ISSN: 1885-5857 Impact factor 2023 7.2
Vol. 69. Num. 10.
Pages 990-992 (October 2016)

Scientific letter
Prognostic Effect of Body Mass Index in Patients With an Implantable Cardioverter-defibrillator for Primary Prevention of Sudden Death

Influencia del índice de masa corporal en el pronóstico de pacientes con desfibrilador automático implantable en prevención primaria de muerte súbita

María Cristina González-CambeiroaMoisés Rodríguez-MañerobAlba Abellas-SequeiroscJosé Moreno-ArribasdDavid Filgueira-RamaeJosé Ramón González-Juanateyb

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To the Editor,

Implantable cardioverter-defibrillators (ICD) are an important therapeutic option for patients with heart diseases that confer a high risk of sudden death (SD).1,2 Randomized studies have demonstrated that ICD implantation in patients with heart failure (HF) and severe ventricular dysfunction reduces mortality.

In addition, the prevalence of obesity has increased notably in recent years. Several studies have demonstrated an association between obesity and overweight and the presence of cardiovascular disease such as ischemic heart disease, HF, and SD. However, recent studies have found a paradoxically favorable prognosis for several diseases (such as HF, ischemic heart disease, atrial fibrillation, and diabetes mellitus)3–6 in patients who are overweight or obese, with lower cardiovascular hospitalization and lower total and cardiovascular mortality. However, prognosis as a function of body mass index (BMI) is unknown for patients with HF and a primary prevention ICD.

We designed a multicenter retrospective study, which was conducted in 15 Spanish hospitals with experience in the field of ICD implantation and follow-up. We enrolled 1174 patients who had received a primary prevention ICD between 2008 and 2014. Eleven patients were lost to follow-up. Only patients with a BMI measurement at the time of ICD implantation were considered; therefore, the final population was 651 patients.

In the study population, 135 individuals had a normal BMI, 283 were overweight, and 233 were obese. The baseline patient characteristics for each group are shown in the Table. The mean age was 61.70 ± 11.13 years, and 120 (18.4%) were women. The mean BMI was 28.37 (range, 18.5-55.36). Of the patients studied, 35.79% were obese, and 79.26% were obese or overweight. Patients with a higher BMI had a higher prevalence of hypertension, diabetes mellitus, dyslipidemia, and obstructive sleep apnea. No significant differences were found between BMI groups regarding treatment with lipid-lowering therapy, beta-blockers, angiotensin-converting enzyme inhibitors, anticoagulants, aldosterone antagonists, amiodarone, and digoxin.

Table.

Demographic Characteristics

Characteristics  BMI < 25 (n = 135)  BMI 25–30 (n = 283)  BMI ≥ 30 (n = 233)  P 
Age, y  61 ± 11.94  62 ± 11.01  61 ± 10.80  .374 
Women  25 (18.50)  47 (16.65)  48 (20.60)  .508 
Hypertension  72 (53.30)  178 (62.90)  163 (70.00)  .006 
Diabetes mellitus  36 (26.70)  77 (27.20)  87 (37.30)  .024 
Dyslipidemia  55 (40.70)  135 (47.70)  129 (55.40)  .022 
Smoking  37 (27.40)  81 (28.60)  72 (30.90)  .748 
COPD  11 (8.10)  33 (11.70)  35 (15.00)  .143 
OSAS  2 (1.50)  14 (4.90)  20 (8.60)  .014 
Peripheral arterial disease  9 (6.70)  30 (10.60)  17 (7.30)  .274 
CVE/TIA  7 (5.20)  27 (9.50)  12 (5.20)  .097 
Cancer  6 (4.40)  8 (2.80)  7 (3.00)  .663 
GFR by MDRD, mL/min/1.74 m2  76.20 ± 32.57  75.01 ± 23.52  74.11 ± 27.16  .778 
LVEF, %  26.00 [10-60]  25.50 [10-62]  26.27 [10-72]  .389 
Sinus rhythm  108 (80.00)  220 (77.70)  171 (73.40)  .088 
AF  42 (31.10)  93 (32.90)  96 (41.20)  .064 
Heart rate, bpm  70 [40-133]  67 [30-126]  70 [35-139]  .016 
Ischemic heart disease  78 (57.80)  164 (58.00)  121 (51.90)  .340 
NYHA III-IV  56 (41.50)  93 (32.90)  89 (38.20)  .188 
QRS duration, ms  124 [78-219]  125 [80-210]  120 [80-210]  .939 
QRS > 120 ms  76 (56.30)  148 (52.30)  121 (51.90)  .578 
Hemoglobin, g/dL  13.30 [9.00-17.80]  13.65 [8.40-17.80]  13.90 [9.60-17.40]  .024 
NT-proBNP, pg/mL  2535 [78-21118]  1276 [116-13 706]  1426 [13-19 098]  .035 
Digoxin  21 (15.60)  41 (14.50)  37 (15.90)  .901 
Beta-blockers  117 (86.70)  239 (84.50)  206 (88.40)  .424 
Calcium channel blockers  4 (3.00)  4 (1.40)  11 (4.70)  .085 
Amiodarone  81 (60.00)  186 (65.70)  134 (57.50)  .147 
Aldosterone antagonists  68 (50.40)  133 (47.00)  109 (46.80)  .771 
ACEI  123 (91.10)  249 (88.00)  212 (91.00)  .447 
Statins  81 (60.00)  186 (65.70)  134 (57.50)  .147 
Antiplatelet agents  70 (51.90)  151 (53.40)  124 (53.20)  .956 
Anticoagulants  38 (28.10)  95 (33.60)  86 (36.90)  .230 
Cardiovascular admission  34 (25.20)  70 (24.70)  54 (23.20)  .878 
Inappropriate shocks  7 (5.20)  31 (11.00)  22 (9.40)  .158 
Appropriate therapies (shocks and/or discharge)  20 (14.81)  53 (18.72)  49 (21.03)  .339 
Electrical storm  1 (0.70)  12 (4.20)  8 (3.40)  .162 
CRT responders  31 (26.9)  78 (27.8)  81 (34.5)  .171 
CRT hyperresponders  5 (3.70)  16 (5.70)  13 (5.50)  .680 
CRT nonresponders  23 (17.2)  29 (13.94)  30 (12.8)  .510 
Mortality  24 (17.80)  46 (16.30)  34 (14.60)  .713 

ACEI, angiotensin converting enzyme inhibitor; AF, atrial fibrillation; BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRT, cardiac resynchronization therapy; CVE, cerebrovascular event; GFR, glomerular filtration rate; LVEF, left ventricular ejection fraction; MDRD, modification of diet in renal disease; NT-proBNP, N-terminal pro-brain natriuretic peptide; NYHA, New York Heart Association functional class; OSAS, obstructive sleep apnea syndrome; TIA, transient ischemic attack.

Values are expressed as no. (%), mean ± standard deviation or mean [interquartile range].

During the 8.65 ± 0.34 years of follow-up, 104 deaths (16%) were registered. Specifically, 24 patients (17.80%) with a normal BMI, 46 (16.30%) overweight patients, and 34 (14.60%) obese patients died. No differences were observed between the 3 groups regarding the number of hospital admissions. The response to cardiac resynchronization therapy was also similar between groups. No differences were found in terms of appropriate shocks, inappropriate shocks, or electrical storms. Likewise, the Kaplan-Meier survival curves showed no differences in mortality for obese and overweight patients vs normal weight patients (Figure).

Figure.

Kaplan-Meyer survival curves according to body mass index.

(0.09MB).

The parameters shown to be predictors of mortality included age, valve disease, heart rate > 70 bpm, anemia (hemoglobin < 13 mg/dL), dyslipidemia, female sex, atrial fibrillation, left ventricular dysfunction (left ventricular ejection fraction < 25%), and renal failure (creatinine > 1.3 mg/dL). No relationship was found between BMI and mortality.

On multivariable analysis, there were no differences in mortality between the overweight and obese subgroups (overweight, hazard ratio [HR] = 0.94; 95% confidence interval [95%CI], 0.57-1.54; P = .805; obesity, HR = 0.837; 95%CI, 0.49-1.42; P = .507). Similarly, there were no differences in the number of admissions for cardiovascular causes (obesity, HR = 0.986; 95%CI, 0.547-1.468; P = .663; overweight, HR = 0.981; 95%CI, 0.611-1.575; P = .936).

The conclusion drawn from this study, based on BMI analysis, is that obesity and overweight show no prognostic differences compared with normal weight for cardiovascular mortality, cardiovascular hospitalization, and appropriate and inappropriate therapies in this population of patients with HF and an ICD implant for primary prevention of SD.

However, the interpretation of these study results should take into account the limitations of the study. First, the conclusions are drawn from BMI analysis, which does not differentiate body fat from lean body mass. Second, we did not analyze distribution of body weight (peripheral vs abdominal) or other measurements of adiposity such as body fat percentage. In addition, no information was available on the proinflammatory and nutritional status of the study population. Furthermore, the available information on BMI was taken from the time of implantation only; therefore, possible changes in this parameter at follow-up were not considered. Lastly, the retrospective design of the study increased the risk of bias.

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