Clinical eHealth最新文献

Background

In March 2022, a severe outbreak of the SARS-CoV-2 Omicron variant occurred in Shanghai. This study aimed to determine disease severity, clinical features, clinical outcome in hospitalized patients with the Omicron variant and evaluate the effectiveness of one-dose, two-dose, and three-dose inactivated vaccines in reducing viral loads, disease course, ICU admissions and severe diseases.

Methods

Retrospective cohort analysis was performed on 5,170 adult patients (≥18 years) identified as severe acute respiratory syndrome coronavirus 2 positive with Reverse Transcription Polymerase Chain Reaction admitted at Shanghai Medical Center for Gerontology between March 2022 and June 2022. Demographic information, laboratory data, immunization status, clinical characteristics and outcomes were extracted from electronic medical records.

Results

Among 5,170 enrolled patients, the median age was 53 years, and 2,861 (55.3 %) were male. 71.0 % were mild COVID-19 cases, and cough (1,137 [22.0 %]), fever (592 [11.5 %]), sore throat (510 [9.9 %]), and fatigue (334 [6.5 %]) were the most common symptoms on the patient’s first admission. The median length of hospital stay was 8.7 ± 4.5 days. In multivariate logistic analysis, booster vaccination can significantly reduce ICU admissions and decrease the severity of COVID-19 outcome when compared with less doses of vaccine (OR = 0.75, 95 %CI, 0.62–0.91, P ≤ 0.005; OR = 0.99, 95 %CI, 0.99–1.00, p < 0.001).

Conclusions

In summary, the most of patients who contracted SARSCoV-2 omicron variant had mild clinical features and patients with vaccination took less time to lower viral loads.

Background

Albumin is believed to be associated with the prediction of various cancers. This retrospective cohort study aimed to explore the non-linear relationship between albumin level and overall survival (OS) in advanced non-small cell lung cancer (NSCLC) patients with anlotinib therapy, utilizing the Generalized additive model (GAM) approach. Specifically, we investigated the potential non-linear associations that might not be captured by conventional linear analyses.

Methods

A retrospective cohort of 211 patients undergoing anlotinib treatment for advanced NSCLC was included in this study. A wide range of albumin levels was considered, and the GAM method was applied to account for potential confounding clinical variables and unveil the non-linear relationship between albumin and OS.

Results

A non-linear relationship with inflection points of 40 g/L and 48 g/L was detected between albumin level and OS after adjusting for potential confounders. The hazard ratio (HR) of the left, middle and right of the inflection points were 0.95 (95 % confidence interval [CI], 0.87 to 1.04, p = 0.2819, n = 71), 0.75 (95 % CI, 0.64 to 0.89, p = 0.0007, n = 133) and 2.79 (95 % CI, 1.39 to 5.61, p = 0.0039, n = 7), respectively. The findings indicate a negative correlation between albumin level and OS when albumin level was between 40 g/L and 48 g/L. For every unit increase in albumin, there was a 25 % reduction in the risk of death. Subgroup analysis revealed that the negative relationship was enhanced with blood urea nitrogen (BUN) level increase and diminished with D-dimer increase.

Conclusions

The relationship between albumin level and OS was non-linear. Albumin level is an independent prognostic factor for OS. In addition, BUN level and D-dimer level could modify the effect of albumin level on the risk of death in advanced NSCLC patients treated with anlotinib.

Background

Digital psychoeducational interventions have been used for self-monitoring symptoms and self-care for outpatients with affective and anxiety disorders associated with a history of adverse childhood experiences (ACEs). Outpatients with affective and anxiety disorders were recruited for digital mental health interventions (smartphone app and e-book) with treatment as usual.

Methods

Participants were evaluated at baseline and at 24-week follow-up with digital mental health interventions. Participants were assessed by questionnaire and scales (the 17-item Hamilton Depressive Rating Scale (HDRS₁₇), Hamilton Anxiety Rating Scale (HAM-A), Adverse Childhood Questionnaire, Sheehan Disability Scale, Quality of Life, Perceived Stress Scale, and by anthropometric measures.

Results

153 participants were classified into two groups: (n = 110) participants reported having experienced at least three ACEs (<4ACEs) and (n = 43) reported having experienced four or more (≥4ACEs). At baseline, the ≥ 4ACEs group presented significantly more unstable relationship, lower quality of life, more functional impairment, more perceived stress, higher HAM-A and HDRS₁₇ scores than < 4ACEs group. After 24 weeks, digital psychoeducational interventions improved the practice of physical activity in both groups. Digital technologies improved severity of depressive symptoms in the < 4ACEs group and decreased functional impairment in the ≥ 4ACEs group. Higher scores on HAM-A were associated with more ACEs, HDRS₁₇ > 7 and lack of physical activities.

Conclusion

These findings highlight the importance of screening for ACEs to use digital psychoeducational interventions for reducing depressive symptoms and improving physical activities in outpatients with mood and anxiety disorders.

Over the last decade, artificial intelligence in medicine has attracted much attention and interest for its robust automation and efficiency in disease diagnosis, treatment and prognosis. It has shown great potential in bringing the revolutionary change in clinical practice. This review aimed to summarize the progress AI has made recently in several fields of medicine, including patient screening, diagnosis and treatment of cancerous and non-cancerous diseases, pharmaceutical development, clinical trial management and basic science. Meanwhile, we will discuss its future direction and important considerations, focusing on its utility, usability, validation, safety and ethnic problem.

People face difficulties in obtaining various care services, which leads to a severe delay in health care treatment, especially in light of the events that accompanied the spread of the Covid-19 epidemic, and therefore obtaining them may cause serious illness or even death. This paper is about MEDICAL+ and MEDICAL PLUS+ applications that were developed to enables the patients to gain healthcare treatment in the easiest, cheapest and timeless way. The applications were developed using web developing languages and tools to enables the user to book appointments, performing online chatting sessions with the healthcare providers and receiving the medical prescriptions directly after the online session in the same application.

The consequences of stroke are often severe and result in the need for rehabilitation of different regions in the body for a defined period. Rehabilitation exercises are often monotonous and quickly become boring, therefore serious games try to include gaming aspects into the rehabilitation process, which was previously shown can support the overall process. To support wrist and hand rehabilitation, the authors propose a new and innovative serious game, which relies only on the Leap Motion Controller together with a PC combining cognitive and physical rehabilitation. The development was based on a user-centred design approach divided into 3 consecutive phases focusing on the therapeutical point of view. In Phase 1, requirements for such a solution were identified and validated in cooperation with one therapist. Phases 2 and 3 pose additional changes gathered from interviews together with four more therapists and one stroke patient to develop a highly sophisticated and flexible prototype. Different and easily adaptable levels were developed in which the patient needs to perform specific movements with one or two hands and wrists to control the avatar throughout a jump-and-run game. Additionally, it includes memory aspects for additional and combined cognitive training. In the end, overall 44 requirements were identified and the gathered feedback during the iterations showed a positive acceptance and massive potential of the proposed solution. These requirements should also pose a good starting point for the development and research of new serious games in that domain as well.

Skin cancer is a significant health risk that requires early detection for effective treatment. This paper discusses two automated techniques, Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), which make use of deep learning techniques for skin cancer detection. Through evaluation of research on skin cancer detection using ANN and CNN, the effectiveness and performance of these techniques in early and efficient diagnosis of skin cancer were established. The study found that ANN and CNN were successful in early detection of skin cancer using different data sets and hybrid models, demonstrating the potential for these technologies to improve accuracy in skin cancer detection. The paper highlights the novelty of using deep learning techniques for skin cancer detection and emphasises the critical need for an automated system for skin lesion recognition to reduce effort and time in the diagnosis process. The possible applications of this study include the development of more efficient and accurate skin cancer detection systems that can lead to earlier diagnosis and improved treatment outcomes. Overall, this research underscores the importance of using advanced technologies, such as ANN and CNN, in the fight against skin cancer and highlights the potential impact of these techniques in improving patient outcomes.

Background

There is an urgent need to find new approaches that improve long-term adherence to a healthy lifestyle for people with cardiovascular disease (CVD). Deposit contracts (a financial incentive in which the participant deposits own money) are inexpensive and effective, but acceptability among CVD patients is unclear. This study investigated the acceptability of a deposit contract intervention for physical activity among CVD patients.

Methods

We approached CVD patients through the Harteraad patient panel of the Dutch CVD patient organization and asked them to fill in an online survey. In total (N = 659) CVD patients with a mean age of 66.2 years completed the survey. The survey assessed acceptability of deposit contracts, responses to a concrete example of a deposit contract for physical activity behavior change, and suitable moments for implementation.

Results

Overall, half of the participants (45.6%) confirmed needing extra commitment to maintain lifestyle change. Yet, a small part of the sample was convinced by the idea that losing money could be motivating (18.8%) and indicated that they would be willing to deposit money themselves (13.2%). Responding to a concrete example of a deposit contract for physical activity, a quarter of the sample (26.2%) reported there was a chance they would participate. Furthermore, 27.1% of the participants found the deposit contract effective and 27.4% found it acceptable. Exploratory analyses showed that a subgroup of younger and lower educated participants responded more favorably. Opinions on when to start with a deposit contract were mixed.

Conclusions

Because acceptability was generally found to be low, future research should also investigate strategies to leverage commitment principles for CVD patients without a cash deposit requirement. When deposit contracts are offered to CVD patients in practice, we recommend offering them as an optional, additional element to existing interventions that patients can opt-in to.