Conectar
In recent decades, all-cause mortality has increased among individuals with chronic kidney disease (CKD), influenced by factors such as aetiology, standards of care and access to kidney replacement therapies (dialysis and transplantation). The recent COVID-19 pandemic also affected mortality over the past few years. Here, we outline the protocol for a systematic review to investigate global temporal trends in all-cause mortality among patients with CKD at any stage from 1990 to current. We also aim to assess temporal trends in the mortality rate associated with the COVID-19 pandemic.
We will conduct a systematic review of studies reporting mortality for patients with CKD following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will search electronic databases, national and multiregional kidney registries and grey literature to identify observational studies that reported on mortality associated with any cause for patients with CKD of all ages with any stage of the disease. We will collect data between April and August 2023 to include all studies published from 1990 to August 2023. There will be no language restriction, and clinical trials will be excluded. Primary outcome will be temporal trends in CKD-related mortality. Secondary outcomes include assessing mortality differences before and during the COVID-19 pandemic, exploring causes of death and examining trends across CKD stages, country classifications, income levels and demographics.
A systematic review will analyse existing data from previously published studies and have no direct involvement with patient data. Thus, ethical approval is not required. Our findings will be published in an open-access peer-reviewed journal and presented at scientific conferences.
CRD42023416084.
Commentary on: Zhang M, Zhang C, Chen C, et al. The experience of diabetic retinopathy patients during hospital-to-home full-cycle care: a qualitative study. BMC Nurs. 2023 Mar 3;22(1):58. doi: 10.1186/s12912-023-01206-y.
Consideration of patients' life experiences, which aids in examining their feelings and patient’ experiences. Future research should further investigate the phenomenological approach in diverse healthcare contexts.
The transition from hospital to home treatment for patients with diabetic retinopathy (DR) is the subject of a study by Zhang and colleagues. The authors attempt to comprehend the substance of this phenomenon a shift towards patient-centric healthcare research by using a phenomenological method.
The descriptive phenomenology method was used to implement this...
The need for public research funding to be more accountable and demonstrate impact beyond typical academic outputs is increasing. This is particularly challenging and the science behind this form of research is in its infancy when applied to collaborative research funding such as that provided by the Australian National Health and Medical Research Council to the Centre for Research Excellence in Digestive Health (CRE-DH).
In this paper, we describe the protocol for applying the Framework to Assess the Impact from Translational health research to the CRE-DH. The study design involves a five-stage sequential mixed-method approach. In phase I, we developed an impact programme logic model to map the pathway to impact and establish key domains of benefit such as knowledge advancement, capacity building, clinical implementation, policy and legislation, community and economic impacts. In phase 2, we have identified and selected appropriate, measurable and timely impact indicators for each of these domains and established a data plan to capture the necessary data. Phase 3 will develop a model for cost–consequence analysis and identification of relevant data for microcosting and valuation of consequences. In phase 4, we will determine selected case studies to include in the narrative whereas phase 5 involves collation, data analysis and completion of the reporting of impact.
We expect this impact evaluation to comprehensively describe the contribution of the CRE-DH for intentional activity over the CRE-DH lifespan and beyond to improve outcomes for people suffering with chronic and debilitating digestive disorders.
This impact evaluation study has been registered with the Hunter New England Human Research Ethics Committee as project 2024/PID00336 and ethics application 2024/ETH00290. Results of this study will be disseminated via medical conferences, peer-reviewed publications, policy submissions, direct communication with relevant stakeholders, media and social media channels such as X (formely Twitter).
by Aishwarya Subramanian, Rachel M. Germain
Animals navigate landscapes based on perceived risks vs. rewards, as inferred from features of the landscape. In the wild, knowing how strongly animal movement is directed by landscape features is difficult to ascertain but widespread disturbances such as wildfires can serve as natural experiments. We tested the hypothesis that wildfires homogenize the risk/reward landscape, causing movement to become less directed, given that fires reduce landscape complexity as habitat structures (e.g., tree cover, dense brush) are burned. We used satellite imagery of a research reserve in Northern California to count and categorize paths made primarily by mule deer (Odocoileus hemionus) in grasslands. Specifically, we compared pre-wildfire (August 2014) and post-wildfire (September 2018) image history layers among locations that were or were not impacted by wildfire (i.e., a Before/After Control/Impact design). Wildfire significantly altered spatial patterns of deer movement: more new paths were gained and more old paths were lost in areas of the reserve that were impacted by wildfire; movement patterns became less directed in response to fire, suggesting that the risk/reward landscape became more homogenous, as hypothesized. We found evidence to suggest that wildfire affects deer populations at spatial scales beyond their scale of direct impact and raises the interesting possibility that deer perceive risks and rewards at different spatial scales. In conclusion, our study provides an example of how animals integrate spatial information from the environment to make movement decisions, setting the stage for future work on the broader ecological implications for populations, communities, and ecosystems, an emerging interest in ecology.To understand community antibiotic practices and their drivers, comprehensively and in contextually sensitive ways, we explored the individual, community and health system-level factors influencing community antibiotic practices in rural West Bengal in India.
Qualitative study using focus group discussions and in-depth interviews.
Two contrasting village clusters in South 24 Parganas district, West Bengal, India. Fieldwork was conducted between November 2019 and January 2020.
98 adult community members (42 men and 56 women) were selected purposively for 8 focus group discussions. In-depth interviews were conducted with 16 community key informants (7 teachers, 4 elected village representatives, 2 doctors and 3 social workers) and 14 community health workers.
Significant themes at the individual level included sociodemographics (age, gender, education), cognitive factors (knowledge and perceptions of modern antibiotics within non-biomedical belief systems), affective influences (emotive interpretations of appropriate medicine consumption) and economic constraints (affordability of antibiotic courses and overall costs of care). Antibiotics were viewed as essential fever remedies, akin to antipyretics, with decisions to halt mid-course influenced by non-biomedical beliefs associating prolonged use with toxicity. Themes at the community and health system levels included the health stewardship roles of village leaders and knowledge brokering by informal providers, pharmacists and public sector accredited social health activists. However, these community resources lacked sufficient knowledge to address people’s doubts and concerns. Qualified doctors were physically and socially inaccessible, creating a barrier to seeking their expertise.
The interplay of sociodemographic, cognitive and affective factors, and economic constraints at the individual level, underscores the complexity of antibiotic usage. Additionally, community leaders and health workers emerge as crucial players, yet their knowledge gaps and lack of empowerment pose challenges in addressing public concerns. This comprehensive analysis highlights the need for targeted interventions that address both individual beliefs and community health dynamics to promote judicious antibiotic use.
by Ausilah Alfraihat, Amer F. Samdani, Sriram Balasubramanian
Anterior Vertebral Body Tethering (AVBT) is a growing alternative treatment for adolescent idiopathic scoliosis (AIS), offering an option besides spinal fusion. While AVBT aims to correct spinal deformity through growth correction, its outcomes have been mixed. To improve surgical outcomes, this study aimed to develop a machine learning-based tool to predict short- and midterm spinal curve correction in AIS patients who underwent AVBT surgery, using the most predictive clinical, radiographic, and surgical parameters. After institutional review board approval and based on inclusion criteria, 91 AIS patients who underwent AVBT surgery were selected from the Shriners Hospitals for Children, Philadelphia. For all patients, longitudinal standing (PA or AP, and lateral) and side bending spinal Radiographs were retrospectively obtained at six visits: preop and first standing, one year, two years, five years postop, and at the most recent follow-up. Demographic, radiographic, and surgical features associated with curve correction were collected. The sequential backward feature selection method was used to eliminate correlated features and to provide a rank-ordered list of the most predictive features of the AVBT correction. A Gradient Boosting Regressor (GBR) model was trained and tested using the selected features to predict the final correction of the curve in AIS patients. Eleven most predictive features were identified. The GBR model predicted the final Cobb angle with an average error of 6.3 ± 5.6 degrees. The model also provided a prediction interval, where 84% of the actual values were within the 90% prediction interval. A list of the most predictive features for AVBT curve correction was provided. The GBR model, trained on these features, predicted the final curve magnitude with a clinically acceptable margin of error. This model can be used as a clinical tool to plan AVBT surgical parameters and improve outcomes.by Prathibha Prasad, Mohamed Jaber, Dinesh Y., Prathibha Ramani, Abdulrahman Arafat, Abdalla Khairy
Personal identification in forensics is possible with gender determination using DNA (deoxyribonucleic acid) analysis. DNA isolation from teeth samples subjected to extreme temperatures has been shown to predict the gender of the deceased. However, the literature lacks studies on DNA extracted from tooth samples exposed to freezing temperatures. This study aimed to isolate the SRY gene from the extirpated pulp of teeth that were subjected to varying temperatures for gender identification. Thirty teeth with vital pulps, divided into 3 groups were included in the study. Each group consisted of 5 male and 5 female tooth samples. The groups were exposed to diverse environmental factors for three weeks. Group 1: room temperature (R group); Group 2: high temperature (H group) and Group 3: freezing temperature (F group). Later, DNA was isolated from the pulp tissue, and the SRY gene was amplified using PCR (Polymerase Chain Reaction). The Sensitivity and Specificity of the results were analyzed. SRY gene detected in the study samples identified accurate gender with a 46.70% Sensitivity and 93.30% Specificity. Significant difference was found in the correlation between gene expression and gender among the three groups (p = 1.000). The study validates that dental pulp tissue can be a reliable source for DNA extraction. And SRY gene amplification from teeth exposed to diverse environmental conditions. Further investigations are required to validate its application in forensics.Implementation of enhanced recovery pathways (ERPs) has resulted in improved patient-centred outcomes and decreased costs. However, there is a lack of high-level evidence for many ERP elements. We have designed a randomised, embedded, multifactorial, adaptive platform perioperative medicine (REMAP Periop) trial to evaluate the effectiveness of several perioperative therapies for patients undergoing complex abdominal surgery as part of an ERP. This trial will begin with two domains: postoperative nausea/vomiting (PONV) prophylaxis and regional/neuraxial analgesia. Patients enrolled in the trial will be randomised to arms within both domains, with the possibility of adding additional domains in the future.
In the PONV domain, patients are randomised to optimal versus supraoptimal prophylactic regimens. In the regional/neuraxial domain, patients are randomised to one of five different single-injection techniques/combination of techniques. The primary study endpoint is hospital-free days at 30 days, with additional domain-specific secondary endpoints of PONV incidence and postoperative opioid consumption. The efficacy of an intervention arm within a given domain will be evaluated at regular interim analyses using Bayesian statistical analysis. At the beginning of the trial, participants will have an equal probability of being allocated to any given intervention within a domain (ie, simple 1:1 randomisation), with response adaptive randomisation guiding changes to allocation ratios after interim analyses when applicable based on prespecified statistical triggers. Triggers met at interim analysis may also result in intervention dropping.
The core protocol and domain-specific appendices were approved by the University of Pittsburgh Institutional Review Board. A waiver of informed consent was obtained for this trial. Trial results will be announced to the public and healthcare providers once prespecified statistical triggers of interest are reached as described in the core protocol, and the most favourable interventions will then be implemented as a standardised institutional protocol.
by Priyanka Dixit, Thiagarajan Sundararaman, Shiva Halli
BackgroundThe role of place of delivery on the neonatal health outcomes are very crucial. Although the quality of care is being improved, there is no consensus about who is the better healthcare provider in low and middle-income countries (LMICs), public or private facilities. The aim of this study is to assess the differentials in neonatal mortality by the type of healthcare providers in India and its states.
MethodsWe used the data from the fourth wave of the National Family Health Survey 2015–16 (NFHS-4). Information on 259,627 live births to women within the five years preceding the survey was examined. Neonatal mortality rates for state and national levels were calculated using DHS methodology. Multi-variate logistics regression was performed to find the effect of birthplace on neonatal deaths. Propensity score matching (PSM) was used to evaluate the relationship between place of delivery and neonatal deaths to account for the bias attributable to observable covariates.
ResultsThe rise in parity of the women and purchasing power influences the choice of healthcare providers. Increased neonatal mortality was found in private hospital delivery compared to public hospitals in Punjab, Rajasthan, Chhattisgarh, Madhya Pradesh, Bihar, Jharkhand, Odisha, Goa, Maharashtra, Andhra Pradesh and Karnataka states using propensity score matching analysis. However, analysis on the standard of pre-natal and post-natal care indicates that private hospitals generally outperformed public hospitals.
ConclusionsThe study observed a significant variation in neonatal mortality among public and private health care systems in India. Findings of the study urges that more attention be paid to the improve care at the place of delivery to improve neonatal health. There is a need of strengthened national health policy and public-private partnerships in order to improve maternal and child health care in both private and public health facilities.
To identify factors associated with malnutrition (undernutrition and overnutrition) and determine appropriate cut-off values for mid-arm circumference (MAC) and calf circumference (CC) among community-dwelling Indian older adults.
Data from the first wave of harmonised diagnostic assessment of dementia for Longitudinal Ageing Study in India (LASI-DAD) were used. Various sociodemographic factors, comorbidities, geriatric syndromes, childhood financial and health status were included. Anthropometric measurements included body mass index (BMI), MAC and CC.
Nationally representative cohort study including 36 Indian states and union territories.
4096 older adults aged >60 years from LASI DAD.
The outcome variable was BMI, categorised as low (2), normal (18.5–22.9 kg/m2) and high (>23 kg/m2). The cut-off values of MAC and CC were derived using ROC curve with BMI as the gold standard.
902 (weighted percentage 20.55%) had low BMI, 1742 (44.25%) had high BMI. Undernutrition was associated with age, wealth-quintile and impaired cognition, while overnutrition was associated with higher education, urban living and comorbidities such as hypertension, diabetes and chronic heart disease. For CC, the optimal lower and upper cut-offs for males were 28.1 cm and >31.5 cm, respectively, while for females, the corresponding values were 26 cm and >29 cm. Similarly, the optimal lower and upper cut-offs for MAC in males were 23.9 cm and >26.9 cm, and for females, they were 22.5 cm and >25 cm.
Our study identifies a high BMI prevalence, especially among females, individuals with higher education, urban residents and those with comorbidities. We establish gender-specific MAC and CC cut-off values with significant implications for healthcare, policy and research. Tailored interventions can address undernutrition and overnutrition in older adults, enhancing standardised nutritional assessment and well-being.
This cross-sectional comparative study aimed to analyse and compare the story-grammar components in Tamil-speaking children with and without hearing impairment (HI) narratives.
The study used a cross-sectional, comparative design to assess and compare narrative structures.
Data were collected at the Sri Ramachandra Institute of Higher Education and Research in Chennai, India.
30 children participated in the study, including 15 children with severe to profound hearing loss who used cochlear implants and 15 with normal hearing. The participants were language-age-matched children aged 3–5 years, proficient in Tamil.
No specific interventions were implemented in this study.
The primary outcome measures focused on story-grammar components, including settings, characters, initiating events, internal plans, attempts, outcomes, and resolution. These components were evaluated through narrative retellings by the children.
Analysis of the narratives revealed significant differences between the two groups. Children with normal hearing demonstrated a higher representation of story-grammar elements than children with HI.
The findings suggest that children with normal hearing exhibit a more proficient understanding and utilisation of story structure in their story-telling than children with HI. This study highlights the importance of narrative analysis in language assessment, particularly for children with HI. Tailored interventions incorporating appropriate language stimulation techniques are needed to enhance children’s narrative skills with HI. Further research in this area is warranted.
by Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Saurav Mallik, Zhongming Zhao
Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.‘Healthier Wealthier Families’ (HWF) seeks to reduce financial hardship in the early years by embedding a referral pathway between Australia’s universal child and family health (CFH) services and financial counselling. This pilot study investigated the feasibility and short-term impacts of HWF, adapted from a successful Scottish initiative.
Setting: CFH services in five sites across two states, coinciding with the COVID-19 pandemic. Participants: Caregivers of children aged 0–5 years experiencing financial hardship (study-designed screen). Design: Mixed methods. With limited progress using a randomised trial (RCT) design in sites 1–3 (March 2020–November 2021), qualitative interviews with service providers identified implementation barriers including stigma, lack of knowledge of financial counselling, low financial literacy, research burden and pandemic disruption. This informed a simplified RCT protocol (site 4) and direct referral model (no randomisation, pre–post evaluation, site 5) (June 2021–May 2022). Intervention: financial counselling; comparator: usual care (sites 1–4). Feasibility measures: proportions of caregivers screened, enrolled, followed up and who accessed financial counselling. Impact measures: finances (quantitative) and other (qualitative) to 6 months post-enrolment.
355/434 caregivers completed the screen (60%–100% across sites). In RCT sites (1–4), 79/365 (19%–41%) reported hardship but less than one-quarter enrolled. In site 5, n=66/69 (96%) caregivers reported hardship and 44/66 (67%) engaged with financial counselling; common issues were utility debts (73%), and obtaining entitlements (43%) or material aid/emergency relief (27%). Per family, financial counselling increased income from government entitlements by an average $A6504 annually plus $A784 from concessions, grants, brokerage and debt waivers. Caregivers described benefits (qualitative) including reduced stress, practical help, increased knowledge and empowerment.
Financial hardship screening via CFH was acceptable to caregivers, direct referral was feasible, but individual randomisation was infeasible. Larger-scale implementation will require careful, staged adaptations where CFH populations and the intervention are well matched and low burden evaluation.
ACTRN12620000154909.
Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs.
This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs.
5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer).
Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases.
The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging.
Background
Online surveys in nursing research have both advantages and disadvantages. Reaching a sample and attaining an appropriate response rate is an ongoing challenge and necessitates careful consideration when designing a nursing research study using an online survey approach.
Objective
In this study, we aimed to explore response rates and survey characteristics of studies by nurse researchers that used online methodologies to survey nurses, nursing students, and nursing faculty.
Methods
We conducted an integrative review of research studies that used online surveys for data collection published from 2011 to 2021. We examined response rates and survey characteristics such as recruitment method, use of incentives, question type, length of survey, time to complete the survey, and use of reminders.
Results
Our review included 51 studies published by nurses with target samples of nurses, nursing students, or nursing faculty. Study sample sizes ranged from 48 to 29,283, the number of respondents ranged from 29 to 3,607, and the response rates ranged from 3.4% to 98%, with an average of 42.46%. Few patterns emerged regarding recruitment or other factors to enhance response rates; only five studies used incentives.
Conclusion
Response rates to online surveys are unlikely to reach the rates seen in older mailed surveys. Researchers need to design online survey studies to be easily accessible, concise, and appealing to participants. 
FreshRSS 