Although we know more about the risk factors, survival for patients with metastatic cutaneous SCC depends on extent of nodal involvement. Therefore, emphasis should remain on prevention and aggressive treatment of cutaneous SCC and vigilant observation for signs and symptoms of metastasis.

The coexistence of coronavirus disease 2019 (COVID-19) and pulmonary embolism (PE), two life-threatening illnesses, in the same patient presents a unique challenge. Guidelines have delineated how best to diagnose and manage patients with PE. However, the unique aspects of COVID-19 confound both the diagnosis and treatment of PE, and therefore require modification of established algorithms. Important considerations include adjustment of diagnostic modalities, incorporation of the prothrombotic contribution of COVID-19, management of two critical cardiorespiratory illnesses in the same patient, and protecting patients and health-care workers while providing optimal care. The benefits of a team-based approach for decision-making and coordination of care, such as that offered by pulmonary embolism response teams (PERTs), have become more evident in this crisis. The importance of careful follow-up care also is underscored for patients with these two diseases with long-term effects. This position paper from the PERT Consortium specifically addresses issues related to the diagnosis and management of PE in patients with COVID-19.

Background Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lead to characteristic patterns in the results of widely available, routine blood tests that could be identified with machine learning methodologies. Machine learning modalities integrating findings from these common laboratory test results might accelerate ruling out COVID-19 in emergency department patients. Objective We sought to develop (ie, train and internally validate with cross-validation techniques) and externally validate a machine learning model to rule out COVID 19 using only routine blood tests among adults in emergency departments. Methods Using clinical data from emergency departments (EDs) from 66 US hospitals before the pandemic (before the end of December 2019) or during the pandemic (March-July 2020), we included patients aged ≥20 years in the study time frame. We excluded those with missing laboratory results. Model training used 2183 PCR-confirmed cases from 43 hospitals during the pandemic; negative controls were 10,000 prepandemic patients from the same hospitals. External validation used 23 hospitals with 1020 PCR-confirmed cases and 171,734 prepandemic negative controls. The main outcome was COVID 19 status predicted using same-day routine laboratory results. Model performance was assessed with area under the receiver operating characteristic (AUROC) curve as well as sensitivity, specificity, and negative predictive value (NPV). Results Of 192,779 patients included in the training, external validation, and sensitivity data sets (median age decile 50 [IQR 30-60] years, 40.5% male [78,249/192,779]), AUROC for training and external validation was 0.91 (95% CI 0.90-0.92). Using a risk score cutoff of 1.0 (out of 100) in the external validation data set, the model achieved sensitivity of 95.9% and specificity of 41.7%; with a cutoff of 2.0, sensitivity was 92.6% and specificity was 59.9%. At the cutoff of 2.0, the NPVs at a prevalence of 1%, 10%, and 20% were 99.9%, 98.6%, and 97%, respectively. Conclusions A machine learning model developed with multicenter clinical data integrating commonly collected ED laboratory data demonstrated high rule-out accuracy for COVID-19 status, and might inform selective use of PCR-based testing.

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Massive pulmonary embolism (PE) refers to large emboli that cause hemodynamic instability, right ventricular failure, and circulatory collapse. According to the 2016 ACCP Antithrombotic Guidelines, therapy for massive PE should include systemic thrombolytic therapy in conjunction with anticoagulation and supportive care. However, in patients with a contraindication to systemic thrombolytics or in those who fail the above interventions, extracorporeal membrane oxygenation (ECMO) and/or surgical embolectomy may be used to improve oxygenation, achieve hemodynamic stability, and successfully treat massive PE. Randomized controlled human trials evaluating ECMO in this context have not been done, and its role has not been well-defined. The European Society of Cardiology 2014 acute PE guidelines briefly mention that ECMO can be used for massive PE as a method for hemodynamic support and as an adjunct to surgical embolectomy. The 2016 CHEST Antithrombotic Therapy for venous thromboembolism Disease guidelines do not mention ECMO in the management of massive PE. However, multiple case reports and small series cited benefit with ECMO for massive PE. Further, ECMO may facilitate stabilization for surgical embolectomy. Unfortunately, ECMO requires full anticoagulation to maintain the functionality of the system; hence, significant bleeding complicates its use in 35% of patients. Contraindications to ECMO include high bleeding risk, recent surgery or hemorrhagic stroke, poor baseline functional status, advanced age, neurologic dysfunction, morbid obesity, unrecoverable condition, renal failure, and prolonged cardiopulmonary resuscitation without adequate perfusion of end organs. In this review, we discuss management of massive PE, with an emphasis on the potential role for ECMO and/or surgical embolectomy.