Evaluation of a Preoperative Adverse Event Risk Index for Patients Undergoing Head and Neck Cancer Surgery
Bibliographic record
Abstract
Importance: Patients 65 years or older are the most frequent users of operative resources and are also the most vulnerable to postoperative adverse events (AEs). Frailty indices are increasingly being used for preoperative risk stratification within head and neck cancer surgery, but most models lack a multifactorial basis and cannot be directly applied to clinical practice. A practical risk index is needed for clinicians to gauge risk factors preoperatively. Objective: To develop a preoperative risk index of short-term major postoperative AEs for patients undergoing head and neck cancer surgery. Design: Cohort analysis of patients from multiple medical centers undergoing inpatient ablative or reconstructive head and neck cancer surgery and registered in the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) from 2006 to 2016. Exposures: Inpatient ablative or reconstructive head and neck cancer surgery. Main Outcomes and Measures: Sociodemographic, frailty-related, and surgical factors in the derivation cohort were evaluated using simple and multiple logistic regression. Risk factors were subsequently integrated into a preoperative head and neck surgery risk index (HNSRI) and compared with existing models using the validation cohort. A composite variable of major postoperative AEs was used, including death within 30 days of surgery. Results: A total of 43 968 operations were found using the ACS NSQIP database. Of these, 12 569 cases were excluded as non-head and neck cancer or emergency surgery. Of the included 31 399 operations reviewed, the mean (SD) patient age was 56.9 (15.4) years, and 16 994 of the patients were women (54.1%). A total of 4556 (14.5%) patients had a major postoperative AE, and 209 (0.7%) died. Older age, male sex, smoking, anticoagulation, recent weight loss, functional dependence, free-tissue transfer, tracheotomy, duration of surgery, wound classification, anemia, leukocytosis, and hypoalbuminemia were independently associated with major AEs or death on multiple regression analysis (C statistic, 0.83). The area under the curve of the HNSRI to predict major AEs including death using the validation cohort (n = 15 699) was 0.84 (95% CI, 0.83-0.85) with a sensitivity of 80.1% (95% CI, 79.4%-80.8%) and specificity, 72.3% (95% CI, 70.3%-74.2%). The HNSRI outperformed existing risk models for prediction of AEs: delta C index of the HNSRI to the modified frailty index 11, 0.23 (95% CI, 0.22-0.25); the American Society of Anesthesiologists classification, 0.14 (95% CI, 0.13-0.16); and the ACS risk calculator, 0.02 (95% CI, 0.01-0.03). Conclusions and Relevance: The proposed HNSRI demonstrated a high sensitivity and specificity for major postoperative AEs and death in the studied population. This risk index can be used to counsel patients awaiting head and neck cancer surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".