Risk calculator of multimorbid risk of rehospitalization and death from heart failure: including the contribution of the gut microbiome
Bibliographic record
Abstract
AIMS: The elucidation of the contributory role of multimorbidity to heart failure (HF) including the gut-heart axis has added a new dimension to our understanding of HF pathophysiology that is not reflected in currently available risk scores. The present investigation aimed to develop and validate a novel risk score model of multimorbidity for HF risk stratification. METHODS AND RESULTS: A risk model was developed based on the contribution of markers associated with HF multimorbidities on outcomes of mortality and/or rehospitalization due to HF (death/HF) at one year. Two independent HF cohorts were combined and randomly split 70:30 using a split-sample validation approach for training and validation cohorts that were not significantly different for investigated variables. Backward logistic regression was used to develop the risk model with a further scoring system to create a simple risk calculator. A final 11-variable risk model (age, previous HF hospitalization, NYHA group III/IV, NT-proBNP, diastolic blood pressure, loop diuretic use, beta-blocker non-use, creatinine, COPD, diabetes, and combined gut metabolites) showed a diagnostic performance of 0.71 in the training cohort (C-statistic validation cohort, 0.70, P < 0.001). A risk score/calculator was further developed based on this model with categorization into three (low-, mid-, and high-) and two (low- and high-) risk groups, with both approaches demonstrating increased incidence of death/HF in patients at the highest risk (P < 0.001). CONCLUSION: A novel risk model and score were derived that showed the contribution of comorbidities including the added value of the gut-heart axis on risk stratification of HF patients on rehospitalization and death. LAY SUMMARY: The contributory role of multimorbidity is not well understood in heart failure (HF), including the more recent addition of the gut microbiome (gut-heart axis). However, in current HF risk scores, the contributory role of multimorbidity is seldom considered. In this study, we developed an 11-variable risk model and a simple risk score calculator for clinical use that considers the contribution of HF multimorbidity, including the gut microbiome. The clinical risk score was developed and validated in a clinical cohort from two combined independent European studies from inpatient heart failure subjects. The outcomes were death due to HF and/or rehospitalization at 1 year. The final model showed diagnostic performance comparable to current HF risk scores. Furthermore, the risk score calculator, developed for clinical use, is able to stratify patients into low-, mid-, and high-risk groups, with worsening outcomes seen with increasing risk group. The importance and novelty of this risk model over current HF risk scores are the contribution of comorbidities including the added value of the gut-heart axis on HF risk stratification.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".