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Respiratory Investigation Jul 2022The joint committee of the Japanese Society of Intensive Care Medicine/Japanese Respiratory Society/Japanese Society of Respiratory Care Medicine on ARDS Clinical...
BACKGROUND
The joint committee of the Japanese Society of Intensive Care Medicine/Japanese Respiratory Society/Japanese Society of Respiratory Care Medicine on ARDS Clinical Practice Guideline has created and released the ARDS Clinical Practice Guideline 2021.
METHODS
The 2016 edition of the Clinical Practice Guideline covered clinical questions (CQs) that targeted only adults, but the present guideline includes 15 CQs for children in addition to 46 CQs for adults. As with the previous edition, we used a systematic review method with the Grading of Recommendations Assessment Development and Evaluation (GRADE) system as well as a degree of recommendation determination method. We also conducted systematic reviews that used meta-analyses of diagnostic accuracy and network meta-analyses as a new method.
RESULTS
Recommendations for adult patients with ARDS are described: we suggest against using serum C-reactive protein and procalcitonin levels to identify bacterial pneumonia as the underlying disease (GRADE 2D); we recommend limiting tidal volume to 4-8 mL/kg for mechanical ventilation (GRADE 1D); we recommend against managements targeting an excessively low SpO (PaO) (GRADE 2D); we suggest against using transpulmonary pressure as a routine basis in positive end-expiratory pressure settings (GRADE 2B); we suggest implementing extracorporeal membrane oxygenation for those with severe ARDS (GRADE 2B); we suggest against using high-dose steroids (GRADE 2C); and we recommend using low-dose steroids (GRADE 1B). The recommendations for pediatric patients with ARDS are as follows: we suggest against using non-invasive respiratory support (non-invasive positive pressure ventilation/high-flow nasal cannula oxygen therapy) (GRADE 2D); we suggest placing pediatric patients with moderate ARDS in the prone position (GRADE 2D); we suggest against routinely implementing NO inhalation therapy (GRADE 2C); and we suggest against implementing daily sedation interruption for pediatric patients with respiratory failure (GRADE 2D).
CONCLUSIONS
This article is a translated summary of the full version of the ARDS Clinical Practice Guideline 2021 published in Japanese (URL: https://www.jrs.or.jp/publication/jrs_guidelines/). The original text, which was written for Japanese healthcare professionals, may include different perspectives from healthcare professionals of other countries.
Topics: Adult; Child; Extracorporeal Membrane Oxygenation; Humans; Prone Position; Respiration, Artificial; Respiratory Distress Syndrome; Tidal Volume
PubMed: 35753956
DOI: 10.1016/j.resinv.2022.05.003 -
Journal of Ambient Intelligence and... 2023Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine...
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
PubMed: 35039756
DOI: 10.1007/s12652-021-03612-z -
Intensive Care Medicine Jun 2020The accuracy of the signs and tests that clinicians use to diagnose ventilator-associated pneumonia (VAP) and initiate antibiotic treatment has not been well... (Meta-Analysis)
Meta-Analysis Review
The accuracy of the signs and tests that clinicians use to diagnose ventilator-associated pneumonia (VAP) and initiate antibiotic treatment has not been well characterized. We sought to characterize and compare the accuracy of physical examination, chest radiography, endotracheal aspirate (ETA), bronchoscopic sampling cultures (protected specimen brush [PSB] and bronchoalveolar lavage [BAL]), and CPIS > 6 to diagnose VAP. We searched six databases from inception through September 2019 and selected English-language studies investigating accuracy of any of the above tests for VAP diagnosis. Reference standard was histopathological analysis. Two reviewers independently extracted data and assessed study quality. We included 25 studies (1639 patients). The pooled sensitivity and specificity of physical examination findings for VAP were poor: fever (66.4% [95% confidence interval [CI]: 40.7-85.0], 53.9% [95% CI 34.5-72.2]) and purulent secretions (77.0% [95% CI 64.7-85.9], 39.0% [95% CI 25.8-54.0]). Any infiltrate on chest radiography had a sensitivity of 88.9% (95% CI 73.9-95.8) and specificity of 26.1% (95% CI 15.1-41.4). ETA had a sensitivity of 75.7% (95% CI 51.5-90.1) and specificity of 67.9% (95% CI 40.5-86.8). Among bronchoscopic sampling methods, PSB had a sensitivity of 61.4% [95% CI 43.7-76.5] and specificity of 76.5% [95% CI 64.2-85.6]; while BAL had a sensitivity of 71.1% [95% CI 49.9-85.9] and specificity of 79.6% [95% CI 66.2-85.9]. CPIS > 6 had a sensitivity of 73.8% (95% CI 50.6-88.5) and specificity of 66.4% (95% CI 43.9-83.3). Classic clinical indicators had poor accuracy for diagnosis of VAP. Reliance upon these indicators in isolation may result in misdiagnosis and potentially unnecessary antimicrobial use.
Topics: Adult; Bronchoalveolar Lavage; Bronchoalveolar Lavage Fluid; Critical Illness; Humans; Pneumonia, Ventilator-Associated; Respiration, Artificial; Sensitivity and Specificity
PubMed: 32306086
DOI: 10.1007/s00134-020-06036-z -
Journal of the American College of... Jan 2023The efficacy and safety of direct oral anticoagulants (DOACs) for patients with thrombotic antiphospholipid syndrome remain controversial. (Meta-Analysis)
Meta-Analysis
BACKGROUND
The efficacy and safety of direct oral anticoagulants (DOACs) for patients with thrombotic antiphospholipid syndrome remain controversial.
OBJECTIVES
The authors performed a systematic review and meta-analysis of randomized controlled trials that compared DOACs with vitamin K antagonists (VKAs).
METHODS
We searched PubMed, EMBASE, and Cochrane Central Register of Controlled Trials through April 9, 2022. The 2 main efficacy outcomes were a composite of arterial thrombotic events and venous thromboembolic events (VTEs). The main safety outcome was major bleeding. Random effects models with inverse variance were used.
RESULTS
Our search retrieved 253 studies. Four open-label randomized controlled trials involving 472 patients were included (mean control-arm time-in-therapeutic-range 60%). All had proper random sequence generation and adequate allocation concealment. Overall, the use of DOACs compared with VKAs was associated with increased odds of subsequent arterial thrombotic events (OR: 5.43; 95% CI: 1.87-15.75; P < 0.001, I = 0%), especially stroke, and the composite of arterial thrombotic events or VTE (OR: 4.46; 95% CI: 1.12-17.84; P = 0.03, I = 0%). The odds of subsequent VTE (OR: 1.20; 95% CI: 0.31-4.55; P = 0.79, I = 0%), or major bleeding (OR: 1.02; 95% CI: 0.42-2.47; P = 0.97; I = 0%) were not significantly different between the 2 groups. Most findings were consistent within subgroups.
CONCLUSIONS
Patients with thrombotic antiphospholipid syndrome randomized to DOACs compared with VKAs appear to have increased risk for arterial thrombosis. No significant differences were observed between patients randomized to DOACs vs VKAs in the risk of subsequent VTE or major bleeding.
Topics: Humans; Administration, Oral; Anticoagulants; Antiphospholipid Syndrome; Fibrinolytic Agents; Hemorrhage; Randomized Controlled Trials as Topic; Thrombosis; Venous Thromboembolism; Vitamin K
PubMed: 36328154
DOI: 10.1016/j.jacc.2022.10.008 -
Journal of Medical Internet Research May 2021Adherence rates of preventative medication for cardiovascular disease (CVD) have been reported as 57%, and approximately 9% of all CVD events in Europe are attributable... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Adherence rates of preventative medication for cardiovascular disease (CVD) have been reported as 57%, and approximately 9% of all CVD events in Europe are attributable to poor medication adherence. Mobile health technologies, particularly mobile apps, have the potential to improve medication adherence and clinical outcomes.
OBJECTIVE
The objective of this study is to assess the effects of mobile health care apps on medication adherence and health-related outcomes in patients with CVD. This study also evaluates apps' functionality and usability and the involvement of health care professionals in their use.
METHODS
Electronic databases (MEDLINE [Ovid], PubMed Central, Cochrane Library, CINAHL Plus, PsycINFO [Ovid], Embase [Ovid], and Google Scholar) were searched for randomized controlled trials (RCTs) to investigate app-based interventions aimed at improving medication adherence in patients with CVD. RCTs published in English from inception to January 2020 were reviewed. The Cochrane risk of bias tool was used to assess the included studies. Meta-analysis was performed for clinical outcomes and medication adherence, with meta-regression analysis used to evaluate the impact of app intervention duration on medication adherence.
RESULTS
This study included 16 RCTs published within the last 6 years. In total, 12 RCTs reported medication adherence as the primary outcome, which is the most commonly self-reported adherence. The duration of the interventions ranged from 1 to 12 months, and sample sizes ranged from 24 to 412. Medication adherence rates showed statistically significant improvements in 9 RCTs when compared with the control, and meta-analysis of the 6 RCTs reporting continuous data showed a significant overall effect in favor of the app intervention (mean difference 0.90, 95% CI 0.03-1.78) with a high statistical heterogeneity (I=93.32%). Moreover, 9 RCTs assessed clinical outcomes and reported an improvement in systolic blood pressure, diastolic blood pressure, total cholesterol, and low-density lipoprotein cholesterol levels in the intervention arm. Meta-analysis of these clinical outcomes from 6 RCTs favored app interventions, but none were significant. In the 7 trials evaluating app usability, all were found to be acceptable. There was a great variation in the app characteristics. A total of 10 RCTs involved health care professionals, mainly physicians and nurses, in the app-based interventions. The apps had mixed functionality: 2 used education, 7 delivered reminders, and 7 provided reminders in combination with educational support.
CONCLUSIONS
Apps tended to increase medication adherence, but interventions varied widely in design, content, and delivery. Apps have an acceptable degree of usability; yet the app characteristics conferring usability and effectiveness are ill-defined. Future large-scale studies should focus on identifying the essential active components of successful apps.
TRIAL REGISTRATION
PROSPERO International Prospective Register of Systematic Reviews CRD42019121385; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=121385.
Topics: Blood Pressure; Cardiovascular Diseases; Humans; Medication Adherence; Mobile Applications; Telemedicine
PubMed: 34032583
DOI: 10.2196/24190 -
American Journal of Respiratory and... Jan 2023Pediatric-specific ventilator liberation guidelines are lacking despite the many studies exploring elements of extubation readiness testing. The lack of clinical...
Executive Summary: International Clinical Practice Guidelines for Pediatric Ventilator Liberation, A Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network Document.
Pediatric-specific ventilator liberation guidelines are lacking despite the many studies exploring elements of extubation readiness testing. The lack of clinical practice guidelines has led to significant and unnecessary variation in methods used to assess pediatric patients' readiness for extubation. Twenty-six international experts comprised a multiprofessional panel to establish pediatrics-specific ventilator liberation clinical practice guidelines, focusing on acutely hospitalized children receiving invasive mechanical ventilation for more than 24 hours. Eleven key questions were identified and first prioritized using the Modified Convergence of Opinion on Recommendations and Evidence. A systematic review was conducted for questions that did not meet an threshold of ⩾80% agreement, with Grading of Recommendations, Assessment, Development, and Evaluation methodologies applied to develop the guidelines. The panel evaluated the evidence and drafted and voted on the recommendations. Three questions related to systematic screening using an extubation readiness testing bundle and a spontaneous breathing trial as part of the bundle met Modified Convergence of Opinion on Recommendations criteria of ⩾80% agreement. For the remaining eight questions, five systematic reviews yielded 12 recommendations related to the methods and duration of spontaneous breathing trials, measures of respiratory muscle strength, assessment of risk of postextubation upper airway obstruction and its prevention, use of postextubation noninvasive respiratory support, and sedation. Most recommendations were conditional and based on low to very low certainty of evidence. This clinical practice guideline provides a conceptual framework with evidence-based recommendations for best practices related to pediatric ventilator liberation.
Topics: Humans; Child; Respiration, Artificial; Ventilator Weaning; Ventilators, Mechanical; Airway Extubation; Sepsis
PubMed: 36583619
DOI: 10.1164/rccm.202204-0795SO -
Circulation Oct 2020
Topics: Advanced Cardiac Life Support; American Heart Association; Cardiology; Cardiology Service, Hospital; Cardiopulmonary Resuscitation; Consensus; Emergencies; Emergency Service, Hospital; Evidence-Based Medicine; Heart Arrest; Humans; Risk Factors; Treatment Outcome; United States
PubMed: 33081530
DOI: 10.1161/CIR.0000000000000918 -
Yonsei Medical Journal Jan 2022Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have... (Meta-Analysis)
Meta-Analysis
PURPOSE
Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases.
MATERIALS AND METHODS
The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity.
RESULTS
A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983).
CONCLUSION
This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.
Topics: Algorithms; Artificial Intelligence; Atrial Fibrillation; Humans; Sensitivity and Specificity; Wearable Electronic Devices
PubMed: 35040610
DOI: 10.3349/ymj.2022.63.S93 -
The British Journal of Radiology Sep 2020In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and...
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public database. Subsequently, we conducted a systematic literature search for recently published clinically relevant studies on AI in cardiac imaging. As a result, 24 and 14 studies using CT and MRI, respectively, were included and summarized. From these studies, it can be concluded that AI is widely applied in cardiac applications in the clinic, including coronary calcium scoring, coronary CT angiography, fractional flow reserve CT, plaque analysis, left ventricular myocardium analysis, diagnosis of myocardial infarction, prognosis of coronary artery disease, assessment of cardiac function, and diagnosis and prognosis of cardiomyopathy. These advancements show that AI has a promising prospect in cardiac imaging.
Topics: Adipose Tissue; Algorithms; Artificial Intelligence; Cardiomyopathies; Computed Tomography Angiography; Coronary Disease; Coronary Stenosis; Databases, Factual; Deep Learning; Fractional Flow Reserve, Myocardial; Heart; Heart Ventricles; Humans; Magnetic Resonance Imaging; Myocardial Infarction; Prognosis; Vascular Calcification
PubMed: 32017605
DOI: 10.1259/bjr.20190812 -
Critical Care (London, England) Nov 2021Extubation failure is an important issue in ventilated patients and its risk factors remain a matter of research. We conducted a systematic review and meta-analysis to... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Extubation failure is an important issue in ventilated patients and its risk factors remain a matter of research. We conducted a systematic review and meta-analysis to explore factors associated with extubation failure in ventilated patients who passed a spontaneous breathing trial and underwent planned extubation. This systematic review was registered in PROPERO with the Registration ID CRD42019137003.
METHODS
We searched the PubMed, Web of Science and Cochrane Controlled Register of Trials for studies published from January 1998 to December 2018. We included observational studies involving risk factors associated with extubation failure in adult intensive care unit patients who underwent invasive mechanical ventilation. Two authors independently extracted data and assessed the validity of included studies.
RESULTS
Sixty-seven studies (involving 26,847 participants) met the inclusion criteria and were included in our meta-analysis. We analyzed 49 variables and, among them, we identified 26 factors significantly associated with extubation failure. Risk factors were distributed into three domains (comorbidities, acute disease severity and characteristics at time of extubation) involving mainly three functions (circulatory, respiratory and neurological). Among these, the physiological respiratory characteristics at time of extubation were the most represented. The individual topic of secretion management was the one with the largest number of variables. By Bayesian multivariable meta-analysis, twelve factors were significantly associated with extubation failure: age, history of cardiac disease, history of respiratory disease, Simplified Acute Physiologic Score II score, pneumonia, duration of mechanical ventilation, heart rate, Rapid Shallow Breathing Index, negative inspiratory force, lower PaO/FiO ratio, lower hemoglobin level and lower Glasgow Coma Scale before extubation, with the latest factor having the strongest association with extubation outcome.
CONCLUSIONS
Numerous factors are associated with extubation failure in critically ill patients who have passed a spontaneous breathing trial. Robust multiparametric clinical scores and/or artificial intelligence algorithms should be tested based on the selected independent variables in order to improve the prediction of extubation outcome in the clinical scenario.
Topics: Airway Extubation; Artificial Intelligence; Bayes Theorem; Critical Illness; Humans; Treatment Failure
PubMed: 34782003
DOI: 10.1186/s13054-021-03802-3