Outcomes following trauma laparotomy for hypotensive trauma patients: A UK military and civilian perspective.

BACKGROUND: The management of trauma patients has changed radically in the last decade, and studies have shown overall improvements in survival. However, reduction in mortality for the many may obscure a lack of progress in some high-risk patients. We sought to examine the outcomes for hypotensive patients requiring laparotomy in UK military and civilian cohorts. METHODS: We undertook a review of two prospectively maintained trauma databases: the UK Joint Theatre Trauma Registry for the military cohort (February 4, 2003, to September 21, 2014) and the trauma registry of the Royal London Hospital major trauma center (January 1, 2012, to January 1, 2017) for civilian patients. Adults undergoing trauma laparotomy within 90 minutes of arrival at the emergency department (ED) were included. RESULTS: Hypotension was present on arrival at the ED in 155 (20.4%) of 761 military patients. Mortality was higher in hypotensive casualties (25.8% vs. 9.7% in normotensive casualties; p < 0.001). Hypotension was present on arrival at the ED in 63 (35.7%) of 176 civilian patients. Mortality was higher in hypotensive patients (47.6% vs. 12.4% in normotensive patients; p < 0.001). In both cohorts of hypotensive patients, neither the average injury severity, the prehospital time, the ED arrival systolic blood pressure, nor mortality rate changed significantly during the study period. CONCLUSIONS: Despite improvements in survival after trauma for patients overall, the mortality for patients undergoing laparotomy who arrive at the ED with hypotension has not changed and appears stubbornly resistant to all efforts. Specific enquiry and research should continue to be directed at this high-risk group of patients. LEVEL OF EVIDENCE: Prognostic/Epidemiologic, level IV

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort

BACKGROUND: Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in forced vital capacity (FVC) is the main indicator of progression; however, missingness prevents long-term analysis of patterns in lung function. We aimed to identify distinct clusters of lung function trajectory among patients with idiopathic pulmonary fibrosis using machine learning techniques. METHODS: We did a secondary analysis of longitudinal data on FVC collected from a cohort of patients with idiopathic pulmonary fibrosis from the PROFILE study; a multicentre, prospective, observational cohort study. We evaluated the imputation performance of conventional and machine learning techniques to impute missing data and then analysed the fully imputed dataset by unsupervised clustering using self-organising maps. We compared anthropometric features, genomic associations, serum biomarkers, and clinical outcomes between clusters. We also performed a replication of the analysis on data from a cohort of patients with idiopathic pulmonary fibrosis from an independent dataset, obtained from the Chicago Consortium. FINDINGS: 415 (71%) of 581 participants recruited into the PROFILE study were eligible for further analysis. An unsupervised machine learning algorithm had the lowest imputation error among tested methods, and self-organising maps identified four distinct clusters (1-4), which was confirmed by sensitivity analysis. Cluster 1 comprised 140 (34%) participants and was associated with a disease trajectory showing a linear decline in FVC over 3 years. Cluster 2 comprised 100 (24%) participants and was associated with a trajectory showing an initial improvement in FVC before subsequently decreasing. Cluster 3 comprised 113 (27%) participants and was associated with a trajectory showing an initial decline in FVC before subsequent stabilisation. Cluster 4 comprised 62 (15%) participants and was associated with a trajectory showing stable lung function. Median survival was shortest in cluster 1 (2·87 years [IQR 2·29-3·40]) and cluster 3 (2·23 years [1·75-3·84]), followed by cluster 2 (4·74 years [3·96-5·73]), and was longest in cluster 4 (5·56 years [5·18-6·62]). Baseline FEV1 to FVC ratio and concentrations of the biomarker SP-D were significantly higher in clusters 1 and 3. Similar lung function clusters with some shared anthropometric features were identified in the replication cohort. INTERPRETATION: Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters might optimise evaluation of intervention efficacy during clinical trials and patient management. FUNDING: National Institute for Health and Care Research, Medical Research Council, and GlaxoSmithKline

Genetic overlap between idiopathic pulmonary fibrosis and COVID-19

Coronavirus disease 2019 (COVID-19) is an infectious disease potentially leading to long lasting respiratory symptoms and has resulted in over 4 million deaths worldwide. Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease (ILD) characterised by an aberrant response to alveolar injury leading to progressive scarring of the lungs. Individuals with ILD are at a higher risk of death from COVID-19 [1]

Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis

Background Considerable clinical heterogeneity in idiopathic pulmonary fibrosis (IPF) suggests the existence of multiple disease endotypes. Identifying these endotypes would improve our understanding of the pathogenesis of IPF and could allow for a biomarker-driven personalised medicine approach. We aimed to identify clinically distinct groups of patients with IPF that could represent distinct disease endotypes. Methods We co-normalised, pooled and clustered three publicly available blood transcriptomic datasets (total 220 IPF cases). We compared clinical traits across clusters and used gene enrichment analysis to identify biological pathways and processes that were over-represented among the genes that were differentially expressed across clusters. A gene-based classifier was developed and validated using three additional independent datasets (total 194 IPF cases). Findings We identified three clusters of patients with IPF with statistically significant differences in lung function (p=0.009) and mortality (p=0.009) between groups. Gene enrichment analysis implicated mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity in the pathogenesis underlying these groups. We developed and validated a 13-gene cluster classifier that predicted mortality in IPF (high-risk clusters vs low-risk cluster: HR 4.25, 95% CI 2.14 to 8.46, p=3.7×10−5). Interpretation We have identified blood gene expression signatures capable of discerning groups of patients with IPF with significant differences in survival. These clusters could be representative of distinct pathophysiological states, which would support the theory of multiple endotypes of IPF. Although more work must be done to confirm the existence of these endotypes, our classifier could be a useful tool in patient stratification and outcome prediction in IPF