An intraoperative 10-point Surgical Apgar Score, based on estimated blood loss, lowest mean arterial pressure, and lowest heart rate, was developed and validated for predicting postoperative complications in patients undergoing vascular and general surgeries. We sought to estimate the ability of this metric to predict major postoperative complications in patients with pancreatic cancer undergoing the Whipple procedure. This is a prospective, observational, single-centre study involving adult patients undergoing the Whipple procedure, at a major tertiary cancer centre. All eligible patients undergoing Whipple surgery in our institute between March 2018 and October 2021 were included in the study. Demographic data, clinicopathological characteristics, comorbidities, intraoperative variables, and postoperative complications were analyzed. The surgical score was calculated from intraoperative blood loss, lowest heart rate, and lowest mean arterial pressure. All the patients were followed up till 30 days postoperatively. Descriptive statistics and univariate and multivariate analyses were used as appropriate. The occurrence of major postoperative complications represented the primary outcome. A total of 253 patients were analyzed. The mean duration of surgery was 436 min. On statistical analyses, the occurrence of major postoperative complications was significantly associated with SAS ≤ 4 (OR = 8.00, 95% CI = 3.78–16.93, p = 0.000), use of intraoperative vasopressor (OR = 2.247, 95% CI = 1.312–3.846, p = 0.003), and body mass index (BMI) (OR = 1.074, 95% CI = 1.010–1.142, p = 0.022). However, we did not find any significant association between other demographic variables like age, comorbidities, duration of surgery, and preoperative s. albumin with the occurrence of postoperative complications. Lower SAS (≤ 4) is the most powerful predictor of postoperative complications in pancreatic cancer patients undergoing Whipple surgery. The score provides a simple and immediate means of measuring and communicating patient outcomes, using data routinely available in any setting.
