Risk factors and predictors of venous thromboembolism in patients with acute spontaneous intracerebral hemorrhage: A systematic review and meta-analysis.

BACKGROUND: Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a common and preventable complication of patients with acute spontaneous intracerebral hemorrhages (ICH). Knowledge of VTE risk factors in patients with acute spontaneous ICH continues to evolve while remains controversial. Therefore, this study aims to summarize the risk factors and predictors of VTE in patients with acute spontaneous ICH. METHODS: EMBASE, PubMed, Web of Science and Cochrane databases were searched for articles containing Mesh words "Cerebral hemorrhage" and "Venous thromboembolism." Eligibility screening, data extraction, and quality assessment of the retrieved articles were conducted independently by two reviewers. We performed meta-analysis to determine risk factors for the development of VTE in acute spontaneous ICH patients. Sensitivity analysis were performed to explore the sources of heterogeneity. RESULTS: Of the 12,362 articles retrieved, 17 cohort studies were included.Meta-analysis showed that longer hospital stay [OR=15.46, 95 % CI (12.54, 18.39), P<0.00001], infection [OR=5.59, 95 % CI (1.53, 20.42), P=0.009], intubation [OR=4.32, 95 % CI (2.79, 6.69), P<0.00001] and presence of intraventricular hemorrhage (IVH) [OR=1.89, 95 % CI (1.50, 2.38), P<0.00001] were significant risk factors for VTE in acute spontaneous ICH patients. Of the 17 studies included, five studies reported six prediction models, including 15 predictors. The area under the receiver operating curve (AUC) ranged from 0.71 to 0.95. One of the models was externally validated. CONCLUSION: Infection, the intubation, presence of IVH and longer hospital stay were risk factors for the development of VTE in acute spontaneous ICH patients. Prediction models of VTE based on acute spontaneous ICH patients have been poorly reported and more research will be needed before such models can be applied in clinical settings.

Development and application of a risk nomogram for the prediction of risk of carbapenem-resistant Acinetobacter baumannii infections in neuro-intensive care unit: a mixed method study.

OBJECTIVE: This study aimed to develop and apply a nomogram with good accuracy to predict the risk of CRAB infections in neuro-critically ill patients. In addition, the difficulties and expectations of application such a tool in clinical practice was investigated. METHODS: A mixed methods sequential explanatory study design was utilized. We first conducted a retrospective study to identify the risk factors for the development of CRAB infections in neuro-critically ill patients; and further develop and validate a nomogram predictive model. Then, based on the developed predictive tool, medical staff in the neuro-ICU were received an in-depth interview to investigate their opinions and barriers in using the prediction tool during clinical practice. The model development and validation is carried out by R. The transcripts of the interviews were analyzed by Maxqda. RESULTS: In our cohort, the occurrence of CRAB infections was 8.63% (47/544). Multivariate regression analysis showed that the length of neuro-ICU stay, male, diabetes, low red blood cell (RBC) count, high levels of procalcitonin (PCT), and number of antibiotics ≥ 2 were independent risk factors for CRAB infections in neuro-ICU patients. Our nomogram model demonstrated a good calibration and discrimination in both training and validation sets, with AUC values of 0.816 and 0.875. Additionally, the model demonstrated good clinical utility. The significant barriers identified in the interview include "skepticism about the accuracy of the model", "delay in early prediction by the indicator of length of neuro-ICU stay", and "lack of a proper protocol for clinical application". CONCLUSIONS: We established and validated a nomogram incorporating six easily accessed indicators during clinical practice (the length of neuro-ICU stay, male, diabetes, RBC, PCT level, and the number of antibiotics used) to predict the risk of CRAB infections in neuro-ICU patients. Medical staff are generally interested in using the tool to predict the risk of CRAB, however delivering clinical prediction tools in routine clinical practice remains challenging.