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Li S, Xu Z, Zhang S, Sun H, Qin X, Zhu L, Jiang T, Zhou J, Yan F, Deng Q. Non-coding RNAs in acute ischemic stroke: from brain to periphery. Neural Regen Res 2025; 20:116-129. [PMID: 38767481 PMCID: PMC11246127 DOI: 10.4103/nrr.nrr-d-23-01292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/09/2023] [Accepted: 12/18/2023] [Indexed: 05/22/2024] Open
Abstract
Acute ischemic stroke is a clinical emergency and a condition with high morbidity, mortality, and disability. Accurate predictive, diagnostic, and prognostic biomarkers and effective therapeutic targets for acute ischemic stroke remain undetermined. With innovations in high-throughput gene sequencing analysis, many aberrantly expressed non-coding RNAs (ncRNAs) in the brain and peripheral blood after acute ischemic stroke have been found in clinical samples and experimental models. Differentially expressed ncRNAs in the post-stroke brain were demonstrated to play vital roles in pathological processes, leading to neuroprotection or deterioration, thus ncRNAs can serve as therapeutic targets in acute ischemic stroke. Moreover, distinctly expressed ncRNAs in the peripheral blood can be used as biomarkers for acute ischemic stroke prediction, diagnosis, and prognosis. In particular, ncRNAs in peripheral immune cells were recently shown to be involved in the peripheral and brain immune response after acute ischemic stroke. In this review, we consolidate the latest progress of research into the roles of ncRNAs (microRNAs, long ncRNAs, and circular RNAs) in the pathological processes of acute ischemic stroke-induced brain damage, as well as the potential of these ncRNAs to act as biomarkers for acute ischemic stroke prediction, diagnosis, and prognosis. Findings from this review will provide novel ideas for the clinical application of ncRNAs in acute ischemic stroke.
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Affiliation(s)
- Shuo Li
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Zhaohan Xu
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Shiyao Zhang
- Department of Neurology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu Province, China
| | - Huiling Sun
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Xiaodan Qin
- General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Lin Zhu
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Teng Jiang
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Junshan Zhou
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Fuling Yan
- Department of Neurology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu Province, China
| | - Qiwen Deng
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
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Melgarejo JD, Gurel K, Compton CR, Liu M, Guzman V, Assuras S, Levin BE, Elkind MSV, Ikram MK, Kavousi M, Ikram MA, Wright C, Crivello F, Laurent A, Tzourio C, Vernooij MW, Rundek T, Zhang Z, Bos D, Gutierrez J. Brain artery diameters and risk of dementia and stroke. Alzheimers Dement 2024; 20:2497-2507. [PMID: 38332543 PMCID: PMC11032539 DOI: 10.1002/alz.13712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 02/10/2024]
Abstract
INTRODUCTION We tested the association of brain artery diameters with dementia and stroke risk in three distinct population-based studies using conventional T2-weighted brain magnetic resonance imaging (MRI) images. METHODS We included 8420 adults > 40 years old from three longitudinal population-based studies with brain MRI scans. We estimated and meta-analyzed the hazard ratios (HRs) of the brain and carotids and basilar diameters associated with dementia and stroke. RESULT Overall and carotid artery diameters > 95th percentile increased the risk for dementia by 1.74 (95% confidence interval [CI], 1.13-2.68) and 1.48 (95% CI, 1.12-1.96) fold, respectively. For stroke, meta-analyses yielded HRs of 1.59 (95% CI, 1.04-2.42) for overall arteries and 2.11 (95% CI, 1.45-3.08) for basilar artery diameters > 95th percentile. DISCUSSION Individuals with dilated brain arteries are at higher risk for dementia and stroke, across distinct populations. Our findings underline the potential value of T2-weighted brain MRI-based brain diameter assessment in estimating the risk of dementia and stroke.
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Affiliation(s)
- Jesus D. Melgarejo
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Studies Coordinating CentreResearch Unit Hypertension and Cardiovascular EpidemiologyKU Leuven Department of Cardiovascular SciencesUniversity of LeuvenLeuvenBelgium
- Institute of NeuroscienceUniversity of Texas Rio Grande ValleyHarlingenTexasUSA
| | - Kursat Gurel
- Department of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Cassidy Rose Compton
- Department of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Minghua Liu
- Department of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Vanessa Guzman
- Department of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Stephanie Assuras
- Department of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
| | - Bonnie E. Levin
- Department of NeurologyMiller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Mitchell S. V. Elkind
- Department of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
- Department of EpidemiologyMailman School of Public Health Columbia UniversityNew YorkNew YorkUSA
| | - M. Kamran Ikram
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of NeurologyErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Maryam Kavousi
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
| | - M. Arfan Ikram
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Clinton Wright
- National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMarylandUSA
| | - Fabrice Crivello
- Institute of Neurodegenerative DiseasesUMR5293, Neurofunctional Imaging GroupBordeauxFrance
| | - Alexandre Laurent
- Institute of Neurodegenerative DiseasesUMR5293, Neurofunctional Imaging GroupBordeauxFrance
| | - Christophe Tzourio
- Bordeaux Population Health Research CenterInserm, University BordeauxBordeauxFrance
| | - Meike W. Vernooij
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Tatjana Rundek
- Department of Public Health Sciences and Evelyn F. McKnight Brain InstituteMiller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Zhen‐Yu Zhang
- Studies Coordinating CentreResearch Unit Hypertension and Cardiovascular EpidemiologyKU Leuven Department of Cardiovascular SciencesUniversity of LeuvenLeuvenBelgium
| | - Daniel Bos
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Studies Coordinating CentreResearch Unit Hypertension and Cardiovascular EpidemiologyKU Leuven Department of Cardiovascular SciencesUniversity of LeuvenLeuvenBelgium
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Jose Gutierrez
- Department of NeurologyVagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNew YorkUSA
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Stroke risk in older British men: Comparing performance of stroke-specific and composite-CVD risk prediction tools. Prev Med Rep 2022; 31:102098. [PMID: 36820364 PMCID: PMC9938339 DOI: 10.1016/j.pmedr.2022.102098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/14/2022] [Accepted: 12/22/2022] [Indexed: 12/25/2022] Open
Abstract
Stroke risk is currently estimated as part of the composite risk of cardiovascular disease (CVD). We investigated if composite-CVD risk prediction tools QRISK3 and Pooled Cohort Equations-PCE, derived from middle-aged adults, are as good as stroke-specific Framingham Stroke Risk Profile-FSRP and QStroke for capturing the true risk of stroke in older adults. External validation for 10y stroke outcomes was performed in men (60-79y) of the British Regional Heart Study. Discrimination and calibration were assessed in separate validation samples (FSRP n = 3762, QStroke n = 3376, QRISK3 n = 2669 and PCE n = 3047) with/without adjustment for competing risks. Sensitivity/specificity were examined using observed and clinically recommended thresholds. Performance of FSRP, QStroke and QRISK3 was further compared head-to-head in 2441 men free of a range of CVD, including across age-groups. Observed 10y risk (/1000PY) ranged from 6.8 (hard strokes) to 11 (strokes/transient ischemic attacks). All tools discriminated weakly, C-indices 0.63-0.66. FSRP and QStroke overestimated risk at higher predicted probabilities. QRISK3 and PCE showed reasonable calibration overall with minor mis-estimations across the risk range. Performance worsened on adjusting for competing non-stroke deaths. However, in men without CVD, QRISK3 displayed relatively better calibration for stroke events, even after adjustment for competing deaths, including in oldest men. All tools displayed similar sensitivity (63-73 %) and specificity (52-54 %) using observed risks as cut-offs. When QRISK3 and PCE were evaluated using thresholds for CVD prevention, sensitivity for stroke events was 99 %, with false positive rate 97 % suggesting existing intervention thresholds may need to be re-examined to reflect age-related stroke burden.
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Key Words
- AF, atrial fibrillation
- BRHS, British Regional Heart Study
- CHD, coronary heart disease
- CIF, cumulative incidence function
- CPI, centred prognostic index
- CVD, cardiovascular disease
- Calibration
- Cardiovascular disease
- Discrimination
- FSRP, Framingham stroke risk profile
- HF, heart failure
- KM, Kaplan-Meier
- MI, myocardial infarction
- NICE, National Institute For Health And Care Excellence
- Older adults
- PCE, pooled cohort equations
- PI, prognostic index
- Risk prediction
- SCORE, systematic coronary risk evaluation
- Sn/Sp, percent sensitivity/percent specificity
- Stroke
- TIA, transient ischemic attack
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Letellier N, Ilango SD, Mortamais M, Tzourio C, Gabelle A, Empana JP, Samieri C, Berr C, Benmarhnia T. Socioeconomic inequalities in dementia risk among a French population-based cohort: quantifying the role of cardiovascular health and vascular events. Eur J Epidemiol 2021; 36:1015-1023. [PMID: 34308532 PMCID: PMC8542549 DOI: 10.1007/s10654-021-00788-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 07/08/2021] [Indexed: 12/29/2022]
Abstract
This study aimed to investigate the role of cardiovascular health (CVH) and vascular events as potential contributors to socioeconomic inequalities in dementia using causal mediation analyses. We used data from the Three-City Cohort, a French population-based study with 12 years of follow-up, with active search of dementia cases and validated diagnosis. Individual socioeconomic status was assessed using education, occupation and income. A CVH score as defined by the American Heart Association and incident vascular events were considered separately as mediators. We performed multi-level Cox proportional and Aalen additive hazard regression models to estimate the total effects of socioeconomic status on dementia risk. To estimate natural direct and indirect effects through CVH and vascular events, we applied two distinct weighting methods to quantify the role of CVH and vascular events: Inverse Odds Ratio Weighting (IORW) and Marginal Structural Models (MSM) respectively. Among 5581 participants, the risk of dementia was higher among participants with primary education (HR 1.60, 95%CI 1.44–1.78), blue-collar workers (HR 1.62, 95%CI 1.43–1.84) and with lower income (HR 1.23, 95%CI 1.09–1.29). Using additive models, 571 (95% CI 288–782) and 634 (95% CI 246–1020) additional cases of dementia per 100 000 person and year were estimated for primary education and blue-collar occupation, respectively. Using IORW, the CVH score mediate the relationship between education or income, and dementia (proportion mediated 17% and 26%, respectively). Yet, considering vascular events as mediator, MSM generated indirect effects that were smaller and more imprecise. Socioeconomic inequalities in dementia risk were observed but marginally explained by CVH or vascular events mediators.
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Affiliation(s)
- Noémie Letellier
- Institute for Neurosciences of Montpellier INM, Univ Montpellier, INSERM, Montpellier, France.
| | - Sindana D Ilango
- School of Public Health, San Diego State University, San Diego, CA, USA
- Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
| | - Marion Mortamais
- Institute for Neurosciences of Montpellier INM, Univ Montpellier, INSERM, Montpellier, France
| | - Christophe Tzourio
- Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France
| | - Audrey Gabelle
- Institute for Neurosciences of Montpellier INM, Univ Montpellier, INSERM, Montpellier, France
- Memory Research and Resources Center, Department of Neurology, Montpellier University Hospital Gui de Chauliac, Montpellier, France
| | - Jean-Philippe Empana
- Université de Paris, INSERM U970, Paris Cardiovascular Research Center, Paris, France
| | - Cécilia Samieri
- Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France
| | - Claudine Berr
- Institute for Neurosciences of Montpellier INM, Univ Montpellier, INSERM, Montpellier, France
- Memory Research and Resources Center, Department of Neurology, Montpellier University Hospital Gui de Chauliac, Montpellier, France
| | - Tarik Benmarhnia
- Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
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Xu W, Huang J, Yu Q, Yu H, Pu Y, Shi Q. A systematic review of the status and methodological considerations for estimating risk of first ever stroke in the general population. Neurol Sci 2021; 42:2235-2247. [PMID: 33783660 DOI: 10.1007/s10072-021-05219-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/23/2021] [Indexed: 01/01/2023]
Abstract
AIMS The methodological quality of development, validation, and modification of those models have not been evaluated via a thoroughly literature review. This study aims to describe the overall status and evaluate the methodological quality of risk prediction models for stroke incidence in the general population. METHODS We searched the database of EMBASE and MEDLINE by the combination of subject words and key words to collect the research on stroke risk prediction model in the general population. The retrieval time was from the establishment of the database to September 2019. It should be mentioned that risk of bias for each model was assessed, and data on population characteristics and model performance was also extracted. RESULTS The search screened 11,386 peer-reviewed publications and 57 citation searching, of which 48 were included in the review, describing the development of 51 prediction models, 47 external validation models, and 12 modification models. Among 51 development models, the predicted outcome concentrated on fatal or non-fatal stroke (n = 37, 73%). Thirty-nine development models (76%) were without internal validation. C-statistic or AUC was adopted for discrimination in 80% models, and Hosmer-Lemeshow test (n = 25, 49%) was also performed for calibration. Twenty-six development models (53%) were externally validated, among which only 2 (8%) were validated by independent researchers. Risk prediction performance was improved when models were modified by adding novel risk factors, such as the internal carotid artery plaque and intima-media thickness. CONCLUSION Models for predicting stroke occurrence need further external validation, recalibration, or modification in different populations, to help interpret those models in the practice of stroke prevention.
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Affiliation(s)
- Wei Xu
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Jiuyi Huang
- Community Prevention Research Unit, Shanghai Institute of Cerebrovascular Disease Prevention, Shanghai, 201203, China
| | - Qingsong Yu
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Hongfan Yu
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Yang Pu
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China
| | - Qiuling Shi
- School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China.
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Kadir RRA, Alwjwaj M, Bayraktutan U. MicroRNA: An Emerging Predictive, Diagnostic, Prognostic and Therapeutic Strategy in Ischaemic Stroke. Cell Mol Neurobiol 2020; 42:1301-1319. [PMID: 33368054 PMCID: PMC9142420 DOI: 10.1007/s10571-020-01028-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 12/09/2020] [Indexed: 02/07/2023]
Abstract
Stroke continues to be the third-leading cause of death and disability worldwide. The limited availability of diagnostic tools approved therapeutics and biomarkers that help monitor disease progression or predict future events remain as the major challenges in the field of stroke medicine. Hence, attempts to discover safe and efficacious therapeutics and reliable biomarkers are of paramount importance. MicroRNAs (miRNAs) are a class of non-coding RNAs that play important roles in regulating gene expression. Since miRNAs also play important roles in key mechanisms associated with the pathogenesis of stroke, including energy failure, inflammation and cell death, it is possible that miRNAs may serve as reliable blood-based markers for risk prediction, diagnosis and prognosis of ischaemic stroke. Discovery of better neurological outcome and smaller cerebral infarcts in animal models of ischaemic stroke treated with miRNA agomirs or antagomirs indicate that miRNAs may also play a cerebrovascular protective role after an ischaemic stroke. Nonetheless, further evidences on the optimum time for treatment and route of administration are required before effective translation of these findings into clinical practice. Bearing these in mind, this paper reviews the current literature discussing the involvement of miRNAs in major pathologies associated with ischaemic stroke and evaluates their value as reliable biomarkers and therapeutics for ischaemic stroke.
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Huang ZX, Chen LH, Xiong R, He YN, Zhang Z, Zeng J, Cai Q, Liu Z. Essen Stroke Risk Score Predicts Carotid Atherosclerosis in Chinese Community Populations. Healthc Policy 2020; 13:2115-2123. [PMID: 33116991 PMCID: PMC7568636 DOI: 10.2147/rmhp.s274340] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 09/28/2020] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Carotid atherosclerosis (CA) is closely related to stroke, and Framingham Risk Score (FRS) has been used for CA risk evaluation. However, FRS could only be used for subjects of up to 74 years old. The present study was to determine if Essen Stroke Risk Score (ESRS) could be used to estimate CA risk in community populations without age limits. METHODS In the present prospective multi-community screening study, we evaluated the prevalence of CA using high-resolution ultrasound in 521 males and 1039 females (35 to 91 years old). Both FRS and ESRS were calculated for the subjects. Multivariate logistic regression analysis was used to determine the predictive values of FRS and ESRS for CA in these subjects. RESULTS Ultrasound data showed that CA was present in 56.2% of the participants (total of 1560). Multivariate logistic regression analysis showed that ESRS was associated with CA with odds ratio (OR): 1.34 (95% confidence interval (CI), 1.12-1.60, p=0.001). Central obesity (OR: 1.40, CI: 1.07-1.83, p=0.015), female (OR: 0.55, CI: 0.39-0.77, p <0.001) and age (OR: 2.63, CI: 2.27-3.06, p <0.001) were also associated with CA. Based on the estimated area under curve (AUC), FRS (AUC 0.775) was better than ESRS (AUC 0.693) (z statistic 6.774, p <0.001) for CA prediction for individuals of ≤74 years old. However, receiver operating characteristic analysis showed ESRS was a good CA predictor for all subjects (AUC of 0.715). CONCLUSION ESRS could be used as an alternative to FRS to predict CA in community population of all age.
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Affiliation(s)
- Zhi-Xin Huang
- Department of Neurology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, People's Republic of China.,Department of Neurology, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Li-Hua Chen
- Department of Neurology, BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China
| | - Ran Xiong
- Department of Ultrasonography, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, People's Republic of China
| | - Yan-Ni He
- Department of Ultrasonography, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, People's Republic of China
| | - Zhu Zhang
- Department of Neurology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, People's Republic of China.,Department of Neurology, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Jie Zeng
- Department of Center for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, People's Republic of China
| | - Qiankun Cai
- Department of Neurology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, People's Republic of China
| | - Zhenguo Liu
- Center for Precision Medicine and Division of Cardiovascular Medicine, Department of Medicine, University of Missouri School of Medicine, Columbia, MO, USA
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Zhang Y, Fang X, Guan S, Wu X, Liu H, Wang C, Zhang Z, Gu X, Liu C, Cheng J. Validation of 10-Year Stroke Prediction Scores in a Community-Based Cohort of Chinese Older Adults. Front Neurol 2020; 11:986. [PMID: 33192957 PMCID: PMC7642878 DOI: 10.3389/fneur.2020.00986] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 07/28/2020] [Indexed: 12/11/2022] Open
Abstract
A stroke prediction model based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project was developed. We compared its predictive ability with the revised Framingham Stroke Risk Score (R-FSRS) for 5-year stroke incidence in a community cohort of Chinese adults, namely the Beijing Longitudinal Study of Aging (BLSA). Calibration, discrimination, and recalibration were used to compare the predictive ability between the two prediction models. Category-less net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values were also assessed. During a mean follow-up duration of 5.1 years, 106 incidents of fatal or non-fatal strokes occurred among 1,203 participants aged 55–84 years. The R-FSRS applied to our cohort underestimated the 5-year risk for stroke in men and women. China-PAR performed better than the R-FSRS in terms of calibration (men, R-FSRS: χ2-value 144.2 [P < 0.001], China-PAR: 10.4 [P = 0.238]; women, R-FSRS: 280.1 [P < 0.001], China-PAR: 12.5 [P = 0.129]). In terms of discrimination, R-FSRS and China-PAR models performed modestly in our cohort (C-statistic 0.603 [95% CI: 0.560–0.644] for men using China-PAR and 0.568 [95% CI: 0.524–0.610] using the R-FSRS; the corresponding numbers for women were 0.602 [95% CI: 0.564–0.639] and 0.575 [95% CI: 0.537–0.613). The recalibrated China-PAR model significantly improved the discrimination in C statistics and produced higher category-less NRI and IDI for stroke incidence than the R-FSRS. Although China-PAR fairly estimated stroke risk in our cohort, it did not sufficiently identify adults at high risk of stroke. Caution would be exercised by practitioners in applying the original China-PAR to Chinese older adults. Further studies are needed to develop an adequate prediction model based on the recalibrated China-PAR or to find new risk markers which could upgrade this model.
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Affiliation(s)
- Yanlei Zhang
- Department of Neurology, the First affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xianghua Fang
- Evidence-based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Shaochen Guan
- Evidence-based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaoguang Wu
- Evidence-based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hongjun Liu
- Evidence-based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chunxiu Wang
- Evidence-based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhongying Zhang
- Geriatric Department, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiang Gu
- Geriatric Department, Friendship Hospital, Capital Medical University, Beijing, China
| | - Chunxiao Liu
- Geriatric Department, Beijing Geriatric Hospital, Beijing, China
| | - Jianhua Cheng
- Department of Neurology, the First affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Singer J, Gustafson D, Cummings C, Egelko A, Mlabasati J, Conigliaro A, Levine SR. Independent ischemic stroke risk factors in older Americans: a systematic review. Aging (Albany NY) 2020; 11:3392-3407. [PMID: 31127075 PMCID: PMC6555455 DOI: 10.18632/aging.101987] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 04/04/2019] [Indexed: 11/25/2022]
Abstract
The Framingham Stroke Risk Profile (FSRP) is a validated model for predicting 10-year ischemic stroke risk in middle-aged adults, yet has not been demonstrated to consistently translate in older populations. This is a systematic review of independent risk factors measured among > 65 year olds, with subsequent first ischemic stroke, using PRISMA guidelines. We appraised peer-reviewed publications that included participants > 65 years old at risk assessment. Combined with other criteria, results were abstracted from 28 papers reporting six types of stroke risk factors: Serologic/Diagnostic, Conventional, Psychosocial, Genetic, Cognitive, and Antibiotic use. These studies demonstrated levels of serum androgens, C-reactive protein, and advanced glycation endproducts; thrombin generation; left ventricular mass; depressive symptoms; phosphodiesterase 4D single nucleotide polymorphisms; coagulation factor XII gene; peak thrombus generation; and lower cognitive functioning were independent risk factors for ischemic stroke in older adults. Plasma adipokines, free fatty acids and antibiotic use did not predict ischemic stroke. Purpose in life and APOEε2 allele were protective for ischemic stroke. This systematic review provides evidence of risk and protective factors for ischemic stroke in older cohorts that are not included in the FSRP. Further studies are needed to understand whether these factors are important enough to comprise a risk score.
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Affiliation(s)
- Jonathan Singer
- Department of Clinical Psychology, University of Nevada, Reno, NV 89557, USA
| | - Deborah Gustafson
- Department of Neurology, Section for NeuroEpidemiology, State University of New York Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Caroline Cummings
- Department of Clinical Psychology, University of Nevada, Reno, NV 89557, USA
| | - Aron Egelko
- State University of New York Downstate Medical Center, Department of Neurology and Stroke Center, New York City Health + Hospitals/Kings County, Brooklyn, NY 11203, USA
| | | | - Alyssa Conigliaro
- Department of Clinical Psychology, Hofstra University, Hempstead, NY 11549, USA
| | - Steven R Levine
- State University of New York Downstate Medical Center, Department of Neurology and Stroke Center, New York City Health + Hospitals/Kings County, Brooklyn, NY 11203, USA
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10
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Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score. PLoS One 2020; 15:e0232414. [PMID: 32437368 PMCID: PMC7241753 DOI: 10.1371/journal.pone.0232414] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 04/14/2020] [Indexed: 12/11/2022] Open
Abstract
Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of this study was to improve upon the established Revised Framingham Stroke Risk Score and design an interactive Non-Linear Stroke Risk Score. Leveraging machine learning algorithms, our work aimed at increasing the accuracy of event prediction and uncovering new relationships in an interpretable fashion. A two-phase approach was used to create our stroke risk prediction score. First, clinical examinations of the Framingham offspring cohort were utilized as the training dataset for the predictive model. Optimal Classification Trees were used to develop a tree-based model to predict 10-year risk of stroke. Unlike classical methods, this algorithm adaptively changes the splits on the independent variables, introducing non-linear interactions among them. Second, the model was validated with a multi-ethnicity cohort from the Boston Medical Center. Our stroke risk score suggests a key dichotomy between patients with history of cardiovascular disease and the rest of the population. While it agrees with known findings, it also identified 23 unique stroke risk profiles and highlighted new non-linear relationships; such as the role of T-wave abnormality on electrocardiography and hematocrit levels in a patient’s risk profile. Our results suggested that the non-linear approach significantly improves upon the baseline in the c-statistic (training 87.43% (CI 0.85–0.90) vs. 73.74% (CI 0.70–0.76); validation 75.29% (CI 0.74–0.76) vs 65.93% (CI 0.64–0.67), even in multi-ethnicity populations. The clinical implications of the new risk score include prioritization of risk factor modification and personalized care at the patient level with improved targeting of interventions for stroke prevention.
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Wang J, Yuan Z, Liu Y, Xue F. A Multi-Center Competing Risks Model and Its Absolute Risk Calculation Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16183435. [PMID: 31527495 PMCID: PMC6765840 DOI: 10.3390/ijerph16183435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 09/06/2019] [Accepted: 09/10/2019] [Indexed: 01/20/2023]
Abstract
In the competing risks frame, the cause-specific hazard model (CSHM) can be used to test the effects of some covariates on one particular cause of failure. Sometimes, however, the observed covariates cannot explain the large proportion of variation in the time-to-event data coming from different areas such as in a multi-center clinical trial or a multi-center cohort study. In this study, a multi-center competing risks model (MCCRM) is proposed to deal with multi-center survival data, then this model is compared with the CSHM by simulation. A center parameter is set in the MCCRM to solve the spatial heterogeneity problem caused by the latent factors, hence eliminating the need to develop different models for each area. Additionally, the effects of the exposure factors in the MCCRM are kept consistent for each individual, regardless of the area they inhabit. Therefore, the coefficient of the MCCRM model can be easily explained using the scenario of each model for each area. Moreover, the calculating approach of the absolute risk is given. Based on a simulation study, we show that the estimate of coefficients of the MCCRM is unbiased and precise, and the area under the curve (AUC) is larger than that of the CSHM when the heterogeneity cannot be ignored. Furthermore, the disparity of the AUC increases progressively as the standard deviation of the center parameter (SDCP) rises. In order to test the calibration, the expected number (E) of strokes is calculated and then compared with the corresponding observed number (O). The result is promising, so the SDCP can be used to select the most appropriate model. When the SDCP is less than 0.1, the performance of the MCCRM and CSHM is analogous, but when the SDCP is equal to or greater than 0.1, the performance of the MCCRM is significantly superior to the CSHM. This suggests that the MCCRM should be selected as the appropriate model.
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Affiliation(s)
- Jintao Wang
- Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China.
- Department of Statistics, School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China.
| | - Yi Liu
- Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China.
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China.
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Gómez-Outes A, Suárez-Gea ML, García-Pinilla JM. When are the cardiovascular and stroke risks too high? Pharmacotherapy for stroke prophylaxis. Expert Opin Pharmacother 2018; 19:1427-1440. [PMID: 30198347 DOI: 10.1080/14656566.2018.1511703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Stroke is a significant source of morbidity and mortality in developed countries. Cardioembolic strokes represent approximately 15-30% of all ischemic strokes. They are frequently related to atrial fibrillation (AF) and have a worse prognosis and high recurrence rates when compared to other causes (e.g. atherosclerosis). AREAS COVERED This review includes a summary of general and specific scores to assess cardiovascular and stroke risks, with a focus on specific scores available in AF. Recommendations for antithrombotic therapy are also reviewed. EXPERT OPINION Several scores are available for the evaluation of stroke risk. They are useful to identify the risk factors that trigger the need for medical interventions. Integrated risk scores with visual interfaces showing the risk of events, with and without the proposed interventions, can aid decision-making. The risk of stroke can definitely be considered too high in those patients with a history of stroke/transient ischemic attack, who need antiplatelet therapy (after a non-cardioembolic stroke) or anticoagulant therapy (after a cardioembolic stroke). For primary prevention of stroke, antiplatelet therapy is not usually recommended, while anticoagulation should be considered if the patient has concomitant AF and at least one additional risk factor unrelated to sex.
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Affiliation(s)
- Antonio Gómez-Outes
- a Division of Pharmacology and Clinical Drug Evaluation, Medicines for Human Use , Spanish Agency for Medicines and Medical Devices (AEMPS) , Madrid , Spain
| | - Mª Luisa Suárez-Gea
- a Division of Pharmacology and Clinical Drug Evaluation, Medicines for Human Use , Spanish Agency for Medicines and Medical Devices (AEMPS) , Madrid , Spain
| | - Jose Manuel García-Pinilla
- b UGC de Cardiología y Cirugía Cardiovascular , Instituto de Biomedicina de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria , Málaga , Spain.,c Centro de Investigación Biomédica en Red-Cardiovascular (CIBER-CV) , Instituto de Salud Carlos III , Madrid , Spain
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Flueckiger P, Longstreth W, Herrington D, Yeboah J. Revised Framingham Stroke Risk Score, Nontraditional Risk Markers, and Incident Stroke in a Multiethnic Cohort. Stroke 2018; 49:363-369. [PMID: 29311270 PMCID: PMC5780220 DOI: 10.1161/strokeaha.117.018928] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Revised: 12/05/2017] [Accepted: 12/11/2017] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Limited data exist on the performance of the revised Framingham Stroke Risk Score (R-FSRS) and the R-FSRS in conjunction with nontraditional risk markers. We compared the R-FSRS, original FSRS, and the Pooled Cohort Equation for stroke prediction and assessed the improvement in discrimination by nontraditional risk markers. METHODS Six thousand seven hundred twelve of 6814 participants of the MESA (Multi-Ethnic Study of Atherosclerosis) were included. Cox proportional hazard, area under the curve, net reclassification improvement, and integrated discrimination increment analysis were used to assess and compare each stroke prediction risk score. Stroke was defined as fatal/nonfatal strokes (hemorrhagic or ischemic). RESULTS After mean follow-up of 10.7 years, 231 of 6712 (3.4%) strokes were adjudicated (2.7% ischemic strokes). Mean stroke risks using the R-FSRS, original FSRS, and Pooled Cohort Equation were 4.7%, 5.9%, and 13.5%. The R-FSRS had the best calibration (Hosmer-Lemeshow goodness-of-fit, χ2=6.55; P=0.59). All risk scores were predictive of incident stroke. C statistics of R-FSRS (0.716) was similar to Pooled Cohort Equation (0.716), but significantly higher than the original FSRS (0.653; P=0.01 for comparison with R-FSRS). Adding nontraditional risk markers individually to the R-FSRS did not improve discrimination of the R-FSRS in the area under the curve analysis, but did improve category-less net reclassification improvement and integrated discrimination increment for incident stroke. The addition of coronary artery calcium to R-FSRS produced the highest category-less net reclassification improvement (0.36) and integrated discrimination increment (0.0027). Similar results were obtained when ischemic strokes were used as the outcome. CONCLUSIONS The R-FSRS downgraded stroke risk but had better calibration and discriminative ability for incident stroke compared with the original FSRS. Nontraditional risk markers modestly improved the discriminative ability of the R-FSRS, with coronary artery calcium performing the best.
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Affiliation(s)
- Peter Flueckiger
- From the Heart and Vascular Center of Excellence, Wake Forest School of Medicine, Winston-Salem, NC (P.F., D.H., J.Y.); and Department of Neurology, University of Washington-Harborview Medical Center, Seattle, WA (W.L.)
| | - Will Longstreth
- From the Heart and Vascular Center of Excellence, Wake Forest School of Medicine, Winston-Salem, NC (P.F., D.H., J.Y.); and Department of Neurology, University of Washington-Harborview Medical Center, Seattle, WA (W.L.)
| | - David Herrington
- From the Heart and Vascular Center of Excellence, Wake Forest School of Medicine, Winston-Salem, NC (P.F., D.H., J.Y.); and Department of Neurology, University of Washington-Harborview Medical Center, Seattle, WA (W.L.)
| | - Joseph Yeboah
- From the Heart and Vascular Center of Excellence, Wake Forest School of Medicine, Winston-Salem, NC (P.F., D.H., J.Y.); and Department of Neurology, University of Washington-Harborview Medical Center, Seattle, WA (W.L.).
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Suemoto CK, Ueda P, Beltrán-Sánchez H, Lebrão ML, Duarte YA, Wong R, Danaei G. Development and Validation of a 10-Year Mortality Prediction Model: Meta-Analysis of Individual Participant Data From Five Cohorts of Older Adults in Developed and Developing Countries. J Gerontol A Biol Sci Med Sci 2017; 72:410-416. [PMID: 27522061 DOI: 10.1093/gerona/glw166] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 07/25/2016] [Indexed: 12/24/2022] Open
Abstract
Background Existing mortality prediction models for older adults have been each developed using a single study from the United States or Western Europe. We aimed to develop and validate a 10-year mortality prediction model for older adults using data from developed and developing countries. Methods We used data from five cohorts, including data from 16 developed and developing countries: ELSA (English Longitudinal Study of Aging), HRS (Health and Retirement Study), MHAS (Mexican Health and Aging Study), SABE-Sao Paulo (The Health, Well-being and Aging), and SHARE (Survey on Health, Ageing and Retirement in Europe). 35,367 older adults were split into training (two thirds) and test (one third) data sets. Baseline predictors included age, sex, comorbidities, and functional and cognitive measures. We performed an individual participant data meta-analysis using a sex-stratified Cox proportional hazards model, with time to death as the time scale. We validated the model using Harrell's C statistic (discrimination) and the estimated slope between observed and predicted 10-year mortality risk across deciles of risk (calibration). Results During a median of 8.6 years, 8,325 participants died. The final model included age, sex, diabetes, heart disease, lung disease, cancer, smoking, alcohol use, body mass index, physical activity, self-reported health, difficulty with bathing, walking several blocks, and reporting date correctly. The model showed good discrimination (Harrell's C = 0.76) and calibration (slope = 1.005). Models for developed versus developing country cohorts performed equally well when applied to data from developing countries. Conclusion A parsimonious mortality prediction model using data from multiple cohorts in developed and developing countries can be used to predict mortality in older adults in both settings.
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Affiliation(s)
- Claudia Kimie Suemoto
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Division of Geriatrics, University of Sao Paulo Medical School, Brazil
| | - Peter Ueda
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Hiram Beltrán-Sánchez
- Department of Community Health Sciences and California Center for Population Research, Fielding School of Public Health, UCLA, Los Angeles
| | | | - Yeda Aparecida Duarte
- Department of Medical Surgical Nursing, School of Nursing, University of Sao Paulo, Brazil
| | - Rebeca Wong
- Department of Preventive Medicine and Community Health, University of Texas, Galveston
| | - Goodarz Danaei
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Dufouil C, Beiser A, McLure LA, Wolf PA, Tzourio C, Howard VJ, Westwood AJ, Himali JJ, Sullivan L, Aparicio HJ, Kelly-Hayes M, Ritchie K, Kase CS, Pikula A, Romero JR, D'Agostino RB, Samieri C, Vasan RS, Chêne G, Howard G, Seshadri S. Revised Framingham Stroke Risk Profile to Reflect Temporal Trends. Circulation 2017; 135:1145-1159. [PMID: 28159800 PMCID: PMC5504355 DOI: 10.1161/circulationaha.115.021275] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Age-adjusted stroke incidence has decreased over the past 50 years, likely as a result of changes in the prevalence and impact of various stroke risk factors. An updated version of the Framingham Stroke Risk Profile (FSRP) might better predict current risks in the FHS (Framingham Heart Study) and other cohorts. We compared the accuracy of the standard (old) and of a revised (new) version of the FSRP in predicting the risk of all-stroke and ischemic stroke and validated this new FSRP in 2 external cohorts, the 3C (3 Cities) and REGARDS (Reasons for Geographic and Racial Differences in Stroke) studies. METHODS We computed the old FSRP as originally described and a new model that used the most recent epoch-specific risk factor prevalence and hazard ratios for individuals ≥55 years of age and for the subsample ≥65 years of age (to match the age range in REGARDS and 3C studies, respectively) and compared the efficacy of these models in predicting 5- and 10-year stroke risks. RESULTS The new FSRP was a better predictor of current stroke risks in all 3 samples than the old FSRP (calibration χ2 of new/old FSRP: in men: 64.0/12.1, 59.4/30.6, and 20.7/12.5; in women: 42.5/4.1, 115.4/90.3, and 9.8/6.5 in FHS, REGARDS, and 3C, respectively). In the REGARDS, the new FSRP was a better predictor among whites compared with blacks. CONCLUSIONS A more contemporaneous, new FSRP better predicts current risks in 3 large community samples and could serve as the basis for examining geographic and racial differences in stroke risk and the incremental diagnostic utility of novel stroke risk factors.
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Affiliation(s)
- Carole Dufouil
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Alexa Beiser
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Leslie A McLure
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Philip A Wolf
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Christophe Tzourio
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Virginia J Howard
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Andrew J Westwood
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Jayandra J Himali
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Lisa Sullivan
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Hugo J Aparicio
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Margaret Kelly-Hayes
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Karen Ritchie
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Carlos S Kase
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Aleksandra Pikula
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Jose R Romero
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Ralph B D'Agostino
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Cécilia Samieri
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Ramachandran S Vasan
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Genevieve Chêne
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - George Howard
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.)
| | - Sudha Seshadri
- From Inserm, Bordeaux Population Health Research Center, UMR 1219, Univ. Bordeaux, France (C.D., C.T., C.S., G.C.); ISPED, Univ. Bordeaux, France (C.D., C.S., G.C.); CHU de Bordeaux, Pole de sante publique, France (C.D., C.T., G.C.); Department of Neurology, School of Medicine (A.B., P.A.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., S.S.), Department of Biostatistics, School of Public Health (A.B., L.S.), and Department of Mathematics (R.B.D.), Boston University, MA; National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (A.B., P.A.W., A.J.W., J.J.H., H.J.A., M.K.-H., C.S.K., A.P., J.R.R., R.B.D., R.S.V., S.S.); Department of Biostatistics, Drexel University School of Public Health, Philadelphia, PA (L.A.M.); Departments of Epidemiology (V.J.H.) and Biostatistics (G.H.), School of Public Health, University of Alabama at Birmingham; and INSERM Unit 1061, Montpellier University, France (K.R.).
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Péquignot R, Dufouil C, Prugger C, Pérès K, Artero S, Tzourio C, Empana JP. High Level of Depressive Symptoms at Repeated Study Visits and Risk of Coronary Heart Disease and Stroke over 10 Years in Older Adults: The Three-City Study. J Am Geriatr Soc 2016; 64:118-25. [PMID: 26782860 DOI: 10.1111/jgs.13872] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKROUND Baseline depressive symptoms have been consistently associated with the onset of cardiovascular disease (CVD). OBJECTIVES Since depressive symptoms vary over time in elderly persons, and to help clarify whether or not depression is an etiological factor for CVD, we quantified the association between the course of depressive symptoms and occurrence of first coronary heart disease (CHD) and stroke events in older adults. DESIGN A population-based prospective observational study. SETTING Participants were randomly selected from the electoral rolls of three large French cities. PARTICIPANTS A total of 9,294 participants were examined at baseline between 1999 and 2001, and thereafter at repeated study visits over 10 years. MEASUREMENTS High levels of depressive symptoms (HLDS) were defined as a score≥16 on the 20-item Center for Epidemiologic Studies Depression Scale. The number of study visits with HLDS was used as a time dependent variable in Cox proportional hazard models. RESULTS There were 7,313 participants (36.6% males) aged 73.8±5.4 years with no history of CHD, stroke or dementia at baseline. After a median follow-up of 8.4 years (SD 2.3 years), 629 first CHD or stroke events occurred. After adjustment for sociodemographic characteristics and vascular risk factors, the risk of CHD and stroke combined increased 1.15-fold (95% CI: 1.06 to 1.25) per each additional study visit with HLDS. The results remained unchanged when accounting for the presence of disability and antidepressant intake at baseline and during follow-up. CONCLUSION Elderly persons exposed to HLDS at several occasions over 10 years showed substantial increased risk of coronary heart disease and stroke events.
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Affiliation(s)
- Renaud Péquignot
- INSERM U970, Paris Cardiovascular Research Center, Paris, F-75015, France.,Paris Descartes University, Sorbonne Paris Cité, Paris, France.,Service de Médecine et Réadaptation, Hôpitaux de Saint-Maurice, Saint-Maurice, F-94415, France
| | - Carole Dufouil
- INSERM, U897, Bordeaux, France.,Bordeaux 2 University, Bordeaux, France.,CIC-1401, Clinical Epidemiology Branch, Bordeaux Hospital, Bordeaux, France
| | - Christof Prugger
- INSERM U970, Paris Cardiovascular Research Center, Paris, F-75015, France.,Paris Descartes University, Sorbonne Paris Cité, Paris, France
| | - Karine Pérès
- INSERM, U897, Bordeaux, France.,Bordeaux 2 University, Bordeaux, France
| | - Sylvaine Artero
- INSERM U1061, Neuropsychiatry, Montpellier, France.,University of Montpellier 1, Montpellier, France
| | | | - Jean-Philippe Empana
- INSERM U970, Paris Cardiovascular Research Center, Paris, F-75015, France.,Paris Descartes University, Sorbonne Paris Cité, Paris, France
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18
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Paulus JK, Lai LYH, Lundquist C, Daneshmand A, Buettner H, Lutz JS, Raman G, Wessler BS, Kent DM. Field Synopsis of the Role of Sex in Stroke Prediction Models. J Am Heart Assoc 2016; 5:JAHA.115.002809. [PMID: 27151514 PMCID: PMC4889171 DOI: 10.1161/jaha.115.002809] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Guidelines for stroke prevention recommend development of sex‐specific stroke risk scores. Incorporating sex in Clinical Prediction Models (CPMs) may support sex‐specific clinical decision making. To better understand their potential to guide sex‐specific care, we conducted a field synopsis of the role of sex in stroke‐related CPMs. Methods and Results We identified stroke‐related CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Database, a systematic summary of cardiovascular CPMs published from January 1990 to May 2012. We report the proportion of models including the effect of sex on stroke incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 92 stroke‐related CPMs, 30 (33%) contained a coefficient for sex or presented sex‐stratified models. Only 12/58 (21%) CPMs predicting outcomes in patients included sex, compared to 18/30 (60%) models predicting first stroke (P<0.0001). Sex was most commonly included in models predicting stroke among a general population (69%). Female sex was consistently associated with reduced mortality after ischemic stroke (n=4) and higher risk of stroke from arrhythmias or coronary revascularization (n=5). Models predicting first stroke versus outcomes among patients with stroke (odds ratio=5.75, 95% CI 2.18–15.14, P<0.001) and those developed from larger versus smaller sample sizes (odds ratio=4.58, 95% CI 1.73–12.13, P=0.002) were significantly more likely to include sex. Conclusions Sex is included in a minority of published CPMs, but more frequently in models predicting incidence of first stroke. The importance of sex‐specific care may be especially well established for primary prevention.
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Affiliation(s)
- Jessica K Paulus
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
| | - Lana Y H Lai
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
| | - Christine Lundquist
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
| | - Ali Daneshmand
- Department of Neurology, Tufts Medical Center, Boston, MA
| | | | - Jennifer S Lutz
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
| | - Gowri Raman
- Center for Clinical Evidence Synthesis, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
| | - Benjamin S Wessler
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA Division of Cardiology, Tufts Medical Center, Boston, MA
| | - David M Kent
- Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA
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Carcaillon L, Plichart M, Zureik M, Rouaud O, Majed B, Ritchie K, Tzourio C, Dartigues JF, Empana JP. Carotid plaque as a predictor of dementia in older adults: the Three-City Study. Alzheimers Dement 2014; 11:239-48. [PMID: 25510384 DOI: 10.1016/j.jalz.2014.07.160] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Revised: 06/18/2014] [Accepted: 07/23/2014] [Indexed: 11/19/2022]
Abstract
BACKGROUND The contribution of carotid atherosclerosis to incident dementia remains unclear. We examined the association between carotid plaques (CP) and common carotid intima media thickness (CCA-IMT) with incident dementia and its subtypes, and their added value for dementia risk prediction. METHODS At baseline, 6025 dementia-free subjects aged 65-86 years underwent bilateral carotid ultrasonography measures of CP and plaque-free CCA-IMT. Subjects were followed-up over 7 years for the detection of dementia. RESULTS After a mean 5.4 years of follow-up, 421 subjects developed dementia including 272 Alzheimer's disease and 83 vascular/mixed dementia (VaD). Only CP were independently related to VaD (HR(≥2 sites with plaques) = 1.92; 95% confidence interval or CI = 1.13-3.22) and improved VaD risk prediction (continuous Net Reclassification Index = 30.1%; 95% CI = 8.4-51.7) beyond known dementia risk factors. Accounting for stroke or competing risk by death marginally modified the results. CONCLUSION In older adults, CP are independent predictors of incident VaD and may improve VaD risk prediction.
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Affiliation(s)
- Laure Carcaillon
- Inserm, CESP Centre for Research in Epidemiology and Population Health, UMR-S1018, Hormones and Cardiovascular Disease, University Paris Sud, Villejuif, France
| | - Matthieu Plichart
- Inserm, UMR-S 970, Paris Descartes University, Sorbonne Paris Cité, Paris Cardiovascular Research Center, Paris, France; Assistance Publique - Hôpitaux de Paris, Hôpital Broca, Paris, France.
| | | | | | - Bilal Majed
- Inserm, UMR-S 970, Paris Descartes University, Sorbonne Paris Cité, Paris Cardiovascular Research Center, Paris, France; Epidemiology and Clinical Research Unit, Arras General Hospital, Arras, France
| | - Karen Ritchie
- Inserm U1061, Neuropsychiatry: Epidemiological and Clinical Research, Hôpital La Colombière, Montpellier, France; Imperial College, Faculty of Medicine, London, United Kingdom; University of Montpellier 1, Faculty of Medicine, Montpellier, France
| | - Christophe Tzourio
- Inserm U708, Neuroepidemiology, Bordeaux, France; University of Victor Segalen Bordeaux2, Bordeaux, France
| | - Jean-François Dartigues
- University of Victor Segalen Bordeaux2, Bordeaux, France; Inserm U897, Epidemiology and Neuropsychology of Brain Aging, Bordeaux, France
| | - Jean-Philippe Empana
- Inserm, UMR-S 970, Paris Descartes University, Sorbonne Paris Cité, Paris Cardiovascular Research Center, Paris, France
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Parmar P, Krishnamurthi R, Ikram MA, Hofman A, Mirza SS, Varakin Y, Kravchenko M, Piradov M, Thrift AG, Norrving B, Wang W, Mandal DK, Barker-Collo S, Sahathevan R, Davis S, Saposnik G, Kivipelto M, Sindi S, Bornstein NM, Giroud M, Béjot Y, Brainin M, Poulton R, Narayan KMV, Correia M, Freire A, Kokubo Y, Wiebers D, Mensah G, BinDhim NF, Barber PA, Pandian JD, Hankey GJ, Mehndiratta MM, Azhagammal S, Ibrahim NM, Abbott M, Rush E, Hume P, Hussein T, Bhattacharjee R, Purohit M, Feigin VL. The Stroke Riskometer(TM) App: validation of a data collection tool and stroke risk predictor. Int J Stroke 2014; 10:231-44. [PMID: 25491651 PMCID: PMC4335600 DOI: 10.1111/ijs.12411] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Accepted: 10/28/2014] [Indexed: 11/26/2022]
Abstract
Background The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the ‘mass’ approach), the ‘high risk’ approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer™, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer™) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results The Stroke Riskometer™ performed well against the FSRS five-year AUROC for both males (FSRS = 75·0% (95% CI 72·3%–77·6%), Stroke Riskometer™ = 74·0(95% CI 71·3%–76·7%) and females [FSRS = 70·3% (95% CI 67·9%–72·8%, Stroke Riskometer™ = 71·5% (95% CI 69·0%–73·9%)], and better than QStroke [males – 59·7% (95% CI 57·3%–62·0%) and comparable to females = 71·1% (95% CI 69·0%–73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51–0·56, D-statistic ranging from 0·01–0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0·006). Conclusions The Stroke Riskometer™ is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer™ will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.
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Pandya A, Gupta A, Kamel H, Navi BB, Sanelli PC, Schackman BR. Carotid artery stenosis: cost-effectiveness of assessment of cerebrovascular reserve to guide treatment of asymptomatic patients. Radiology 2014; 274:455-63. [PMID: 25225841 DOI: 10.1148/radiol.14140501] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
PURPOSE To project and compare the lifetime health benefits, health care costs, and incremental cost-effectiveness of a decision rule based on assessment of cerebrovascular reserve (CVR) compared with medical therapy and immediate revascularization in asymptomatic patients with carotid artery stenosis for prevention of stroke. MATERIALS AND METHODS The three strategies compared included immediate revascularization (carotid endarterectomy) and ongoing medical therapy (with antiplatelet, statin, and antihypertensive agents plus lifestyle modification), medical therapy-based treatment with revascularization only for patients who progressed, and use of a CVR-based decision rule for treatment in which patients with CVR impairment undergo immediate revascularization and all others receive medical therapy. A decision analytic model was developed to project lifetime quality-adjusted life years (QALYs) and costs for asymptomatic patients with carotid stenosis with 70%-89% carotid luminal narrowing at presentation. Risks of clinical events, costs, and quality-of-life values were estimated on the basis of those in published sources. The analysis was conducted from a health care system perspective, with health and cost outcomes discounted at 3%. Results Total costs per person and lifetime QALYs were lowest for the medical therapy-based strategy ($14 597, 9.848 QALYs), followed by CVR testing ($16 583, 9.934 QALYs) and immediate revascularization ($20 950, 9.940 QALYs). The incremental cost-effectiveness ratio for the CVR-based strategy compared with the medical therapy-based strategy was $23 000 per QALY and for the immediate revascularization versus the CVR-based strategy was $760 000 per QALY. RESULTS were sensitive to variations in model inputs for revascularization costs and complication risks and baseline stroke risk. CONCLUSION CVR testing can be a cost-effective tool to identify asymptomatic patients with carotid stenosis who are most likely to benefit from revascularization.
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Affiliation(s)
- Ankur Pandya
- From the Departments of Healthcare Policy and Research (A.P., B.R.S.), Radiology (A.G., P.C.S.), and Neurology (H.K., B.B.N.), Weill Cornell Medical College, 402 East 67th St, 2nd Floor, LA 241, New York, NY 10065
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Ferket BS, van Kempen BJH, Wieberdink RG, Steyerberg EW, Koudstaal PJ, Hofman A, Shahar E, Gottesman RF, Rosamond W, Kizer JR, Kronmal RA, Psaty BM, Longstreth WT, Mosley T, Folsom AR, Hunink MGM, Ikram MA. Separate prediction of intracerebral hemorrhage and ischemic stroke. Neurology 2014; 82:1804-12. [PMID: 24759844 DOI: 10.1212/wnl.0000000000000427] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
OBJECTIVES To develop and validate 10-year cumulative incidence functions of intracerebral hemorrhage (ICH) and ischemic stroke (IS). METHODS We used data on 27,493 participants from 3 population-based cohort studies: the Atherosclerosis Risk in Communities Study, median age 54 years, 45% male, median follow-up 20.7 years; the Rotterdam Study, median age 68 years, 38% male, median follow-up 14.3 years; and the Cardiovascular Health Study, median age 71 years, 41% male, median follow-up 12.8 years. Among these participants, 325 ICH events, 2,559 IS events, and 9,909 nonstroke deaths occurred. We developed 10-year cumulative incidence functions for ICH and IS using stratified Cox regression and competing risks analysis. Basic models including only established nonlaboratory risk factors were extended with diastolic blood pressure, total cholesterol/high-density lipoprotein cholesterol ratio, body mass index, waist-to-hip ratio, and glomerular filtration rate. The cumulative incidence functions' performances were cross-validated in each cohort separately by Harrell C-statistic and calibration plots. RESULTS High total cholesterol/high-density lipoprotein cholesterol ratio decreased the ICH rates but increased IS rates (p for difference across stroke types <0.001). For both the ICH and IS models, C statistics increased more by model extension in the Atherosclerosis Risk in Communities and Cardiovascular Health Study cohorts. Improvements in C statistics were reproduced by cross-validation. Models were well calibrated in all cohorts. Correlations between 10-year ICH and IS risks were moderate in each cohort. CONCLUSIONS We developed and cross-validated cumulative incidence functions for separate prediction of 10-year ICH and IS risk. These functions can be useful to further specify an individual's stroke risk.
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Affiliation(s)
- Bart S Ferket
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Bob J H van Kempen
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Renske G Wieberdink
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Ewout W Steyerberg
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Peter J Koudstaal
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Albert Hofman
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Eyal Shahar
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Rebecca F Gottesman
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Wayne Rosamond
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Jorge R Kizer
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Richard A Kronmal
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Bruce M Psaty
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - W T Longstreth
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Thomas Mosley
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - Aaron R Folsom
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - M G Myriam Hunink
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA
| | - M Arfan Ikram
- From the Departments of Epidemiology (B.S.F., B.J.H.v.K., R.G.W., A.H., M.G.M.H., M.A.I.), Radiology (B.S.F., B.J.H.v.K., M.G.M.H., M.A.I.), Neurology (R.G.W., P.J.K., M.A.I.), and Public Health (E.W.S.), Erasmus Medical Center, Rotterdam, the Netherlands; Division of Epidemiology and Biostatistics (E.S.), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson; Department of Neurology (R.F.G.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology (W.R.), Gillings School of Global Public Health, University of North Carolina, Chapel Hill; Department of Medicine, and Department of Epidemiology of Population Health (J.R.K.), Albert Einstein College of Medicine, Bronx, NY; Departments of Biostatistics (R.A.K.), Neurology (W.T.L.), and Epidemiology (W.T.L.), and Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services (B.M.P.), University of Washington, Seattle; Group Health Research Institute (B.M.P.), Group Health Cooperative, Seattle, WA; Departments of Medicine (Geriatrics) and Neurology (T.M.), University of Mississippi Medical Center, Jackson; Division of Epidemiology and Community Health (A.R.F.), University of Minnesota, Minneapolis; and Department of Health Policy and Management (M.G.M.H.), Harvard School of Public Health, Boston, MA.
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Bushnell C, McCullough LD, Awad IA, Chireau MV, Fedder WN, Furie KL, Howard VJ, Lichtman JH, Lisabeth LD, Piña IL, Reeves MJ, Rexrode KM, Saposnik G, Singh V, Towfighi A, Vaccarino V, Walters MR. Guidelines for the prevention of stroke in women: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014; 45:1545-88. [PMID: 24503673 PMCID: PMC10152977 DOI: 10.1161/01.str.0000442009.06663.48] [Citation(s) in RCA: 622] [Impact Index Per Article: 62.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE The aim of this statement is to summarize data on stroke risk factors that are unique to and more common in women than men and to expand on the data provided in prior stroke guidelines and cardiovascular prevention guidelines for women. This guideline focuses on the risk factors unique to women, such as reproductive factors, and those that are more common in women, including migraine with aura, obesity, metabolic syndrome, and atrial fibrillation. METHODS Writing group members were nominated by the committee chair on the basis of their previous work in relevant topic areas and were approved by the American Heart Association (AHA) Stroke Council's Scientific Statement Oversight Committee and the AHA's Manuscript Oversight Committee. The panel reviewed relevant articles on adults using computerized searches of the medical literature through May 15, 2013. The evidence is organized within the context of the AHA framework and is classified according to the joint AHA/American College of Cardiology and supplementary AHA Stroke Council methods of classifying the level of certainty and the class and level of evidence. The document underwent extensive AHA internal peer review, Stroke Council Leadership review, and Scientific Statements Oversight Committee review before consideration and approval by the AHA Science Advisory and Coordinating Committee. RESULTS We provide current evidence, research gaps, and recommendations on risk of stroke related to preeclampsia, oral contraceptives, menopause, and hormone replacement, as well as those risk factors more common in women, such as obesity/metabolic syndrome, atrial fibrillation, and migraine with aura. CONCLUSIONS To more accurately reflect the risk of stroke in women across the lifespan, as well as the clear gaps in current risk scores, we believe a female-specific stroke risk score is warranted.
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Majed B, Tafflet M, Kee F, Haas B, Ferrieres J, Montaye M, Ruidavets JB, Arveiler D, Yarnell J, Amouyel P, Ducimetiere P, Empana JP. External validation of the 2008 Framingham cardiovascular risk equation for CHD and stroke events in a European population of middle-aged men. The PRIME study. Prev Med 2013; 57:49-54. [PMID: 23603213 DOI: 10.1016/j.ypmed.2013.04.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2013] [Revised: 04/05/2013] [Accepted: 04/08/2013] [Indexed: 10/26/2022]
Abstract
OBJECTIVE To test the applicability of the sex-specific 2008 Framingham general cardiovascular risk equation for coronary heart disease (CHD) and stroke in European middle-aged men from Ireland and France. METHODS In the PRIME study, 9638 healthy middle-aged men recruited in France and Ireland were surveyed for 10 years for the occurrence of first CHD and stroke events. The original Framingham equation, the partially calibrated Framingham equation (using the PRIME baseline survival at 10 years), and the completely calibrated Framingham equation (additionally using risk factor means calculated in PRIME) were assessed. Model fit (expected versus observed events) and discrimination ability were assessed using a modified Hosmer-Lemeshow Chi-square statistic and Harrell's c-index respectively. RESULTS The original (uncalibrated) Framingham equation overestimated by 1.94-fold the risk of CHD and stroke combined in PRIME, and by 2.23 and 1.42-fold in PRIME-France and PRIME-Ireland respectively. Adequate fit was found after complete calibration. However, discrimination ability of the Framingham equation was poor as shown by Harrell's c-index lower than 0.70. CONCLUSION The (completely) calibrated 2008 Framingham equation predicted accurate number of CHD and stroke events but discriminated poorly individuals at higher from those at lower event risk in a European population of middle-aged men.
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Affiliation(s)
- Bilal Majed
- Paris Cardiovascular Research Centre (PARCC), University Paris Descartes, Sorbonne Paris Cité, UMR-S970 Paris, France.
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Péquignot R, Tzourio C, Péres K, Ancellin ML, Perier MC, Ducimetière P, Empana JP. Depressive symptoms, antidepressants and disability and future coronary heart disease and stroke events in older adults: the Three City Study. Eur J Epidemiol 2013; 28:249-56. [PMID: 23338904 DOI: 10.1007/s10654-013-9765-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2012] [Accepted: 01/07/2013] [Indexed: 01/15/2023]
Abstract
To investigate the association between baseline depressive symptoms and first fatal and non fatal coronary heart disease (CHD) and stroke in older adults, taking antidepressants and disability into account. In the Three City Study, a community-based prospective multicentric observational study cohort, 7,308 non-institutionalized men and women aged ≥65 years with no reported history of CHD, stroke or dementia, completed the 20-item Center for Epidemiologic Studies depression scale (CESD) questionnaire. First CHD and stroke events during follow-up were adjudicated by an independent expert committee. Hazard ratios (HRs) were estimated by Cox proportional hazard model. After a median follow-up of 5.3 years, 338 subjects had suffered a first non-fatal CHD or stroke event, and 82 had died from a CHD or stroke. After adjustment for study center, baseline socio-demographic characteristics, and conventional risk factors, depressive symptoms (CESD ≥ 16) were associated with fatal events only: fatal CHD plus stroke (HR = 2.50; 95% CI 1.57-3.97), fatal CHD alone (n = 57; HR = 2.21 ; 95%CI 1.27-3.87), and fatal stroke alone (n = 25; HR = 3.27; 95% CI 1.42-7.52). These associations were even stronger in depressed subjects receiving antidepressants (HR = 4.17; 95% CI 1.84-9.46) and in depressed subjects with impaired Instrumental Activities of Daily Living (HR = 8.93; 95% CI 4.60-17.34). By contrast, there was no significant association with non fatal events (HR for non-fatal CHD or stroke = 0.94; 95% CI 0.66-1.33). In non-institutionalized elderly subjects without overt CHD, stroke or dementia, depressive symptoms were selectively and robustly associated with first fatal CHD or stroke events.
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Affiliation(s)
- Renaud Péquignot
- INSERM U970, Paris Cardiovascular Research Centre, Paris Descartes University, Sorbonne Paris Cité, UMR-S970, 75015, Paris, France.
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Abstract
Treatment of type 2 diabetes in the elderly represents a major challenge both in terms of clinical management and public health. Aging is causing a marked increase in the pandemic of diabetes in elderly people. However, scientific evidence to support the most appropriate treatment for diabetes in the elderly is scarce. Given the heterogeneity of the elderly population, which includes subjects with very different functional and cognitive capacities, co-morbidities, and life expectancy, it is critical to make a comprehensive assessment from a biopsychosocial perspective, to address the vascular risk factors integrally, and to establish individually tailored targets for glycemic control. In frail elderly or individuals with a short life expectancy, it may be reasonable to maintain HbA1c between 7.6%-8.5%. The therapeutic strategy for elderly patients with type 2 diabetes should be individualized and agreed with the patient and their caregivers, according to the objective. Improving quality of life, assuring patient safety and avoiding the adverse effects of antidiabetic treatment should be prioritized. Given the increased susceptibility of the elderly to severe hypoglycemia and its consequences, antidiabetic therapies that minimize the risk of hypoglycemic events should be selected.
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Firmo JOA, Peixoto SV, Loyola Filho AID, Uchôa E, Lima-Costa MF. Birth cohort differences in hypertension control in a Brazilian population of older elderly: the Bambuí Cohort Study of Aging (1997 and 2008). CAD SAUDE PUBLICA 2012; 27 Suppl 3:S427-34. [PMID: 21952864 DOI: 10.1590/s0102-311x2011001500013] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2010] [Accepted: 05/09/2011] [Indexed: 01/13/2023] Open
Abstract
We examined for cohort differences in hypertension control between participants in the Bambuí Cohort Study of Aging of aging born 1916-1926 and 1927-1937. Participants included hypertensive elderly aged 71-81 at baseline (n = 313) and at 11th wave follow up (n = 484). Prevalence of hypertension awareness (70.9% and 81.2%, respectively), median medical appointments in previous 12 months (2 and 3, respectively) and antihypertensive drug use (74.4% and 90.7% respectively) increased significantly from older to younger cohort. However, prevalence of appropriately controlled hypertension among those treated (< 140/< 90 mmHg) was similar in both cohorts (44.6% and 40.1%, p = 0.255). Multivariate analysis returned schooling as the only factor independently associated with appropriately controlled hypertension. Despite increased medical appointments and drug use in the recent cohort, the prevalence of appropriately controlled hypertension remained as low as in the older cohort.
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Affiliation(s)
- Josélia O A Firmo
- Núcleo de Estudos em Saúde Pública e Envelhecimento, Fundação Oswaldo Cruz/Universidade Federal de Minas Gerais, Belo Horizonte, Brasil.
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Lima-Costa MF, Cesar CC, Chor D, Proietti FA. Self-rated health compared with objectively measured health status as a tool for mortality risk screening in older adults: 10-year follow-up of the Bambuí Cohort Study of Aging. Am J Epidemiol 2012; 175:228-35. [PMID: 22193172 DOI: 10.1093/aje/kwr290] [Citation(s) in RCA: 89] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Interest in self-rated health (SRH) as a tool for use in disease and mortality risk screening is increasing. The authors assessed the discriminatory ability of baseline SRH to predict 10-year mortality rates compared with objectively measured health status. Principal component analysis was used to create a health score that included systolic blood pressure, presence of diabetes mellitus, body mass index, electrocardiographic parameters, B-type natriuretic peptide, and other biochemical and hematologic measures. From 1997 to 2007, a total of 474 of the 1,388 baseline participants died and 81 were lost to follow-up, yielding 11,833 person-years of observation. The adjusted hazard ratio for death was 1.74 (95% confidence interval (CI): 1.32, 2.29) for persons reporting poor health versus those reporting good health. When combined with age and sex, SRH had a C statistic to predict death equal to 0.69 (95% CI: 0.67, 0.71), which was comparable to that of the inclusive health score (C = 0.69, 95% CI: 0.67, 0.72). The addition of other parameters, such as lifestyle, physical functioning, mental symptoms, and physical symptoms, had little effect on these 2 predictive models (C = 0.71 (95% CI: 0.69, 0.73) and C = 0.71 (95% CI: 0.69, 0.74), respectively). The abilities of the SRH and the health score models to predict death decreased in parallel fashion over time. These results suggest that older adults who report poor health warrant particular attention as persons who have accumulated biologic markers of disease.
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Ho JS, Cannaday JJ, Barlow CE, Reinhardt DB, Wade WA. Computed tomography detection of carotid calcium and subclinical carotid atherosclerosis. Int J Cardiovasc Imaging 2011; 28:1601-7. [PMID: 22101625 DOI: 10.1007/s10554-011-9980-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2011] [Accepted: 11/10/2011] [Indexed: 01/07/2023]
Abstract
Computed tomography (CT) detection of coronary calcium has become a popular technique for assessing coronary atherosclerosis. Whether CT detection of carotid calcium could similarly assess carotid atherosclerosis is unknown. We thus performed a study evaluating the feasibility of carotid calcium scoring by CT. We also looked for an association between carotid calcium and subclinical carotid atherosclerosis. Subjects (n = 876) underwent non-contrast CT scanning of their carotid arteries. Carotid calcium was quantified by the Agatston method. Stenoses were detected by subsequent CT angiography. Significant subclinical atherosclerosis was defined by the presence of a ≥30% carotid stenosis. The frequency of a ≥30% carotid stenosis was then analyzed as a function of carotid calcium scores and various cardiovascular risk factors. CT detection of carotid calcium was feasible, robust, and reliable. Significant univariate associations for a ≥30% carotid stenosis included age (P < 0.001), diabetes (P = 0.02), hypertension (P = 0.01), and the carotid calcium score (P < 0.001). Those with a ≥30% carotid stenosis exhibited a median (25th, 75th percentile) carotid calcium score of 153 (19, 489), while those without a ≥30% carotid stenosis had a median (25th, 75th percentile) carotid calcium score of 0 (0, 89). Conversely, when no carotid calcium was detected, there was a low (1%) frequency of significant carotid atherosclerosis. The frequency of a ≥30% carotid stenosis increased as the carotid calcium score increased (P < 0.001 for trend). This association remained significant after adjustment for age, hypertension, diabetes, hyperlipidemia, and tobacco use. CT detection of carotid calcium can assess the burden of carotid atherosclerosis.
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Affiliation(s)
- John S Ho
- Cooper Clinic, 12200 Preston Road, Dallas, TX 75230, USA.
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Beer C, Alfonso H, Flicker L, Norman PE, Hankey GJ, Almeida OP. Traditional Risk Factors for Incident Cardiovascular Events Have Limited Importance in Later Life Compared With the Health in Men Study Cardiovascular Risk Score. Stroke 2011; 42:952-9. [DOI: 10.1161/strokeaha.110.603480] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Christopher Beer
- From the Western Australian Centre for Health and Ageing (C.B., H.A., L.F., O.P.A.), Centre for Medical Research, Western Australian Institute for Medical Research, Perth, Australia; School of Medicine and Pharmacology (C.B., L.F., G.J.H.), University of Western Australia, Perth, Australia; Department of Geriatric Medicine (C.B., L.F.), Royal Perth Hospital, Perth, Australia; School of Psychiatry and Clinical Neurosciences (O.P.A.), University of Western Australia, Perth, Australia; Department of
| | - Helman Alfonso
- From the Western Australian Centre for Health and Ageing (C.B., H.A., L.F., O.P.A.), Centre for Medical Research, Western Australian Institute for Medical Research, Perth, Australia; School of Medicine and Pharmacology (C.B., L.F., G.J.H.), University of Western Australia, Perth, Australia; Department of Geriatric Medicine (C.B., L.F.), Royal Perth Hospital, Perth, Australia; School of Psychiatry and Clinical Neurosciences (O.P.A.), University of Western Australia, Perth, Australia; Department of
| | - Leon Flicker
- From the Western Australian Centre for Health and Ageing (C.B., H.A., L.F., O.P.A.), Centre for Medical Research, Western Australian Institute for Medical Research, Perth, Australia; School of Medicine and Pharmacology (C.B., L.F., G.J.H.), University of Western Australia, Perth, Australia; Department of Geriatric Medicine (C.B., L.F.), Royal Perth Hospital, Perth, Australia; School of Psychiatry and Clinical Neurosciences (O.P.A.), University of Western Australia, Perth, Australia; Department of
| | - Paul E. Norman
- From the Western Australian Centre for Health and Ageing (C.B., H.A., L.F., O.P.A.), Centre for Medical Research, Western Australian Institute for Medical Research, Perth, Australia; School of Medicine and Pharmacology (C.B., L.F., G.J.H.), University of Western Australia, Perth, Australia; Department of Geriatric Medicine (C.B., L.F.), Royal Perth Hospital, Perth, Australia; School of Psychiatry and Clinical Neurosciences (O.P.A.), University of Western Australia, Perth, Australia; Department of
| | - Graeme J. Hankey
- From the Western Australian Centre for Health and Ageing (C.B., H.A., L.F., O.P.A.), Centre for Medical Research, Western Australian Institute for Medical Research, Perth, Australia; School of Medicine and Pharmacology (C.B., L.F., G.J.H.), University of Western Australia, Perth, Australia; Department of Geriatric Medicine (C.B., L.F.), Royal Perth Hospital, Perth, Australia; School of Psychiatry and Clinical Neurosciences (O.P.A.), University of Western Australia, Perth, Australia; Department of
| | - Osvaldo P. Almeida
- From the Western Australian Centre for Health and Ageing (C.B., H.A., L.F., O.P.A.), Centre for Medical Research, Western Australian Institute for Medical Research, Perth, Australia; School of Medicine and Pharmacology (C.B., L.F., G.J.H.), University of Western Australia, Perth, Australia; Department of Geriatric Medicine (C.B., L.F.), Royal Perth Hospital, Perth, Australia; School of Psychiatry and Clinical Neurosciences (O.P.A.), University of Western Australia, Perth, Australia; Department of
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Powers BJ, Danus S, Grubber JM, Olsen MK, Oddone EZ, Bosworth HB. The effectiveness of personalized coronary heart disease and stroke risk communication. Am Heart J 2011; 161:673-80. [PMID: 21473965 DOI: 10.1016/j.ahj.2010.12.021] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2010] [Accepted: 12/22/2010] [Indexed: 11/17/2022]
Abstract
BACKGROUND Current guidelines recommend global risk assessment to guide vascular risk factor management; however, most provider-patient communication focuses on individual risk factors in isolation. We sought to evaluate the impact of personalized coronary heart disease and stroke risk communication on patients' knowledge, beliefs, and health behavior. METHODS We conducted a randomized controlled trial testing personalized risk communication based on Framingham stroke and coronary heart disease risk scores compared with a standard risk factor education. A total of 89 patients were recruited from primary care clinics and followed up for 3 months. Outcomes included the following: risk perception and worry, risk factor knowledge, risk reduction preferences and decision conflict, medication adherence, health behaviors, and blood pressure. RESULTS Participants had a very low understanding of numeric information, high perceived risk for stroke or myocardial infarction, and high proportion of medication nonadherence. Patients' ability to identify vascular risk factors increased with personalized risk communication (mean 1.8 additional risk factors, 95% CI 1.3-2.2) and standard risk factor education (mean 1.6 additional risk factors, 95% CI 1.1-2.1) immediately after the intervention but was not sustained at 3 months. Patients in the personalized group had less decision conflict than the standard risk factor education group over intended risk reduction strategies (5.9 vs 10.1, P = .003). There was no appreciable impact of either communication strategy on medication adherence, exercise, smoking cessation, or blood pressure. CONCLUSIONS Personalized risk communication was preferred by patients and had a small impact on risk reduction preferences and decision conflict but had no impact on patient beliefs or behavior compared with standard risk factor education.
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Affiliation(s)
- Benjamin J Powers
- Center for Health Services Research in Primary Care, Durham VA Medical Center, NC 27705, USA
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Bozorgmanesh M, Hadaegh F, Azizi F. Predictive accuracy of the 'Framingham's general CVD algorithm' in a Middle Eastern population: Tehran Lipid and Glucose Study. Int J Clin Pract 2011; 65:264-73. [PMID: 21314863 DOI: 10.1111/j.1742-1241.2010.02529.x] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
AIMS To examine the performance of 'the Framingham's general CVD algorithm' in estimating 5-year risk of cardiovascular disease (CVD) and coronary heart disease (CHD) in an adult Middle Eastern community. METHODS In a population-based prospective cohort, the Tehran Lipid and Glucose Study (TLGS), CVD risk estimates were calculated for 3838 individuals (2183 women) free of CVD at baseline. RESULTS Over 8.6 years, there were 119 CVD (106 CHD) events among women, and 164 (137) among men aged ≥ 30 years. Standard risk factors [age, systolic blood pressure (SBP), antihypertensive treatment, total and high-density lipoprotein cholesterol, smoking and diabetes mellitus], measured at baseline, were significantly related to the incidence of CVD. The only exception, lack of significance for coefficients of SBP among women, was attributable to collinearity between age and SBP. For most risk factors, hazard ratios (HRs) for CVD were similar for the TLGS and the Framingham cohorts' participants. Few exceptions were lower HR for SBP among TLGS' women and lower HR for age among TLGS' men. The Framingham equations quite correctly discriminated participants [C statistic: CVD, 0.818 (women) and 0.774 (men); CHD: 0.822 (women) and 0.751 (men)] with good calibration (all calibration χ(2) values < 15, ps > 0.1). DISCUSSION AND CONCLUSIONS The Framingham's CVD algorithm was effective at ranking individuals and could be used to quantify risk and to guide preventive care in Iranian adults. For regions without established cohort, recalibration using risk factors and CVD (CHD) rates may be an effective method to develop CVD (CHD) risk prediction algorithms for local practice.
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Affiliation(s)
- M Bozorgmanesh
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University (M.C.), Tehran, Iran
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Empana JP, Tafflet M, Escolano S, Vergnaux AC, Bineau S, Ruidavets JB, Montaye M, Haas B, Czernichow S, Balkau B, Ducimetiere P. Predicting CHD risk in France: a pooled analysis of the D.E.S.I.R., Three City, PRIME, and SU.VI.MAX studies. ACTA ACUST UNITED AC 2011; 18:175-85. [PMID: 21450663 DOI: 10.1177/1741826710389354] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND We aimed to develop and validate a simple coronary heart disease (CHD) risk algorithm applicable to asymptomatic men and women in France, and to compare its accuracy with that of the last published version of the Framingham risk function for cardiovascular disease. DESIGN A pooled analysis of four French prospective general-population studies. METHODS The baseline and follow-up data from D.E.S.I.R., PRIME, Three City, and SU.VI.MAX studies were used. The 10-year CHD risk was estimated by the Cox proportional hazards model with candidate variables including age, gender, body mass index, waist circumference, family history of coronary heart disease, smoking status, diabetes status, systolic blood pressure, and total and high-density lipoprotein (HDL) cholesterol. RESULTS The study population included 22,256 subjects (61.4% men) aged (SD) 56.0 years (8.3) without a personal history of CHD at baseline. After a mean follow-up of 8.0 years (2.3), 788 first CHD events occurred, 726 in men and 62 in women. The final model included age, gender, age × gender interaction, current smoking status, diabetes status, systolic blood pressure, total and HDL cholesterol. Using this model, the number of predicted coronary events fitted that given by the 10-year Kaplan-Meier survival estimates within each decile of estimated risk (calibration). This model had fair discrimination: Harrell C-index, 0.7831 (95% CI: 0.7704-0.7957). For comparison, the recalibrated Framingham risk function had equivalent performances compared to the French risk equation. CONCLUSION Our 10-year French CHD risk equation based on traditional risk factors performed at least as well as the recalibrated Framingham cardiovascular disease risk function.
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Affiliation(s)
- J P Empana
- INSERM U970, Paris Cardiovascular Research Centre PARCC, 56 rue Leblanc, Paris, France.
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Lima-Costa MF, Matos DL, Ribeiro ALP. Chagas Disease Predicts 10-Year Stroke Mortality in Community-Dwelling Elderly. Stroke 2010; 41:2477-82. [DOI: 10.1161/strokeaha.110.588061] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Maria Fernanda Lima-Costa
- From the Centro de Pesquisas Rene Rachou (M.F.L.-C.), Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brasil, and Faculdade de Medicina, Universidade Federal de Minas Grais, Belo Horizonte, Minas Gerais, Brasil; Centro de Pesquisas Rene Rachou (D.L.M.), Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brasil, and Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brasil; and Faculdade de Medicina (A.L.P.R.), Universidade Federal de Minas Grais,
| | - Divane L. Matos
- From the Centro de Pesquisas Rene Rachou (M.F.L.-C.), Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brasil, and Faculdade de Medicina, Universidade Federal de Minas Grais, Belo Horizonte, Minas Gerais, Brasil; Centro de Pesquisas Rene Rachou (D.L.M.), Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brasil, and Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brasil; and Faculdade de Medicina (A.L.P.R.), Universidade Federal de Minas Grais,
| | - Antônio Luiz P. Ribeiro
- From the Centro de Pesquisas Rene Rachou (M.F.L.-C.), Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brasil, and Faculdade de Medicina, Universidade Federal de Minas Grais, Belo Horizonte, Minas Gerais, Brasil; Centro de Pesquisas Rene Rachou (D.L.M.), Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brasil, and Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brasil; and Faculdade de Medicina (A.L.P.R.), Universidade Federal de Minas Grais,
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Lee M, Saver JL, Chang KH, Liao HW, Chang SC, Ovbiagele B. Impact of microalbuminuria on incident stroke: a meta-analysis. Stroke 2010; 41:2625-31. [PMID: 20930164 DOI: 10.1161/strokeaha.110.581215] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Microalbuminuria, a marker of both kidney disease and endothelial dysfunction, may be associated with global vascular risk, but the nature and magnitude of the link between microalbuminuria and incident stroke has not been clearly defined. The purpose of this study was to assess the consistency and strength of the association of microalbuminuria with risk of stroke in prospective studies using meta-analysis. METHODS We conducted a systematic search of electronic databases and bibliographies for studies reporting a multivariate-adjusted estimate, represented as relative risk with 95% CI, of the association between microalbuminuria and stroke risk. Studies were excluded if a majority of study participants had established kidney disease or pre-eclampsia. Estimates were combined using a random-effect model. RESULTS We identified 12 studies, with a total of 48 596 participants and 1263 stroke events. Overall, presence of microalbuminuria was associated with greater stroke risk (relative risk, 1.92; 95% CI, 1.61 to 2.28; P < 0.001) after adjustment for established cardiovascular risk factors. There was evidence of significant heterogeneity in the magnitude of the association across studies (P for heterogeneity < 0.001, I² = 68%), which was partially explained by differences in study population, microalbuminuria definition, and different microalbuminuria-related risk among stroke subtypes. However, in stratified analyses, microalbuminuria was associated with increased risk of subsequent stroke in all subgroups (general population, diabetics, those with known stroke). CONCLUSIONS Microalbuminuria is strongly and independently associated with incident stroke risk. Future studies should explore whether microalbuminuria is just a risk marker or a modifiable risk factor for stroke.
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Affiliation(s)
- Meng Lee
- Stroke Center and Department of Neurology, University of California, Los Angeles, CA 90095, USA
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