The Use of Polymerase Chain Reaction Amplification for the Detection of Viruses and Bacteria in Severe Community-Acquired Pneumonia.

BACKGROUND: Pathogens are often not identified in severe community-acquired pneumonia (CAP), and the few studies using polymerase chain reaction (PCR) techniques for virus detection are from temperate countries. OBJECTIVE: This study assesses if PCR amplification improves virus and bacteria detection, and if viral infection contributes to mortality in severe CAP in a tropical setting, where respiratory pathogens have less well-defined seasonality. METHODS: In this cohort study of patients with severe CAP in an intensive care unit, endotracheal aspirates for intubated patients and nasopharyngeal swabs for non-intubated patients were sent for PCR amplification for respiratory viruses. Blood, endotracheal aspirates for intubated patients, and sputum for non-intubated patients were analysed using a multiplex PCR system for bacteria. RESULTS: Out of 100 patients, using predominantly cultures, bacteria were identified in 42 patients; PCR amplification increased this number to 55 patients. PCR amplification identified viruses in 32 patients. In total, only bacteria, only viruses, and both bacteria and viruses were found in 37, 14, and 18 patients, respectively. The commonest viruses were influenza A H1N1/2009 and rhinovirus; the commonest bacterium was Streptococcus pneumoniae. Hospital mortality rates for patients with no pathogens, bacterial infection, viral infection, and bacterial-viral co-infection were 16.1, 24.3, 0, and 5.6%, respectively (p = 0.10). On multivariable analysis, virus detection was associated with lower mortality (adjusted odds ratio 0.12, 95% confidence interval 0.2-0.99; p = 0.049). CONCLUSIONS: Viruses and bacteria were detected in 7 of 10 patients with severe CAP with the aid of PCR amplification. Viral infection appears to be independently associated with lower mortality.

IDSA/ATS minor criteria aid pre-intensive care unit resuscitation in severe community-acquired pneumonia.

The effect of employing severity scores to identify severe community-acquired pneumonia (SCAP) cases for early aggressive resuscitation is unknown. Optimising pre-intensive care unit (ICU) care may improve outcomes in patients at risk of SCAP. We conducted a before-and-after study of patients classified into control and intervention groups (January 2004 to December 2007 and January 2008 to December 2010, respectively). Our intervention was two-pronged, using the 2007 Infectious Diseases Society of America (IDSA)/American Thoracic Society (ATS) minor criteria to identify SCAP for aggressive emergency department resuscitation. Patients with SCAP, defined as those with three or more IDSA/ATS minor criteria, were targeted. Differences in mortality, triage and compliance with emergency department resuscitation were compared between the groups. The hospital mortality rate was lower in the intervention versus the control group (5.7% versus 23.8%, p<0.001). On multivariate analysis, the intervention group was associated with lower mortality (OR 0.24, 95% CI 0.09-0.67). ICU admission rates decreased from 52.9% to 38.6% (p=0.008) and inappropriately delayed ICU admissions decreased from 32.0% to 14.8% (p<0.001). There was increased compliance with the aggressive resuscitation protocol after the intervention. A combined intervention, using a pneumonia score to identify those at risk of SCAP early and an aggressive pre-ICU resuscitation protocol may reduce mortality and ICU admissions.

Diagnostic characteristics of clinical prediction models for obstructive sleep apnea in different clinic populations.

PURPOSE: As predictive factors and their diagnostic values are affected by the characteristics of the population studied, clinical prediction model for obstructive sleep apnea (OSA) may exhibit different diagnostic characteristics in different populations. We aimed to compare the diagnostic characteristics of clinical prediction models developed in two different populations. METHODS: One hundred seventeen consecutive clinic patients (group 1) were evaluated to develop a clinical prediction model for OSA (local model). The diagnostic characteristics of this local model were compared with those of a foreign model by applying both models to another group of 52 patients who were referred to the same clinic (group 2). All patients underwent overnight polysomnography. RESULTS: The local model had an area under receiver operator characteristics curve of 79%. A cutoff of 0.6 was associated with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 77.9%, 72.5%, 84.5%, and 63.0%, respectively. The overall diagnostic accuracy, sensitivity, specificity, PPV, and NPV of the local model using data from patients in group 2 were 69.0%, 78.1%, 45.0%, 69.4%, and 56.3%, respectively. The foreign model had an overall diagnostic accuracy of 64.0% when applied to data from patients in group 2. At the optimal cutoff of 17, the foreign model was associated with sensitivity of 38.2%, specificity of 83.3%, NPV of 41.7% and PPV of 81.3%. CONCLUSIONS: Clinical prediction model for OSA derived from a foreign population exhibits markedly different diagnostic characteristics from one that is developed locally, even though the overall accuracy is similar. Our findings challenge the predictive usefulness and the external validity of clinical prediction models.