Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.


Journal

JAMA
ISSN: 1538-3598
Titre abrégé: JAMA
Pays: United States
ID NLM: 7501160

Informations de publication

Date de publication:
24 01 2023
Historique:
entrez: 24 1 2023
pubmed: 25 1 2023
medline: 27 1 2023
Statut: ppublish

Résumé

Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Identifiants

pubmed: 36692561
pii: 2800662
doi: 10.1001/jama.2022.24683
pmc: PMC10408266
doi:

Types de publication

Comparative Study Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

306-317

Subventions

Organisme : NINDS NIH HHS
ID : R61 NS120246
Pays : United States
Organisme : NINDS NIH HHS
ID : U01 NS041588
Pays : United States

Commentaires et corrections

Type : ErratumIn

Références

Am J Epidemiol. 1989 Apr;129(4):687-702
pubmed: 2646917
BMC Med Inform Decis Mak. 2011 Jun 22;11:45
pubmed: 21696604
J Clin Epidemiol. 2019 Jun;110:12-22
pubmed: 30763612
Stroke. 1991 Mar;22(3):312-8
pubmed: 2003301
Stroke. 1994 Jan;25(1):40-3
pubmed: 8266381
Circulation. 2017 Mar 21;135(12):1145-1159
pubmed: 28159800
Sci Rep. 2021 Apr 7;11(1):7567
pubmed: 33828178
Neuroepidemiology. 2005;25(3):135-43
pubmed: 15990444
Science. 2019 Oct 25;366(6464):447-453
pubmed: 31649194
BMJ. 2016 Jan 25;352:i6
pubmed: 26810254
BMC Med Ethics. 2017 Mar 1;18(1):19
pubmed: 28249596
Stat Methods Med Res. 2019 Sep;28(9):2768-2786
pubmed: 30032705
BMJ. 2015 Jan 07;350:g7594
pubmed: 25569120
Stroke. 2017 Jul;48(7):1737-1743
pubmed: 28526763
Stat Med. 2011 May 10;30(10):1105-17
pubmed: 21484848
Stroke. 2014 Dec;45(12):3754-832
pubmed: 25355838
Am J Epidemiol. 2002 Nov 1;156(9):871-81
pubmed: 12397006
J Am Med Inform Assoc. 2021 Mar 1;28(3):549-558
pubmed: 33236066
BMC Med Res Methodol. 2016 Nov 3;16(1):148
pubmed: 27809784
Stroke. 2011 Feb;42(2):517-84
pubmed: 21127304
NPJ Digit Med. 2018 May 8;1:18
pubmed: 31304302
J Stat Softw. 2011 Mar;39(5):1-13
pubmed: 27065756
Circulation. 2021 Feb 23;143(8):e254-e743
pubmed: 33501848
J Am Coll Cardiol. 2014 Jul 1;63(25 Pt A):2886
pubmed: 24768878
Am J Epidemiol. 2017 Jun 1;185(11):1093-1102
pubmed: 30052741

Auteurs

Chuan Hong (C)

Duke AI Health, Durham, North Carolina.
Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina.

Michael J Pencina (MJ)

Duke AI Health, Durham, North Carolina.
Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
Duke Clinical Research Institute, Durham, North Carolina.

Daniel M Wojdyla (DM)

Duke Clinical Research Institute, Durham, North Carolina.

Jennifer L Hall (JL)

American Heart Association, Dallas, Texas.

Suzanne E Judd (SE)

School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama.

Michael Cary (M)

Duke AI Health, Durham, North Carolina.
Duke University School of Nursing, Durham, North Carolina.

Matthew M Engelhard (MM)

Duke AI Health, Durham, North Carolina.
Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina.

Samuel Berchuck (S)

Department of Statistical Science, Duke University School of Medicine, Durham, North Carolina.

Ying Xian (Y)

Department of Neurology, University of Texas Southwestern Medical Center, Dallas.

Ralph D'Agostino (R)

Department of Mathematics & Statistics, Boston University Arts and Sciences, Boston, Massachusetts.

George Howard (G)

School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama.

Brett Kissela (B)

College of Medicine, University of Cincinnati, Cincinnati, Ohio.

Ricardo Henao (R)

Duke AI Health, Durham, North Carolina.
Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina.

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