Blood RNA Biomarkers Identify Bacterial and Biofilm Coinfections in COVID-19 Intensive Care Patients.

Purpose: Secondary opportunistic coinfections are a significant contributor to morbidity and mortality in intensive care unit (ICU) patients, but can be difficult to identify. Presently, new blood RNA biomarkers were tested in ICU patients to diagnose viral, bacterial, and biofilm coinfections. Methods: COVID-19 ICU patients had whole blood drawn in RNA preservative and stored at -80°C. Controls and subclinical infections were also studied. Droplet digital polymerase chain reaction (ddPCR) quantified 6 RNA biomarkers of host neutrophil activation to bacterial (DEFA1), biofilm (alkaline phosphatase [ALPL], IL8RB/CXCR2), and viral infections (IFI27, RSAD2). Viral titer in blood was measured by ddPCR for SARS-CoV2 (SCV2). Results: RNA biomarkers were elevated in ICU patients relative to controls. DEFA1 and ALPL RNA were significantly higher in severe versus incidental/moderate cases. SOFA score was correlated with white blood cell count (0.42), platelet count (-0.41), creatinine (0.38), and lactate dehydrogenase (0.31). ALPL RNA (0.59) showed the best correlation with SOFA score. IFI27 (0.52) and RSAD2 (0.38) were positively correlated with SCV2 viral titer. Overall, 57.8% of COVID-19 patients had a positive RNA biomarker for bacterial or biofilm infection. Conclusions: RNA biomarkers of host neutrophil activation indicate the presence of bacterial and biofilm coinfections in most COVID-19 patients. Recognizing coinfections may help to guide the treatment of ICU patients.

Actigraphy informs distinct patient-centered outcomes in Pre-COPD.

BACKGROUND: Actigraphy can provide useful patient-centered outcomes for quantification of physical activity in the "real-world" setting. METHODS: To characterize the relationship of actigraphy outputs with "in-laboratory" measures of cardiopulmonary function and respiratory symptoms in pre-COPD, we obtained actigraphy data for 8 h/day for 5 consecutive days a week before in-laboratory administration of respiratory questionnaires, PFT, and CPET to a subgroup of subjects participating in the larger study of the health effects of exposure to secondhand tobacco smoke who had air trapping but no spirometric obstruction (pre-COPD). Using machine learning approaches, we identified the most relevant actigraphy predictors and examined their associations with symptoms, lung function, and exercise outcomes. RESULTS: Sixty-one subjects (age = 66±7 years; BMI = 24±3 kg/m2; FEV1/FVC = 0.75 ± 0.05; FEV1 = 103 ± 17 %predicted) completed the nested study. In the hierarchical cluster analysis, the activity, distance, and energy domains of actigraphy, including moderate to vigorous physical activity, were closely correlated with each other, but were only loosely associated with spirometric and peak exercise measures of oxygen consumption, ventilation, oxygen-pulse, and anaerobic threshold (VO2AT), and were divergent from symptom measures. Conversely, the sedentary domain clustered with respiratory symptoms, air trapping, airflow indices, and ventilatory efficiency. In Regression modeling, sedentary domain was inversely associated with baseline lung volumes and tidal breathing at peak exercise, while the activity domains were associated with VO2AT. Respiratory symptoms and PFT data were not associated with actigraphy outcomes. DISCUSSION: Outpatient actigraphy can provide information for "real-world" patient-centered outcomes that are not captured by standardized respiratory questionnaires, lung function, or exercise testing. Actigraphy activity and sedentary domains inform of distinct outcomes.

Patient Adherence in an Academic Medical Center's Low-dose Computed Tomography Screening Program.

OBJECTIVES: Low-dose computed tomography (LDCT) screening is an important tool for reducing lung cancer mortality. This study describes a single center's experience with LDCT and attempts to identify any barriers to compliance with standard guidelines. MATERIALS AND METHODS: This is a retrospective review of a single university-based hospital system from 2015 to 2019. All individuals who met eligibility for lung cancer screening were entered into a database. The definition of adherence with the screening program was determined by the recommended timeline for the follow-up LDCT. Cohorts were split by adherence and demographics were compared. RESULTS: A total of 203 LDCTs were performed in 121 patients who met eligibility for LDCT and had appropriate surveillance from 2015 to 2019. The average age was 64 years old. The overall adherence rate for prescribed LDCTs was 59.1%. Patients with Lung-RADS score 2 had 2.43 times higher odds of adherence relative to patients with Lung-RADS score 1 (odds ratio [OR]=2.43; 95% confidence interval [CI]: 1.23-4.83; P=0.011). African American patients had 42% lower odds of adherence relative to white patients (OR=0.58; 95% CI: 0.32-1.06; P=0.076). Patients with non-District of Columbia zip codes had 57% higher odds of adherence relative to those with District of Columbia zip codes, although this did not reach statistical significance (OR=1.57; 95% CI: 0.87-2.82; P=0.136). CONCLUSIONS: Despite the implementation of a multidisciplinary, academic LDCT screening program, overall adherence rate to prescribed follow-up scans was suboptimal. Socioeconomic disparities and African American race may negatively affect adherence to lung cancer screening LDCT guidelines. Patients with concerning findings on initial LDCT had a higher association of adherence to guidelines.

Lung volume indices predict morbidity in smokers with preserved spirometry.

BACKGROUND: Abnormal lung volumes that reflect air trapping are common in COPD. However, their significance in smokers with preserved spirometry (normal FEV1 to FVC ratio) is unclear. METHODS: Using the Veterans Administration Informatics and Computing Infrastructure database, we identified 7479 patients at risk for COPD (ever smokers >40 years of age without restrictive lung disease) who had preserved spirometry and concomitant lung volume measurements, and examined their subsequent health records for clinical diagnoses of COPD, healthcare utilisation, follow-up spirometry and mortality. RESULTS: Air trapping was prevalent, with 31% of patients having residual volume to total lung capacity ratio (RV:TLC) greater than the upper limit of normal (ULN). RV:TLC varied widely from 14% to 77% (51% to 204% of predicted) across the normal ranges of FEV1:FVC and FEV1. Patients with RV:TLC greater than the ULN were more likely to receive subsequent clinical diagnoses of COPD (HR (95% CI)=1.55 (1.42 to 1.70), p<0.001) and had higher all-cause mortality (HR (95% CI)=1.41 (1.29 to 1.54), p<0.001). They had higher rates of respiratory medication prescriptions and hospital and intensive care unit admissions. Other air trapping and static hyperinflation indices showed similar associations with health outcomes. Additionally, high-normal RV:TLC was associated with intermediate adverse health outcomes compared with low-normal and abnormal RV:TLC. Abnormal RV:TLC predicted higher likelihood of progression to spirometric COPD (OR (95% CI)=1.30 (1.03 to 1.65), p=0.027). CONCLUSION: In this study of the Veterans Affairs electronic health records, air trapping was common in smokers with preserved spirometry and predicted adverse respiratory outcomes and progression to overt COPD.