Physical Fitness in Subacute Stroke (PHYS-Stroke) - Statistical Analysis Plan

Statistical Analysis Plan of the PHYS-Stroke trial<br

Detailed results of the unadjusted ROC analysis for all 3 raters at 1.5 and 3 T.

<p>At both 1.5 and 3 T, the ability of a relative DWI- or ADC threshold to predict the presence of lesion in FLAIR-imaging was investigated individually for 3 raters. The given threshold is the optimal relative intensity value cutoff determined by the Youden-Index. For each threshold, the corresponding sensitivity, specificity, PPV and NPV and their means are shown. 95% confidence intervals are given for each individual AUC. DWI-rSI performed better in discriminating hyperintensities in FLAIR imaging than ADC-rSI. AUC, Area under the curve; PPV, positive predictive value; NPV, negative predictive value.</p

Database screening results and final study inclusion rate.

<p>In both databases the exclusion criterion with the highest exclusion rate was a stroke-to-imaging-time higher than 12 hours. In the 1.5 T database, the number of patients, which had to be excluded due to insufficient image quality (mainly of FLAIR images), was much higher (17.1%) than in the 3 T database (2.0%).</p

Clinical data, imaging data and comparison of patient groups.

<p>Data are given as median and IQR (interquartile range); Groups were compared using the Mann-Whitney U rank sum test, significant differences are marked by an asterisk; n, number; h, hours; y, years; ACA: anterior cerebral artery; MCA: middle cerebral artery; PCA: posterior cerebral artery; ICA/CCA: internal/common carotid artery; VA: vertebral artery; BA: basilar artery. ^a =  if patients had occlusion in two different vessels at the same time (e.g. ICA and MCA), occlusion was indicated for both vessels.</p

Detailed results of the adjusted ROC analysis for ADC and all 3 raters at 1.5 and 3 T.

a<p>m0: adjusted model, adjusted for lesion volume, sex, thrombolysis, NIHSS).</p>b<p>m1: m0 additionally adjusted for age.</p>c<p>m2: m1 additionally adjusted for time (stroke-to-imaging).</p>d<p>m3: m1 and rater specific ADC-ROI values.</p><p>In contrast to DWI (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092295#pone-0092295-t003" target="_blank">table 3</a>), adding the ADC-ROI-values for each rater (model 3[m3]) as a variable led to only a bad to moderate accuracy for the prediction of FLAIR-hyperintensities for each rater in comparison with the basic models (m0 and m1). The AUC was even inferior to m2, which was based on “time-from-stroke-onset”. Thus, ADC maps cannot reliably predict FLAIR-hyperintensities in contrast to DWI-maps. Please see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092295#pone-0092295-g003" target="_blank">figure 3</a> for the respective ROC-curves for each model. AUC, Area under the curve.</p

Adjusted linear regression analysis to evaluate the association of relative DWI-intensity and time-from-stroke-onset.

<p>Adjusted linear regression analysis was performed to identify a possible association of relative (A) DWI-intensity and (B) ADC-intensity (y-axis,) and time-from-stroke-onset (x-axis) at 1.5 T (blue circles) and 3 T (green circle). At both field strengths, a significant association was found for DWI (A) with moderate adjusted Rsquare values (1.5 T: 0.28; 3 T: 0.44). Adjusted correlation (Spearman's rank correlation) was: 1.5 T = 0.45 (p<0.001), 3 T = 0.69 (p<0.001). In contrast, no association was found for ADC-maps (B) with adjusted Rsquare values near zero (1.5 T: 0.04; 3 T: 0.01) and weak to no adjusted correlation (1.5 T = −0.22, 3 T = 0.05). Plots are shown in logarithmic scale.</p

Adjusted ROC curves for the detection of presence of FLAIR-lesions by a relative DWI- and ADC-threshold.

<p>ROC-curves belonging to the detailed data presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092295#pone-0092295-t003" target="_blank">table 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092295#pone-0092295-t004" target="_blank">4</a> (please see legends of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092295#pone-0092295-t003" target="_blank">table 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092295#pone-0092295-t004" target="_blank">4</a> for further details). DWI-models for Group A (1.5 T) and B (3 T) (A,B) and ADC-models for Group A and B (C,D).</p

Labeling of lesions in DWI imaging according to the FLAIR pattern.

<p>A) In case of a FLAIR lesion encompassing the whole DWI-lesion, the hyperintensity was delineated as a region of interest (ROI) (A, second row). The FLAIR-ROI was then copied on the DWI and filled with 6 mm circular ROIs (A, third row)). These ROIs were classified as FLAIR+. B) In cases, in which the FLAIR-ROI did not completely match the DWI-lesion (B, second row), ROIs inside the FLAIR-ROI were classified as FLAIR+, and those outside as FLAIR- (B, third row). C) If no FLAIR lesion was identified (C, second row), the whole DWI lesion was filled with circular ROIs, which were classified as FLAIR- (C, third row). These steps were performed equally in ADC-maps. D), E) and F) show examples in analogy to the scheme, D) showing a patient, where the delineated FLAIR-ROI encompasses the whole DWI-lesion, E) depicting a patient, where the FLAIR-lesion only partially covers the DWI-lesion. Lastly, F) shows a patient, where all DWI-ROIs were labelled as FLAIR- in the absence of a visible FLAIR lesion.</p

data_sheet_1_Impact of Lesion Load Thresholds on Alberta Stroke Program Early Computed Tomographic Score in Diffusion-Weighted Imaging.docx

Background and aims<p>Assessment of ischemic lesions on computed tomography or MRI diffusion-weighted imaging (DWI) using the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is widely used to guide acute stroke treatment. However, it has never been defined how many voxels need to be affected to label a DWI-ASPECTS region ischemic. We aimed to assess the effect of various lesion load thresholds on DWI-ASPECTS and compare this automated analysis with visual rating.</p>Materials and methods<p>We analyzed overlap of individual DWI lesions of 315 patients from the previously published predictive value of fluid-attenuated inversion recovery study with a probabilistic ASPECTS template derived from 221 CT images. We applied multiple lesion load thresholds per DWI-ASPECTS region (>0, >1, >10, and >20% in each DWI-ASPECTS region) to compute DWI-ASPECTS for each patient and compared the results to visual reading by an experienced stroke neurologist.</p>Results<p>By visual rating, median ASPECTS was 9, 84 patients had a DWI-ASPECTS score ≤7. Mean DWI lesion volume was 22.1 (±35) ml. In contrast, by use of >0, >1-, >10-, and >20%-thresholds, median DWI-ASPECTS was 1, 5, 8, and 10; 97.1% (306), 72.7% (229), 41% (129), and 25.7% (81) had DWI-ASPECTS ≤7, respectively. Overall agreement between automated assessment and visual rating was low for every threshold used (>0%: κ_w = 0.020 1%: κ_w = 0.151; 10%: κ_w = 0.386; 20% κ_w = 0.381). Agreement for dichotomized DWI-ASPECTS ranged from fair to substantial (≤7: >10% κ = 0.48; >20% κ = 0.45; ≤5: >10% κ = 0.528; and >20% κ = 0.695).</p>Conclusion<p>Overall agreement between automated and the standard used visual scoring is low regardless of the lesion load threshold used. However, dichotomized scoring achieved more comparable results. Varying lesion load thresholds had a critical impact on patient selection by ASPECTS. Of note, the relatively low lesion volume and lack of patients with large artery occlusion in our cohort may limit generalizability of these findings.</p

PDX AML cells allow genetic engineering without altering molecular sample characteristics.

<p><b>(A)</b> Scheme of the process of generating transgenic PDX (t-PDX) AML cells. PDX cells were transduced after first or second retransplantation cycle. <b>(B)</b> Scheme of the vector constructs. <b>(C)</b> Transduction rate in t-PDX AML cells after lentiviral transduction and cell amplification in mice was measured by FACS analysis of fluorochrome or NGFR expression. Each mark visualizes data obtained from a single transduction. Open mark: no transgenic cells were detectable. <b>(D)</b> Enrichment of transgenic cells using flow cytometry was measured using mCherry expression after cell amplification in mice. <b>(E)</b> Genetic engineering does not alter immunophenotype; primary cells, untransduced PDX cells after fourth retransplantation and enriched transgenic t-PDX cells were analyzed by multicolor flow cytometry; specific fluorescence intensity is depicted. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120925#pone.0120925.s003" target="_blank">S3C Fig.</a> for exemplary FACS plots of AML-372. Raw data is depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120925#pone.0120925.s011" target="_blank">S3 Table</a>. <b>(F)</b> Genetic engineering does not markedly alter AML-specific mutations; genomic DNA was isolated out of primary cells, untransduced PDX cells and enriched transgenic t-PDX cells; VAF of mutations was profiled by targeted resequencing. <i>BCOR</i> (BCL-6 corepressor); <i>KRAS</i> (Kirsten rat sarcoma viral oncogene homolog); <i>NRAS</i> (neuroblastoma RAS viral oncogene homolog); <i>TP53</i> (tumor protein p53). Raw data is depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120925#pone.0120925.s010" target="_blank">S2 Table</a>.</p