Emergency Medicine

Accidental hypothermia: Factors related to a prolonged hospital stay – A nationwide observational study in Japan

a b s t r a c t

Background: The incidence of accidental hypothermia (AH) is low, and the length of hospital stay in patients with AH remains poorly understood. The present study explored which factors were related to prolonged hospitaliza- tion among patients with AH using Japan’s nationwide registry data.

Methods: The data from the Hypothermia STUDY 2018, which included patients >=18 years old with a body tem- perature <= 35 ?C, were obtained from a multicenter registry for AH conducted at 89 institutions throughout Japan, collected from December 1, 2018, to February 28, 2019. The patients were divided into a "short-stay patients" group (within 7 days) and "long-stay patients" group (more than 7 days). A logistic regression analysis after mul- tiple imputation was performed to obtain odds ratios (ORs) for Prolonged hospitalization with age, frailty, loca- tion, causes underlying the hypothermia, temperature, pH, potassium level, and disseminated intravascular coagulation score as independent variables.

Results: In total, 656 patients were included in the study, of which 362 were eligible for the analysis. The median length of hospital stay was 17 days. Of the 362 patients, 265 (73.2%) stayed in the hospital for more than 7 days. The factors associated with prolonged hospitalization were frailty (OR, 2.11; 95% confidence interval [CI], 1.09-4.10; p = 0.027), the occurrence of indoor (OR, 3.20; 95% CI, 1.58-6.46; p = 0.001), alcohol intoxication

(OR, 0.17; 95% CI, 0.05-0.56; p = 0.004), pH (OR, 0.07; 95% CI, 0.01-0.76; p = 0.029), potassium level (OR,

1.36; 95% CI, 1.00-1.85; p = 0.048), and DIC score (OR, 1.54; 95% CI, 1.13-2.10; p = 0.006).

Conclusions: Frailty, indoor situation, alcohol intoxication, pH value, potassium level, and DIC score were factors contributing to prolonged hospitalization in patients with AH. Preventing frailty may help reduce the length of hospital stay in patients with AH. In addition, measuring the pH value and potassium level by an arterial blood gas analysis at the ED is recommended for the early evaluation of AH.

(C) 2021

Abbreviations: AH, Accidental hypothermia; ORs, odds ratios; CI, confidence interval; DIC, disseminated intravascular coagulation; EMS, emergency medical services; GCS, Glasgow coma scale; SOFA, Sequential Organ Failure Assessment; V-A ECMO, venous- arterial extracorporeal membrane oxygenation; CPC, Cerebral Performance Category; CFS, clinical frailty scale; CT, computed tomography; CPA, cardiopulmonary arrest; ECG, electrocardiogram; ED, emergency department; VF, ventricular fibrillation; PEA, pulseless electrical activity; ICU, intensive-care unit.

* Corresponding author at: Department of Emergency Medicine, Asahikawa Medical University, 2-1, Midorigaoka higashi, Asahikawa 078-8510, Japan.

E-mail address: [email protected] (S. Takauji).

  1. Introduction

Accidental hypothermia (AH) is defined by a body core temperature of <35 ?C [1,2]. While it has a low incidence, the mortality rate is high for cases of severe hypothermia [3,4]. Previous studies regarding AH have shown that prognostic factors.in patients with AH were the age, pH, and potassium level [3-7]. However, these factors were useful in de- ciding whether to perform invasive Rewarming procedures, such as ex- tracorporeal membrane oxygenation (ECMO), but were insufficient for prescribing prophylactic measures to prevent hypothermia. In contrast,

https://doi.org/10.1016/j.ajem.2021.03.079

0735-6757/(C) 2021

studies focusing on the length of hospital stay have proven beneficial for preventing AH and considering the public health regarding AH. For in- stance, shortening hospitalization can increase the bed turnover rate, thereby reducing Medical costs. However, few studies have investigated which factors are associated with the length of hospital stay [8].

We hypothesized that the patients with AH who required prolonged hospitalization potential had factors that differed from the prognostic factors. The characteristics of patients with AH in Japan include an older age, high rate of occurring indoors, and high mortality rate [9,10]. Japan is the most rapidly aging society in the world [11]. We be- lieve that analyzing the AH registry in Japan may be helpful for prescrib- ing prophylactic measures to prevent AH in other countries that are also experiencing the aging of their societies.

The present study investigated which factors are associated with long-term hospitalization in patients with AH using Japan’s nationwide registry data of hypothermia.

  1. Methods
    1. Study design and setting

We performed a prospective, observational, multi-center registry of hypothermia: the Hypothermia STUDY 2018. This study was conducted from December 1, 2018, to February 28, 2019, among a consortium of 89 academic and community medical centers from different geographic re- gions across Japan. The study was approved by the institutional review board of each hospital listed in the acknowledgements, and the require- ment for informed consent was waived due to the observational nature of the study.

    1. Patient selection and data collection

The present study included consecutive patients whose body tem- perature measured by emergency medical services (EMS) or at the emergency department (ED) was less than 35 ?C. Patients younger than 18 years old were excluded. The following data were collected: age, sex, any Pre-existing conditions, activities of daily living (ADL), life- style, location, causes underlying the hypothermia (acute medical ill- ness [stroke, ischemic cardiac disease, infectious disease, malnutrition, arrhythmia, diabetes mellitus, renal disease, hypoglycemia, cardiac fail- ure, endocrine disease and gastrointestinal disease], trauma [submer- sion, distress], alcohol intoxication, others [including drugs]), Charlson Comorbidity Index (CCI), Glasgow coma scale (GCS), Sequential Organ Failure Assessment score [12], disseminated intravascular coag- ulation (DIC) score [13], laboratory data, temperature, dysrhythmias, cardiac arrest during pre-hospital, venous-arterial extracorporeal mem- brane oxygenation (V-A ECMO) rewarming, intubation, hospital pe- riods, survival, and Cerebral Performance Category score at 30 days after admission.

The Clinical Frailty Scale score was determined using the activ- ities of daily living and pre-existing conditions, as described previously

[14]: CFS 1, very fit, defined as ADL 1 (independent) and CCI 0; CFS 2, well, defined as ADL 1 and CCI >=1, or ADL 2 (sometimes out of the door) and CCI 0; CFS 3, well with treated comorbid disease, defined as ADL 2 and CCI 1-2; CFS 4, apparently vulnerable, defined as ADL 2 and CCI >=3, or ADL 3 (indoors); CFS 5, mildly frail, defined as ADL 4 (almost needing assistance) and CCI <=2; CFS 6, moderately frail, defined as ADL 4 and CCI >=3; and CFS 7, severely frail, defined ADL 5 (needing total assis- tance). Patients were defined as frail if they had a CFS score of >=5 before hospital admission. Temperature was recorded as the core temperature from the rectum, urinary bladder, or esophagus if available; otherwise, the peripheral temperature from the axilla or ear was noted. Hypother- mia was classified according to the temperature as mild (35-32 ?C), moderate (32-28 ?C), of severe (<28 ?C).

The laboratory data included the pH value, potassium level, platelet count, and CPK level at the ED. The pH value in principle was evaluated

by an arterial blood gas analysis, and the pH measured using the venous blood gas was adjusted as described in a previous study [15]. Complica- tions during hospitalization were recorded and classified as pneumonia, pancreatitis, or other. Pneumonia was defined as an obvious shadow on chest radiography or computed tomography (CT). Pancreatitis was de- fined as cases meeting at least two of the following conditions: 1) ab- dominal pain, 2) elevation of pancreatic enzyme levels in the blood, and 3) edema of the pancreas or peripancreatic effusion on ultra- sound/CT. In the present study, patients were excluded from the analy- sis if they were 30-day non-survivors, because it is likely that many of these patients were critically ill, there was a potential bias. Similarly, the patients who did not stay in a hospital, or in whom the length of hospital stay or body temperature was unknown were excluded from the present analysis.

Previous studies showed that the median length of hospital stay in patients with AH was 4-9 days [4,8,10]. According to a previous study, we therefore divided the patients with AH into 2 groups: the “short- stay patients” group (<=7 days) and "long-stay patients" group (>7 days).

    1. Data analyses

Data are expressed as the number (%) or median (interquartile range), as appropriate. Intergroup comparisons were made using Fisher’s exact test for categorical data and the Mann-Whitney U test for continuous data. For a further evaluation, we conducted multivariate logistic regression analyses to control for the potentially confounding roles of age, frailty, location, causes underlying the hypothermia, tem- perature, pH, potassium, and DIC score. These potential confounders were selected based on a previous study regarding AH [8] or the consid- eration of clinically significant variables. Furthermore, a multiple Linear regression analysis including similar variables was performed. Missing data were managed with multiple imputation by chained equations [16,17]. The variables included in the imputation model were those from the logistic regression analysis. Twenty-five dataset were imputed with 10 iterations each. Multivariate logistic regression and multiple liner regression analyses were applied to the 25 imputed datasets, and final estimates were obtained by averaging the 25 estimates according to Rubin’s rules. Furthermore, a complete data set was used for the sen- sitive analysis. All tests were two-sided with P values of less than 0.05 considered statistically significant.

Statistical analyses were performed with the EZR software program

(Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R software. Multiple imputation was per- formed using mice package in the R software program, version 4.0.1 (R Foundation for Statistical Computing, Vienna, Austria).

  1. Results

Of the 656 patients with hypothermia included in the Hypothermia STUDY 2018, 294 were excluded from the present study because of death within 30 days (N = 192), non-hospital stay (N = 57), unknown length of hospital stay (N = 42), or others, including an unknown temperature (N = 3). The remaining 362 patients were eligible for the present analysis. The patient flow diagram is shown in Fig. 1. The non- hospital-stay patients were younger, had lower severity scores (e.g. SOFA and DIC scores), and had a higher occurrence outdoors than the hospital-stay patients (Supplement table 1). In addition, clinical data showed that the non-hospital-stay patients had a greater incidence of Mild hypothermia and higher GCS, systolic BP, pH value, and platelet count with a lower CPK level than the hospital-stay patients (Supple- ment table 2). In the present study, depending on the length of hospital stay, the 362 patients were divided into the “short-stay patients” group (N = 97) and “long-stay patients” group (N = 265). The overall mortal- ity rate within 30 days, including death at the ED, was 192 among 656

Image of Fig. 1

Fig. 1. Study flowchart. Of the 656 patients with hypothermia, 362 patients were enrolled, and 294 patients were excluded because of death within 30 days (N = 192), non-hospital stay (N = 57), unknown length of hospital stay (N = 42), or other reasons, including an unknown temperature (N = 3). The 362 patients were divided into a “short-stay patient” group (N = 97) and a “long-stay patient” group (N = 265). ED, emergency department.

patients (29.3%). Among the remaining 362 patients, 265 (73.2%) stayed in the hospital for more than 7 days.

    1. Baseline characteristics of the study population

Table 1 shows the baseline characteristics of the study population and a comparison of the clinical characteristics between the “short- stay patients” and “long-stay patients” groups. The median patient age

was 78 years old, and 75.1% of AH cases occurred indoors. The age, SOFA score, and DIC score were higher in the “long-stay patients” group than in the “short-stay patients” group. There was a significant difference in the CFS score, and causes underlying the hypothermia be- tween the “short-stay patients” and “long-stay patients” groups. In the “long-stay patients” group, the rate of occurrence indoors was higher than that in the “short-stay patients” group. Regarding the causes un- derlying the hypothermia, acute medical illness was the predominant cause in the “long-stay patients” group, whereas the proportion of cases caused by alcohol intoxication was higher in “short-stay patients” group than in the “long-stay patients” group.

    1. Clinical and laboratory data

The clinical and laboratory data are presented in Table 2. There were no significant differences in the severity grade of temperature, GCS, blood pressure, heart rate, respiratory rate, platelet level, or rate of intu- bation between the “short-stay patients” and “long-stay patients” groups. However, the pH values were significantly lower and the potas- sium and CPK levels higher in the “long-stay patient” group than in the “short-stay patient” group. In addition, the “long-stay patient” group showed a significantly higher Incidence of arrhythmia than the “short- stay patient” group. A total of 222 (61.3%) patients were admitted to the intensive-care unit (ICU), although there was no significant differ- ence in the rate of ICU admission between the groups.

    1. Complications, neurological score, and hospital length of stay

There was no significant difference in the incidence of complications between the “short-stay patients” and “long-stay patients” groups (Table 3). However, at the neurological assessment, the “long-stay pa- tients” group showed a higher rate of worsened neurological score (CPC 3-5) at 30 days after admission than the “short-stay patients” group, while the “short-stay patients” group showed a significantly

Table 1

Characteristics of patients with hypothermia

All patients

Missing

Short-stay patients

Long-stay patients

p-value

n = 362

n, (%)

n = 97

n = 265

Age, years

78 (68-87)

0

75 (62-86)

79 (69-87)

0.035

Males

197 (54.4%)

0

51 (52.6%)

146 (55.1%)

0.721

Charlson comorbidity index Severity

SOFA total

1 (0-1)

4 (2-7)

0

29 (8.0)

1 (0-1)

3 (2-5)

1 (0-2)

5 (3-7)

0.341

<0.001

DIC score

2 (1-3)

1 (0.0)

2 (1-2)

2 (1-3)

<0.001

Clinical Frailty Scale score

1: very fit

93 (25.7%)

11 (3.0)

32 (34.0%)

61 (23.7%)

0.009

2: well

115 (31.8%)

30 (31.9%)

85 (33.1%)

3: well with treated comorbid disease

21 (5.8%)

6 (6.4%)

15 (5.8%)

4: apparently vulnerable

45 (12.4%)

2 (2.1%)

43 (16.7%)

5: mildly frail

47 (13.0%)

17 (18.1%)

30 (11.7%)

6: moderately frail

13 (3.6%)

4 (4.3%)

9 (3.5%)

7: severely frail Lifestyle

Living alone

17 (4.7%)

128 (36.2%)

8 (2.2)

3 (3.2%)

30 (32.3%)

14 (5.4%)

98 (37.5%)

0.866

Not living alone

210 (59.3%)

59 (63.4%)

151 (57.9%)

Homelessness

1 (0.3%)

0 (0.0%)

1 (0.4%)

Nursing home

10 (2.8%)

3 (3.2%)

7 (2.7%)

Unknown Location

Outdoor

5 (1.4%)

86 (24.9%)

16 (4.4)

1 (1.1%)

40 (42.6%)

4 (1.5%)

46 (18.3%)

<0.001

Indoor

Causes underlying the hypothermia Acute medical illness

260 (75.1%)

192 (53.0%)

21 (5.8)

54 (57.4%)

35 (39.8%)

206 (81.7%)

157 (62.1%)

<0.001

Trauma, submersion, and distress

53 (14.6%)

15 (17.0%)

38 (15.0%)

Alcohol intoxication

22 (6.1%)

16 (18.2%)

6 (2.4%)

Others (Unknown, drug)

74 (20.4%)

22 (25.0%)

52 (20.6%)

SOFA, Sequential Organ Failure Assessment; DIC, disseminated intravascular coagulopathy The data are expressed as the number (%) or median (interquartile range).

Clinical and laboratory data of patients with hypothermia

All patients

Missing

Short-stay patients

Long-stay patients

p-value

n = 362

n, (%)

n = 97

n = 265

Temperature

30.8 (28.5-33.2)

0

31.8 (29.4-33.7)

30.6 (28.2-33.1)

0.036

Mild (35-32 ?C)

143 (39.5%)

47 (48.5%)

96 (36.2%)

0.089

Moderate (32-28 ?C)

144 (39.8%)

35 (36.1%)

109 (41.1%)

Severe (<28 ?C)

75 (20.7%)

15 (15.5%)

60 (22.6%)

GCS

11 (9-14)

14 (3.9)

13 (9-14)

11 (8-14)

0.086

Systolic BP (mmHg)

120 (96-146)

25 (6.9)

126 (98-153)

119 (95-142)

0.197

Diastolic BP (mmHg)

71 (55-89)

30 (8.3)

76 (60-90)

70 (52-87)

0.115

Heart rate

70 (54-91)

7 (1.9)

73 (56-93)

70 (53-90)

0.474

Respiratory rate

18 (15-22)

27 (7.5)

17 (14-20)

18 (15-22)

0.106

pH

7.32 (7.21-7.37)

48 (13.3)

7.34 (7.27-7.39)

7.31 (7.21-7.37)

0.018

Potassium (mEq/L)

4.2 (3.6-4.9)

7 (1.9)

3.9 (3.5-4.4)

4.3 (3.7-5.0)

0.001

Plt (x104/uL)

18.7 (13.1-25.3)

7 (1.9)

19.5 (14.1-25.5)

18.4 (12.5-24.8)

0.328

CPK (U/L)

378 (157-1210)

26 (7.2)

219 (118-593)

459 (180-1409)

<0.001

ECG at ED

19 (5.3)

0.001

Normal

147 (40.6%)

54 (59.3%)

93 (36.9%)

Abnormal

196 (54.1%)

37 (40.7%)

159 (63.1%)

CPA

13

0

0

13

0.024

Intubation

47

20 (5.5)

8

39

0.153

V-A ECMO

9

0

0

9

0.120

Admitted to ICU

222 (61.3%)

0

52 (53.6%)

170 (64.2%)

0.088

GCS, Glasgow Coma Scale; ECG, electrocardiogram; ED, emergency department; CPA, cardiopulmonary arrest; ECMO, extracorporeal membrane oxygenation The data are expressed as the number (%) or median (interquartile range).

higher rate of patients with a favorable neurological outcome (CPC 1-2) than the “long-stay patients” group. The median length of ICU stay was 4 days, and the median length of hospital stay was 17 days for the 362 total patients. Supplementary Fig. S1 shows the distribution of the hos- pital stay duration.

    1. Multivariate logistic regression analyses

Table 4 shows that the factors associated with the length of hospital stay, based on a multivariate logistic regression analysis after multiple imputation, were frailty (odds ratio [OR], 2.11; 95% confidence interval [CI], 1.09-4.10; p = 0.027), indoor occurrence (OR, 3.20; 95% CI, 1.58-6.46; p = 0.001), causes underlying the hypothermia (alcohol in- toxication) (OR, 0.17; 95% CI, 0.05-0.56; p = 0.004), pH (OR, 0.07; 95% CI, 0.01-0.76; p = 0.029), Potassium levels (OR, 1.36; 95% CI, 1.00-1.85; p = 0.048), and the DIC score (OR, 1.54; 95% CI, 1.13-2.10; p = 0.006).

Table 3

Complications, neurological score and hospital length of stay

All patients

n = 362

Short-stay patients

n = 97

Long-stay patients

n = 265

p-value

Length of stay in the ICU length of stay in the hospital

CPC at 30 days Good (1-2)

4 (2-7)

17

(7-32)

167

2 (2-3)

4 (2-6)

45

5 (3-9)

24

(14-38)

122

<0.001

<0.001

0.008

Poor (3-5)

Complications

72

9

67

0.768

None

357

97

260

Pneumonia

1

0

1

Pancreatitis

1

0

1

Other

3

0

3

Complications of arrhythmia

None

354

96

258

1.000

VF

2

0

2

PEA

1

0

1

Bradycardia

4

1

3

Other

1

0

1

VF, ventricular fibrillation; PEA, pulseless electrical activity; ICU, intensive-care unit The data are presented as the median (interquartile range).

In the sensitivity analysis, pH (OR, 0.10; 95% CI, 0.01-1.11, p = 0.061) was not significantly associated with the length of hospital stay, but the result was close to significance, and the sensitivity analysis showed similar results to the analysis after multiple imputation. Thus, the re- sults of the analysis after multiple imputation were considered robust.

    1. Multiple liner regression analyses

Table 5 shows the correlation between variables and the length of hospital stay, based on a multiple liner regression analysis after multiple imputation. Indoor occurrence (? = 7.19; 95% CI, 1.14-13.24, P = 0.020), causes underlying the hypothermia (alcohol intoxication) (? = -10.82; 95% CI, -20.92-0.71, P = 0.036), and the DIC score

(? = 2.84; 95% CI, 0.41-5.28, P = 0.022) were significantly correlated with the length of hospital stay. In the sensitivity analysis, alcohol intox- ication (? = -8.95; 95% CI, -20.82-2.91, p = 0.139) was not signifi- cantly associated with the length of hospital stay, but the result was close to significance, so the interpretation of results did not make much difference. Thus, the results of the analysis after multiple imputa- tion were considered robust.

  1. Discussion

The present nationwide study showed that the hospital-stay pa- tients with AH were older and had higher SOFA and DIC scores with lower body temperatures than non-hospital-stay patients with AH. A total of 75.1% of hospital-stay patients with AH developed AH indoors. A total of 73.2% of hospital-stay patients with AH stayed for more than 7 days. The median length of hospital stay was 17 days. The rate of com- plications was low, there was no association between complications and the length of hospital stay. The factors related to long-term hospitaliza- tion were frailty, indoor occurrence, alcohol intoxication, pH value, po- tassium level, and the DIC score.

Among these factors related to a prolonged hospital stay, the pH value, potassium levels, and DIC score were not preventable, while frailty and an indoor occurrence were potentially preventable with in- tervention. Previous studies have shown many patients with AH in urban settings develop AH indoors [9,10], which was in agreement with the present findings. In addition, the patients who develop AH in- doors have a higher Death rate and longer hospital stay because of their

Table 4

Results of a multivariate logistic regression analysis for factors associated with the length of hospital stay

Model without imputation (N = 281) Model with imputation (N = 362)

OR

95% CI

P-value

OR

95% CI

P-value

Age, years

1.02

0.99-1.05

0.069

1.02

0.99-1.04

0.103

Frailty (CFS >= 5)

2.59

1.19-5.63

0.017

2.11

1.09-4.10

0.027

Location

Outdoor

Reference

Reference

Indoor

3.57

1.55-8.23

0.003

3.20

1.58-6.46

0.001

Causes underlying the hypothermia

Acute medical illness

Reference

Reference

Trauma, submersion, and distress

0.88

0.32-2.45

0.804

0.95

0.40-2.28

0.913

Alcohol intoxication

0.20

0.05-0.73

0.015

0.17

0.05-0.56

0.004

Others (Unknown, drug)

0.72

0.34-1.55

0.408

0.68

0.34-1.34

0.263

Temperature (per 1 ?C)

0.97

0.87-1.07

0.510

0.96

0.87-1.05

0.330

pH (per 1)

0.10

0.01-1.11

0.061

0.07

0.01-0.76

0.029

Potassium (per 1 mEq/L)

1.45

1.02-2.07

0.039

1.36

1.00-1.85

0.048

DIC score (per 1)

1.83

1.24-2.69

0.002

1.54

1.13-2.10

0.006

OR, odds ratio; CI, confidence interval; DIC, disseminated intravascular coagulopathy

tendency to have an advanced age [10,18]. However, of note, the pres- ent study showed that the factors associated with the length of hospital stay were not just the age but the frailty. Recently, frailty, which is highly prevalent in older individuals and confers a high risk of falls, dis- ability, hospitalization, and mortality [19], has also been noted in criti- cally ill patients [20,21]. To our knowledge, limited data exist regarding the relationship between AH and frailty. The present findings are of critical importance. Clinically, the risk factors of frailty and an in- door occurrence may be useful for the early detection of AH patients likely to require a prolonged hospital stay.

In the present study, the pH value and potassium level also were fac- tors associated with a prolonged hospital stay. We can easily measure these factors through an arterial blood gas analysis at the ED, making them useful markers for the initial assessment predicting a prolonged hospital stay. In addition, previous studies have shown that these factors were associated with the prognosis in patients with AH [3,6]. Therefore, an arterial blood gas analysis at the ED is an essential laboratory test for the early evaluation of AH. Other factors associated with a prolonged hospitalization include DIC score and alcohol intoxication. The DIC score is generally increased by sepsis and organ failure. Accordingly, AH patients complicated by such issues may have a significantly prolonged hospital stay. In contrast, alcohol intoxication was associated with a short hospital stay. We speculate that these patients had acute al- cohol intoxication, which resulted in an early discharge from the hospi- tal after rewarming and awakening.

A previous study indicated that a lower core temperature, lower de- gree of consciousness, and lower platelet count were factors related to a prolonged hospital stay [8]. These factors are inconsistent with the re- sults of the present study. Several reasons may explain this discrepancy. First, the age of the patients in the present study was significantly higher in comparison to that in the previous study. The patients in the previous study were mostly healthy, younger, and AH occurred outdoors, such as in cases of avalanche or submersion in freezing water [3,22,23], resulting in an early discharge from the hospital. In contrast, the pa- tients in the present study were older individuals with underlying dis- eases, and AH tended to occur indoors. These differences may affect the factors associated with the hospital stay. Japan is the most rapidly aging society in the world [11]. In the near future, the results of this study may be helpful for prescribing prophylactic measures to prevent AH in industrialized countries, which– similarly to Japan–have increas- ingly aging populations. Second, the previous study was performed at a single institution and extracted from a univariate analysis, so potential confounders were not considered for adjustment. The present study was a multicenter, nationwide study associated with AH in Japan and involved a multivariate analysis of credible data.

The present study had several limitations. First, differences in the so- cial environment and medical care system may influence the length of hospitalization. However, we considered these differences to be negligi- ble, not affecting the interpretation of the present study. Second, this registry did not include data on the CFS score, and instead, the CFS

Table 5

Results of a multiple linear regression analysis for factors associated with the length of hospital stay

Model without imputation (N = 281) Model with imputation (N = 362)

95% CI

95% CI

Partial regression coefficient ?

Lower

Upper

P-value

Partial regression coefficient ?

Lower

Upper

P-value

Age, years

0.04

-0.16

0.24

0.705

0.02

-0.15

0.19

0.845

Frailty (CFS >= 5)

5.22

-1.41

11.84

0.122

3.81

-1.78

9.39

0.181

Location

Outdoor

Reference

Reference

Indoor

8.75

1.45

16.06

0.019

7.19

1.14

13.24

0.020

Causes underlying the hypothermia

Acute medical illness

Trauma, submersion, and distress

Reference

5.77

-2.91

14.46

0.192

Reference

3.31

-3.70

10.31

0.354

Alcohol intoxication

-8.95

-20.82

2.91

0.139

-10.82

-20.92

-0.71

0.036

Others (Unknown, drug)

1.39

-5.09

7.88

0.673

-0.29

-5.96

5.37

0.919

Temperature (per 1 ?C)

-0.56

-1.45

0.34

0.222

-0.54

-1.30

0.22

0.166

pH (per 1)

-12.65

-30.11

4.80

0.155

-15.63

-31.98

0.72

0.061

Potassium (per 1 mEq/L)

0.64

-1.83

3.10

0.611

0.55

-1.67

2.77

0.629

DIC score (per 1)

4.07

1.08

7.07

0.008

2.84

0.41

5.28

0.022

CI, confidence interval; DIC, disseminated intravascular coagulopathy

score was evaluated using the activities of daily living and pre-existing conditions. Finally, although we adjusted for variables, the possibility of residual confounding factors cannot be avoided.

  1. Conclusion

This study found, that the hospital-stay patients were older and had more severe cases than the non-hospital-stay patients with AH, and among the hospital-stay patients with AH, three-quarters needed to be hospitalized for more than 7 days. The factors related to a prolonged hospital stay were frailty, indoor occurrence, pH value, potassium level, and the DIC score. Conversely, alcohol intoxication was associated with a short hospital stay. Taken together, these findings suggest that reduc- ing frailty and maintaining adequate activities of daily living may help reduce the length of hospital stay in patients with AH. In addition, an ar- terial blood gas analysis at the ED is recommended for the early evalua- tion of AH.

Authors’ contributions

ST and JK contributed to the conception and design of this analysis. ST wrote the manuscript. TH, SY, YK, and KH supervised the work. YS provided statistical advice on the study design and the analyzed data. KS, HY and AY contributed to the interpretation of the results. All au- thors read and approved the final manuscript.

Funding

None.

Availability of data and materials

No data is available.

Ethics approval and consent to participate

The institutional review board of each hospital listed in the acknowl- edgements approved the study, and the requirement for informed con- sent was waived due to the observational nature of the study.

Consent for publication

Not applicable.

Declaration of Competing Interest

The authors declare that they have no competing interests.

Acknowledgements

List of hospitals participating in the present study. Aizawa Hospital.

Akita Red Cross Hospital. Asahikawa City Hospital.

Asahikawa Medical university hospital.

Center Hospital of the National Center for Global Health and Medicine.

Daiyukai General Hospital.

Dokkyo Medical University Nikko Medical Center. Dokkyo Medical University Saitama Medical Center. Esashi Hospital.

Fujieda Municipal General Hospital. Fujisawa City Hospital.

Fukui Prefectual Hospital.

Funabashi Municipal Medical Center. Gifu Prefectual General Medical Center. Gifu University Hospital.

Hamamatsu Medical Center. Hidaka Tokushukai Hospital. Hokkaido Medical Center.

Hyogo emergency medical center. Hyogo Prefectual Nishinomiya Hospital. Ina Central Hospital.

Ise Red Cross Hospital.

Ishikawa Prefectual Central Hospital. Ishinomaki Red Cross Hospital.

Iwata City Hospital.

Iwate Prefectual Central Hospital. Japanese Red Cross Society Kyoto Daiichi Hospital. Juntendo University Nerima Hospital.

Juntendo University Urayasu Hospital. Kagawa University Hospital.

Kansai Medical University Hospital. Kasugai Municipal Hospital.

Kawaguchi Municipal Medical Center. Kawasaki Municipal Hospital.

Kimitsu Chuo Hospital. Kumamoto Red Cross Hospital. Kyorin University Hospital.

Kyoto University Hospital. Mie Prefectual Genaral Medical Center. Miyazaki Prefectual Nobeoka Hospital. Nagano Red Cross Hospital.

Nagasaki University Hospital. Nagoya Ekisaikai Hospital.

Nagoya University Hospital. Narita Red Cross Hospital. Nasu Red Cross Hospital.

National Defense Medical College Hospital.

National Hospital Organization Mito Medical Center. National Hospital Organization Nagoya Medical Center. National Hospital Organization Osaka National Hospital. Nayoro City General Hospital.

Nihon University Hospital.

Nihon University Itabashi Hospital. Nihonkai General Hospital.

Niigata University Medical & Dental Hospital. Nippon Medical School Hospital.

Nippon Medical School Tamanagayama Hospital. Oita University Hospital.

Ome Municipal Central Hospital. Osaka City General Hospital.

Ota Memorial Hospital.

Rinku General Medical Center. Saiseikai Shiga Hospital.

Saiseikai Utsunomiya Hospital. Sapporo City General Hospital. Seirei Hamamatsu General Hospital. Seirei Mikatahara General Hospital. Shinshu University Hospital.

Shonan Kamakura General Hospital. St.Mary’s Hospital.

Steel Memorial Hirohata Hospital. Takasaki General Medical Center. Teikyo University Hospital.

Teine Keijinkai Hospital. Tenshi Hospital.

Toho University Omori Medical Center. Tokushima Prefectual Miyoshi Hospital. Tokuyama Central Hospital.

Tokyo Metropolitan Tama Medical Center. Tosei General Hospital.

Toyama University Hospital. Tsuyama Chuo Hospital.

University of Tokyo Hospital. University of Yamanashi Hospital. Yamagata University Hospital.

Yamaguchi University Hospital. Yamanashi Prefectual Central Hospital. Yokkaichi Municipal Hospital.

Yokohama Minami Kyosai Hospital.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.ajem.2021.03.079.

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