Hyeong-Seok Lim (1), Seung-Min Kim (2), Ailing Cui (1), Dong-Wha Kang (3)
(1) Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, Ulsan University College of Medicine, Seoul, Republic of Korea, (2) Department of Neurology, Veterans Health Service Medical Center, Seoul, Republic of Korea, (3) Department of Neurology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Republic of Korea
Objectives: Stroke is one of the most common causes of disability worldwide. The levels of disability and handicap among the survivors may vary [1]. Of clinical and imaging variables, ischemic lesion volume measured on brain imaging is an objective surrogate of stroke severity and burden, which is analogous to tumor size in cancer patients. As the ischemic lesions dynamically change over time during the acute phase of stroke, it is believed that the assessment of ischemic lesion growth at multiple times may be a more reliable predictor than lesion volume at a single time. It has been demonstrated that ischemic lesion growth is an independent predictor of poor outcome in stroke patients with any stroke subtype as well as in patients who have received thrombolysis [2–6]. We sought to identify the predictive factors for the degree of disability 3 months after stroke [7] and to evaluate the quantitative predictive ability of each identified predictor for the prognosis using modeling and simulation analysis.
Methods: Prospectively collected clinical data from 405 patients with acute ischemic stroke including brain magnetic resonance images (MRIs) and disability outcomes assessed using the modified Rankin Scale (mRS) 3 month after the onset of disease were analyzed and the potential covariates were then tested with regard to whether they improved the model significantly [8]. A proportional odds cumulative logit model was implemented in NONMEM. The relationship between the difference in lesion volume (DLV) — lesion volume measured by brain MRI 5 days later — lesion volume at the onset of the disease, and the mRS measured at 3 months (mRS3) was modeled first, and the potential covariates were tested. For internal validation of the pharmacodynamic models by comparing the observed proportion of each mRS3 value and the model-predicted value by DLV, simulation with 1000 replicates was performed using the original data set, and the results are visualized in bar plots based on prespecified intervals of DLV using R software.
Results: Inclusion of TDLV in the baseline proportional odds cumulative logit model in the form of a simple maximum effect (Emax) model best described the relationship between TDLV and the logit probability of mRS3, and improved the model fit significantly compared with baseline model without TDLV. In the final mod el, TDLV, NIHSS, age, and the comorbidity of DM were identified as significant covariates.
Conclusions: The quantitative model constructed in the current analysis will enable us to predict the long-term disabilities of the patients with acute ischemic stroke using the patient-specific MRI and other clinical information. Our study findings will be useful for individualizing therapies and these results could also be applied to stratify or enrich clinical trials by predicting the prognosis of patients, thus enabling more efficient clinical drug development for patients with ischemic stroke.
References:
[1] Sturm JW, Dewey HM, Donnan GA, et al. Stroke. Handicap after stroke: how does it relate to disability, perception of recovery, and stroke subtype: the north North East Melbourne Stroke Incidence Study (NEMESIS). Stroke. 2002; 33:762-769.
[2] Cho KH, Kang DW, Kwon SU, et al. Lesion volume increase is related to neurologic progression in patients with subcortical infarction. J Neurol Sci. 2009; 284:163–167.
[3] Warach S, Kaufman D, Chiu D, et al. Effect of the Glycine Antagonist Gavestinel on cerebral infarcts in acute stroke patients, a randomized placebo-controlled trial: The GAIN MRI Substudy. Cerebrovasc Dis. 2006;21;106–11.
[4] Olivot JM, Mlynash M, Thijs VN, et al. Relationships between infarct growth, clinical outcome, and early recanalization in diffusion and perfusion imaging for understanding stroke evolution (DEFUSE). Stroke. 2008; 39:2257–63.
[5] Cho KH, Kwon SU, Lee DH, et al. Early infarct growth predicts long-term clinical outcome after thrombolysis. J Neurol Sci. 2012; 316:99–103.
[6] Barrett KM, Ding YH, Wagner DP, et al. Change in diffusion weighted imaging infarct volume predicts neurologic outcome at 90 days: results of the Acute Stroke Accurate Prediction (ASAP) trial serial imaging substudy. Stroke. 2009; 40:2422–2427.
[7] Rangaraju S, Haussen D, Nogueira RG, Nahab F, Frankel M. Comparison of 3-month stroke disability and quality of life across modified Rankin scale categories. Interv Neurol. 2017; 6:36–41.
[8] Kim SM, Kwon SU, Kim JS, et al. Early infarct growth predicts long-term clinical outcome in ischemic stroke. J Neurol Sci. 2014; 347:205–209
Reference: PAGE 27 (2018) Abstr 8596 [www.page-meeting.org/?abstract=8596]
Poster: Drug/Disease Modelling - CNS