Baseline factors identified for prediction of good responders in patients with end-stage diffuse coronary artery disease undergoing intracoronary CD34+ cell therapy
Bibliographic record
Abstract
Abstract Background: Treating patients with end-stage diffuse coronary artery disease (EnD-CAD) unsuitable for coronary intervention remains a clinical challenge. They usually express refractory angina and have high risk for mortality. Although growing data have indicated cell therapy is an alternative solution to medical or invasive therapy, there are still lacking useful markers to predict whether heart function will improve in the EnD-CAD patients who underwent circulatory-derived CD34+ cell therapy. By utilizing the baseline variables and results from our previous phase I/II clinical trials, the aim of this study tried to elucidate the variables predictive of the “good response” to CD34+ cell therapy.Methods: This retrospective study included 38 patients in the phase I clinical trial (2011-2014), and 30 patients in the phase II clinical trial (2013-2017). These patients were categorized into “good responders” and “non-responders” according to their 1-year improvement of LVEF ≥7.0% or <7.0% after intracoronary CD34+ cell therapy. Univariate and multivariate logistic regression models were performed to identify potential independent predictors of good responder to cell therapy, followed by Hosmer–Lemeshow (H-L) test for goodness of fit and prediction power.Results: Among baseline data, multivariate analysis demonstrated that history of former smoker was independently predictive of good responders (p=0.006). On the other hand, male gender, the baseline Canadian Cardiovascular Society angina score ≥3 and grades of LV diastolic dysfunction ≥2 were significantly negative predictors of good responders (all p<0.01). After administration of subcutaneous granulocyte-colony stimulating factor (G-CSF), a higher post-G-CSF neutrophil count in addition to the above four baseline variables also played crucial roles in early prediction of good response to CD34+ cell therapy for EnD-CAD (all p<0.03). The H-L test displayed a good prediction power with sensitivity 83.3%, specificity 85.3% and accuracy 84.4%. Conclusions: Using the results of our phase I/II clinical trials, previous smoking habit, female sex, lower grades of angina score and diastolic dysfunction were identified to be independently predictive of “good response” to CD34+ cell therapy in the patients with EnD-CAD.Trial registration: This is a retrospective analysis based on the phase I (ISRCTN72853206) and II (ISRCTN26002902) clinical trials
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".