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Dynamics of CTP parameters show specific spatiotemporal patterns that allow delayed cerebral ischemia prediction after subarachnoid hemorrhage
Position du problème et objectif(s) de l’étude
Aneurysmal subarachnoid hemorrhage (SAH) is a severe condition in which prognosis is worsened by delayed cerebral ischemia (DCI). Even though DCI pathophysiology is still a major research subject, preclinical and clinical studies have pointed towards an early and progressive deterioration in cerebral perfusion. As a consequence, perfusion computed-tomography (CTP) has been investigated as a DCI prognostic and diagnostic tool. However, canonical spatial and temporal evolution of the various CTP-derived metrics have not been established in SAH patients, such that optimal CTP periodicity for monitoring and metrics thresholds for triggering intervention remain unclear.
Matériel et méthodes
We conducted a one-year retrospective observational study in SAH patients hospitalized in a french University Hospital, and who underwent multiple CTP exams (study approved by IRB : 2018-IRB-MTP-02-11). We developed an image processing pipeline that allowed quantitative analysis of CTP parameters' dynamics after images registration into the MNI space. Parametric volumes were segmented using an arterial atlas including 8 arterial territories. For each region of interest (ROI) and each exam, the average of the perfusion parameters (TMax, MTT, CBF) was calculated as well as the associated coefficient of variation (CV, ratio of the standard deviation over the mean value of the parameter within the ROI). Cerebral vasospasm (CVS), and Delayed Cerebral Ischemia (DCI) occurrences were noted and 3 categories of patients were defined: "noCVS", including patients who presented neither CVS nor DCI; "CVS", including patients who presented CVS but no DCI; "DCI", including patients who presented DCI. To investigate cerebral perfusion dynamics following SAH at the group level, data were referenced to the exact SAH date (D0) and individual pattern could be overlaid. Similarly, for further investigations in the DCI group, data were referenced to the DCI diagnosis date.
Résultats & Discussion
62 patients were included, 33% classified as WFNS III-V. Cerebral vasospasm (CVS) occurrence was 68%, that of DCI 15%. CTP parameters displayed specific dynamics in each of the no-CVS, CVS, and DCI groups. In the DCI category, CBF showed an early increase and homogenization throughout the brain (Fig 1.c), while TMax showed a secondary increase both in its mean and coefficient of variation (Fig. 1 a.d.). These two features were included in a DCI predictive model (AUC of the ROC = 0.94 after bootstrapping correction). Besides, single-patients data allowed to discern between two types of dynamics in DCI patients, one characterized by high asymmetry between hemispheric parameters (Fig. 2. b.e.), the other by a rapid whole-brain deterioration of brain perfusion (Fig 2. a.d. and c.f.). We suggest that CTP monitoring comprising at least 2 exams at the early stage, for instance D0 and D2 to measure early CBF variation, and exams spaced 2 days apart from one another during the high-risk period, for example at days 5, 7 and 9 to measure secondary increase in TMax, would allow DCI prediction and early diagnosis.
Conclusion
We described spatial and temporal CTP parameters dynamics in SAH patients and showed specific patterns for CVS and DCI. These dynamics corroborate an early brain perfusion alteration accompanied with CBF overshoot, followed by a secondary deterioration characterized by an increased perfusion heterogeneity. These pathological features allow to efficiently predict DCI with CTP monitoring.
Auteurs
Vivien SZABO (1) , Quentin MESNILDREY (2), Cyril DARGAZANLI (3), Florentin KUCHARCZAK (4), Alexandre KOBBAI (5), Pierre-François PERRIGAULT (2), Kévin CHALARD (1) - (1)Department Of Critical Care Medicine And Anesthesiology Gui De Chauliac, Chu Montpellier, Igf, Univ. Montpellier, Cnrs, Inserm, Montpellier, France, (2)Department Of Critical Care Medicine And Anesthesiology Gui De Chauliac, Chu Montpellier, Montpellier, France, (3)Department Of Neuroradiology Gui De Chauliac, Chu Montpellier, Montpellier, France, (4)Department Of Biostatistics, Epidemiology, Public Health And Innovation In Methodology, Chu Nîmes, Lirmm, Univ. Montpellier, Cnrs, Nîmes, France, (5)Department Of Biostatistics, Epidemiology, Public Health And Innovation In Methodology, Chu Nîmes, Montpellier, France