Pressure injuries (PIs) have now become a common complication of the elderly patients. Some studies have observed that pressure injuries may increase mortality, but this area of evidence has not been evaluated and summarised. The aim of this study was to compare the mortality of patients with pressure injuries and those without pressure injuries. A meta-analysis of observational studies was performed. PubMed, Cochrane Library, Embase, and Web of Science were searched up to April 2019. Studies about mortality among the elderly patients with and without pressure injuries were included. Methodological quality was assessed by the Newcastle-Ottawa Scale (NOS). The fixed effect or random effect model was determined by the test of heterogeneity. The subgroup analysis was performed based on the pressure injuries stages, the region, and the type of study design. The meta-regression analysis was performed to investigate the relationship between the mortality and patients' enrolled year, average age, the incidence of pressure injuries, and gender ratio. The sensitivity analysis was used to explore the impact of an individual study by excluding one at a time. The hazard ratio (HR) and 95% confidence intervals (CIs) in terms of the comparison of two groups were extracted for meta-analysis. A survival curve between two groups by individual patient-level was drew. Eight studies with 5523 elderly patients were included in the analysis.Follow-up periods for the included studies ranged from about 0.5 to 3 years. The elderly patients who complicated with pressure injuries had a higher risk of death. The pooled HR was 1.78 (95% CI 1.46-2.16). A funnel plot showed no publication bias. Further subgroup analysis showed that HR values for the patient stage 3 to 4 pressure injuries (HR:2.41; 95% CI:1.08-5.37) were higher than stage 1-4 and 2-4 pressure injuries (HR: 1.66; 95% CI: 1.35-2.05; HR: 1.74; 95% CI: 1.16-2.60). The meta-regression analysis found that patients' enrolled year, average age, the incidence of pressure injuries, and gender ratio were not the sources of heterogeneity. Sensitivity analyses showed that the outcomes of the study did not change after removing the Onder's article. The survival curve at the individual patient-level also indicated that patients complicated with pressure injuries significantly increased the risk of death (HR: 1.958; 95% CI: 1.79-2.14) in elderly patients. Our metaanalysis indicated that patients complicated with pressure injuries are estimated to

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Background Surgery-related pressure injury (SRPI) is a serious problem in patients who undergo cardiovascular surgery. Identifying patients at a high risk of SRPI is important for clinicians to recognize and prevent it expeditiously. Machine learning (ML) has been widely used in the field of healthcare and is well suited to predictive analysis. Purpose The aim of this study was to develop an ML-based predictive model for SRPI in patients undergoing cardiovascular surgery. Methods This secondary analysis of data was based on a single-center, prospective cohort analysis of 149 patients who underwent cardiovascular surgery. Data were collected from a 1,000-bed university-affiliated hospital. We developed the ML model using the XGBoost algorithm for SRPI prediction in patients undergoing cardiovascular surgery based on major potential risk factors. Model performance was tested using a receiver operating characteristic curve and the C-index. Results Of the sample of 149 patients, SRPI developed in 37, an incidence rate of 24.8%. The five most important predictors included duration of surgery, patient weight, duration of the cardiopulmonary bypass procedure, patient age, and disease category. The ML model had an area under the receiver operating characteristic curve of 0.806, which indicates that the ML model has a moderate prediction value for SRPI. Conclusions Applying ML to clinical data may be a reliable approach to the assessment of the risk of SRPI in patients undergoing cardiovascular surgical procedures. Future studies may deploy the ML model in the clinic and focus on applying targeted interventions for SRPI and related diseases.

Our meta-analysis indicated that maternal viral load was an important risk factor for MTCT in HBeAg-positive mothers, and maternal viral load was dose-dependent with HBV MTCT incidence.

Our meta-analysis indicated that prenatal smoking was associated with postpartum depression.