Positive predictive value and stroke workflow outcomes using automated vessel density (RAPID-CTA) in stroke patients: One year experience

被引:22
作者
Adhya, Julie [1 ]
Li, Charles [1 ]
Eisenmenger, Laura [2 ]
Cerejo, Russell [3 ]
Tayal, Ashis [3 ]
Goldberg, Michael [1 ]
Chang, Warren [1 ]
机构
[1] Allegheny Hlth Network, Dept Radiol, Pittsburgh, PA USA
[2] Univ Wisconsin, Dept Radiol, Sch Med & Publ Hlth, Madison, WI 53706 USA
[3] Allegheny Hlth Network, Dept Neurol, Pittsburgh, PA USA
关键词
Stroke; mechanical thrombectomy; artificial intelligence; computed tomography angiography; ACUTE ISCHEMIC-STROKE; ARTIFICIAL-INTELLIGENCE; ENDOVASCULAR TREATMENT; THROMBECTOMY; TIME; ONSET; BRAIN;
D O I
10.1177/19714009211012353
中图分类号
R445 [影像诊断学];
学科分类号
100207 ;
摘要
Purpose Several new techniques have emerged for detecting anterior circulation large vessel occlusion by quantifying relative vessel density including RAPID-CTA, potentially allowing for faster triage and decreased time to mechanical thrombectomy. We present our one-year experience on positive predictive value of RAPID-CTA for the detection of large vessel occlusion in patients presenting with stroke symptoms and its effect on treatment time and clinical outcomes. Materials and methods Three hundred and ten patients presenting with stroke symptoms with relative vessel density <60% on RAPID-CTA were included (average age 70 years, 145 male, 165 female). Examinations were considered positive if there was evidence of large vessel occlusion or high grade stenosis. Computed tomography angiography to groin puncture time was calculated during one-year time intervals before and after RAPID-CTA installation. Ninety-day Modified Rankin Scale scores were obtained for patients in each cohort. Results Of the 310 patients, 270 had large vessel occlusion or high grade stenosis (87% positive predictive value), with 161 having large vessel occlusion. Using 45% relative vessel density threshold, 129/161 large vessel occlusion were detected (80% sensitivity) and 163/172 examinations were positive (95% positive predictive value). Computed tomography angiography to groin puncture time was significantly lower after deployment of RAPID-CTA (93 min vs 68 min, p<0.05). Average 90 day modified Rankin Scale score was lower in the RAPID-CTA group with a higher percentage of patients with functional independence, although the data was not statistically significant. Conclusion RAPID-CTA had high positive predictive value for large vessel occlusion with a 45% relative vessel density threshold, which could facilitate active worklist reprioritization. Time to treatment was significantly lower and clinical outcomes were improved after deployment of RAPID-CTA.
引用
收藏
页码:476 / 481
页数:6
相关论文
共 47 条
  • [1] Abbafati C, 2020, LANCET, V396, P1204
  • [2] Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging
    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.
    Sarraj, A.
    Kasner, S. E.
    Ansari, S. A.
    Yeatts, S. D.
    Hamilton, S.
    Mlynash, M.
    Heit, J. J.
    Zaharchuk, G.
    Kim, S.
    Carrozzella, J.
    Palesch, Y. Y.
    Demchuk, A. M.
    Bammer, R.
    Lavori, P. W.
    Broderick, J. P.
    Lansberg, M. G.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2018, 378 (08) : 708 - 718
  • [3] Fast Automatic Detection of Large Vessel Occlusions on CT Angiography
    Amukotuwa, Shalini
    Straka, Matus
    Dehkharghani, Seena
    Bammer, Roland
    [J]. STROKE, 2019, 50 (12) : 3431 - 3438
  • [4] Automated Detection of Intracranial Large Vessel Occlusions on Computed Tomography Angiography: A Single Center Experience
    Amukotuwa, Shalini A.
    Straka, Matus
    Smith, Heather
    Chandra, Ronil V.
    Dehkharghani, Seena
    Fischbein, Nancy J.
    Bammer, Roland
    [J]. STROKE, 2019, 50 (10) : 2790 - 2798
  • [5] Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration
    Arbabshirani, Mohammad R.
    Fornwalt, Brandon K.
    Mongelluzzo, Gino J.
    Suever, Jonathan D.
    Geise, Brandon D.
    Patel, Aalpen A.
    Moore, Gregory J.
    [J]. NPJ DIGITAL MEDICINE, 2018, 1
  • [6] Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study
    Bacchi, Stephen
    Zerner, Toby
    Oakden-Rayner, Luke
    Kleinig, Timothy
    Patel, Sandy
    Jannes, Jim
    [J]. ACADEMIC RADIOLOGY, 2020, 27 (02) : E19 - E23
  • [7] Comparing the outcomes of two independent computed tomography perfusion softwares and their impact on therapeutic decisions in acute ischemic stroke
    Bathla, Girish
    Ortega-Gutierrez, Santiago
    Klotz, Ernst
    Juergens, Markus
    Zevallos, Cynthia B.
    Ansari, Sameer
    Ward, Caitlin E.
    Policeni, Bruno
    Samaniego, Edgar
    Derdeyn, Colin
    [J]. JOURNAL OF NEUROINTERVENTIONAL SURGERY, 2020, 12 (10) : 1028 - 1032
  • [8] A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke
    Berkhemer, O. A.
    Fransen, P. S. S.
    Beumer, D.
    van den Berg, L. A.
    Lingsma, H. F.
    Yoo, A. J.
    Schonewille, W. J.
    Vos, J. A.
    Nederkoorn, P. J.
    Wermer, M. J. H.
    van Walderveen, M. A. A.
    Staals, J.
    Hofmeijer, J.
    van Oostayen, J. A.
    Nijeholt, G. J. Lycklama A.
    Boiten, J.
    Brouwer, P. A.
    Emmer, B. J.
    de Bruijn, S. F.
    van Dijk, L. C.
    Kappelle, L. J.
    Lo, R. H.
    Van Dijk, E. J.
    de Vries, J.
    de Kort, P. L. M.
    van Rooij, W. J. J.
    van den Berg, J. S. P.
    van Hasselt, B. A. A. M.
    Aerden, L. A. M.
    Dallinga, R. J.
    Visser, M. C.
    Bot, J. C. J.
    Vroomen, P. C.
    Eshghi, O.
    Schreuder, T. H. C. M. L.
    Heijboer, R. J. J.
    Keizer, K.
    Tielbeek, A. V.
    den Hertog, H. M.
    Gerrits, D. G.
    van den Berg-Vos, R. M.
    Karas, G. B.
    Steyerberg, E. W.
    Flach, H. Z.
    Marquering, H. A.
    Sprengers, M. E. S.
    Jenniskens, S. F. M.
    Beenen, L. F. M.
    van den Berg, R.
    Koudstaal, P. J.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2015, 372 (01) : 11 - 20
  • [9] Bhalla, RADIOLOGY ARTIFICIAL, V3, P1
  • [10] Artificial intelligence for decision support in acute stroke - current roles and potential
    Bivard, Andrew
    Churilov, Leonid
    Parsons, Mark
    [J]. NATURE REVIEWS NEUROLOGY, 2020, 16 (10) : 575 - 585