N. Shaikh1,2, N. Afzal2, M. Lakhdir3, K. Abdul Rahim4, R. Ahmad2, S. Kamran Bakhshi1,2, M. Ali9, Z. Samad7, A. H. Haider1,3,8,10 1Aga Khan University Medical College, Department Of Surgery, Karachi, Sindh, Pakistan 2Aga Khan University Medical College, Dean’s Office, Karachi, Sindh, Pakistan 3Aga Khan University Medical College, Department Of Community Health Sciences, Karachi, Sindh, Pakistan 4Aga Khan University Medical College, Department Of GI And Surgery, Karachi, Sindh, Pakistan 7Aga Khan University Medical College, Chair, Department Of Medicine, Karachi, Sindh, Pakistan 8Brigham And Women’s Hospital, Center For Surgery And Public Health, Department Of Surgery, Boston, MA, USA 9Aga Khan University Medical College, Department Of Medicine, Karachi, Sindh, Pakistan 10Aga Khan University Medical College, Dean, Medical College, Karachi, Sindh, Pakistan
Introduction:
Negative impact of comorbidity on Emergency General Surgery (EGS) outcomes is well known. In lesser developed countries with inconsistent documentation of comorbid conditions, undiagnosed and progressively worsening comorbidities can have an even worse impact on EGS outcomes. Understanding profiles of patients that may have significant comorbid index can help identify at-risk populations. Hence, the objective of this study is to discern predictors of higher comorbidity index and major complications in EGS using a large South Asian sample population.
Methods:
Data of adult patients with American Association for Surgery of Trauma defined EGS diagnoses at a tertiary teaching hospital, presenting between 2010 and 2019 was retrieved. Patients were categorized into predefined EGS groups using ICD codes. Primary outcome was severity of comorbidity using Charlson Comorbidity Index (CCI), categorized into Mild (CCI score = 1), Moderate (CCI score = 2) and Severe (CCI score >/= 3). Secondary outcome included incidence of a major complication. Multinomial logistic regression was performed to ascertain factors predicting severe CCI and complications from the following domains: age, sex, financial status, admission type and residency. Adjusted Odds Ratio along with 95% CI was reported for all significant predictors. Each CCI component was then assessed for its association with complication rates to identify comorbidities resulting in the highest burden of complications.
Results:
Analysis of 31,297 operated patients with primary EGS diagnosis at index admission showed a mean age of 48.73 years (SD + 0.09) with a near-equal gender distribution (53.5% male, 46.5% female). Multinomial logistic regression in Table 1 identified increasing age, emergent admission status, lack of insurance and male gender as factors that were more likely to result in a severe CCI scores. Overall comorbidity rate was 3.19%. Highest burden of comorbidity was from Diabetes Mellitus (42.27%) followed by HIV/AIDS (13.32%) and solid tumors (11.48%); these were also associated with the highest number of complications. Overall complication rate was 11.32% with acute renal failure (6.28%), renal insufficiency (5.36%) and sepsis (3.97%) identified as the most common in-hospital complications.
Conclusion:
This study determined patient-level factors predicting higher CCI and worse outcomes in EGS. In countries where data on comorbid conditions is not well documented, identifying predictive patient-level factors and subsequently stratifying population sub-sets at risk of worse outcomes can provide valuable insight on disease progression and aid decision making in EGS patients. Early recognition and control can help improve survival and prevent complications.
