MétaCan
Menu
Retour à la cohorte
Enregistrement W3217162145 · doi:10.1016/j.xjon.2021.11.009

Commentary: In cardiac surgery, you are only as old as you feel

2021· editorial· en· W3217162145 sur OpenAlexaboutno aff
Michael C. Grant

Notice bibliographique

RevueJTCVS Open · 2021
Typeeditorial
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésScopusMedicineBypass graftingCardiac surgeryPopulationCohortMEDLINEGerontologyGeneral surgeryInternal medicineArtery

Résumé

récupéré en direct d'OpenAlex

Central MessageFrailty is potentially an age-independent predictor of outcomes. Care of the frail patient requires consensus regarding frailty diagnosis and establishment of goals for preoperative optimization.See Article page 491. Frailty is potentially an age-independent predictor of outcomes. Care of the frail patient requires consensus regarding frailty diagnosis and establishment of goals for preoperative optimization. See Article page 491. There is broad consensus among experts that frailty is associated with worse outcomes after cardiac surgery. In the last year alone, there have been numerous studies devoted to the subject, providing evidence that frailty predicts greater mortality, greater resource use, and lower functional outcomes and even dictates the nature and location of discharge from the hospital.1McIsaac D.I. Fottinger A. Sucha E. McDonald B. Association of frailty with days alive at home after cardiac surgery: a population-based cohort study.Br J Anaesth. 2021; 126: 1103-1110Abstract Full Text Full Text PDF Scopus (1) Google Scholar, 2Bäck C. Hornum M. Jørgensen M.B. Lorenzen U.S. Olsen P.S. Møller C.H. et al.Comprehensive assessment of frailty score supplements the existing cardiac surgical risk scores.Eur J Cardiothorac Surg. 2021; 60: 710-716Crossref Scopus (0) Google Scholar, 3Dobaria V. Hadaya J. Sanaiha Y. Aguayo E. Sareh S. Benharash P. The pragmatic impact of frailty on outcomes of coronary artery bypass grafting.Ann Thorac Surg. 2021; 112: 108-115Abstract Full Text Full Text PDF PubMed Scopus (7) Google Scholar, 4Nakano M. Nomura Y. Suffredini G. Bush B. Tian J. Yamaguchi A. et al.Functional outcomes of frail patients after cardiac surgery: an observational study.Anesth Analg. 2020; 130: 1534-1544Crossref Scopus (2) Google Scholar, 5Lee J.A. Yanagawa B. An K.R. Arora R.C. Verma S. Friedrich J.O. Canadian Cardiovascular Surgery Meta-Analysis Working GroupFrailty and pre-frailty in cardiac surgery: a systematic review and meta-analysis of 66,448 patients.J Cardiothorac Surg. 2021; 16: 184Crossref Scopus (4) Google Scholar As a result, not only are select markers of frailty now incorporated into the Society of Thoracic Surgeons database, but groups are calling for more comprehensive preoperative frailty screening as a means to identify and triage patients at greatest risk.6Yanagawa B. Graham M.M. Afilalo J. Hassan A. Arora R.C. Frailty as a risk predictor in cardiac surgery: beyond the eyeball test.J Thorac Cardiovasc Surg. 2019; 157: 1905-1909Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar The challenge, of course, is there are either no universally accepted criteria for defining frailty, and established means involve cumbersome, time-consuming exercises or require specialized training and equipment.7Sarkar S. MacLeod J.B. Hassan A. Dutton D.J. Brunt K.R. Légaré J.F. An age-independent hospital record-based frailty score correlates with adverse outcomes after heart surgery and increased health care costs.J Thorac Cardiovasc Surg Open. 2021; 8: 491-502Google Scholar In addition, despite the fact that most literature classifies patients into categories, including pre-frail and frail designations,5Lee J.A. Yanagawa B. An K.R. Arora R.C. Verma S. Friedrich J.O. Canadian Cardiovascular Surgery Meta-Analysis Working GroupFrailty and pre-frailty in cardiac surgery: a systematic review and meta-analysis of 66,448 patients.J Cardiothorac Surg. 2021; 16: 184Crossref Scopus (4) Google Scholar it is increasingly accepted that frailty is more accurately described along a spectrum, with varying degrees of severity. It is in this context that Sarkar and colleagues7Sarkar S. MacLeod J.B. Hassan A. Dutton D.J. Brunt K.R. Légaré J.F. An age-independent hospital record-based frailty score correlates with adverse outcomes after heart surgery and increased health care costs.J Thorac Cardiovasc Surg Open. 2021; 8: 491-502Google Scholar may provide additional clarity, having retrospectively evaluated patients undergoing cardiac surgery to develop a 20-point frailty score that incorporates binary risk variables across a host of patient-specific domains. Although these multifaceted rubrics are not necessarily novel8Solomon J. Moss E. Morin J.F. Langlois Y. Cecere R. de Varennes B. et al.The essential frailty toolset in older adults undergoing coronary artery bypass surgery.J Am Heart Assoc. 2021; 10: e020219Crossref Scopus (0) Google Scholar—evidenced by the fact that the authors embellished upon a deficit-based model provided by others9Eckart A. Hauser S.I. Haubitz S. Struja T. Kutz A. Koch D. et al.Validation of the hospital frailty risk score in a tertiary 396 care hospital in Switzerland: results of a prospective, observational study.BMJ Open. 2019; 9: e026923Crossref PubMed Scopus (21) Google Scholar—the method offered by Sarkar and colleagues7Sarkar S. MacLeod J.B. Hassan A. Dutton D.J. Brunt K.R. Légaré J.F. An age-independent hospital record-based frailty score correlates with adverse outcomes after heart surgery and increased health care costs.J Thorac Cardiovasc Surg Open. 2021; 8: 491-502Google Scholar is particularly compelling because their results suggest that it is (1) age-independent, which casts the first stone against the basic tenet that age is inextricably linked to frailty, highlighting that age alone is a poor surrogate for surgical outcome; and (2) computed from data readily available through existing electronic health records, akin to widely used cardiac risk scores (ie, Society of Thoracic Surgeons and European System for Cardiac Operative Risk Evaluation), which suggests it has greater practical application compared with more labor-intensive assessment strategies. As with all medical inquiry, the 2 steps forward offered by this study are accompanied by a cautious step back. In analytics, any model such as the one put forward by Sarkar and colleagues7Sarkar S. MacLeod J.B. Hassan A. Dutton D.J. Brunt K.R. Légaré J.F. An age-independent hospital record-based frailty score correlates with adverse outcomes after heart surgery and increased health care costs.J Thorac Cardiovasc Surg Open. 2021; 8: 491-502Google Scholar is strengthened with additional data, allowing for improved internal validation and codification. However, as the authors admit, the model still requires prospective external validation through not only its application to separate patient cohorts, but also through comparison with existing frailty-assessment modalities. Further, any exercise that identifies a vulnerable population in advance of cardiac surgery should be coupled with targeted interventions to mitigate risk. To that end, fledgling examples of preoperative optimization (or “prehabilitation”) have been focused on addressing individual modifiable risk factors, including preoperative anemia, sarcopenia, and exercise tolerance.6Yanagawa B. Graham M.M. Afilalo J. Hassan A. Arora R.C. Frailty as a risk predictor in cardiac surgery: beyond the eyeball test.J Thorac Cardiovasc Surg. 2019; 157: 1905-1909Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar,10Waite I. Deshpande R. Baghai M. Massey T. Wendler O. Greenwood S. Home-based preoperative rehabilitation (prehab) to improve physical function and reduce hospital length of stay for frail patients undergoing coronary artery bypass graft and valve surgery.J Cardiothorac Surg. 2017; 12: 91Crossref PubMed Scopus (77) Google Scholar, 11Arthur H.M. Daniels C. McKelvie R. Hirsh J. Rush B. Effect of a preoperative intervention on preoperative and postoperative outcomes in low-risk patients awaiting elective coronary artery bypass graft surgery: a randomized, controlled trial.Ann Intern Med. 2000; 133: 253-262Crossref PubMed Scopus (271) Google Scholar, 12Engelman D.T. Ben Ali W. Williams J.B. Perrault L.P. Reddy V.S. Arora R.C. et al.Guidelines for perioperative care in cardiac surgery: Enhanced Recovery After Surgery Society Recommendations.JAMA Surg. 2019; 154: 755-766Crossref PubMed Scopus (232) Google Scholar However, in much the same fashion that preoperative risk assessment has expanded to acknowledge the many interrelated domains that contribute to the frailty diagnosis, preoperative optimization should be equally multifaceted, with protocols developed to comprehensively address highlighted deficits. Time will tell if more automated risk stratification can inform better care for our most vulnerable patients, but our growing understanding of frailty suggests that the old saying is true: age is only a number. An age-independent hospital record-based frailty score correlates with adverse outcomes after heart surgery and increased health care costsJTCVS OpenVol. 8PreviewGlobally, an increasing number of vulnerable or frail patients are undergoing cardiac surgery. However, large-scale frailty data are often limited by the need for time-consuming frailty assessments. This study aimed to (1) create a retrospective registry-based frailty score (FS), (2) determine its effect on outcomes and age, and (3) health care costs. Full-Text PDF Open Access

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,135
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

Tête enseignante Opus0,022
Tête enseignante GPT0,322
Écart entre enseignants0,300 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJTCVS OpenMême sujetFrailty in Older AdultsTravaux en français237 207