Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis

Autoři: Ruili Wei aff001;  Chaonan Wang aff002;  Fangping He aff001;  Lirong Hong aff003;  Jie Zhang aff004;  Wangxiao Bao aff001;  Fangxia Meng aff001;  Benyan Luo aff001
Působiště autorů: Department of Neurology, Brain Medical Centre, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China aff001;  Department of Geriatrics, Shulan (Hangzhou) Hospital, Hangzhou, China aff002;  Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China aff003;  Department of Rehabilitation, Hangzhou Hospital of Zhejiang CAPR, Hangzhou, China aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0226295


Accurate prediction of the neurological outcome following hypoxic–ischemic brain injury (HIBI) remains difficult. Diffusion-weighted imaging (DWI) can detect acute and subacute brain abnormalities following global cerebral hypoxia. Therefore, DWI can be used to predict the outcomes of HIBI. To this end, we searched the PubMed, EMBASE, and Cochrane Library databases for studies that examine the diagnostic accuracy of DWI in predicting HIBI outcomes in adult patients between January1995 and September 2019. Next, we conducted a comprehensive meta-analysis using the Meta-DiSc and several complementary techniques. Following the application of inclusion and exclusion criteria, a total of 28 studies were included with 98 data subsets. The overall sensitivity and specificity, with 95% confidence interval, were 0.613(0.599–0.628) and 0.958(0.947–0.967), respectively, and the area under the curve was 0.9090. Significant heterogeneity among the included studies and a threshold effect were observed (p<0.001). Different positive indices were the major sources for the heterogeneity, followed by the anatomical region examined, both of which significantly affected the prognostic accuracy. In conclusion, we demonstrated that DWI can be an instrumental modality in predicting the outcome of HIBI with good prognostic accuracy. However, the lack of clear and generally accepted positive indices limits its clinical application. Therefore, using more reliable positive indices and combining DWI with other clinical predictors may improve the diagnostic accuracy of HIBI.

Klíčová slova:

Brain damage – Brainstem – Cardiac arrest – Diffusion weighted imaging – Hypothermia – Magnetic resonance imaging – Neuroimaging


1. Busl KM, Greer DM. Hypoxic-ischemic brain injury: pathophysiology, neuropathology and mechanisms. NeuroRehabilitation. 2010;26(1):5–13. doi: 10.3233/NRE-2010-0531 20130351.

2. Becker LB, Ostrander MP, Barrett J, Kondos GT. Outcome of CPR in a large metropolitan area—where are the survivors? Annals of emergency medicine. 1991;20(4):355–61. doi: 10.1016/s0196-0644(05)81654-3 2003661.

3. Blackhall LJ, Ziogas A, Azen SP. Low survival rate after cardiopulmonary resuscitation in a county hospital. Archives of internal medicine. 1992;152(10):2045–8. 1417377.

4. Wijdicks EF, Hijdra A, Young GB, Bassetti CL, Wiebe S, Quality Standards Subcommittee of the American Academy of N. Practice parameter: prediction of outcome in comatose survivors after cardiopulmonary resuscitation (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2006;67(2):203–10. doi: 10.1212/ 16864809.

5. Sandroni C, Cariou A, Cavallaro F, Cronberg T, Friberg H, Hoedemaekers C, et al. Prognostication in comatose survivors of cardiac arrest: an advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine. Resuscitation. 2014;85(12):1779–89. doi: 10.1016/j.resuscitation.2014.08.011 25438253.

6. Sandroni C, D'Arrigo S. Neurologic Prognostication: Neurologic Examination and Current Guidelines. Seminars in neurology. 2017;37(1):40–7. doi: 10.1055/s-0036-1593857 28147417.

7. Samaniego EA, Mlynash M, Caulfield AF, Eyngorn I, Wijman CA. Sedation confounds outcome prediction in cardiac arrest survivors treated with hypothermia. Neurocritical care. 2011;15(1):113–9. doi: 10.1007/s12028-010-9412-8 20680517; PubMed Central PMCID: PMC3153345.

8. Blondin NA, Greer DM. Neurologic prognosis in cardiac arrest patients treated with therapeutic hypothermia. The neurologist. 2011;17(5):241–8. doi: 10.1097/NRL.0b013e318224ee0e 21881465.

9. Samaniego EA, Persoon S, Wijman CA. Prognosis after cardiac arrest and hypothermia: a new paradigm. Current neurology and neuroscience reports. 2011;11(1):111–9. doi: 10.1007/s11910-010-0148-9 20927660; PubMed Central PMCID: PMC3357920.

10. Bernard SA, Gray TW, Buist MD, Jones BM, Silvester W, Gutteridge G, et al. Treatment of comatose survivors of out-of-hospital cardiac arrest with induced hypothermia. The New England journal of medicine. 2002;346(8):557–63. doi: 10.1056/NEJMoa003289 11856794.

11. Nolan JP, Soar J, Cariou A, Cronberg T, Moulaert VR, Deakin CD, et al. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015: Section 5 of the European Resuscitation Council Guidelines for Resuscitation 2015. Resuscitation. 2015;95:202–22. doi: 10.1016/j.resuscitation.2015.07.018 26477702.

12. Bandettini PA. What's new in neuroimaging methods? Annals of the New York Academy of Sciences. 2009;1156:260–93. doi: 10.1111/j.1749-6632.2009.04420.x 19338512; PubMed Central PMCID: PMC2716071.

13. Kim SH, Choi SP, Park KN, Youn CS, Oh SH, Choi SM. Early brain computed tomography findings are associated with outcome in patients treated with therapeutic hypothermia after out-of-hospital cardiac arrest. Scand J Trauma Resusc Emerg Med. 2013;21:57. doi: 10.1186/1757-7241-21-57 23870424; PubMed Central PMCID: PMC3726374.

14. Els T, Kassubek J, Kubalek R, Klisch J. Diffusion-weighted MRI during early global cerebral hypoxia: A predictor for clinical outcome? Acta neurologica Scandinavica. 2004;110(6):361–7. doi: 10.1111/j.1600-0404.2004.00342.x 15527448

15. Gonzalez RG, Schaefer PW, Buonanno FS, Schwamm LH, Budzik RF, Rordorf G, et al. Diffusion-weighted MR imaging: diagnostic accuracy in patients imaged within 6 hours of stroke symptom onset. Radiology. 1999;210(1):155–62. doi: 10.1148/radiology.210.1.r99ja02155 9885601.

16. Haku T, Miyasaka N, Kuroiwa T, Kubota T, Aso T. Transient ADC change precedes persistent neuronal death in hypoxic-ischemic model in immature rats. Brain research. 2006;1100(1):136–41. doi: 10.1016/j.brainres.2006.05.018 16774743.

17. Desmond PM, Lovell AC, Rawlinson AA, Parsons MW, Barber PA, Yang Q, et al. The value of apparent diffusion coefficient maps in early cerebral ischemia. AJNR American journal of neuroradiology. 2001;22(7):1260–7. Epub 2001/08/11. 11498412.

18. Järnum H, Knutsson L, Rundgren M, Siemund R, Englund E, Friberg H, et al. Diffusion and perfusion MRI of the brain in comatose patients treated with mild hypothermia after cardiac arrest: a prospective observational study. Resuscitation [Internet]. 2009; 80(4):[425–30 pp.]. Available from: doi: 10.1016/j.resuscitation.2009.01.004 19211182

19. Wijman CAC, Mlynash M, Caulfield AF, Hsia AW, Eyngorn I, Bammer R, et al. Prognostic value of brain diffusion- after weighted imaging cardiac arrest. Annals of neurology. 2009;65(4):394–402. doi: 10.1002/ana.21632 19399889

20. Wijdicks EF, Campeau NG, Miller GM. MR imaging in comatose survivors of cardiac resuscitation. AJNR American journal of neuroradiology. 2001;22(8):1561–5. Epub 2001/09/18. 11559506.

21. Wu O, Sorensen AG, Benner T, Singhal AB, Furie KL, Greer DM. Comatose patients with cardiac arrest: Predicting clinical outcome with diffusion-weighted MR imaging. Radiology. 2009;252(1):173–81. doi: 10.1148/radiol.2521081232 19420318

22. Hirsch KG, Mlynash M, Eyngorn I, Pirsaheli R, Okada A, Komshian S, et al. Multi-Center Study of Diffusion-Weighted Imaging in Coma After Cardiac Arrest. Neurocritical care. 2016;24(1):82–9. doi: 10.1007/s12028-015-0179-9 26156112

23. Brain Resuscitation Clinical Trial ISG. Randomized clinical study of thiopental loading in comatose survivors of cardiac arrest. The New England journal of medicine. 1986;314(7):397–403. doi: 10.1056/NEJM198602133140701 2868412.

24. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36. doi: 10.7326/0003-4819-155-8-201110180-00009 22007046.

25. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics in medicine. 2002;21(11):1539–58. doi: 10.1002/sim.1186 12111919.

26. Vamvakas EC. Meta-analyses of studies of the diagnostic accuracy of laboratory tests: a review of the concepts and methods. Archives of pathology & laboratory medicine. 1998;122(8):675–86. 9701328.

27. Honest H, Khan KS. Reporting of measures of accuracy in systematic reviews of diagnostic literature. BMC health services research. 2002;2:4. doi: 10.1186/1472-6963-2-4 11884248; PubMed Central PMCID: PMC100326.

28. Kim KW, Lee J, Choi SH, Huh J, Park SH. Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part I. General Guidance and Tips. Korean J Radiol. 2015;16(6):1175–87. doi: 10.3348/kjr.2015.16.6.1175 26576106; PubMed Central PMCID: PMC4644738.

29. Arends LR, Hamza TH, van Houwelingen JC, Heijenbrok-Kal MH, Hunink MG, Stijnen T. Bivariate random effects meta-analysis of ROC curves. Medical decision making: an international journal of the Society for Medical Decision Making. 2008;28(5):621–38. doi: 10.1177/0272989X08319957 18591542.

30. Dinnes J, Deeks J, Kirby J, Roderick P. A methodological review of how heterogeneity has been examined in systematic reviews of diagnostic test accuracy. Health Technol Assess. 2005;9(12):1–113, iii. doi: 10.3310/hta9120 15774235.

31. Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC medical research methodology. 2006;6:31. doi: 10.1186/1471-2288-6-31 16836745; PubMed Central PMCID: PMC1552081.

32. Song F, Khan KS, Dinnes J, Sutton AJ. Asymmetric funnel plots and publication bias in meta-analyses of diagnostic accuracy. International journal of epidemiology. 2002;31(1):88–95. doi: 10.1093/ije/31.1.88 11914301.

33. Barrett KM, Freeman WD, Weindling SM, Brott TG, Broderick DF, Heckman MG, et al. Brain injury after cardiopulmonary arrest and its assessment with diffusion-weighted magnetic resonance imaging. Mayo Clinic proceedings. 2007;82(7):828–35. doi: 10.4065/82.7.828 17605963

34. Bevers MB, Scirica BM, Avery KR, Henderson GV, Lin AP, Lee JW. Combination of Clinical Exam, MRI and EEG to Predict Outcome Following Cardiac Arrest and Targeted Temperature Management. Neurocritical care. 2018;29(3):396–403. Epub 2018/07/02. doi: 10.1007/s12028-018-0559-z 29949008.

35. Choi SP, Park KN, Park HK, Kim JY, Youn CS, Ahn KJ, et al. Diffusion-weighted magnetic resonance imaging for predicting the clinical outcome of comatose survivors after cardiac arrest: A cohort study. Critical Care. 2010;14(1). doi: 10.1186/cc8874 20152021

36. Choi DW, Lee SW, Jeong SH, Park JS, Kim H. Early diffusion-weighted imaging and outcome prediction of comatose survivors after suicidal hanging. American Journal of Emergency Medicine. 2018. doi: 10.1016/j.ajem.2018.04.027 29793774

37. Cronberg T, Rundgren M, Westhall E, Englund E, Siemund R, Rosen I, et al. Neuron-specific enolase correlates with other prognostic markers after cardiac arrest. Neurology. 2011;77(7):623–30. doi: 10.1212/WNL.0b013e31822a276d 21775743.

38. Greer D, Scripko P, Bartscher J, Sims J, Camargo E, Singhal A, et al. Clinical MRI interpretation for outcome prediction in cardiac arrest. Neurocritical care. 2012;17(2):240–4. doi: 10.1007/s12028-012-9716-y 22565633

39. Greer DM, Scripko PD, Wu O, Edlow BL, Bartscher J, Sims JR, et al. Hippocampal magnetic resonance imaging abnormalities in cardiac arrest are associated with poor outcome. Journal of stroke and cerebrovascular diseases: the official journal of National Stroke Association. 2013;22(7):899–905. Epub 2012/09/22. doi: 10.1016/j.jstrokecerebrovasdis.2012.08.006 22995378.

40. Hirsch KG, Mlynash M, Jansen S, Persoon S, Eyngorn I, Krasnokutsky MV, et al. Prognostic Value of A Qualitative Brain MRI Scoring System After Cardiac Arrest. Journal of Neuroimaging. 2015;25(3):430–7. doi: 10.1111/jon.12143 25040353

41. Jeon CH, Park JS, Lee JH, Kim H, Kim SC, Park KH, et al. Comparison of brain computed tomography and diffusion-weighted magnetic resonance imaging to predict early neurologic outcome before target temperature management comatose cardiac arrest survivors. Resuscitation. 2017;118:21–6. doi: 10.1016/j.resuscitation.2017.06.021 28668700

42. Kim J, Choi BS, Kim K, Jung C, Lee JH, Jo YH, et al. Prognostic performance of diffusion-weighted MRI combined with NSE in comatose cardiac arrest survivors treated with mild hypothermia. Neurocritical care. 2012;17(3):412–20. doi: 10.1007/s12028-012-9773-2 22932993

43. Kim J, Kim K, Hong S, Kwon B, Yun ID, Choi BS, et al. Low apparent diffusion coefficient cluster-based analysis of diffusion-weighted MRI for prognostication of out-of-hospital cardiac arrest survivors. Resuscitation. 2013;84(10):1393–9. doi: 10.1016/j.resuscitation.2013.04.011 23603152

44. Kim J, Kim K, Suh GJ, Kwon WY, Kim KS, Shin J, et al. Prognostication of cardiac arrest survivors using low apparent diffusion coefficient cluster volume. Resuscitation. 2016;100:18–24. Epub 2016/01/18. doi: 10.1016/j.resuscitation.2015.12.013 26774174.

45. Luyt CE, Galanaud D, Perlbarg V, Vanhaudenhuyse A, Stevens RD, Gupta R, et al. Diffusion tensor imaging to predict long-term outcome after cardiac arrest: a bicentric pilot study. Anesthesiology. 2012;117(6):1311–21. Epub 2012/11/09. doi: 10.1097/ALN.0b013e318275148c 23135257.

46. Mettenburg JM, Agarwal V. Discordant Observation of Brain Injury by MRI and Malignant Electroencephalography Patterns in Comatose Survivors of Cardiac Arrest following Therapeutic Hypothermia. 2016. doi: 10.3174/ajnr.A4839 27313132.

47. Mlynash M, Campbell DM, Leproust EM, Fischbein NJ, Bammer R, Eyngorn I, et al. Temporal and spatial profile of brain diffusion-weighted MRI after cardiac arrest. Stroke. 2010;41(8):1665–72. doi: 10.1161/STROKEAHA.110.582452 20595666

48. Moon HK, Jang J, Park KN, Kim SH, Lee BK, Oh SH, et al. Quantitative analysis of relative volume of low apparent diffusion coefficient value can predict neurologic outcome after cardiac arrest. Resuscitation. 2018;126:36–42. doi: 10.1016/j.resuscitation.2018.02.020 29474879

49. Oren NC, Chang E, Yang CW, Lee SK. Brain Diffusion Imaging Findings May Predict Clinical Outcome after Cardiac Arrest. Journal of neuroimaging: official journal of the American Society of Neuroimaging. 2019;29(4):540–7. Epub 2019/05/21. doi: 10.1111/jon.12626 31107566.

50. Park JS, Lee SW, Kim H, Min JH, Kang JH, Yi KS, et al. Efficacy of diffusion-weighted magnetic resonance imaging performed before therapeutic hypothermia in predicting clinical outcome in comatose cardiopulmonary arrest survivors. Resuscitation. 2015;88:132–7. doi: 10.1016/j.resuscitation.2014.11.031 25541428

51. Reynolds AS, Guo X, Matthews E, Brodie D, Rabbani LE, Roh DJ, et al. Post-anoxic quantitative MRI changes may predict emergence from coma and functional outcomes at discharge. Resuscitation. 2017;117:87–90. doi: 10.1016/j.resuscitation.2017.06.010 28624592

52. Ryoo SM, Jeon SB, Sohn CH, Ahn S, Han C, Lee BK, et al. Predicting Outcome With Diffusion-Weighted Imaging in Cardiac Arrest Patients Receiving Hypothermia Therapy: Multicenter Retrospective Cohort Study. Critical care medicine. 2015;43(11):2370–7. Epub 2015/08/19. doi: 10.1097/CCM.0000000000001263 26284621.

53. Topcuoglu MA, Oguz KK, Buyukserbetci G, Bulut E. Prognostic value of magnetic resonance imaging in post-resuscitation encephalopathy. Internal Medicine. 2009;48(18):1635–45. doi: 10.2169/internalmedicine.48.2091 19755766

54. Velly L, Perlbarg V, Boulier T, Adam N, Delphine S, Luyt CE, et al. Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. The Lancet Neurology. 2018;17(4):317–26. doi: 10.1016/S1474-4422(18)30027-9 29500154

55. Wallin E, Larsson IM, Kristofferzon ML, Larsson EM, Raininko R, Rubertsson S. Acute brain lesions on magnetic resonance imaging in relation to neurological outcome after cardiac arrest. Acta anaesthesiologica Scandinavica. 2018;62(5):635–47. doi: 10.1111/aas.13074 29363101

56. Keijzer HM, Hoedemaekers CWE, Meijer FJA, Tonino BAR, Klijn CJM, Hofmeijer J. Brain imaging in comatose survivors of cardiac arrest: Pathophysiological correlates and prognostic properties. Resuscitation. 2018;133:124–36. doi: 10.1016/j.resuscitation.2018.09.012 30244045.

57. Howard RS, Holmes PA, Siddiqui A, Treacher D, Tsiropoulos I, Koutroumanidis M. Hypoxic-ischaemic brain injury: imaging and neurophysiology abnormalities related to outcome. QJM: monthly journal of the Association of Physicians. 2012;105(6):551–61. doi: 10.1093/qjmed/hcs016 22323616.

58. Bazarian JJ, Freeman WD, Chiota NA, Cronberg T, Rundgren M, Westhall E, et al. Neuron-specific enolase correlates with other prognostic markers after cardiac arrest. Neurology. 2011;77(20):1856–7. doi: 10.1212/WNL.0b013e31823c1141 22084277

59. Haataja L, Mercuri E, Guzzetta A, Rutherford M, Counsell S, Flavia Frisone M, et al. Neurologic examination in infants with hypoxic-ischemic encephalopathy at age 9 to 14 months: use of optimality scores and correlation with magnetic resonance imaging findings. The Journal of pediatrics. 2001;138(3):332–7. doi: 10.1067/mpd.2001.111325 11241038.

Článok vyšiel v časopise


2019 Číslo 12