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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 -
Journal of Evidence-based Medicine Sep 2023Technology including artificial intelligence (AI) may play a key role to strengthen primary health care services in resource-poor settings. This systematic review aims...
AIM
Technology including artificial intelligence (AI) may play a key role to strengthen primary health care services in resource-poor settings. This systematic review aims to explore the evidence on the use of AI and digital health in improving primary health care service delivery.
METHODS
Three electronic databases were searched using a comprehensive search strategy without providing any restriction in June 2023. Retrieved articles were screened independently using the "Rayyan" software. Data extraction and quality assessment were conducted independently by two review authors. A narrative synthesis of the included interventions was conducted.
RESULTS
A total of 4596 articles were screened, and finally, 48 articles were included from 21 different countries published between 2013 and 2021. The main focus of the included studies was noncommunicable diseases (n = 15), maternal and child health care (n = 11), primary care (n = 8), infectious diseases including tuberculosis, leprosy, and HIV (n = 7), and mental health (n = 6). Included studies considered interventions using AI, and digital health of which mobile-phone-based interventions were prominent. m-health interventions were well adopted and easy to use and improved the record-keeping, service deliver, and patient satisfaction.
CONCLUSION
AI and the application of digital technologies improve primary health care service delivery in resource-poor settings in various ways. However, in most of the cases, the application of AI and digital health is implemented through m-health. There is a great scope to conduct further research exploring the interventions on a large scale.
PubMed: 37691394
DOI: 10.1111/jebm.12547 -
American Journal of Surgery Sep 2023The role of metabolic and bariatric surgery (MBS), in synergy with left ventricular assist device (LVAD) implantation, in the scope of end-stage heart failure management... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The role of metabolic and bariatric surgery (MBS), in synergy with left ventricular assist device (LVAD) implantation, in the scope of end-stage heart failure management for patients with severe obesity is not well elucidated.
METHODS
We conducted a meta-analysis using Cochrane, Embase, PubMed, and Scopus databases to include articles from their inception to November 2022.
RESULTS
A total of 271 patients who underwent MBS during or after the LVAD implantation were included from eleven separate studies. After surgery, 67.4% of patients were listed on the heart transplant waitlist with 32.5% undergoing a successful transplant. We reported a mean listing time of 13.8 months. Finally, the pooled postoperative complication rate, 30-day readmission rate, and one-year mortality rate were 47.6%, 23.6% and 10.2% respectively.
CONCLUSIONS
MBS and LVAD is a safe and effective approach to bridge patients with severe obesity and end-stage heart failure for definitive heart transplantation.
Topics: Humans; Obesity, Morbid; Heart-Assist Devices; Heart Failure; Heart Transplantation; Bariatric Surgery; Treatment Outcome; Retrospective Studies
PubMed: 37355375
DOI: 10.1016/j.amjsurg.2023.06.014 -
International Journal of Cardiology Nov 2023Right Ventricular Pacing (RVP) may have detrimental effects in ventricular function. Left Bundle Branch Area Pacing (LBBAP) is a new pacing strategy that appears to have... (Meta-Analysis)
Meta-Analysis
Safety and efficacy of left bundle branch area pacing compared with right ventricular pacing in patients with bradyarrhythmia and conduction system disorders: Systematic review and meta-analysis.
BACKGROUND
Right Ventricular Pacing (RVP) may have detrimental effects in ventricular function. Left Bundle Branch Area Pacing (LBBAP) is a new pacing strategy that appears to have better results. The aim of this systematic review and meta-analysis is to compare the safety and efficacy of LBBAP vs RVP in patients with bradyarrhythmia and conduction system disorders.
METHODS
MEDLINE, EMBASE and Pubmed databases were searched for studies comparing LBBAP with RVP. Outcomes were all-cause mortality, atrial fibrillation (AF) occurrence, heart failure hospitalizations (HFH) and complications. QRS duration, mechanical synchrony and LVEF changes were also assessed. Pairwise meta-analysis was conducted using random and fixed effects models.
RESULTS
Twenty-five trials with 4250 patients (2127 LBBAP) were included in the analysis. LBBAP was associated with lower risk for HFH (RR:0.33, CI 95%:0.21 to 0.50; p < 0.001), all-cause mortality (RR:0.52 CI 95%:0.34 to 0.80; p = 0.003), and AF occurrence (RR:0.43 CI 95%:0.27 to 0.68; p < 0.001) than RVP. Lead related complications were not different between the two groups (p = 0.780). QRSd was shorter in the LBBAP group at follow-up (WMD: -32.20 msec, CI 95%: -40.70 to -23.71; p < 0.001) and LBBAP achieved better intraventricular mechanical synchrony than RVP (SMD: -1.77, CI 95%: -2.45 to -1.09; p < 0.001). LBBAP had similar pacing thresholds (p = 0.860) and higher R wave amplitudes (p = 0.009) than RVP.
CONCLUSIONS
LBBAP has better clinical outcomes, preserves ventricular electrical and mechanical synchrony and has excellent pacing parameters, with no difference in complications compared to RVP.
Topics: Humans; Bradycardia; Cardiac Pacing, Artificial; Cardiac Conduction System Disease; Heart Conduction System; Atrial Fibrillation; Electrocardiography; Treatment Outcome; Bundle of His
PubMed: 37527751
DOI: 10.1016/j.ijcard.2023.131230 -
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 -
Europace : European Pacing,... Nov 2023Transvenous lead extraction (TLE) is performed using non-laser and laser techniques with overall high efficacy and safety. Variation in outcomes between the two... (Meta-Analysis)
Meta-Analysis
AIMS
Transvenous lead extraction (TLE) is performed using non-laser and laser techniques with overall high efficacy and safety. Variation in outcomes between the two approaches does exist with limited comparative evidence in the literature. We sought to compare non-laser and laser TLE in a meta-analysis.
METHODS AND RESULTS
We searched Medline, Embase, Scopus, ClinicalTrials.gov, and CENTRAL databases for TLE studies published between 1991 and 2021. From the included 68 studies, safety and efficacy data were carefully evaluated and extracted. Aggregated cases of outcomes were used to calculate odds ratio (OR), and pooled rates were synthesized from eligible studies to compare non-laser and laser techniques. Subgroup comparison of rotational tool and laser extraction was also performed. Non-laser in comparison with laser had lower procedural mortality (pooled rate 0% vs. 0.1%, P < 0.01), major complications (pooled rate 0.7% vs. 1.7%, P < 0.01), and superior vena cava (SVC) injury (pooled rate 0% vs. 0.5%, P < 0.001), with higher complete success (pooled rate 96.5% vs. 93.8%, P < 0.01). Non-laser comparatively to laser was more likely to achieve clinical [OR 2.16 (1.77-2.63), P < 0.01] and complete [OR 1.87 (1.69-2.08), P < 0.01] success, with a lower procedural mortality risk [OR 1.6 (1.02-2.5), P < 0.05]. In the subgroup analysis, rotational tool compared with laser achieved greater complete success (pooled rate 97.4% vs. 95%, P < 0.01) with lower SVC injury (pooled rate 0% vs. 0.7%, P < 0.01).
CONCLUSION
Non-laser TLE is associated with a better safety and efficacy profile when compared with laser methods. There is a greater risk of SVC injury associated with laser sheath extraction.
Topics: Humans; Defibrillators, Implantable; Vena Cava, Superior; Device Removal; Lasers; Cardiac Catheterization; Pacemaker, Artificial; Treatment Outcome; Retrospective Studies
PubMed: 37882609
DOI: 10.1093/europace/euad316 -
Journal of Nuclear Cardiology :... Dec 2023Bone scintigraphy imaging is frequently used to investigate patients with suspected transthyretin cardiac amyloidosis (ATTR-CM). However, the reported accuracy for... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Bone scintigraphy imaging is frequently used to investigate patients with suspected transthyretin cardiac amyloidosis (ATTR-CM). However, the reported accuracy for interpretation approaches has changed over time. We performed a systematic review and meta-analysis to determine the diagnostic accuracy of visual planar grading, heart-to-contralateral (HCL) ratio, and quantitative analysis of SPECT imaging and evaluate reasons for shifts in reported accuracy.
METHODS
We performed a systematic review to identify studies of the diagnostic accuracy of bone scintigraphy for ATTR-CM from 1990 until February 2023 using PUBMED and EMBASE. Studies were reviewed separately by two authors for inclusion and for risk of bias assessment. Summary receiver operating characteristic curves and operating points were determined with hierarchical modeling.
RESULTS
Out of a total of 428 identified studies, 119 were reviewed in detail and 23 were included in the final analysis. The studies included a total of 3954 patients, with ATTR-CM diagnosed in 1337 (39.6%) patients and prevalence ranging from 21 to 73%. Visual planar grading and quantitative analysis had higher diagnostic accuracy (.99) than HCL ratio (.96). Quantitative analysis of SPECT imaging had the highest specificity (97%) followed by planar visual grade (96%) and HCL ratio (93%). ATTR-CM prevalence accounted for some of the observed between study heterogeneity.
CONCLUSIONS
Bone scintigraphy imaging is highly accurate for identifying patients with ATTR-CM, with between study heterogeneity in part explained by differences in disease prevalence. We identified small differences in specificity, which may have important clinical implications when applied to low-risk screening populations.
Topics: Humans; Prealbumin; Amyloid Neuropathies, Familial; Tomography, X-Ray Computed; Radionuclide Imaging; Cardiomyopathies
PubMed: 37226006
DOI: 10.1007/s12350-023-03297-1 -
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 -
Computer Methods and Programs in... Feb 2024Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is... (Review)
Review
BACKGROUND AND OBJECTIVES
Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data.
METHODS
We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information".
RESULTS
We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches.
CONCLUSION
AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.
Topics: Humans; Artificial Intelligence; Biomarkers; Biopsy; Liver; Liver Cirrhosis; Non-alcoholic Fatty Liver Disease; Ultrasonography
PubMed: 38008040
DOI: 10.1016/j.cmpb.2023.107932 -
Heart & Lung : the Journal of Critical... 2024The use of sedative and analgesic drugs during non-invasive ventilation (NIV) in patients with acute respiratory failure (ARF) is controversial. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The use of sedative and analgesic drugs during non-invasive ventilation (NIV) in patients with acute respiratory failure (ARF) is controversial.
OBJECTIVES
To assess the clinical effectiveness of sedative and analgesic medications used during NIV for patients with ARF to no sedation or analgesia. In addition, to investigate the characteristics of dexmedetomidine in comparison to other medications.
METHODS
PubMed, Embase, Web of Science, Cochrane Library and China National Knowledge Infrastructure (CNKI) were searched. Mean differences (MDs) or pooled risk ratios (RRs) were computed using random-effects models. We applied the Cochrane risk-of-bias assessment tool 2.0 to assess the methodological quality of eligible studies and the GRADE approach to evaluate the evidence certainty.
RESULTS
Twenty-one studies were selected. Whether in Group A (using sedative and analgesic drugs vs. nonuse) or Group B (using dexmedetomidine vs. other drugs), the rates of tracheal intubation and delirium, the length of NIV, and the length of stay in the intensive care unit (ICU LOS) all decreased in both experimental groups (P < 0.05). And there were no significant differences in all-cause mortality and the incidence of hypotension between the two groups (P > 0.05), while both Group A and Group B's experimental groups had greater incidences of bradycardia.
CONCLUSIONS
Administering sedative and analgesic medications during NIV can reduce the risk of tracheal intubation and delirium. Additionally, dexmedetomidine outperformed other sedative medications in terms of these clinical outcomes, making it the better option when closely monitoring patients' vital signs.
Topics: Humans; Respiration, Artificial; Dexmedetomidine; Hypnotics and Sedatives; Pain; Intensive Care Units; Noninvasive Ventilation; Analgesics; Analgesia; Delirium
PubMed: 37769542
DOI: 10.1016/j.hrtlng.2023.09.005