Skip to main content

Advertisement

SpringerLink
Log in

Clinical Prediction Models for Aneurysmal Subarachnoid Hemorrhage: A Systematic Review

  • Original Article
  • Published:
Neurocritical Care Aims and scope Submit manuscript

Abstract

Background

Clinical prediction models can enhance clinical decision-making and research. However, available prediction models in aneurysmal subarachnoid hemorrhage (aSAH) are rarely used. We evaluated the methodological validity of SAH prediction models and the relevance of the main predictors to identify potentially reliable models and to guide future attempts at model development.

Methods

We searched the EMBASE, MEDLINE, and Web of Science databases from January 1995 to June 2012 to identify studies that reported clinical prediction models for mortality and functional outcome in aSAH. Validated methods were used to minimize bias.

Results

Eleven studies were identified; 3 developed models from datasets of phase 3 clinical trials, the others from single hospital records. The median patient sample size was 340 (interquartile range 149–733). The main predictors used were age (n = 8), Fisher grade (n = 6), World Federation of Neurological Surgeons grade (n = 5), aneurysm size (n = 5), and Hunt and Hess grade (n = 3). Age was consistently dichotomized. Potential predictors were prescreened by univariate analysis in 36 % of studies. Only one study was penalized for model optimism. Details about model development were often insufficiently described and no published studies provided external validation.

Conclusions

While clinical prediction models for aSAH use a few simple predictors, there are substantial methodological problems with the models and none have had external validation. This precludes the use of existing models for clinical or research purposes. We recommend further studies to develop and validate reliable clinical prediction models for aSAH.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Steyerberg EW. Clinical prediction models. A practical approach to development, validation and updating. New York: Springer; 2009.

    Google Scholar 

  2. Al-Khindi T, Macdonald RL, Schweizer TA. Cognitive and functional outcome after aneurysmal subarachnoid hemorrhage. Stroke. 2010;41:e519–36.

    Article  PubMed  Google Scholar 

  3. Lovelock CE, Rinkel GJ, Rothwell PM. Time trends in outcome of subarachnoid hemorrhage: Population-based study and systematic review. Neurology. 2010;74:1494–501.

    Article  PubMed  CAS  Google Scholar 

  4. den BS, de Keizer NF, de JE. Performance of prognostic models in critically ill cancer patients—a review. Crit Care. 2005;9:R458–63.

    Article  Google Scholar 

  5. Ariesen MJ, Algra A, van der Worp HB, Rinkel GJ. Applicability and relevance of models that predict short term outcome after intracerebral haemorrhage. J Neurol Neurosurg Psychiatry. 2005;76:839–44.

    Article  PubMed  CAS  Google Scholar 

  6. Beneciuk JM, Bishop MD, George SZ. Clinical prediction rules for physical therapy interventions: a systematic review. Phys Ther. 2009;89:114–24.

    Article  PubMed  Google Scholar 

  7. Toll DB, Janssen KJ, Vergouwe Y, Moons KG. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol. 2008;61:1085–94.

    Article  PubMed  CAS  Google Scholar 

  8. Mallett S, Royston P, Waters R, Dutton S, Altman DG. Reporting performance of prognostic models in cancer: a review. BMC Med. 2010;8:21.

    Article  PubMed  Google Scholar 

  9. Noterman J, Brotchi J. A brief history and a few remarks about classifications of aneurysmal subarachnoid hemorrhages and scales of follow-up. Neurochirurgie. 2006;52:83–92.

    Article  PubMed  CAS  Google Scholar 

  10. van Gijn J, Bromberg JE, Lindsay KW, Hasan D, Vermeulen M. Definition of initial grading, specific events, and overall outcome in patients with aneurysmal subarachnoid hemorrhage. A survey [Review] [24 refs]. Stroke. 1994;25:1623–7.

    Article  PubMed  Google Scholar 

  11. Cavanagh SJ, Gordon VL. Grading scales used in the management of aneurysmal subarachnoid hemorrhage: a critical review. J Neurosci Nurs. 2002;34:288–95.

    Article  PubMed  Google Scholar 

  12. Ducati A. The clinical grading of subarachnoid hemorrhage [Review] [8 refs] [Italian]. Minerva Anestesiol. 1998;64:109–12.

    PubMed  CAS  Google Scholar 

  13. Rosen DS, Macdonald RL. Subarachnoid hemorrhage grading scales: a systematic review. Neurocrit Care. 2005;2:110–8.

    Article  PubMed  Google Scholar 

  14. Tamargo RJ, Walter KA, Oshiro EM. Aneurysmal subarachnoid hemorrhage: prognostic features and outcomes [Review] [70 refs]. New Horiz. 1997;5:364–75.

    PubMed  CAS  Google Scholar 

  15. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.

    Article  PubMed  Google Scholar 

  16. Ingui BJ, Rogers MA. Searching for clinical prediction rules in MEDLINE. J Am Med Inform Assoc. 2001;8:391–7.

    Article  PubMed  CAS  Google Scholar 

  17. Risselada R, Lingsma HF, Bauer-Mehren A, et al. Prediction of 60 day case-fatality after aneurysmal subarachnoid haemorrhage: results from the International Subarachnoid Aneurysm Trial (ISAT). Eur J Epidemiol. 2010;25:261–6.

    Article  PubMed  CAS  Google Scholar 

  18. Risselada R, Lingsma HF, Molyneux AJ, et al. Prediction of two month modified Rankin Scale with an ordinal prediction model in patients with aneurysmal subarachnoid haemorrhage. BMC Med Res Methodol. 2010;10:86.

    Article  PubMed  Google Scholar 

  19. Le Roux PD, Elliott JP, Newell DW, Grady MS, Winn HR. Predicting outcome in poor-grade patients with subarachnoid hemorrhage: a retrospective review of 159 aggressively managed cases. J Neurosurg. 1996;85:39–49.

    Article  PubMed  Google Scholar 

  20. Mocco J, Ransom ER, Komotar RJ, et al. Preoperative prediction of long-term outcome in poor-grade aneurysmal subarachnoid hemorrhage. Neurosurgery. 2006;59:529–38.

    Article  PubMed  CAS  Google Scholar 

  21. Claassen J, Vu A, Kreiter KT, et al. Effect of acute physiologic derangements on outcome after subarachnoid hemorrhage. Crit Care Med. 2004;32:832–8.

    Article  PubMed  Google Scholar 

  22. Turck N, Vutskits L, Sanchez-Pena P, et al. A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage. Intensive Care Med. 2010;36:107–15.

    Article  PubMed  Google Scholar 

  23. Germanson TP, Lanzino G, Kongable GL, Torner JC, Kassell NF. Risk classification after aneurysmal subarachnoid hemorrhage. Surg Neurol. 1998;49:155–63.

    Article  PubMed  CAS  Google Scholar 

  24. Lagares A, Gomez PA, Alen JF, et al. A comparison of different grading scales for predicting outcome after subarachnoid haemorrhage. Acta Neurochir (Wien). 2005;147:5–16.

    Article  CAS  Google Scholar 

  25. Ogilvy CS, Quinones-Hinojosa A. Surgical treatment of vertebral and posterior inferior cerebellar artery aneurysms [Review] [51 refs]. Neurosurg Clin N Am. 1998;9:851–60.

    PubMed  CAS  Google Scholar 

  26. Rosen DS, Macdonald RL. Grading of subarachnoid hemorrhage: modification of the World Federation of Neurosurgical Societies scale on the basis of data for a large series of patients. Neurosurgery. 2004;54:566–75.

    Article  PubMed  Google Scholar 

  27. Salary M, Quigley MR, Wilberger JE Jr. Relation among aneurysm size, amount of subarachnoid blood, and clinical outcome. J Neurosurg. 2007;107:13–7.

    Article  PubMed  Google Scholar 

  28. de TP, Rios PM, Ledezma A, Sanchis A, Alen JF, Lagares A. Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques. IEEE Trans Inf Technol Biomed. 2009;13:794–801.

    Article  Google Scholar 

  29. Degen LA, Dorhout Mees SM, Algra A, Rinkel GJ. Interobserver variability of grading scales for aneurysmal subarachnoid hemorrhage. Stroke. 2011;42:1546–9.

    Article  PubMed  Google Scholar 

  30. Smith ML, Abrahams JM, Chandela S, Smith MJ, Hurst RW, Le Roux PD. Subarachnoid hemorrhage on computed tomography scanning and the development of cerebral vasospasm: the Fisher grade revisited. Surg Neurol. 2005;63:229–34.

    Article  PubMed  Google Scholar 

  31. Frontera JA, Claassen J, Schmidt JM, et al. Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified Fisher scale. Neurosurgery. 2006;59:21–7.

    Article  PubMed  Google Scholar 

  32. Jennett B, Bond M. Assessment of outcome after severe brain damage. A practical scale. Lancet. 1975;1:480–4.

    Article  PubMed  CAS  Google Scholar 

  33. van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van GJ. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19:604–7.

    Article  PubMed  Google Scholar 

  34. Mushkudiani NA, Hukkelhoven CW, Hernandez AV, et al. A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes. J Clin Epidemiol. 2008;61:331–43.

    Article  PubMed  Google Scholar 

  35. Whiteley W, Chong WL, Sengupta A, Sandercock P. Blood markers for the prognosis of ischemic stroke: a systematic review. Stroke. 2009;40:e380–9.

    Article  PubMed  Google Scholar 

  36. Mocco J, Ransom ER, Komotar RJ, et al. Racial differences in cerebral vasospasm: a systematic review of the literature. Neurosurgery. 2006;58:305–14.

    Article  PubMed  CAS  Google Scholar 

  37. Bossuyt PM, Reitsma JB, Bruns DE, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD Initiative. Ann Intern Med. 2003;138:40–4.

    PubMed  Google Scholar 

  38. Altman DG, McShane LM, Sauerbrei W, Taube SE. Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. PLoS Med. 2012;9:e1001216.

    Article  PubMed  Google Scholar 

  39. Moher D, Schulz KF, Altman D. The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. JAMA. 2001;285:1987–91.

    Article  PubMed  CAS  Google Scholar 

  40. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62:1006–12.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

Funded by a Grant from the Canadian Institutes for Health Research.

Disclosures

Michael D. Cusimano receives grant support from the Canadian Institutes for Health Research. Nima Etminan receives grant support from the Physicians Services Incorporated Foundation. David Hasan receives grant funding from the National Institutes of Health and Brain Aneurysm Foundation. Peter Le Roux receives research funding from the National Institutes of Health and Integra and is a consultant for Codman, Integra, and Edge Therapeutics. R. Loch Macdonald receives grant funding from the Canadian Institutes for Health Research, Heart and Stroke Foundation of Canada, Canadian Stroke Network, Brain Aneurysm Foundation, and Physicians Services Incorporated Foundation. He is a consultant for Actelion Pharmaceuticals, Ltd. and the Chief Scientific Officer of Edge Therapeutics, Inc. Stephan Mayer is a consultant for Actelion Pharmaceuticals, Ltd. Andrew Molyneux is a consultant for Micrus Endovascular and receives Grant support from the Medical Research Council UK and Cerecyte Coil Trial. Tom A. Schweizer receives grant support from the Heart and Stroke Foundation of Canada, the Canadian Institutes of Health Research, and the Ontario Ministry of Research and Innovation. Michael Todd receives grant support from the National Institutes of Health. James Torner receives grant support from the National Institutes of Health. Mervyn D. I. Vergouwen receives Grant support from the Netherlands Thrombosis Foundation and Netherlands Heart Foundation. George K. C. Wong receives grant support from the Hong Kong Food and Health Bureau and the Hong Kong University Grant Committee.

Author information

Authors and Affiliations

  1. St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada

    Blessing N. R. Jaja, Michael D. Cusimano, Don Ilodigwe, Benjamin Lo, Ada Louffat-Olivares, Tom A. Schweizer, Julian Spears & Jeff Singh

  2. Heinrich Heine University, Düsseldorf, Germany

    Nima Etminan & Daniel Hanggi

  3. University of Iowa, Iowa, IA, USA

    David Hasan, Michael Todd & James Torner

  4. Columbia University, New York, NY, USA

    Hector Lantigua & Stephan Mayer

  5. University of Pennsylvania, Philadelphia, PA, USA

    Peter Le Roux

  6. Oxford University, Oxford, UK

    Andrew Molyneux

  7. Leeds Teaching Hospitals NHS Trust, Leeds, UK

    Audrey Quinn

  8. King’s College London, London, UK

    Thomas Schenk

  9. University Medical Center Utrecht, Utrecht, Netherlands

    Mervyn D. I. Vergouwen

  10. Chinese University of Hong Kong, Hong Kong, China

    George K. C. Wong

  11. Division of Neurosurgery, St. Michael’s Hospital, University of Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada

    R. Loch Macdonald

Authors
  1. Blessing N. R. Jaja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael D. Cusimano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nima Etminan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Hanggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Don Ilodigwe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hector Lantigua
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Peter Le Roux
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Benjamin Lo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ada Louffat-Olivares
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Stephan Mayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Andrew Molyneux
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Audrey Quinn
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tom A. Schweizer
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Thomas Schenk
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Julian Spears
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Michael Todd
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. James Torner
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Mervyn D. I. Vergouwen
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. George K. C. Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jeff Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. R. Loch Macdonald
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Loch Macdonald.

Additional information

This study was conducted for the SAHIT Collaboration.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jaja, B.N.R., Cusimano, M.D., Etminan, N. et al. Clinical Prediction Models for Aneurysmal Subarachnoid Hemorrhage: A Systematic Review. Neurocrit Care 18, 143–153 (2013). https://doi.org/10.1007/s12028-012-9792-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12028-012-9792-z

Keywords

Search

Navigation