Nevin Manimala

Medicine (Baltimore). 2023 Jul 21;102(29):e34322. doi: 10.1097/MD.0000000000034322.

ABSTRACT

The “3 noes right-sided infective endocarditis” (3no-RSIE: no left-sided, no drug users, no cardiac devices) was first described more than a decade ago. We describe the largest series to date to characterize its clinical, microbiological, echocardiographic and prognostic profile. Eight tertiary centers with surgical facilities participated in the study. Patients with right-sided endocarditis without left sided involvement, absence of drug use history and no intracardiac electronic devices were retrospectively included in a multipurpose database. A total of 53 variables were analyzed in every patient. We performed a univariate analysis of in-hospital mortality to determine variables associated with worse prognosis. the study was comprised of 100 patients (mean age 54.1 ± 20 years, 65% male) with definite 3no-RSIE were included (selected from a total of 598 patients with RSIE of all the series, which entails a 16.7% of 3no-RSIE). Most of the episodes were community-acquired (72%), congenital cardiopathies were frequent (32% of the group of patients with previous known predisposing heart disease) and fever was the main manifestation at admission (85%). The microbiological profile was led by Staphylococci spp (52%). Vegetations were detected in 94% of the patients. Global in-hospital mortality was 19% (5.7% in patients operated and 26% in patients who received only medical treatment, P < .001). Non-community acquired infection, diabetes mellitus, right heart failure, septic shock and acute renal failure were more common in patients who died. the clinical profile of 3no-RSIE is closer to other types of RSIE than to LSIE, but mortality is higher than that reported on for other types of RSIE. Surgery may play an important role in improving outcome.

PMID:37478259 | DOI:10.1097/MD.0000000000034322

Medicine (Baltimore). 2023 Jul 21;102(29):e34386. doi: 10.1097/MD.0000000000034386.

ABSTRACT

BACKGROUND: The presence of malignant pleural effusion in lung cancer patients often suggests a poor prognosis. We plan to investigate which regimen of vascular targeting drug is preferable to control the malignant pleural effusion in such patients.

METHODS: Two investigators dependently searched and screened for randomized controlled trials in PubMed, Embase, Web of Science and China National Knowledge Infrastructure from the database inception to August 2022. R software was applied to build a network model in Bayesian method. Objective response rate of malignant pleural effusion is the primary outcome measure. Besides, the incidence of 3 adverse events were compared, including gastrointestinal reaction, leukopenia and hypertension. Due to the disconnection of network, we analysis and discuss the short-term treatment (3-4 weeks) and long-term treatment (6-12 weeks) respectively.

RESULTS: 31 studies with 2093 patients were identified. Four targeting drugs contain bevacizumab (Bev), anlotinib, apatinib and Endostar. Two administration routes include intracavity perfusion (icp) and intravenous injection. Based on the current evidence, for short-term treatments, compared with single-agent chemotherapy (CT), Bev_icp + CT, anlotinib + CT, Bev_icp and anlotinib + endorstar_icp present better objective response, and no statistical significance was found in objective response between Bev_icp + CT, anlotinib + CT and Bev_icp. For long-term treatments, compared with doublet or triplet chemotherapy (2CT or 3CT), Bev_icp + 2CT, apatinib + 2CT, Bev_icp + 3CT, and Bev_intravenous injection + 2CT are more effective option, but no statistical significance was found in objective response between the 4 combination regimens with chemotherapy.

CONCLUSION: Our findings suggest that no statistical significance between above vascular targeting regimens. Pathological type of lung cancer may affect the effect of bevacizumab intracavity infusion plus chemotherapy. The influence of different administration routes of vascular targeting drugs on efficacy remains to be investigated. There are some concerns with the quality of the studies, and some limitations should be considered when interpreting these results, which includes limited geographical region and sample size of studies. Despite these limitations, this study may inform vascular targeting therapy choice in such a patient population.

PMID:37478250 | DOI:10.1097/MD.0000000000034386

Medicine (Baltimore). 2023 Jul 21;102(29):e34385. doi: 10.1097/MD.0000000000034385.

ABSTRACT

BACKGROUND: Postoperative nausea and vomiting (PONV) is one of the common adverse reactions after surgery. Recent randomized controlled trials (RCTs) investigating antiemetic drugs suggest that aprepitant has the strongest antiemetic effect of any single drug. This meta-analysis aimed to explore the efficacy of aprepitant for preventing PONV based on the existing literature.

METHODS: To identify RCTs investigating the use of aprepitant for PONV prevention, we searched PubMed, Embase, and Cochrane Library databases for articles published prior to March 20, 2022. Seventeen RCTs were identified, with 3299 patients, meeting the inclusion criteria. PONV incidence, complete response, 80 mg aprepitant combined with dexamethasone and ondansetron, vomiting, nausea, and analgesic dose-response were the main outcomes measured.

RESULTS: Compared with the control group, PONV incidence was significantly reduced among those receiving aprepitant (odds ratio [OR]: 0.34; 95% confidence interval [CI]: 0.26, 0.44; P < .0001), with a more complete response (OR: 1.35; 95% CI: 1.14, 1.59; P = .0004). Supplementation of 80 mg aprepitant in combination with dexamethasone and ondansetron substantially improved the effects of PONV (OR: 0.36; 95% CI: 0.16, 0.82; P = .01). Further, administration of 80 mg aprepitant was better at preventing vomiting than nausea (OR: 8.6; 95% CI: 3.84, 19. 29; P < .00001). No statistically significant difference between the dose-response of analgesics was identified (mean difference: -1.09; 95% CI: -6.48, 4.30; P = .69). The risk of bias was assessed independently by paired evaluators.

CONCLUSION: Aprepitant effectively reduces the incidence of PONV; however, the effects of postoperative analgesia require further exploration.

PMID:37478247 | DOI:10.1097/MD.0000000000034385

Medicine (Baltimore). 2023 Jul 21;102(29):e34205. doi: 10.1097/MD.0000000000034205.

ABSTRACT

This research aimed to assess gray matter (GM), white matter (WM), lesions of multiple sclerosis (MS) and the therapeutic effect using diffusion kurtosis imaging (DKI). From January 2018 to October 2019, 78 subjects (48 of MS and 30 of health) perform routine MR scan and DKI of cervical spinal cord. The MS patients were divided into 2 groups according to the presence or absence of T2 hyperintensity. DKI-metrics were measured in the lesions, normal-appearing GM and WM. Significant differences were detected in DKI metrics between MS and healthy (P < .05) and between patients with cervical spinal cord T2-hyperintense and without T2-hyperintense (P < .001). Compared to healthy, GM-mean kurtosis (MK), GM-radial kurtosis, and WM-fractional anisotropy, WM-axial diffusion were statistically reduced in patients without T2-hyperintense (P < .05). Significant differences were observed in DKI metrics between patients with T2-hyperintense after therapy (P < .05), as well as GM-MK and WM-fractional anisotropy, WM-axial diffusion in patients without T2-hyperintense (P < .05); Expanded Disability Status Scale was correlated with MK values, as well as Expanded Disability Status Scale scores and MK values after therapy. Our results indicate that DKI-metrics can detect and quantitatively evaluate the changes in cervical spinal cord micropathological structure.

PMID:37478237 | DOI:10.1097/MD.0000000000034205

Medicine (Baltimore). 2023 Jul 21;102(29):e34158. doi: 10.1097/MD.0000000000034158.

ABSTRACT

BACKGROUND: This study aimed to explore suitable clustering algorithms for author collaborations (ACs) in bibliometrics and investigate which countries frequently coauthored with others in recent years. To achieve this, the study developed a method called the Follower-Leading Clustering Algorithm (FLCA) and used it to analyze ACs and cowords in the Journal of Medicine (Baltimore) from 2020 to 2022.

METHODS: This study extracted article metadata from the Web of Science and used the statistical software R to implement FLCA, enabling efficient and reproducible analysis of ACs and cowords in bibliometrics. To determine the countries that easily coauthored with other countries, the study observed the top 20 countries each year and visualized the results using network charts, heatmaps with dendrograms, and Venn diagrams. The study also used chord diagrams to demonstrate the use of FLCA on ACs and cowords in Medicine (Baltimore).

RESULTS: The study observed 12,793 articles, including 5081, 4418, and 3294 in 2020, 2021, and 2022, respectively. The results showed that the FLCA algorithm can accurately identify clusters in bibliometrics, and the USA, China, South Korea, Japan, and Spain were the top 5 countries that commonly coauthored with others during 2020 and 2022. Furthermore, the study identified China, Sichuan University, and diagnosis as the leading entities in countries, institutes, and keywords based on ACs and cowords, respectively. The study highlights the advantages of using cluster analysis and visual displays to analyze ACs in Medicine (Baltimore) and their potential application to coword analysis.

CONCLUSION: The proposed FLCA algorithm provides researchers with a comprehensive means to explore and understand the intricate connections between authors or keywords. Therefore, the study recommends the use of FLCA and visualizations with R for future research on ACs with cluster analysis.

PMID:37478228 | DOI:10.1097/MD.0000000000034158

JMIR Ment Health. 2023 Jul 21;10:e47084. doi: 10.2196/47084.

ABSTRACT

BACKGROUND: Although suicide is a leading cause of death among children, the optimal approach for using health care data sets to detect suicide-related emergencies among children is not known.

OBJECTIVE: This study aimed to assess the performance of suicide-related International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and suicide-related chief complaint in detecting self-injurious thoughts and behaviors (SITB) among children compared with clinician chart review. The study also aimed to examine variations in performance by child sociodemographics and type of self-injury, as well as develop machine learning models trained on codified health record data (features) and clinician chart review (gold standard) and test model detection performance.

METHODS: A gold standard classification of suicide-related emergencies was determined through clinician manual review of clinical notes from 600 emergency department visits between 2015 and 2019 by children aged 10 to 17 years. Visits classified with nonfatal suicide attempt or intentional self-harm using the Centers for Disease Control and Prevention surveillance case definition list of ICD-10-CM codes and suicide-related chief complaint were compared with the gold standard classification. Machine learning classifiers (least absolute shrinkage and selection operator-penalized logistic regression and random forest) were then trained and tested using codified health record data (eg, child sociodemographics, medications, disposition, and laboratory testing) and the gold standard classification. The accuracy, sensitivity, and specificity of each detection approach and relative importance of features were examined.

RESULTS: SITB accounted for 47.3% (284/600) of the visits. Suicide-related diagnostic codes missed nearly one-third (82/284, 28.9%) and suicide-related chief complaints missed more than half (153/284, 53.9%) of the children presenting to emergency departments with SITB. Sensitivity was significantly lower for male children than for female children (0.69, 95% CI 0.61-0.77 vs 0.84, 95% CI 0.78-0.90, respectively) and for preteens compared with adolescents (0.66, 95% CI 0.54-0.78 vs 0.86, 95% CI 0.80-0.92, respectively). Specificity was significantly lower for detecting preparatory acts (0.68, 95% CI 0.64-0.72) and attempts (0.67, 95% CI 0.63-0.71) than for detecting ideation (0.79, 95% CI 0.75-0.82). Machine learning-based models significantly improved the sensitivity of detection compared with suicide-related codes and chief complaint alone. Models considering all 84 features performed similarly to models considering only mental health-related ICD-10-CM codes and chief complaints (34 features) and models considering non-ICD-10-CM code indicators and mental health-related chief complaints (53 features).

CONCLUSIONS: The capacity to detect children with SITB may be strengthened by applying a machine learning-based approach to codified health record data. To improve integration between clinical research informatics and child mental health care, future research is needed to evaluate the potential benefits of implementing detection approaches at the point of care and identifying precise targets for suicide prevention interventions in children.

PMID:37477974 | DOI:10.2196/47084

JMIR Med Educ. 2023 Jul 21;9:e47777. doi: 10.2196/47777.

ABSTRACT

BACKGROUND: Many health professions faculty members lack training on fundamental lesbian, gay, bisexual, transgender, and queer (LGBTQ+) health topics. Faculty development is needed to address knowledge gaps, improve teaching, and prepare students to competently care for the growing LGBTQ+ population.

OBJECTIVE: We conducted a program evaluation of the massive open online course Teaching LGBTQ+ Health: A Faculty Development Course for Health Professions Educators from the Stanford School of Medicine. Our goal was to understand participant demographics, impact, and ongoing maintenance needs to inform decisions about updating the course.

METHODS: We evaluated the course for the period from March 27, 2021, to February 24, 2023, guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. We assessed impact using participation numbers, evidence of learning, and likelihood of practice change. Data included participant demographics, performance on a pre- and postcourse quiz, open-text entries throughout the course, continuing medical education (CME) credits awarded, and CME course evaluations. We analyzed demographics using descriptive statistics and pre- and postcourse quiz scores using a paired 2-tailed t test. We conducted a qualitative thematic analysis of open-text responses to prompts within the course and CME evaluation questions.

RESULTS: Results were reported using the 5 framework domains. Regarding Reach, 1782 learners participated in the course, and 1516 (85.07%) accessed it through a main course website. Of the different types of participants, most were physicians (423/1516, 27.9%) and from outside the sponsoring institution and target audience (1452/1516, 95.78%). Regarding Effectiveness, the median change in test scores for the 38.1% (679/1782) of participants who completed both the pre- and postcourse tests was 3 out of 10 points, or a 30% improvement (P<.001). Themes identified from CME evaluations included LGBTQ+ health as a distinct domain, inclusivity in practices, and teaching LGBTQ+ health strategies. A minority of participants (237/1782, 13.3%) earned CME credits. Regarding Adoption, themes identified among responses to prompts in the course included LGBTQ+ health concepts and instructional strategies. Most participants strongly agreed with numerous positive statements about the course content, presentation, and likelihood of practice change. Regarding Implementation, the course cost US $57,000 to build and was intramurally funded through grants and subsidies. The course faculty spent an estimated 600 hours on the project, and educational technologists spent another 712 hours. Regarding Maintenance, much of the course is evergreen, and ongoing oversight and quality assurance require minimal faculty time. New content will likely include modules on transgender health and gender-affirming care.

CONCLUSIONS: Teaching LGBTQ+ Health improved participants’ knowledge of fundamental queer health topics. Overall participation has been modest to date. Most participants indicated an intention to change clinical or teaching practices. Maintenance costs are minimal. The web-based course will continue to be offered, and new content will likely be added.

PMID:37477962 | DOI:10.2196/47777

J Med Internet Res. 2023 Jul 21;25:e42483. doi: 10.2196/42483.

ABSTRACT

BACKGROUND: The COVID-19 pandemic has increased the use of digital solutions in medical care, especially for patients in remote areas and those requiring regular medical care. However, internet access is essential for the implementation of digital health care. The digital divide is the unequal distribution of access to digital technology, and the first level digital divide encompasses structural barriers. Brazil, a country with economic inequality and uneven population distribution, faces challenges in achieving internet access for all.

OBJECTIVE: This study aims to provide a comprehensive overview of the first-level digital divide in Brazil, estimate the relationship between variables, and identify the challenges and opportunities for digital health care implementation.

METHODS: Data were retrieved from the Brazilian Institute of Geography and Statistics National Continuous House survey database, including demographic, health, and internet-related variables. Statistical analysis included 2-tailed t tests, chi-square, and multivariate logistic regression to assess associations between variables.

RESULTS: Our analysis included 279,382 interviews throughout Brazil. The sample included more houses from the northeast (n=99,553) and fewer houses from the central west (n=30,804). A total of 223,386 (80.13%) of the interviewed population used the internet, with urban areas having higher internet access (187,671/212,109, 88.48%) than rural areas (35,715/67,077, 53.24%). Among the internet users, those interviewed who lived in urban houses, were women, were younger, and had higher income had a statistically higher prevalence (P<.001). Cell phones were the most common device used to access the internet (141,874/143,836, 98.63%). Reasons for not using the internet included lack of interest, knowledge, availability, and cost, with regional variations. The prevalence of internet access also varied among races, with 84,747 of 98,968 (85.63%) White respondents having access, compared to 22,234 of 28,272 (78.64%) Black respondents, 113,518 of 148,191 (76.6%) multiracial respondents, and 2887 of 3755 (76.88%) other respondents. In the southeast, central west, and south regions, the numbers of people with internet access were 49,790 of 56,298 (88.44%), 27,209 of 30,782 (88.39%), and 27,035 of 31,226 (86.58%), respectively, and in the north and northeast, 45,038 of 61,404 (73.35%) and 74,314 of 99,476 (74.7%). The income of internet users was twice the income of internet nonusers. Among those with diabetes-related limitations in daily activities, 945 of 2377 (39.75%) did not have internet access, and among those with daily activity restrictions, 1381 of 3644 (37.89%) did not have access. In a multivariate logistic regression analysis, women (odds ratio [OR] 1.147, 95% CI 0.118-0.156; P<.001), urban households (OR 6.743, 95% CI 1.888-1.929; P<.001), and those earning more than the minimum wage (OR 2.087, 95% CI 0.716-0.756; P<.01) had a positive association with internet access.

CONCLUSIONS: Brazil’s diverse regions have different demographic distributions, house characteristics, and internet access levels, requiring targeted measures to address the first-level digital divide in rural areas and reduce inequalities in digital health solutions. Older people, poor, and rural populations face the greatest challenges in the first level digital divide in Brazil, highlighting the need to tackle the digital divide in order to promote equitable access to digital health care.

PMID:37477958 | DOI:10.2196/42483

JMIR Ment Health. 2023 Jul 21;10:e47285. doi: 10.2196/47285.

ABSTRACT

BACKGROUND: With many digital mental health interventions failing to engage clients for enough time to demonstrate substantive changes to their well-being and with only 2% of all digital solutions on app stores having undergone randomized controlled trials, the rising demand for mental health prevention and early intervention care is not being met. Young adults in particular struggle to find digital well-being apps that suit their needs.

OBJECTIVE: This study explored the effects of eQuoo, an evidence-based mental health game that teaches psychological skills through gamification, on resilience, depression, anxiety, and attrition in a student population.

METHODS: In total, 1165 students from 180 universities in the United Kingdom participated in a 5-week, 3-armed randomized controlled trial. Participants were randomly allocated into 1 of 3 groups: eQuoo users, users of a treatment-as-usual evidence-based cognitive behavioral health app called Sanvello, and a no-intervention waitlist. The Rugged Resilience Scale, Generalized Anxiety Disorder-7, and Patient Health Questionnaire-8 were administered to all participants at baseline and every 7 days until completion.

RESULTS: A repeated measures-ANOVA revealed statistically significant increases in resilience scores in the test group (P<.001) compared with both control groups (Sanvello: P=.10 and waitlist: P=.82) over 5 weeks. The app also significantly decreased anxiety and depression scores (both P<.001). With 64.5% (251/389) adherence, the eQuoo group retained 42% more participants than the control groups.

CONCLUSIONS: Digital health interventions such as eQuoo are effective, scalable, and low-cost solutions for supporting young adults and are available on all leading mobile platforms. Further investigation could clarify the extent to which specific elements of the eQuoo app (including gamification) led to better outcomes.

TRIAL REGISTRATION: German Clinical Trials Register (DRKS) DRKS00027638; https://drks.de/search/en/trial/DRKS00027638.

PMID:37477955 | DOI:10.2196/47285

Europace. 2023 Jul 21:euad211. doi: 10.1093/europace/euad211. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Intracardiac echocardiography (ICE) is a useful but operator-dependent tool for left atrial (LA) anatomical rendering during atrial fibrillation (AF) ablation. The CARTOSOUND FAM Module, a new deep learning (DL) imaging algorithm, has the potential to overcome this limitation. This study aims to evaluate feasibility of the algorithm compared to cardiac computed tomography (CT) in patients undergoing AF ablation.

METHODS: In 28 patients undergoing AF ablation, baseline patient information were recorded and 3D shells of LA body and anatomical structures (LAA/LSPV/LIPV/RSPV/RIPV) were reconstructed using the DL algorithm. The selected ultrasound frames were gated to end-expiration and max LA volume. Ostial diameters of these structures and carina-to-carina distance between left and right pulmonary veins were measured and compared with CT measurements. Anatomical accuracy of the DL algorithm was evaluated by three independent electrophysiologists using a 3-anchor scale for LA anatomical structures and a 5-anchor scale for LA body. Ablation related characteristics were summarized.

RESULTS: The algorithm generated 3D reconstruction of LA anatomies and 2D contours overlaid on ultrasound input frames. Average calculation time for LA reconstruction was 65s. Mean ostial diameters and carina-to-carina distance were all comparable to CT without statistical significance. Ostial diameters and carina-to-carina distance also showed moderate to high correlation (r=0.52-0.75) except for RIPV (r=0.20). Qualitative ratings showed good agreement without between-rater differences. Average procedure time was 143.7±43.7min, with average RF time 31.6±10.2min. All patients achieved ablation success and no immediate complications were observed.

CONCLUSION: DL algorithm integration with ICE demonstrated considerable accuracy compared to CT and qualitative physician assessment. The feasibility of ICE with this algorithm can potentially further streamline AF ablation workflow.

PMID:37477946 | DOI:10.1093/europace/euad211