Can risk modelling improve treatment decisions in asymptomatic carotid stenosis?
Autor: | Burke JF; Deparment of Neurology, University of Michigan, Ann Arbor, USA. jamesbur@med.umich.edu.; Deparment of Neurology, Ann Arbor VA, Ann Arbor, USA. jamesbur@med.umich.edu.; RWJ Clinical Scholars Program, Room G100-36, Building 14, NCRC, 2800 Plymouth Rd, Ann Arbor, MI, 48109, USA. jamesbur@med.umich.edu., Morgenstern LB; Deparment of Neurology, University of Michigan, Ann Arbor, USA., Hayward RA; Deparment of Internal Medicine, University of Michigan, Ann Arbor, USA.; The Center for Clinical Management and Research, Ann Arbor VA, Ann Arbor, USA. |
---|---|
Jazyk: | angličtina |
Zdroj: | BMC neurology [BMC Neurol] 2019 Nov 22; Vol. 19 (1), pp. 295. Date of Electronic Publication: 2019 Nov 22. |
DOI: | 10.1186/s12883-019-1528-7 |
Abstrakt: | Background: Carotid endarterectomy (CEA) is routinely performed for asymptomatic carotid stenosis, yet its average net benefit is small. Risk stratification may identify high risk patients that would clearly benefit from treatment. Methods: Retrospective cohort study using data from the Asymptomatic Carotid Atherosclerosis Study (ACAS). Risk factors for poor outcomes were included in backward and forward selection procedures to develop baseline risk models estimating the risk of non-perioperative ipsilateral stroke/TIA. Baseline risk was estimated for all ACAS participants and externally validated using data from the Atherosclerosis Risk in Communities (ARIC) study. Baseline risk was then included in a treatment risk model that explored the interaction of baseline risk and treatment status (CEA vs. medical management) on the patient-centered outcome of any stroke or death, including peri-operative events. Results: Three baseline risk factors (BMI, creatinine and degree of contralateral stenosis) were selected into our baseline risk model (c-statistic 0.59 [95% CI 0.54-0.65]). The model stratified absolute risk between the lowest and highest risk quintiles (5.1% vs. 12.5%). External validation in ARIC found similar predictiveness (c-statistic 0.58 [0.49-0.67]), but poor calibration across the risk spectrum. In the treatment risk model, CEA was superior to medical management across the spectrum of baseline risk and the magnitude of the treatment effect varied widely between the lowest and highest absolute risk quintiles (3.2% vs. 10.7%). Conclusion: Even modestly predictive risk stratification tools have the potential to meaningfully influence clinical decision making in asymptomatic carotid disease. However, our ACAS model requires target population recalibration prior to clinical application. |
Databáze: | MEDLINE |
Externí odkaz: | |
Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |