Suspecting non-cystic fibrosis bronchiectasis: What the busy primary care clinician needs to know.

AIMS: Non-cystic fibrosis bronchiectasis (NCFB) is a chronic, progressive respiratory disorder characterised by irreversibly and abnormally dilated airways, persistent cough, excessive sputum production and recurrent pulmonary infections. In the last several decades, its prevalence has increased, making it likely to be encountered in the primary care setting. The aim was to review the clinical presentation and diagnosis of NCFB, with an emphasis on the role of computed tomography (CT). METHODS: For this review, trials and reports were identified from PubMed/Medline and ClinicalTrials.gov from the US NIH and the Cochrane Register of Controlled Trials. The search used keywords: bronchiectasis, non-cystic fibrosis bronchiectasis, chronic pulmonary infection and computed tomography. No date/language restrictions were used. RESULTS: Non-cystic fibrosis bronchiectasis often coexists with other respiratory conditions, such as chronic obstructive pulmonary disease. The prevalence of NCFB is increasing, particularly in women and older individuals, possibly as a result of increased physician awareness and widespread use of CT, which is the gold standard for the diagnosis of NCFB. CT can assist in identifying an underlying cause of NCFB and determining the extent and severity of the disease. DISCUSSION: Non-cystic fibrosis bronchiectasis should be suspected in the primary care setting in patients with chronic cough, purulent sputum and frequent respiratory infections that tend to resolve slowly or partially. Early diagnosis and determination of the extent and severity of the disease by CT and other tests are critical to establish therapy to improve quality of life and potentially slow progressive decline of lung function in patients with NCFB.

Endothelial adhesion molecules and multiple organ failure in patients with severe sepsis.

OBJECTIVE: To determine if serum levels of endothelial adhesion molecules were associated with the development of multiple organ failure (MOF) and in-hospital mortality in adult patients with severe sepsis. DESIGN: This study was a secondary data analysis of a prospective cohort study. SETTING: Patients were admitted to two tertiary intensive care units in San Antonio, TX, between 2007 and 2012. PATIENTS: Patients with severe sepsis at the time of intensive care unit (ICU) admission were enrolled. Inclusion criteria were consistent with previously published criteria for severe sepsis or septic shock in adults. Exclusion criteria included immunosuppressive medications or conditions. INTERVENTIONS: None. MEASUREMENTS: Baseline serum levels of the following endothelial cell adhesion molecules were measured within the first 72h of ICU admission: Intracellular Adhesion Molecule 1 (ICAM-1), Vascular Cell Adhesion Molecule-1 (VCAM-1), and Vascular Endothelial Growth Factor (VEGF). The primary and secondary outcomes were development of MOF (⩾2 organ dysfunction) and in-hospital mortality, respectively. MAIN RESULTS: Forty-eight patients were enrolled in this study, of which 29 (60%) developed MOF. Patients that developed MOF had higher levels of VCAM-1 (p=0.01) and ICAM-1 (p=0.01), but not VEGF (p=0.70) compared with patients without MOF (single organ failure only). The area under the curve (AUC) to predict MOF according to VCAM-1, ICAM-1 and VEGF was 0.71, 0.73, and 0.54, respectively. Only increased VCAM-1 levels were associated with in-hospital mortality (p=0.03). These associations were maintained even after adjusting for APACHE and SOFA scores using logistic regression. CONCLUSIONS: High levels of serum ICAM-1 was associated with the development of MOF. High levels of VCAM-1 was associated with both MOF and in-hospital mortality.

Effects of intrapulmonary percussive ventilation on airway mucus clearance: A bench model.

AIM: To determine the ability of intrapulmonary percussive ventilation (IPV) to promote airway clearance in spontaneously breathing patients and those on mechanical ventilation. METHODS: An artificial lung was used to simulate a spontaneously breathing patient (Group 1), and was then connected to a mechanical ventilator to simulate a patient on mechanical ventilation (Group 2). An 8.5 mm endotracheal tube (ETT) connected to the test lung, simulated the patient airway. Artificial mucus was instilled into the mid-portion of the ETT. A filter was attached at both ends of the ETT to collect the mucus displaced proximally (mouth-piece filter) and distally (lung filter). The IPV machine was attached to the proximal end of the ETT and was applied for 10-min each to Group 1 and 2. After each experiment, the weight of the various circuit components were determined and compared to their dry weights to calculate the weight of the displaced mucus. RESULTS: In Group 1 (spontaneously breathing model), 26.8% ± 3.1% of the simulated mucus was displaced proximally, compared to 0% in Group 2 (the mechanically ventilated model) with a P-value of < 0.01. In fact, 17% ± 1.5% of the mucus in Group 2 remained in the mid-portion of the ETT where it was initially instilled and 80% ± 4.2% was displaced distally back towards the lung (P < 0.01). There was an overall statistically significant amount of mucus movement proximally towards the mouth-piece in the spontaneously breathing (SB) patient. There was also an overall statistically significant amount of mucus movement distally back towards the lung in the mechanically ventilated (MV) model. In the mechanically ventilated model, no mucus was observed to move towards the proximal/mouth piece section of the ETT. CONCLUSION: This bench model suggests that IPV is associated with displacement of mucus towards the proximal mouthpiece in the SB patient, and distally in the MV model.

Improving outcomes in chronic obstructive pulmonary disease: the role of the interprofessional approach.

Chronic obstructive pulmonary disease (COPD) is associated with significant morbidity, places substantial time and cost burden on the health care system, and is now the third leading cause of death in the US. Many interventions are available to appropriately manage patients with COPD; however, fully implementing these strategies to help improve outcomes may be difficult. Collaboration between an interprofessional team of health care professionals (which includes physicians, nurses, respiratory therapists, physical therapists, dietitians, pharmacists, and many others) and COPD patients and caregivers is necessary to optimally manage these patients and to truly impact outcomes in this devastating disease. Prescribing evidence-based non-pharmacological and pharmacological therapies is an important start, but a true team-based approach is critical to successfully implement comprehensive care in patients with COPD. The goal of this review is to employ a case-based approach to provide practical information regarding the roles of the interprofessional team in implementing strategies to optimally manage COPD patients.

A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem.

Frequent COPD exacerbations have a large impact on morbidity, mortality and health-care expenditures. By 2020, the World Health Organization expects COPD and COPD exacerbations to be the third leading cause of death world-wide. Furthermore, In 2005 it was estimated that COPD exacerbations cost the U.S. health-care system 38 billion dollars. Studies attempting to determine factors related to COPD readmissions are still very limited. Moreover, few have used a organized machine-learning, sensitivity analysis approach, such as a Random Forest (RF) statistical model, to analyze this problem. This study utilized the RF machine learning algorithm to determine factors that predict risk for multiple COPD exacerbations in a single year. This was a retrospective study with a data set of 106 patients. These patients were divided randomly into training (80%) and validating (20%) data-sets, 100 times, using approximately sixty variables intially, which in prior studies had been found to be associated with patient readmission for COPD exacerbation. In an interactive manner, an RF model was created using the training set and validated on the testing dataset. Mean area-under-curve (AUC) statistics, sensitivity, specificity, and negative/positive predictive values (NPV, PPV) were calculated for the 100 runs. THE FOLLOWING VARIABLES WERE FOUND TO BE IMPORTANT PREDICTORS OF PATIENTS HAVING AT LEAST TWO COPD EXACERBATIONS WITHIN ONE YEAR: employment, body mass index, number of previous surgeries, administration of azithromycin/ceftriaxone/moxifloxacin, and admission albumin level. The mean AUC was 0.72, sensitivity of 0.75, specificity of 0.56, PPV of 0.7 and NPV of 0.63. Histograms were used to confirm consistent accuracy. The RF design has consistently demonstrated encouraging results. We expect to validate our results on new patient groups and improve accuracy by increasing our training dataset. We hope that identifying patients at risk for frequent readmissions will improve patient outcome and save valuable hospital resources.