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Canadian Journal of Anesthesia/Journal canadien d'anesthésie

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28.12.2022 | Reports of Original Investigations

Performance of administrative database frailty instruments in predicting clinical outcomes and cost for patients undergoing transcatheter aortic valve implantation: a historical cohort study

verfasst von: Zhe Li, PhD, MPH, Harindra C. Wijeysundera, MD, PhD, Rodrigo Bagur, MD, PhD, FAHA, FSCAI, Davy Cheng, MD, Janet Martin, PharmD, MSc, Bob Kiaii, MD, Feng Qiu, MSc, Jiming Fang, PhD, Ava John-Baptiste, PhD

Erschienen in: Canadian Journal of Anesthesia/Journal canadien d'anesthésie | Ausgabe 1/2023

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Abstract

Purpose

Frailty instruments may improve prognostic estimates for patients undergoing transcatheter aortic valve implantation (TAVI). Few studies have evaluated and compared the performance of administrative database frailty instruments for patients undergoing TAVI. This study aimed to examine the performance of administrative database frailty instruments in predicting clinical outcomes and costs in patients who underwent TAVI.

Methods

We conducted a historical cohort study of 3,848 patients aged 66 yr or older who underwent a TAVI procedure in Ontario, Canada from 1 April 2012 to 31 March 2018. We used the Johns Hopkins Adjusted Clinical Group (ACG) frailty indicator and the Hospital Frailty Risk Score (HFRS) to assign frailty status. Outcomes of interest were in-hospital mortality, one-year mortality, rehospitalization, and healthcare costs. We compared the performance of the two frailty instruments with that of a reference model that adjusted baseline covariates and procedural characteristics. Accuracy measures included c-statistics, Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated discrimination improvement (IDI), net reclassification index (NRI), bias, and accuracy of cost estimates.

Results

A total of 863 patients (22.4%) were identified as frail using the Johns Hopkins ACG frailty indicator and 865 (22.5%) were identified as frail using the HFRS. Although agreement between the frailty instruments was fair (Kappa statistic = 0.322), each instrument classified different subgroups as frail. Both the Johns Hopkins ACG frailty indicator (rate ratio [RR], 1.13; 95% confidence interval [CI], 1.06 to 1.20) and the HFRS (RR, 1.14; 95% CI, 1.07 to 1.21) were significantly associated with increased one-year costs. Compared with the reference model, both the Johns Hopkins ACG frailty indicator and HFRS significantly improved NRI for one-year mortality (Johns Hopkins ACG frailty indicator: NRI, 0.160; P < 0.001; HFRS: NRI, 0.146; P = 0.001) and rehospitalization (Johns Hopkins ACG frailty indicator: NRI, 0.201; P < 0.001; HFRS: NRI, 0.141; P = 0.001). These improvements in NRI largely resulted from classification improvement among those who did not experience the event. With one-year mortality, there was a significant improvement in IDI (IDI, 0.003; P < 0.001) with the Johns Hopkins ACG frailty indicator. This improvement in performance resulted from an increase in the mean probability of the event among those with the event.

Conclusion

Preoperative frailty assessment may add some predictive value for TAVI outcomes. Use of administrative database frailty instruments may provide small but significant improvements in case-mix adjustment when profiling hospitals for certain outcomes.

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ICES (formerly Institute for Clinical Evaluative Sciences). Available from URL: https://www.ices.on.ca (accessed August 2022).

Drug Product Database. Available from URL: https://www.canada.ca/en/health-canada/services/drugs-health-products/drug-products/drug-product-database.html (accessed September 2022).

Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001; 56: M146–56. https://doi.org/10.1093/gerona/56.3.m146CrossRefPubMed

Afilalo J, Alexander KP, Mack MJ, et al. Frailty assessment in the cardiovascular care of older adults. J Am Coll Cardiol 2014; 63: 747–62. https://doi.org/10.1016/j.jacc.2013.09.070CrossRefPubMed

Li Z, Dawson E, Moodie J, et al. Measurement and prognosis of frail patients undergoing transcatheter aortic valve implantation: a systematic review and meta-analysis. BMJ Open 2021; 11: e040459. https://doi.org/10.1136/bmjopen-2020-040459CrossRefPubMedPubMedCentral

Afilalo J, Lauck S, Kim DH, et al. Frailty in older adults undergoing aortic valve replacement the FRAILTY-AVR study. J Am Coll Cardiol 2017; 70: 689–700. https://doi.org/10.1016/j.jacc.2017.06.024CrossRefPubMed

Green P, Woglom AE, Genereux P, et al. The impact of frailty status on survival after transcatheter aortic valve replacement in older adults with severe aortic stenosis: a single-center experience. JACC Cardiovasc Interv 2012; 5: 974–81. https://doi.org/10.1016/j.jcin.2012.06.011CrossRefPubMedPubMedCentral

Goldfarb M, Bendayan M, Rudski LG, et al. Cost of cardiac surgery in frail compared with nonfrail older adults. Can J Cardiol 2017; 33: 1020–6. https://doi.org/10.1016/j.cjca.2017.03.019CrossRefPubMed

Zampieri FG, Iwashyna TJ, Viglianti EM, et al. Association of frailty with short-term outcomes, organ support and resource use in critically ill patients. Intensive Care Med 2018; 44: 1512–20. https://doi.org/10.1007/s00134-018-5342-2CrossRefPubMed

Nishimura RA, Otto CM, Bonow RO, et al. 2017 AHA/ACC focused update of the 2014 AHA/ACC guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2017; 70: 252–89. https://doi.org/10.1016/j.jacc.2017.03.011CrossRefPubMed

Kim DH, Kim CA, Placide S, Lipsitz LA, Marcantonio E. Preoperative frailty assessment and outcomes at 6 months or later in older adults undergoing cardiac surgical procedures: a systematic review. Ann Intern Med 2016; 165: 650–60. https://doi.org/10.7326/m16-0652CrossRefPubMedPubMedCentral

10.

Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: a review. Eur J Intern Med 2016; 31: 3–10. https://doi.org/10.1016/j.ejim.2016.03.007CrossRefPubMed

11.

Gilbert T, Neuburger J, Kraindler J, et al. Development and validation of a hospital frailty risk score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet 391: 1775–82. https://doi.org/10.1016/s0140-6736(18)30668-8

12.

McIsaac DI, Wong CA, Huang A, Moloo H, van Walraven C. Derivation and validation of a generalizable preoperative frailty index using population-based health administrative data. Ann Surg 2019; 270: 102–8. https://doi.org/10.1097/sla.0000000000002769CrossRefPubMed

13.

Alkadri J, Hage D, Nickerson LH, et al. A systematic review and meta-analysis of preoperative frailty instruments derived from electronic health data. Anesth Analg 2021; 133: 1094–1106. https://doi.org/10.1213/ane.0000000000005595CrossRefPubMed

14.

Kundi H, Popma JJ, Reynolds MR, et al. Frailty and related outcomes in patients undergoing transcatheter valve therapies in a nationwide cohort. Eur Heart J 2019; 40: 2231–9. https://doi.org/10.1093/eurheartj/ehz187CrossRefPubMedPubMedCentral

15.

Sami F, Ranka S, Shah A, Torres C, Villablanca P. Impact of frailty on outcomes in patients undergoing transcatheter aortic valve replacement: a report from national inpatient sample. J Am Coll Cardiol 2020; 75: 1487. https://doi.org/10.1016/S0735-1097(20)32114-8CrossRef

16.

Sternberg SA, Bentur N, Abrams C, et al. Identifying frail older people using predictive modeling. Am J Manag Care 2012; 18: 392–7.

17.

Malik AH, Yandrapalli S, Zaid S, et al. Impact of frailty on mortality, readmissions, and resource utilization after TAVI. Am J Cardiol 2020; 127: 120–7. https://doi.org/10.1016/j.amjcard.2020.03.047CrossRefPubMed

18.

Udell JA, Koh M, Qiu F, et al. Outcomes of women and men with acute coronary syndrome treated with and without percutaneous coronary revascularization. J Am Heart Assoc. 2017; 6(1): 1–10. https://doi.org/10.1161/JAHA.116.004319

19.

Jaakkimainen RS, Bronskill SE, Tierney MC, et al. Identification of physician-diagnosed Alzheimer’s disease and related dementias in population-based administrative data: a validation study using family physicians’ electronic medical records. J Alzheimers Dis 2016; 54: 337–49. https://doi.org/10.3233/jad-160105CrossRefPubMed

20.

Wammes JJ, van der Wees PJ, Tanke MA, Westert GP, Jeurissen PP. Systematic review of high-cost patients’ characteristics and healthcare utilisation. BMJ Open 2018; 8: e023113. https://doi.org/10.1136/bmjopen-2018-023113

21.

Wodchis WP, Bushmeneva K, Nikitovic M, Mckillop I. Guidelines on person-level costing using administrative databases in Ontario, 2013. Available from URL: https://tspace.library.utoronto.ca/handle/1807/87373 (accessed July 2022).

22.

Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput 2009; 38: 1228–34. https://doi.org/10.1080/03610910902859574CrossRef

23.

Mihaylova B, Briggs A, O’Hagan A, Thompson SG. Review of statistical methods for analysing healthcare resources and costs. Health Econ 2011; 20: 897–916. https://doi.org/10.1002/hec.1653CrossRefPubMed

24.

Blough DK, Ramsey SD. Using generalized linear models to assess medical care costs. Health Serv Outcomes Res Methodol 2000; 1: 185–202.CrossRef

25.

Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med 2005; 37: 360–3.PubMed

26.

Austin PC, Tu JV, Alter DA. Comparing hierarchical modeling with traditional logistic regression analysis among patients hospitalized with acute myocardial infarction: should we be analyzing cardiovascular outcomes data differently? Am Heart J 2003; 145: 27–35. https://doi.org/10.1067/mhj.2003.23CrossRefPubMed

27.

Wijeysundera HC, Li L, Braga V, et al. Drivers of healthcare costs associated with the episode of care for surgical aortic valve replacement versus transcatheter aortic valve implantation. Open Heart 2016; 3: e000468. https://doi.org/10.1136/openhrt-2016-000468CrossRefPubMedPubMedCentral

28.

Akinwande MO, Dikko HG, Samson A. Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis. Open J Stat 2015; 5(07): 754.CrossRef

29.

Cundill B, Alexander ND. Sample size calculations for skewed distributions. BMC Med Res Methodol 2015; 15: 28. https://doi.org/10.1186/s12874-015-0023-0CrossRefPubMedPubMedCentral

30.

Van Smeden M, de Groot JA, Moons KG, et al. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Med Res Methodol 2016; 16: 1–12. https://doi.org/10.1186/s12874-016-0267-3CrossRef

31.

Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008; 27: 157–72. https://doi.org/10.1002/sim.2929CrossRefPubMed

32.

Austin PC, Steyerberg EW. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med 2014; 33: 517–35. https://doi.org/10.1002/sim.5941CrossRefPubMed

33.

Pencina MJ, D’Agostino RB Sr. Evaluating discrimination of risk prediction models: the C statistic. JAMA 2015; 314: 1063–4. https://doi.org/10.1001/jama.2015.11082CrossRefPubMed

34.

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837–45. https://doi.org/10.2307/2531595CrossRefPubMed

35.

Neath AA, Cavanaugh JE. The Bayesian information criterion: background, derivation, and applications. Wiley Interdiscip Rev Comput Stat 2012; 4: 199–203. https://doi.org/10.1002/wics.199CrossRef

36.

Vrieze SI. Model selection and psychological theory: a discussion of the differences between the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Psychol Methods 2014; 17: 228–43. https://doi.org/10.1037/a0027127CrossRef

37.

Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21: 128–38. https://doi.org/10.1097/ede.0b013e3181c30fb2CrossRefPubMedPubMedCentral

38.

Austin PC, Ghali WA, Tu JV. A comparison of several regression models for analysing cost of CABG surgery. Stat Med 2003; 22: 2799–815. https://doi.org/10.1002/sim.1442CrossRefPubMed

39.

Muratov S, Lee J, Holbrook A, et al. Incremental healthcare utilisation and costs among new senior high to cost users in Ontario, Canada: A retrospective matched cohort study. BMJ Open. 2019; 9(10): 1–9. https://doi.org/10.1136/bmjopen-2018-028637

40.

Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29–36. https://doi.org/10.1148/radiology.143.1.7063747CrossRefPubMed

41.

Tjur T. Coefficients of determination in logistic regression models - a new proposal: the coefficient of discrimination. Am Stat 2009; 63: 366–72. https://doi.org/10.1198/tast.2009.08210CrossRef

42.

Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation 2016; 133: 601–9. https://doi.org/10.1161/circulationaha.115.017719CrossRefPubMedPubMedCentral

43.

Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. Am J Epidemiol 2009; 170: 244–56. https://doi.org/10.1093/aje/kwp107CrossRefPubMedPubMedCentral

44.

Kundi H, Valsdottir LR, Popma JJ, et al. Impact of a claims-based frailty indicator on the prediction of long-term mortality after transcatheter aortic valve in Medicare beneficiaries. Circ Cardiovasc Qual Outcomes 2018; 11: 1–8. https://doi.org/10.1161/circoutcomes.118.005048CrossRef

45.

Aguayo GA, Donneau AF, Vaillant MT, et al. Practice of epidemiology agreement between 35 published frailty scores in the general population. Am J Epidemiol 2017; 186: 420–34. https://doi.org/10.1093/aje/kwx061CrossRefPubMedPubMedCentral

46.

Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr 2008; 8: 1–10. https://doi.org/10.1186/1471-2318-8-24CrossRef

47.

Henning KA, Ravindran M, Qiu F, et al. Impact of procedural capacity on transcatheter aortic valve replacement wait times and outcomes: A study of regional variation in Ontario, Canada. Open Heart 2020; 7: e001241. https://doi.org/10.1136/openhrt-2020-001241CrossRefPubMedPubMedCentral

48.

Czarnecki A, Qiu F, Henning KA, et al. Comparison of 1-year pre- and post-transcatheter aortic valve replacement hospitalization rates: a population-based cohort study. Can J Cardiol 2020; 36: 1616–23. https://doi.org/10.1016/j.cjca.2020.01.009CrossRefPubMed

Titel: Performance of administrative database frailty instruments in predicting clinical outcomes and cost for patients undergoing transcatheter aortic valve implantation: a historical cohort study
verfasst von: Zhe Li, PhD, MPH
Harindra C. Wijeysundera, MD, PhD
Rodrigo Bagur, MD, PhD, FAHA, FSCAI
Davy Cheng, MD
Janet Martin, PharmD, MSc
Bob Kiaii, MD
Feng Qiu, MSc
Jiming Fang, PhD
Ava John-Baptiste, PhD
Publikationsdatum: 28.12.2022
Verlag: Springer International Publishing
Erschienen in: Canadian Journal of Anesthesia/Journal canadien d'anesthésie / Ausgabe 1/2023
Print ISSN: 0832-610X
Elektronische ISSN: 1496-8975
DOI: https://doi.org/10.1007/s12630-022-02354-6

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Performance of administrative database frailty instruments in predicting clinical outcomes and cost for patients undergoing transcatheter aortic valve implantation: a historical cohort study

Abstract

Purpose

Methods

Results

Conclusion

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Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

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