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Surgical site infection (SSI) is one of the most common complications of posterior cervical surgery. It is difficult to diagnose in the early stage and may lead to severe consequences such as wound dehiscence and central nervous system infection. This retrospective study included patients who underwent posterior cervical surgery at The Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University from September 2018 to June 2022. We employed several machine learning methods, such as the gradient boosting (GB), random forests (RF), artificial neural network (ANN) and other popular machine learning models. To minimize the variability introduced by random splitting, the results underwent 10-fold cross-validation repeated 10 times. Five measurements were averaged across 10 repetitions with 10-fold cross-validation, the RF model achieved the highest AUROC (0.9916), specificity (0.9890) and precision (0.9759). The GB model achieved the highest sensitivity (0.9535) and the KNN achieved the highest sensitivity (0.9958). The application of machine learning techniques facilitated the development of a precise model for predicting SSI after posterior cervical surgery. This dynamic model can be served as a valuable tool for clinicians and patients to assess SSI risk and prevent it in clinical practice.
Atrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies.
We will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month prediction horizon, that is, a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states.
Permission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.
A systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).
To (i) assess the adherence of long-term care (LTC) facilities to the COVID-19 prevention and control recommendations, (ii) identify predictors of this adherence and (iii) examine the association between the adherence level and the impact of the pandemic on selected unfavourable conditions.
Cross-sectional survey.
Managers (n = 212) and staff (n = 2143) of LTC facilities (n = 223) in 13 countries/regions (Brazil, Egypt, England, Hong Kong, Indonesia, Japan, Norway, Portugal, Saudi Arabia, South Korea, Spain, Thailand and Turkey) evaluated the adherence of LTC facilities to COVID-19 prevention and control recommendations and the impact of the pandemic on unfavourable conditions related to staff, residents and residents' families. The characteristics of participants and LTC facilities were also gathered. Data were collected from April to October 2021. The study was reported following the STROBE guidelines.
The adherence was significantly higher among facilities with more pre-pandemic in-service education on infection control and easier access to information early in the pandemic. Residents' feelings of loneliness and feeling down were the most affected conditions by the pandemic. More psychological support to residents was associated with fewer residents' aggressive behaviours, and more psychological support to staff was associated with less work–life imbalance.
Pre-pandemic preparedness significantly shaped LTC facilities' response to the pandemic. Adequate psychological support to residents and staff might help mitigate the negative impacts of infection outbreaks.
This is the first study to comprehensively examine the adherence of LTC facilities to COVID-19 prevention and control recommendations. The results demonstrated that the adherence level was significantly related to pre-pandemic preparedness and that adequate psychological support to staff and residents was significantly associated with less negative impacts of the pandemic on LTC facilities' staff and residents. The results would help LTC facilities prepare for and respond to future infection outbreaks.
No Patient or Public Contribution.
Nursing interventions in the post-operative time period including psychological and emotional support, adverse event education, and instructions for follow-up care contribute patient satisfaction, safety, and quality of life. However, the time spent in the post-anesthesia care unit (PACU) and hospital continues to shorten around the world to reduce health care spending and improve patient outcomes. Nurses conducting research during the important post-operative recovery period need to utilize unique techniques and emerging technologies to contact, recruit and collect data outside of the hospital setting including the Research Electronic Data Capture (REDCap) platform.
This paper describes the feasibility and acceptability, facilitators and barriers of the software application, REDCap, to complete a repeated-measures, descriptive correlational study in patients undergoing outpatient breast cancer surgeries.
The recruitment, data collection and storage were completed utilizing the secure REDCap Platform. The Institutional Research Board (IRB)-approved study was a repeated-measures, descriptive, correlational study with data collection at three time points. The data points aligned with important transitions and routine visits to improve data collection feasibility and increase relevance to clinical practice.
The sample consisted of women diagnosed with breast cancer undergoing breast conserving surgery between August 15 and October 15, 2020. There were 123 potential participants, of which 76 started the surveys and 75 participated (61%) responded and participated in the study on Post-operative Day 1. Fifty-nine participants (78%) completed the surveys on post-operative Day 14.
As the frequency of outpatient treatment increases, nurses conducting post-operative research will need to collect the data outside of the hospital setting.
Email provides a method of studying new phenomena by recruiting participants, providing information about the study, and collecting results in a non-traditional setting. REDCap provides a method to facilitate nursing research through a securely encrypted integrated process.
by Nataliya Rybnikova, Dani Broitman, Murielle Mary-Krause, Maria Melchior, Yakov Ben-Haim
Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample’s status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example.The study aimed to identify factors associated with participation in Phase II cardiac rehabilitation and to assess patient perceptions towards the usage of technologies in cardiac rehabilitation.
Despite efforts to promote utilisation of cardiac rehabilitation (CR), participation among patients remains unsatisfactory. Little is known of patient decision to participate Phase II CR in a multi-ethnic country.
A cross-sectional study design.
A consecutive sampling of 240 patients with coronary heart disease completed Coronary Artery Disease Education Questionnaire (CADE-Q) II, Hospital Anxiety and Depression Scale (HADS), Multidimensional Scale of Perceived Social Support (MSPSS) and Cardiac Rehabilitation Barriers Scale (CRBS).
Seventy per cent of patients (mean age 60.5 [SD = 10.6] years, 80.8% male) participated in phase II cardiac rehabilitation. Self-driving to cardiac rehabilitation centres, higher barriers in perceived need/health care and logistical factors were significantly associated with decreased odds of participation. Patients with more barriers from comorbidities/functional status, higher perceived social support from friends, and anxiety were more likely to participate. Chinese and Indians were less likely to participate when compared with Malays. More than 80% of patients used both home and mobile broadband internet, and 72.9% of them would accept the usage of technologies, especially educational videos, instant messenger, and video calls to partially replace the face-to-face, centre-based cardiac rehabilitation approach.
Several barriers were associated with non-participation in phase II cardiac rehabilitation. With the high perceived acceptance of technology usage in cardiac rehabilitation, home-based and hybrid cardiac rehabilitation may represent potential solutions to improve participation.
By addressing the barriers to cardiac rehabilitation, patients are more likely to be ready to adopt health behaviour changes and adhere to the cardiac rehabilitation programme. The high perceived acceptance of using technologies in cardiac rehabilitation may provide insights into new delivery models that can improve and overcome barriers to participation.
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