) were transferred to inpatient wards. The discharge rate was n\u0026thinsp;=\u0026thinsp;6 (4.6%), and the mortality rate was n\u0026thinsp;=\u0026thinsp;28 (21.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics, clinical characteristics, and outcomes of emergency care\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u0026ndash;24\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003cp\u003e(18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003cp\u003e(88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003cp\u003e(43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003cp\u003e(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.0\u003c/p\u003e \u003cp\u003e(60.5-\u003c/p\u003e \u003cp\u003e76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003cp\u003e(62.5-\u003c/p\u003e \u003cp\u003e75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003cp\u003e(57.0-\u003c/p\u003e \u003cp\u003e68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003cp\u003e(63.5-\u003c/p\u003e \u003cp\u003e79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003cp\u003e(57.0-\u003c/p\u003e \u003cp\u003e74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003cp\u003e(59.0-\u003c/p\u003e \u003cp\u003e75.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;44 years %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;64 years %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003cp\u003e(53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65 years %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003cp\u003e(19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003cp\u003e(17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003cp\u003e(74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eForm of arrival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy ambulance %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003cp\u003e(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003cp\u003e(129)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriage category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSTR I %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003cp\u003e(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003cp\u003e(61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSTR II %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.7\u003c/p\u003e \u003cp\u003e(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003cp\u003e(58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSTR III %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSTR IV %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSTR V %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCore temperature at arrival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTymp. \u0026deg;C median\u003c/p\u003e \u003cp\u003e(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003cp\u003e(25.4-\u003c/p\u003e \u003cp\u003e32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003cp\u003e(25.9-\u003c/p\u003e \u003cp\u003e31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003cp\u003e(27.8-\u003c/p\u003e \u003cp\u003e31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003cp\u003e(27.6-\u003c/p\u003e \u003cp\u003e32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003cp\u003e(27.0-\u003c/p\u003e \u003cp\u003e30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003cp\u003e(26.1\u003c/p\u003e \u003cp\u003e31.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwiss staging model for hypothermia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003cp\u003e(30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.6\u003c/p\u003e \u003cp\u003e(48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage V %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWilderness Medical Society classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003cp\u003e(19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003cp\u003e(55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003cp\u003e(47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCare and outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian length of stay\u003c/p\u003e \u003cp\u003e(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003cp\u003e(7.3-\u003c/p\u003e \u003cp\u003e23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003cp\u003e(12.4-\u003c/p\u003e \u003cp\u003e20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003cp\u003e(11.6-\u003c/p\u003e \u003cp\u003e17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003cp\u003e(11.1-\u003c/p\u003e \u003cp\u003e20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003cp\u003e(8.93-\u003c/p\u003e \u003cp\u003e15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003cp\u003e(9.5-\u003c/p\u003e \u003cp\u003e18.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarged %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003cp\u003e(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWards %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003cp\u003e(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003cp\u003e(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003cp\u003e(29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.8\u003c/p\u003e \u003cp\u003e(81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003cp\u003e(16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDied in ED %\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003cp\u003e(28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictors and univariate correlations with severe outcomes\u003c/h3\u003e\n\u003cp\u003eThe triage category was the strongest predictor (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a medium\u0026ndash;strong association (Cramer's V\u0026thinsp;=\u0026thinsp;0.349) with the primary outcome. The Swiss staging system also showed a significant but moderate-strength association with the primary outcome (p\u0026thinsp;=\u0026thinsp;0.032; Cramer's V\u0026thinsp;=\u0026thinsp;0.259). There was also a moderate but significant association for the WMS classification (p\u0026thinsp;=\u0026thinsp;0.017; Cramer's V\u0026thinsp;=\u0026thinsp;0.249). The initial core temperature was negatively correlated (p\u0026thinsp;=\u0026thinsp;0.002; Pearson's r=-0.270) with ICU admission and ED death. In contrast, ambient external temperature was not significantly associated with our outcome measure (p\u0026thinsp;=\u0026thinsp;0.694; r=-0.034) (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eMultivariate logistic regression analysis\u003c/h3\u003e\n\u003cp\u003eThe results of multivariate logistic regression indicate that a more severe triage category is significantly associated with a greater likelihood of progressive deterioration (ICU admission or ED mortality) (OR\u0026thinsp;=\u0026thinsp;0.310, 95% CI: 0.157\u0026ndash;0.610; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A critical outcome is 17.2% less likely to occur with each 1\u0026deg;C increase in core temperature (OR\u0026thinsp;=\u0026thinsp;0.828, 95% CI: 0.738\u0026ndash;0.928, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk significantly increased with increasing disease stage, as indicated by Swiss hypothermia staging (OR\u0026thinsp;=\u0026thinsp;1.877, 95% CI: 1.208\u0026ndash;2.919, p\u0026thinsp;=\u0026thinsp;0.005). Moreover, the WMS classification (OR\u0026thinsp;=\u0026thinsp;2.035, 95% CI: 1.203\u0026ndash;3.442, p\u0026thinsp;=\u0026thinsp;0.008) indicated a more than twofold increase in the likelihood of ICU admission or ED mortality (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCombined models\u003c/h2\u003e \u003cp\u003eImproved predictive accuracy was observed in the bivariate models. Compared with any of the univariate models, the combination of core temperature and triage category (OR\u0026thinsp;=\u0026thinsp;0.390, 95% CI: 0.197\u0026ndash;0.774, p\u0026thinsp;=\u0026thinsp;0.007; Nagelkerke R\u0026sup2; = 0.199) offered significantly more precise predictions. Similarly, the results of the combination of Swiss classification and triage category (OR\u0026thinsp;=\u0026thinsp;0.372, 95% CI: 0.189\u0026ndash;0.733, p\u0026thinsp;=\u0026thinsp;0.004; Nagelkerke R\u0026sup2; = 0.183) were comparable (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePredictive performance\u003c/h2\u003e \u003cp\u003eThe diagnostic accuracy of predicting critical resolution was assessed via receiver operating characteristic (ROC) curves. Among the triage category (AUC\u0026thinsp;=\u0026thinsp;0.683, 95% CI: 0.587\u0026ndash;0.779), initial core temperature (AUC\u0026thinsp;=\u0026thinsp;0.666, 95% CI: 0.566\u0026ndash;0.766), Swiss- (AUC\u0026thinsp;=\u0026thinsp;0.644, 95% CI: 0.543\u0026ndash;0.745) and WMS classification (AUC\u0026thinsp;=\u0026thinsp;0.637, 95% CI: 0.536\u0026ndash;0.738), the triage category was found to be the strongest independent predictor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), but the combination of triage classification and core temperature (AUC\u0026thinsp;=\u0026thinsp;0.740, 95% CI: 0.650\u0026ndash;0.831) provided the best predictive performance for critical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For this combination, the positive and negative likelihood ratios (LR\u0026thinsp;+\u0026thinsp;=\u0026thinsp;6.920, LR-=0.052) were also very good (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsistent with earlier epidemiological findings, our data confirm that hypothermia cases in emergency care follow a seasonal trend, reaching their peak in winter [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The greater proportion of male patients is also consistent with prior research, which links this trend to differences in exposure and behavioral factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe age distribution of the patients indicated that middle-aged individuals were also prominently represented, whereas hypothermia primarily affected the older age group, as previously reported in large-scale studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although comorbidities and age-related decreases in thermoregulatory ability are significant variables, hypothermia may also be caused by nonphysiological factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe significance of prehospital detection and timely intervention is well documented, particularly in the context of hypothermia, where rapid recognition and management can enhance outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Most of our patients arrived via ambulances, underscoring the critical importance of emergency medical services in these circumstances. The predictive function of decision-making systems in emergency treatment was substantiated by the strong association between critical outcomes and triage categorization. The MSTR exhibited greater accuracy for the primary outcome than hypothermia-specific categorization methods did. Although their predictive power was not as strong as that of the triage classification, the Wilderness Medical Society classification and the Swiss staging model for hypothermia both correlated with critical outcomes. Although the Swiss staging model and other hypothermia classification systems are good for determining severity, the involvement of additional clinical parameters within triage systems may aid in risk stratification and guide treatment in emergency environments [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCritical outcomes were associated with lower core temperatures. In isolation, the core temperature only had a negligible predictive value. The absence of a significant correlation between the primary outcome and outside temperature aligns with the hypothesis that the severity of hypothermia is influenced by exposure duration, comorbidities, and other environmental variables, in addition to ambient temperature. Combining triage with the initial core temperature results in a more accurate prediction than does depending on only a few factors. Our results imply that advanced risk assessment models in emergency care perform better than single clinical indicators do and may improve patient management and resource allocation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe retrospective, single-center design may limit generalizability. Our reliance on routinely documented ED indicators potentially introduced inconsistency and residual confounding, in addition to the lack of comorbidity profiles and psychosocial variables. We only assessed short-term outcomes, and the limited sample size may also affect the statistical reliability. The triage categorization of patients with a core temperature of less than 32\u0026deg;C may have introduced collinearity, regardless of our statistical effort. The use of monthly average temperatures may have resulted in oversimplified weather patterns, potentially resulting in the absence of finer meteorological effects. We did not assess additional ED workflow factors, such as waiting times or resource utilization.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccidental hypothermia continues to pose a substantial clinical challenge in Hungary and other temperate regions, necessitating the implementation of specialized prehospital and in-hospital care. Although the Swiss staging model and the Wilderness Medical Society guidelines can be beneficial in the assessment of severity, our results suggest that triage alone has significant discriminatory power. For critical outcomes, risk stratification is enhanced by further integrating the core temperature into the CTAS-based MSTR classification. Future research should focus on sophisticating severity scales by including more clinical parameters and dynamic thresholds for core temperature.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e \u0026ndash; area under the ROC curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVPU\u003c/strong\u003e \u0026ndash; alert, verbal, pain, unresponsive (neurological responsiveness scale)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eATS\u003c/strong\u003e \u0026ndash; Australasian Triage Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e \u0026ndash; confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCramer\u0026rsquo;s V\u003c/strong\u003e \u0026ndash; measure of association for nominal variables\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCTAS\u003c/strong\u003e \u0026ndash; Canadian Triage and Acuity Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e \u0026ndash; emergency department\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICD-10\u003c/strong\u003e \u0026ndash; International Classification of Diseases, Tenth Revision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICU\u003c/strong\u003e \u0026ndash; intensive care unit\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e \u0026ndash; interquartile range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLR+\u003c/strong\u003e \u0026ndash; positive likelihood ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLR-\u003c/strong\u003e \u0026ndash; negative likelihood ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMSTR\u003c/strong\u003e \u0026ndash; Hungarian Emergency Triage System (Magyar S\u0026uuml;rgőss\u0026eacute;gi Triage Rendszer)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMTS\u003c/strong\u003e \u0026ndash; Manchester Triage System\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e \u0026ndash; number (of patients or observations)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e \u0026ndash; odds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evalue (p)\u003c/strong\u003e \u0026ndash; probability value (statistical significance indicator)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR\u0026sup2; (Cox-Snell, Nagelkerke)\u003c/strong\u003e \u0026ndash; coefficient(s) of determination in logistic regression\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e \u0026ndash; receiver operating characteristic (curve)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e \u0026ndash; standard deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSPSS\u003c/strong\u003e \u0026ndash; Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT68\u003c/strong\u003e \u0026ndash; ICD-10 code for hypothermia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTymp.\u003c/strong\u003e \u0026ndash; tympanic (temperature measurement)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e \u0026ndash; variance inflation factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWMS\u003c/strong\u003e \u0026ndash; Wilderness Medical Society\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and affiliations\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eDepartment of Emergency Medicine, Semmelweis University, 1082 Budapest, \u0026Uuml;llői \u0026uacute;t 78/A, Budapest, Hungary\u003c/li\u003e\n \u003cli\u003eKorn\u0026eacute;l \u0026Aacute;d\u0026aacute;m, Anna Stelkovics, Barbara Zadravecz-Heider, D\u0026oacute;ra Melicher, B\u0026aacute;nk G. Fenyves, Szabolcs Ga\u0026aacute;l-Marschal \u0026amp; Csaba Varga\u003c/li\u003e\n \u003cli\u003eDepartment of Emergency Medicine, St. John\u0026rsquo;s Hospital, K\u0026uacute;tv\u0026ouml;lgyi Hospital. 1125 Budapest, K\u0026uacute;tv\u0026ouml;lgyi \u0026uacute;t 4., Hungary\u003c/li\u003e\n \u003cli\u003eSzabolcs Ga\u0026aacute;l-Marschal\u003c/li\u003e\n \u003cli\u003eDepartment of Emergency Medicine Kaposi M\u0026oacute;r Teaching Hospital, 7400 Kaposv\u0026aacute;r, Talli\u0026aacute;n Gyula u. 20-32, Hungary\u003c/li\u003e\n \u003cli\u003eCsaba Varga\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK. \u0026Aacute;. executed the statistical analyses, conceptualized and designed the study, and composed the manuscript. A. S. and B. Z-H. assisted in patient and meteorological data retrieval. B. G. F. and D. M. contributed to the manuscript\u0026apos;s revision and conducted a critical review. Sz. G-M. cleaned the interpretation of the data in the finalization phase. Cs. V. provided expert guidance on hypothermia classification systems and supervised the study to ensure alignment with clinical and methodological standards.\u003c/p\u003e\n\u003cp\u003eAll the authors have read, reviewed, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Korn\u0026eacute;l \u0026Aacute;d\u0026aacute;m.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthical approval was provided by the Semmelweis University Regional and Institutional Review Board (SE RKEB 274/2024. ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos) that would require consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einterest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrown DJ, Brugger H, Boyd J, Paal P. Accidental hypothermia. N Engl J Med. 2012;367(20):1930\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMikes MZ, Pieczka I, Dezső Z. Characteristics and observed seasonal changes in Cold Air Outbreaks in Hungary using station data (1901\u0026ndash;2020). Hungarian Geographical Bulletin. 2024;73(2):115\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eVarga C, Lelovics Z, So\u0026oacute;s V, Ol\u0026aacute;h T. Patient turnover in a multidisciplinary emergency department (Betegforgalmi trendek multidiszciplin\u0026aacute;ris s\u0026uuml;rgőss\u0026eacute;gi oszt\u0026aacute;lyon). Orv Hetil. 2017;158(21):811\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003ePaal P, Pasquier M, Darocha T, Lechner R, Kosinski S, Wallner B, et al. Accidental Hypothermia: 2021 Update. Int J Environ Res Public Health. 2022;19(1).\u003c/li\u003e\n\u003cli\u003eT\u0026ouml;rő K. A kl\u0026iacute;mav\u0026aacute;ltoz\u0026aacute;s \u0026eacute;s a mortalit\u0026aacute;s k\u0026ouml;z\u0026ouml;tti \u0026ouml;sszef\u0026uuml;gg\u0026eacute;sek meg\u0026iacute;t\u0026eacute;l\u0026eacute;se, k\u0026uuml;l\u0026ouml;n\u0026ouml;s tekintettel az igazs\u0026aacute;g\u0026uuml;gyi orvostani szempontokra (in Hungarian). In: \u0026Eacute;let-Tudom\u0026aacute;ny-T\u0026ouml;rt\u0026eacute;nelem: Tanulm\u0026aacute;nyok az MTA \u0026Eacute;lettudom\u0026aacute;nyok-t\u0026ouml;rt\u0026eacute;nete Munkabizotts\u0026aacute;g tev\u0026eacute;kenys\u0026eacute;g\u0026eacute;ből, 2016\u0026ndash;2023. Budapest: L\u0026Eacute;TRA Alap\u0026iacute;tv\u0026aacute;ny, Kaleidoscope K\u0026ouml;nyvek; 2023. p. 54\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eDurrer B, Brugger H, Syme D. The medical on-site treatment of hypothermia: ICAR-MEDCOM recommendation. High Alt Med Biol. 2003;4(1):99\u0026ndash;103.\u003c/li\u003e\n\u003cli\u003eMusi ME, Sheets A, Zafren K, Brugger H, Paal P, H\u0026ouml;lzl N, et al. Clinical staging of accidental hypothermia: The Revised Swiss System: Recommendation of the International Commission for Mountain Emergency Medicine (ICAR MedCom). Resuscitation. 2021;162:182\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eDow J, Giesbrecht GG, Danzl DF, Brugger H, Sagalyn EB, Walpoth B, et al. Wilderness Medical Society Clinical Practice Guidelines for the Out-of-Hospital Evaluation and Treatment of Accidental Hypothermia: 2019 Update. Wilderness Environ Med. 2019;30(4S):S47\u0026ndash;S69.\u003c/li\u003e\n\u003cli\u003eT\u0026oacute;th Z. The importance and methods of triage in emergency medicine (A betegoszt\u0026aacute;lyoz\u0026aacute;s (triage) jelentős\u0026eacute;ge \u0026eacute;s m\u0026oacute;dszerei a s\u0026uuml;rgőss\u0026eacute;gi betegell\u0026aacute;t\u0026aacute;sban). \u0026Uacute;jra\u0026eacute;leszt\u0026eacute;s (Resuscitatio Hungarica). 2007;5:9\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eManchester Triage Group. Emergency Triage. 3rd ed. Chichester: Wiley-Blackwell; 2014.\u003c/li\u003e\n\u003cli\u003eBullard MJ, Musgrave E, Warren D, Unger B, Skeldon T, Grierson R, et al. Revisions to the Canadian Emergency Department Triage and Acuity Scale (CTAS) Guidelines 2016. CJEM. 2017;19(S2):S18\u0026ndash;S27.\u003c/li\u003e\n\u003cli\u003eBocsi R, Botos P. Tri\u0026aacute;zs tank\u0026ouml;nyv \u0026ndash; 2.0 verzi\u0026oacute; (in Hungarian). MSOTKE; 2016.\u003c/li\u003e\n\u003cli\u003eCommonwealth of Australia. Emergency Triage Education Kit, Australasian Triage Scale (ATS) Guidelines. Commonwealth of Australia; 2009.\u003c/li\u003e\n\u003cli\u003eDepartment of Health and Aging. Emergency Triage Education Kit \u0026ndash; Triage Workbook. Commonwealth of Australia; 2009.\u003c/li\u003e\n\u003cli\u003eSorić M, Miletić W, Bar\u0026scaron;ić Gračanin T, Delić B, Grabovac V, Žiga S. Efficiency of triage in the emergency department of an urban academic clinical hospital-a retrospective study. Signa Vitae. 2017;13:16\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003ePodsiadło P, Brožek T, Balik M, Nowak E, Mendrala K, Hymczak H, et al. Predictors of cardiac arrest in severe accidental hypothermia. Am J Emerg Med. 2024;78:145\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eMetnet.hu. Napi időj\u0026aacute;r\u0026aacute;si adatok (in Hungarian) [Internet]. Available from: https://www.metnet.hu/napi-adatok?sub=2 (accessed 2025 Feb 5).\u003c/li\u003e\n\u003cli\u003eTakauji S, Hifumi T, Saijo Y, Yokobori S, Kanda J, Kondo Y, et al. Association between frailty and mortality among patients with accidental hypothermia: a nationwide observational study in Japan. BMC Geriatr. 2021;21(1):507.\u003c/li\u003e\n\u003cli\u003eHaverkamp FJC, Giesbrecht GG, Tan E. The prehospital management of hypothermia \u0026ndash; An up-to-date overview. Injury. 2018;49(2):149\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eR\u0026ouml;sli D, Schn\u0026uuml;riger B, Candinas D, Haltmeier T. The impact of accidental hypothermia on mortality in trauma patients overall and patients with traumatic brain injury specifically: a systematic review and meta-analysis. World J Surg. 2020;44(12):4106\u0026ndash;17.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"accidental hypothermia, triage, emergency service, hospital, risk assessment, logistic models, ROC curve, mortality, global warming","lastPublishedDoi":"10.21203/rs.3.rs-6202034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6202034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccidental hypothermia, defined as a core temperature below 35\u0026deg;C, can cause metabolic, respiratory, and circulatory disturbances; fatal arrhythmias; or cardiac arrest. Our objective was to analyze the profile of patients presenting at a Hungarian emergency department and to identify predictors of critical outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort analysis from 2020\u0026ndash;2024 at the Department of Emergency Medicine, Semmelweis University. Patients whose core temperature was less than 35\u0026deg;C were included, and their demographics and triage categories were documented. Hypothermia severity was assessed via the Swiss staging model and the Wilderness Medical Society classification. The primary outcome was a composite of admissions to the intensive care unit and mortality in the emergency department. We tested the ability of hypothermia-specific scales and triage categories, admission temperature, and their combined models to predict the primary outcome. Predictive accuracy was evaluated via receiver operating characteristic (ROC) analysis. The strength of the correlations was quantified via logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 131 patients met the inclusion criteria. The median age was 67.5 years (IQR: 59.0\u0026ndash;75.0). Eighty-eight patients (67.2%) were male. The median admission core temperature was 29.3\u0026deg;C (IQR: 26.1\u0026ndash;31.4\u0026deg;C). The median length of stay was 13.7 hours (IQR: 9.5\u0026ndash;18.9 hours). Severe hypothermia (\u0026lt;\u0026thinsp;30\u0026deg;C) was present in 47 patients (34.6%). Intensive care unit admission was required for 16 patients (12.2%), and 28 patients (21.4%) died during emergency care. Ambient temperature seasonally affected the incidence of hypothermia but had no influence on the probability of critical outcomes. The triage category outperformed hypothermia-specific stratification tools and was the strongest single predictor of critical outcomes (AUC\u0026thinsp;=\u0026thinsp;0.683). The combination of triage category and admission core temperature had the highest predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.740, 95% CI: 0.650\u0026ndash;0.831) for the primary outcome.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAccidental hypothermia is a serious and potentially lethal emergency despite milder winters associated with climate change. The admission core temperature improves the predictive performance of general triage systems for critical outcomes. To identify and manage high-risk hypothermic patients in environments with sudden temperature fluctuations, comprehensive, integrated risk assessment methods are essential.\u003c/p\u003e","manuscriptTitle":"Hypothermia in Emergency Care: Longitudinal Demographic Trends and Predictors of Critical Outcomes in Hungary","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 16:17:33","doi":"10.21203/rs.3.rs-6202034/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7521e329-5c1a-480a-9f30-04a17d0a5ca7","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T06:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 16:17:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6202034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6202034","identity":"rs-6202034","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}