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International Journal of Medical... Aug 2023As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the... (Review)
Review
BACKGROUND
As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx.
METHOD
A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).
RESULTS
Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center.
CONCLUSIONS
While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.
Topics: Humans; Artificial Intelligence; Big Data; Heart Transplantation; Prognosis; Retrospective Studies
PubMed: 37285695
DOI: 10.1016/j.ijmedinf.2023.105110 -
Clinical Rheumatology Oct 2023Cardiovascular manifestations are common in patients suffering axial spondyloarthritis and can result in substantial morbidity and disease burden. To give an overview of... (Review)
Review
Cardiovascular manifestations are common in patients suffering axial spondyloarthritis and can result in substantial morbidity and disease burden. To give an overview of this important aspect of axial spondyloarthritis, we conducted a systematic literature search of all articles published between January 2000 and 25 May 2023 on cardiovascular manifestations. Using PubMed and SCOPUS, 123 out of 6792 articles were identified and included in this review. Non-radiographic axial spondyloarthritis seems to be underrepresented in studies; thus, more evidence for ankylosing spondylitis exists. All in all, we found some traditional risk factors that led to higher cardiovascular disease burden or major cardiovascular events. These specific risk factors seem to be more aggressive in patients with spondyloarthropathies and have a strong connection to high or long-standing disease activity. Since disease activity is a major driver of morbidity, diagnostic, therapeutic, and lifestyle interventions are crucial for better outcomes. Key Points • Several studies on axial spondyloarthritis and associated cardiovascular diseases have been conducted in the last few years addressing risk stratification of these patients including artificial intelligence. • Recent data suggest distinct manifestations of cardiovascular disease entities among men and women which the treating physician needs to be aware of. • Rheumatologists need to screen axial spondyloarthritis patients for emerging cardiovascular disease and should aim at reducing traditional risk factors like hyperlipidemia, hypertension, and smoking as well as disease activity.
Topics: Male; Humans; Female; Spondylarthritis; Cardiovascular Diseases; Artificial Intelligence; Risk Factors; Spondylitis, Ankylosing; Heart Disease Risk Factors
PubMed: 37418034
DOI: 10.1007/s10067-023-06655-z -
Cureus Aug 2023Cardiovascular diseases (CVDs) present a significant global health challenge and remain a primary cause of death. Early detection and intervention are crucial for... (Review)
Review
A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?
Cardiovascular diseases (CVDs) present a significant global health challenge and remain a primary cause of death. Early detection and intervention are crucial for improved outcomes in acute coronary syndrome (ACS), particularly acute myocardial infarction (AMI) cases. Artificial intelligence (AI) can detect heart disease early by analyzing patient information and electrocardiogram (ECG) data, providing invaluable insights into this critical health issue. However, the imbalanced nature of ECG and patient data presents challenges for traditional machine learning (ML) algorithms in performing unbiasedly. Investigators have proposed various data-level and algorithm-level solutions to overcome these challenges. In this study, we used a systematic literature review (SLR) approach to give an overview of the current literature and to highlight the difficulties of utilizing ML, deep learning (DL), and AI algorithms in predicting, diagnosing, and prognosis of heart diseases. We reviewed 181 articles from reputable journals published between 2013 and June 15, 2023, focusing on eight selected papers for in-depth analysis. The analysis considered factors such as heart disease type, algorithms used, applications, and proposed solutions and compared the benefits of algorithms combined with clinicians versus clinicians alone. This systematic review revealed that the current ML-based diagnostic approaches face several open problems and issues when implementing ML, DL, and AI in real-life settings. Although these algorithms show higher sensitivities, specificities, and accuracies in detecting heart disease, we must address the ethical concerns while implementing these models into clinical practice. The transparency of how these algorithms operate remains a challenge. Nevertheless, further exploration and research in ML, DL, and AI are necessary to overcome these challenges and fully harness their potential to improve health outcomes for patients with AMI.
PubMed: 37674942
DOI: 10.7759/cureus.43003 -
Sensors (Basel, Switzerland) Aug 2023Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms... (Review)
Review
Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.
Topics: Humans; Artificial Intelligence; Awareness; Chronic Disease; Heart Failure; Wearable Electronic Devices
PubMed: 37571678
DOI: 10.3390/s23156896 -
Open Heart Jul 2023Fabry disease (FD) is an X-linked lysosomal storage disorder caused by enzyme deficiency, leading to glycosphingolipid accumulation. Cardiac accumulation triggers local...
INTRODUCTION
Fabry disease (FD) is an X-linked lysosomal storage disorder caused by enzyme deficiency, leading to glycosphingolipid accumulation. Cardiac accumulation triggers local tissue injury, electrical instability and arrhythmia. Bradyarrhythmia and atrial fibrillation (AF) incidence are reported in up to 16% and 13%, respectively.
OBJECTIVE
We conducted a systematic review evaluating AF burden and bradycardia requiring permanent pacemaker (PPM) implantation and report any predictive risk factors identified.
METHODS
We conducted a literature search on studies in adults with FD published from inception to July 2019. Study outcomes included AF or bradycardia requiring therapy. Databases included Embase, Medline, PubMed, Web of Science, CINAHL and Cochrane. The Risk of Bias Agreement tool for Non-Randomised Studies (RoBANS) was utilised to assess bias across key areas.
RESULTS
11 studies were included, eight providing data on AF incidence or PPM implantation. Weighted estimate of event rates for AF were 12.2% and 10% for PPM. Age was associated with AF (OR 1.05-1.20 per 1-year increase in age) and a risk factor for PPM implantation (composite OR 1.03). Left ventricular hypertrophy (LVH) was associated with AF and PPM implantation.
CONCLUSION
Evidence supporting AF and bradycardia requiring pacemaker implantation is limited to single-centre studies. Incidence is variable and choice of diagnostic modality plays a role in detection rate. Predictors for AF (age, LVH and atrial dilatation) and PPM (age, LVH and PR/QRS interval) were identified but strength of association was low. Incidence of AF and PPM implantation in FD are variably reported with arrhythmia burden likely much higher than previously thought.
PROSPERO DATABASE
CRD42019132045.
Topics: Adult; Humans; Bradycardia; Atrial Fibrillation; Fabry Disease; Incidence; Pacemaker, Artificial
PubMed: 37460269
DOI: 10.1136/openhrt-2023-002316 -
Clinical Cardiology Jul 2023Cardiac resynchronization therapy (CRT) strategy for heart failure with mildly reduced ejection fraction (HFmrEF) is controversial. Left bundle branch area pacing... (Meta-Analysis)
Meta-Analysis Review
Cardiac resynchronization therapy (CRT) strategy for heart failure with mildly reduced ejection fraction (HFmrEF) is controversial. Left bundle branch area pacing (LBBAP) is an emerging pacing modality and an alternative option to CRT. This analysis aimed to perform a systematic review of the literature and meta-analysis on the impact of the LBBAP strategy in HFmrEF, with left ventricular ejection fraction (LVEF) between 35% and 50%. PubMed, Embase, and Cochrane Library were searched for full-text articles on LBBAP from inception to July 17, 2022. The outcomes of interest were QRS duration and LVEF at baseline and follow-up in mid-range heart failure. Data were extracted and summarized. A random-effect model incorporating the potential heterogeneity was used to synthesize the results. Out of 1065 articles, 8 met the inclusion criteria for 211 mid-range heart failure patients with an implant LBBAP across the 16 centers. The average implant success rate with lumenless pacing lead use was 91.3%, and 19 complications were reported among all 211 enrolled patients. During the average follow-up of 9.1 months, the average LVEF was 39.8% at baseline and 50.5% at follow-up (MD: 10.90%, 95% CI: 6.56-15.23, p < .01). Average QRS duration was 152.6 ms at baseline and 119.3 ms at follow-up (MD: -34.51 ms, 95% CI: -60.00 to -9.02, p < .01). LBBAP could significantly reduce QRS duration and improve systolic function in a patient with LVEF between 35% and 50%. Application of LBBAP as a CRT strategy for HFmrEF may be a viable option.
Topics: Humans; Stroke Volume; Cardiac Pacing, Artificial; Ventricular Function, Left; Heart Conduction System; Cardiac Resynchronization Therapy; Heart Failure; Electrocardiography; Treatment Outcome
PubMed: 37144691
DOI: 10.1002/clc.24028 -
Frontiers in Human Neuroscience 2023Autonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce...
BACKGROUND
Autonomous navigation of catheters and guidewires in endovascular interventional surgery can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment.
OBJECTIVE
To determine from recent literature, through a systematic review, the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous navigation of catheters and guidewires for endovascular interventions.
METHODS
PubMed and IEEEXplore databases were searched to identify reports of AI applied to autonomous navigation methods in endovascular interventional surgery. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). PROSPERO: CRD42023392259.
RESULTS
Four hundred and sixty-two studies fulfilled the search criteria, of which 14 studies were included for analysis. Reinforcement learning (RL) (9/14, 64%) and learning from expert demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. These studies evaluated models on physical phantoms (10/14, 71%) and (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while non-anatomical vessel platforms "idealized" for simple navigation were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalizability were present across studies. No procedures were performed on patients in any of the studies reviewed. Moreover, all studies were limited due to the lack of patient selection criteria, reference standards, and reproducibility, which resulted in a low level of evidence for clinical translation.
CONCLUSION
Despite the potential benefits of AI applied to autonomous navigation of endovascular interventions, the field is in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come.
SYSTEMATIC REVIEW REGISTRATION
identifier: CRD42023392259.
PubMed: 37600553
DOI: 10.3389/fnhum.2023.1239374 -
BMC Biology Nov 2023Traditionally, in biomedical animal research, laboratory rodents are individually examined in test apparatuses outside of their home cages at selected time points....
BACKGROUND
Traditionally, in biomedical animal research, laboratory rodents are individually examined in test apparatuses outside of their home cages at selected time points. However, the outcome of such tests can be influenced by various factors and valuable information may be missed when the animals are only monitored for short periods. These issues can be overcome by longitudinally monitoring mice and rats in their home cages. To shed light on the development of home cage monitoring (HCM) and the current state-of-the-art, a systematic review was carried out on 521 publications retrieved through PubMed and Web of Science.
RESULTS
Both the absolute (~ × 26) and relative (~ × 7) number of HCM-related publications increased from 1974 to 2020. There was a clear bias towards males and individually housed animals, but during the past decade (2011-2020), an increasing number of studies used both sexes and group housing. In most studies, animals were kept for short (up to 4 weeks) time periods in the HCM systems; intermediate time periods (4-12 weeks) increased in frequency in the years between 2011 and 2020. Before the 2000s, HCM techniques were predominantly applied for less than 12 h, while 24-h measurements have been more frequent since the 2000s. The systematic review demonstrated that manual monitoring is decreasing in relation to automatic techniques but still relevant. Until (and including) the 1990s, most techniques were applied manually but have been progressively replaced by automation since the 2000s. Independent of the year of publication, the main behavioral parameters measured were locomotor activity, feeding, and social behaviors; the main physiological parameters were heart rate and electrocardiography. External appearance-related parameters were rarely examined in the home cages. Due to technological progress and application of artificial intelligence, more refined and detailed behavioral parameters have been investigated in the home cage more recently.
CONCLUSIONS
Over the period covered in this study, techniques for HCM of mice and rats have improved considerably. This development is ongoing and further progress as well as validation of HCM systems will extend the applications to allow for continuous, longitudinal, non-invasive monitoring of an increasing range of parameters in group-housed small rodents in their home cages.
Topics: Male; Female; Mice; Animals; Rats; Behavior, Animal; Artificial Intelligence; Social Behavior; Heart Rate; Animals, Domestic
PubMed: 37953247
DOI: 10.1186/s12915-023-01751-7 -
Cureus Dec 2023Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can... (Review)
Review
Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.
PubMed: 38213372
DOI: 10.7759/cureus.50395 -
European Respiratory Review : An... Sep 2023The number of patients completing unsupervised home spirometry has recently increased due to more widely available portable technology and the COVID-19 pandemic, despite... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The number of patients completing unsupervised home spirometry has recently increased due to more widely available portable technology and the COVID-19 pandemic, despite a lack of solid evidence to support it. This systematic methodology review and meta-analysis explores quantitative differences in unsupervised spirometry compared with spirometry completed under professional supervision.
METHODS
We searched four databases to find studies that directly compared unsupervised home spirometry with supervised clinic spirometry using a quantitative comparison ( Bland-Altman). There were no restrictions on clinical condition. The primary outcome was measurement differences in common lung function parameters (forced expiratory volume in 1 s (FEV), forced vital capacity (FVC)), which were pooled to calculate overall mean differences with associated limits of agreement (LoA) and confidence intervals (CI). We used the I statistic to assess heterogeneity, the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess risk of bias and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess evidence certainty for the meta-analyses. The review has been registered with PROSPERO (CRD42021272816).
RESULTS
3607 records were identified and screened, with 155 full texts assessed for eligibility. We included 28 studies that quantitatively compared spirometry measurements, 17 of which reported a Bland-Altman analysis for FEV and FVC. Overall, unsupervised spirometry produced lower values than supervised spirometry for both FEV with wide variability (mean difference -107 mL; LoA= -509, 296; I=95.8%; p<0.001; very low certainty) and FVC (mean difference -184 mL, LoA= -1028, 660; I=96%; p<0.001; very low certainty).
CONCLUSIONS
Analysis under the conditions of the included studies indicated that unsupervised spirometry is not interchangeable with supervised spirometry for individual patients owing to variability and underestimation.
Topics: Humans; COVID-19; Forced Expiratory Volume; Pandemics; Respiratory Tract Diseases; Spirometry
PubMed: 37673426
DOI: 10.1183/16000617.0248-2022