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Pressure injuries (PIs) are one of the leading potentially preventable hospital-acquired complications associated with prolonged hospital length, poor quality of life and financial burden. The relationship between body mass index (BMI) and PIs occurrence is controversial. The aim of this study was to further examine relationships between BMI and PIs occurrence in hospitalized patients. A multi-center prospective study. 39 hospitals located in northwest China from April 2021 to July 2023. 175,960 hospitalized patients aged over 18 years were enrolled, and 170,800 patients were included in the final analysis. BMI and clinical characteristics were assessed at baseline. PIs assessment were performed by trained nurses, with data recorded for the presence, the location and stage of each PI. For staging PIs, the National Pressure Ulcer Advisory Panel（NPUAP） staging system were used. The multivariate logistic regressions analysis and restricted cubic splines (RCS) models were used to explore associations between BMI and PIs, adjusting for potential confounders. Of 175,960 participants, 5160 were excluded from analyses. The multivariate logistic regression model identified a positive relationship between under-weight BMI and risk of PIs occurrence (OR = 1.60, 95% CI:1.18–2.17). We also found U shaped association between BMI and PIs occurrence (non-linear P < 0.001). BMI less than 23 kg/m2 significantly increased risk of PIs, and there was a tendency to increase risk of PIs at BMI higher than 30 kg/m2. We stratified participants by sex to further investigate their association and found the risk of PIs increases substantially in women at BMI below 17 kg/m2 and in men at BMI below 23 kg/m2. The present study indicated that there was an approximate U shaped relationship between BMI and PIs occurrence, and this association was potentially different between men and women. • Pressure injuries (PIs) are one of the leading potentially preventable adverse events in the hospital. • Early identification of hospitalized patients at risk of PIs can lead to early and precise intervention. • The relationship between body mass index (BMI) and PIs occurrence is controversial. • There was an approximate U shaped relationship between BMI and PIs occurrence. • The relationships between BMI and PIs occurrence was potentially different between men and women. [ABSTRACT FROM AUTHOR]

To examine the association between cognitive frailty and the risk of future falls among older adults. Systematic review and meta-analysis. Older people aged ≥60 years with cognitive frailty from community, hospital, or both. PubMed, EMBASE, Web of Science, the Cochrane Library, Wanfang Database, China Knowledge Resource Integrated Database (CNKI), Weipu Database (VIP), and Chinese Biomedical Database (CBM) were searched for relevant studies published from the inception of the database until June 14, 2022. Stata 16.0 software was used to perform the meta-analysis. A random effects model was used to pool the prevalence of falls in older adults over age 60 years with cognitive frailty and the strength of the association between cognitive frailty and falls [odds ratios (ORs) and 95% CIs]. Quality assessment, heterogeneity, and sensitivity analyses were also conducted. A study protocol was registered in PROSPERO (CRD42022331323). The review included 18 studies in qualitative synthesis, 14 of which were in meta-analysis. Eleven sets of cross-sectional data involving 23,025 participants and 5 sets of longitudinal data involving 11,924 participants were used in the meta-analysis. The results showed that the overall prevalence of falls in 1742 people with cognitive frailty was 36.3% (95% CI 27.9-44.8, I 2 = 93.4%). Longitudinal study results showed that cognitively frail individuals had a higher risk of falls (OR 3.02, 95% CI 2.11-4.32, I 2 = 0.0%, P =.406), compared to robust participants without cognitive impairment; physically frail people (alone) had a moderate risk of falls (OR 2.16, 95% CI 1.42-3.30, I 2 = 9.7%, P =.351); cognitively impaired people (alone) had a lower risk of falls (OR 1.36, 95% CI 1.03-1.79, I 2 = 0.0%, P =.440). Among cross-sectional studies, cognitive frailty was associated with the risk of falls (OR 2.74, 95% CI 2.20-3.40, I 2 = 53.1%, P =.019). Although high heterogeneity was noted among 11 cross-sectional studies reporting ORs, the sensitivity analysis showed that no single study significantly affected the final pooled results. This systematic review and meta-analysis confirms the findings that cognitive frailty was demonstrated to be a significant predictor of future falls in older adults. However, further prospective investigations are warranted. [ABSTRACT FROM AUTHOR]

Previous studies have indicated the potential occurrence of alexithymia among stroke patients, yet the prevalence of alexithymia in this population remains disparate across different investigations without a synthesized overview. To systematically evaluate the prevalence and characteristics of alexithymia in stroke patients. A systematic review and meta-analysis was conducted following the PRISMA guidelines. PubMed, Embase, Web of Science, The Cochrane Library, CINAHL, China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database, and Weipu Database (VIP) were searched from inception to December 31,2022, two independent researchers extracted data and evaluated article quality. Seventeen studies were included, reporting on the prevalence of alexithymia or Toronto Alexithymia Scale-20 (TAS-20) scores among stroke patients. The pooled prevalence was found to be 35.0% (95%CI= 23.0–47.0%; I2 =97.5%), and the total scores (TS) of TAS-20 was 59.90 (95% CI=56.34–63.47; I2 =100.0%). Subgroup analysis revealed significant variation in TAS-20 scores across different geographical regions. Specifically, the total TAS-20 score in Chinese stroke patients (62.95, 95%CI=58.75-67.14; I2 =100%) was higher compared to non-Chinese stroke patients (52.58, 95%CI=49.12-56.04; I2 = 99.0%). The prevalence of alexithymia is high among stroke patients, with TAS-20 scores surpassing those observed in patients with certain other medical conditions. This underscores the importance of addressing alexithymia in stroke patients promptly through assessment and intervention to mitigate negative emotional consequences and enhance overall quality of life. Future research could explore the influence of demographic factors such as age and sex on alexithymia in stroke patients, enabling a more comprehensive understanding of alexithymia. [ABSTRACT FROM AUTHOR]

To assess the effect of different noninvasive ventilation interfaces on preventing the facial pressure injury. This network meta -analysis was conducted following the PRISMA reporting guidelines. Seven electronic databases were systematically searched for randomised controlled trials about the comparative effectiveness of different interfaces in preventing facial pressure injury with noninvasive ventilation in adults and newborns from inception to June 2023. The acronym of PICOS was used and the keywords as well as inclusion/exclusion criteria were determined. Study selection and data extraction were performed by two independent reviewers. The Cochrane risk of bias assessment tool was used to assess the methodological quality. A total of 78 randomised controlled trials involving 7,291 patients were included. The results of network meta -analysis showed that the effectiveness of the eight noninvasive ventilation interfaces on the prevention of facial pressure injury was in the order of: nasal cannula > full-face mask > rotation of nasal mask with nasal prongs > helmet > nasal mask > oronasal mask > nasal prongs > face mask. The use of full-face mask in adults and nasal cannula in newborns had the best effect on preventing the incidence of facial pressure injury. The use of full-face mask in adults and nasal cannula in newborns had the most clinical advantage in preventing the incidence of facial pressure injury and were worthy promoting in clinical practice. This study provides a certain theoretical basis for the selection of appropriate interface for patients with noninvasive ventilation. Clinical practitioners should choose the appropriate interfaces based on the patient's specific condition to reduce the incidence of facial pressure injury, enhance patient comfort, and improve the effectiveness of respiratory therapy. [ABSTRACT FROM AUTHOR]

This study aimed to build and validate a nomogram model to estimate the risk of pressure injuries in intensive care unit patients. Multicenter prospective cohort study. 33 tertiary hospitals in the Gansu Province, China. This study included 6420 patients between April 2021 to October 2022 from an information platform of pressure injury risk management called the "Long Hu Hui." Univariate and multivariate logistic regression analyses identified pressure injury risk factors to be included in the nomogram. The resulting nomogram was tested for calibration discrimination, and clinical usefulness. Of the included patients, 77 developed pressure injuries, representing an incidence rate of 1.2 %. Analysis of binary logistic regression revealed that the estimation nomogram included weight loss greater than 5 kg in the last three months, pneumotomy cannula, thoracic catheter, isoproterenol, norepinephrine, abnormal skin color, ruptured erythema, stroke, increased body temperature and nonspecific patients (specific patients include paralysis, unconsciousness, dementia, forced body position). The area under the receiver operating characteristic curve for the training cohort was 0.806 (95 % CI 0.755–0.857), and the AUC of the text cohort was 0.737 (95 % CI 0.574–0.901). The model has excellent calibration in both the training cohort (H-L test: χ 2 = 6.34, P = 0.61) and the text cohort (H-L test: χ 2 = 4.50, P = 0.81). Furthermore, the decision curve analysis revealed the preferred net benefit and the threshold probability in the estimation nomogram. The nomogram model accurately estimated the risk of pressure injuries among intensive care patients, it should be used to inform risk assessment and facilitate early intervention strategies in future practice. The nomogram allows intensive care providers to dynamically assess the patient's risk of pressure injuries and to implement more targeted interventions accordingly. [ABSTRACT FROM AUTHOR]

Despite the fact that the estimated prevalence and risk factors of falls in adults with rheumatoid arthritis (RA) are widely reported, these results have not been synthesized. The systematic review and meta-analysis aimed to investigate the prevalence and risk factors of falls in adults with RA. PubMed, EMBASE, Web of Science, the Cochrane Library, Cumulative Index to Nursing & Allied Health Literature (CINAHL), Wanfang Database, China Knowledge Resource Integrated Database (CNKI), Weipu Database (VIP), and Chinese Biomedical Database (CBM) were searched for relevant studies published from the inception of the database until July 4, 2022. Stata 15.0 Software was used to perform the meta-analysis. For the prevalence of falls in adults with RA and risk factors that were investigated in at least 2 studies in a comparable way, we calculated pooled incidence and odds ratios (ORs) using random-effects models, with a test for heterogeneity. A study protocol was registered in PROSPERO (CRD42022358120). A total of 6,470 articles were screened and data from 34 studies involving 24,123 subjects were used in meta-analysis. The pooled prevalence of any falls was 34% (95% confidence interval, CI: 29% to 38%, I2 =97.7%, P <0.001), and 16% for recurrent falls (95% CI: 12% to 20%, I2 =97.5%, P <0.001). 25 risk factors were considered, including sociodemographic, medical and psychological, medication, and physical function. The strongest associations were found for history of falls (OR=3.08, 95%CI: 2.32 to 4.08, I2 =0.0%, P = 0.660), history of fracture (OR=4.03, 95%CI: 3.12 to 5.21, I2 =97.3%, P <0.001), walking aid use (OR=1.60, 95%CI: 1.23 to 2.08, I2 =67.7%, P = 0.026), dizziness (OR=1.95, 95%CI: 1.43 to 2.64, I2 =82.9%, P = 0.003), psychotropic medication use (OR=1.79, 95%CI: 1.39 to 2.30, I2 =22.0%, P = 0.254), antihypertensive medicine/diuretic (OR=1.83, 95%CI: 1.37 to 2.46, I2 =51.4%, P = 0.055), taking four or more medicine(OR=1.51, 95%CI: 1.26 to 1.81, I2 =26.0%, P = 0.256), and HAQ score(OR=1.54, 95%CI: 1.40 to 1.69, I2 =36.9%, P = 0.135). This meta-analysis provides a comprehensive evidence-based assessment of the prevalence and risk factors for falls in adults with RA, confirming their multifactorial etiology. Understanding the risk factors of falls can provide healthcare personnel with a theoretical basis for the management and prevention of RA patients. [Display omitted] [ABSTRACT FROM AUTHOR]

To critically appraise and quantify the performance studies by employing machine learning (ML) to predict delirium. A systematic review and meta-analysis. Articles reporting the use of ML to predict delirium in adult patients were included. Studies were excluded if (1) the primary goal was only the identification of various risk factors for delirium; (2) the full-text article was not found; and (3) the article was published in a language other than English/Chinese. PubMed, Embase, Cochrane Library database, Web of Science, Grey literature, and other relevant databases for the related publications were searched (from inception to December 15, 2021). The data were extracted using a standard checklist, and the risk of bias was assessed through the prediction model risk of bias assessment tool. Meta-analysis with the area under the receiver operating characteristic curve, sensitivity, and specificity as effect measures, was performed with Metadisc software. Cochran Q and I 2 statistics were used to assess the heterogeneity. Meta-regression was performed to determine the potential effect of adjustment for the key covariates. A total of 22 studies were included. Only 4 of 22 studies were quantitatively analyzed. The studies varied widely in reporting about the study participants, features and selection, handling of missing data, sample size calculations, and the intended clinical application of the model. For ML models, the overall pooled area under the receiver operating characteristic curve for predicting delirium was 0.89, sensitivity 0.85 (95% confidence interval 0.84‒0.85), and specificity 0.80 (95% confidence interval 0.81–0.80). We found that the ML model showed excellent performance in predicting delirium. This review highlights the potential shortcomings of the current approaches, including low comparability and reproducibility. Finally, we present the various recommendations on how these challenges can be effectively addressed before deploying these models in prospective analyses. [ABSTRACT FROM AUTHOR]