SOX9-regulated matrix proteins predict poor outcomes in patients with COVID-19 and pulmonary fibrosis (preprint)

Pulmonary fibrosis is an increasing and major cause of death worldwide. Understanding the cellular and molecular mechanisms underlying the pathophysiology of lung fibrosis may lead to urgently needed diagnostic and prognostic strategies for the disease. SOX9 is a core transcription factor that has been associated with fibrotic disease, however its role and regulation in acute lung injury and/or fibrosis have not been fully defined. In this study we apply a hypothesis based approach to uncover unique SOX9-protein signatures associated with both acute lung injury and fibrotic progression. Using in vivo models of lung injury in the presence or absence of SOX9, our study shows SOX9 is essential to the damage associated response of alveolar epithelial cells from an early time-point in lung injury. In parallel, as disease progresses, SOX9 is responsible for regulating tissue damaging ECM production from pro-fibrotic fibroblasts. In determining the in vivo role of SOX9 we identified secreted ECM components downstream of SOX9 as markers of acute lung injury and fibrosis. To underscore the translational potential of our SOX9-regulated markers, we analysed serum samples from acute COVID19, post COVID19 and idiopathic pulmonary fibrosis (IPF) patient cohorts. Our hypothesis driven SOX9-panels showed significant capability in all cohorts at identifying patients who had poor disease outcomes. This study shows that SOX9 is functionally critical to disease in acute lung injury and pulmonary fibrosis and its regulated pathways have diagnostic, prognostic and therapeutic potential in both COVID19 and IPF disease.

Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning (preprint)

Background: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 9.3 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 22.8 days. Conclusions: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.