Evaluation of a Preoperative Adverse Event Risk Index for Patients Undergoing Head and Neck Cancer Surgery
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».