JMIR Publications

ABSTRACT

Background:

Post-operative complications following emergency laparotomy remain a major global health challenge, with mortality rates ranging from 8% to 18% worldwide and up to 25% within two years of surgery. 1,2. In low- and middle-income countries (LMICs), the burden is particularly high, with reported complication rates of 28%–40.8% and mortality rates between 7.9% and 18% in Kenya and Rwanda, respectively. 3,4 Given the high risks associated with these procedures, including infections, organ failure, and mortality, it is essential to use effective prognostic tools that can guide early interventions. The Surgical Apgar Score (SAS), first proposed by Gawande et al., has shown promise in predicting complications across various surgical disciplines. 3 However, its utility in the specific context of emergency laparotomies, especially within diverse clinical environments, has not been extensively studied. 5 This multi-centre study in Uganda seeks to address this gap by assessing the effectiveness of SAS across different healthcare settings, thereby determining its potential as a universal prognostic tool in predicting post-operative outcomes. The premise of this study rests on the belief that early perioperative indicators can serve as reliable predictors of a patient’s risk for severe complications or mortality. The SAS incorporates three critical intraoperative parameters—estimated blood loss, the lowest heart rate, and the lowest mean arterial pressure. 6 This may provide vital insight into the likelihood of adverse outcomes. 7 Early recognition of high-risk patients through SAS could enable clinicians to implement timely interventions, ultimately improving survival and recovery rates. 8 In Uganda, data from Mulago Regional Referral Hospital reported an in-hospital mortality rate of 10.6% among both emergency and elective surgical patients, aligning with the 5.5% to 22.4% range observed in other resource-limited settings. 9 Despite demonstrating fair discriminatory ability of the Surgical Apgar Score (SAS), most local studies, including the Onen et al. single-center study, faced several limitations; complications after laparotomy were reported as either present or absent, focusing only on major events. This limits the measure of the true burden of postoperative morbidity; blood loss estimation relied on subjective visual methods, potentially misclassifying SAS scores. The absence of preoperative data limited contextual risk adjustment, and outcomes were restricted to in-hospital events, possibly underestimating true complication and mortality rates. These constraints, coupled with early discharge, underscore the need for multicenter validation and integration of SAS into broader perioperative risk models tailored for low-resource settings.

Objective:

To predict emergency laparotomy adverse outcomes using the Surgical APGAR Score based stratification system.

Methods:

This was a multicenter hospital-based prospective observational cohort study in which we evaluated the predictive value of SAS in patients undergoing emergency laparotomy. The study was conducted in three tertiary health facilities. All patients who underwent emergency laparotomy were informed about the research and asked to participate. Those who accepted participation and satisfied all the inclusion criteria were given an informed consent document to sign, and a questionnaire was completed to obtain their social demographic and clinical characteristics. This work has been reported in line with the STROCSS Criteria, and the study was conducted from April to July 2025. 10 Inclusion criteria All adults aged >18 years, patients who presented to the study centers during the study period for an emergency laparotomy, and consented to participate in the study. Exclusion criteria Patients who presented to the hospital for emergency laparotomy but had polytrauma requiring any other surgery; patients with known advanced intra-abdominal malignancy or head injury; patients with known bleeding disorders; patients who had reoperation within the same admission or within 30 days. Sample size The Cochran (1977) formula was used to estimate the sample size to enable the prediction accuracy of the model at a power (1-β) of 80%, a type 1 error of less than 5%, and a 95% confidence interval. Using the Cochran formula. n = required sample size z = confidence level at 95% (standard value of 1.96) p = mortality rate, The mortality rate of laparotomy in a Ugandan study, according to Onen et al., 9 of 10.6%. was used in the calculation d = margin of error at 5% (standard value of 0.05) Substituting in the formula, This was a systematic non-probability consecutive sampling approach that was employed for all emergency laparotomy patients who met the inclusion criteria. Data was collected within 24 hours after surgery using a data collection sheet by the principal investigator and trained study assistants. Using the anesthesia data sheet, blood pressure and heart rate were monitored every fifteen minutes from induction to reversal of anesthesia. Mean arterial pressure was calculated using the below formula: [(2 x diastolic pressure) + systolic pressure /3] 9. Estimated blood loss was determined by estimating the hematocrit and hemoglobin level by getting the full complete blood count (CBC) result pre- and postoperatively. Inpatients and outpatients were followed up to determine any postoperative complications within thirty (30) days after the surgery. 11 Blood loss was calculated using the formula below: Blood loss = [EBV X (Hi – Hf)/ (HCTi + HCTf)/2] + (500 x Tu)12 Where EBV = estimated blood loss is assumed to be 70 cm³/kg, Hi = pre-operative hemoglobin, Hf = post-operative hemoglobin, HCTi = pre-operative packed cells, HCTf = post-operative packed cells, Tu = sum of whole blood, packed cells, and cell unit transfused13 The Apgar score was calculated using the following data from the participants: age, sex, weight, diagnosis, duration of the operation (in minutes), preoperative CBC and postoperative CBC, SAS taken from estimated blood loss, lowest recorded mean arterial pressure, lowest recorded pulse rate, the occurrence of major complications, and mortality within 30 days postoperatively.3 For risk stratification, patients were grouped into three categories based on the Surgical Apgar score (Table 1). Data analysis Descriptive Statistics: mean ± standard deviation (SD), frequencies, and percentages were used. Chi-square test (or Fisher’s exact test where appropriate) for association between SAS groups & CDC grade. Kruskal-Wallis was used to compare CCI scores across SAS groups. A 0.05 p-value or less will statistically be considered significant. Spearman’s rank correlation between SAS and CDC/CCI score. Ordinal and linear logistic regression with SAS as a predictor of CDC for complication severity, and SAS as a predictor of continuous CCI score were used. ROC Curves were used to assess accuracy; curves were plotted for SAS predicting major complications (CDC ≥ 3) and mortality (CDC grade 5). An AUC with 95% CI was calculated, and the Youden’s Index was used for SAS cut-off values. Sensitivity, specificity, and false positive rates were reported. Statistical Significance: p-value < 0.05 = statistically significant; tables and graphs were used to display results.

Results:

Patient Demographics and Clinical Characteristics In this study, a total of 146 patients who underwent emergency laparotomy were included. The largest age group was 40–60 years, comprising 43.2% of patients. Males predominated, accounting for 71.9% (n=105). The average patient weight was 72.9 ± 6.8 kg. The most common diagnoses were small bowel obstruction (36.3%), blunt abdominal trauma (19.2%), and large bowel obstruction (15.1%). Most patients were classified as ASA II (81.5%). Surgery duration was ≥120 minutes in 54.1% of cases, and 22.6% (n=33) received intraoperative blood transfusions. The majority (85.5%) had Surgical Apgar Scores (SAS) between 5 and 7, while 7.6% (n=11) had low SAS (≤4). Overall mortality was 4.8% (n=7), corresponding to CDC grade 5 and a CCI score of 100%. Postoperative complications occurred in 31.5% of patients. Baseline characteristics are summarized. (Table 2) Patients were scored using the Surgical Apgar Score (SAS), ranging from 0 to 10, and categorized into three SAS risk groups: high-risk (0–4), moderate-risk (5–6), and low-risk (7– 10). High-risk SAS were 11 (7.5%), of which 5 (45.5%) died; 4 major complications and 2 minor complications. Moderate-risk group: 95 patients (65.1%); mixed complication severity; 2.1% mortality. Low-risk group: 40 (27.4%); predominantly minor; no deaths. Postoperative complications were classified using the Clavien-Dindo Classification (CDC) and its corresponding CCI burden. A cross-tabulation between SAS risk groups and postoperative outcomes by CDC showed a significant association (χ² = 48.160, p < 0.001). Fisher’s Exact Test (p < 0.001). (Table 3) Most deaths occurred in the high-risk group (SAS 0–4), while minor complications were dominant in the moderate and low-risk groups, with no deaths reported in the low-risk group. Complication severity was measured from Grade 0 (no complication) to Grade 5 (death), with the corresponding Comprehensive Complication Index (CCI) ranging from 0 to 100, where 100 indicates death. Among the 7 patients who had Grade 5 (death) complications, 5 (71.4%) were in the high-risk SAS group, and 2 (28.6%) were in the moderate-risk SAS group. A significant negative correlation was observed between SAS and CDC. (χ² = 51.612, p < 0.001 Spearman’s rho = –0.325, p < 0.001. Complication frequency shows a clear decreasing trend in the mean CCI scores across SAS categories, Spearman’s rho: 0.325, p < 0.001, Patients with lower SAS had significantly higher CCI scores: SAS 0–4: Mean CCI = 54.27 ± 45.15, SAS 5– 6: Mean CCI = 8.70 ± 17.51, SAS 7–10: Mean CCI = 3.06 ± 7 (Table 4). Although means and standard deviations were calculated for SAS risk groups, the Shapiro-Wilk test for normality revealed significant deviations from normal distribution in all groups (p < 0.05). Consequently, the Kruskal–Wallis test was applied, and a significant difference in mean CCI ranks across SAS groups was seen. K-Wallis (χ² = 23.298, p < 0.001). Patients in the High-Risk group had the highest mean rank CCI 118.91. Correlation Between Surgical Apgar Score and CDC/CCI: A Spearman’s rank correlation was conducted to assess the relationship with SAS. There was a statistically significant, negative correlation between SAS and CCI (ρ = -0.388, p < 0.001). Ordinal Logistic Regression Output for SAS and CDC; An ordinal logistic regression was used with CDC grades as the dependent variable; the SAS score shows a statistically significant inverse complication prediction (β = – 0.930, p < 0.001) (Wald = 21.729, p < .001). The test of the parallel lines assumption was not violated (p = 0.216), suggesting that the model explains approximately 18.3% of the variance in (Nagelkerke R² = .183). Linear Regression for SAS Predicting CCI burden, SAS showed a strong negative prediction; for each one-point increase in SAS, the CCI decreases by about 4.71 units. (p < 0.001). ROC Curve for major complications and mortality shows good and excellent discriminative ability for major complications, with an AUC of 0.780 (SE = 0.063, p < 0.001), 95% CI 0.656 to 0.904 (figures 1 and 2). The optimal Youden’s Index cut-off point was identified at SAS ≥ 5, which yielded a sensitivity of 96.9% and specificity of 38.9%, balancing between true positive and false positive rates, with excellent discriminative ability for mortality about 85% of the time. AUC = 0.852 (95% CI: 0.704–0.999, p = 0.003), Cut-off of ≥ 5 (Sensitivity: 89.7%, False Positive Rate: 28.6%), the lower bound (0.704) still suggests acceptable accuracy, though the wide CI likely reflects a limited sample size. p-value < 0.003

Conclusions:

Conclusion The SAS demonstrates significant predictive value for postoperative complication severity; its performance is enhanced when interpreted in conjunction with established classification systems like CDC and CCI. Particularly in high-burden, low-income settings, integrating SAS into routine surgical practice could enhance patient safety, optimize resource utilization, and improve surgical outcomes. Clinical Trial: PACTR202511513563586. Pan African Clinical Trial Registry (pactr.samrc.ac.za) database,