Sex-related differences in functional capacity and its implications in risk stratification before major non-cardiac surgery: a post hoc analysis of the international METS study
Notice bibliographique
Résumé
Background Poor functional capacity has been identified as an important modifiable risk factor for postoperative complications. Cardiopulmonary exercise testing (CPET) provides objective parameters of functional capacity (e.g., oxygen consumption at peak exercise, peak VO 2 ), with significant prognostication for postoperative complications. However, sex-specific thresholds for functional capacity to predict surgical risk are yet to be established. Therefore, we performed a post hoc analysis of the international, multicentre, prospective observational METS (Measurement of Exercise Tolerance before Surgery) study to evaluate if sex-specific thresholds of peak VO 2 improve risk prediction of postoperative complications. Methods We undertook a post hoc analysis (HREC/71824/PMCC) of the METS study, which was performed between March 2013 and March 2016. We investigated whether sex-specific differences exist for CPET-derived parameters and associated thresholds for predicting postoperative complications in this large cohort of patients that had major non-cardiac surgery (n = 1266). Logistic regression models were analyzed for the association of low peak VO 2 with moderate-to-severe in-hospital postoperative complications. Optimal sex-specific peak VO 2 thresholds were obtained by maximizing the Youden index of receiver operating characteristic (ROC) curves. Finally, multivariable logistic regression models tested the resulting sex-specific thresholds against the established non-sex-specific peak VO 2 threshold (14 mL kg −1 min −1 ) adjusted for clinically relevant features such as comorbidities and surgical complexity. Models were evaluated by bootstrapping optimism-corrected area under the ROC curve and the net reclassification improvement index (NRI). Findings Female patients (n = 480) had a lower mean (SD) peak VO 2 than males (16.7 (4.9) mL kg −1 min −1 versus 21.2 (6.5) mL kg −1 min −1 , p < 0.001) and a lower postoperative complication rate (10.4% versus 15.3%; p = 0.018) than males (n = 786). The optimal peak VO 2 threshold for predicting postoperative complications was 12.4 mL kg −1 min −1 for females and 22.3 mL kg −1 min −1 for males, respectively. In the multivariable regression model, low non-sex-specific peak VO 2 did not independently predict postoperative complications. In contrast, low sex-specific peak VO 2 was an independent predictor of postoperative complications (OR 2.29; 95% CI: 1.60, 3.30; p < 0.001). The optimism-corrected AUC-ROC of the sex-specific model was higher compared with the non-sex-specific model (0.73 versus 0.7; DeLong's test: p = 0.021). The sex-specific model classified 39% of the patients more correctly than the baseline model (NRI = 0.39; 95% CI: 0.24, 0.55). In contrast, the non-sex-specific model only classified 9% of the patients more correctly when compared against the baseline model (NRI = 0.09; 95% CI: −0.04, 0.22). Interpretation Our data report sex-specific differences in preoperative CPET-derived functional capacity parameters. Sex-specific peak VO 2 thresholds identify patients at increased risk for postoperative complications with a higher discriminatory ability than a sex-unspecific threshold. As such, sex-specific threshold values should be considered in preoperative CPET to potentially improve risk stratification and to guide surgical decision-making, including eligibility for surgery, preoperative optimization strategies (prehabilitation) or seeking non-surgical options. Funding There was no funding for the present study. The original METS study was funded by Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, Ontario Ministry of Health and Long-Term Care, Ontario Ministry of Research, Innovation and Science, UK National Institute of Academic Anaesthesia, UK Clinical Research Collaboration, Australian and New Zealand College of Anaesthetists, and Monash University.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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 ».