NISAR-F SCORE: a simple risk stratification tool for patients implanted with cardiac resynchronization therapy
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Introduction Individualized estimation of prognosis after cardiac resynchronization therapy (CRT) remains challenging. Outcomes in this group of patients are influenced by multiple factors and a comprehensive and customized approach to estimate prognosis after CRT is lacking Aims To develop and validate a simple prognostic score for patients implanted with CRT (NISAR-F score), based on readily available clinical and echocardiographic variables to predict the combined endpoints of death or hospitalization in 24 months. Methods A single-centre retrospective study was conducted with inclusion of all consecutive patients who underwent CRT implantation between 2012 and 2019. Follow-up started after CRT implantation and ended upon death, hospitalization or 24 months after study entry. Survival analysis was performed using a multivariate Cox regression model, in order to analyze the effect on survival /hospitalization in 24 months of the following factors: age, gender, NYHA Class III-IV, ischemic heart failure, type 2 diabetes, arterial hypertension, dyslipidemia and ejection fraction < 21%. According to the analysis, points were attributed to each factor. Afterwards, the NISAR-F score was calculated for each patient, summing the points of each variable. The authors finally created ROC curves for the NISAR-F score to predict the occurrence of the combined endpoint in 2 groups of patients: CRT responders (ejection fraction increase of at least 10% after CRT implantation) and CRT non-responders. The statistical analysis was performed in SPSS. Results 102 patients were included in the study (75.4% male, mean age 68 ± 10.46 years). 10(9.8%) of the patients were re-hospitalized and 8 (7.8%) died during the 24-month follow-up. After calculating NISAR-F score for each patient, area under ROC curves were obtained. The analysis of the ROC curves allows us to confirm the good performance of the score created [responders group (AUC 0.812) vs non-responders (AUC 0.721)]. Conclusion The NISAR-F score is a useful tool to predict the combined endpoint (mortality and hospitalization in 24 months) after CRT implantation, in both responders and non-responders, revealing good performance of this new and simple score based only on clinical and echocardiographic variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".