Limited disease progression in endocrine surgery patients with treatment delays due to COVID-19.

BACKGROUND: The COVID-19 pandemic profoundly impacted the delivery of care and timing of elective surgical procedures. Most endocrine-related operations were considered elective and safe to postpone, providing a unique opportunity to assess clinical outcomes under protracted treatment plans. METHODS: American Association of Endocrine Surgeon members were surveyed for participation. A Research Electronic Data Capture survey was developed and distributed to 27 institutions to assess the impact of COVID-19-related delays. The information collected included patient demographics, primary diagnosis, resumption of care, and assessment of disease progression by the surgeon. RESULTS: Twelve out of 27 institutions completed the survey (44.4%). Of 850 patients, 74.8% (636) were female; median age was 56 (interquartile range, 44-66) years. Forty percent (34) of patients had not been seen since their original surgical appointment was delayed; 86.2% (733) of patients had a delay in care with women more likely to have a delay (87.6% vs 82.2% of men, χ2 = 3.84, P = .05). Median duration of delay was 70 (interquartile range, 42-118) days. Among patients with a delay in care, primary disease site included thyroid (54.2%), parathyroid (37.2%), adrenal (6.5%), and pancreatic/gastrointestinal neuroendocrine tumors (1.3%). In addition, 4.0% (26) of patients experienced disease progression and 4.1% (24) had a change from the initial operative plan. The duration of delay was not associated with disease progression (P = .96) or a change in operative plan (P = .66). CONCLUSION: Although some patients experienced disease progression during COVID-19 delays to endocrine disease-related care, most patients with follow-up did not. Our analysis indicated that temporary delay may be an acceptable course of action in extreme circumstances for most endocrine-related surgical disease.

Ethanol ablation for the treatment of benign thyroid nodules.

BACKGROUND: Ethanol ablation (EA) is a non-surgical option for the treatment of benign cystic thyroid nodules. This study summarizes our preliminary experience with the efficacy and safety of EA. METHODS: A retrospective analysis was performed of patients undergoing EA for symptomatic, benign, cystic and predominantly cystic (≥75%) thyroid nodules. Baseline nodule volume, cosmetic scores, and symptom scores were assessed, as well as volume reduction ratio (VRR), cosmetic and symptom scores at post-procedure months 1, 3, 6, and 12. RESULTS: 31 patients underwent an uncomplicated EA for a single cyst with an average volume of 21.3 cc (range: 1.7-101.4 cc). Follow-up was limited by the COVID-19 pandemic. Mean nodule VRRs were 66 ± 20% (1 m, n = 17), 87 ± 15% (3 m, n = 9), 72 ± 20% (6 m, n = 7), and 78% (12 m, n = 3). Mean symptom and cosmetic scores decreased concurrently post-procedure. CONCLUSION: EA is a safe, effective option for benign cystic and predominantly cystic thyroid nodules.

Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials.

Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity: i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction.

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials.

OBJECTIVE: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data. MATERIALS AND METHODS: On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020-June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death. RESULTS: There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4-28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event. DISCUSSION: By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients. CONCLUSIONS: This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.

The COVID-19 Trial Finder.

Clinical trials are the gold standard for generating reliable medical evidence. The biggest bottleneck in clinical trials is recruitment. To facilitate recruitment, tools for patient search of relevant clinical trials have been developed, but users often suffer from information overload. With nearly 700 coronavirus disease 2019 (COVID-19) trials conducted in the United States as of August 2020, it is imperative to enable rapid recruitment to these studies. The COVID-19 Trial Finder was designed to facilitate patient-centered search of COVID-19 trials, first by location and radius distance from trial sites, and then by brief, dynamically generated medical questions to allow users to prescreen their eligibility for nearby COVID-19 trials with minimum human computer interaction. A simulation study using 20 publicly available patient case reports demonstrates its precision and effectiveness.