Developing and validating an appendectomy algorithm for use in a South African database

Background: Identifying surgical patients through administrative and clinical data can inform the quality and demand for surgical care. In South Africa, a database exists that comprises data from the public health sector. However, algorithms are lacking to identify surgical procedures like appendectomy in these systems in our setting. Aim: To develop and validate an appendectomy algorithm for use in a South African database. Setting: Data from public hospitals in South Africa were the reference standard and comprised appendectomy and other general surgery procedure controls. The index test was the appendectomy algorithm developed and validated using the provincial database in the country. Methods: A diagnostic test accuracy study was done. The algorithm was developed using four phases: exploration and selection, development, refinement and validation. Data analyses were performed using STATA version 18. Results: The final algorithm comprised two procedures and nine diagnostic codes and reached a sensitivity of 91.3% and a specificity of 96%. Conclusion: Our study is the first to validate an appendectomy algorithm in a low-and middle-income country setting. While not the first globally, it addresses a critical gap in the literature by demonstrating that robust, high-specificity algorithms can be developed in resource-constrained settings. Future research should focus on applying the algorithm to evaluate median delays in accessing care within the public health system. Contribution: This study demonstrates that surgical procedure algorithms can be developed and validated with sufficient sensitivity and specificity using diagnostic and procedure codes for application in a low- and middle-income country setting.