Subsegmental pulmonary embolism and anticoagulant therapy: the impact of clinical context.

BACKGROUND: Anticoagulation for subsegmental pulmonary embolism (SSPE) is controversial. AIM: To assess the impact of clinical context on anticoagulation and outcomes of SSPE. METHODS: We electronically searched computed tomography pulmonary angiogram reports to identify SSPE. We extracted demographic, risk factor, investigations and outcome data from the electronic medical record. We stratified patients according to anticoagulation and no anticoagulation. RESULTS: From 1 January 2017 to 31 December 2019, we identified 166 patients with SSPE in 5827 pulmonary angiogram reports. Of these, 123 (74%) received anticoagulation. Compared with non-anticoagulated patients, such patients had a different clinical context: higher rates of previous venous thromboembolism (11% vs 0%; P = 0.019), more recent surgery (26% vs 9%; P = 0.015), more elevated serum D-dimer (22% vs 5%; P = 0.004), more lung parenchymal abnormalities (76% vs 61%; P = 0.037) and were almost twice as likely to require inpatient care (76% vs 42%; P < 0.001). Such patients also had twice the all-cause mortality at 1 year (32% vs 16%). CONCLUSIONS: SSPE is diagnosed in almost 3% of pulmonary angiograms and is associated with high mortality, regardless of anticoagulation, due to coexistent disease processes rather than SSPE. Anticoagulation appears dominant but markedly affected by the clinical context of risk factors, alternative indications and illness severity. Thus, the controversy is partly artificial because anticoagulation after SSPE is clinically contextual with SSPE as only one of several factors.

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study.

BACKGROUND: Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention. METHODS: We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction. RESULTS: ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested. CONCLUSIONS: ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.

Neuromyelitis Optica Spectrum Disorder and Anti-Aquaporin 4 Channel Immunoglobulin in an Australian Pediatric Demyelination Cohort.

Neuromyelitis optica spectrum disorder is uncommon in children, and often seronegative for aquaporin-4 immunoglobulin G (AQP4-IgG). We conducted a retrospective study of 67 children presenting to a single Australian center with acquired demyelinating syndromes over a 7-year period. All patients were tested for AQP4-IgG. Five children (7.5%) had neuromyelitis optica spectrum disorder. One child was seropositive for AQP4-IgG (1.5%) and had a relapsing disease course with mild residual deficits. She also had a concomitant motor axonal neuropathy that improved with immunosuppressive therapy. Of the remaining 4 children, 3 had a monophasic course and 1 a relapsing course. Two were tested for anti-myelin oligodendrocyte glycoprotein (anti-MOG) antibody and both were seropositive. This study confirms that neuromyelitis optica spectrum disorder is uncommon in children, and that AQP4-IgG seropositivity is rare. Anti-MOG antibodies should be tested in children with neuromyelitis optica spectrum disorder.