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Background: Osteoporosis is a very common bone disease in the elderly population and can lead to fractures and disability. Malnutrition can lead to osteoporosis. The geriatric nutritional risk index (GNRI) is a tool used to assess the risk of malnutrition and complications associated with nutritional status in older patients and is a crucial predictor of many diseases. Hence, this study investigated the association between the GNRI and the presence of osteoporosis and assessed the value of this index for predicting osteoporosis in patients with type 2 diabetes mellitus (T2DM). Methods: This cross-sectional study enrolled 610 elderly patients with T2DM. General and laboratory data of the patients were collected, along with their measurements of bone mineral density (BMD). The GNRI was calculated based on ideal body weight and serum albumin (ABL) levels. Correlation analysis was performed to determine the relationship between the GNRI and BMD and bone metabolism indices. The GNRI predictive value for osteoporosis development was analyzed through logistic regression analysis and by creating a receiver operating characteristic curve (ROC), calculating the area under the curve (AUC). Results: All patients were divided into the no-nutritional risk and nutritional risk groups. Compared with the no-nutritional risk group, the nutritional risk group had a longer diabetes course, older age, higher HbA1c levels, higher prevalence of osteoporosis; lower BMI, ABL,triglyceride (TG),Calcium (Ca),25-hydroxy-vitamin-D(25(OH)D),and parathyroid hormone(PTH) and lower femoral neck BMD,total hip BMD (P < 0.05). All patients were also assigned to the non-osteoporosis and osteoporosis groups. The non-osteoporosis group had higher GNRI values than the osteoporosis group (P < 0.05). Correlation analysis revealed a positive correlation between the GNRI and lumbar BMD, femoral neck BMD, and total hip BMD (P < 0.05). After the adjustment for confounding factors, Spearman's correlation analysis revealed that the GNRI was positively correlated with Ca, 25(OH)D, and PTH and negatively correlated with alkaline phosphatase (ALP) and procollagen of type-1 N-propeptide (P1NP). Regression analysis exhibited that the GNRI was significantly associated with osteoporosis. The ROC curve analysis was performed using the GNRI as the test variable and the presence of osteoporosis as the status variable. This analysis yielded an AUC for the GNRI of 0.695 and was statistically significant (P < 0.05). Conclusions: A lower GNRI among T2DM patients in northern China is associated with a higher prevalence of osteoporosis. [ABSTRACT FROM AUTHOR]

Objective: Myocardial injury leads to higher mortality in COVID‐19, but the causes and risk factors are variable. We evaluated the potential risk factors for myocardial injury in COVID‐19 patients to improve treatment strategies and reduce mortality. Methods: This retrospective analysis enrolled 325 COVID‐19 patients in Shanghai, China. Results: The median age in our cohort was 51 [range 15‐88] years, 26 (8%) were critically ill, and 177 patients (19.7%) had myocardial injury. The myocardial injury group comprised older, more critically ill patients with hypertension, other comorbidities, history of angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker use, lower peripheral blood lymphocyte count and higher D‐dimer levels. Binary logistic regression analysis identified only age was an independent risk factor for myocardial injury (odds ratio 1.019; 95% confidence interval 1.003‐1.036; age increase by 1 year = myocardial injury risk increase by 1.9%). Conclusions: Older age was associated with a higher incidence of myocardial injury for COVID‐19 patients. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the current status of physical restraint of patients in the neurosurgical intensive care unit (NSICU) and analyze the factors influencing this measure using a cross-sectional study design. A total of 312 patients from four tertiary hospitals in NSICU were investigated in Beijing, China. The rate of physical restraint of patients in the NSICU was 42.9%. In 41.8% of cases, nurses performed physical restraint based on experience, and 45.5% of patients had physical restraint-related nursing records. Binary logistic regression analyses revealed that physical restraint was associated with delirium, mild-to-moderate disturbance of consciousness, history of extubation, surgery, and use of sedatives within 24 hour. Analysis of related factors can provide a reference for nurses and managers to improve physical restraint strategies. [ABSTRACT FROM AUTHOR]

Background: Neurosurgical intensive care unit patients are at high risk for delirium. A risk prediction model could help the staff screen for patients at high risk for delirium. On the basis of this risk, preventive measures could be taken to reduce the undesired effects of delirium. Objectives: To establish a delirium prediction model for neurosurgical intensive care unit patients and to verify the sensitivity and specificity of this model. Design A prospective, observational, single‐centre study. Methods: Data were collected from a total of 310 patients admitted to the neurosurgery intensive care unit between January 2017 and February 2018. A risk factor prediction model was then created using multivariate logistic regression. Further data were collected from another 60 patients between March 2018 and June 2018 to validate the model. Results: The model consisted of six predictors, namely, cognitive dysfunction on admission, fever, hypoalbuminaemia, abnormal liver function, sedative use four or more times, and physical restraint. The area under the curve of the model was 0.80, with sensitivity and specificity of 0.68 and 0.83, respectively. Conclusions: This study established a delirium prediction model for neurosurgical intensive care unit patients, which we believe would help focused prevention of delirium in intensive care unit patients. SUMMARY STATEMENT: What is already known about this topic? Delirium is an acute medical condition commonly seen in general hospitals.There are many reasons underpinning delirium in ICU patients, and early screening and prevention constitute the first steps of delirium management.There are a limited number of studies that have addressed delirium in neurosurgical ICU patients. What this paper adds? This study established and validated a delirium prediction model on the basis of the risk factors associated with neurosurgical ICU patients. The implications of this paper: This delirium prediction model for neurosurgical ICU patients allows medical staff to adopt early interventional measures for patients at high risk for developing delirium, thereby preventing and reducing the incidence of this condition. [ABSTRACT FROM AUTHOR]

Background: Hospital-acquired infection (HAI) is a serious complication of neurosurgery. In recent years, the medical body has paid increasing attention to this issue.Aim: We investigated the status of HAIs in patients who had undergone surgery for intracranial aneurysms and analysed their risk factors.Methods: A retrospective analysis was carried out on the medical records of 542 patients with intracranial aneurysms after they were admitted for neurosurgery at Xuanwu Hospital of Capital Medical University between January and December 2016. Cases studied were divided into an infection group and a control group. Logistic regression analysis of the data was carried out.Findings: Of the 542 patients with intracranial aneurysms who underwent surgery, 77 HAIs occurred in 64 patients, with an infection prevalence of 11.8% and prevalence of infection cases of 14.2%. Logistic regression showed that an admission Glasgow Coma Scale (GCS) score of less than 8 points (odds ratio = 4.261, 95% confidence interval 1.102-16.476), hyperglycaemia (2.759, 1.159-6.564), hypothermia treatment (6.557, 2.244-19.159), and central venous catheterisation (CVC) (8.853, 2.860-27.398) were independent risk factors for HAIs in patients with intracranial aneurysm who underwent surgery.Conclusion: Being comatose upon hospital admission, having hyperglycaemia or hypothermia, and indwelling CVC are major risk factors for HAIs in patients undergoing surgery for intracranial aneurysms. [ABSTRACT FROM AUTHOR]

Aims and objectives To explore the incidence of delirium in cerebrovascular patients admitted in our Neurosurgery Intensive Care Unit and analyse the risk factors leading to delirium. Background Delirium is one of the most common mental disorders in general hospitals, but the incidence of different kinds of diseases and studies varies. Cerebrovascular patients in our Neurosurgery Intensive Care Unit are high-risk groups for delirium; identifying risk factors for delirium and taking early interventions are crucial for patient prognosis. Design A prospective study. Methods A convenience sampling method was used to collect data from 128 patients in the Neurosurgery Intensive Care Unit of Xuanwu Hospital, Capital Medical University, Beijing, China, between May 2016-January 2017. Researchers used Confusion Assessment Method for the Intensive Care Unit (Chinese version) to assess each patient's delirium statement twice a day at regular times. We also collected other independent data variables and followed up the short-term clinical outcomes daily. Results On the basis of Confusion Assessment Method for the Intensive Care Unit evaluation, patients were divided into a delirium group and a nondelirium group. The prevalence of delirium among the 128 patients was 42.2%. Multivariate analysis showed that severity of illness, fever, the use of physical restraints and sleep deprivation were independent predictors of delirium in cerebrovascular patients in the Neurosurgery Intensive Care Unit. Conclusions Cerebrovascular patients in the Neurosurgery Intensive Care Unit with a critical condition, fever or use of physical restraints or experiencing sleep deprivation were more prone to delirium. Relevance to clinical practice Cerebrovascular patients in the Neurosurgery Intensive Care Unit showed a high incidence of delirium. There are many risk factors leading to delirium, some of which are independent predictors of intensive care delirium. Patients with delirium will suffer various adverse effects upon their short-term clinical outcomes. Therefore, nurses should pay close attention to changes in a patient's mental state and learn about the risk factors associated with delirium, in order to be able to take early measures to prevent delirium. [ABSTRACT FROM AUTHOR]