Is mRankin scale correlated with mTICI? A systematic review and meta-regression on RCTs and registries.
De Rubeis, G.; Pampana, E.; Prosperini, L.; Fabiano, S.; Bertaccini, L.; Anticoli, S.; Saba, L.; Gasperini, C.; Cotroneo, E.
Show abstract
Background and PurposemTICI [≥]2b/3 is one of the strongest positive predictors of mRS [≤]2. Quantitative analysis is poorly investigated. Reconcile results from RCT and registries is still a challenge. The purpose was to evaluate the numeric correlation between mTICI[≥]2b/3 and mRS[≤]2 in RCT and registries. MethodsLiterature research was performed on Pubmed for studies in 2015-2020. mTICI, mRS and sample size were recorded. Exclusion criteria were monocentric study, not-human and not-English. Studies quality were assessed with MINORS and RoB2. Meta-logistic and meta-linear regressions were used to correlate mTICI and mRS in both RCTs and registries. Z-test was used for comparing coefficients between RCTs and registries. ResultsTwenty-six studies were evaluated (13 registries; 14 RCTs) for 24423 patients (21914 from registries [average per registry 1685{+/-}1277]; 2509 from RCTs [average per RCT 179{+/-}160]). RCTs involved anterior circulation only, 7/13 (53.8%) registries considered also posterior one. The OR of obtaining a mRS[≤]2 for a singular increased of mTICI [≥]2b rate was 1.65 (CI95% 1.22-2.01) for all studies, 1.65 (CI95% 1.10-2.46) for RCTs and 1.50 (CI95% 1.00-2.23) for registries. mTICI[≥]2b and mRS had a positive correlation with a coefficient of 0.49 (CI95% 0.19-0.80, p=0.001) for all studies, 0.54 (CI95% 0.09-1.00) for RCTs and 0.42 (CI 95% 0.04-0.81) for registries. No differences were found in the coefficients between RCTs and registries (p=0.63; p=0.65; respectively). ConclusionsUnitary increased of mTICI[≥]2b rate correspond to an augment of mRS[≤]2 by 0.50 (CI95% 0.19-0.89) with OR of obtaining mRS[≤]2 of 1.65 (CI95% 1.22-2.01), without significantly differences in coefficients.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Consistency of superb microvascular imaging and contrast enhanced ultrasonography in detecting intraplaque neovascularization: a meta-analysis 96%
- Stent For Life Initiative in Portugal: progress through years and Covid-19 Impact 95%
- A systematic review and meta-analysis on the effectiveness of an early invasive strategy compared to a conservative approach in elderly patients with non-ST elevation acute coronary syndrome 94%
Similar papers in this journal
- CT-Perfusion absolute Ghost Infarct Core is a rare phenomenon associated with poor collateral status in acute ischemic stroke patients 93%
- Upper limb functional recovery in chronic stroke patients after COVID-19-interrupted rehabilitation: An observational study 92%
- Screening for Right Ventricular Dysfunction in the Emergency Department Using a Smartphone ECG Analysis Application: An External Validation Study with Acute Pulmonary Embolism Patients 92%
Similar papers in this journal
- The PBL teaching method in Neurology Education in the Traditional Chinese Medicine undergraduate students: An Observational Study 92%
- The Impact of Coronavirus Disease 2019 (COVID-19) on Liver Injury in China: A Systematic Review and Meta-analysis 92%
- Decreased door-to-balloon time in patients with ST-segment elevation myocardial infarction during the early COVID-19 pandemic in South Korea – an observational study 91%
Similar papers in this journal
- Long term stability of patients undergoing endovascular parent artery occlusion of their intracranial artery 94%
- Computed tomography perfusion parameters predictive of symptomatic intracranial hemorrhage after mechanical thrombectomy in patients with cerebral large vessel occlusion 94%
- Applying a Random Forest Approach in Predicting Health Status in Carotid Artery Stenosis Patients 30 Days Post-Stenting 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.