Onoja Akpa1, Fred S Sarfo2, Mayowa Owolabi3, Albert Akpalu4, Kolawole Wahab5, Reginald Obiako6, Morenikeji Komolafe7, Lukman Owolabi8, Godwin O Osaigbovo9, Godwin Ogbole10, Hemant K Tiwari11, Carolyn Jenkins12, Adekunle G Fakunle13, Samuel Olowookere14, Ezinne O Uvere13, Joshua Akinyemi15, Oyedunni Arulogun13, Josephine Akpalu2, Moyinoluwalogo M Tito-Ilori13, Osahon J Asowata1, Philip Ibinaiye6, Cynthia Akisanya16, Olalekan I Oyinloye5, Lambert Appiah2, Taofik Sunmonu17, Paul Olowoyo18, Atinuke M Agunloye19, Abiodun M Adeoye19, Joseph Yaria20, Daniel T Lackland12, Donna Arnett21, Ruth Y Laryea22, Taiwo O Adigun13, Akinkunmi P Okekunle1, Benedict Calys-Tagoe23, Okechukwu S Ogah20, Mayowa Ogunronbi14, Olugbo Y Obiabo24, Suleiman Y Isah8, Hamisu A Dambatta8, Raelle Tagge25, Obande Ogenyi20, Bimbo Fawale7, Chimdinma L Melikam6, Akinola Onasanya14, Sunday Adeniyi5, Rufus Akinyemi26, Bruce Ovbiagele25. 1. Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Nigeria; Cardiology Research Unit, Institute of Cardiovascular Diseases, College of Medicine, University of Ibadan, Nigeria. 2. Department of Medicine, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. 3. Department of Medicine, University of Ibadan, Nigeria; Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, University College Hospital Ibadan, and Blossom Specialist Medical Center, Ibadan, Nigeria. Electronic address: mayowaowolabi@yahoo.com. 4. Department of Medicine, University of Ghana Medical School, Accra, Ghana. 5. Department of Medicine, University of Ilorin Teaching Hospital, Ilorin, Nigeria. 6. Department of Medicine, Ahmadu Bello University, Zaria, Nigeria. 7. Department of Medicine, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. 8. Department of Medicine, Aminu Kano Teaching Hospital, Kano, Nigeria. 9. Jos University Teaching Hospital Jos, Nigeria. 10. Department of Radiology, University of Ibadan, Nigeria. 11. University of Alabama at Birmingham, Birmingham, AL, USA. 12. Medical University of South Carolina, SC, USA. 13. College of Medicine, University of Ibadan, Nigeria. 14. Federal Medical Centre, Abeokuta, Nigeria. 15. Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Nigeria. 16. Federal Teaching Hospital, Ido-Ekiti, Ado-Ekiti, Nigeria. 17. Federal Medical Centre, Owo, Ondo State, Nigeria. 18. Federal Teaching Hospital, Ido-Ekiti, Ekiti State, Nigeria. 19. College of Medicine, University of Ibadan, Nigeria; University College Hospital, Ibadan, Nigeria. 20. University College Hospital, Ibadan, Nigeria. 21. College of Public Health, University of Kentucky, USA. 22. Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, University College Hospital Ibadan, and Blossom Specialist Medical Center, Ibadan, Nigeria. 23. Department of Community Health, University of Ghana Medical School, Accra, Ghana. 24. Delta State University/Delta State University Teaching Hospital, Oghara, Nigeria. 25. Weill Institute for Neurosciences, School of Medicine, University of California San-Francisco, USA. 26. Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, University College Hospital Ibadan, and Blossom Specialist Medical Center, Ibadan, Nigeria; Federal Medical Centre, Abeokuta, Nigeria.
Abstract
BACKGROUND: Stroke risk can be quantified using risk factors whose effect sizes vary by geography and race. No stroke risk assessment tool exists to estimate aggregate stroke risk for indigenous African. OBJECTIVES: To develop Afrocentric risk-scoring models for stroke occurrence. MATERIALS AND METHODS: We evaluated 3533 radiologically confirmed West African stroke cases paired 1:1 with age-, and sex-matched stroke-free controls in the SIREN study. The 7,066 subjects were randomly split into a training and testing set at the ratio of 85:15. Conditional logistic regression models were constructed by including 17 putative factors linked to stroke occurrence using the training set. Significant risk factors were assigned constant and standardized statistical weights based on regression coefficients (β) to develop an additive risk scoring system on a scale of 0-100%. Using the testing set, Receiver Operating Characteristics (ROC) curves were constructed to obtain a total score to serve as cut-off to discriminate between cases and controls. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) at this cut-off. RESULTS: For stroke occurrence, we identified 15 traditional vascular factors. Cohen's kappa for validity was maximal at a total risk score of 56% using both statistical weighting approaches to risk quantification and in both datasets. The risk score had a predictive accuracy of 76% (95%CI: 74-79%), sensitivity of 80.3%, specificity of 63.0%, PPV of 68.5% and NPV of 76.2% in the test dataset. For ischemic strokes, 12 risk factors had predictive accuracy of 78% (95%CI: 74-81%). For hemorrhagic strokes, 7 factors had a predictive accuracy of 79% (95%CI: 73-84%). CONCLUSIONS: The SIREN models quantify aggregate stroke risk in indigenous West Africans with good accuracy. Prospective studies are needed to validate this instrument for stroke prevention.
BACKGROUND: Stroke risk can be quantified using risk factors whose effect sizes vary by geography and race. No stroke risk assessment tool exists to estimate aggregate stroke risk for indigenous African. OBJECTIVES: To develop Afrocentric risk-scoring models for stroke occurrence. MATERIALS AND METHODS: We evaluated 3533 radiologically confirmed West African stroke cases paired 1:1 with age-, and sex-matched stroke-free controls in the SIREN study. The 7,066 subjects were randomly split into a training and testing set at the ratio of 85:15. Conditional logistic regression models were constructed by including 17 putative factors linked to stroke occurrence using the training set. Significant risk factors were assigned constant and standardized statistical weights based on regression coefficients (β) to develop an additive risk scoring system on a scale of 0-100%. Using the testing set, Receiver Operating Characteristics (ROC) curves were constructed to obtain a total score to serve as cut-off to discriminate between cases and controls. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) at this cut-off. RESULTS: For stroke occurrence, we identified 15 traditional vascular factors. Cohen's kappa for validity was maximal at a total risk score of 56% using both statistical weighting approaches to risk quantification and in both datasets. The risk score had a predictive accuracy of 76% (95%CI: 74-79%), sensitivity of 80.3%, specificity of 63.0%, PPV of 68.5% and NPV of 76.2% in the test dataset. For ischemic strokes, 12 risk factors had predictive accuracy of 78% (95%CI: 74-81%). For hemorrhagic strokes, 7 factors had a predictive accuracy of 79% (95%CI: 73-84%). CONCLUSIONS: The SIREN models quantify aggregate stroke risk in indigenous West Africans with good accuracy. Prospective studies are needed to validate this instrument for stroke prevention.
Authors: L P Fried; N O Borhani; P Enright; C D Furberg; J M Gardin; R A Kronmal; L H Kuller; T A Manolio; M B Mittelmark; A Newman Journal: Ann Epidemiol Date: 1991-02 Impact factor: 3.797
Authors: Joep Perk; Guy De Backer; Helmut Gohlke; Ian Graham; Zeljko Reiner; Monique Verschuren; Christian Albus; Pascale Benlian; Gudrun Boysen; Renata Cifkova; Christi Deaton; Shah Ebrahim; Miles Fisher; Giuseppe Germano; Richard Hobbs; Arno Hoes; Sehnaz Karadeniz; Alessandro Mezzani; Eva Prescott; Lars Ryden; Martin Scherer; Mikko Syvänne; Wilma J M Scholte op Reimer; Christiaan Vrints; David Wood; Jose Luis Zamorano; Faiez Zannad Journal: Eur Heart J Date: 2012-05-03 Impact factor: 29.983