Validation of the DECAF score to predict hospital mortality in acute exacerbations of COPD

Background Hospitalisation due to acute exacerbations of COPD (AECOPD) is common, and subsequent mortality high. The DECAF score was derived for accurate prediction of mortality and risk strati fi cation to inform patient care. We aimed to validate the DECAF score, internally and externally, and to compare its performance to other predictive tools. Methods The study took place in the two hospitals within the derivation study (internal validation) and in four additional hospitals (external validation) between January 2012 and May 2014. Consecutive admissions were identi fi ed by screening admissions and searching coding records. Admission clinical data, including DECAF indices, and mortality were recorded. The prognostic value of DECAF and other scores were assessed by the area under the receiver operator characteristic (AUROC) curve. Results In the internal and external validation cohorts, 880 and 845 patients were recruited. Mean age was 73.1 (SD 10.3) years, 54.3% were female, and mean (SD) FEV 1 45.5 (18.3) per cent predicted. Overall mortality was 7.7%. The DECAF AUROC curve for inhospital mortality was 0.83 (95% CI 0.78 to 0.87) in the internal cohort and 0.82 (95% CI 0.77 to 0.87) in the external cohort, and was superior to other prognostic scores for inhospital or 30-day mortality. Conclusions DECAF is a robust predictor of mortality, using indices routinely available on admission. Its generalisability is supported by consistent strong performance; it can identify low-risk patients (DECAF 0 – 1) potentially suitable for Hospital at Home or early supported discharge services, and high-risk patients (DECAF 3 – 6) for escalation planning or appropriate early palliation. Trial registration number UKCRN ID 14214

Discovery of distinct immune phenotypes using machine learning in pulmonary arterial hypertension

RATIONALE: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, though it remains unknown if distinct immune phenotypes exist. OBJECTIVE: Identify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles. METHODS AND RESULTS: In a prospective observational study of Group 1 PAH patients evaluated at Stanford University (discovery cohort, n=281) and University of Sheffield (validation cohort, n=104) between 2008-2014, we measured a circulating proteomic panel of 48 cytokines, chemokines, and factors using multiplex immunoassay. Unsupervised machine learning (consensus clustering) was applied in both cohorts independently to classify patients into proteomic immune clusters, without guidance from clinical features. To identify central proteins in each cluster, we performed partial correlation network analysis. Clinical characteristics and outcomes were subsequently compared across clusters. Four PAH clusters with distinct proteomic immune profiles were identified in the discovery cohort. Cluster 2 (n=109) had low cytokine levels similar to controls. Other clusters had unique sets of upregulated proteins central to immune networks- cluster 1 (n=58)(TRAIL, CCL5, CCL7, CCL4, MIF), cluster 3 (n=77)(IL-12, IL-17, IL-10, IL-7, VEGF), and cluster 4 (n=37)(IL-8, IL-4, PDGF-β, IL-6, CCL11). Demographics, PAH etiologies, comorbidities, and medications were similar across clusters. Non-invasive and hemodynamic surrogates of clinical risk identified cluster 1 as high-risk and cluster 3 as low-risk groups. Five-year transplant-free survival rates were unfavorable for cluster 1 (47.6%, CI 35.4-64.1%) and favorable for cluster 3 (82.4%, CI 72.0-94.3%)(across-cluster p<0.001). Findings were replicated in the validation cohort, where machine learning classified four immune clusters with comparable proteomic, clinical, and prognostic features. CONCLUSIONS: Blood cytokine profiles distinguish PAH immune phenotypes with differing clinical risk that are independent of World Health Organization Group 1 subtypes. These phenotypes could inform mechanistic studies of disease pathobiology and provide a framework to examine patient responses to emerging therapies targeting immunity

The detection of novelty relies on dopaminergic signaling: evidence from apomorphine's impact on the novelty N2

Despite much research, it remains unclear if dopamine is directly involved in novelty detection or plays a role in orchestrating the subsequent cognitive response. This ambiguity stems in part from a reliance on experimental designs where novelty is manipulated and dopaminergic activity is subsequently observed. Here we adopt the alternative approach: we manipulate dopamine activity using apomorphine (D1/D2 agonist) and measure the change in neurological indices of novelty processing. In separate drug and placebo sessions, participants completed a von Restorff task. Apomorphine speeded and potentiated the novelty-elicited N2, an Event-Related Potential (ERP) component thought to index early aspects of novelty detection, and caused novel-font words to be better recalled. Apomorphine also decreased the amplitude of the novelty-P3a. An increase in D1/D2 receptor activation thus appears to potentiate neural sensitivity to novel stimuli, causing this content to be better encoded. © 2013 Rangel-Gomez et al

Variability in the freshwater balance of northern Marguerite Bay, Antarctic Peninsula: results from δ18O

We investigate the seasonal variability in freshwater inputs to the Marguerite Bay region (Western Antarctic Peninsula) using a time series of oxygen isotopes in seawater from samples collected in the upper mixed layer of the ocean during 2002 and 2003. We find that meteoric water, mostly in the form of glacial ice melt, is the dominant freshwater source, accounting for up to 5% of the near-surface ocean during the austral summer. Sea ice melt accounts for a much smaller percentage, even during the summer (maximum around 1%). The seasonality in meteoric water input to the ocean (around 2% of the near-surface ocean) is not dissimilar to that of sea ice melt (around 2% in 2002 and 1% in 2003), contradicting the assumption that sea ice processes dominate the seasonal evolution of the physical ocean environment close to the Antarctic continent. Three full-depth profiles of oxygen isotopes collected in successive Decembers (2001, 2002 and 2003) indicate that around 4 m of meteoric water is present in the water column at this time of year, and around 1 m of sea ice formed from this same water column. The predominance of glacial melt is significant, since it is known to be an important factor in the operation of the ecosystem, for example by providing a source of nutrients and modifying the physical environment to control the spatial extent and magnitude of phytoplankton blooms. The Western Antarctic Peninsula is undergoing a very rapid change in climate, with increasing ocean and air temperatures, retreating glaciers and increases in precipitation associated with changes in atmospheric circulation. As climate change continues, we expect meteoric water inputs to the adjacent ocean to rise further. Sea ice in this sector of the Antarctic has shown a climatic decrease, thus we expect a reduction in oceanic sea ice melt fractions if this change continues. Continued monitoring of the oceanic freshwater budget at the western Peninsula is needed to track these changes as they occur, and to better understand their ecological consequences