Objective To explore application effect of hierarchical management based on Intensive Care Nursing Scoring System ( ICNSS) in intensive care unit ( ICU) patients with brain injury .Methods A total of 65 ICU patients with brain injury from June 2013 to May 2014 were treated as control group;another 60 cases of ICU patients with brain injury from June 2014 to May 2015 were treated as observation group .Patients in the control group were implemented with routine nursing , while the observation group with hierarchical management based on ICNSS system.Prognosis, complications, nurse-patient disputes events, nursing risk events, and patient satisfaction of two groups were compared .Results In observation group, plant survival rate (3.33%), incidence of complications (6.67%), nurse dispute event rate (3.33%), incidence of nursing risk events (5.00%) were lower than those of the control group (χ2 =4.296,4.720,4.296,5.354;P<0.05).Among patient satisfaction of the observation group , scores of health education ( 3.85 ±0.45 ) points, medical environment(3.79 ±0.53)points, psychological nursing(3.65 ±0.78)points, service attitude(3.38 ±0.56) points, operational care(3.49 ±0.29)points were higher than those of the control group (t=40.821,36.362, 23.801,24.972,43.465;P<0.01).Conclusions Based on the ICNSS, hierarchical management model can allocate human resources in ICU reasonably and effectively , improve quality of care for patients with traumatic brain in ICU , reduce the incidence nursing risk events and elevate patient and family satisfaction .

Objective To understand the prevalence of nosocomial infection and its potential risk factors through a cross-sectional study, to construct a predictive model of the probability of nosocomial infection, and to provide a basis for nosocomial infection management. Methods The prevalence rate of nosocomial infection and potential risk factors of all inpatients in a tertiary general hospital were investigated on a certain day. The possible risk factors of nosocomial infection were analyzed, and a nomogram prediction model on the probability of nosocomial infection was established. The calibration curve and ROC curve were used to evaluate the predictive efficiency of the model. Results A total of 419 hospitalized patients were investigated, and the prevalence rate of nosocomial infection was 3.58%. The top three nosocomial infections were in ICU, neurosurgery, and cardiac surgery. The top three infection sites were surgical site infections, lower respiratory tract infections, and urinary tract infections. The results of univariate analysis showed that the length of hospital stay, surgery, antimicrobial use and underlying diseases were statistically related to the occurrence of nosocomial infections (all P<0.05). Logistic regression analysis showed that compared with the length of stay (LOS)＜14, the risk of nosocomial infection in patients with long LOS (≥14) was 5.48 (95% CI: 1.68-19.16). The risk of nosocomial infection in patients with two basic diseases was 7.61 times that (95%CI: 1.50-44.79) of patients without underlying diseases. The risk of nosocomial infection in patients with surgery was 4.88 times that of patients without surgery (95%CI: 1.47-19.6). According to the coefficients of the related risk factors calculated by logistic regression, a nomogram model of the occurrence probability of nosocomial infection was established. The C-index of the model was 0.839, and the area under the ROC curve for predictive efficiency was 0.809 (95%CI: 0.740-0.942). Conclusion Nosocomial infection control and management should be strengthened. Individual risk assessment of patients' nosocomial infection should consider about the age, underlying diseases, surgical status, glucocorticoid or immunosuppressive agents, and antimicrobial drug use. It is essential to identify the high-risk groups as soon as possible and take prevention and control measures to reduce the prevalence rate of nosocomial infection.