External Validation of Predictive Models for Diagnosis, Management and Severity of Pediatric Appendicitis

Ricards Marcinkevics¹Kacper Sokol^1*Akhil Paulraj²Melinda A Hilbert³Vivien Rimili⁴Sven Wellmann⁵Christian Knorr⁶Bertram Reingruber⁷Julia E Vogt^1*Patricia Reis Wolfertstetter^5*

• ¹ETH Zürich, Zurich, Switzerland
• ²Department of Electrical and Computer Engineering, School of Engineering and Applied Science, Princeton University, Princeton, New Jersey, United States
• ³St. Vincentius Clinics Karlsruhe, Karlsruhe, Baden-Württemberg, Germany
• ⁴University Hospital of Cologne, Cologne, North Rhine-Westphalia, Germany
• ⁵University Medical Center Regensburg, Regensburg, Bavaria, Germany
• ⁶RoMed Klinikum, Rosenheim, Bavaria, Germany
• ⁷Florence Nightingale Hospital Kaiserswerther Diakonie, Düsseldorf, Germany

Background Appendicitis is a common condition among children and adolescents. Machine learning models can offer much-needed tools for improved diagnosis, severity assessment and management guidance for pediatric appendicitis. However, to be adopted in practice, such systems must be reliable, safe and robust across various medical contexts, e.g., hospitals with distinct clinical practices and patient populations. Methods We performed external validation of models predicting the diagnosis, management and severity of pediatric appendicitis. Trained on a cohort of 430 patients admitted to the Children's Hospital St. Hedwig (Regensburg, Germany), the models were validated on an independent cohort of 301 patients from the Florence-Nightingale-Hospital (Düsseldorf, Germany). The data included demographic, clinical, scoring, laboratory and ultrasound parameters. In addition, we explored the benefits of model retraining and inspected variable importance. Results The distributions of most parameters differed between the datasets. Consequently, we saw a decrease in predictive performance for diagnosis, management and severity across most metrics. After retraining with a portion of external data, we observed gains in performance, which, nonetheless, remained lower than in the original study. Notably, the most important variables were consistent across the datasets. Conclusions While the performance of transferred models was satisfactory, it remained lower than on the original data. This study demonstrates challenges in transferring models between hospitals, especially when clinical practice and demographics differ or in the presence of externalities such as pandemics. We also highlight the limitations of retraining as a potential remedy since it could not restore predictive performance to the initial level.