The Influence of the Charlson Comorbidity Index on Procedural Characteristics, VARC-2 Endpoints and 30-Day Mortality Among Patients Who Undergo Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
BACKGROUND: Aortic stenosis (AS) is a common valvular abnormality and transcatheter aortic valve implantation (TAVI) is being increasingly used to treat patients considered too high risk for conventional surgery. We aimed to assess the prevalence of comorbid conditions in patients undergoing TAVI using the Charlson Comorbidity Index (CCI) and to assess their impact on clinical and procedural outcomes. METHODS: We analysed 158 patients who underwent a TAVI at our institution between June 2009 and September 2015 to define their co-morbid burden as measured with CCI, and study its impact on procedural characteristics and mortality at 30 days. RESULTS: One hundred fifty-eight (158) patients with a mean age of 82±8years and a mean CCI score of 2.67 underwent a TAVI. Only 12/158 patients had a CCI of 0. The commonest cardiovascular comorbidities were previous myocardial infarction (24%), congestive heart failure (15%) and diabetes mellitus (23%) whilst the commonest non-cardiovascular comorbidities were renal disease (46%) and chronic obstructive pulmonary disease (COPD) (29%). After multivariable adjustment, CCI was not independently associated with adverse clinical outcomes. The addition of CCI to scoring systems such as Logistic EuroScore (LES) and Society of Thoracic Surgeons (STS) risk models improved the area under the curve from 0.75 (95%CI: 0.44-1.00) and 0.83 (95%CI: 0.64-1.00) to 0.78 (95%CI: 0.53-1.00) and 0.89 (95%CI: 0.78-1.00) respectively. CONCLUSIONS: The burden of comorbid conditions in patients undergoing TAVI is significant. The CCI score was not independently associated with a higher risk of death but can be useful in addition to LES and STS risk models in informing decision making on the selection of patients for TAVI.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".