Virtual Care for Patients with Advanced Well Differentiated Gastroenteropancreatic Neuroendocrine Tumor (GEP-NET).

INTRODUCTION: The COVID-19 pandemic resulted in an unprecedent shift towards virtual cancer care, including the care of gastroenteropancreatic neuroendocrine tumors (GEP-NETs). The aim of this study was to evaluate the use of virtual care for GEP-NETs during the COVID-19 pandemic at a high-volume academic cancer center. METHODS: This retrospective, observational study performed at the Ottawa Hospital Cancer Center in Canada evaluated adult patients with GEP-NETs seen in consultation by medical oncology between 1 June 2019 and 31 December 2022. Demographic, clinicopathologic, cancer treatment and visit data were collected. Univariable and multivariable analyses assessed the relationship between patient characteristics and virtual care use. RESULTS: A total of 103 patients with well-differentiated GEP-NETS were included. Overall, 18/103 (17.5%) consults and 594/781 (76.1%) follow-ups were performed virtually. All consultation visits returned to in-person assessment by 2022, while 67.0% and 41.4% follow-ups remained virtual in 2022 and 2023, respectively. The year of follow-up, sex, employment and Charlston comorbidity index were associated with virtual follow-up use in the multivariable analysis. DISCUSSION: Virtual care remained a predominant method of GEP-NET patient assessment in the peri-pandemic period. These results highlight an opportunity to improve access to subspecialty neuroendocrine cancer care through the continued use of virtual care.

Performance and Head-to-Head Comparison of Three Clinical Models to Predict Occurrence of Postthrombotic Syndrome: A Validation Study.

OBJECTIVE: The SOX-PTS, Amin, and Méan models are three different clinical prediction scores stratifying the risk for postthrombotic syndrome (PTS) development in patients with acute deep vein thrombosis (DVT) of the lower limbs. Herein, we aimed to assess and compare these scores in the same cohort of patients. METHODS: We retrospectively applied the three scores in a cohort of 181 patients (196 limbs) who participated in the SAVER pilot trial for an acute DVT. Patients were stratified into PTS risk groups using positivity thresholds for high-risk patients as proposed in the derivation studies. All patients were assessed for PTS 6 months after index DVT using the Villalta scale. We calculated the predictive accuracy for PTS and area under receiver operating characteristic (AUROC) curve for each model. RESULTS: The Méan model was the most sensitive (sensitivity 87.7%; 95% confidence interval [CI]: 77.2-94.5) with the highest negative predictive value (87.5%; 95% CI: 76.8-94.4) for PTS. The SOX-PTS was the most specific score (specificity 97.5%; 95% CI: 92.7-99.5) with the highest positive predictive value (72.7%; 95% CI: 39.0-94.0). The SOX-PTS and Méan models performed well for PTS prediction (AUROC: 0.72; 95% CI: 0.65-0.80 and 0.74; 95% CI: 0.67-0.82), whereas the Amin model did not (AUROC: 0.58; 95% CI: 0.49-0.67). CONCLUSION: Our data support that the SOX-PTS and Méan models have good accuracy to stratify the risk for PTS.