Prediction of Infarct Growth and Neurological Deterioration in Patients with Vertebrobasilar Artery Occlusions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patient Selection
2.2. Variables and Image Analysis
2.3. Identification of the Clinical-Core Mismatch Criteria That Predicts Infarct Growth
2.4. Identification of Factors Predictive of Neurological Deterioration
2.5. Statistical Analysis
3. Results
3.1. Infarct Growth and Generation of Clinical-Core Mismatch Criteria
3.1.1. Generation of Core Criterion
3.1.2. Generation of Clinical Criterion That Can Predict Infarct Growth
3.2. Neurological Deterioration and Its Predictors
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Goyal, M.; Menon, B.K.; van Zwam, W.H.; Dippel, D.W.; Mitchell, P.J.; Demchuk, A.M.; Dávalos, A.; Majoie, C.B.; van der Lugt, A.; de Miquel, M.A.; et al. Endovascular thrombectomy after large-vessel ischaemic stroke: A meta-analysis of individual patient data from five randomised trials. Lancet 2016, 387, 1723–1731. [Google Scholar] [CrossRef]
- Kayan, Y.; Meyers, P.M.; Prestigiacomo, C.J.; Kan, P.; Fraser, J.F.; Society of NeuroInterventional Surgery. Current endovascular strategies for posterior circulation large vessel occlusion stroke: Report of the Society of NeuroInterventional Surgery Standards and Guidelines Committee. J. Neurointerv. Surg. 2019, 11, 1055–1062. [Google Scholar] [CrossRef]
- Mattle, H.P.; Arnold, M.; Lindsberg, P.J.; Schonewille, W.J.; Schroth, G. Basilar artery occlusion. Lancet Neurol. 2011, 10, 1002–1014. [Google Scholar] [CrossRef]
- Nogueira, R.G.; Jadhav, A.P.; Haussen, D.C.; Bonafe, A.; Budzik, R.F.; Bhuva, P.; Yavagal, D.R.; Ribo, M.; Cognard, C.; Hanel, R.A.; et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N. Engl. J. Med. 2018, 378, 11–21. [Google Scholar] [CrossRef]
- Nogueira, R.G.; Kemmling, A.; Souza, L.M.; Payabvash, S.; Hirsch, J.A.; Yoo, A.J.; Lev, M.H. Clinical diffusion mismatch better discriminates infarct growth than mean transit time-diffusion weighted imaging mismatch in patients with middle cerebral artery-M1 occlusion and limited infarct core. J. Neurointerv. Surg. 2017, 9, 127–130. [Google Scholar] [CrossRef]
- Albers, G.W.; Marks, M.P.; Kemp, S.; Christensen, S.; Tsai, J.P.; Ortega-Gutierrez, S.; McTaggart, R.A.; Torbey, M.T.; Kim-Tenser, M.; Leslie-Mazwi, T.; et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N. Engl. J. Med. 2018, 378, 708–718. [Google Scholar] [CrossRef] [PubMed]
- Baek, J.H.; Kim, B.M.; Kim, D.J.; Heo, J.H.; Nam, H.S.; Song, D.; Bang, O.Y. Importance of truncal-type occlusion in stentriever-based thrombectomy for acute stroke. Neurology 2016, 87, 1542–1550. [Google Scholar] [CrossRef] [PubMed]
- Ferbert, A.; Bruckmann, H.; Drummen, R. Clinical features of proven basilar artery occlusion. Stroke 1990, 21, 1135–1142. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lee, J.S.; Hong, J.M.; Lee, K.S.; Suh, H.I.; Demchuk, A.M.; Hwang, Y.H.; Kim, B.M.; Kim, J.S. Endovascular Therapy of Cerebral Arterial Occlusions: Intracranial Atherosclerosis versus Embolism. J. Stroke Cerebrovasc. Dis. 2015, 24, 2074–2080. [Google Scholar] [CrossRef] [PubMed]
- Baek, J.H.; Kim, B.M. Angiographical Identification of Intracranial, Atherosclerosis-Related, Large Vessel Occlusion in Endovascular Treatment. Front. Neurol. 2019, 10, 298. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.S.; Hong, J.M.; Kim, J.S. Diagnostic and Therapeutic Strategies for Acute Intracranial Atherosclerosis-related Occlusions. J. Stroke 2017, 19, 143–151. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Baek, J.H.; Kim, B.M.; Heo, J.H.; Kim, D.J.; Nam, H.S.; Kim, Y.D. Outcomes of Endovascular Treatment for Acute Intracranial Atherosclerosis-Related Large Vessel Occlusion. Stroke 2018, 49, 2699–2705. [Google Scholar] [CrossRef] [PubMed]
- Lee, S.J.; Hong, J.M.; Choi, J.W.; Park, J.H.; Park, B.; Kang, D.H.; Kim, Y.W.; Kim, Y.S.; Hong, J.H.; Yoo, J.; et al. Predicting Endovascular Treatment Outcomes in Acute Vertebrobasilar Artery Occlusion: A Model to Aid Patient Selection from the ASIAN KR Registry. Radiology 2020, 294, 628–637. [Google Scholar] [CrossRef] [PubMed]
- Puetz, V.; Khomenko, A.; Hill, M.D.; Dzialowski, I.; Michel, P.; Weimar, C.; Wijman, C.A.; Mattle, H.P.; Engelter, S.T.; Muir, K.W.; et al. Extent of hypoattenuation on CT angiography source images in basilar artery occlusion: Prognostic value in the Basilar Artery International Cooperation Study. Stroke 2011, 42, 3454–3459. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nagel, S.; Herweh, C.; Kohrmann, M.; Huttner, H.B.; Poli, S.; Hartmann, M.; Hahnel, S.; Steiner, T.; Ringleb, P.; Hacke, W. MRI in patients with acute basilar artery occlusion—DWI lesion scoring is an independent predictor of outcome. Int. J. Stroke 2012, 7, 282–288. [Google Scholar] [CrossRef] [PubMed]
- Khatibi, K.; Nour, M.; Tateshima, S.; Jahan, R.; Duckwiler, G.; Saver, J.; Szeder, V. Posterior Circulation Thrombectomy-pc-ASPECT Score Applied to Preintervention Magnetic Resonance Imaging Can Accurately Predict Functional Outcome. World Neurosurg. 2019, 129, e566–e571. [Google Scholar] [CrossRef] [PubMed]
- Baek, J.H.; Kim, B.M.; Yoo, J.; Nam, H.S.; Kim, Y.D.; Kim, D.J.; Heo, J.H.; Bang, O.Y. Predictive Value of Computed Tomography Angiography-Determined Occlusion Type in Stent Retriever Thrombectomy. Stroke 2017, 48, 2746–2752. [Google Scholar] [CrossRef]
- Alemseged, F.; Shah, D.G.; Diomedi, M.; Sallustio, F.; Bivard, A.; Sharma, G.; Mitchell, P.J.; Dowling, R.J.; Bush, S.; Yan, B.; et al. The Basilar Artery on Computed Tomography Angiography Prognostic Score for Basilar Artery Occlusion. Stroke 2017, 48, 631–637. [Google Scholar] [CrossRef] [Green Version]
- Lin, L.C.; Lee, T.H.; Chang, C.H.; Chang, Y.J.; Liou, C.W.; Chang, K.C.; Lee, J.D.; Peng, T.Y.; Chung, J.; Chen, S.C.; et al. Predictors of clinical deterioration during hospitalization following acute ischemic stroke. Eur. Neurol. 2012, 67, 186–192. [Google Scholar] [CrossRef]
- Hsieh, F.Y.; Bloch, D.A.; Larsen, M.D. A simple method of sample size calculation for linear and logistic regression. Stat. Med. 1998, 17, 1623–1634. [Google Scholar] [CrossRef] [Green Version]
- Alemseged, F.; Van der Hoeven, E.; Di Giuliano, F.; Shah, D.; Sallustio, F.; Arba, F.; Kleinig, T.J.; Bush, S.; Dowling, R.J.; Yan, B.; et al. Response to Late-Window Endovascular Revascularization Is Associated With Collateral Status in Basilar Artery Occlusion. Stroke 2019, 50, 1415–1422. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Dai, Q.; Ye, R.; Zi, W.; Liu, Y.; Wang, H.; Zhu, W.; Ma, M.; Yin, Q.; Li, M.; et al. Endovascular treatment versus standard medical treatment for vertebrobasilar artery occlusion (BEST): An open-label, randomised controlled trial. Lancet Neurol. 2020, 19, 115–122. [Google Scholar] [CrossRef]
- Schonewille, W.J. A Randomized Acute Stroke Trial of Endovascular Therapy in Acute Basilar Artery Occlusion. Presented at the ESO-WSO 2020 Major Clinical Trials Webinar, ESOC European Stroke Organisation, 13 May 2020; Available online: https://eso-wso-conference.org/eso-wso-may-webinar/ (accessed on 12 October 2020).
- Bang, O.Y. Intracranial atherosclerosis: Current understanding and perspectives. J. Stroke 2014, 16, 27–35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kim, Y.W.; Sohn, S.I.; Yoo, J.; Hong, J.H.; Kim, C.H.; Kang, D.H.; Kim, Y.S.; Lee, S.J.; Hong, J.M.; Choi, J.W.; et al. Local tirofiban infusion for remnant stenosis in large vessel occlusion: Tirofiban ASSIST study. BMC Neurol. 2020, 20, 284. [Google Scholar] [CrossRef]
- Hwang, Y.H.; Kang, D.H.; Kim, Y.W.; Kim, Y.S.; Park, S.P.; Liebeskind, D.S. Impact of time-to-reperfusion on outcome in patients with poor collaterals. AJNR Am. J. Neuroradiol. 2015, 36, 495–500. [Google Scholar] [CrossRef] [Green Version]
- Chang, F.C.; Luo, C.B.; Chung, C.P.; Kuo, K.H.; Chen, T.Y.; Lee, H.J.; Lin, C.J.; Lirng, J.F.; Guo, W.Y. Influence of Vertebrobasilar Stenotic Lesion Rigidity on the Outcome of Angioplasty and Stenting. Sci. Rep. 2020, 10, 3923. [Google Scholar] [CrossRef] [Green Version]
- Monnin, P.; Sfameni, N.; Gianoli, A.; Ding, S. Optimal slice thickness for object detection with longitudinal partial volume effects in computed tomography. J. Appl. Clin. Med. Phys. 2017, 18, 251–259. [Google Scholar]
Variables | Infarct Growth (N = 43) | No Infarct Growth (N = 80) | p Value |
---|---|---|---|
Age | 72 (59–80) | 66 (55–75) | 0.114 |
Sex, male | 23 (53.5%) | 62 (77.5%) | 0.006 |
HTN | 21 (48.8%) | 47 (58.8%) | 0.292 |
DM | 13 (30.2%) | 22 (27.5%) | 0.749 |
Atrial fibrillation | 12 (27.9%) | 17 (21.3%) | 0.407 |
Presence of prodrome | 8 (18.6%) | 11 (13.8%) | 0.477 |
Onset-to-door time (h) | 3 (1–7) | 3 (2–11.75) | 0.857 |
NIHSS at presentation | 18 (8–22) | 7 (3–16) | 0.007 |
NIHSS ≥11 | 29 (67.4%) | 32 (40.0%) | 0.004 |
Subset mental status scores | 4 (1–6) | 1 (0–4) | 0.065 |
Mental status ≥1 | 36 (83.7%) | 41 (51.2%) | <0.001 |
Subset motor scores | 6 (2–8) | 2 (0–5.75) | 0.003 |
Motor ≥5 | 25 (58.1%) | 22 (27.5%) | 0.001 |
Subset cranial and cerebellar scores | 5 (3–7) | 3 (2–6) | 0.048 |
Cranial and cerebellar ≥4 | 32 (74.4%) | 39 (48.8%) | 0.006 |
Occlusion degree | 0.518 | ||
Complete occlusion | 34 (79.1%) | 67 (83.8%) | |
Incomplete occlusion | 9 (20.9%) | 13 (16.3%) | |
Occlusion location | 0.014 | ||
distal BA | 11 (25.6%) | 10 (12.5%) | |
proximal BA | 23 (53.5%) | 33 (41.3%) | |
VA | 9 (20.9%) | 37 (46.3%) | |
Occlusion type | 0.061 | ||
Truncal-type occlusion | 24 (55.8%) | 58 (72.5%) | |
Branching-site occlusion | 19 (44.2%) | 22 (27.5%) | |
PC-ASPECTS | 9 (8–10) | 9 (8–10) | 0.928 |
BATMAN | 5 (3–7) | 6 (5–8) | 0.090 |
Reperfusion (EVT ± IV thrombo-lysis) | 26 (60.5%) | 29 (36.3%) | 0.010 |
Final PC-ASPECTS | 5 (3–7) | 9 (8–10) | <0.001 |
Good outcomes (mRS 0–2) | 6 (14.0%) | 52 (65.0%) | <0.001 |
Variables | OR | 95% CI | p Value | |
---|---|---|---|---|
Model 1 | NIHSS at presentation≥11 | 2.16 | 0.83–5.65 | 0.116 |
Age | 1.01 | 0.98–1.05 | 0.570 | |
Sex | 2.39 | 0.96–5.91 | 0.060 | |
TTO (vs. BSO) | 0.93 | 0.35–2.43 | 0.876 | |
EVT ± IV thrombolysis | 1.58 | 0.66–3.79 | 0.309 | |
PC-ASPECTS (per 1 point decrease) | 0.93 | 0.65–1.33 | 0.682 | |
BATMAN (per 1 point decrease) | 1.16 | 0.92–1.46 | 0.220 | |
Model 2 | Subset mental status ≥ 1 | 3.34 | 1.19–9.38 | 0.022 |
Age | 1.01 | 0.98–1.05 | 0.586 | |
Sex | 2.19 | 0.88–5.43 | 0.092 | |
TTO (vs. BSO) | 1.00 | 0.38–2.65 | 0.996 | |
EVT ± IV thrombolysis | 1.48 | 0.61–3.55 | 0.383 | |
PC-ASPECTS (per 1 point decrease) | 0.95 | 0.67–1.34 | 0.760 | |
BATMAN (per 1 point decrease) | 1.17 | 0.92–1.47 | 0.200 | |
Model 3 | Subset motor ≥ 5 | 2.44 | 0.96–6.25 | 0.062 |
Age | 1.01 | 0.97–1.04 | 0.673 | |
Sex | 2.13 | 0.86–5.30 | 0.104 | |
TTO (vs. BSO) | 0.95 | 0.36–2.50 | 0.909 | |
EVT ± IV thrombolysis | 1.50 | 0.61–3.65 | 0.376 | |
PC-ASPECTS (per 1 point decrease) | 0.93 | 0.65–1.33 | 0.695 | |
BATMAN (per 1 point decrease) | 1.19 | 0.94–1.49 | 0.150 | |
Model 4 | Subset cranial and cerebellar ≥ 4 | 1.73 | 0.64–4.69 | 0.285 |
Age | 1.01 | 0.98–1.05 | 0.542 | |
Sex | 0.44 | 0.18–1.07 | 0.069 | |
TTO (vs. BSO) | 0.85 | 0.33–2.18 | 0.729 | |
EVT ± IV thrombolysis | 1.57 | 0.63–3.94 | 0.333 | |
PC-ASPECTS (per 1 point decrease) | 0.99 | 0.71–1.39 | 0.963 | |
BATMAN (per 1 point decrease) | 1.15 | 0.92–1.45 | 0.229 |
Variables | ND (N = 13) | No-ND (N = 60) | p Value |
---|---|---|---|
Age | 73 (48–77) | 69.5 (56–77.75) | 0.375 |
Sex, male | 11 (84.6%) | 43 (71.7%) | 0.335 |
HTN | 9 (69.2%) | 35 (58.3%) | 0.467 |
DM | 7 (53.8%) | 19 (31.7%) | 0.130 |
Atrial fibrillation | 1 (7.7%) | 18 (30.0%) | 0.097 |
Presence of prodrome | 6 (46.2%) | 7 (11.7%) | 0.003 |
Onset-to-door time (h) | 5 (2–28.5)] | 7 (3–24) | 0.870 |
NIHSS at presentation | 7 (2.5–13.5) | 4.5 (1.25–16.75) | 0.438 |
Subset mental status scores | 0 (0–2) | 1 (0–3.75) | 0.577 |
Subsetmotorscores | 2 (0–5) | 0 (0–4.75) | 0.318 |
Subset cranial and cerebellar scores | 3 (2–5) | 2.5 (1–5.75) | 0.934 |
Occlusion degree | 0.020 | ||
Complete occlusion | 7 (53.8%) | 50 (83.3%) | |
Incomplete occlusion | 6 (46.2%) | 10 (16.7%) | |
Occlusion location | 0.250 | ||
distal BA | 0 (0.0%) | 8 (13.3%) | |
proximal BA | 5 (38.5%) | 27 (45.0%) | |
VA | 8 (61.5%) | 25 (41.7%) | |
Occlusion type | 0.018 | ||
Truncal-type occlusion | 13 (100.0%) | 41 (68.3%) | |
Branching-site occlusion | 0 (0.0%) | 19 (31.7%) | |
PC-ASPECTS | 8 (7–9.5) | 9.5 (8–10) | 0.144 |
BATMAN | 5 (3.5–6) | 6 (5–8) | 0.077 |
Final PC-ASPECTS | 8 (6–9.5) | 9 (7–10) | 0.935 |
Good outcomes (mRS 0–2) | 2 (15.4%) | 38 (63.3%) | 0.002 |
Variables | OR | 95% CI | p Value |
---|---|---|---|
Incomplete occlusion (vs. complete occlusion) | 6.17 | 1.11–34.25 | 0.037 |
BATMAN (per 1 point decrease) | 1.91 | 1.17–3.11 | 0.009 |
PC-ASPECTS (per 1 point decrease) | 1.96 | 1.11–3.48 | 0.021 |
Age | 0.96 | 0.91–1.02 | 0.208 |
Onset-to-door time (h) | 0.97 | 0.94–1.00 | 0.087 |
NIHSS at presentation (per 1 point increase) | 0.88 | 0.77–1.01 | 0.072 |
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Koh, S.; Park, J.H.; Park, B.; Choi, M.H.; Lee, S.E.; Lee, J.S.; Hong, J.M.; Lee, S.-J. Prediction of Infarct Growth and Neurological Deterioration in Patients with Vertebrobasilar Artery Occlusions. J. Clin. Med. 2020, 9, 3759. https://doi.org/10.3390/jcm9113759
Koh S, Park JH, Park B, Choi MH, Lee SE, Lee JS, Hong JM, Lee S-J. Prediction of Infarct Growth and Neurological Deterioration in Patients with Vertebrobasilar Artery Occlusions. Journal of Clinical Medicine. 2020; 9(11):3759. https://doi.org/10.3390/jcm9113759
Chicago/Turabian StyleKoh, Seungyon, Ji Hyun Park, Bumhee Park, Mun Hee Choi, Sung Eun Lee, Jin Soo Lee, Ji Man Hong, and Seong-Joon Lee. 2020. "Prediction of Infarct Growth and Neurological Deterioration in Patients with Vertebrobasilar Artery Occlusions" Journal of Clinical Medicine 9, no. 11: 3759. https://doi.org/10.3390/jcm9113759
APA StyleKoh, S., Park, J. H., Park, B., Choi, M. H., Lee, S. E., Lee, J. S., Hong, J. M., & Lee, S. -J. (2020). Prediction of Infarct Growth and Neurological Deterioration in Patients with Vertebrobasilar Artery Occlusions. Journal of Clinical Medicine, 9(11), 3759. https://doi.org/10.3390/jcm9113759