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The Centers for Disease Control and Prevention (CDC) has developed an approach to ventilator-associated events (VAE) surveillance. Using these methods, this study was performed to investigate VAE incidences and to test whether VAEs are associated with poorer outcomes in China.
A 4-month, prospective multicenter surveillance study between April and July 2013.
Our study included 15 adult intensive care units (ICUs) of 15 hospitals in China.
Patients admitted to ICUs during the study period
Patients on mechanical ventilation (MV) were monitored for VAEs: ventilator-associated conditions (VACs), infection-related ventilator-associated complications (IVACs), and possible or probable ventilator-associated pneumonia (VAP). Patients with and without VACs were compared with regard to duration of MV, ICU length of stay (LOS), overall hospital LOS, and mortality rate.
During the study period, 2,356 of the 5,256 patients admitted to ICUs received MV for 8,438 ventilator days. Of these patients, 636 were on MV >2 days. VACs were identified in 94 cases (4.0%; 11.1 cases per 1,000 ventilator days), including 31 patients with IVACs and 16 with possible VAP but none with probable VAP. Compared with patients without VACs, patients with VACs had longer ICU LOS (by 6.2 days), longer duration on MV (by 7.7 days), and higher hospital mortality rate (50.0% vs 27.3%). The mortality rate attributable to VACs was 11.7%. Compared with those with VACs alone, patients with IVACs had longer duration on MV and increased ICU LOS but no higher mortality rates.
In China, surveillance of VACs and IVACs is able to identify MV patients with poorer outcomes. However, surveillance of possible and probable VAP can be problematic.
Infect. Control Hosp. Epidemiol. 2015;36(12):1388–1395
This paper is concerned with the initial boundary-value problem for the p-system with nonlinear damping. We prove the existence of a global smooth solution under the assumption that only the C0-norm of the derivative of the initial data is sufficiently small, while the C0-norm of the initial data is not necessarily small. The proof is based on several key a priori estimates, the maximum principle and the characteristic method.
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