Preoperative Risk Stratification and Surgical Outcome Prediction: Integrating Clinical Scoring Systems, Data-Driven Models, and Patient-Centered Optimization

Abstract

Risk stratification before surgery is a key component of contemporary perioperative medicine that allows clinicians to foresee morbidity and mortality in the postoperative period, direct shared decision-making, and deploy perioperative resources efficiently. Over the last three decades, a wide variety of risk prediction tools have emerged, ranging from simple clinical scoring systems to complex machine-learning models. This paper critically reviews the history, clinical application, and limitations of preoperative risk stratification systems, and examines how they contribute to the prediction of surgical outcomes and optimisation of perioperative processes. Based on recent literature and institutional outcome data from 210 surgical patients, the research examines the effectiveness of structured risk assessment in predicting outcomes, identifying modifiable risk factors, and personalising perioperative care. Statistical analysis demonstrates a significant correlation between preoperative risk category and postoperative outcomes. The results highlight that risk stratification is most effective when implemented as part of multidisciplinary processes combined with risk-modifying interventions, rather than as a standalone predictive activity.

Keywords