Journal of Clinical Anesthesia (2016) xx, xxx–xxx
Correspondence Evaluation on preoperative assessment of obese patients☆,☆☆ Editor, Obesity is rising rapidly across developed countries, with the current figures in the United States (35.7%) and United Kingdom (26.1%) expected to double by 2050 [1]. Estimates predict that up to 66% of patients undergoing surgery in the United Kingdom are overweight [2]. Currently, there is limited evidence on preoperative management of obese patients in dedicated high-risk preassessment clinics. Recent published guidelines by the Association of Anaesthetists of Great Britain and Ireland recommended that all obese patients receive preoperative assessment by an anesthetist in high-risk clinics. However, these recommendations are made based on expert opinion. At Queen Elizabeth Hospital Birmingham (QEHB), all patients undergoing surgical procedures are referred by the surgeon to dedicated preassessment clinics and divided into low risk and high risk, corresponding to levels 1, 2A, 2B, and 3, respectively. Low-risk clinics are led by trained preassessment nurses and high-risk clinics by more experienced nursing staff and consultant anesthetists. Currently, patients are risk assessed by surgeons before allocation into these clinics. Hence, we aimed to evaluate service of all obese patients undergoing preoperative assessment for gastrointestinal surgeries retrospectively. Data were collected during routine clinical practice from March 2015 to May 2015 at QEHB. Consecutively, adult patients (≥18 years) with a body mass index (BMI) greater than 30 kg/m2 undergoing gastrointestinal or hepatobiliary surgery were included in the study. Eligible procedures were those involving surgery on any part of the gastrointestinal tract or biliary tree. Patients undergoing day-case urologic, gynecologic, vascular, or transplant procedures were excluded. The primary outcome measure was evaluating allocation of obese patients to preassessment clinics. Secondary outcome measures include 30-day major complication rate, length of hospital stay, postoperative care setting, and cancellation rates at the time of surgery. Baseline characteristics were compared between the levels of preoperative assessment using χ2 test for categorical ☆ ☆☆
Assistance with the article: None. Funding/Conflicts of interest: None.
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variables and t test for continuous variables. Binary logistic regressions were used to determine the strength association between risk factors for postoperative complications and length of hospital stay. Data analyses were performed using IBM SPSS statistics version 22.0.
Ethics The protocol was reviewed and approved as a service evaluation by the QEHB local ethics committee department. The date of approval was August 28, 2015. Because this study was retrospective and observational in nature, a full and formal ethical review was not required. A total of 139 patients were included in our analysis. Baseline demographics are shown in Table 1. Patients in the highrisk clinics (levels 2B and 3) were more likely to be older and have a higher surgery grade. Overall complication rate was 15.6% (21/139), where 26.5% (13/49) were from the highrisk clinics and 9.2% (8/87) in low-risk clinics. The most common postoperative complication was surgical site infection. Median length of hospital stay was 2 days (range, 1-59 days). Using stepwise regression analysis, surgery grade, not BMI, was the strongest predictor of postoperative complications (P = .006) and length of hospital stay (P b .0001). Patients from high-risk clinics were more likely to be sent to higherintensity care settings such as wards or intensive care units compared with patients from low-risk clinics (odds ratio, 8.20; 95% confidence interval, 3.46-19.44; P b .0001) In fact, 89.1% (41/46) of patients in the high-risk group were sent to wards and/or intensive care units as part of the planned surgical pathway. Cancellation rates were 5% (7/139) and they were similar between both groups. This retrospective audit addresses an important issue on preoperative management in obese patients. Here, we evaluated the association between BMI and postoperative outcomes such as complication rates and length of hospital stay. We chose these 2 outcomes as they are relevant to perioperative care for surgical patients. From our service evaluation, it appears that BMI may not predict postoperative complications and that surgery grade is a better predictor of preoperative risk. Body mass index as a risk factor for postoperative complications has been a matter of debate for some time and the evidence base for this has always been equivocal [3-6]. Hence,
2
Correspondence Table 1
Baseline demographics and operative details by preassessment clinics
Age (y) BMI (kg/m2) Sex Ethnicity
Surgery grade
Comorbidities
Mean (SD) Mean (SD) Male Female White Nonwhite Grade 1 Grade 2 Grade 3 Grade 4 None 1-2 3-4 5+
Level 1 (n = 67)
Level 2A (n = 22)
Level 2B (n = 11)
Level 3 (n = 39)
53.4 (14.6) 35.8 (4.7) 29 (43.3) 38 (56.7) 49 (73.1) 18 (26.9) 32 (47.8) 24 (35.8) 7 (10.4) 4 (6.0) 19 (28.4) 29 (43.3) 17 (25.4) 2 (3.0)
57.8 (17.7) 34.2 (3.5) 10 (45.5) 12 (54.5) 21 (95.5) 1 (4.5) 5 (22.7) 12 (54.5) 1 (4.5) 4 (18.2) 9 (40.9) 12 (54.5) 0 (0.0) 1 (4.5)
63.2 (9.6) 33.9 (3.9) 5 (45.5) 6 (54.5) 10 (90.9) 1 (9.1) 0 (0.0) 2 (18.2) 0 (0.0) 9 (81.8) 0 (0.0) 7 (63.6) 4 (36.4) 0 (0.0)
63.6 (13.1) 33.4 (3.1) 19 (48.7) 20 (51.3) 35 (89.7) 4 (10.3) 3 (7.7) 12 (30.8) 0 (0.0) 24 (61.5) 7 (17.9) 17 (43.6) 13 (33.3) 2 (5.1)
P .002⁎ .269⁎ .961 .034
b.001
.083
Data are n (%) except where indicated. All tests χ2, except where * indicates Kruskal-Wallis test. BMI = body mass index.
the idea behind preassessment service is to allocate high BMI patients to high-risk clinics to improve outcomes and reduce risk of complications as they would be better managed. Considering that our audit and other studies have shown that BMI does not affect outcomes, this may not be an important factor to consider when allocating patients into preassessment clinics. Instead, patients with high BMI should be allocated to high-risk clinics if they are known to pose a risk to anesthesia such as obstructive sleep apnea making intubation difficult before surgery. The main limitation of this study is the usual bias associated with the retrospective nature of the design. It was difficult to obtain information on the patients' American Society of Anesthesiologists grade from their notes. In this study, only obese patients were included making it difficult to compare rates of complications in normal and overweight BMI categories. Furthermore, we have chosen to include all gastrointestinal procedures, both minor and major surgeries. This may potentially bias the result as the effect of high BMI may be apparent in patients undergoing major surgery. Lastly, complications were not assessed using the Clavien-Dindo classification system, making it difficult to assess if obese patients may be at risk of developing major complications as compared with minor ones. In summary, obesity and preoperative care is becoming increasingly important as part of perioperative management to improve outcomes of surgical patients. Future prospective studies should aim to validate our findings in a much larger cohort to assess the effect of BMI on outcomes, and if management of obese patients in high-risk preoperative clinics will improve outcomes.
Sivesh K. Kamarajah BMedSci* Mustafa Sowida University of Birmingham, Birmingham, UK *Corresponding author: Sivesh K Kamarajah, College of Medical and Dental Sciences, University of Birmingham Edgbaston Birmingham B15 2TT, United Kingdom Tel.: +44 7471397404 E-mail address:
[email protected] Christina Reihill FRCN Queen Elizabeth Hospital, Birmingham, UK http://dx.doi.org/10.1016/j.jclinane.2016.09.005
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