Prognosis of severe acquired brain injury: Short and long-term outcome determinants and their potential clinical relevance after rehabilitation. A comprehensive approach to analyze cohort studies

Autoři: Bernardo Lanzillo aff001;  Giuseppe Piscosquito aff001;  Laura Marcuccio aff001;  Anna Lanzillo aff001;  Dino Franco Vitale aff001
Působiště autorů: Istituti Clinici Maugeri, IRCCS di Telese Terme, Via Bagni Vecchi 1, Telese T, BN, Italy aff001;  Casa di Cura San Michele, Via Montella 16, Maddaloni, CE, Italy aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0216507



Accurate prognostic evaluation is a key factor in the clinical management of patients affected by severe acute brain injury (ABI) and helps planning focused therapies, better caregiver’s support and allocation of resources. Aim of the study was to assess factors independently associated with both the short and long-term outcomes after rehabilitation in patients affected by ABI in the setting of a single Rehabilitation Unit specifically allocated to these patients.

Methods and findings

In all patients (567) with age ≥ 18 years discharged from the Unit in the period 2006/2015 demographic, etiologic, comorbidity indicators, and descriptors of the disability burden (at hospital admission and discharge) were evaluated as potential prognostic factors of both short-term (4 classes of disability status at discharge) and long-term (mortality) outcomes. A comprehensive analytical method was adopted to combine several tasks. Select the factors with a significant independent association with the outcome, assess the relative weights and the “stability” (by bootstrap resampling) of them and estimate the role of the prognostic models in the clinical framework considering “cost” and “benefits”. The generalized ordered logistic model for ordinal dependent variables was used for the short-term outcome while the Cox proportional hazard model was used for the long-term outcome. The final short-term model identified 7 factors that independently account for 37% of the outcome variability as shown by pseudo R2 (pR2) = 0.37. The disability status descriptors show the strongest association since they account for more than 60% of the pR2, followed by age (14.8%), the presence of percutaneous endoscopic gastrostomy or nasogastric intubation (14.4%), a longer stay in the acute ward (5.9%) and concomitant coronary disease (1.3%). The final multivariable Cox model identified 4 factors that independently account for 52% of the outcome variability (R2 = 0.52). The disability extent and the disability recovered lead the long-term mortality since they account for the 53% of the global R2. The relevant effect of age (42%) is appreciable only after 2 years given the significant interaction with time. A longer stay in the acute ward explains the remaining fraction (5%). Considering ‘cost and benefits’, the decision curve analysis shows that the clinical benefit achieved by using both prognostic models is greater than the other possible action strategies, namely ‘treat all’ and ‘treat none. Several less obvious characteristics of the prognostic models are appreciated by integrating the results of multiple analytical methods.


The comprehensive analytical tool aimed to integrate statistical significance, weight, “stability” and clinical “net” benefit, gives back a prognostic framework explaining a relevant portion of both outcomes’ variability in which the strong association of the disability status with both outcomes is comparable to and followed by a time modulated role of age. Our data do not support a differentiated association of traumatic vs non-traumatic etiology. The results encourage the use of integrated approach to analyze cohort data.

Klíčová slova:

Cognitive impairment – Coronary heart disease – Death rates – Etiology – Glomerular filtration rate – Hospitals – Chronic obstructive pulmonary disease – Neurorehabilitation


1. Colantonio A, Gerber G, Bayley M, Deber R, Yin J, Kim H. Differential profiles for patients with traumatic and non-traumatic brain injury. J Rehabil Med. 2011 Mar;43(4):311–5. doi: 10.2340/16501977-0783 21347507

2. Smania N, Avesani R, Roncari L, Ianes P, Girardi P, Varalta V et al. Factors predicting functional and cognitive recovery following severe traumatic, anoxic, and cerebrovascular brain damage. J Head Trauma Rehabil. 2013 Mar-Apr;28(2):131–40. doi: 10.1097/HTR.0b013e31823c0127 22333677

3. Avesani R, Roncari L, Khansefid M, Formisano R, Boldrini P, Zampolini M et al. The Italian National Registry of severe acquired brain injury: epidemiological, clinical and functional data of 1469 patients. Eur J Phys Rehabil Med. 2013 Oct;49(5):611–8. 23558700

4. Shaun Gray D, Burnham RS. Preliminary outcome analysis of a long-term rehabilitation program for severe acquired brain injury. Arch Phys Med Rehabil. 2000;81:1447–56. doi: 10.1053/apmr.2000.16343 11083347

5. Menon DK, Zahed C. Prediction of outcome in severe traumatic brain injury. Curr Opin Crit Care. 2009 Oct;15(5):437–41. doi: 10.1097/MCC.0b013e3283307a26 19713837

6. Silverberg ND, Gardner AJ, Brubacher JR, Panenka WJ, Li JJ, Iverson GL. Systematic review of multivariable prognostic models for mild traumatic brain injury. J Neurotrauma. 2015 Apr 15;32(8):517–26. doi: 10.1089/neu.2014.3600 25222514

7. Gao J, Zheng Z. Development of prognostic models for patients with traumatic brain injury: a systematic review. Int J Clin Exp Med 2015 Nov 15;8(11):19881–5. 26884899

8. Maas AI, Murray GD, Roozenbeek B, Lingsma HF, Butcher I, McHugh GS et al.; International Mission on Prognosis Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) Study Group. Advancing care for traumatic brain injury: findings from the IMPACT studies and perspectives on future research. Lancet Neurol. 2013 Dec;12(12):1200–10. doi: 10.1016/S1474-4422(13)70234-5 24139680

9. Cullen NK, Park YG, Bayley MT. Functional recovery following traumatic vs non-traumatic brain injury: a case-controlled study. Brain Inj. 2008 Dec;22(13–14):1013–20. doi: 10.1080/02699050802530581 19117180

10. Stevens RD, Sutter R. Prognosis in severe brain injury. Crit Care Med. 2013 Apr;41(4):1104–23. doi: 10.1097/CCM.0b013e318287ee79 23528755

11. Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak. 2006 Nov 14;6:38. doi: 10.1186/1472-6947-6-38 17105661

12. Lanzillo B, Matarazzo G, Calabrese C, Vitale DF. Normalization of functional independence measure variation improves assessment of stroke rehabilitation outcome. Eur J Phys Rehabil Med. 2015 Oct; 51(5):587–96. 25573600

13. Williams R. Generalized ordered logit/partial proportional odds models for ordinal dependent variables. Stata J. 2006;6(1):58–82.

14. Nieto FJ, Coresh J. Adjusting survival curves for confounders: a review and a new method. Am J Epidemiol 1996;143:1059–68. doi: 10.1093/oxfordjournals.aje.a008670 8629613

15. Royston P, Lambert PC. Flexible parametric survival analysis using Stata:beyond the Cox model. College Station, Texas: Stata Press; 2011. pp 275–282.

16. Rengo G, Pagano G, Filardi PP, Femminella GD, Parisi V, Cannavo A et al. Prognostic Value of Lymphocyte G Protein-Coupled Receptor Kinase-2 Protein Levels in Patients With Heart Failure. Circ Res. 2016 Apr 1;118(7):1116–24. doi: 10.1161/CIRCRESAHA.115.308207 26884616

17. Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999;28:964–974. doi: 10.1093/ije/28.5.964 10597998

18. Shorrocks AF. Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. J Econ Inequal. 2013;11:99–126.

19. Royston P, Sauerbrei W. Multivariate model building. A pragmatic approach to regression analysis based on fractional polynomials for modeling continuous variables. Chichester, UK: Wiley; 2008.pp 183–199.

20. Sauerbrei W, Royston P, Look M. A New Proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biom J. 2007 Jun;49(3):453–73. doi: 10.1002/bimj.200610328 17623349

21. Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat. 2008;62:314–320. doi: 10.1198/000313008X370302 19132141

22. Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008 Nov 26;8:53. doi: 10.1186/1472-6947-8-53 19036144

23. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007 Oct 20;370(9596):1453–7. doi: 10.1016/S0140-6736(07)61602-X 18064739

24. Formisano R, Azicnuda E, Sefid MK, Zampolini M, Scarponi F, Avesani R. Early rehabilitation: benefits in patients with severe acquired brain injury. Neurol Sci. 2017 Jan;38(1):181–184. doi: 10.1007/s10072-016-2724-5 27696274

25. Fu TS, Jing R, McFaull SR, Cusimano MD. Recent trends in hospitalization and in-hospital mortality associated with traumatic brain injury in Canada: a nationwide, population-based study. J Trauma Acute Care Surg. 2015 Sep;79(3):449–54. 26535433

Článek vyšel v časopise


2019 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Zvyšte si kvalifikaci online z pohodlí domova

Antiseptika a prevence ve stomatologii
nový kurz
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Citikolin v neuroprotekci a neuroregeneraci: od výzkumu do klinické praxe nejen očních lékařů
Autoři: MUDr. Petr Výborný, CSc., FEBO

Zánětlivá bolest zad a axiální spondylartritida – Diagnostika a referenční strategie
Autoři: MUDr. Monika Gregová, Ph.D., MUDr. Kristýna Bubová

Diagnostika a léčba deprese pro ambulantní praxi
Autoři: MUDr. Jan Hubeňák, Ph.D

Význam nemocničního alert systému v době SARS-CoV-2
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D., prim. MUDr. Václava Adámková

Všechny kurzy
Kurzy Doporučená témata