Introduction: A significant increase in the use of computed tomography with pulmonary angiography (CTPA) for the diagnosis of pulmonary embolism (PE) has been observed in the past twenty years. We aimed to investigate whether the validated diagnostic predictive tools and D-dimers were adequately utilized in a large public hospital in New York City. Methods: We conducted a retrospective review of patients who underwent CTPA for the specific indication of ruling out PE over a period of one year. Two independent reviewers, blinded to each other and to the CTPA and D-dimer results, estimated the clinical probability (CP) of PE using Well’s score, the YEARS algorithm, and the revised Geneva score. Patients were classified based on the presence or absence of PE in the CTPA. Results: A total of 917 patients were included in the analysis (median age: 57 years, female: 59%). The clinical probability of PE was considered low by both independent reviewers in 563 (61.4%), 487 (55%), and 184 (20.1%) patients based on Well’s score, the YEARS algorithm, and the revised Geneva score, respectively. D-dimer testing was conducted in less than half of the patients who were deemed to have low CP for PE by both independent reviewers. Using a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off in patients with a low CP of PE would have missed only a small number of mainly subsegmental PE. All three tools, when combined with D-dimer < 500 ng/mL or 95%. Conclusion: All three validated diagnostic predictive tools were found to have significant diagnostic value in ruling out PE when combined with a D-dimer cut-off of <500 ng/mL or the age-adjusted cut-off. Excessive use of CTPA was likely secondary to suboptimal use of diagnostic predictive tools.

Hyperlipidemia has been associated with worse outcomes in patients with Coronavirus disease 2019 (COVID-19). However, lower LDL-C (low-density lipoprotein cholesterol) levels have been associated with increased COVID-19 severity and mortality. We conducted a retrospective observational study of patients with COVID-19 admitted to New York City Health and Hospitals from 1 March 2020 to 31 October 2020, comparing pre-COVID-19 LDL-C levels or LDL-C levels obtained during COVID-19 hospitalization, with the need for invasive mechanical ventilation and death. Propensity score matching was performed using logistic regression models, and standardized mean differences were calculated. A total of 3020 patients (median age 61 years; 36% women) were included. In the matched cohort, on multivariate logistic regression analysis, LDL was inversely associated with in-hospital death (OR: 0.99, 95% CI: 0.986–0.999, p = 0.036). As a categorical variable, LDL > 70 mg/dL was associated with 47% lower likelihood of invasive mechanical ventilation (OR: 0.53, 95% CI: 0.29–0.95, p = 0.034). No significant association between pre-COVID-19 LDL and death or invasive mechanical ventilation was found (OR: 1.00, 95% CI 0.99–1.01, p = 0.833). Low LDL-C level measured during COVID-19 was associated with a higher likelihood of invasive mechanical ventilation and in-hospital death. A similar association was not found between pre-COVID-19 LDL-C and these outcomes. LDL-C levels obtained during COVID-19 are likely not reflective of the baseline lipid profile.

Lung cancer remains the leading cause of cancer-related mortality worldwide, and non-small cell lung cancer (NSCLC) accounts for most cases. Advances in targeted therapies and immunotherapy have improved outcomes, but their benefit depends on timely molecular characterization. Early next-generation sequencing (NGS), performed at or near diagnosis, is increasingly used to identify actionable alterations and inform first-line and perioperative treatment planning. In this review, we examine the clinical, operational, and economic rationale for early comprehensive NGS in NSCLC. Upfront sequencing can improve diagnostic yield, preserve limited tissue, reduce turnaround time, and help avoid non-matched systemic therapy. Practical implementation strategies include Molecular Tumor Boards, low-input assays, liquid biopsy integration, and selected artificial intelligence-supported workflow tools, as well as emerging applications such as minimal residual disease monitoring. Improving access to testing and integrating it more consistently into routine care will be important to extend the benefits of comprehensive genomic profiling to a broader range of patients with NSCLC.