Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery

Bariatric surgery is currently the only treatment available for morbid obesity that has been shown to offer significant weight loss over time [1], reduce cardiovascular morbidity [2], diabetes [3‐5], cancer [6], and improve quality-of-life [7‐9]. Each year, approximately 500,000 bariatric procedures are performed worldwide [10]. In the preoperative evaluation of the patient, it is important to identify factors that may be associated with adverse outcome. A few risk prediction models for postoperative mortality have been described previously [11, 12], but mortality rates following bariatric surgery today are so low that it is more important to consider severe postoperative morbidity when assessing adverse outcome in the early postoperative period [13‐15]. Prediction models for postoperative complication could also enable case-mix comparison of results from different bariatric surgical centres. Risk prediction models for adverse postoperative outcome previously reported in the literature [14, 16, 17] have shown discriminatory values that are too low to be clinically useful in the individual assessment of patients [18].

The purpose of the present study was to construct and validate a risk prediction model using preoperatively available risk factors for severe postoperative complications.

A total of 37,811 patients operated between January 1, 2010 and December 31, 2014 were identified in the SOReg. These patients were included in the study group. Follow-up at day 30 was 98.2% (n = 37,134). A further 6250 patients operated between January 1, 2015 and December 31, 2015 constituted the validation group. In this group, follow-up at day 30 was 94.7% (n = 5919).

The two groups were comparable with regard to baseline characteristics (Table 1). The most common primary bariatric procedure in the study group was gastric bypass (n = 34,161, 90.3%), followed by sleeve gastrectomy (n = 1774, 4.7%), duodenal switch (n = 252, 0.7%), gastric banding (n = 111, 0.3%), and other procedures (n = 146, 0.4%). In addition, 1367 patients (3.6%) underwent revision surgery. Most procedures were laparoscopic (n = 36,539, 96.6%), while 1037 patients (2.7%) underwent a planned open procedure and 235 patients (0.6%) were converted to open surgery. In the study group, 3116 patients (8.4%) suffered from some form of complication. A severe postoperative complication occurred in 1220 cases (3.3%).

In the validation group, 4403 patients (70.4%) were operated with gastric bypass, 1696 (27.1%) with sleeve gastrectomy, 55 (0.9%) with duodenal switch, 25 (0.6%) with another bariatric procedure, and 71 patients (1.1%) underwent revision surgery. Most procedures were laparoscopic (n = 6129, 98.1%), while 98 patients (1.6%) underwent a planned open procedure and 23 patients (0.4%) were converted to open surgery. In the validation group, 482 patients (8.1%) suffered from some form of postoperative complication. A severe postoperative complication occurred in 188 cases (3.2%). Potential risk factors for severe postoperative complication analysed in the study group are presented in Table 2.

The factors included in the risk prediction model are presented in Table 3. The largest contribution came from revision surgery (19.0%), followed by low body mass index (10.8%), age (9.7%), year of surgery (9.5%), waist circumference (8.8%), and dyspepsia/GERD (8.2%). When the model was recalculated using only primary gastric bypass procedures, no significant difference was found (data not shown).

Validation of the model tested on patients operated in 2015 resulted in an area under the ROC curve of 0.53, a Hosmer-Lemeshow goodness of fit 17.91 (p = 0.056) and Nagelkerke R2 0.013. The sensitivity/specificity graph is presented in Fig. 1.

The differences in case-mix, estimated as predicted probability of severe postoperative complication, between different centres during 2015 is presented in Fig. 2.

In the present study, a number of factors known at the time of surgery were identified as being associated with increased risk for severe postoperative complication. In general, the risk prediction model based on these factors had a high specificity, but a low sensitivity. Despite the large study population, it was not possible to create a prediction model that was sensitive enough to be useful for individual patients in clinical practice. Indeed, surgery-specific risk factors seem to be the most important factors when predicting severe postoperative complications [13, 18]. The present study supports the results of a review by Geubbels reporting that risk prediction models based on preoperatively known risk factors alone are inadequate in predicting postoperative severe complications [18]. There are, however, a number of preoperative factors associated with increased risk for severe postoperative complication.

In the present study, high age, dyspepsia/GERD, relatively low BMI, large waist circumference, and revision surgery were associated with increased risk for severe postoperative complication within 30 days after surgery. Age has previously been reported to be an important patient-specific risk factor for severe postoperative complication [13, 14, 17, 18]. An association between lower BMI and increased risk for postoperative complication has also been reported previously [13, 16]. The subgroup of patients with a relatively low BMI accepted for surgery may have a different metabolic profile than the average bariatric surgical patient. The combination of large waist circumference and a relatively low BMI indicates a fat distribution associated with higher metabolic burden [23, 24]. This could also be associated with increased insulin resistance as a response to the surgical trauma, or simply the cause of more technically demanding surgery. Insulin resistance has previously been suggested as a factor associated with postoperative complications [25, 26]. Dyspepsia/GERD may increase the risk for severe pulmonary complications following surgery [27]. In the present study, an association between dyspepsia/GERD and severe postoperative complication was also seen, and likely explained by the increased risk for postoperative gastric and pulmonary complications. However, dyspepsia/GERD may also serve as a confounder for other risk factors, such as psychosomatic disorders, not measured within this study [28‐30]. Revision surgery is more technically demanding than primary surgery and complication rates are also known to be higher [31]. Finally, our model included year of surgery. This factor is not relevant when considering risk in a specific case, but since complication rates improve with time, it is an important factor to consider when designing a prediction model.

This study was based on data from almost all bariatric surgery patients operated in Sweden during the study period. Follow-up at 30 days after surgery was also high. There are, however, a number of limitations that must be acknowledged. HbA1c did not fit in the present risk prediction model. Although an association between HbA1c and risk has been established in diabetic non-obese subjects undergoing cardiovascular surgery [26], for morbidly obese patients undergoing bariatric surgery the association has only been established in non-diabetic patients [25]. Since our aim was to create one model for all bariatric surgery patients, both diabetic and non-diabetic patients were grouped together, though it is possible that risk association is somewhat different in diabetic compared to non-diabetic morbidly obese patients. A model incorporating several potential predictor variables may have multicollinearity problems. However, in our model, multicollinearity did not affect how well the model fitted. The study was a register-based observational study. A register-based study is limited to the specific definitions of the register and to the quality of registration. Registration in the SOReg is subjected to continuous validation, and so far, the validity has been shown to be very high [19]. Cardiovascular comorbidity and pulmonary comorbidity other than sleep apnea are not mandatory variables in the SOReg and could thus not be evaluated as potential risk factors in this study. Although cardiovascular disease is over-represented in obese patients [32], the prevalence of severe cardiovascular comorbidity in this group of patients at the time of surgery is not that high in European studies [18]. Furthermore, patient-specific socio-economic factors such as level of education, employment/unemployment, married or not, or immigration, could play an important role. Such data were not available for this study as they are not entered in the SOReg.

Although prediction models based on preoperative factors have not been found to be useful in the preoperative assessment of patients prior to bariatric surgery, the results of the present study indicate that dyspepsia/GERD, high age, and large waist circumference relative to BMI (signalling significant amount of visceral fat) should be considered as risk factors in the individual case, in particular patients considered for revision surgery. To optimize these patients, pharmacological treatment for dyspepsia/GERD and a preoperative weight loss of 5–10% TBW [33] may be considered.

Although our prediction model had too low a sensitivity for use at the individual level, the specificity was very high and our results could be used for case-mix adjustments on a group level. This could be useful when comparing results between different units or in reimbursement systems. For prediction, classification and pattern recognition purposes, when traditional statistical modelling methods (dealing with finding relationship between variables to predict an outcome) do not work, machine learning methods (which deals with building systems that can learn from data, instead of explicitly programmed instructions) may be promising alternatives.