COMPOSICÃO CORPORAL NA UTI: O que deve preocupar o Intensivista ? XX CBMI Costa do Sauípe, BA Novembro, 2015 Obesidade Sarcopênica: Já sabemos qual seu impacto na UTI? C Sarcopenia e Fraqueza UTI: Tratamento Autofagia Haroldo Falcão R. Cunha Intensivista AMIB Nutrólogo ABRAN SBNPE – Capítulo RJ
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COMPOSICÃO CORPORAL NA UTI: O que deve preocupar
o Intensivista ?
XX CBMI Costa do Sauípe, BA
Novembro, 2015 Obesidade Sarcopênica: Já sabemos qual seu impacto na UTI?
C
Sarcopenia e Fraqueza UTI:
Tratamento
Autofagia
Haroldo Falcão R. Cunha
Intensivista AMIB
Nutrólogo ABRAN
SBNPE – Capítulo RJ
Sem potencial conflito de interesse em relação ao tema proposto
MECANISMOS
PARADOXO DA OBESIDADE
POPULAÇÃO EM GERAL IMC>30
DOENÇAS - ICC - DM - DPOC
Reserva ?
Anti-citocinas
Idade ?
Pickkers et al. Crit Care Med 2013; 41:1878–1883.
Clinical Investigations
www.ccmjournal.org 1881
there were no firm conclusions concerning the effect of BMI on the overall mortality rate. These meta-analyses were statisti-cally very heterogeneous, which indicates the need for caution in interpreting pooled estimates. Importantly, adjustments, for example, severity of illness and age were not applied, and some studies reported more than 30% of the data missing, compared with 7.8% of the patients in our study. Our database of over 150,000 patients now makes it clear that an inverse J-shaped outcome association with BMI for critically ill patients actu-ally exists, and the mortality rate is greatest for patients with cachexia. This rate becomes progressively lower for normal, overweight, and obese patients and only tends to increase again
for patients who are morbidly obese. The optimal BMI for criti-cally ill patients appears to be higher than that of patients whose illness is not critical (19).
There are several limitations inherent to the observational nature of our study. Some may argue that these observational associations are not real phenomena but reflect selection bias with the more severe patients or those with poorer progno-ses. That is to say, due to the presence of, for example, malig-nancies, they have lower BMIs as an epiphenomenon of their poorer outcomes. Furthermore, obese patients who are less ill may be admitted earlier to the ICU because of the need for more nursing staff not available on the ward. This could affect the unadjusted data presented in the previous meta-analyses but appears to be less important in the adjusted data of this study. Although it is likely that unmeasured or residual con-founding factors remain, adjusted odds ratios in our analysis show that BMI influences outcome independently of gender, neoplasm, and severity of disease. Importantly, the distribution of BMI in ICU patients (BMI > 25 kg/m2, 48%; 25–30 kg/m2, 34%; and > 30 kg/m2, 14%) is very similar to that of the gen-eral Dutch population (BMI > 25 kg/m2, 47%; 25–30 kg/m2, 36%; and > 30 kg/m2, 12%) (20, 21), also arguing against bias. In addition, our study shows that reverse causality (e.g., when occult or preexisting diseases [such as cancer] that increase mortality also cause weight loss) does not explain the higher mortality rate of patients with a lower BMI.
Naturally, our study is not suited to explain the mecha-nism of the observed beneficial effect of obesity, and we are not able to determine whether body fat distribution modifies the observed association. Previous studies have shown that after adjustment for BMI, waist circumference and the waist-to-hip ratio were strongly associated with the risk of death for nonhospitalized patients (1), whereas a higher mortality rate was observed for adults with a thigh circumference less than 60 cm (22).
Thus, although obesity is associated with various comorbid conditions, physiologic derangements, physical limitations, and pharmacologic alterations, all of which may complicate acute illness and impede therapeutic measures, obese patients still have a better outcome. This may not be as counterintui-tive as it seems when we consider that, during human evo-lution, a better nutritional state enabled people to overcome periods of physical crisis. Nevertheless, this does not explain the mechanism or nature of the potential protective factors, and the influence of obesity on acute illnesses is still poorly understood. Several explanations have been put forward to explain the apparently beneficial effects of obesity on critically ill patients, including more nutritional reserves, higher levels of anti-inflammatory cytokines (9, 23), and the higher choles-terol and lipid levels common in obese patients, which bind endotoxin during critical illness and provide the precursors for adrenal steroid synthesis (24). In addition, neutrophil dys-function, attenuation of acute lung injury (25), and diaphrag-matic remodeling due to chronically increased weight (26) and chronically increased intra-abdominal pressure (27) are
TABLE 2. Estimated Odds Ratio of Confounding Variables in Logistic Regression Model
Variable Odds Ratio 95% CI
SAPS II, per 10 points 1.57 1.52–1.63
Log (SAPS II +1) 3.36 2.85–3.96
Sex (female) 0.95 0.92–0.98
Age, per 10 yr 1.14 1.13–1.16
Surgical admission 0.74 0.71–0.77
Neoplasm 1.02 0.95–1.08
AIDS 0.51 0.40–0.66
Hematologic malignancy 1.07 0.97–1.19
Immunologic insufficiency 1.31 1.23–1.40
Mechanical ventilation 0.97 0.93–1.00
Calendar time (yr) 0.93 0.92–0.93
Body-mass index (kg/m2)
Relative risk
0.25
0.5
1.0
2.5
5
10
20
10 20 30 40 50
15
20
25
30
Absolute mortality rate (%)
Figure 2. Relative risks for hospital mortality with respect to body mass index (BMI) (kg/m²). Relative risks adjusted for Simplified Acute Physiology Score II, age, gender, admission type, neoplasm, AIDS, hematologic malignancy, immunologic insufficiency, mechanical ventilation, and calendar year. A reference value of BMI = 25.0 was used. The upper lines and lower lines represent 95% CIs for the estimated relative risks.