The Development, Internal and External Validation of a Circumcision Complications Risk Calculator for an African Population: Prevention of Circumcision Complications via Pre-circumcision Complication Risk Profiling in Ghana

Abstract

Background and objective Circumcision complications from clinical and non-clinical procedures pose significant health risks in Ghana. In light of this, tools that predict and prevent these mishaps using individual sociodemographic risk factors as digital biomarkers are urgently needed. Mobile health (mHealth) technologies offer a promising platform for improving circumcision safety through digital risk profiling. In this study, we aimed to develop a mobile app-based digital risk calculator for preventing circumcision complications in Ghana by leveraging digital biomarkers and risk profiling. Methods We conducted a five-year retrospective analysis of hospital-based data involving a total of 217 participants (186 for model development and 31 for external validation), identifying key risk factors including demographics, circumciser skill level, and provider facility type. Embedded, but not explicit, was the circumcision-seeking behavior of participants and thus, the geospatial distribution of complications. These variables were integrated into a logistic regression model. Internal and external validation of the model was conducted. The model was then deployed via an "R: A Language and Environment for Statistical Computing" platform, embedded into a mobile app designed for healthcare providers and parents. Pilot testing assessed app usability in 30 adult participants. Results The app categorized patients into low, moderate, and high-risk groups. The diagnostic model achieved a specificity of 96.08%, a positive predictive value (PPV) of 64.71%, and a negative predictive value (NPV) of 86.98%, correctly classifying 84.95% of cases. Sensitivity was 33.33%. The Hosmer-Lemeshow goodness-of-fit test (χ2 = 11.05, p <i>= 0.199</i>) confirmed the model fit. The receiver operating characteristic (ROC) analysis showed excellent discrimination [area under the curve (AUC) = 0.8895]. External validation and usability testing yielded favorable results. Conclusions This mobile app offers a valuable tool for real-time circumcision risk assessment, enhancing safety outcomes. Future research should aim to incorporate machine learning to optimize predictive performance.