Regional outcomes of severe acute respiratory syndrome coronavirus 2 infection in hospitalised patients with haematological malignancy.

OBJECTIVES: We sought to characterise the outcomes of patients with haematological malignancy and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hospital in our regional network of 7 hospitals. METHODS: Consecutive hospitalised patients with haematological malignancy and SARS-CoV-2 infection were identified from 01/03/2020 to 06/05/2020. Outcomes were categorised as death, resolved or ongoing. The primary outcome was preliminary case fatality rate (pCFR), defined as the number of cases resulting in death as a proportion of all diagnosed cases. Analysis was primarily descriptive. RESULTS: 66 Patients were included, overall pCFR was 51.5%. Patients ≥ 70 years accounted for the majority of hospitalised cases (42, 63%) and fatalities (25, 74%). Mortality was similar between females (52%) and males (51%). Immunosuppressive or cytotoxic treatment within 3 months of the diagnosis of SARS-CoV-2 infection was associated with a significantly higher pCFR of 70%, compared with 28% in those not on active treatment (P = .0013, 2 proportions z test). CONCLUSIONS: Mortality rates in patients with haematological malignancy and SARS-CoV-2 infection in hospital are high supporting measures to minimise the risk of infection in this population.

The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis.

Artificial neural networks are machine-learning algorithms designed to analyse data without a pre-existing hypothesis as to any associations that may exist. This technique has not previously been applied to the risk stratification of patients referred with suspected deep vein thrombosis (DVT). Current assessment is usually with a points-based clinical score, which may be combined with a D-dimer blood test. A neural network was trained to risk-stratify patients presenting with suspected DVT and its performance compared with existing tools. Data from 11 490 cases of suspected DVT presenting consecutively between 1 January 2011 and 31 December 2017 were analysed, and 7080 for whom all components of the Wells' score, a D-dimer and an ultrasound result were available were included in the analysis. The data were broken into a training set of 5270 patients, used to develop the algorithm, and a testing set of 1810 patients to assess performance of the trained algorithm. This network was able to exclude DVT without the need for ultrasound in significantly more patients than existing risk assessment scores, whilst retaining very low false negatives rates. More generally, this approach may improve the analysis of complex data to support decision-making in other areas of clinical medicine.