Obesity Surgery, 14, 275-281

Increased Body Mass Index and Peri-operative Risk in Patients Undergoing Non-cardiac Surgery Joachim Klasen, Dr med; Axel Junger, PD Dr med; Bernd Hartmann, Dr med; Andreas Jost; Matthias Benson, PD Dr med; Tsovinar Virabjan; Gunter Hempelmann, Professor Dr med Dr hc Department of Anesthesiology, Intensive Care Medicine, and Pain Management University Hospital Giessen, Germany Background: Increased BMI is a well known risk factor for morbidity and mortality in hospitalized nonsurgical patients. However, the published evidence for a comparable effect in surgical patients is scarce. Methods: This retrospective study was designed to assess the attributable effects of increased BMI (>30 kg/m 2) on outcome (hospital mortality, admission to the intensive care unit (ICU), and incidence of intraoperative cardiovascular events (CVE)) in patients undergoing non-cardiac surgery by a computerized anesthesia record-keeping system. The study is based on data-sets of 28,065 patients. Cases were defined as patients with BMI >30; controls (BMI 20-25) were automatically selected according to matching variables (ASA physical status, high risk and urgency of surgery, age and sex) in a stepwise fashion. Differences in outcome measures were assessed using univariate analysis. Stepwise regression models were developed to predict the impact of increased BMI on the different outcome measures. Results: 4,726 patients (16.8%) were found with BMI >30. Matching was successful for 41.5% of the cases, leading to 1,962 cases and controls. The crude mortality rates were 1.1% (cases) vs 1.2% (controls); P=0.50, power=0.88). Admission to ICU was deemed necessary in 6.8% (cases) vs 7.5% (controls), P=0.42, Reprint requests to: PD Dr. med. Axel Junger, Abteilung Anaesthesiologie, Intensivmedizin, Schmerztherapie, Universitätsklinikum Giessen, Rudolf-Buchheim-Str. 7, 35392 Giessen, Germany. Fax: +49-641-9944499; e-mail: [email protected] Financial support provided in part by grant from IMESO GmbH (Hüttenberg, Germany). The founding agreement ensured the authors’ independence in designing the study, interpreting the data, writing and publishing the report. PD Dr. M. Benson is a partner in IMESO GmbH and an employee of the University Hospital Giessen. None of the other authors have any financial interest in the subject matter, materials, or equipment discussed or in competing materials. © FD-Communications Inc.

power=0.65, and CVE were detected from the database in 22.3% (cases) vs 21.6% (controls), P=0.30, power=0.60. Using logistic regression analyses, no significant association between higher BMI and outcome measures could be verified. Conclusion: Increased BMI alone was not a factor leading to an increased perioperative risk in non-cardiac surgery. This fact may be due to an elevated level of attention while caring for obese patients.

Key words: Anesthesia, obesity, risk factors, outcome, computers

Introduction Body mass index (BMI) is a simple measure of nutritional status which is known to be associated with increased overall mortality.1,2 However, the impact of obesity on risk and outcome of hospitalized and especially surgical patients remains controversial.3,4 Whereas some authors found an increased risk for perioperative events in obese patients, 5,6 none of the established perioperative risk indices7-9 considers an increased BMI as an independent risk factor or predictor of morbidity or mortality. We designed a matched case-control study to utilize the resources of our computerized anesthesia record-keeping system to assess the effects attributable to increased BMI (>30 kg/m2) on hospital mortality, prolonged hospital length of stay, admission to the intensive care unit (ICU), and the incidence of intraoperative cardiovascular events in patients undergoing non-cardiac surgery. Obesity Surgery, 14, 2004

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Material and Methods The study is based on data-sets of 28,065 patients who underwent non-cardiac surgery at a tertiary care teaching hospital. Data acquisition was performed with an online computerized anesthesia record-keeping system, NarkoData (IMESO GmbH, Hüttenberg, Germany).10,11 Patients under age 18 were excluded from this study. The record-keeping system collects all data relevant to anesthesia during the procedure, including biometrical data, administered drugs, laboratory results, vital data and the data-set, for quality assurance according to the German Society of Anesthesiology and Intensive Care Medicine (DGAI).12 Systolic (SAP), diastolic (DAP) and mean arterial blood-pressure (MAP), as well as heart rate (HR) are recorded online at least every 5 minutes using non-invasive measurement and every 3 minutes using invasive measurement. Any drugs applied are entered manually at the moment of administration. All patient-related data collected during the preoperative ward round, informed consent of the patient, results of clinical examination, and additional investigations are recorded by the anesthesiologist in the electronic anesthesia record on the day preceding an elective procedure, and data of emergency procedures are assessed immediately before the operation. On termination of the anesthesia procedure, files are imported into the database (Oracle 7®, Oracle Corp, Redwood Shores, CA, USA) after running through plausibility and integrity checks. All data-fields used for this study were mandatory and provided improved data quality. Patients undergoing elective or urgent surgery were asked their current height and weight as part of the demographic data. In case of unconsciousness these variables were estimated by the responsible anesthesiologist. In order to evaluate the impact of increased BMI on hospital mortality, length of stay, admission to ICU, and incidence of intraoperative cardiovascular events, the method of matched pairs was used. Cases were defined as patients with BMI >30. Matched controls were automatically selected among all patients of the mentioned data-pool over the study period, according to matching variables. 276 Obesity Surgery, 14, 2004

Control patients were matched if they had a BMI of 20-25. Matching criteria included: • American Society of Anesthesiologists (ASA) physical status 13 • High risk surgery (intracranial, thoracic, abdominal and major vascular surgery) • Urgency of surgery – elective; urgent (surgery within 6 hours after admission); emergency (surgery within 2 hours after admission) • Age • Sex The selection of the matched controls was performed in a stepwise manner, first attempting to match on ASA physical status, then type of surgery, then urgency of surgery, then age, and finally sex; only one control was matched to each case. Hospital mortality, length of stay, and admission to ICU were derived from the hospital information system. Length of hospital stay was considered prolonged if the patient was not discharged home after 21 days (value of the 75%-quartile of the complete study population). Crude mortality ratio was the ratio of the hospital mortality rate in cases divided by the mortality rate in matched controls. Structured query language (SQL queries) were used for retrospective detection of intraoperative cardiovascular events (hypotension, hypertension, bradycardia and tachycardia) out of the database according to the definition of the German Society of Anaesthesiology and Intensive Care Medicine (DGAI).12 Relevant cardiovascular events were defined as follows: • Hypotension: Decrease of MAP >30% within a 10-minute interval and administration of a vasoconstrictor or a positive inotropic drug within 20 minutes after beginning of the decrease (epinephrine, norepinephrine, dopamine, dobutamine, dopexamine, amezinium metilsulfate (Supratonin), cafedrine/theodrenaline (Akrinor‚), enoximone, milrinone). Additional volume administration was not considered. • Hypertension: Increase of MAP >30% within a 10-minute interval and administration of an antihypertensive drug within 20 minutes after beginning of the increase (nifedipine, urapidile, clonidine, hydralazine, droperidol, glyceryl trinitrate, sodium nitroprusside). • Bradycardia: HR <50 min-1 for at least 5 minutes

Increased Body Mass Index and Peri-operative Risk

and intravenous drug administration to increase HR within 15 minutes after beginning of bradycardia (atropine, orciprenaline, ipratropium bromide, epinephrine, or pacemaker). • Tachycardia: HR >100 min-1 for at least 5 minutes and intravenous drug administration to decrease of HR within 15 minutes after beginning of tachycardia (beta blocker, calcium antagonist, cardiac glycoside, sodium channel blocker, (Vaughan Williams, class I), potassium channel blocker (Vaughan Williams class III), cardio-version, defibrillation).

Statistical Analyses Data were exported from the database into the SPSS® statistics program (SPSS Software GmbH, Munich, Germany). Either c2-test or Fisher’s exact test (for independent samples) were used to detect statistically significant differences between case patients and matched controls in outcome variables. Metric variables were compared with the non-parametric Mann-Whitney U-test. Level of significance was set at P<0.05. The power (1–b-failure) was determined using the software GPOWER Version 2.0.14 Since the patients in the case group and the matched control group were not randomly assigned with respect to risk of a BMI >30 kg/m2, we developed logistic regression models using the enter method to predict the impact of an increased BMI on hospital mortality, prolonged length of stay, admission to ICU, and incidence of intraoperative cardiovascular events. Independent variables in the 4 models included all match criteria as well as a BMI >30. Independent variables were analyzed as categories using dummy variables for BMI >30, ASA physical status, high-risk surgery, urgency of surgery, and sex. Age was handled as a continuous variable.

Results In our study, 4,726 patients (16.8%) were found with BMI >30. Matching was successful for 41.5% of the cases, leading to 1,962 cases and controls. The case patients had a mean BMI (±SD) of 34.4 ± 7.4, and the matched control patients had a mean

BMI of 22.8 ± 1.4 (median [range] kg/m2: 32.7 [31.4; 35.5] vs 23.0 [21.6; 24.1]). Mean age of the case patients was 53.9 ± 16.7 years, and of the matched control patients 53.6 ± 16.6 years (median [range] years: 56 [41; 67] vs 55 [40; 66]). The distribution of the categorical matching variables in cases and matched controls is shown in Table 1. The matched control patients had a crude mortality rate of 1.1% (n=22) vs 1.2% (n=23) for the case patients with an increased BMI (P=0.50, power=0.88, Table 2). The crude mortality ratio of cases to controls was 0.92. The mean length of stay was 18.3 ± 21.2 days for the case patients and 17.7 ± 20.4 days for the matched control patients (median [range] d: 11 [7; 22] vs 11 [7; 20]; P=0.46). Admission to ICU was necessary in 6.8% of the patients (n=133), compared with 7.5% of the matched control patients (n=147; P=0.42, power=0.65, Table 2). Case patients had an average ICU length of stay of 4.7 ± 9.8 days and the matched controls an average ICU length of stay of 4.8 ± 8.6 days (median [range] d: 1 [1; 4] vs 1 [1; 3]; P=0.61). At least one adverse intraoperative cardiovascular event was detected in 22.3% of the case patients (n=438) and in 21.6% of the matched control patients (n=423; P=0.30, power=0.60, Table 2). A prolonged length of stay was observed in 12.5% Table 1. Distribution of the categorical matching variables in cases and matched controls Variable

Case Patients n %

Matched Controls n %

Sex Male 984 50.2 917 46.7 Female 978 49.8 1045 53.3 ASA physical status I 275 14.0 275 14.0 II 989 50.4 990 50.5 III 584 29.8 583 29.7 IV 110 5.6 110 5.6 V 4 0.2 4 0.2 Urgency of Surgery Elective 1672 85.2 1664 84.8 Urgent 232 11.8 235 12.0 Emergency 58 3.0 63 3.2 High-risk surgery (intracranial, thoracic, abdominal and major vascular surgery) No 1674 85.3 1674 85.3 Yes 288 14.7 288 14.7

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Klasen et al Table 2. Outcome measures in cases and matched controls

P-values power

Hospital mortality

n

%

Case patients Matched controls

23 22

1.2 1.1

0.50

ICU admission Case patients Matched controls

133 147

6.8 7.5

0.42

0.65

Cardiovascular events Case patients 438 Matched controls 423

22.3 21.6

0.30

0.60

0.45

0.53

Prolonged length of hospital stay Case patients 246 12.5 Matched controls 228 11.6

.88

of the case patients (n=246), compared with 11.6% of the matched case patients (n=228; P=0.45, power=0.53, Table 2). The logistic regression models revealed only two variables that were associated with an increased risk of mortality, ICU admission, cardiovascular events, and prolonged length of hospital stay: ASA physical status and high-risk surgery (P<0.05, Table 3). Paradoxically, in patients undergoing high-risk surgery, a decreased incidence of prolonged length of stay was found (odds ratio=0.75, Table 3). In male patients an increased risk for prolonged length of hospital stay and ICU admission was observed. Older age was associated with an increased incidence of cardiovascular events (P<0.05). Higher BMI was neither associated with an increased risk of mortality nor with a higher risk for other assessed outcome measures (Table 3).

Discussion Obesity increases morbidity, impairs quality of life and is one of the most important causes of mortality.15,16 A number of epidemiological studies have been performed to estimate the impact of obesity on mortality. Stevens et al17 observed that the association between greater BMI and mortality is agedependent. Whereas mortality was increased in obese adults up to the age of 74, this effect declined 278 Obesity Surgery, 14, 2004

Table 3. Results of the logistic regression models using the four outcome measures as dependent and all matched criteria as well as BMI >30 kg/m2 as independent variables Hospital mortality Variables

Age (Years) Male gender BMI class>30 kg/m2 ASA physical status High-risk surgery Urgency of surgery ICU admission Variables

Age (Years) Male gender BMI class>30 kg/m2 ASA physical status High-risk surgery Urgency of surgery

P-value 0.543 0.178 0.787 0.000 0.007 0.143

1.01 [0.99; 1.03] 1.56 [0.82; 300] 1.09 [0.59; 2.03] 6.94 [4.33; 11.13] 2.44 [1.27; 4.68] 1.36 [0.90; 2.04]

odds ratio

95% CI

0.522 0.023 0.440 0.000 0.000 0.435

1.00 1.35 0.90 2.46 3.71 1.01

[0.99; 1.01] [1.04; 1.76] [0.70; 1.17] [2.05; 2.94] [2.82; 4.88] [0.72; 1.15]

odds ratio

95% CI

1.02 1.02 1.04 1.36 2.53 1.01

[1.02; 1.03] [0.87; 1.19] [0.89; 1.22] [1.21; 1.52] [2.08; 3.08] [0.86; 1.20]

0.000 0.851 0.614 0.000 0.000 0.886

Prolonged length of hospital stay Variables P-value odds ratio Age (Years) Male gender BMI class>30 kg/m2 ASA physical status High-risk surgery Urgency of surgery

95% CI

P-value

Cardiovascular events Variables P-value

Age (Years) Male gender BMI class>30 kg/m2 ASA physical status High-risk surgery Urgency of surgery

odds ratio

0.832 0.001 0.432 0.000 0.042 0.149

1.00 1.39 1.08 2.33 0.75 1.15

95% CI

[0.99; 1.01] [1.15; 1.70] [0.89; 1.31] [2.03; 2.67] [0.57; 0.99] [0.95; 1.39]

in older age. Calle and colleagues,18 unlike the aforementioned authors, have found that the risk of death from all causes increases throughout the range of moderate and severe obese conditions for both men and women in all age groups. In longitudinal studies, the association between BMI and mortality

Increased Body Mass Index and Peri-operative Risk

is U-shaped, with an increased risk in the lowest and highest percentiles of the distribution.1 However, only few data exist on the impact of BMI on mortality in hospitalized patients. Landi and colleagues3 studied the data of 18,316 patients to identify the relationship between age, BMI, and mortality. They found an association between BMI and mortality among hospitalized patients, with increased death rates at the lowest and highest BMI rankings in younger patients and an increased death rate at the lowest BMIs with only a slight elevation at the highest BMIs (>35 kg/m2). However the aim of their study was to correlate obesity in conjunction with their sequelae, to age. Consequently, they did not implement a correction factor to control for obesity-related morbidity. The relationship between obesity and mortality may be substantially different between healthy subjects in longitudinal studies and hospitalized patients. Galanos et al19 studied critical care patients and could not reproduce the U-shaped curve. They suggested a protective effect for BMI >30 kg/m2 in certain diseases. The authors’ explanation was that these patients may need a nutritional reserve, or excess BMI, to survive the acute illness and the concomitant aggressive hospital care. However, these authors performed their study in medical patients, and they did not correct for any medical conditions which may be associated with obesity. Comparable to medical patients, obesity commonly is considered as an independent risk factor in surgical patients, but the degree of risk has not been quantified in a precise manner. There is little evidence that excessive body weight in itself should contraindicate general surgery. However, obesity is often associated with abnormal cardiorespiratory and metabolic function and hemostasis, which may predispose to morbidity and mortality after surgery.20-22 Most reports dealing with perioperative events in obese patients were based on small sample sizes or considered special settings and surgical procedures.23-26 In a German quality assurance project, Schwilk et al27 observed that the incidence of major adverse events was almost doubled in obese young patients preoperatively assessed as “young and healthy”, when compared with normal weight patients of the same age. Paradoxically, in the presence of preexisting cardiovascular disease, an

increased BMI had little impact on perioperative adverse events. The authors concluded, that nutritional disorder is an important epidemiological factor in anesthesia. However, none of the mentioned studies on mortality in surgical patients provided a risk stratification in their patients according to the ASA physical status or the type of surgery. Because we wanted to find out whether obesity by itself is an independent risk factor for morbidity or mortality, we designed our study as a matched pairs case control study. There are no published data on the impact of any of these parameters on mortality. We took into account possible risk factors of perioperative adverse events. The ASA classification is well recognized as a simple and reliable predictor of mortality. 28 Furthermore, it is obvious that the type of surgery has a major impact on the surgical risk.9 We tried – comparable to the revised cardiac risk index by Lee et al9 – to make a simple classification of surgical risk as high (intracranial, thoracic, abdominal and major vascular) or low (all other surgical procedures). Other possibly confounding factors considered for our study included urgency of surgery, age and sex. The results of our matched case-control study suggest that an increased BMI alone does not carry a major prognostic implication for patients undergoing non-cardiac surgery. Furthermore, according to our results, a possible increased mortality in obese patients is probably due to preexisting morbidities and the type of surgery. As we did not investigate any other parameters, we cannot conclude that there may be other risk factors potentially compromising perioperative outcome. However, we are not aware of any major additional factor which may have contributed to the results of our study. One additional explanation for our results may be the fact that obesity is obvious for everyone involved in surgery and perioperative care of the patient. As it generally is considered a significant risk factor, this may have led to a higher degree of vigilance, to more timely therapeutic intervention, and to optimization of perioperative management. Limitations of the present study must be addressed. We know, that self-reported height and weight, as well as proxy estimate of the same, may not be optimal. Kuskowska-Wolk and colleagues29 assessed the accuracy of the BMI (kg/m2), which is derived from these data and often considered as a Obesity Surgery, 14, 2004

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reliable indirect estimate of relative body weight. They observed that reliance upon questionnairederived self-reports will lead to considerable underestimation of the prevalence of obesity, although some studies have analyzed the accuracy of selfreported weight and height data in survey studies. 30 These authors found that systematic differences in weight were observed between self-reported and objective values. These differences, however, were of minor importance (underestimation 1.6% in men, and 3.1% in women). However, in case of significant differences between self-reported weight and the clinical impression of the attending anesthesiologist, an objective evaluation is the standard procedure at our intitution. Despite the satisfactory power of the study which found no differences in hospital mortality, the results should be considered cautiously, given the relative low number of deaths in both groups. Secondly, the nature of the study is retrospective, and obesity was quantified by a single calculation at only one time point. Furthermore, we had no direct measure of adiposity or of lean body mass, and we had no measure of central adiposity, such as the waist-to-hip ratio. The BMI cannot distinguish adequately between fat mass and lean tissue mass, and it may be a less useful indicator of adiposity, especially among the older patients who have a greater amount of body fat at a given BMI than younger ones because of age-related declines in muscle mass.31 In conclusion, our results suggest that increased BMI cannot be used as a predictor for perioperative outcome in patients undergoing non-cardiac surgery. Whether this is an effect of higher attention to care of obese patients cannot be answered. The data of this paper are a relevant part of T. Virabjan’s medical doctoral thesis. We thank Moredata GmbH, Giessen, for assistance in data management and statistical evaluation.

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