Conectar
by Dong Min Jung, Yong Jae Kwon, Yong Wan Cho, Jong Geol Baek, Dong Jae Jang, Yongdo Yun, Seok-Ho Lee, Gahee Son, Hyunjong Yoo, Min Cheol Han, Jin Sung Kim
Volumetric modulated arc therapy (VMAT) for lung cancer involves complex multileaf collimator (MLC) motion, which increases sensitivity to interplay effects with tumour motion. Current dynamic conformal arc methods address this issue but may limit the achievable dose distribution optimisation compared with standard VMAT. This study examined the clinical utility of a VMAT technique with monitor unit limits (VMATliMU) to mimic conformal arc delivery and reduce interplay effects while maintaining plan quality. VMATliMU was implemented by applying monitor unit limitations during VMAT reoptimisation to minimise MLC encroachment into target volumes. Using mesh-type reference computational phantom CT images, treatment plans were generated for a simulated stage I lung cancer case prescribed to 45 Gy in three fractions. VMATliMU, conventional VMAT, VMAT with leaf speed limitations, dynamic conformal arc therapy, and constant dynamic conformal arc therapy were compared. Plans were optimised for multiple isodose line prescriptions (50%, 60%, 70%, 80%, and 90%) to investigate the impact of dose distribution. Evaluation parameters included MLC positional accuracy using area difference ratios, dosimetric indices, gradient metrics, and organ-at-risk doses. VMATliMU prevented MLC encroachment into the internal target volume across 60%–90% isodose lines, showing superior MLC accuracy compared with other methods. At the challenging 50% isodose line, VMATliMU had 4.5 times less intrusion than VMAT with leaf speed limits. VMAT plans had better dosimetric indices than dynamic conformal arc plans. VMATliMU reduced monitor units by 5.1%–19.2% across prescriptions. All plans met the clinical dose constraints, with the aortic arch below tolerance and acceptable lung doses. VMATliMU combines VMAT’s dosimetric benefits with the dynamic conformal arcs’s simplicity, minimising MLC encroachment while maintaining plan quality. Reduced monitor units lower low-dose exposure, treatment time, and interplay effects. VMATliMU is usable in existing planners with monitor unit limits, offering a practical solution for lung stereotactic body radiation therapy.The evidence for the optimal duration of psychotherapy for borderline personality disorder (BPD) is scarce. Two previous trials have compared different durations of psychotherapy. The first compared 6 months versus 12 months of dialectical behaviour therapy for BPD (the FASTER trial). The second compared 5 months versus 14 months of mentalisation-based therapy for BPD (the MBT-RCT trial). The primary objective of the present study will be to provide an individual patient data pooled analysis of two randomised clinical trials by combining the two short-term groups and the two long-term groups from the FASTER and MBT-RCT trials, thereby providing greater statistical power than the individual trials. Accordingly, we will evaluate the overall evidence on the effects of short-term versus long-term psychotherapy for BPD and investigate whether certain subgroups might benefit from short-term versus long-term psychotherapy.
An individual patient data pooled analysis of the FASTER trial and the MBT-RCT trial will be conducted. The primary outcome will be a composite of the proportion of participants with a suicide, a suicide attempt or a psychiatric hospitalisation. The secondary outcome will be the proportion of participants with self-harm. Exploratory outcomes will be BPD symptoms, symptom distress, level of functioning and quality of life. We will primarily assess outcomes at 15 months after randomisation for the FASTER trial and at 16 months after randomisation for the MBT-RCT trial. Predefined subgroups based on the design variables in the original trials will be tested for interaction with the intervention as follows: trial, sex (male compared with female), age (below or at 30 years compared with above 30 years) and baseline level of functioning (Global Assessment of Functioning baseline score at 0–49 compared with 50–100).
The statistical analyses will be performed on anonymised trial data that have already been approved by the respective ethical committees that originally assessed the included trials. The final analysis will be published in a peer-reviewed scientific journal and the results will be presented at national seminars and international conferences.
CRD42024612840.
To examine trends in Chuna manual therapy utilisation for musculoskeletal disorders (MSDs) following its inclusion in the National Health Insurance (NHI) system in Korea in 2019 using claims data from the Health Insurance Review & Assessment Service (HIRA).
Retrospective analysis of NHI claims data.
Nationwide medical institutions, based on HIRA claims data from April 2019 to December 2021.
All patients who received at least one Chuna therapy session during the study period.
Primary outcome: Annual trends in Chuna manual therapy claims. Secondary outcome: Patient demographics, therapy frequency, MSD diagnoses and concurrent therapies.
A total of 12 729 625 Chuna therapy claims were analysed, showing a gradual annual increase in utilisation from 2019 to 2021. The most common age group was 45–54 years (22.3%), with female patients comprising a higher proportion (55.8%) than male patients.
Low back pain (M54.5), lumbar sprain and strain (S33.5) and cervicalgia (M54.2) were the most common diagnoses. Patients receiving Complex Chuna (50% co-payment) had more treatment sessions than those receiving Simple Chuna or Complex Chuna (80% co-payment), with spinal disorders such as spinal stenosis (M48.0) and intervertebral disc disorders (M51.1, M50.1) associated with higher treatment frequency. Acupuncture was the most common concurrent therapy (97.4%).
This study is the first to comprehensively analyse Chuna therapy utilisation using nationwide NHI claims data. The findings confirm that Chuna therapy is widely used for MSDs, particularly among middle-aged and elderly patients with spinal or muscle-related conditions. Patients with severe or chronic spinal diseases were more likely to receive frequent Chuna therapy sessions. These results provide insights into the utilisation patterns of Chuna therapy and highlight the need for further research to refine reimbursement policies based on disease severity and patient characteristics.
Elder neglect by both informal and formal caregivers is common, particularly among persons with dementia, and has serious health consequences but is under-recognised and under-reported. Persons with dementia are often unable to report neglect due to memory and language impairments, increasing their vulnerability. Screening for elder mistreatment and initiation of intervention in primary care clinics may be helpful, but few evidence-based tools or strategies exist. We plan to: (1) develop a novel primary care screening tool to identify elder neglect in persons with dementia, (2) develop an innovative technology-driven intervention for caregivers and (3) pilot both for feasibility and acceptability in primary care.
We will use a multistep process to develop a screening tool, including a modified Delphi approach with experts, and multivariable analysis comparing confirmed cases of neglect in patients with dementia from the existing data registry to non-neglected controls. We will develop an evidence-based, technology-driven caregiving intervention for neglect with an expert panel and iterative beta testing. Following the development of the protocol for implementation of the tool and intervention with associated training, we will pilot test both the tool and intervention in older adult patients and caregivers. We will conduct provider focus groups and interviews with patients and caregivers to assess usability and will modify the tool and intervention. These studies are in preparation for a future randomised trial.
Initial phases of this project have been reviewed and approved by the Weill Cornell Medicine Institutional Review Board, protocol #22-06024967, with initial approval on 1 July 2022. We aim to disseminate our results in peer-reviewed journals, at national and international conferences and among interested patient groups and the public.
To describe self-reported treatment and exercise strategies among patients with long-lasting low back pain (LBP) 1 month after consultation at a specialised hospital-based Medical Spine Clinic and evaluate their associations with changes in pain and disability 1 and 3 months after consultation.
Prospective cohort study using questionnaire data before consultation (baseline) and 1 and 3 months after consultation.
Specialised hospital-based Medical Spine Clinic, Denmark.
1686 patients with long-lasting LBP completed the baseline questionnaire; 908 patients responded at 1 month, of them 623 responded at 3 month.
Patients were categorised by treatment (physiotherapy, chiropractic treatment, physiotherapy+chiropractic treatment and no recommended treatment) and exercise strategy (exercise continued, exercise ceased, exercise initiated and not exercising).
Pain was assessed by the numeric rating scale (NRS: 0–10), and disability was assessed by the Oswestry disability index (ODI: 0–100).
1-month postconsultation, half of the patients received no recommended treatment; most others received physiotherapy (42%). Nearly half of the patients continued exercise, 28% continued to be inactive, and 22% initiated exercise. For the population as a whole, pain changed by –0.74 (95% CI –0.90; –0.58) and –1.02 (95% CI –1.22; –0.83) points on the NRS at 1- and 3-month follow-up, respectively, and disability by –2.65 (95% CI –3.51; –1.78) and –4.48 (95% CI –5.59; –3.38) points on the ODI. Differences between treatment strategies were small. However, the two groups not exercising improved less compared with those who continued exercise when adjusted for age, sex and baseline level (order of magnitude from 0.07 to 1.18 points on the NRS and from 4.01 to 9.08 points on the ODI). For pain, these group differences were statistically significant at 1 month (p
Mean improvement was negligible, with no differences between treatment strategies. However, patients not exercising showed no or less improvement, highlighting the importance of exercise in managing long-lasting LBP.
To develop deep learning models to predict nursing need proxies among hospitalised patients and compare their predictive efficacy to that of a traditional regression model.
This methodological study employed a cross-sectional secondary data analysis.
This study used de-identified electronic health records data from 20,855 adult patients aged 20 years or older, admitted to the general wards at a tertiary hospital. The models utilised patient information covering the preceding 2 days, comprising vital signs, biomarkers and demographic data. To create nursing need proxies, we identified the six highest-workload nursing tasks. We structured the collected data sequentially to facilitate processing via recurrent neural network (RNN) and long short-term memory (LSTM) algorithms. The STROBE checklist for cross-sectional studies was used for reporting.
Both the RNN and LSTM predicted nursing need proxies more effectively than the traditional regression model. However, upon testing the models using a sample case dataset, we observed a notable reduction in prediction accuracy during periods marked by rapid change.
The RNN and LSTM, which enhanced predictive performance for nursing needs, were developed using iterative learning processes. The RNN and LSTM demonstrated predictive capabilities superior to the traditional multiple regression model for nursing need proxies.
Applying these predictive models in clinical settings where medical care complexity and diversity are increasing could substantially mitigate the uncertainties inherent in decision-making processes.
We used de-identified electronic health record data of 20,855 adult patients about vital signs, biomarkers and nursing activities.
The authors state that they have adhered to relevant EQUATOR guidelines: STROBE statement for cross-sectional studies.
Despite widespread adoption of deep learning algorithms in various industries, their application in nursing administration for workload distribution and staffing adequacy remains limited. This study amalgamated deep learning technology to develop a predictive model to proactively forecast nursing need proxies. Our study demonstrates that both the RNN and LSTM models outperform a traditional regression model in predicting nursing need proxies. The proactive application of deep learning methods for nursing need prediction could help facilitate timely detection of changes in patient nursing demands, enabling the effective and safe nursing services.
Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field.
Systematic review of prospective studies.
CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol.
Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes.
Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health.
We have registered our review in PROSPERO (registration number: CRD 42023415232).
The systematic review aims to synthesize the literature examining the effectiveness of nurse-led remote digital support on health outcomes in adults with chronic conditions.
Adults with chronic diseases have increased rates of mortality and morbidity and use health care resources at a higher intensity than those without chronic conditions—placing strain on the patient, their caregivers and health systems. Nurse-led digital health disease self-management interventions have potential to improve outcomes for patients with chronic conditions by facilitating care in environments other that the hospital setting.
We searched PubMed/MEDLINE, Embase, PsycINFO and Cochrane Central databases from inception to 7 December 2022. We included randomized controlled trials assessing the impact of nurse-led remote digital support interventions compared to usual care on health-related outcomes in adults with chronic illness. The Cochrane risk-of-bias tool was used to assess bias in studies. Outcomes were organized into four categories: self-management, clinical outcomes, health care resource use and satisfaction with care. Results are presented narratively based on statistical significance.
Forty-four papers pertaining to 40 unique studies were included. Interventions most targeted diabetes (n = 11) and cardiovascular disease (n = 8). Websites (n = 10) and mobile applications (n = 10) were the most used digital modalities. Nurses supported patients either in response to incoming patient health data (n = 14), virtual appointment (n = 8), virtual health education (n = 5) or through a combination of these approaches (n = 13). Positive impacts of nurse-led digital chronic disease support were identified in each outcome category. Mobile applications were the most effective digital modality.
Results show that nurse-led remote digital support interventions significantly improve self-management capacity, clinical health outcomes, health care resource use and satisfaction with care. Such interventions have potential to support overall health for adults with chronic conditions in their home environments.
This study aimed to explore digital literacy among community-dwelling older adults in urban South Korea. A semistructured interview guide was developed using the Digital Competence ( 2.0 framework, which emphasizes the competencies for full digital participation in five categories: information and data literacy, communication and collaboration, content creation, safety, and problem-solving. The data were analyzed using combined inductive and deductive content analysis. Inductive analysis identified three main categories: perceived ability to use digital technology, responses to digital technology, and contextual factors. In the results of deductive analysis, participants reported varying abilities in using digital technologies for information and data literacy, communication or collaboration, and problem-solving. However, their abilities were limited in handling the safety or security of digital technology and lacked in creating digital content. Responses to digital technology contain subcategories of perception (positive or negative) and behavior (trying or avoidance). Regarding contextual factors, aging-related physical and cognitive changes were identified as barriers to digital literacy. The influence of families or peers was viewed as both a facilitator and a barrier. Our participants recognized the importance of using digital devices to keep up with the trend of digitalization, but their digital literacy was mostly limited to relatively simple levels. This scoping review aims to describe published work on the symptoms and management of long COVID conditions.
Symptoms and management of COVID-19 have focused on the acute stage. However, long-term consequences have also been observed.
A scoping review was performed based on the framework suggested by Arksey and O’Malley. We conducted a literature search to retrieve articles published from May 2020 to March 2021 in CINHAL, Cochrane library, Embase, PubMed and Web of science, including backward and forward citation tracking from the included articles. Among the 1880 articles retrieved, 34 articles met our criteria for review: 21 were related to symptom presentation and 13 to the management of long COVID.
Long COVID symptoms were described in 21 articles. Following COVID-19 treatment, hospitalised patients most frequently reported dyspnoea, followed by anosmia/ageusia, fatigue and cough, while non-hospitalised patients commonly reported cough, followed by fever and myalgia/arthralgia. Thirteen studies described management for long COVID: Focused on a multidisciplinary approach in seven articles, pulmonary rehabilitation in three articles, fatigue management in two articles and psychological therapy in one study.
People experience varied COVID-19 symptoms after treatment. However, guidelines on evidence-based, multidisciplinary management for long COVID conditions are limited in the literature. The COVID-19 pandemic may extend due to virus mutations; therefore, it is crucial to develop and disseminate evidence-based, multidisciplinary management guidelines.
A rehabilitation care plan and community healthcare plans are necessary for COVID-19 patients before discharge. Remote programmes could facilitate the monitoring and screening of people with long COVID.
The literature cites many factors that influence a nurse's decision when choosing their workplace. However, it is unclear which attributes matter the most to newly graduated nurses. The study aimed to identify the relative importance of workplace preference attributes among newly graduated nurses.
A cross-sectional study.
We conducted an online survey and data were collected in June 2022. A total of 1111 newly graduated nurses in South Korea participated. The study employed best–worst scaling to quantify the relative importance of nine workplace preferences and also included questions about participants' willingness to pay for each workplace preferences. The relationships between the relative importance of the workplace attribute and the willingness to pay were determined using a quadrant analysis.
The order according to the relative importance of workplace preferences is as follows: salary, working conditions, organizational climate, welfare program, hospital location, hospital level, hospital reputation, professional development, and the chance of promotion. The most important factor, salary, was 16.67 times more important than the least important factor, the chance of promotion, in terms of choosing workplace. In addition, working conditions and organizational climate were recognized as high economic value indicators.
Newly graduated nurses nominated better salaries, working conditions, and organizational climate as having a more important role in choosing their workplace.
The findings of this study have important implications for institutions and administrators in recruiting and retaining newly graduated nurses.
FreshRSS 