Abstract
Background
The association of body mass index with outcome in patients treated with targeted temperature management (TTM) after out-of-hospital cardiac arrest (OHCA) is unclear. The purpose of this study was to examine the effect of body mass index (BMI) on neurological outcomes and mortality in resuscitated patients treated with TTM after OHCA.
Methods
This multicenter, prospective, observational study was performed with data from 22 hospitals included in the Korean Hypothermia Network KORHN-PRO registry. Comatose adult patients treated with TTM after OHCA between October 2015 and December 2018 were enrolled. The BMI of each patient was calculated and classified according to the criteria of the World Health Organization (WHO). Each group was analyzed in terms of demographic characteristics and associations with six-month neurologic outcomes and mortality after cardiac arrest (CA).
Results
Of 1,373 patients treated with TTM identified in the registry, 1,315 were included in this study. One hundred two patients were underweight (BMI <18.5 kg/m2), 798 were normal weight (BMI 18.5–24.9 kg/m2), 332 were overweight (BMI 25–29.9 kg/m2), and 73 were obese (BMI ≥ 30 kg/m2). The higher BMI group had younger patients and a greater incidence of diabetes and hypertension. Six-month neurologic outcomes and mortality were not different among the BMI groups (p = 0.111, p = 0.234). Univariate and multivariate analyses showed that BMI classification was not associated with six-month neurologic outcomes or mortality. In the subgroup analysis, the underweight group treated with TTM at 33°C was associated with poor neurologic outcomes six months after CA (OR 2.090, 95% CI 1.010–4.325, p = 0.047), whereas the TTM at 36°C group was not (OR 0.88, 95% CI 0.249–3.112, p = 0.843).
Conclusions
BMI was not associated with six-month neurologic outcomes or mortality in patients surviving OHCA. However, in the subgroup analysis, underweight patients were associated with poor neurologic outcomes when treated with TTM at 33°C.
Introduction
The number of obese patients has been steadily increasing worldwide, and in the United States, the proportion increased from 30.5% in 1999–2000 to 42.4% in 2017–2018 [1]. Body mass index (BMI) is a simple tool that uses height and weight to measure obesity. As the number of obese patients increases, an increasing number of studies on the association between body mass index and various diseases has been performed, and the results are controversial. Obesity is known to be a major risk factor for chronic diseases, such as hypertension, type 2 diabetes mellitus, metabolic syndrome, myocardial infarction, and heart failure, which may increase the risk of cardiac arrest [2, 3]. However, the paradox of obesity is that obese patients with sepsis, which has high mortality rates, have better outcomes and survival [4, 5]. Another study found that overweight patients had the lowest mortality rate, and a lower BMI tended to increase all-cause mortality rates [6, 7].
Previous studies examining the relationship between out-of-hospital cardiac arrest (OHCA) outcomes and BMI had controversial results. One single-center, retrospective study showed that an overweight BMI was associated with lower six-month mortality and poor neurologic outcomes in OHCA patients treated with targeted temperature management (TTM) [8]. Another study showed that obesity is a risk factor for mortality in OHCA patients treated with therapeutic hypothermia [9, 10], and other studies showed that BMI was not associated with survival or good neurologic outcomes at hospital discharge [11, 12]. However, in all of the above studies, patients were treated at a target temperature of 33°C, and neurologic outcomes were evaluated at hospital discharge.
In the current study, we aimed to evaluate whether BMI influences six-month neurologic outcomes in patients treated with TTM after cardiac arrest using a multicenter, prospective registry. Through subgroup analysis, we aimed to investigate differences in neurological outcomes according to BMI in the TTM 33 and TTM 36 groups.
Methods
Study setting and participation
This registry-based study was conducted prospectively with data from 22 centers included in the Korean Hypothermia Network, KORHN-PRO registry (NCT02827422). The study included an informed consent form approved by all participating hospitals, including the institutional review board (IRB) of Seoul St. Mary’s Hospital (XC150IMI0081K). Between October 2015 and December 2018, comatose adult patients (≥ 18 years old) who were resuscitated following OHCA and treated with TTM were registered in the KORHN-PRO registry. The registry excluded prearrest CPC 3 or 4; known disease resulting in death at 180 days; and cardiac arrest caused by trauma, stroke, or intracranial bleeding. The web-based registry consisted of patient information, resuscitation variables, TTM information, in-hospital treatment, outcomes and outcome prediction modalities. Each center received approval to participate by its Institutional Review Board. Written informed consent was obtained from all patients.
The TTM protocol of all centers included the same TTM parameters. The TTM regimen involved cooling from 32°C to 36°C for 12 to 24 h and controlled normothermia for 72 h after the return of spontaneous circulation. The participating hospitals treated the patients according to their own treatment protocols, and the TTM device and target temperature selection were based on individual preferences. For subgroup analysis, the patients were divided into two groups according to their intended regimen. Patients planning to be treated with a low temperature setting of 33–34°C were considered the TTM 33 group, and those planning to be treated with a high temperature setting of 35–36°C were considered the TTM 36 group. Neurologic outcomes were investigated by researchers at each hospital using multimodal tests.
Data collection and definitions
We obtained data from the KORHN-PRO registry in the Utstein style. We included adult OHCA survivors who underwent TTM with a target temperature of 32–36°C for 24 h and excluded patients 1) whose weight or height were missed and 2) who had no data on 6-month neurologic outcomes. The following information was obtained: 1) patients characteristics: sex, age, comorbidities (coronary heart disease, hypertension, diabetes mellitus, cardiovascular accident, lung disease and renal disease), height and weight; 2) resuscitation variables: arrest location, initial arrest rhythm, witness of collapse, bystander cardiopulmonary resuscitation (CPR), arrest etiology (cardiac or noncardiac) and anoxic time (collapse to ROSC time); 3) TTM: TTM methods, target temperature, induction time (time from start of TTM to the time of achieving the target temperature), rewarming time, time from ROSC to start of TTM, and the time from ROSC to target temperature achievement; and 4) in-hospital data: shock after ROSC (systolic blood pressure < 90 mmHg for > 30 minutes, or the need for supportive measures to maintain a blood pressure of 90 mmHg), advanced cardiac treatment (coronary angiography, percutaneous coronary intervention, use of thrombolytics, extracorporeal bypass) and adverse events within 7 days to maintain a ROSC (seizure), and advanced cardiac treatment (coronary angiography).
According to the criteria of the World Health Organization (WHO), BMI was calculated as the weight in kilograms (kg) divided by height in meters squared. Based on the weight and height from the registry data, the calculated BMI was categorized as underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2) or obese (≥ 30 kg/m2).
Primary outcome and subgroup analysis
The primary outcome was the neurologic outcome 6 months after resuscitation following OHCA. The outcomes were assessed by investigators at each center through face-to-face or telephone interviews. The neurologic outcome was expressed as good or poor; good performance (CPC 1) or moderate disability (CPC 2) were categorized as a good outcome; and severe disability (CPC 3), vegetative state (CPC 4) or death/brain death (CPC 5) were classified as a poor outcome. Subgroup analysis was performed to assess the effect of BMI on neurologic outcome in terms of patient characteristics and TTM variables.
Statistical analysis
Recorded data were analyzed using IBM SPSS 24.0 for Windows (IBM corp., Armonk, NY, USA). Continuous variables are presented as the mean ± SD or median and interquartile range (IQR). Categorical variables are presented as counts (percentage). For comparisons among BMI groups, continuous variables were compared using the Kruskal-Wallis test when they did not follow a normal distribution according to the Kolmogorov-Smirnov test. The χ2 test was used for comparisons of categorical data. To determine the relationship between BMI and six-month neurologic outcome, univariate and multivariate analyses were performed with logistic regression using normal BMI as the reference category. The Hosmer-Lemeshow test was employed to examine goodness of fit, and odds ratios (ORs) and 95% confidence intervals (CIs) were computed. Subgroup analysis was conducted using forest plots and p-values to investigate the effect of various variables on the association between neurologic outcome and BMI. All statistical tests were two-tailed, and p-values < 0.05 indicated significant differences.
Results
Of 1,373 patients identified in the KORHN-PRO registry, 24 with missing height and weight data and 34 without six-month neurologic outcomes in the database were excluded. Finally, 1,315 patients were enrolled and analyzed in this study. BMI was calculated and categorized into underweight, normal weight, overweight and obese groups according to the WHO criteria (Fig 1).
Fig 1. Flow chart of the study.
Abbreviation: OHCA out-of-hospital cardiac arrest, TTM targeted temperature management, BMI body mass index.
The baseline characteristics of the patients were analyzed by BMI category (Table 1). The majority of all BMI groups consisted of males (p < 0.001). Significant differences among the groups existed in age, hypertension, diabetes mellitus, lung disease, initial rhythm, arrest etiology, total anoxic time, induction time, ROSC to target temperature and reperfusion treatment. In particular, older patients had lower BMIs (p < 0.001), and patients with higher BMIs had greater incidences of hypertension (p < 0.001), diabetes mellitus (p = 0.003) and witnessed collapse (p = 0.047) and a longer induction time (p < 0.001) and time from ROSC to the target temperature (p < 0.001).
Table 1. Baseline characteristics of patients stratified according to body mass index classification.
| Total | Underweight | Normal | Overweight | Obese | p-value | |
|---|---|---|---|---|---|---|
| (n = 1315) | (n = 112) | (n = 798) | (n = 332) | (n = 73) | ||
| Male sex | 933 (71.0%) | 65 (58.0%) | 579 (72.6%) | 249 (75.0%) | 40 (54.8%) | < 0.001 |
| Age, years, median (IQR) | 53.0 (48.0–70.0) | 60.0 (47.5–72.0) | 59.0 (48.0–70.0) | 57.0 (47.0–69.0) | 54.0 (44.0–63.0) | 0.042 |
| Comorbidities | ||||||
| CAD | 194 (14.8%) | 7 (6.3%) | 120 (15.0%) | 56 (16.9%) | 11 (15.1%) | 0.053 |
| Hypertension | 473 (36.0%) | 19 (17.0%) | 281 (35.2%) | 134 (40.4%) | 39 (53.4%) | < 0.001 |
| Diabetes mellitus | 318 (24.2%) | 15 (13.4%) | 192 (24.1%) | 84 (25.3%) | 27 (37.0%) | 0.003 |
| CVA | 73 (5.6%) | 8 (7.1%) | 47 (5.9%) | 15 (4.5%) | 3 (4.1%) | 0.641 |
| Lung disease | 84 (6.4%) | 22 (19.6%) | 41 (5.1%) | 17 (5.1%) | 4 (5.5%) | < 0.001 |
| Renal disease | 100 (7.6%) | 6 (5.4%) | 57 (7.1%) | 28 (8.4%) | 9 (12.3%) | 0.297 |
| BMI, kg/m2, median (IQR) | 23.36 (20.96–25.71) | 17.30 (16.26–17.96) | 22.48 (20.76–23.76) | 26.49 (25.71–27.72) | 32.67 (31.06–35.23) | < 0.001 |
| Arrest location | 0.366 | |||||
| Home/residence | 674 (51.3%) | 64 (57.1%) | 410 (51.4%) | 160 (48.2%) | 40 (54.8%) | |
| Other | 641 (48.7%) | 48 (42.9%) | 388 (48.6%) | 172 (51.8%) | 33 (45.2%) | |
| Initial arrest rhythm | 0.012 | |||||
| Shockable | 443 (33.7%) | 26 (23.2%) | 270 (33.8%) | 128 (38.6%) | 19 (26.0%) | |
| Non-shockable | 872 (66.3%) | 86 (76.8%) | 528 (66.2%) | 204 (61.4%) | 54 (74.0%) | |
| Witnessed collapse | 910 (69.2%) | 74 (66.1%) | 534 (66.9%) | 247 (74.4%) | 55 (75.3%) | 0.047 |
| Bystander CPR | 810 (61.6%) | 66 (58.9%) | 486 (60.9%) | 210 (63.3%) | 48 (65.8%) | 0.701 |
| Arrest etiology | 0.001 | |||||
| Cardiac etiology | 812 (61.7%) | 52 (46.4%) | 492 (61.7%) | 226 (68.1%) | 42 (57.5%) | |
| Other etiology | 503 (38.3%) | 60 (53.6%) | 308 (38.3%) | 106 (31.9%) | 31 (42.5%) | |
| Anoxic time, minutes, median (IQR) | 3.0 (17.0–43.0) | 24.0 (13.0–38.0) | 28.0 (17.0–40.0) | 28.0 (17.0–43.0) | 32.0 (15.0–45.5) | 0.047 |
| TTM methods | 0.756 | |||||
| Surface cooling | 1178 (89.6%) | 99 (88.4%) | 717 (89.8%) | 296 (89.2%) | 66 (90.4%) | |
| Intravascular cooling | 122 (9.3%) | 13 (11.6%) | 71 (8.9%) | 31 (9.3%) | 7 (9.6%) | |
| Other | 15 (1.1%) | 0 (0.0%) | 10 (1.3%) | 5 (1.5%) | 0 (0.0%) | |
| TTM target temperature | 0.275 | |||||
| 33°C | 1035 (78.7%) | 85 (75.9%) | 618 (77.4%) | 272 (81.9%) | 60 (82.2%) | |
| 36°C | 280 (21.3%) | 27 (24.1%) | 180 (22.6%) | 60 (18.1%) | 13 (17.8%) | |
| Induction time, hours, median (IQR) | 2.33 (1.08–4.25) | 1.42 (1.00–3.50) | 2.25 (1.07–4.19) | 3.00 (1.83–4.92) | 3.75 (1.58–5.88) | < 0.001 |
| Rewarming time, hours, median (IQR) | 13.67 (9.27–16.00) | 13.00 (8.63–17.80) | 13.5 (9.00–16.00) | 14.00 (10.00–16.00) | 14.00 (8.50–16.00) | 0.226 |
| ROSC to TTM, hours, median (IQR) | 3.42 (2.17–4.87) | 3.62 (2.18–7.21) | 3.45 (2.17–5.06) | 3.33 (2.20–4.73) | 4.00 (2.83–5.13) | 0.466 |
| ROSC to target, hours, median (IQR) | 6.20 (4.28–9.00) | 5.42 (3.89–7.38) | 6.22 (4.26–9.00) | 6.83 (4.88–9.72) | 7.13 (4.93–10.10) | < 0.001 |
| Shock after ROSC | 638 (48.5%) | 41 (36.6%) | 396 (49.8%) | 164 (49.5%) | 37 (50.7%) | 0.067 |
| Reperfusion | < 0.001 | |||||
| None | 831 (63.2%) | 91 (81.3%) | 495 (62.0%) | 204 (61.4%) | 41 (56.2%) | |
| CAG only | 285 (21.7%) | 13 (11.6%) | 191 (23.9%) | 62 (18.7%) | 19 (26.0%) | |
| CAG with PCI | 199 (15.1%) | 8 (7.1%) | 112 (14.0%) | 66 (19.9%) | 13 (17.8%) | |
| tPA | 40 (3.0%) | 3 (2.7%) | 24 (3.0%) | 12 (3.6%) | 1 (1.4%) | 0.774 |
| ECMO | 59 (4.5%) | 2 (1.8%) | 35 (4.4%) | 18 (5.4%) | 4 (5.5%) | 0.428 |
| Adverse events | ||||||
| Seizure | 316 (24.0%) | 39 (34.8%) | 194 (24.3%) | 66 (19.9%) | 17 (23.3%) | 0.016 |
| Bleeding | 64 (4.9%) | 5 (4.5%) | 40 (5.0%) | 17 (5.1%) | 2 (2.7%) | 0.96 |
| Pneumonia | 512 (38.9%) | 55 (49.1%) | 304 (38.1%) | 127 (38.3%) | 26 (35.6%) | 0.138 |
| Sepsis | 153 (11.6%) | 15 (13.4%) | 89 (11.2%) | 39 (11.7%) | 10 (13.7%) | 0.844 |
| Rearrest | 248 (18.9%) | 15 (13.4%) | 140 (17.5%) | 74 (22.3%) | 19 (26.0%) | 0.044 |
| Six-month neurologic outcome | 0.111 | |||||
| Good outcome | 410 (31.2%) | 25 (22.3%) | 262 (32.8%) | 104 (31.3%) | 19 (26.0%) | |
| Poor outcome | 905 (68.8%) | 87 (77.7%) | 536 (67.2%) | 228 (68.7%) | 54 (74.0%) | |
| Six-month mortality | 541 (41.1%) | 39 (34.8%) | 342 (42.9%) | 135 (40.8%) | 25 (34.2%) | 0.234 |
Abbreviations: IQR interquartile range, CAD coronary artery disease, CVA cerebrovascular accident, BMI body mass index, CPR cardiopulmonary resuscitation, TTM targeted temperature management, ROSC return of spontaneous circulation, CAG coronary angiography, PCI percutaneous coronary intervention, tPA tissue plasminogen activator, ECMO extracorporeal membrane oxygenation.
Continuous variables are presented as the median (Q1-Q3) and tested by using the Kruskal-Wallis test, and categorical variables are presented as N (%) and tested by using the chi-squared test.
*p < 0.05 was significant.
The primary neurologic outcomes were compared by BMI classification. Of the 1,315 included patients, 6 months after cardiac arrest, 905 patients had poor neurologic outcomes, and 541 patients had not survived. The number and frequency of patients in each BMI group are presented in Table 1. The results showed no significant difference among the groups.
Univariate and multivariate logistic regression analyses were performed to identify the relationship between BMI and the primary outcome (Table 2). In the univariate analysis, underweight patients were more frequently associated with poor neurologic outcomes (odds ratio [OR], 1.701; 95% confidence interval [CI], 1.065–2.718, p = 0.026). Both univariate and multivariate analyses revealed significant associations with age, initial arrest rhythm, arrest etiology, anoxic time and shock after ROSC. However, in the multivariate logistic regression analysis, we found that BMI was not associated with poor neurologic outcome after adjusting for sex, age, history of hypertension, diabetes mellitus, lung disease, renal disease, arrest location, initial arrest rhythm, witnessed collapse, bystander CPR, arrest etiology, anoxic time, shock after ROSC.
Table 2. Univariate and multivariate logistic regression analysis for poor neurologic outcome at 6 months.
| Variables | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| Unadjusted OR | 95% CI for the OR | p-value | Adjusted OR | 95% CI for the OR | p-value | |
| Sex (Male/Female) | 0.602 | 0.459–0.790 | < 0.001 | 0.778 | 0.535–1.131 | 0.189 |
| Age (per years) | 1.032 | 1.024–1.039 | < 0.001 | 1.044 | 1.031–1.057 | < 0.001 |
| Comorbidities | ||||||
| Coronary heart disease | 1.263 | 0.917–1.739 | 0.154 | |||
| Hypertension | 1.545 | 1.202–1.987 | 0.001 | 1.037 | 0.705–1.524 | 0.853 |
| Diabetes mellitus | 2.109 | 1.557–2.857 | < 0.001 | 1.299 | 0.838–2.013 | 0.241 |
| CVA | 1.409 | 0.817–2.431 | 0.218 | |||
| Lung disease | 4.026 | 1.996–8.121 | < 0.001 | 0.971 | 0.425–2.218 | 0.945 |
| Renal disease | 2.334 | 1.367–3.987 | 0.002 | 1.242 | 0.625–2.466 | 0.536 |
| Arrest location (Home/Others) | 1.674 | 1.323–2.119 | < 0.001 | 1.092 | 0.784–1.520 | 0.603 |
| Initial arrest rhythm (Shockable/Non-shockable) | 0.089 | 0.068–0.117 | < 0.001 | 0.183 | 0.126–0.264 | < 0.001 |
| Witnessed collapse | 0.32 | 0.238–0.431 | < 0.001 | 0.687 | 0.460–1.026 | 0.066 |
| Bystander CPR | 0.677 | 0.530–0.866 | 0.002 | 1.099 | 0.780–1.550 | 0.589 |
| Cardiac arrest etiology | 0.135 | 0.097–0.187 | < 0.001 | 0.232 | 0.148–0.364 | < 0.001 |
| Time from arrest to ROSC (per minutes) | 1.061 | 1.051–1.071 | < 0.001 | 1.064 | 1.052–1.076 | < 0.001 |
| TTM target temperature (33°C/36°C) | 0.965 | 0.726–1.282 | 0.805 | |||
| ROSC to TTM start time (per hours) | 0.98 | 0.940–1.021 | 0.332 | |||
| ROSC to target temperature time (per hours) | 1 | 0.999–1.001 | 0.513 | |||
| Shock after ROSC (Yes/No) | 2.819 | 2.209–3.597 | < 0.001 | 1.888 | 1.362–2.618 | < 0.001 |
| BMI, kg/m2 | ||||||
| Normal (15.5–24.9) | Reference | - | - | |||
| Underweight (< 18.5) | 1.701 | 1.065–2.718 | 0.026 | 1.668 | 0.903–3.080 | 0.102 |
| Overweight (25.0–29.9) | 1.072 | 0.814–1.411 | 0.622 | 1.444 | 0.981–2.125 | 0.062 |
| Obese (> 30.0) | 1.389 | 0.807–2.392 | 0.236 | 1.421 | 0.684–2.951 | 0.347 |
Abbreviations: OR odds ratio, CI confidence interval, CVA Cerebrovascular accident, CPR cardiopulmonary resuscitation, TTM targeted temperature management, ROSC return of spontaneous circulation, BMI body mass index.
*p<0.05 was significant.
Subgroup analysis showed that BMI had no effect on neurologic outcomes except that underweight patients treated with a target temperature of 33°C tended to have poor neurologic outcomes (odds ratio [OR] 2.090; 95% confidence interval [CI] 1.010–4.325; p = 0.047) (Table 3, Fig 2).
Table 3. Comparison of odds ratios by body mass index group for poor neurologic outcomes based on the target temperature.
| OR | 95% CI for the OR | p-value | ||
|---|---|---|---|---|
| 33°C | Underweight | 2.09 | 1.010–4.325 | 0.047 |
| Overweight | 1.399 | 0.908–2.156 | 0.128 | |
| Obese | 1.964 | 0.827–4.663 | 0.126 | |
| 36°C | Underweight | 0.88 | 0.249–3.112 | 0.843 |
| Overweight | 1.641 | 0.653–4.123 | 0.292 | |
| Obese | 0.519 | 0.100–2.704 | 0.436 | |
Abbreviations: OR odds ratio, CI confidence interval.
*Adjusted sex, age, HTN, DM, lung disease, renal disease, arrest location, initial rhythm, witnessed arrest, bystander CPR, cardiac etiology, TTM shock.
*p<0.05 was significant.
Fig 2. Forest plots of the odds ratios among the BMI groups according to the target temperature.
(A) The odds ratio of 33°C targeted temperature management (B) The odds ratio of 36°C targeted temperature management.
Discussion
The purpose of this study was to investigate the influence of BMI on the neurologic outcome of OHCA survivors treated with TTM. Because obesity is a risk factor for cardiovascular disease and death [2, 3], obese and overweight patients who survive CA are assumed to have a worse prognosis. When comparing characteristics among the BMI groups, our data showed that patients with higher BMIs had a greater incidence of hypertension and diabetes mellitus. Additionally, obese and overweight patients required more time to reach the target temperature. Although these differences existed, neurologic outcome was not associated with BMI in the univariate and multivariate logistic regression analyses. Only in the subgroup analysis between those treated with target temperatures of 33°C and 36°C was underweight status and treatment at 33°C associated with poor neurologic outcomes.
The association between BMI and OHCA outcomes has been the subject of several studies. Our study found that BMI was not associated with poor neurologic outcomes, although higher BMIs increased the incidence of underlying disease and cardiovascular death. More than half of the enrolled patients were initially found to have a nonshockable rhythm, asystole in particular, which may cause poor outcomes in CA [13]. Moreover, the anoxic time from collapse to ROSC was a significant factor in the neurologic outcome of patients who experienced CA [14]. In the KORHN-PRO registry, the median anoxic time was 30 minutes, which contributed considerably to unfavorable neurologic outcomes. In particular, patients in the obese group had more nonshockable rhythms at arrest and much longer total anoxic times than those in the other groups, while witnessed CA was significantly more common in the obese group. Since the relative importance of BMI and other cardiac arrest factors could not be weighted, the effect of BMI on neurologic outcomes might be affected. One possible explanation for this result is that the ischemia-reperfusion process after ROSC leads to systemic inflammatory injury, called sepsis-like syndrome, and affects patient outcomes [15]. In a previous study on sepsis and BMI, adipose tissue was shown to regulate immunity by secreting anti-inflammatory adipokines and providing fuel in acute life-threatening illness [16, 17]. In this registry, there were some demographic factors that could adversely affect the prognosis of obese patients, but for this reason, the neurological prognosis after 6 months may not be significantly different from that of other groups.
Previous studies demonstrated that a higher BMI was related to a delay in induction time for TTM. Yong Hun Jung et al. showed a prolonged induction duration and slower cooling rate in the obese group [18]. These findings were supported by adipose tissue functioning as an insulator and heat trap [19]. Therefore, obese and overweight patients require a relatively extended time to reach the target temperature, and more aggressive and complex cooling methods might be necessary.
In the subgroup analysis, TTM at 33°C in underweight patients with OHCA resulted in unfavorable neurologic outcomes. This might be explained by underweight patients being sensitive to low temperature and in turn achieving poor outcomes. A prior study highlighted that patients with lower BMIs suffered from more cold injury and peripheral vascular disease than patients with higher BMIs due to a shortage of fat tissue [20]. Furthermore, the results were correlated with the obesity paradox and better outcomes with higher BMIs [21]. In a meta-analysis by Ma, Y., 24,822 patients from 7 studies were analyzed to clarify the relationship between BMI and the clinical outcomes of patients who experienced CA. The study found that overweight patients had a favorable neurological prognosis after CA [22]. Gupta stated that obese patients had higher risk-adjusted odds of survival not only in OCHA but also in IHCA (in-hospital cardiac arrest) [23]. These phenomena resulted from adipose tissue providing a metabolic reserve and lipid soluble nutrients during a highly active metabolic state, such as CA and low temperature during TTM [24]. Consequently, when considering TTM at 33°C for underweight patients resuscitated from OHCA, clinicians should pay special attention to their treatment approach to achieve favorable outcomes.
One of the strengths of this study was that the KORHN-PRO registry contained a large number of OHCA patients from multiple centers. In addition, few patients decided to withdraw life support treatment, and the risk of self-fulfilling prophecy could be avoided. Another strength of this study was that all enrolled patients were provided with well-organized TTM and intensive care. This alleviated treatment gaps among hospitals. Consequently, the body temperature of patients was maintained at a constant temperature, and fluctuation was minimized during TTM. To the best of our knowledge, the current study is the first registry study comparing TTM 33°C and 36°C treatments among BMI groups.
This study had several limitations. First, our study is an observational prospective study, and there is a risk of selection bias and residual confounding. Second, even though the TTM protocol was uniform, clinicians set the target temperature of TTM based on individual preferences, which may influence outcomes. Third, only a few obese patients were included, so the effect of the obese group might be underestimated because the number of Asians with a BMI above 30 was small. Fourth, we used BMI as an indicator of underweight and obesity. Because BMI does not consider body fat distribution or distinguish lean body mass from fat mass, supplementary values could have been applied [25].
Conclusions
By examining the KORHN-PRO registry, we found that BMI was not an independent risk factor for poor neurologic outcomes at 6 months in CA survivors treated with TTM. In addition, aggressive and additional cooling methods should be considered for patients with higher BMIs, and special attention might be needed for underweight patients in the TTM 33 group.
Supporting information
(PDF)
Acknowledgments
The following investigators participated in the Korean Hypothermia Network.Network chair: Seung Pill Choi (The Catholic University of Korea, Eunpyeong St. Mary’s Hospital);principal investigators of each hospital: Kyu Nam Park (The Catholic University of Korea, Seoul St.Mary’s Hospital), Minjung Kathy Chae (Ajou University Medical Center), Won Young Kim (AsanMedical Center), Byung Kook Lee (Chonnam National University Hospital), Dong Hoon Lee (Chung-Ang University Hospital), Tae Chang Jang (Daegu Catholic University Medical Center), Jae Hoon Lee (Dong-A University Hospital), Yoon Hee Choi (Ewha Womans University Mokdong Hospital),Je Sung You (Gangnam Severance Hospital), Young Hwan Lee (Hallym University Sacred HeartHospital), In Soo Cho (Hanil General Hospital), Su Jin Kim (Korea University Anam Hospital), Jong-Seok Lee (Kyung Hee University Medical Center), Yong Hwan Kim (Samsung Changwon Hospital),Min Seob Sim (Samsung Medical Center), Jonghwan Shin (Seoul Metropolitan Government SeoulNational University Boramae Medical Center), Yoo Seok Park (Severance Hospital), Hyung Jun Moon(Soonchunhyang University Hospital Cheonan), Won Jung Jeong (The Catholic University of Korea,St. Vincent’s Hospital), Joo Suk Oh (The Catholic University of Korea, Uijeongbu St. Mary’s Hospital),Seung Pill Choi (The Catholic University of Korea, Yeouido St. Mary’s Hospital), Kyoung-Chul Cha(Wonju Severance Christian Hospital).
Data Availability
All relevant data are within the manuscript and its Supporting Information files.
Funding Statement
The author(s) received no specific funding for this work.
References
- 1.Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of Obesity and Severe Obesity Among Adults: United States, 2017–2018. NCHS Data Brief. 2020:1–8. [PubMed] [Google Scholar]
- 2.Lavie CJ, Alpert MA, Arena R, Mehra MR, Milani RV, Ventura HO. Impact of obesity and the obesity paradox on prevalence and prognosis in heart failure. JACC Heart Fail. 2013;1:93–102. doi: 10.1016/j.jchf.2013.01.006 [DOI] [PubMed] [Google Scholar]
- 3.Ortega-Loubon C, Fernandez-Molina M, Singh G, Correa R. Obesity and its cardiovascular effects. Diabetes Metab Res Rev. 2019;35:e3135. doi: 10.1002/dmrr.3135 [DOI] [PubMed] [Google Scholar]
- 4.Pepper DJ, Demirkale CY, Sun JF, Rhee C, Fram D, Eichacker P, et al. Does Obesity Protect Against Death in Sepsis? A Retrospective Cohort Study of 55,038 Adult Patients. Critical Care Medicine. 2019;47:643–50. doi: 10.1097/CCM.0000000000003692 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Mewes C, Bohnke C, Alexander T, Buttner B, Hinz J, Popov AF, et al. Favorable 90-Day Mortality in Obese Caucasian Patients with Septic Shock According to the Sepsis-3 Definition. Journal of Clinical Medicine. 2020;9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Flegal KM, Kit BK, Orpana H, Graubard BI. Association of All-Cause Mortality With Overweight and Obesity Using Standard Body Mass Index Categories A Systematic Review and Meta-analysis. Jama-J Am Med Assoc. 2013;309:71–82. doi: 10.1001/jama.2012.113905 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Veronese N, Cereda E, Solmi M, Fowler SA, Manzato E, Maggi S, et al. Inverse relationship between body mass index and mortality in older nursing home residents: a meta-analysis of 19,538 elderly subjects. Obes Rev. 2015;16:1001–15. doi: 10.1111/obr.12309 [DOI] [PubMed] [Google Scholar]
- 8.Jung YH, Lee BK, Lee DH, Lee SM, Cho YS, Jeung KW. The association of body mass index with outcomes and targeted temperature management practice in cardiac arrest survivors. American Journal of Emergency Medicine. 2017;35:268–73. doi: 10.1016/j.ajem.2016.10.070 [DOI] [PubMed] [Google Scholar]
- 9.Breathett K, Mehta N, Yildiz V, Abel E, Husa R. The impact of body mass index on patient survival after therapeutic hypothermia after resuscitation. American Journal of Emergency Medicine. 2016;34:722–5. doi: 10.1016/j.ajem.2015.12.077 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Geri G, Savary G, Legriel S, Dumas F, Merceron S, Varenne O, et al. Influence of body mass index on the prognosis of patients successfully resuscitated from out-of-hospital cardiac arrest treated by therapeutic hypothermia. Resuscitation. 2016;109:49–55. doi: 10.1016/j.resuscitation.2016.09.011 [DOI] [PubMed] [Google Scholar]
- 11.Lee H, Oh J, Kang H, Lim TH, Ko BS, Choi HJ, et al. Association between the body mass index and outcomes of patients resuscitated from out-of-hospital cardiac arrest: a prospective multicentre registry study. Scand J Trauma Resus. 2021;29. doi: 10.1186/s13049-021-00837-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Leary M, Cinousis MJ, Mikkelsen ME, Gaieski DF, Abella BS, Fuchs BD. The association of body mass index with time to target temperature and outcomes following post-arrest targeted temperature management. Resuscitation. 2014;85:244–7. doi: 10.1016/j.resuscitation.2013.10.027 [DOI] [PubMed] [Google Scholar]
- 13.Lemiale V, Dumas F, Mongardon N, Giovanetti O, Charpentier J, Chiche JD, et al. Intensive care unit mortality after cardiac arrest: the relative contribution of shock and brain injury in a large cohort. Intensive Care Med. 2013;39:1972–80. doi: 10.1007/s00134-013-3043-4 [DOI] [PubMed] [Google Scholar]
- 14.Grimaldi D, Dumas F, Perier MC, Charpentier J, Varenne O, Zuber B, et al. Short- and long-term outcome in elderly patients after out-of-hospital cardiac arrest: a cohort study. Crit Care Med. 2014;42:2350–7. doi: 10.1097/CCM.0000000000000512 [DOI] [PubMed] [Google Scholar]
- 15.Adrie C, Adib-Conquy M, Laurent I, Monchi M, Vinsonneau C, Fitting C, et al. Successful cardiopulmonary resuscitation after cardiac arrest as a "sepsis-Like" syndrome. Circulation. 2002;106:562–8. doi: 10.1161/01.cir.0000023891.80661.ad [DOI] [PubMed] [Google Scholar]
- 16.Ahima RS. Adipose tissue as an endocrine organ. Obesity. 2006;14:242–9. doi: 10.1038/oby.2006.317 [DOI] [PubMed] [Google Scholar]
- 17.Zeve D, Tang W, Graff J. Fighting Fat with Fat: The Expanding Field of Adipose Stem Cells. Cell Stem Cell. 2009;5:472–81. doi: 10.1016/j.stem.2009.10.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Jung YH, Lee BK, Lee DH, Lee SM, Cho YS, Jeung KW. The association of body mass index with outcomes and targeted temperature management practice in cardiac arrest survivors. Am J Emerg Med. 2017;35:268–73. doi: 10.1016/j.ajem.2016.10.070 [DOI] [PubMed] [Google Scholar]
- 19.Speakman JR. Obesity and thermoregulation. Handb Clin Neurol. 2018;156:431–43. doi: 10.1016/B978-0-444-63912-7.00026-6 [DOI] [PubMed] [Google Scholar]
- 20.Min JY, Choi YS, Lee HS, Lee S, Min KB. Increased cold injuries and the effect of body mass index in patients with peripheral vascular disease. BMC Public Health. 2021;21:294. doi: 10.1186/s12889-020-09789-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Lavie CJ, De Schutter A, Parto P, Jahangir E, Kokkinos P, Ortega FB, et al. Obesity and Prevalence of Cardiovascular Diseases and Prognosis-The Obesity Paradox Updated. Prog Cardiovasc Dis. 2016;58:537–47. doi: 10.1016/j.pcad.2016.01.008 [DOI] [PubMed] [Google Scholar]
- 22.Ma Y, Huang L, Zhang L, Yu H, Liu B. Association between body mass index and clinical outcomes of patients after cardiac arrest and resuscitation: A meta-analysis. Am J Emerg Med. 2018;36:1270–9. doi: 10.1016/j.ajem.2018.03.079 [DOI] [PubMed] [Google Scholar]
- 23.Gupta T, Kolte D, Mohananey D, Khera S, Goel K, Mondal P, et al. Relation of Obesity to Survival After In-Hospital Cardiac Arrest. Am J Cardiol. 2016;118:662–7. doi: 10.1016/j.amjcard.2016.06.019 [DOI] [PubMed] [Google Scholar]
- 24.Sakr Y, Elia C, Mascia L, Barberis B, Cardellino S, Livigni S, et al. Being overweight or obese is associated with decreased mortality in critically ill patients: a retrospective analysis of a large regional Italian multicenter cohort. J Crit Care. 2012;27:714–21. doi: 10.1016/j.jcrc.2012.08.013 [DOI] [PubMed] [Google Scholar]
- 25.Antonopoulos AS, Tousoulis D. The molecular mechanisms of obesity paradox. Cardiovasc Res. 2017;113:1074–86. doi: 10.1093/cvr/cvx106 [DOI] [PubMed] [Google Scholar]