Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.</p

Telemonitoring for heart failure:a meta-analysis

AIMS: Telemonitoring modalities in heart failure (HF) have been proposed as being essential for future organization and transition of HF care, however, efficacy has not been proven. A comprehensive meta-analysis of studies on home telemonitoring systems (hTMS) in HF and the effect on clinical outcomes are provided. METHODS AND RESULTS: A systematic literature search was performed in four bibliographic databases, including randomized trials and observational studies that were published during January 1996-July 2022. A random-effects meta-analysis was carried out comparing hTMS with standard of care. All-cause mortality, first HF hospitalization, and total HF hospitalizations were evaluated as study endpoints. Sixty-five non-invasive hTMS studies and 27 invasive hTMS studies enrolled 36 549 HF patients, with a mean follow-up of 11.5 months. In patients using hTMS compared with standard of care, a significant 16% reduction in all-cause mortality was observed [pooled odds ratio (OR): 0.84, 95% confidence interval (CI): 0.77-0.93, I2: 24%], as well as a significant 19% reduction in first HF hospitalization (OR: 0.81, 95% CI 0.74-0.88, I2: 22%) and a 15% reduction in total HF hospitalizations (pooled incidence rate ratio: 0.85, 95% CI 0.76-0.96, I2: 70%). CONCLUSION: These results are an advocacy for the use of hTMS in HF patients to reduce all-cause mortality and HF-related hospitalizations. Still, the methods of hTMS remain diverse, so future research should strive to standardize modes of effective hTMS.</p

Photoplethysmography and intracardiac pressures:early insights from a pilot study

Aims: Invasive haemodynamic monitoring of heart failure (HF) is used to detect deterioration in an early phase thereby preventing hospitalizations. However, this invasive approach is costly and presently lacks widespread accessibility. Hence, there is a pressing need to identify an alternative non-invasive method that is reliable and more readily available. In this pilot study, we investigated the relation between wrist-derived photoplethysmography (PPG) signals and the invasively measured pulmonary capillary wedge pressure (PCWP). Methods and results: Fourteen patients with aortic valve stenosis who underwent transcatheter aortic valve replacement with concomitant right heart catheterization and PPG measurements were included. Six unique features of the PPG signals [heart rate, heart rate variability, systolic amplitude (SA), diastolic amplitude, crest time (CT), and large artery stiffness index (LASI)] were extracted. These features were used to estimate the continuous PCWP values and the categorized PCWP (low &lt; 12mmHg vs. high ≥ 12mmHg). All PPG features resulted in regression models that showed low correlations with the invasively measured PCWP. Classification models resulted in higher performances: the model based on the SA and the model based on the LASI both resulted in an area under the curve (AUC) of 0.86 and the model based on the CT resulted in an AUC of 0.72. Conclusion: These results demonstrate the capability to non-invasively classify patients into clinically meaningful categories of PCWP using PPG signals from a wrist-worn wearable device. To enhance and fully explore its potential, the relationship between PPG and PCWP should be further investigated in a larger cohort of HF patients.</p

Automated cardiac arrest detection using a photoplethysmography wristband:algorithm development and validation in patients with induced circulatory arrest in the DETECT-1 study

Background: Unwitnessed out-of-hospital cardiac arrest is associated with low survival chances because of the delayed activation of the emergency medical system in most cases. Automated cardiac arrest detection and alarming using biosensor technology would offer a potential solution to provide early help. We developed and validated an algorithm for automated circulatory arrest detection using wrist-derived photoplethysmography from patients with induced circulatory arrests. Methods: In this prospective multicentre study in three university medical centres in the Netherlands, adult patients (aged 18 years or older) in whom short-lasting circulatory arrest was induced as part of routine practice (transcatheter aortic valve implantation, defibrillation testing, or ventricular tachycardia induction) were eligible for inclusion. Exclusion criteria were a known bilateral significant subclavian artery stenosis or medical issues interfering with the wearing of the wristband. After providing informed consent, patients were equipped with a photoplethysmography wristband during the procedure. Invasive arterial blood pressure and electrocardiography were continuously monitored as the reference standard. Development of the photoplethysmography algorithm was based on three consecutive training cohorts. For each cohort, patients were consecutively enrolled. When a total of 50 patients with at least one event of circulatory arrest were enrolled, that cohort was closed. Validation was performed on the fourth set of included patients. The primary outcome was sensitivity for the detection of circulatory arrest. Findings: Of 306 patients enrolled between March 14, 2022, and April 21, 2023, 291 patients were included in the data analysis. In the development phase (n=205), the first training set yielded a sensitivity for circulatory arrest detection of 100% (95% CI 94–100) and four false positive alarms; the second training set yielded a sensitivity of 100% (94–100), with six false positive alarms; and the third training set yielded a sensitivity of 100% (94–100), with two false positive alarms. In the validation phase (n=86), the sensitivity for circulatory arrest detection was 98% (92–100) and 11 false positive circulatory arrest alarms. The positive predictive value was 90% (95% CI 82–94). Interpretation: The automated detection of induced circulatory arrests using wrist-derived photoplethysmography is feasible with good sensitivity and low false positives. These promising findings warrant further development of this wearable technology to enable automated cardiac arrest detection and alarming in a home setting. Funding: Dutch Heart Foundation (Hartstichting).</p

Chest wall injuries due to cardiopulmonary resuscitation and the effect on in-hospital outcomes in survivors of out-of-hospital cardiac arrest

BACKGROUND: This study aimed to assess the prevalence of chest wall injuries due to cardiopulmonary resuscitation for out-of-hospital cardiac arrest (OHCA) and to compare in-hospital outcomes in patients with versus without chest wall injuries. METHODS: A retrospective cohort study of all intensive care unit (ICU)-admitted patients who underwent cardiopulmonary resuscitation for OHCA between January 1, 2007, and December 2019 was performed. The primary outcome was the occurrence of chest wall injuries, as diagnosed on chest computed tomography. Chest wall injury characteristics such as rib fracture location, type, and dislocation were collected. Secondary outcomes were in-hospital outcomes and subgroup analysis of patients with good neurological recovery to identify those who could possibly benefit from the surgical stabilization of rib fractures. RESULTS: Three hundred forty-four patients were included, of which 291 (85%) sustained chest wall injury. Patients with chest wall injury had a median of 8 fractured ribs (P25-P75, 4-10 ribs), which were most often undisplaced (on chest computed tomography) (n = 1,574 [72.1%]), simple (n = 1,948 [89.2%]), and anterior (n = 1,785 [77.6%]) rib fractures of ribs 2 to 7. Eight patients (2.3%) had a flail segment, and 136 patients (39.5%) had an anterior flail segment. Patients with chest wall injury had fewer ventilator-free days (0 days [P25-P75, 0-16 days] vs. 13 days [P25-P75, 2-22 days]; p = 0.006) and a higher mortality rate (n = 102 [54.0%] vs. n = 8 [22.2%]; p < 0.001) than those without chest wall injury. For the subgroup of patients with good neurological recovery, the presence of six or more rib fractures or a single displaced rib fracture was associated with longer hospital and ICU length of stay, respectively. CONCLUSION: Cardiopulmonary resuscitation-related chest wall injuries in survivors of OHCA and especially rib fractures are common. Patients with chest wall injury had fewer ventilator-free days and a higher mortality rate. Patients with good neurological recovery might represent a subgroup of patients who could benefit from surgical stabilization of rib fractures. LEVEL OF EVIDENCE: Therapeutic, level IV; Epidemiological, Level IV

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long‐Term Mortality Risk

Background We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high‐sensitivity cardiac troponin T, N‐terminal pro‐B‐type natriuretic peptide, high‐sensitivity C‐reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long‐term mortality risk. Methods and Results BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high‐frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker‐based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all‐cause death were evaluated using accelerated failure time models (median follow‐up, 9.1 years; 141 deaths). Three biomarker‐based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long‐term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44–0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39–1.32; P=0.281) compared with patients with a repeat ACS. Conclusions Patients with subphenotypes of post‐ACS with different all‐cause mortality risks during long‐term follow‐up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year