Our aim was to prospectively determine the predictive capabilities of SEPSIS-1 and SEPSIS-3 definitions in the emergency departments and general wards. Patients with National Early Warning Score (NEWS) of 3 or above and suspected or proven infection were enrolled over a 24-h period in 13 Welsh hospitals. The primary outcome measure was mortality within 30 days. Out of the 5422 patients screened, 431 fulfilled inclusion criteria and 380 (88%) were recruited. Using the SEPSIS-1 definition, 212 patients had sepsis. When using the SEPSIS-3 definitions with Sequential Organ Failure Assessment (SOFA) score ≥ 2, there were 272 septic patients, whereas with quickSOFA score ≥ 2, 50 patients were identified. For the prediction of primary outcome, SEPSIS-1 criteria had a sensitivity (95%CI) of 65% (54-75%) and specificity of 47% (41-53%); SEPSIS-3 criteria had a sensitivity of 86% (76-92%) and specificity of 32% (27-38%). SEPSIS-3 and SEPSIS-1 definitions were associated with a hazard ratio (95%CI) 2.7 (1.5-5.6) and 1.6 (1.3-2.5), respectively. Scoring system discrimination evaluated by receiver operating characteristic curves was highest for Sequential Organ Failure Assessment score (0.69 (95%CI 0.63-0.76)), followed by NEWS (0.58 (0.51-0.66)) (p < 0.001). Systemic inflammatory response syndrome criteria (0.55 (0.49-0.61)) and quickSOFA score (0.56 (0.49-0.64)) could not predict outcome. The SEPSIS-3 definition identified patients with the highest risk. Sequential Organ Failure Assessment score and NEWS were better predictors of poor outcome. The Sequential Organ Failure Assessment score appeared to be the best tool for identifying patients with high risk of death and sepsis-induced organ dysfunction.

Primary pericardial mesothelioma (PPM) is an extremely rare malignancy with a very poor prognosis. It poses a diagnostic challenge given its often late and non-specific presentation. This report describes a 74-year-old man who presented with central pleuritic chest pain and mild breathlessness. The patient was febrile and mildly tachycardic with crepitations in the right lung base. Blood tests revealed raised inflammatory markers and chest X-ray showed no acute pathology. Following admission, CT pulmonary angiogram showed a large left-sided mediastinal mass (approximately 110 x 70 x 85 mm) centered on the pericardium. Further post venous phase CT imaging identified possible myocardial invasion alongside suspicious liver nodules. Later, outpatient fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging highlighted further FDG avid pleural and liver lesions. CT-guided biopsy of the pericardial lesion was undertaken, with histology and immunohistochemistry indicating epitheliod-type mesothelioma. A significant malignant pericardial effusion was also identified, which ultimately required pericardial window formation. Immunotherapy was commenced utilizing dual nivolumab and ipilimumab, a novel regime for the treatment of mesothelioma. Palliative radiotherapy to the pericardial lesion will also be performed. Here, we demonstrate the diagnostic challenge of this vanishingly rare condition, which is usually diagnosed upon the development of associated complications. Early recognition gives the best chance of improved mortality, however, diagnosis requires a high index of clinical suspicion alongside prompt investigation, primarily involving cross-sectional imaging.

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The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease.

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