BACKGROUND: Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system.
METHODS: Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor\'s measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility.
RESULTS: The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base.
CONCLUSIONS: We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios.

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software\'s capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

UNASSIGNED: Surveillance of SARS-CoV-2 circulation is mainly based on real-time reverse transcription-polymerase chain reaction, which requires laboratory facilities and cold chain for sample transportation. This is difficult to achieve in remote rural areas of resource-limited settings. The use of dried blood spots shipped at room temperature has shown good efficiency for the detection of arboviral RNA. Using a similar approach, we conducted a study at 3 provincial hospitals in Laos to compare the detection of SARS-CoV-2 from neat and dried spot samples.
UNASSIGNED: Between January 2022 and March 2023, patients with respiratory symptoms were recruited. Nasopharyngeal/oropharyngeal swabs in virus transport medium (VTM), dry swabs, saliva, and dried saliva spotted on filter paper were collected. All samples were tested by SARS-CoV-2 real-time reverse transcription-polymerase chain reaction.
UNASSIGNED: In total, 479 participants were included. The VTM samples tested positive for 288 (60.1%). High positive percent agreements were observed for dry swab (84.8%; 95% CI, 80.2%-88.8%) and saliva (89.2%; 95% CI, 85.1%-92.6%) as compared with VTM. There was a loss of sensitivity when saliva was dried on filter paper (73.6%; 95% CI, 68.1%-78.6%) as compared with saliva. SARS-CoV-2 variant (Delta or Omicron) had no significant impact on the performance of the different sample types.
UNASSIGNED: Our findings suggest that dry swabs could be a good alternative for sample collection and permit easy shipping at ambient temperature for subsequent viral SARS-CoV-2 RNA purification and molecular investigation. This is a useful tool to consider for a rapid implementation of large-scale surveillance of SARS-CoV-2 in remote areas, which could be extrapolated to other respiratory targets during routine surveillance or in the case of a novel emerging pandemic.

Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases-SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.

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This study aimed to assess the prevalence and reasons for nonadherence to cotrimoxazole prophylaxis therapy. A cross-sectional study was conducted among people living with HIV attending Ayder Comprehensive Specialized Hospital. Data were collected through interviews and reviews of medical records. Binary logistic regression was employed to analyze factors associated with CPT nonadherence. Approximately two-thirds (65.5%) of the participants were non-adherent to co-trimoxazole prophylaxis therapy. The main reasons for non-adherence were side effects, pill fatigue and forgetfulness. Strategies to improve adherence to co-trimoxazole prophylaxis therapy should focus on the combined patient, clinical and medication related issues of people living with HIV.

OBJECTIVE: To develop and validate a multiplex conventional PCR assay to simultaneously detect Cryptosporidium spp., Entamoeba histolytica, and Giardia lamblia in diarrheal samples as a rapid, cost-effective, and sensitive diagnostic tool for prevalent co-infections for improved diagnostic accuracy and efficiency in resource-limited settings.
METHODS: Stool samples collected from patients with gastrointestinal symptoms after taking written consent, processed via wet mount, iodine mount, and PCR assays. Cohen\'s kappa statistical analysis was done to test agreement.
RESULTS: Among 240 patients, 28.75% showed intestinal protozoa via Microscopy; Single-plex and multiplex PCR demonstrated 100% concordance, detecting 27.9%; confirmed by sequencing. Highest parasite positivity was observed in transplant and immunocompromised patients, with moderate to almost perfect agreement between microscopy and molecular methods.
CONCLUSIONS: Multiplex-conventional PCR offers superior sensitivity and specificity over microscopy and 100% concordance with single-plex PCR, enabling rapid, cost-effective diagnosis of multiple parasites from single stool sample. Its adoption could revolutionize parasitic infection management in routine diagnostics.

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BACKGROUND: Pacemakers (PMs) are used to treat patients with severe bradycardia symptoms. They do, however, pose several complications. Even with these risks, there are only a few studies assessing PM implantation outcomes in resource-limited settings like Ethiopia and other sub-Saharan countries in general. Therefore, this study aims to assess the mid-term outcome of PM implantation in patients who have undergone PM implantation in the Cardiac Center of Ethiopia by identifying the rate and predictors of complications and death.
METHODS: This retrospective study was conducted at the Cardiac Center of Ethiopia from October 2023 to January 2024 on patients who had PM implantation from September 2012 to August 2023 to assess the midterm outcome of the patients. Complication rate and all-cause mortality rate were the outcomes of our study. Multivariable logistic regression was used to identify factors associated with complications and death. To analyze survival times, a Kaplan-Meier analysis was performed.
RESULTS: This retrospective follow-up study included 182 patients who underwent PM implantation between September 2012 and August 2023 and were at least 18 years old. The patients\' median follow-up duration was 72 months (Interquartile range (IQR): 36-96 months). At the end of the study, 26.4% of patients experienced complications. The three most frequent complications were lead dislodgement, which affected 6.6% of patients, PM-induced tachycardia, which affected 5.5% of patients, and early battery depletion, which affected 5.5% of patients. Older age (Adjusted Odds Ratio (AOR) 1.1, 95% CI 1.04-1.1, p value < 0.001), being female (AOR 4.5, 95%CI 2-9.9, p value < 0.001), having dual chamber PM (AOR 2.95, 95%CI 1.14-7.6, p value = 0.006) were predictors of complications. Thirty-one (17%) patients died during the follow-up period. The survival rates of our patients at 3, 5, and 10 years were 94.4%, 92.1%, and 65.5% respectively with a median survival time of 11 years. Patients with a higher Charlson comorbidity index before PM implantation (AOR 1.2, 95% CI 1.1-1.8, p = 0.04), presence of complications (AOR 3.5, 95% CI 1.2-10.6, p < 0.03), and New York Heart Association (NYHA) class III or IV (AOR 3.3, 95% CI 1.05-10.1, p = 0.04) were associated with mortality.
CONCLUSIONS: Many complications were experienced by patients who had PMs implanted, and several factors affected their prognosis. Thus, it is essential to identify predictors of both complications and mortality to prioritize and address the manageable factors associated with both mortality and complications.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.