Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".