Noninvasive Multiparameter Monitoring for the Detection of Decompensated Heart Failure: Exploratory Study
Notice bibliographique
Résumé
Background: Remote patient monitoring strategies in patients with heart failure (HF) are often based on manual readings and interpretation of various parameters by health care professionals. Automated multiparameter predictive models (MPMs) have the potential to improve early recognition of decompensated HF and to reduce the workload for both health care professionals and patients. To reduce costs and facilitate large-scale implementation, these models should preferably be based on noninvasive measurements, with user-friendly devices. Objective: This exploratory study aimed to evaluate whether an MPM, using various parameters from a wrist-worn device supplied with a photoplethysmography sensor and a triaxial accelerometer, contributes to the detection of decompensated HF and death in patients with unstable HF. Methods: Patients who were admitted to the hospital with acute decompensated HF, regardless of etiology or left ventricular ejection fraction, were instructed to wear a research-grade wrist-worn device from the moment of discharge. The device measured heart rate (HR), interbeat intervals (IBIs), respiration rate (RR), activity counts (AC), energy expenditure (EE), and sleep. Participants were instructed to wear the device 24 hours a day for 3 consecutive months. We evaluated 7 classifiers under four strategies for handling extreme class imbalance; the best model was then tested via leave-one-subject-out cross-validation on untouched data. The combined end point of interest was hospital readmission due to decompensated HF, decompensated HF treated at the outpatient clinic by increasing the loop diuretic dose, or death due to HF. Results: A total of 17 patients participated in the study (median age 77, IQR 70-84 y; n=9, 53% male). During follow-up, the device-wearing compliance was 78% (55%-81%). The activity-related parameters (EE and AC) performed best with respect to data quality: 72% and 79% of the data were of high quality, respectively. Concerning HR, 46% of the data were of high quality, whereas only 29% of the IBI and 14% of the RR data were of high quality. Sleep data were lacking 99% of the time during follow-up, resulting in exclusion from training the classifier. The most optimal model for the detection of the combined end point of HF deterioration showed a specificity of 97.2% and a sensitivity of 5.3% in the 2 weeks prior to an event (area under the curve=0.59) after leave-one-subject-out cross-validation analysis. Conclusions: An MPM using a noninvasive wrist-worn device, measuring HR, IBI, RR, AC, and EE, showed high specificity but low sensitivity for the prediction of decompensated HF and HF-related mortality. Low sensitivity likely reflects the extreme class imbalance and sequences with low data quality (especially HR, RR, and sleep), resulting in exclusion from training the MPM in our older, real-world HF cohort. Future studies should improve data fidelity and enroll larger cohorts to address class imbalance and enhance predictive performance.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».