Purpose: The aim of this study was to verify the potential risk factors of ventilator-associated pneumonia (VAP) in elderly Chinese patients receiving mechanical ventilation (MV). The secondary aim of this study was to present logistical regression prediction models of VAP occurrence in elderly Chinese patients receiving MV. Methods: Patients (aged 80 years or above) receiving MV for ≥48 h were enrolled from the Chinese People’s Liberation Army (PLA) General Hospital from January 2011 to December 2015. A chi-squared test and Mann–Whitney U-test were used to compare the data between participants with VAP and without VAP. Univariate logistic regression models were performed to explore the relationship between risk factors and VAP. Results: A total of 901 patients were included in the study, of which 156 were diagnosed as VAP (17.3%). The incidence density of VAP was 4.25/1,000 ventilator days. Logistic regression analysis showed that the independent risk factors for elderly patients with VAP were COPD (OR =1.526, P 0.05), intensive care unit (ICU) admission (OR=1.947, P 0.01), the MV methods ( P 0.023), the number of antibiotics administered (OR=4.947, P 0.01), the number of central venous catheters (OR=1.809, P 0.05), the duration of indwelling urinary catheter (OR=1.805, P 0.01) and the use of corticosteroids prior to MV (OR=1.618, P 0.05). Logistic regression prediction model of VAP occurrence in the Chinese elderly patients with mechanical ventilation: Conclusion: VAP occurrence is associated with a variety of controllable factors including the MV methods and the number of antibiotics administered. A model was established to predict VAP occurrence so that high-risk patients could be identified as early as possible.

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BackgroundFrailty has been recognized as an important prognostic indicator in patients with acute myocardial infarction (AMI). However, no study has focused on critical AMI patients. We aimed to determine the impact of frailty on short- and long-term mortality risk in critical AMI patients.MethodsData from the Medical Information Mart for Intensive Care (MIMIC)-IV database was used. Frailty was assessed using the Hospital Frailty Risk Score (HFRS). Outcomes were in-hospital mortality and 1-year mortality. Logistic regression and Cox proportional-hazards models were used to investigate the association between frailty and outcomes.ResultsAmong 5,003 critical AMI patients, 2,176 were non-frail (43.5%), 2,355 were pre-frail (47.1%), and 472 were frail (9.4%). The in-hospital mortality rate was 13.8%, and the 1-year mortality rate was 29.5%. In our multivariable model, frailty was significantly associated with in-hospital mortality [odds ratio (OR) = 1.30, 95% confidence interval (CI): 1.20–1.41] and 1-year mortality [hazard ratio (HR) = 1.29, 95% CI: 1.24–1.35] as a continuous variable (per five-score increase). When assessed as categorical variables, pre-frailty and frailty were both associated with in-hospital mortality (OR = 2.80, 95% CI: 2.19–3.59 and OR = 2.69, 95% CI: 1.93–3.73, respectively) and 1-year mortality (HR = 2.32, 95% CI: 2.00–2.69 and HR = 2.81, 95% CI: 2.33–3.39, respectively) after adjustment for confounders. Subgroup analysis showed that frailty was only associated with in-hospital mortality in critically ill patients with non-ST-segment elevation myocardial infarction (STEMI) but not STEMI (p for interaction = 0.012). In addition, frailty was associated with 1-year mortality in both STEMI and non-STEMI patients (p for interaction = 0.447). The addition of frailty produced the incremental value over the initial model generated by baseline characteristics for both in-hospital and 1-year mortality.ConclusionFrailty, as assessed by the HFRS, was associated with both in-hospital and 1-year mortality in critical AMI patients. Frailty improves the prediction of short- and long-term mortality in critical AMI patients and may have potential clinical applications.

In this study, a sensitive electrochemical immunosensor for prostate specific antigen (PSA) detection is described using nitrogen-doped graphene (NG) and gold nanoparticles (Au NPs) as a sensing interface.