bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standard
Authors: Ronner E , Van Vliet M , Monnink S , Kuiper M , Hoftijzer D , Constandse J 
Published: November 2023

Abstract

Background
Blood pressure is a key paramater in acute and chronic cardiovascular diseases (CVD) [1]. However, obtaining reliable and reproducible cuff blood pressure (BP) measurements is challenging. We demonstrate that a wristband-based PPG measurement in combination with a custom developed BP algorithm represents a possible alternative to cuff BP measurements.

Purpose
This study aimed to validate a novel cuffless photopletysmography (PPG)-based non-invasive wristband for continuous BP monitoring in accordance with ISO 81060-2:2019.

Methods
The study compared PPG-guided BP algorithm predictions with subclavian arterial reference measurements taken during cardiac catheterization. Eligible patients were consecutively included, and eligibility was screened following ISO 81060-2:2019 requirements. Reference measurements were performed using a validated invasive BP monitoring device with a sampling rate of 100Hz. PPG signals were collected using six light emission diodes and two photodiodes at a sampling rate of 128Hz. Three sequential initialization measurements were taken using a validated blood pressure cuff before the cardiac catheterization exam. These measurements, along with approximately 100 additional features (PPG-derived and based on patient demographics), were used as input for the machine learning-based BP algorithm. Correlation, mean error, and standard deviation (SD) were determined for systolic and diastolic BP measurements between the BP algorithm predictions and invasive reference measurements

Results
The study included 97 patients from whom 420 individual 30-second samples were obtained. The mean age, weight, and height of the analysed subjects were 67.1 (SD 11.1), 83.4 (SD 16.1), and 174.1 (SD 10.0), respectively. In 48 samples (11%), systolic BP was ≤100mmHg, while in 106 samples (25%), systolic BP was ≥160mmHg. Diastolic BP was ≤70 mmHg in 222 samples (53%) and ≥85 mmHg in 99 samples (24%). The BP algorithm predictions showed a high correlation with invasive reference measurements for systolic (r = 0.985) and diastolic (r = 0.961) BP measurements. The mean error of the BP algorithm predictions compared to the invasive reference measurements was ±3.7 mmHg (SD 4.4 mmHg) and ±2.5 mmHg (SD 3.7 mmHg) for systolic and diastolic BP, respectively. Results were similar within each gender and skin colour category (Fitzpatrick I-VI).

Conclusion
This study demonstrates that a wristband-based PPG measurement in combination with the developed BP algorithm can provide accurate continuous BP measurements across a wide range of BP distributions. Therefore, wristband BP monitoring may serve as a valid and less burdensome alternative to cuff BP measurements for both in-hospital and at-home BP monitoring. However, further research is necessary to evaluate the precision of the BP algorithm during movement and the stability of the predictions over time.figure of BP
Risk stratification of coronary artery disease using the artificial intelligence-enabled electrocardiogram
Authors: Awasthi S , Sachadeva N , Abbou R , Gupta Y , Anto A , Asfahan S , Hegstrom L , Alger H , Medina-Inojosa J , Mccully R , Lerman A , Friedman P , Attia Z , Soundararajan V , Lopez-Jiminez F 
Published: November 2023

Abstract

Background
Atherosclerotic cardiovascular disease (ASCVD), of which coronary artery disease (CAD) is the primary manifestation, is the leading cause of death worldwide. The AHA/ACC Pooled Cohort Equation (PCE) facilitates risk stratification and initiation of primary prevention of ASCVD, however their performance is suboptimal.

Purpose
We develop an artificial intelligence-based electrocardiogram (ECG) analysis (AI-ECG) algorithm to detect different stages of CAD and assessed the value of AI-ECG over PCE to predict incident myocardial infarction (MI) and survival over 3-years.

Methods
Using electronic health record data from approximately 7 million patients seen at over 70 hospitals and clinics across 5 states in the USA, we created independent AI-ECG models to identify elevated coronary artery calcium (CAC) by computer tomography, the presence of obstructive CAD by stress test or angiography, and left ventricular regional akinesis by echocardiogram, presumed to represent prior MI. Deep structured data, AI-enabled natural language processing of unstructured data were used to ensure cohort, and physician review of labeling were used to ensure cohort accuracy. These models were ensembled into a single model that was used to assess the utility of AI-ECG for predicting new occurrence MI in PCE- and age-stratified cohorts of patient with no prior history of ASCVD, over a 3-year timeframe using Cox’s proportional hazards analysis.

Results
A total of 6,080 patients (15,192 ECGs) were labeled positive for CAC ≥ 300, 12,561 patients (18,915 ECGs) positive for obstructive CAD, and 10,767 patients (23,870 ECGs) positive for regional akinesis. AI-ECG for identifying elevated CACS, obstructive CAD, and regional akinesis achieved AUROCs of 0.86, 0.80, and 0.93, respectively. All AI-ECG models identified increased 3-year risk for MI and death in AI-ECG positive versus AI-ECG negative patients and subgroups with 2 or 3 positive results had stepwise increased risk (Figure A). In a 3-year survival analysis of all-comer patients, the hazard ratio for those that tested positive on 1, 2, or 3 versus 0 positive disease-specific models were 1.61 (1.53-1.69). 2.40 (2.27-2.55), 3.42 (3.21-3.65), respectively. Risk stratification was also observed in cohorts without prior ASCVD stratified by low and high PCE-derived 10-year ASCVD risk (Figure B) and in cohorts stratified by age, including under the age of 40.

Conclusions
AI-ECG has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimate ASCVD risk, and in whom risk assessment over periods of time shorter than 10 years is desired.
Biomarker associated trends in mortality in myocardial infarction as an example of clinical data warehouse analyses - new opportunities of data-driven cardiovascular research
Authors: Geng J , Gessler N , Reimers J , Bohnen S , Dreher A , Wohlmuth P , Hakmi S , Willems S , Tigges E P , Kaiser L 
Published: November 2023

Abstract

Background
Healthcare data collected during clinical routine bears an immense potential for various clinical research questions. With the rapid use and development of structured electronic health records, this abundant source of data can be utilized to efficiently comprise large datasets based on actual, unselected patients. In this study, we examined 4,205 patients who presented with myocardial infarction (MI) since the implementation of a new hospital information system at a tertiary hospital. We sought to evaluate mortality trends using routinely collected biomarkers.

Methods
4,205 patients admitted with MI at a high-volume tertiary hospital between September 2016 and September 2022, were retrospectively analyzed. Statistical analyses comprised data from a data warehouse sourced from the hospital information system including ICD and OPS codes, biometric data, and biomarker values.

Results
Of the 4,205 patients with MI, 28% (1,176) were female, the mean age was 67 years. 28% (1,171) were diagnosed with ST-segment elevation myocardial infarction (STEMI). 15% of all MI patients (645) showed coronary single-vessel disease, whereas 65% of patients (2.746) suffered from multi-vessel disease. Percutaneous coronary intervention was performed in 47% (1,961) of all included NSTEMI and STEMI patients. Concomitant arterial hypertension was present in 51% of STEMI patients compared to 65% of the NSTEMI population. Regarding STEMI, diabetes mellitus (DM) was prevalent in 22% of cases, whereas 31% in NSTEMI were diagnosed with DM. We observed a significant linear association between in-hospital mortality and peak biomarker levels of high-sensitive Troponin I (hsTnI), serum lactate, and creatine kinase myocardial band (CKMB) (figure 1). MI patients with a peak lactate measurement of &gt; 17.01 mmol/l had a mortality rate of &gt; 80%. Two-thirds of patients (66%) with CKMB values of &gt; 1474.50 U/I died during the index hospitalization.

Conclusion
In our study, we observed significant linear associations between biomarker levels and in-hospital mortality in patients with myocardial infarction. Furthermore, we demonstrated that hospital information systems can efficiently be used to generate large, comprehensive datasets not only to characterize real-world patient populations but also to evaluate data of prognostic relevance. With careful evaluation of data quality and within the frameworks of patient privacy, future research in this field holds promising potential.
Does a digitally enabled telerehabilitation program reduce hospital admissions and length of stay for private health insurance patients in Australia?
Authors: Braver J , Afoakwah C , Yiallourou S , Salim A , Keating C , Marwick T , Oldenburg B , Carrington M 
Published: November 2023

Abstract

Introduction
Traditional cardiac rehabilitation (CR) programs are associated with reduced cardiovascular morbidity, hospital admissions and mortality. However, participation continues to be poor. Novel digital health models of care have emerged, providing a new frontier in healthcare delivery, but evidence for their impact on healthcare utilization is limited. We implemented and investigated a telehealth program complemented by a CR mobile app for patients following a hospitalization for coronary heart disease (CHD).

Purpose
To assess whether a digitally enabled CR program is equivalent to traditionally delivered CR (F2F-CR) and whether it reduces rehospitalizations and total hospital days compared to patients who did not undertake CR (no-CR).

Methods
Privately insured patients, following a hospitalization for CHD, were recruited nationally, across Australia, between October 2019 - 2020. Participants received weekly telehealth consultations for 8 weeks, supplemented by a CR mobile app. Using administrative private health insurance claims data, we assessed differences in 90-day and 12-month rehospitalizations and total hospital days for the digital-CR group (n=138) compared with a) F2F-CR (n=115) and b) no-CR (n=266).

Results
Among the cohort, 27% lived in regional/rural areas, 71% were male and the majority were aged over 60 years (83%). The digital-CR group had lower total hospital days relative to the no-CR group in the 12 months post index event (4.5 vs 7.2 days; mean difference 2.8 days; p=0.019; 95% CI: 5.1-0.5). Readmission rates were lower at 90 days and similar at 12 months post discharge in the digital-CR group compared with the no-CR group, although not statistically significant. There were 0.17 and 0.21 (p=0.43) readmissions per patient at 90 days post discharge and 1.08 and 1.07 (p=0.94) readmissions per patient within 12 months in the digital-CR group and no-CR group respectively. There were no significant differences for digital-CR relative to F2F-CR on any measures.

Conclusion
Rehospitalization rates were similar between the groups, whilst the digital-CR group had significantly fewer days in hospital than the no-CR group. Digital-CR may reduce the severity of rehospitalizations highlighted by fewer days in hospital. The digital-CR and F2F-CR outcomes were similar, confirming that digitally enabled CR programs provide similar benefits regarding reduced days in hospital, whilst providing additional benefits of improved access and choice. Digital-CR should be made widely available for cardiac patients post hospital discharge.Mean hospital days Digital-CR vs No-CRMean hospital days Digital-CR vs F2F-CR
Long-term impact of remote monitoring after hospitalisations for acute decompensated heart failure: a systematic review and meta-analysis of randomised controlled trials
Authors: Rodrigues Coutinho Clemente M , Felix N S , Navalha D D P , Ferreira R O M , Pasqualotto E P , Nogueira A K S , Marinho A D , Alexandre F K B , Amanda A D F 
Published: November 2023

Abstract

Background
Remote monitoring (RM) has demonstrated to improve outcomes of patients with heart failure (HF) in different settings. However, the long-term efficacy of RM after discharge of patients hospitalised for acute decompensated heart failure (ADHF) remains uncertain.

Purpose
We aimed to perform a systematic review and meta-analysis comparing RM with usual care (UC) in patients recently hospitalised for ADHF.

Methods
We systematically searched PubMed, EMBASE, and Cochrane Library in February 2023 in accordance with PRISMA guidelines for randomised controlled trials (RCTs) comparing RM with UC initiated within 6 weeks of discharge after an admission for ADHF, with a minimum follow-up of 6 months. Statistical analyses were performed using R Software 4.1.0., and heterogeneity was assessed using I² statistics. We extracted the data following the intention-to-treat principle.

Results
We included six RCTs comprising 2,817 patients, of whom 1,436 (51.0%) were randomised to RM. Follow-up ranged from 6 to 15 months. In the pooled analysis, RM was associated with a significant reduction in cardiovascular mortality (OR 0.59; 95% CI 0.36–0.97; p=0.036; I²=0%; Fig. 1A) and cardiovascular hospitalisations (OR 0.71; 95% CI 0.57–0.88; p=0.002; I²=0%; Fig. 1B), with a low heterogeneity between studies. Nonetheless, there was no significant difference between RM and UC with regards to hospitalisations for HF (OR 0.88; 95% CI 0.67–1.15; p=0.340; I²=66%; Fig. 2A) or all-cause hospitalisations (OR 0.89; 95% CI 0.73–1.08; p=0.241; I²=67%; Fig. 2B), albeit with a high heterogeneity between studies.

Conclusion
In this meta-analysis, post-discharge RM was associated with a reduction in cardiovascular mortality and cardiovascular hospitalisations rates as compared with UC in patients admitted for ADHF.Figure 1Figure 2
Mathematical software tool that helps clinicians to classify clinicand and spurious alerts in patients with heart failure and cardiac implantable devices
Authors: Sanchez Vallejo C , Flores Soler J , Garcia-Torrent M , Canadas-Godoy V , Salgado Aranda R , Garcia Gonzalez F , Casas De La Cal L , Herreros Gil M , Perez Serrano M , Perez-Villacastin Dominguez J , Perez Castellano N 
Published: November 2023

Abstract

Introduction
Heart failure (HF) is the leading cause of cardiovascular hospitalisation in developed countries. More and more HF patients are receiving cardiac implantable electronic devices (CIED) that collect physiological data that may be used to predict HF decompensations and avoid hospitalisation through early medical interventions. Some CIEDs have a Multiparameter heart failure index (HF-I), which is an algorithm based on the information provided by five sensors (heart sounds, respiration, chest impedance, heart rate and activity level) capable of predicting 34 days before HF decompensation episodes when the reported index is ≥16 (HF-I alert) with a sensitivity of 70%, although with a significant rate of false positive HF diagnosis.

Purpose
Develop a computational tool to analyse HF-I curve dynamics to find the mathematical variables with the highest predictive accuracy of true clinical events in a tertiary hospital.

Methods
We developed a computational tool to analyse curves of 71 HF-I alerts in 25 patients with HF who were implanted with an implantable cardiac defibrillator (ICD) or cardiac resynchronisation-defibrillator (CRT-D) between 2017 and 2021, using mathematical software (fig 1). It includes peak, time to peak, area under the curve, increase and decrease slopes, variability and monotony. Alerts (HF-I ≥16) were clinically classified as real HF decompensation (clinical alerts) and false positive HF decompensation (spurious/subclinical alerts).

Results
Median age of the study group was 68 years, and 60% had non-ischemic cardiomyopathy. Median left ventricle ejection fraction (LVEF) was 29%; 60% had non-ischaemic cardiomyopathy. Patients with clinical alerts had higher raise slopes 7 days after alert onset (p=0.0055), higher HF-I values 3 days after crossing the alert, higher raise variability and less monotonic curves. Additionally, significant differences were observed in alert duration (p=0.004), HF-I maximum value (p&lt; 0.0001), area under the overall alert curve (p&lt; 0.0001), area under the alert curve at 3 / 7 days (p=0.04/0.002 respectively) and time to maximum peak of the alert (p=0,0032). HF-I value at the 7th day from the alert onset was an early and robust predictor of clinical events (P&lt; 0.0001). A value at day 7 from alert onset ≥ 28 had a 95% sensitivity and 100% specificity for detecting clinical events (fig 2).

Conclusion
a computational mathematical tool can help to classify true positive clinical alerts versus spurious alerts in patients with a multiparameter heart failure index and CIED.Figure 1.Figure 2.
The beneficial effects of adherence to outpatient telecare intervention on clinical outcomes after an episode of acute heart failure: insights from the prospective randomised controlled AMULET trial
Authors: Krzesinski P , Galas A , Piotrowicz K , Chrom P , Jankowska E A , Siebert J , Stanczyk A , Siwolowski P , Gutknecht P , Szalewska D , Banasiak W , Ponikowski P , Gielerak G 
Published: November 2023

Abstract

Background
The prevention of hospitalisations and deaths is a major therapeutic aim in patients with heart failure (HF). In our previously published prospective randomised controlled trial, we demonstrated that an outpatient intervention comprising nurse-led non-invasive assessments, telemedicine support and remote cardiologist decision-making (AMULET telecare) reduced the risk of HF hospitalisation or cardiovascular death by 31% during the 12-month follow-up in HF patients after an episode of acute HF.

Purpose
To present the results of the prespecified per-protocol analysis of the AMULET trial that reveal the impact of AMULET telecare adherence on the prognosis of patients.

Methods
Patients with HF and left ventricular ejection fraction (LVEF) ≤49% and who experienced an episode of acute HF in the past 6 months were randomly assigned to receive either AMULET telecare (n=298) or standard care (n=305). Those who completed the predefined schedule of AMULET telecare (n=190) and all standard care patients were included in the per-protocol population.

Results
In this per-protocol analysis, when compared to standard care, the implementation of AMULET telecare reduced the risk of the primary outcome, a composite of unplanned HF hospitalisation or cardiovascular death (by 51%), and the following secondary outcomes: first unplanned HF hospitalisation, first unplanned cardiovascular hospitalisation, first all-cause unplanned hospitalisation, total unplanned HF hospitalisations and the number of days lost due to HF hospitalisations or death. There was no statistically significant difference in cardiovascular mortality, mortality due to HF and overall mortality between the study groups (Table).

Conclusions
Adherence to AMULET telecare enhances the positive effects of this intervention on the prognosis of HF patients with an LVEF≤49% after an episode of acute HF.
The frequency of arrhythmia in patients with palpitations who had COVID-19 with 24-hour holter compared to the control group
Authors: Korkmaz K , Tulunay Kaya C , Tan Kurklu T S 
Published: November 2023

Abstract

Introduction
COVID-19 affects many systems especially cardiovascular system. Cardiovascular outcomes includes acute coronary syndrome, arrhythmias, myocarditis, cardiogenic shock, sudden cardiac deaths. Overall arrhythmia incidence of %17. Almost all arrhythmias can occur in COVID-19.
Proposition: In this study, we aimed to calculate an arrhythmia incidence, including parameters that have not been studied before, with 24-hour holter results between the COVID-19 survivors and the control group.

Method
The participants of the study were selected among the patients who applied to the Cardiology Department between March 2020 and March 2021 with the complaint of palpitation. From patients with 24-hour rhythm holter (approximately 1500 patients) retrospectively, patients who had COVID-19 at any time before holter insertion (n=190) were identified. The same number of patients (n=221) with similar characteristics were taken as the control group.

Results
The probability of having AF in the COVID-19 group was found to be 3.2 times higher (%95 Cl, 1.002-10.48) than control group. There was no difference in the development of arrhythmia in patients who had COVID-19, when patients with CT involvement and patients without involvement were compared. In addition, there was no difference between having cardiac risk factors in terms of development of arrhythmia in both groups, even when looked at separately. Average rate was lower in serology positive group (p=0.04). Heart rate was lower in those who received favipiravir than those who received plaquenil or favipravir plus plaquenil (p=0.03). Atrial extra systole incidence were higher in those who were taken plaquenil (p=0.48).

Conclusion
According to our knowledge, there is no other study investigating the effect of pharmacological treatment changes used in Covid-19 on arrhythmia frequency. Also CT involvement has no effect on the incidence of arrhythmia.
The main result is when multivariate regression analysis was performed, being COVID-19 was an independent indicator of the presence of atrial fibrillation (p=0.05).