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Background Infection with the novel severe acute respiratory syndrome coronavirus 2 has been associated with a hypercoagulable state. Emerging data from China and Europe have consistently shown an increased incidence of venous thromboembolism (VTE). We aimed to identify the VTE incidence and early predictors of VTE at our high-volume tertiary care center. Methods We performed a retrospective cohort study of 147 patients who had been admitted to Temple University Hospital with coronavirus disease 2019 (COVID-19) from April 1, 2020 to April 27, 2020. We first identified the VTE (pulmonary embolism [PE] and deep vein thrombosis [DVT]) incidence in our cohort. The VTE and no-VTE groups were compared by univariable analysis for demographics, comorbidities, laboratory data, and treatment outcomes. Subsequently, multivariable logistic regression analysis was performed to identify the early predictors of VTE. Results The 147 patients (20.9% of all admissions) admitted to a designated COVID-19 unit at Temple University Hospital with a high clinical suspicion of acute VTE had undergone testing for VTE using computed tomography pulmonary angiography and/or extremity venous duplex ultrasonography. The overall incidence of VTE was 17% (25 of 147). Of the 25 patients, 16 had had acute PE, 14 had had acute DVT, and 5 had had both PE and DVT. The need for invasive mechanical ventilation (adjusted odds ratio, 3.19; 95% confidence interval, 1.07-9.55) and the admission D-dimer level ≥1500 ng/mL (adjusted odds ratio, 3.55; 95% confidence interval, 1.29-9.78) were independent markers associated with VTE. The all-cause mortality in the VTE group was greater than that in the non-VTE group (48% vs 22%; P = .007). Conclusions Our study represents one of the earliest reported from the United States on the incidence rate of VTE in patients with COVID-19. Patients with a high clinical suspicion and the identified risk factors (invasive mechanical ventilation, admission D-dimer level ≥1500 ng/mL) should be considered for early VTE testing. We did not screen all patients admitted for VTE; therefore, the true incidence of VTE could have been underestimated. Our findings require confirmation in future prospective studies.

Clinical pharmacy has a rich history of advancing practice through innovation. These innovations helped to mold clinical pharmacy into a patient-centered discipline recognized for its contributions to improving medication therapy outcomes. However, innovations in clinical pharmacy practice have now waned. In our view, the growth of academic–practice partnerships could reverse this trend and stimulate innovation among the next generation of pioneering clinical pharmacists. Although collaboration facilitates innovation,academic institutions and health care systems/organizations are not taking full advantage of this opportunity. The academic–practice partnership can be optimized by making both partners accountable for the desired outcomes of their collaboration, fostering symbiotic relationships that promote value-added clinical pharmacy services and emphasizing continuous quality improvement in the delivery of these services. Optimizing academic–practice collaboration on a broader scale requires both partners to adopt a culture that provides for dedicated time to pursue innovation, establishes mechanisms to incubate ideas, recognizes where motivation and vision align, and supports the purpose of the partnership. With appropriate leadership and support, a shift in current professional education and training practices, and a commitment to cultivate future innovators, the academic–practice partnership can develop new and innovative practice advancements that will improve patient outcomes.

We present an approach to the automatic construction of decision procedures, via a detailed example in propositional logic. The approach adapts the methods of proofdplanning and the heuristics for induction to a new domain, that of metatheoretic procedures. This approach starts by providing an alternative characterisation of validitys the proofs of the correctness and completeness of this characterisation, and the existence of a decision procedure, are then amenable to automation in the way we describe. In this paper we identify a set of principled extensions to the heuristics for induction needed to tackle the proof obligations arising in the new problem domain and discuss their integration within the clamdOyster system.