Peripherally inserted central catheter (PICC) is extensively used in critical care settings, because it plays a vital role in providing safe central venous entry. However, PICC is associated with several complications, which should be detected to shorten the duration of patients' improvement, reduce health care cost, and lessen the incidence of various PICC-related complications. Therefore, this study aimed to outline current literature on PICC procedures, potential complications, and measures for prevention. Understanding evidence-based guidelines regarding insertion technique, early detection of complications, and care bundle of PICC is significant in complication prevention. Implementation of education, training, and appropriate multidisciplinary approaches on PICC care among nurses and caregivers is the key to preventing complications. Thus, the strict care of indwelling PICC lines, the targeted and reasonable PICC-associated complication prevention, and nursing care have a major clinical significance in reducing the occurrence of potential PICC complications.

Background: Venous thromboembolism (VTE) can be fatal if not treated promptly, and individual studies have reported wide variability in rates of VTE associated with peripherally inserted central catheters (PICC). We thus conducted this meta-analysis to investigate the overall incidence and risk of developing PICC-related VTE in hospitalized patients.Methods: We searched PubMed, Embase, Scopus, and Web of Science databases from inception until January 26, 2022. In studies with a non-comparison arm, the pooled incidence of PICC-related VTE was calculated. The pooled odds ratio (OR) was calculated to assess the risk of VTE in the studies that compared PICC to the central venous catheter (CVC). The Newcastle-Ottawa Scale was used to assess methodological quality.Results: A total of 75 articles (58 without a comparison arm and 17 with), including 109292 patients, were included in the meta-analysis. The overall pooled incidence of symptomatic VTE was 3.7% (95% CI: 3.1–4.4) in non-comparative studies. In the subgroup meta-analysis, the incidence of VTE was highest in patients who were in a critical care setting (10.6%; 95% CI: 5.0–17.7). Meta-analysis of comparative studies revealed that PICC was associated with a statistically significant increase in the odds of VTE events compared with CVC (OR, 2.48; 95% CI, 1.83–3.37; P < 0.01). However, in subgroup analysis stratified by the study design, there was no significant difference in VTE events between the PICC and CVC in randomized controlled trials (OR, 2.28; 95% CI, 0.77–6.74; P = 0.13).Conclusion: Best practice standards such as PICC tip verification and VTE prophylaxis can help reduce the incidence and risk of PICC-related VTE. The risk-benefit of inserting PICC should be carefully weighed, especially in critically ill patients. Cautious interpretation of our results is important owing to substantial heterogeneity among the studies included in this study.

Gerontechnology has great potential in promoting older adults’ well-being. With the accelerated aging process, gerontechnology has a promising market prospect. However, most technological developers and healthcare professionals attached importance to products’ effectiveness, and ignored older adults’ demands and user experience, which reduced older adults' adoption intention of gerontechnology use. The inclusion of older adults in the design process of technologies is essential to maximize the effect. This study explored older adults’ demands for a self-developed intelligent medication administration system and proposed optimization schemes, thus providing reference to developing geriatric-friendly technologies and products. A cross-sectional survey was conducted to explore older adults’ technological demands for the self-developed intelligent medication administration system, and data were analyzed based on the Kano model. A self-made questionnaire was administered from July 2020 to October 2020 after participants used this system for two weeks. The study was registered with the Chinese Clinical Trial Registry (ChiCTR2000040644). A total of 354 older adults participated in the survey. Four items, namely larger font size, simpler operation process, scheduled medication reminders and reliable hardware, were classified as must-be attributes; three items, namely searching drug instructions through WeChat, more sensitive system and longer battery life, as attractive attributes; one item, viewing disease-related information through WeChat, as the one-dimensional attribute; and the rest were indifferent attributes, including simple and beautiful displays, blocking advertisements automatically, providing user privacy protection protocol, viewing personal medical information only by logged-in users, recording all the medications, ordering medications through WeChat. The satisfaction values were between 0.24 and 0.69, and dissatisfaction values were between 0.06 and 0.94. This study suggested that older adults had personalized technology demands. Including their technological demands and desire may assist in decreasing the digital divide and promoting the satisfaction of e-health and/or m-health. Based on older adults’ demands, our study proposed optimization schemes of the intelligent medication administration system, which may help developers design geriatric-friendly intelligent products and nurses to perform older adults-centered and efficient medication management.

Xiao L, et al. Korean Circ J. 2016 Sep;48:e40. https://doi.org/10.4070/kcj.2017.0312

Purpose: Understanding the factors that affect nursing staffs’ intention and practice of physical restraint (PR) on older adults help develop restraint-reduction programs. This study aimed to identify the relationship between the Theory of Planned Behavior (TPB) constructs and nursing staffs’ practice to use PR in long-term care (LTC) facilities. Patients and Methods: A cross-sectional survey was conducted via convenience sampling among 316 nursing staff in six Chinese LTC facilities. PR-TPB questionnaire and the practice subscale of the Chinese version of the Staff Knowledge, Attitudes and Practices Questionnaire regarding PR were used to collect the data. Structural equation modeling (SEM) was used to examine the relationship between variables. Results: The SEM fit well with the data (χ²/df =1.639, RMSEA = 0.045, CFI= 0.955, GFI=0.945). Attitude (β=0.536, P< 0.001) and perceived behavioral control (PBC) (β=0.139, P< 0.05) predicted intention (R²=0.359). PBC was a significant predictor of practice, with R² accounting for 0.151. Conclusion: TPB provided useful insight into better understanding nursing staffs’ PR practices, although it did not support all the TPB principles significantly. Prospective studies may be conducted to design and implement multi-component interventions based on TPB and explore the effectiveness of PR reduction in LTC facilities in-depth.

Background: Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals. Objective: We aimed to develop machine learning (ML)–based models to predict the risk of PR in older adults. Methods: This study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors’ direct observation. A total of 15 candidate predictors (older adults’ demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naïve Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP). Results: A total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube. Conclusions: The RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.