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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
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Predicting Complications in Head and Neck Surgery: Comparing Calculators to Surgeons.

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Neither surgeons nor surgical risk calculators accurately predict patient complications. Preoperative smoking is a key predictor, highlighting the need for improved risk assessment tools in surgical planning.

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Area of Science:

  • Surgical outcomes research
  • Predictive analytics in medicine
  • Healthcare quality assessment

Background:

  • Surgical outcomes significantly influence hospital rankings, reputation, and funding.
  • Objective surgical risk calculators (SRCs) are commonly used, but surgeons' own assessments are often overlooked.
  • There is a need to compare the predictive accuracy of surgeons versus SRCs.

Purpose of the Study:

  • To prospectively evaluate whether surgeons' preoperative risk assessments or objective surgical risk calculators (SRCs) are more accurate in predicting patient surgical outcomes.
  • To compare the performance of surgeons and the American College of Surgeons (ACS) tool in predicting any complication and serious complication.

Main Methods:

  • Prospective cohort study design involving 101 patients.
  • Collected surgeons' preoperative risk assessments and calculated patient risk using an SRC.
  • Compared predictions against actual patient outcomes and against each other using receiver operating characteristic (ROC) analysis.

Main Results:

  • 36.6% of patients experienced any complication, and 17.8% experienced a serious complication.
  • Preoperative smoking significantly increased overall complication rates (2.49 times higher).
  • Neither surgeons (AUC 0.51) nor the ACS tool (AUC 0.58) accurately predicted any complication; performance for serious complications was also not statistically significant (surgeons AUC 0.55, ACS tool AUC 0.60).

Conclusions:

  • Current validated risk calculators and surgeons demonstrate poor accuracy in predicting perioperative risk.
  • Preoperative smoking is the most significant factor for improving complication prediction, alongside age and surgery type.
  • Risk calculators may be inappropriate for assessing hospital performance; improved predictive tools, potentially incorporating AI, are needed.