Machine Learning to Predict Patients at Risk for Frailty in Postoperative Cardiac Patients Using Electrocardiogram and Accelerometer Data
Bibliographic record
Abstract
Purpose: Frailty, characterized by diminished physiological reserves, predicts adverse outcomes in older adults undergoing cardiac surgery. This study aimed to identify predictors of frailty using wearable-derived electrocardiogram (ECG) and accelerometry data. The goal was to enhance postoperative risk stratification and rehabilitation. Methods: Data from 80 patients after cardiac surgery were sourced from PhysioNet, including ECG and accelerometry during rehabilitation exercises. The Edmonton Frail Scale dichotomized patients at ≥7 for high frailty. Electrocardiogram features and accelerometry metrics were extracted after signal processing. Missing data were imputed using mean and mode methods, and multicollinearity was addressed. Logistic Regression, Random Forest, and Neural Network models predicted frailty status, with performance evaluated through accuracy, recall, and F1 scores. Results: Frail patients were older (75 vs 72 years, P = .011) and had shorter 6-minute walk distances (259.47 vs 300.92 meters, P = .0424). They also exhibited longer walk times (P = .007) and higher gait entropy (P = .0279). Logistic regression achieved the best performance (accuracy = 0.84, recall = 0.59, F1 = 0.61). Key predictors included step length asymmetry, 6-minute walk test time, age, and maximum heart rate (odds ratios >4). The model showed strong discriminative ability (area under the curve = 0.92). Conclusions: Wearable-derived ECG and accelerometry features effectively predict frailty postcardiac surgery. These predictions enable screening and tailored rehabilitation. Future research should explore longitudinal changes and combined feature impacts on clinical outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".