Book contents
- Transient Ischemic Attack and Stroke
- Transient Ischemic Attack and Stroke
- Copyright page
- Contents
- Preface to the Second Edition
- Section 1 Epidemiology, Risk Factors, Pathophysiology, and Causes of Transient Ischemic Attacks and Stroke
- Section 2 Clinical Features, Diagnosis, and Investigation
- Section 3 Prognosis of Transient Ischemic Attack and Stroke
- Chapter 14 Methods of Determining Prognosis
- Chapter 15 Short-Term Prognosis after Transient Ischemic Attack and Minor Stroke
- Chapter 16 Short-Term Prognosis after Major Stroke
- Chapter 17 Long-Term Prognosis after Transient Ischemic Attack and Stroke
- Section 4 Treatment of Transient Ischemic Attack and Stroke
- Section 5 Secondary Prevention
- Section 6 Miscellaneous Disorders
- Index
- References
Chapter 14 - Methods of Determining Prognosis
from Section 3 - Prognosis of Transient Ischemic Attack and Stroke
Published online by Cambridge University Press: 01 August 2018
Book contents
- Transient Ischemic Attack and Stroke
- Transient Ischemic Attack and Stroke
- Copyright page
- Contents
- Preface to the Second Edition
- Section 1 Epidemiology, Risk Factors, Pathophysiology, and Causes of Transient Ischemic Attacks and Stroke
- Section 2 Clinical Features, Diagnosis, and Investigation
- Section 3 Prognosis of Transient Ischemic Attack and Stroke
- Chapter 14 Methods of Determining Prognosis
- Chapter 15 Short-Term Prognosis after Transient Ischemic Attack and Minor Stroke
- Chapter 16 Short-Term Prognosis after Major Stroke
- Chapter 17 Long-Term Prognosis after Transient Ischemic Attack and Stroke
- Section 4 Treatment of Transient Ischemic Attack and Stroke
- Section 5 Secondary Prevention
- Section 6 Miscellaneous Disorders
- Index
- References
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- Transient Ischemic Attack and StrokeDiagnosis, Investigation and Treatment, pp. 213 - 230Publisher: Cambridge University PressPrint publication year: 2018
References
Altman, DG (2001). Systematic reviews of evaluations of prognostic variables. British Medical Journal 323:224–228Google Scholar
Altman, DG, Royston, P (2000). What do we mean by validating a prognostic model? Statistics in Medicine 19:453–4733.0.CO;2-5>CrossRefGoogle ScholarPubMed
Altman, DG, Royston, P (2007). Evaluating the performance of prognostic models. In Treating Individuals: From Randomized Trials to Personalised Medicine, Rothwell, PM (ed.), pp. 213–30. London: ElsevierGoogle Scholar
Babyak, MA (2004). What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine 66:411–421Google Scholar
Boissel, JP, Collet, JP, Lievre, M et al. (1993). An effect model for the assessment of drug benefit: Example of antiarrhythmic drugs in postmyocardial infarction patients. Journal of Cardiovascular Pharmacology 22:356–363Google Scholar
Burton, A, Altman, DG (2004). Missing covariate data within cancer prognostic studies: A review of current reporting and proposed guidelines. British Journal of Cancer 91:4–8CrossRefGoogle ScholarPubMed
Christensen, E (1987). Multivariate survival analysis using Cox’s regression model. Hepatology 7:1346–1358Google Scholar
Clark, TG, Altman, DG (2003). Developing a prognostic model in the presence of missing data: An ovarian cancer case study. Journal of Clinical Epidemiology 56:28–37Google Scholar
Collins, GS, Mallett, S, Omar, O et al. (2011). Developing risk prediction models for type 2 diabetes: A systematic review of methodology and reporting. BMC Medicine 9:103Google Scholar
Collins, GS, Omar, O, Shanyinde, M et al. (2013). A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods. Journal of Clinical Epidemiology 66:268–277CrossRefGoogle ScholarPubMed
Collins, GS, Reitsma, JB, Altman, DG et al. (2015). Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD statement. Annals of Internal Medicine 162:600Google ScholarPubMed
Concato, J, Feinstein, AR, Holford, TR (1993). The risk of determining risk with multivariable models. Annals of Internal Medicine 118:201–210Google Scholar
Coste, J, Fermanian, J, Venot, A (1995). Methodological and statistical problems in the construction of composite measurement scales: A survey of six medical and epidemiological journals. Statistics in Medicine 14:331–345Google Scholar
Counsell, C, Dennis, M (2001). Systematic review of prognostic models in patients with acute stroke. Cerebrovascular Diseases 12:159–170Google Scholar
Dahlof, B, Lindholm, LH, Hansson, L et al. (1991). Morbidity and mortality in the Swedish trial in old patients with hypertension (STOP-hypertension). Lancet 338:1281–1285Google Scholar
Feinstein, AR (1996). Multivariable Analysis: An Introduction. New Haven, CT: Yale University PressGoogle Scholar
Friberg, L, Rosenqvist, M, Lip, GY (2012). Evaluation of risk stratification schemes for ischemic stroke and bleeding in 182678 patients with atrial fibrillation: The Swedish Atrial Fibrillation cohort study. European Heart Journal 33:1500–1510CrossRefGoogle Scholar
Gage, BF, van Walraven, C, Pearce, LA et al. (2004). Selecting patients with atrial fibrillation for anticoagulation. Stroke risk stratification in patients taking aspirin. Circulation 110:2287–2292Google Scholar
Giles, MF, Rothwell, PM (2007). Risk of stroke early after transient ischaemic attack: A systematic review and meta-analysis. Lancet Neurology 6:1063–1072Google Scholar
Grover, SA, Lowensteyn, I, Esrey, KL et al. (1995). Do doctors accurately assess coronary risk in their patients? Preliminary results of the coronary health assessment study. British Medical Journal 310:975–978CrossRefGoogle ScholarPubMed
Hackett, ML, Anderson, CS (2005). Predictors of depression after stroke: A systematic review of observational studies. Stroke 36:2296–2301Google Scholar
Harrell, FE Jr., Lee, KL, Califf, RM et al. (1984). Regression modelling strategies for improved prognostic prediction. Statistics in Medicine 3:143–152Google Scholar
Harrell, FE Jr., Lee, KL, Mark, DB (1996). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15:361–3873.0.CO;2-4>CrossRefGoogle ScholarPubMed
Hart, RG (2007). Antithrombotic therapy to prevent stroke in patients with atrial fibrillation. In Treating Individuals: From Randomized Trials to Personalised Medicine, Rothwell, PM (ed.) pp. 265–278. London: ElsevierGoogle Scholar
Hayden, JA, Côté, P, Bombardier, C (2006). Evaluation of the quality of prognosis studies in systematic reviews. Annals of Internal Medicine 144:427–437CrossRefGoogle ScholarPubMed
Henderson, R, Keiding, N (2005). Individual survival time prediction using statistical models. Journal of Medical Ethics 31:703–706Google Scholar
Hodges, JR, Warlow, CP (1990a). Syndromes of transient amnesia: Towards a classification. A study of 153 cases. Journal of Neurology, Neurosurgery Psychiatry 53:834–843Google Scholar
Hodges, JR, Warlow, CP (1990b). The aetiology of transient global amnesia. A case–control study of 114 cases with prospective follow-up. Brain 113:639–657Google Scholar
International Study of Unruptured Intracranial Aneurysms Investigators (1998). Unruptured intracranial aneurysms: Risks of rupture and risks of surgical intervention. New England Journal of Medicine 1998 339:1725–1733Google Scholar
Jacob, M, Lewsey, JD, Sharpin, C et al. (2005). Systematic review and validation of prognostic models in liver transplantation. Liver Transplantation 11:814–825Google Scholar
Johnston, SC, Rothwell, PM, Nguyen-Huynh, MN et al. (2007). Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack. Lancet 369:283–292CrossRefGoogle ScholarPubMed
Justice, AC, Covinsky, KE, Berlin, JA (1999). Assessing the generalizability of prognostic information. Annals of Internal Medicine 130:515–524CrossRefGoogle ScholarPubMed
Kernan, WN, Feinstein, AR, Brass, LM (1991). A methodological appraisal of research on prognosis after transient ischemic attacks. Stroke 22:1108–1116Google Scholar
Kim, LG, Johnson, TL, Marson, AG for the MRC MESS Study Group (2006). Prediction of risk of seizure recurrence after a single seizure and early epilepsy: Further results from the MESS trial. Lancet Neurology 5:317–322Google Scholar
Laupacis, A, Boysen, G, Connolly, S et al. (1994). Risk factors for stroke and efficacy of antithrombotic therapy in atrial fibrillation. Analysis of pooled data from five randomised controlled trials. Archives of Internal Medicine 154:1449–1457Google Scholar
Laupacis, A, Sekar, N, Stiell, IG (1997). Clinical prediction rules. A review and suggested modifications of methodological standards. Journal of the American Medical Association 277:488–494Google Scholar
Li, W, Boissel, JP, Girard, P et al. (1998). Identification and prediction of responders to a therapy: A model and its preliminary application to actual data. Journal of Epidemiology and Biostatistics 3:189–197Google Scholar
Lip, GY, Nieuwlaat, R, Pisters, R et al. (2010). Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: The euro heart survey on atrial fibrillation. Chest 137:263–272Google Scholar
Mackillop, WJ, Quirt, CF (1997). Measuring the accuracy of prognostic judgments in oncology. Journal of Clinical Epidemiology 50:21–29CrossRefGoogle ScholarPubMed
Mallett, S, Royston, P, Dutton, S et al. (2010). Reporting methods in studies developing prognostic models in cancer: A review. BMC Medicine 8:20Google Scholar
Medical Research Council Working Party (1985). MRC trial of treatment of mild hypertension: Principal results. British Medical Journal 291:97–104Google Scholar
Morrow, J, Russell, A, Guthrie, E et al. (2006). Malformation risks of antiepileptic drugs in pregnancy: A prospective study from the UK Epilepsy and Pregnancy Register. Journal of Neurology, Neurosurgery Psychiatry 77:193–198Google Scholar
Pagliaro, L, D’Amico, G, Soronson, TIA et al. (1992). Prevention of bleeding in cirrhosis. Annals of Internal Medicine 117:59–70Google Scholar
Reilly, BM, Evans, AT (2006). Translating clinical research into clinical practice: Impact of using prediction rules to make decisions. Annals of Internal Medicine 144:201–209Google Scholar
Rothwell, PM (2003). Incidence, risk factors and prognosis of stroke and TIA: The need for high-quality, large-scale epidemiological studies and meta-analyses. Cerebrovascular Diseases 16(Suppl 3):2–10Google Scholar
Rothwell, PM, Mehta, Z, Howard, SC et al. (2005). From subgroups to individuals: General principles and the example of carotid endartectomy. Lancet 365:256–265Google Scholar
Royston, P, Sauerbrei, W (2004). A new measure of prognostic separation in survival data. Statistics in Medicine 23:723–748CrossRefGoogle ScholarPubMed
Royston, P, Altman, DG, Sauerbrei, W (2006). Dichotomizing continuous predictors in multiple regression: A bad idea. Statistics in Medicine 25:127–141Google Scholar
Sackett, DL, Whelan, G (1980). Cancer risk in ulcerative colitis: Scientific requirements for the study of prognosis. Gastroenterology 78:1632–1635Google Scholar
Sanmuganathan, PS, Ghahramani, P, Jackson, PR et al. (2001). Aspirin for primary prevention of coronary heart disease: Safety and absolute benefit related to coronary risk derived from meta-analysis of randomised trials. Heart 85:265–271CrossRefGoogle ScholarPubMed
Schafer, JL, Graham, JW (2002). Missing data: Our view of the state of the art. Psychological Methods 7:147–177CrossRefGoogle ScholarPubMed
Schmoor, C, Sauerbrei, W, Schumacher, M (2000). Sample size considerations for the evaluation of prognostic factors in survival analysis. Statistics in Medicine 19:441–452Google Scholar
Schumacher, M, Hollander, N, Sauerbrei, W (1997). Resampling and cross-validation techniques: a tool to reduce bias caused by model building? Statistics in Medicine 16:2813–2827Google Scholar
Stevens, DL, Matthews, WB (1973). Cryptogenic drop attacks: An affliction of women. British Medical Journal 1:439–442Google Scholar
Stroke Prevention in Atrial Fibrillation Investigators (1995). Risk factors for thromboembolism during aspirin therapy in patients with atrial fibrillation: The Stroke Prevention in Atrial Fibrillation study. Journal of Stroke and Cerebrovascular Diseases 1995; 5:147–157Google Scholar
Sun, GW, Shook, TL, Kay, GL (1996). Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. Journal of Clinical Epidemiology. 49:907–916Google Scholar
Vach, W (1997). Some issues in estimating the effect of prognostic factors from incomplete covariate data. Statistics in Medicine 16:57–72Google Scholar
van Houwelingen, HC, Thorogood, J (1995). Construction, validation and updating of a prognostic model for kidney graft survival. Statistics in Medicine 14:1999–2008Google Scholar
van Koningsveld, R, Steyerberg, EW, Hughes, RA et al. (2007). A clinical prognostic scoring system for Guillain–Barré syndrome. Lancet Neurology 6:589–594Google Scholar
Verweij, PJ, van Houwelingen, HC (1993). Cross-validation in survival analysis. Statistics in Medicine 12:2305–2314Google Scholar
Walgaard, C, Lingsma, HF, Ruts, L et al. (2011). Early recognition of poor prognosis in Guillain-Barré syndrome. Neurology 76:968–975Google Scholar
West of Scotland Coronary Prevention Group (1996). West of Scotland Coronary Prevention Study: Identification of high-risk groups and comparison with other cardiovascular intervention trials. Lancet 348:1339–1342CrossRefGoogle Scholar
Wyatt, JC, Altman, DG (1995). Commentary. Prognostic models: Clinically useful or quickly forgotten? British Medical Journal 311:1539–1541Google Scholar
Yusuf, S, Zucker, D, Peduzzi, P et al. (1994). Effect of coronary artery bypass graft surgery on survival: Overview of 10-year results from randomised trials by the Coronary Artery Bypass Graft Surgery Trialists’ Collaboration. Lancet 344:563–570CrossRefGoogle ScholarPubMed