Cardiometabolic Center of Excellence: A Novel Care Delivery Model for Secondary Prevention of Cardiovascular Disease in Type 2 Diabetes
2021; Lippincott Williams & Wilkins; Volume: 14; Issue: 10 Linguagem: Inglês
10.1161/circoutcomes.120.007682
ISSN1941-7705
AutoresMerrill Thomas, Melissa Magwire, Kensey Gosch, Yasser Sammour, Rane Mehta, James H. O’Keefe, Michael E. Nassif, Mikhail Kosiborod,
Tópico(s)Heart Failure Treatment and Management
ResumoHomeCirculation: Cardiovascular Quality and OutcomesVol. 14, No. 10Cardiometabolic Center of Excellence: A Novel Care Delivery Model for Secondary Prevention of Cardiovascular Disease in Type 2 Diabetes Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toSupplementary MaterialsFree AccessResearch ArticlePDF/EPUBCardiometabolic Center of Excellence: A Novel Care Delivery Model for Secondary Prevention of Cardiovascular Disease in Type 2 Diabetes Merrill Thomas, MD, Melissa Magwire, RN, MSN, CDE, Kensey Gosch, MS, Yasser Sammour, MD, Rane Mehta, RN, FNP-C, DNP, James O'Keefe, MD, Michael E. Nassif, MD, MS and Mikhail Kosiborod, MD Merrill ThomasMerrill Thomas Correspondence to: Merrill Thomas, MD, Department of Cardiovascular Medicine, St Luke's Mid America Heart Institute, 4401 Wornall Rd, CV Research 9th Floor, Kansas City, MO 64111. Email E-mail Address: [email protected] https://orcid.org/0000-0003-3418-6424 Saint Luke's Mid America Heart Institute, Kansas City, MO (M.T., M.M., K.G., R.M., J.O., M.E.N., M.K.). University of Missouri-Kansas City School of Medicine (M.T., Y.S., J.O., M.E.N., M.K.). , Melissa MagwireMelissa Magwire Saint Luke's Mid America Heart Institute, Kansas City, MO (M.T., M.M., K.G., R.M., J.O., M.E.N., M.K.). , Kensey GoschKensey Gosch https://orcid.org/0000-0003-2934-6913 Saint Luke's Mid America Heart Institute, Kansas City, MO (M.T., M.M., K.G., R.M., J.O., M.E.N., M.K.). , Yasser SammourYasser Sammour https://orcid.org/0000-0002-1763-9340 University of Missouri-Kansas City School of Medicine (M.T., Y.S., J.O., M.E.N., M.K.). , Rane MehtaRane Mehta Saint Luke's Mid America Heart Institute, Kansas City, MO (M.T., M.M., K.G., R.M., J.O., M.E.N., M.K.). , James O'KeefeJames O'Keefe Saint Luke's Mid America Heart Institute, Kansas City, MO (M.T., M.M., K.G., R.M., J.O., M.E.N., M.K.). University of Missouri-Kansas City School of Medicine (M.T., Y.S., J.O., M.E.N., M.K.). , Michael E. NassifMichael E. Nassif Saint Luke's Mid America Heart Institute, Kansas City, MO (M.T., M.M., K.G., R.M., J.O., M.E.N., M.K.). University of Missouri-Kansas City School of Medicine (M.T., Y.S., J.O., M.E.N., M.K.). and Mikhail KosiborodMikhail Kosiborod https://orcid.org/0000-0002-3750-9789 Saint Luke's Mid America Heart Institute, Kansas City, MO (M.T., M.M., K.G., R.M., J.O., M.E.N., M.K.). University of Missouri-Kansas City School of Medicine (M.T., Y.S., J.O., M.E.N., M.K.). Originally published30 Sep 2021https://doi.org/10.1161/CIRCOUTCOMES.120.007682Circulation: Cardiovascular Quality and Outcomes. 2021;14Cardiovascular disease (CVD) is the leading cause of morbidity and mortality among patients with type 2 diabetes (T2D).1 Although evidence-based therapies reduce the risk of cardiovascular events and are embraced by professional societies, optimal guideline-directed medical therapy (GDMT) is delivered to 10% being considered clinically relevant.For baseline variables, continuous variables are reported using mean and SDs, and categorical variables using counts and percentages. Descriptive variables were compared using standardized differences. Linear regression models (adjusted for age, sex, baseline weight, HbA1c, SBP, history of CVD, time between visits) were used to compare between-group differences for changes in weight, LDL-C, SBP, and HbA1c. A similar model was used to assess change in insulin dose (among those on insulin at baseline), additionally adjusting for baseline insulin dose. Modified Poisson regression models for medication status at follow-up, adjusted for the same covariates, were also performed. Typical analyses for binary outcomes use logistic regression to estimate odds ratios, which are generally interpreted as relative risks. However, in this study, odds ratios are poor estimates of relative risks since the outcomes being modeled are not rare. Poisson method yields relative risks and appropriately adjusts CIs using robust error variance to model binomial data. All continuous covariates in the models included a restricted cubic spline term to account for possible nonlinear associations.All statistical analyses were performed using SAS version 9.4 software (SAS Institute, Inc, Cary, NC). Two-sided P<0.05 were considered statistically significant.ResultsThere were 130 patients with at least one follow-up visit at CMC and 3149 patients with at least one follow-up visit in conventional care settings during the same timeframe. Of the 130 CMC patients, 129 were propensity matched (1:3) to 387 patients in conventional settings. Postpropensity match, baseline characteristics were similar between groups, with all standardized differences <10% except for atherosclerotic CVD (standardized difference 11.5%; Table I in the Data Supplement).Based on the propensity matched, modified Poisson models, at follow-up, CMC patients had a higher rate of GDMT, ACE inhibitor, high-intensity statins, and SGLT-2i or GLP-1RA (Table). Use of ARBs was similar.Table. Comparison of Guideline-Directed Medical Therapies at Follow-UpCardiometabolic clinic (n=129)Control (n=387)RR (CI)P valueGDMT53 (41.1%)9 (2.3%)17.75 (8.94–35.26)<0.0001SGLT-2i/GLP-1RA124 (96.1%)99 (25.7%)3.61 (3.03–4.30)<0.0001ACE inhibitor39 (30.2%)35 (9.1%)2.77 (1.78–4.31)<0.0001Statin111 (86.0%)299 (77.7%)1.08 (0.99–1.19)0.07High-intensity statin81 (62.8%)190 (51.4%)1.25 (1.07–1.46)<0.01ARB39 (30.2%)128 (33.2%)0.95 (0.71–1.28)0.76GDMT defined as high-intensity statin, antiplatelet or anticoagulant, ACE inhibitor/ARB, and either SGLT-2i or GLP-1RA. ACE indicates angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; GDMT, guideline-directed medical therapy; GLP-1RA, glucagon-like peptide 1 receptor agonists; and SGLT-2i, sodium-glucose cotransporter 2 inhibitor.Similarly, in the propensity-matched linear regression models, CMC patients had a greater reduction in weight (−10.9 versus −1.5 lbs, P<0.001), HbA1c (−0.5% versus −0.2%, P=0.02), SBP (−3.6 versus +1.4 mm Hg, P<0.01), LDL (12.1 versus −2.8 mg/dL, P<0.01), and total daily insulin dose (−31.6 versus +1.1 units, P<0.001) as compared with the control group (Figures II and III in the Data Supplement).Baseline covariate data were complete on all 516 patients. Missing outcome data was minimal for changes in weight (0.8%), SBP (0.8%), insulin dose (0.4%), and medications (0%). However, there was larger missingness for follow-up LDL (51%) and HbA1C (42%; nearly all in control group). For these outcomes, inverse propensity weighting was implemented to account for possible bias. We calculated a nonparsimonious propensity score with successful follow-up lab data as the dependent variable. An inversely weighted propensity score was assigned to each patient with lab outcomes available to provide greater weight to the patients who were most like those with missing follow-up lab data. Results were comparable with and without weighting thus only unweighted analyses are presented.Translation to Other SettingsWe think that this novel care delivery model is replicable, scalable, and implementable in other health systems. While the navigator role is essential for the model, effective education can be provided to nurses to adapt their current skillsets to take on these duties. We are working with key stakeholders to develop such training for nurses at other centers. Additionally, there is an opportunity for other clinicians such as PharmDs and certified diabetes educators to fulfill the navigator role. Our model has preventive cardiologist champions leading the initial evaluation, but advanced practice providers, such as nurse practitioners or physician assistants, could take their place in many cases. If the standards of practice are well developed, codified in protocols, and there is appropriate oversight, this model can be easily adapted across different settings.Summary, Future Directions, and ChallengesDespite numerous calls by professional societies to develop effective multidisciplinary collaborative approaches to care of complex patients with T2D and CVD, such models have been nascent or nonexistent. In this single-center study, we successfully initiated a care model that uses a patient-centered, multidisciplinary, team-based approach to provide evidence-based multifaceted secondary risk reduction in patients with T2D and CVD. Our early results show significantly greater improvement in cardiovascular risk and higher rates of GDMT and suggest that this approach can rapidly improve the quality of care. Therefore, care delivery models such as this should be an integral part of future efforts to improve chronic disease management in high-risk patients with multiple comorbidities, especially in settings that emphasize value-based care.Despite the known benefits, composite CVD risk factor reduction and use of optimal GDMT in patients with T2D is low.2 Barriers to effective care include lack of time and resources, failure to adopt guideline-based recommendations, and uncertainty where clinical responsibility lies due to changing professional boundaries. Given the availability of new effective therapies, the complexity in caring and personalizing treatment for patients with T2D is increasing. With such barriers, a multidisciplinary team approach for comprehensive management of both T2D and CVD represents an ideal solution. We demonstrate that a center focused squarely on these priorities improves both uptake of GDMT and risk factor control.Key to the success of the CMC is the navigator role. Navigator at the CMC ensures appropriateness of patient selection and performs a cardiometabolic assessment that includes both T2D and CVD history and a preappointment coverage evaluation for access to GDMT. However, the navigator role has been approached differently in a study evaluating optimization of GDMT in patients with heart failure and reduced ejection fraction.7 In that study, a navigator was used to direct medication changes and assess symptoms, blood pressure, and laboratory tests, which resulted in more optimal use of GDMT. Collectively, these studies suggest that a navigator-driven approach can help to optimize GDMT in complex patients likely by overcoming time and resource constraints typically faced by other clinicians.Although CMC has demonstrated success in improving patient outcomes, to expand and remain successful, there is a need for more clinicians. The expansion of the cardiometabolic team concept to other institutions will likely need involvement from clinician champions and collaborators across specialties, including cardiologists, endocrinologists, nephrologists, primary care, and internal medicine. In addition, we hope that PharmDs, physician assistants, and nurse practitioners can take on a larger role over time, especially in regard to follow-up care. To prove the value of this model, we will continue to track patient outcomes and resource use through the registry and leverage these results. As this model is scalable and replicable, and given a large unmet need, we plan on future expansion to other health care systems through the Cardiometabolic Center Alliance by helping other institutions build similar Centers of Excellence. Additionally, we think that the findings from the registry could be used to support an implementation trial, which could then allow this model to be explored for other complex medical conditions.In conclusion, we demonstrated that a patient-centered, team-based multidisciplinary approach to comprehensive cardiovascular risk reduction is feasible and highly effective within a short time period. Our experience can serve as a roadmap for implementation of similar care delivery models at other institutions.Sources of FundingDr Thomas is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number T32HL110837.Supplemental MaterialsTable IFigures I–IIIDisclosures Dr Magwire has served on the advisory/consultant board for Novo Nordisk and Boehringer Ingelheim. Dr O'Keefe has received honoraria from Astra Zeneca, Boehringer Ingelheim, Amgen, and Regeneron. Dr Nassif has served as a consultant for Roche Diagnostics, Amgen, and Vifor and has received honorarium from Abbott. Dr Kosiborod has served on the advisory board/consultant for Amgen, Applied Therapeutics, Astra Zeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Janssen, Merck (Diabetes), Novo Nordisk, Sanofi, and Vifor Pharma; has received research grants from Astra Zeneca and Boehringer Ingelheim; has received other research support from Astra Zeneca. The other authors report no conflicts.FootnotesThe Data Supplement is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCOUTCOMES.120.007682.For Sources of Funding and Disclosures, see page 1090.Correspondence to: Merrill Thomas, MD, Department of Cardiovascular Medicine, St Luke's Mid America Heart Institute, 4401 Wornall Rd, CV Research 9th Floor, Kansas City, MO 64111. Email [email protected]eduReferences1. National Diabetes Statistics Report 2020. U.S. Department of Health and Human Services, Center for Disease Control and Prevention, 2020.Date accessed: March 4, 2020. Available at: https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf.Google Scholar2. Arnold SV, de Lemos JA, Rosenson RS, Ballantyne CM, Liu Y, Mues KE, Alam S, Elliott-Davey M, Bhatt DL, Cannon CP, et al.; GOULD Investigators. Use of guideline-recommended risk reduction strategies among patients with diabetes and atherosclerotic cardiovascular disease.Circulation. 2019; 140:618–620. doi: 10.1161/CIRCULATIONAHA.119.041730LinkGoogle Scholar3. Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes.N Engl J Med. 2008; 358:580–591. doi: 10.1056/NEJMoa0706245CrossrefMedlineGoogle Scholar4. Arnold SV, Goyal A, Inzucchi SE, McGuire DK, Tang F, Mehta SN, Sperling LS, Maddox TM, Einhorn D, Wong ND, et al.. Quality of care of the initial patient cohort of the diabetes collaborative registry®.J Am Heart Assoc. 2017; 6:e005999. doi: 10.1161/JAHA.117.005999LinkGoogle Scholar5. Weng W, Tian Y, Kong SX, Ganguly R, Hersloev M, Brett J, Hobbs T. The prevalence of cardiovascular disease and antidiabetes treatment characteristics among a large type 2 diabetes population in the United States.Endocrinol Diabetes Metab. 2019; 2:e00076. doi: 10.1002/edm2.76CrossrefMedlineGoogle Scholar6. American Diabetes Association. 10 Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2019.Diabetes Care. 2019; 42:S103–123. doi: 10.2337/dc19-S010CrossrefMedlineGoogle Scholar7. Desai AS, Maclean T, Blood AJ, Bosque-Hamilton J, Dunning J, Fischer C, Fera L, Smith KV, Wagholikar K, Zelle D, et al.. Remote optimization of guideline-directed medical therapy in patients with heart failure with reduced ejection fraction.JAMA Cardiol. 2020; 5:1430–1434. doi: 10.1001/jamacardio.2020.3757CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails October 2021Vol 14, Issue 10Article InformationMetrics Download: 157 © 2021 American Heart Association, Inc.https://doi.org/10.1161/CIRCOUTCOMES.120.007682PMID: 34587753 Originally publishedSeptember 30, 2021 Keywordsmorbidityhypertensionmortalityblood glucosecardiovascular diseasePDF download SubjectsCardiovascular DiseaseSecondary PreventionRisk FactorsDiabetes, Type 2
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