Analysis of tissue lipidomics and computed tomography pulmonary fat attenuation volume (CTPFAV ) in idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: There is increasing interest in the role of lipids in processes that modulate lung fibrosis with evidence of lipid deposition in idiopathic pulmonary fibrosis (IPF) histological specimens. The aim of this study was to identify measurable markers of pulmonary lipid that may have utility as IPF biomarkers. STUDY DESIGN AND METHODS: IPF and control lung biopsy specimens were analysed using a unbiased lipidomic approach. Pulmonary fat attenuation volume (PFAV) was assessed on chest CT images (CTPFAV ) with 3D semi-automated lung density software. Aerated lung was semi-automatically segmented and CTPFAV calculated using a Hounsfield-unit (-40 to -200HU) threshold range expressed as a percentage of total lung volume. CTPFAV was compared to pulmonary function, serum lipids and qualitative CT fibrosis scores. RESULTS: There was a significant increase in total lipid content on histological analysis of IPF lung tissue (23.16 nmol/mg) compared to controls (18.66 mol/mg, p = 0.0317). The median CTPFAV in IPF was higher than controls (1.34% vs. 0.72%, p < 0.001) and CTPFAV correlated significantly with DLCO% predicted (R2 = 0.356, p < 0.0001) and FVC% predicted (R2 = 0.407, p < 0.0001) in patients with IPF. CTPFAV correlated with CT features of fibrosis; higher CTPFAV was associated with >10% reticulation (1.6% vs. 0.94%, p = 0.0017) and >10% honeycombing (1.87% vs. 1.12%, p = 0.0003). CTPFAV showed no correlation with serum lipids. CONCLUSION: CTPFAV is an easily quantifiable non-invasive measure of pulmonary lipids. In this pilot study, CTPFAV correlates with pulmonary function and radiological features of IPF and could function as a potential biomarker for IPF disease severity assessment.

Genomic attributes of airway commensal bacteria and mucosa.

Microbial communities at the airway mucosal barrier are conserved and highly ordered, in likelihood reflecting co-evolution with human host factors. Freed of selection to digest nutrients, the airway microbiome underpins cognate management of mucosal immunity and pathogen resistance. We show here the initial results of systematic culture and whole-genome sequencing of the thoracic airway bacteria, identifying 52 novel species amongst 126 organisms that constitute 75% of commensals typically present in heathy individuals. Clinically relevant genes encode antimicrobial synthesis, adhesion and biofilm formation, immune modulation, iron utilisation, nitrous oxide (NO) metabolism and sphingolipid signalling. Using whole-genome content we identify dysbiotic features that may influence asthma and chronic obstructive pulmonary disease. We match isolate gene content to transcripts and metabolites expressed late in airway epithelial differentiation, identifying pathways to sustain host interactions with microbiota. Our results provide a systematic basis for decrypting interactions between commensals, pathogens, and mucosa in lung diseases of global significance.

Delayed hospital discharges and the trolley crisis.

We audited use of acute hospital beds in Connolly Hospital over a 3-month period (January-March 2020) which coincided with increased provision of step-down (nursing home) beds. Our results show both ineffective and inefficient baseline uses of these acute beds. Increased step-down beds improve patient care by reducing the trolley count, shortening average length of stay and reducing waiting lists. These data confirm that more step-down beds are a high priority for our Health Service to improve the effectiveness and efficiency of our hospitals i.e. better care at less cost.

Prediction of low pulse oxygen saturation in COVID-19 using remote monitoring post hospital discharge.

BACKGROUND: Monitoring systems have been developed during the COVID-19 pandemic enabling clinicians to remotely monitor physiological measures including pulse oxygen saturation (SpO2), heart rate (HR), and breathlessness in patients after discharge from hospital. These data may be leveraged to understand how symptoms vary over time in COVID-19 patients. There is also potential to use remote monitoring systems to predict clinical deterioration allowing early identification of patients in need of intervention. METHODS: A remote monitoring system was used to monitor 209 patients diagnosed with COVID-19 in the period following hospital discharge. This system consisted of a patient-facing app paired with a Bluetooth-enabled pulse oximeter (measuring SpO2 and HR) linked to a secure portal where data were available for clinical review. Breathlessness score was entered manually to the app. Clinical teams were alerted automatically when SpO2 < 94 %. In this study, data recorded during the initial ten days of monitoring were retrospectively examined, and a random forest model was developed to predict SpO2 < 94 % on a given day using SpO2 and HR data from the two previous days and day of discharge. RESULTS: Over the 10-day monitoring period, mean SpO2 and HR increased significantly, while breathlessness decreased. The coefficient of variation in SpO2, HR and breathlessness also decreased over the monitoring period. The model predicted SpO2 alerts (SpO2 < 94 %) with a mean cross-validated. sensitivity of 66 ± 18.57 %, specificity of 88.31 ± 10.97 % and area under the receiver operating characteristic of 0.80 ± 0.11. Patient age and sex were not significantly associated with the occurrence of asymptomatic SpO2 alerts. CONCLUSION: Results indicate that SpO2 alerts (SpO2 < 94 %) on a given day can be predicted using SpO2 and heart rate data captured on the two preceding days via remote monitoring. The methods presented may help early identification of patients with COVID-19 at risk of clinical deterioration using remote monitoring.

Quantitative Airway Assessment of Diffuse Idiopathic Pulmonary Neuroendocrine Cell Hyperplasia (DIPNECH) on CT as a Novel Biomarker.

Objectives: Diffuse idiopathic pulmonary neuroendocrine cell hyperplasia (DIPNECH) occurs due to abnormal proliferation of pulmonary neuroendocrine cells. We hypothesized that performing a quantitative analysis of airway features on chest CT may reveal differences to matched controls, which could ultimately help provide an imaging biomarker. Methods: A retrospective quantitative analysis of chest CTs in patients with DIPNECH and age matched controls was carried out using semi-automated post-processing software. Paired segmental airway and artery diameters were measured for each bronchopulmonary segment, and the airway:artery (AA) ratio, airway wall thickness:artery ratio (AWTA ratio) and wall area percentage (WAP) calculated. Nodule number, size, shape and location was recorded. Correlation between CT measurements and pulmonary function testing was performed. Results: 16 DIPNECH and 16 control subjects were analysed (all female, mean age 61.7 +/− 11.8 years), a combined total of 425 bronchopulmonary segments. The mean AwtA ratio, AA ratio and WAP for the DIPNECH group was 0.57, 1.18 and 68.8%, respectively, compared with 0.38, 1.03 and 58.3% in controls (p < 0.001, <0.001, 0.03, respectively). DIPNECH patients had more nodules than controls (22.4 +/− 32.6 vs. 3.6 +/− 3.6, p = 0.03). AA ratio correlated with FVC (R2 = 0.47, p = 0.02). A multivariable model incorporating nodule number, AA ratio and AWTA-ratio demonstrated good performance for discriminating DIPNECH and controls (AUC 0.971; 95% CI: 0.925−1.0). Conclusions: Quantitative CT airway analysis in patients with DIPNECH demonstrates increased airway wall thickness and airway:artery ratio compared to controls. Advances in knowledge: Quantitative CT measurement of airway wall thickening offers a potential imaging biomarker for treatment response.

Acute Pulmonary Exacerbation Phenotypes in Patients with Cystic Fibrosis.

Rationale: The etiology of cystic fibrosis (CF) pulmonary exacerbations (PEx) is likely multifactorial with viral, bacterial, and non-infectious pathways contributing. Objectives: To determine whether viral infection status and CRP (C-reactive protein) can classify subphenotypes of PEx that differ in outcomes and biomarker profiles. Methods: Patients were recruited at time of admission for a PEx. Nasal swabs and sputum samples were collected and processed using the respiratory panel of the FilmArray multiplex polymerase chain reaction (PCR). Serum and plasma biomarkers were measured. PEx were classified using serum CRP and viral PCR: "pauci-inflammatory" if CRP < 5 mg/L, "non-viral with systemic inflammation" if CRP ⩾ 5 mg/L and no viral infection detected by PCR and "viral with systemic inflammation" if CRP ⩾ 5 mg/L and viral infection detected by PCR. Results: Discovery cohort (n = 59) subphenotype frequencies were 1) pauci-inflammatory (37%); 2) non-viral with systemic inflammation (41%); and 3) viral with systemic inflammation (22%). Immunoglobulin G, immunoglobulin M, interleukin-10, interleukin-13, serum calprotectin, and CRP levels differed across phenotypes. Reduction from baseline in forced expiratory volume in 1 second as percent predicted (FEV1pp) at onset of exacerbation differed between non-viral with systemic inflammation and viral with systemic inflammation (-6.73 ± 1.78 vs. -13.5 ± 2.32%; P = 0.025). Non-viral with systemic inflammation PEx had a trend toward longer duration of intravenous antibiotics versus pauci-inflammation (18.1 ± 1.17 vs. 14.8 ± 1.19 days, P = 0.057). There were no differences in percent with lung function recovery to <10% of baseline FEV1pp. Similar results were seen in local and external validation cohorts comparing a pauci-inflammatory to viral/non-viral inflammatory exacerbation phenotypes. Conclusions: Subphenotypes of CF PEx exist with differences in biomarker profile, clinical presentation, and outcomes.