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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 -
Frontiers in Cardiovascular Medicine 2023Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on... (Review)
Review
INTRODUCTION
Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.
METHODS
We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.
RESULTS
Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx ( = 13) and post durable MCS ( = 7), as well as post HTx and MCS management ( = 7, = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.
CONCLUSION
Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.
PubMed: 36910520
DOI: 10.3389/fcvm.2023.1127716 -
Age and Ageing Jan 2023walking is crucial for an active and healthy ageing, but the perspectives of individuals living with walking impairment are still poorly understood.
BACKGROUND
walking is crucial for an active and healthy ageing, but the perspectives of individuals living with walking impairment are still poorly understood.
OBJECTIVES
to identify and synthesise evidence describing walking as experienced by adults living with mobility-impairing health conditions and to propose an empirical conceptual framework of walking experience.
METHODS
we performed a systematic review and meta-ethnography of qualitative evidence, searching seven electronic databases for records that explored personal experiences of walking in individuals living with conditions of diverse aetiology. Conditions included Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture, heart failure, frailty and sarcopenia. Data were extracted, critically appraised using the NICE quality checklist and synthesised using standardised best practices.
RESULTS
from 2,552 unique records, 117 were eligible. Walking experience was similar across conditions and described by seven themes: (i) becoming aware of the personal walking experience, (ii) the walking experience as a link between individuals' activities and sense of self, (iii) the physical walking experience, (iv) the mental and emotional walking experience, (v) the social walking experience, (vi) the context of the walking experience and (vii) behavioural and attitudinal adaptations resulting from the walking experience. We propose a novel conceptual framework that visually represents the walking experience, informed by the interplay between these themes.
CONCLUSION
a multi-faceted and dynamic experience of walking was common across health conditions. Our conceptual framework of the walking experience provides a novel theoretical structure for patient-centred clinical practice, research and public health.
Topics: Humans; Qualitative Research; Anthropology, Cultural; Walking
PubMed: 36729471
DOI: 10.1093/ageing/afac233 -
Healthcare (Basel, Switzerland) Jun 2023IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial... (Review)
Review
BACKGROUND
IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR.
METHODS
We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures.
RESULTS
We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability ( = 8, 40%), followed by the biochemical or biological markers ( = 5, 25%), DNA profiling data ( = 2, 10%), Doppler indices ( = 3, 15%), MRI data ( = 1, 5%), and physiological, clinical, or socioeconomic data ( = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG.
CONCLUSIONS
our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.
PubMed: 37297757
DOI: 10.3390/healthcare11111617 -
International Journal of Environmental... Jul 2022Using digital technology to provide support, medical consultations, healthcare services, and to track the spread of the coronavirus has been identified as an important... (Review)
Review
INTRODUCTION
Using digital technology to provide support, medical consultations, healthcare services, and to track the spread of the coronavirus has been identified as an important solution to curb the transmission of the virus. This research paper aims to (1) summarize the digital technologies used during the COVID-19 pandemic to mitigate the transmission of the COVID-19; (2) establish the extent to which digital technology applications have facilitated mitigation of the spread of COVID-19; and (3) explore the facilitators and barriers that impact the usability of digital technologies throughout the pandemic.
METHODS
A rapid electronic search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted of available records up to June 2022 on the medical databases PubMed, Ovid, Embase, CINHAIL, the Cochrane Library, Web of Science, and Google Scholar.
RESULTS
An increasing number and variety of digital health applications have been available throughout the pandemic, such as telehealth, smartphone mobile health apps, machine learning, and artificial intelligence. Each technology has played a particular role in curbing COVID-19 transmission. Different users have gained benefits from using digital technology during the COVID-19 pandemic and different determinants have contributed to accelerating the wheel of digital technology implementation during the pandemic.
CONCLUSION
Digital health during the COVID-19 pandemic has evolved very rapidly, with different applications and roles aimed at curbing the pandemic.
Topics: Artificial Intelligence; COVID-19; Digital Technology; Humans; Pandemics; Telemedicine
PubMed: 35886139
DOI: 10.3390/ijerph19148287 -
The Cochrane Database of Systematic... Jan 2022Pharmacotherapies such as loop diuretics are the cornerstone treatment for acute heart failure (AHF), but resistance and poor response can occur. Ultrafiltration (UF) is... (Review)
Review
BACKGROUND
Pharmacotherapies such as loop diuretics are the cornerstone treatment for acute heart failure (AHF), but resistance and poor response can occur. Ultrafiltration (UF) is an alternative therapy to reduce congestion, however its benefits, efficacy and safety are unclear.
OBJECTIVES
To assess the effects of UF compared to diuretic therapy on clinical outcomes such as mortality and rehospitalisation rates.
SEARCH METHODS
We undertook a systematic search in June 2021 of the following databases: CENTRAL, MEDLINE, Embase, Web of Science CPCI-S and ClinicalTrials.gov. We also searched the WHO ICTRP platform in October 2020.
SELECTION CRITERIA
We included randomised controlled trials (RCTs) that compared UF to diuretics in adults with AHF.
DATA COLLECTION AND ANALYSIS
Two review authors independently assessed trial quality and extracted data. We contacted study authors for any further information, and language interpreters to translate texts. We assessed risk of bias in included studies using Risk of Bias 2 (RoB2) tool and assessed the certainty of the evidence using GRADE.
MAIN RESULTS
We included 14 trials involving 1190 people. We included people who had clinical signs of acute hypervolaemia. We excluded critically unwell people such as those with ischaemia or haemodynamic instability. Mean age ranged from 57.5 to 75 years, and the setting was a mix of single and multi-centre. Two trials researched UF as a complimentary therapy to diuretics, while the remaining trials withheld diuretic use during UF. There was high risk of bias in some studies, particularly with deviations from the intended protocols from high cross-overs as well as missing outcome data for long-term follow-up. We are uncertain about the effect of UF on all-cause mortality at 30 days or less (risk ratio (RR) 0.61, 95% confidence interval (CI) 0.13 to 2.85; 3 studies, 286 participants; very low-certainty evidence). UF may have little to no effect on all-cause mortality at the longest available follow-up (RR 1.00, 95% CI 0.73 to 1.36; 9 studies, 987 participants; low-certainty evidence). UF may reduce all-cause rehospitalisation at 30 days or less (RR 0.76, 95% CI 0.53 to 1.09; 3 studies, 337 participants; low-certainty evidence). UF may slightly reduce all-cause rehospitalisation at longest available follow-up (RR 0.91, 95% CI 0.79 to 1.05; 6 studies, 612 participants; low-certainty evidence). UF may reduce heart failure-related rehospitalisation at 30 days or less (RR 0.62, 95% CI 0.37 to 1.04; 2 studies, 395 participants; low-certainty evidence). UF probably reduces heart failure-related rehospitalisation at longest available follow-up, with a number needed to treat for an additional beneficial effect (NNTB) of 10 (RR 0.69, 95% CI 0.53 to 0.90; 4 studies, 636 participants; moderate-certainty evidence). No studies measured need for mechanical ventilation. UF may have little or no effect on serum creatinine change at 30 days since discharge (mean difference (MD) 14%, 95% CI -12% to 40%; 1 study, 221 participants; low-certainty evidence). UF may increase the risk of new initiation of renal replacement therapy at longest available follow-up (RR 1.42, 95% CI 0.42 to 4.75; 4 studies, 332 participants; low-certainty evidence). There is an uncertain effect of UF on the risk of complications from central line insertion in hospital (RR 4.16, 95% CI 1.30 to 13.30; 6 studies, 779 participants; very low-certainty evidence). AUTHORS' CONCLUSIONS: This review summarises the latest evidence on UF in AHF. Moderate-certainty evidence shows UF probably reduces heart failure-related rehospitalisation in the long term, with an NNTB of 10. UF may reduce all-cause rehospitalisation at 30 days or less and at longest available follow-up. The effect of UF on all-cause mortality at 30 days or less is unclear, and it may have little effect on all-cause mortality in the long-term. While UF may have little or no effect on serum creatinine change at 30 days, it may increase the risk of new initiation of renal replacement therapy in the long term. The effect on complications from central line insertion is unclear. There is insufficient evidence to determine the true impact of UF on AHF. Future research should evaluate UF as an adjunct therapy, focusing on outcomes such as heart failure-related rehospitalisation, cardiac mortality and renal outcomes at medium- to long-term follow-up.
Topics: Adult; Aged; Heart Failure; Humans; Middle Aged; Renal Replacement Therapy; Respiration, Artificial; Ultrafiltration
PubMed: 35061249
DOI: 10.1002/14651858.CD013593.pub2 -
Entropy (Basel, Switzerland) May 2021The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big... (Review)
Review
The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.
PubMed: 34073201
DOI: 10.3390/e23060667 -
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 -
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 -
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