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Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke

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Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke. / Quandt, Fanny; Flottmann, Fabian; Madai, Vince I; Alegiani, Anna; Küpper, Clemens; Kellert, Lars; Hilbert, Adam; Frey, Dietmar; Liebig, Thomas; Fiehler, Jens; Goyal, Mayank; Saver, Jeffrey L; Gerloff, Christian; Thomalla, Götz; Tiedt, Steffen; GSR investigators; VISTA-Endovascular Collaborators.

In: TRANSL STROKE RES, Vol. 14, No. 3, 06.2023, p. 311-321.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Quandt, F, Flottmann, F, Madai, VI, Alegiani, A, Küpper, C, Kellert, L, Hilbert, A, Frey, D, Liebig, T, Fiehler, J, Goyal, M, Saver, JL, Gerloff, C, Thomalla, G, Tiedt, S, GSR investigators & VISTA-Endovascular Collaborators 2023, 'Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke', TRANSL STROKE RES, vol. 14, no. 3, pp. 311-321. https://doi.org/10.1007/s12975-022-01040-5

APA

Quandt, F., Flottmann, F., Madai, V. I., Alegiani, A., Küpper, C., Kellert, L., Hilbert, A., Frey, D., Liebig, T., Fiehler, J., Goyal, M., Saver, J. L., Gerloff, C., Thomalla, G., Tiedt, S., GSR investigators, & VISTA-Endovascular Collaborators (2023). Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke. TRANSL STROKE RES, 14(3), 311-321. https://doi.org/10.1007/s12975-022-01040-5

Vancouver

Quandt F, Flottmann F, Madai VI, Alegiani A, Küpper C, Kellert L et al. Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke. TRANSL STROKE RES. 2023 Jun;14(3):311-321. https://doi.org/10.1007/s12975-022-01040-5

Bibtex

@article{4aeefc5e4eaf4b16b8b071a4bb02fa49,
title = "Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke",
abstract = "Whether endovascular thrombectomy (EVT) improves functional outcome in patients with large-vessel occlusion (LVO) stroke that do not comply with inclusion criteria of randomized controlled trials (RCTs) but that are considered for EVT in clinical practice is uncertain. We aimed to systematically identify patients with LVO stroke underrepresented in RCTs who might benefit from EVT. Following the premises that (i) patients without reperfusion after EVT represent a non-treated control group and (ii) the level of reperfusion affects outcome in patients with benefit from EVT but not in patients without treatment benefit, we systematically assessed the importance of reperfusion level on functional outcome prediction using machine learning in patients with LVO stroke treated with EVT in clinical practice (N = 5235, German-Stroke-Registry) and in patients treated with EVT or best medical management from RCTs (N = 1488, Virtual-International-Stroke-Trials-Archive). The importance of reperfusion level on outcome prediction in an RCT-like real-world cohort equaled the importance of EVT treatment allocation for outcome prediction in RCT data and was higher compared to an unselected real-world population. The importance of reperfusion level was magnified in patient groups underrepresented in RCTs, including patients with lower NIHSS scores (0-10), M2 occlusions, and lower ASPECTS (0-5 and 6-8). Reperfusion level was equally important in patients with vertebrobasilar as with anterior LVO stroke. The importance of reperfusion level for outcome prediction identifies patient target groups who likely benefit from EVT, including vertebrobasilar stroke patients and among patients underrepresented in RCT patients with low NIHSS scores, low ASPECTS, and M2 occlusions.",
author = "Fanny Quandt and Fabian Flottmann and Madai, {Vince I} and Anna Alegiani and Clemens K{\"u}pper and Lars Kellert and Adam Hilbert and Dietmar Frey and Thomas Liebig and Jens Fiehler and Mayank Goyal and Saver, {Jeffrey L} and Christian Gerloff and G{\"o}tz Thomalla and Steffen Tiedt and {GSR investigators} and {VISTA-Endovascular Collaborators}",
note = "{\textcopyright} 2022. The Author(s).",
year = "2023",
month = jun,
doi = "10.1007/s12975-022-01040-5",
language = "English",
volume = "14",
pages = "311--321",
journal = "TRANSL STROKE RES",
issn = "1868-4483",
publisher = "SPRINGER US",
number = "3",

}

RIS

TY - JOUR

T1 - Machine Learning-Based Identification of Target Groups for Thrombectomy in Acute Stroke

AU - Quandt, Fanny

AU - Flottmann, Fabian

AU - Madai, Vince I

AU - Alegiani, Anna

AU - Küpper, Clemens

AU - Kellert, Lars

AU - Hilbert, Adam

AU - Frey, Dietmar

AU - Liebig, Thomas

AU - Fiehler, Jens

AU - Goyal, Mayank

AU - Saver, Jeffrey L

AU - Gerloff, Christian

AU - Thomalla, Götz

AU - Tiedt, Steffen

AU - GSR investigators

AU - VISTA-Endovascular Collaborators

N1 - © 2022. The Author(s).

PY - 2023/6

Y1 - 2023/6

N2 - Whether endovascular thrombectomy (EVT) improves functional outcome in patients with large-vessel occlusion (LVO) stroke that do not comply with inclusion criteria of randomized controlled trials (RCTs) but that are considered for EVT in clinical practice is uncertain. We aimed to systematically identify patients with LVO stroke underrepresented in RCTs who might benefit from EVT. Following the premises that (i) patients without reperfusion after EVT represent a non-treated control group and (ii) the level of reperfusion affects outcome in patients with benefit from EVT but not in patients without treatment benefit, we systematically assessed the importance of reperfusion level on functional outcome prediction using machine learning in patients with LVO stroke treated with EVT in clinical practice (N = 5235, German-Stroke-Registry) and in patients treated with EVT or best medical management from RCTs (N = 1488, Virtual-International-Stroke-Trials-Archive). The importance of reperfusion level on outcome prediction in an RCT-like real-world cohort equaled the importance of EVT treatment allocation for outcome prediction in RCT data and was higher compared to an unselected real-world population. The importance of reperfusion level was magnified in patient groups underrepresented in RCTs, including patients with lower NIHSS scores (0-10), M2 occlusions, and lower ASPECTS (0-5 and 6-8). Reperfusion level was equally important in patients with vertebrobasilar as with anterior LVO stroke. The importance of reperfusion level for outcome prediction identifies patient target groups who likely benefit from EVT, including vertebrobasilar stroke patients and among patients underrepresented in RCT patients with low NIHSS scores, low ASPECTS, and M2 occlusions.

AB - Whether endovascular thrombectomy (EVT) improves functional outcome in patients with large-vessel occlusion (LVO) stroke that do not comply with inclusion criteria of randomized controlled trials (RCTs) but that are considered for EVT in clinical practice is uncertain. We aimed to systematically identify patients with LVO stroke underrepresented in RCTs who might benefit from EVT. Following the premises that (i) patients without reperfusion after EVT represent a non-treated control group and (ii) the level of reperfusion affects outcome in patients with benefit from EVT but not in patients without treatment benefit, we systematically assessed the importance of reperfusion level on functional outcome prediction using machine learning in patients with LVO stroke treated with EVT in clinical practice (N = 5235, German-Stroke-Registry) and in patients treated with EVT or best medical management from RCTs (N = 1488, Virtual-International-Stroke-Trials-Archive). The importance of reperfusion level on outcome prediction in an RCT-like real-world cohort equaled the importance of EVT treatment allocation for outcome prediction in RCT data and was higher compared to an unselected real-world population. The importance of reperfusion level was magnified in patient groups underrepresented in RCTs, including patients with lower NIHSS scores (0-10), M2 occlusions, and lower ASPECTS (0-5 and 6-8). Reperfusion level was equally important in patients with vertebrobasilar as with anterior LVO stroke. The importance of reperfusion level for outcome prediction identifies patient target groups who likely benefit from EVT, including vertebrobasilar stroke patients and among patients underrepresented in RCT patients with low NIHSS scores, low ASPECTS, and M2 occlusions.

U2 - 10.1007/s12975-022-01040-5

DO - 10.1007/s12975-022-01040-5

M3 - SCORING: Journal article

C2 - 35670996

VL - 14

SP - 311

EP - 321

JO - TRANSL STROKE RES

JF - TRANSL STROKE RES

SN - 1868-4483

IS - 3

ER -