1. Please check and comment entries here.
Table of Contents

    Topic review

    Body Mass Index

    Subjects: Emergency Medicine
    View times: 6
    Submitted by: Jaehoon Oh

    Definition

    Increased body mass index (BMI) is a risk factor for cardiovascular disease, stroke, and metabolic diseases. A high BMI may affect outcomes of post-cardiac arrest patients, but the association remains debatable.

    1. Introduction

    High BMI may affect the outcomes of post-cardiac arrest patients. Oxygen consumption, carbon dioxide production, and the risk of rapid desaturation are increased in obese patients [1]. Surgical airway and endotracheal intubation as well as bag-mask ventilation can be difficult in obese patients [2]. Proper hand position and compression depth for high-quality chest compressions may be changed in obese cardiac arrest patients [3]. All of these factors may influence the outcomes of obese cardiac arrest patients compared with those in patients with a normal BMI.

    However, previous evidence has shown a U- or J-shaped relationship between better outcomes and BMI, termed the “obesity paradox” [4][5]. There is still a debate on the association between BMI and the outcomes in cardiac arrest patients [4][5]. Additionally, in the underweight population, increased risk of poor prognosis after cardiovascular disease may be associated with various clinical factors, such as aging, sarcopenia, and poor nutritional status [6]. Several previous studies have reported that a low BMI (underweight patient) is associated with poor outcomes in critically ill and post-cardiac-arrest patients [7][8][9].

    Recent meta-analyses have shown an impact of BMI on the outcomes of post-cardiac-arrest patients, including both in-hospital (IHCA) and out-of-hospital cardiac arrests (OHCA) [10][11]. However, IHCA and OHCA patients differ in age, comorbidities, initial shockable rhythm, and time to resuscitation [12]. Based on these differences, IHCA patients have better outcomes than OHCA patients [12]. Ma et al. showed no difference in survival conditions with OHCA in the subgroup analysis between overweight or obese patients and normal-weight patients [11]. However, previous meta-analysis studies have various errors in data collection and meta-analysis procedures; for example, in one study, both IHCA and OHCA patients were recruited for subgroup analysis, the included population had different inclusion criteria, and the association between underweight status and outcomes in OHCA patients was not presented [11]. Moreover, several studies comparing the outcomes between BMI categories in OHCA patients have been reported recently [13][14].

    Early prediction of outcomes in OHCA patients may help to decide appropriate management plan, such as targeted temperature management (TTM). To the best of our knowledge, a meta-analysis identifying the association between BMI and outcomes in patients with ROSC after OHCA has not been performed yet. Therefore, we focused on and compared the neurological and survival outcomes of OHCA in underweight, normal weight, overweight, and obese patients using a meta-analytical approach.

    2. Testing

    We followed the Meta-analysis of Observational Studies in Epidemiology and the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines [15][16]. The protocol is registered at http://www.crd.york.ac.uk/PROSPERO/ (CRD42021220047).

    The title, abstract, and study type of each study were examined by two reviewers (H.L. and H.S.). The exclusion criteria were as follows: irrelevant population (IHCA patients, patients without sustained ROSC), irrelevant outcome measure, irrelevant control group, reviews, case reports, editorials, conference abstracts, meta-analyses, reports with animal studies, or studies on pediatrics. Duplicate articles were excluded manually after comparing titles, authors, and year of publication of all identified studies. In case of a disagreement between the two reviewers, a third reviewer (J.O.) intervened, and differences were discussed until a consensus was reached. After eliminating the excluded abstracts, we acquired full-text versions of the selected articles, which were rescreened and evaluated more thoroughly for eligibility using the same inclusion and exclusion criteria.

    Two reviewers (H.L. and H.S.) independently extracted the characteristics and results from the selected studies. The following variables were collected from all studies: the name of the first author; year of publication; the country where the study was conducted; inclusion period; study design; study population; number of patients with poor neurological outcome (PNO) and in-hospital mortality; BMI classification; assessment tool of neurological outcome measurement, such as the Cerebral Performance Category (CPC) score; and time points of outcome measurement. Patients were categorized according to the World Health Organization classification as underweight (<18.5 kg/m 2), normal weight (18.5–24.9 kg/m 2), overweight (25.0–29.9 kg/m 2), or obese (≥30 kg/m 2) [17]. The neurological outcome scores were divided into good or poor based on the CPC score (1–2: good neurological outcome; or 3–5: poor neurological outcome). We requested further information concerning the variables of interest that had not been described in the studies from the corresponding author via email. Studies with insufficient data despite contacting the authors were excluded.

    In the main analysis, we examined the association between BMI and outcomes in patients with ROSC after OHCA. The strength of the association between BMI and outcomes was calculated as a risk ratio (RR) with a 95% confidence interval (CI) and presented using forest plots. There are two types of statistical models in meta-analysis: random-effects model and fixed effects model. In this study, the random-effects model was used to synthesize the individual data of the included studies, considering variations from the diversity of countries and inclusion periods [18]. Heterogeneity in meta-analysis occurs when observed intervention effects is more different from each other than one would expect due to random errors alone [19]. To measure and quantify heterogeneity, the proportion of between-study inconsistencies using I 2 tests was estimated, with values of 25%, 50%, and 75% considered to be low, moderate, and high, respectively [20]. Subgroup analysis was performed to investigate the potential causes of heterogeneity based on study quality (high quality study vs. low quality study).

    The main attributes of all the studies are shown in Table 1, and the characteristics of included patients are presented in Supplementary Table S2 . All studies had neurological outcomes as the main outcome measurement, and five studies recorded in-hospital mortality as a patient outcome. The six studies included a total of 2427 patients, with 1639 (67.5%) of those patients having poor neurological outcomes. Of a total of 1910 patients from five studies, 1084 (56.8%) patients had in-hospital mortalities. All the studies assessed neurological outcomes according to the CPC scale. Of the six included studies, five were multicenter, and one was a single-center observational study. The time point of neurological outcome measurement at or after hospital discharge is shown in Table 1.

    Table 1. Study characteristics.

    Author. Year Country Inclusion Period Study Type Sample Size PNO, n (%) Mortality, n (%) BMI Classification Time Point of Neurologic Outcome Assessment
    Aoki 2018 Japan 2012 mPOS 517 36/46 (78.3)
    251/331 (75.8)
    79/108 (73.2)
    30/32 (93.8)
    NR Underweight (<18.5)
    Normal (18.5–24.9)
    Overweight (25.0–29.9)
    Obese (≥30.0)
    1 month
    Bunch 2007 USA 1990–2006 mPOS 213 37/68 (54.4)
    42/78 (53.9)
    35/67 (52.2)
    37/68 (54.4)
    42/78 (53.9)
    35/67 (52.2)
    Low to normal weight (<25)
    Overweight (25–30)
    Obese (>30)
    at hospital discharge
    Chen 2021 USA 2011–2018 mROS 261 6/6 (100)
    60/79 (75.9)
    58/88 (65.9)
    61/88 (69.3)
    5/6 (83.3)
    36/79 (45.6)
    47/88 (53.4)
    56/88 (63.6)
    Underweight (<18.5)
    Normal (18.5–24.9)
    Overweight (25.0–29.9)
    Obese (≥30.0)
    at hospital discharge
    Galatianou 2017 Greece 2014 sPOS 13 0/1 (0.0)
    5/12 (41.7)
    0/1 (0.0)
    5/12 (41.7)
    Normal (<25)
    Elevated BMI (≥25)
    at ICU discharge
    Geri 2016 France 2005–2012 mPOS 818 21/25 (84.0)
    237/352 (67.3)
    164/238 (68.9)
    114/136 (83.8)
    20/27 (74.1)
    227/377 (60.2)
    159/264 (60.2)
    113/150 (75.3)
    Underweight (<18.5)
    Normal (18.5–25)
    Overweight (25–30)
    Obese (>30)
    at ICU discharge
    Lee 2021 Korea 2015–2018 mPOS 605 64/75 (85.3)
    213/333 (64.0)
    107/163 (65.6)
    19/34 (55.9)
    47/75 (62.7)
    157/333 (47.1)
    82/163 (50.3)
    16/34 (47.1)
    Underweight (<18.5)
    Normal (18.5–25)
    Overweight (25–30)
    Obese (≥30)
    at hospital discharge
    Abbreviations, PNO, poor neurologic outcome; BMI, body mass index; TTM, targeted temperature management; mPOS, multi-center prospective observational study; NR, not reported; CPC, cerebral performance categories; sPOS, single-center prospective observational study; ICU, intensive care unit.

     

    Of the six studies included in this study, three studies were rated as low-quality studies using the quality scoring system defined in the Methods section [21][22][23]. The major factors that affected study quality were attrition bias by incomplete outcome data and reporting bias. The risk of bias assessment of the studies is summarized in Supplementary Figure S1 .

    Subgroup analysis was performed according to the quality of study (three studies, high quality; one study, low quality). Neurological outcomes in underweight patients were significantly poorer than those in normal-weight patients in the high-quality studies (RR = 1.29; 95% CI = 1.18–1.42; p < 0.001; I 2 = 0%).

    Additionally, underweight patients had increased mortality (RR = 1.28; 95% CI = 1.12–1.47; p < 0.001; I 2 = 0%) compared with patients who were not underweight (normal-to-obese). Obese patients had decreased mortality (RR = 0.87; 95% CI = 0.76–1.00; p = 0.04; I 2 = 38%) compared with patients who were not obese (underweight-to-overweight). However, there was no significant difference in mortality between underweight-to-normal weight and overweight-to-obese patients ( p = 0.11) ( Figure 5 ).

    The entry is from 10.3390/ijerph18168389

    References

    1. Parker, B.K.; Manning, S.; Winters, M.E. The crashing obese patient. West. J. Emerg. Med. 2019, 20, 323–330.
    2. Moon, T.S.; Fox, P.E.; Somasundaram, A.; Minhajuddin, A.; Gonzales, M.X.; Pak, T.J.; Ogunnaike, B. The influence of morbid obesity on difficult intubation and difficult mask ventilation. J. Anesth. 2019, 33, 96–102.
    3. Lee, J.; Oh, J.; Lim, T.H.; Kang, H.; Park, J.H.; Song, S.Y.; Shin, G.H.; Song, Y. Comparison of optimal point on the sternum for chest compression between obese and normal weight individuals with respect to body mass index, using computer tomography: A retrospective study. Resuscitation 2018, 128, 1–5.
    4. Lavie, C.J.; De Schutter, A.; Milani, R.V. Healthy obese versus unhealthy lean: The obesity paradox. Nat. Rev. Endocrinol. 2015, 11, 55–62.
    5. Chalkias, A.; Xanthos, T. The obesity paradox in cardiac arrest patients. Int. J. Cardiol. 2014, 171, 101–102.
    6. Park, D.; Lee, J.H.; Han, S. Underweight: Another risk factor for cardiovascular disease? Medicine 2017, 96, 1–7.
    7. Flegal, K.M.; Graubard, B.I.; Williamson, D.F.; Gail, M.H. Excess deaths associated with underweight, overweight, and obesity. J. Am. Med. Assoc. 2005, 293, 1861–1867.
    8. Finkielman, J.D.; Gajic, O.; Afessa, B. Underweight is independently associated with mortality in post-operative and non-operative patients admitted to the intensive care unit: A retrospective study. BMC Emerg. Med. 2004, 4, 1–7.
    9. Geri, G.; Savary, G.; Legriel, S.; Dumas, F.; Merceron, S.; Varenne, O.; Livarek, B.; Richard, O.; Mira, J.P.; Bedos, J.P.; 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.
    10. Kakavas, S.; Georgiopoulos, G.; Oikonomou, D.; Karayiannis, D.; Masi, S.; Karlis, G.; Xanthos, T. The impact of body mass index on post resuscitation survival after cardiac arrest: A meta-analysis. Clin. Nutr ESPEN 2018, 24, 47–53.
    11. 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, 1270–1279.
    12. Høybye, M.; Stankovic, N.; Holmberg, M.; Christensen, H.C.; Granfeldt, A.; Andersen, L.W. In-hospital vs. out-of-hospital cardiac arrest: Patient characteristics and survival. Resuscitation 2021, 158, 157–165.
    13. Lee, H.; Oh, J.; Kang, H.; Lim, T.H.; Ko, B.S.; Choi, H.J.; Park, S.M.; Jo, Y.H.; Lee, J.S.; Park, Y.S.; 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. Resusc. Emerg. Med. 2021, 29, 1–11.
    14. Chen, C.T.; Lin, M.C.; Lee, Y.J.; Li, L.H.; Chen, Y.J.; Hou, P.C.; How, C.K. Association between body mass index and clinical outcomes in out-of-hospital cardiac arrest survivors treated with targeted temperature management. J. Chin. Med. Assoc. 2021, 84, 504–509.
    15. Stroup, D.F.; Berlin, J.A.; Morton, S.C.; Olkin, I.; Williamson, G.D.; Rennie, D.; Moher, D.; Becker, B.J.; Sipe, T.A.; Thacker, S.B. Meta-analysis of observational studies in epidemiology: A proposal for reporting. J. Am. Med. Assoc. 2000, 283, 2008–2012.
    16. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. PLoS Med. 2009, 6, e1000100.
    17. Akram, D.S.; Astrup, A.V.; Atinmo, T.; Boissin, J.L.; Bray, G.A.; Carroll, K.K.; Chitson, P.; Chunming, C.; Dietz, W.H.; Hill, J.O.; et al. Obesity: Preventing and managing the global epidemic. World Heal. Organ. Tech. Rep. Ser. 2000, 894, 1–253.
    18. DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control Clin. Trials. 1986, 7, 177–188.
    19. Higgins, J.P.T.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M.J.; Welch, V.A. (Eds.) Cochrane Handbook for Systematic Reviews of Interventions, version 6.2; The Cochrane Collaboration: Cochrane, ON, USA, 2021; Available online: www.training.cochrane.org/handbook (accessed on 20 February 2021).
    20. Higgins, J.P.T.; Thompson, S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 2002, 21, 1539–1558.
    21. Aoki, M.; Hagiwara, S.; Oshima, K.; Suzuki, M.; Sakurai, A.; Tahara, Y.; Nagao, K.; Yonemoto, N.; Yaguchi, A.; Morimura, N. Obesity was associated with worse neurological outcome among Japanese patients with out-of-hospital cardiac arrest. Intensive Care Med. 2018, 44, 665–666.
    22. Galatianou, I.; Karlis, G.; Apostolopoulos, A.; Intas, G.; Chalari, E.; Gulati, A.; Iacovidou, N.; Chalkias, A.; Xanthos, T. Body mass index and outcome of out-of-hospital cardiac arrest patients not treated by targeted temperature management. Am. J. Emerg. Med. 2017, 35, 1247–1251.
    23. Bunch, T.J.; White, R.D.; Lopez-Jimenez, F.; Thomas, R.J. Association of body weight with total mortality and with ICD shocks among survivors of ventricular fibrillation in out-of-hospital cardiac arrest. Resuscitation 2008, 77, 351–355.
    More