Posttreatment Variables Improve Outcome Prediction after Intra-Arterial Therapy for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: There are multiple clinical and radiographic factors that influence outcomes after endovascular reperfusion therapy (ERT) in acute ischemic stroke (AIS). We sought to derive and validate an outcome prediction score for AIS patients undergoing ERT based on readily available pretreatment and posttreatment factors. METHODS: The derivation cohort included 511 patients with anterior circulation AIS treated with ERT at 10 centers between September 2009 and July 2011. The prospective validation cohort included 223 patients with anterior circulation AIS treated in the North American Solitaire Acute Stroke registry. Multivariable logistic regression identified predictors of good outcome (modified Rankin score ≤2 at 3 months) in the derivation cohort; model β coefficients were used to assign points and calculate a risk score. Discrimination was tested using C statistics with 95% confidence intervals (CIs) in the derivation and validation cohorts. Calibration was assessed using the Hosmer-Lemeshow test and plots of observed to expected outcomes. We assessed the net reclassification improvement for the derived score compared to the Totaled Health Risks in Vascular Events (THRIVE) score. Subgroup analysis in patients with pretreatment Alberta Stroke Program Early CT Score (ASPECTS) and posttreatment final infarct volume measurements was also performed to identify whether these radiographic predictors improved the model compared to simpler models. RESULTS: Good outcome was noted in 186 (36.4%) and 100 patients (44.8%) in the derivation and validation cohorts, respectively. Combining readily available pretreatment and posttreatment variables, we created a score (acronym: SNARL) based on the following parameters: symptomatic hemorrhage [2 points: none, hemorrhagic infarction (HI)1-2 or parenchymal hematoma (PH) type 1; 0 points: PH2], baseline National Institutes of Health Stroke Scale score (3 points: 0-10; 1 point: 11-20; 0 points: >20), age (2 points: <60 years; 1 point: 60-79 years; 0 points: >79 years), reperfusion (3 points: Thrombolysis In Cerebral Ischemia score 2b or 3) and location of clot (1 point: M2; 0 points: M1 or internal carotid artery). The SNARL score demonstrated good discrimination in the derivation (C statistic 0.79, 95% CI 0.75-0.83) and validation cohorts (C statistic 0.74, 95% CI 0.68-0.81) and was superior to the THRIVE score (derivation cohort: C statistic 0.65, 95% CI 0.60-0.70; validation cohort: C-statistic 0.59, 95% CI 0.52-0.67; p < 0.01 in both cohorts) but was inferior to a score that included age, ASPECTS, reperfusion status and final infarct volume (C statistic 0.86, 95% CI 0.82-0.91; p = 0.04). Compared with the THRIVE score, the SNARL score resulted in a net reclassification improvement of 34.8%. CONCLUSIONS: Among AIS patients treated with ERT, pretreatment scores such as the THRIVE score provide only fair prognostic information. Inclusion of posttreatment variables such as reperfusion and symptomatic hemorrhage greatly influences outcome and results in improved outcome prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".