Objective To identify risk factors independently predictive of pressure injury (also known as pressure ulcer) development among critical-care patients Design We undertook a systematic review of primary research based on standardized criteria set forth by the Institute of Medicine. Data Sources We searched the following databases: CINAHL (EBSCOhost), the Cochrane Library (Wilson), Dissertations & Theses Global (ProQuest), PubMed (National Library of Medicine), and Scopus. There was no language restriction. Method A research librarian coordinated the search strategy. Articles that potentially met inclusion criteria were screened by two investigators. Among the articles that met selection criteria, one investigator extracted data and a second investigator reviewed the data for accuracy. Based on a literature search, we developed a tool for assessing study quality using a combination of currently available tools and expert input. We used the method developed by Coleman and colleagues in 2014 to generate evidence tables and a summary narrative synthesis by domain and subdomain. Results Of 1753 abstracts reviewed, 158 were identified as potentially eligible and 18 fulfilled eligibility criteria. Five studies were classified as high quality, two were moderate quality, nine were low quality, and two were of very low quality. Age, mobility/activity, perfusion, and vasopressor infusion emerged as important risk factors for pressure injury development, whereas results for risk categories that are theoretically important, including nutrition, and skin/pressure injury status, were mixed. Methodological limitations across studies limited the generalizability of the results, and future research is needed, particularly to evaluate risk conferred by altered nutrition and skin/pressure injury status, and to further elucidate the effects of perfusion-related variables. Conclusions Results underscore the importance of avoiding overinterpretation of a single study, and the importance of taking study quality into consideration when reviewing risk factors. Maximal pressure injury prevention efforts are particularly important among critical-care patients who are older, have altered mobility, experience poor perfusion, or who are receiving a vasopressor infusion.

Background Hospital-acquired pressure injuries are a serious problem among critical care patients. Some can be prevented by using measures such as specialty beds, which are not feasible for every patient because of costs. However, decisions about which patient would benefit most from a specialty bed are difficult because results of existing tools to determine risk for pressure injury indicate that most critical care patients are at high risk. Objective To develop a model for predicting development of pressure injuries among surgical critical care patients. Methods Data from electronic health records were divided into training (67%) and testing (33%) data sets, and a model was developed by using a random forest algorithm via the R package “randomforest.” Results Among a sample of 6376 patients, hospital-acquired pressure injuries of stage 1 or greater (outcome variable 1) developed in 516 patients (8.1%) and injuries of stage 2 or greater (outcome variable 2) developed in 257 (4.0%). Random forest models were developed to predict stage 1 and greater and stage 2 and greater injuries by using the testing set to evaluate classifier performance. The area under the receiver operating characteristic curve for both models was 0.79. Conclusion This machine-learning approach differs from other available models because it does not require clinicians to input information into a tool (eg, the Braden Scale). Rather, it uses information readily available in electronic health records. Next steps include testing in an independent sample and then calibration to optimize specificity. (American Journal of Critical Care. 2018; 27:461–468)

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The coronavirus disease (COVID-19) pandemic has been particularly challenging for nursing home staff and residents. Centers for Medicare & Medicaid Services regulation waivers are burdening staff and affecting how care is delivered. Residents are experiencing social isolation, which can result in physical and behavioral health issues, particularly for persons with dementia. These challenges can be addressed in part through technology adaptations. Full integration of electronic health record systems can improve workflow and care quality. Telehealth can improve access to outside providers, provide remote monitoring, and improve social connectedness. Electronic and audiovisual programs can be used for end-of-life planning and information sharing between nursing home staff and families. Online learning systems and other online resources provide flexible options for staff education and training. Investing in and adapting technology can help mitigate workforce stress and improve the quality of nursing home care during and after the COVID-19 crisis.