Detailed, precise information on a pharmaceutical’s projected therapeutic use is required for horizon scanning. Inferring an estimated indication from trial protocols is a key skill of horizon scanners. The International Horizon Scanning Initiative (IHSI) database utilizes semi-automated data collection. This pilot aimed to verify that the extraction of relevant word sets to generate an estimated indication could be semi-automated.

Ten drugs approved in Europe in 2021 were selected as the pilot test set. The test set included drugs approved for the treatment of rare diseases (n=4), haemato-oncology (n=3), and non-oncology conditions (n=3). Eight of the drugs were approved based on phase III trials. The assessment comprised a review of the pivotal trial that supported product registration for these drugs. We undertook a comparison between a human curator and a natural language processing (NLP) algorithm in generating granular tags relating to key aspects of the drugs’ estimated indication (stage of disease, patient-specific subgroup, and place in treatment).

In 50 percent of cases, the NLP accurately tagged a word or word set related to stage of disease, patient-specific subgroup, or place in treatment, which was also tagged by human curators. In 50 percent of cases, the NLP did not identify words or word sets tagged by human curators. Where relevant, the NLP successfully tagged the same word sets relating to stage of disease for all drugs in the test set. The same word sets relating to patient-specific subgroup were successfully tagged for three drugs in the set. NLP successfully tagged word sets relating to place in treatment for two drugs.

The NLP algorithm is successful in extracting relevant word sets, which can be used to generate an estimated indication in an automated or semi-automated process. The pilot highlighted that further testing is required to advance the sensitivity of the algorithm. Further piloting exploring both unsupervised and supervised modeling approaches (named entity recognition and deep neural networks, respectively) is planned.

Detail on a technology’s projected therapeutic use is required for horizon scanning. The International Horizon Scanning Initiative (IHSI) database will utilize natural language processing (NLP) augmented by human curation to generate an estimated indication for technologies in development. We compared the estimated indication, generated as a test-set for NLP, with health technology developers’ (HTDs) proposed indications identified from Ireland’s horizon scanning system (HSS).

Eight oncology technologies common to both Ireland’s HSS and the IHSI database were analyzed. The analysis included unlicensed technologies in late-stage development that have not submitted a European marketing authorization application. Ireland’s HSS receives data on proposed indications for technologies from HTDs. IHSI database curators extract and convert terms from clinical trials into structured inputs (condition, combination therapy, stage of disease, place in treatment, patient/disease-specific subgroups) to produce an estimated indication for a technology. We sought to identify, by structured input, the degree of alignment between HTDs’ proposed indications with the IHSI database’s estimated indication.

There was 100 percent alignment between the HTD’s proposed indication and the estimated indication generated in the IHSI database for five of the eight included technology records. There was 83 percent alignment for two records and 67 percent alignment for one record. Across all records there was full alignment on condition, combination therapy details, patient-specific subgroup, disease-specific subgroup, and place in treatment. Stage of disease was the only element where data was either not generated for the IHSI database’s estimated indication, not aligned with the HTD’s proposed indication, or reported in an incorrect field.

There is a high degree of alignment between an HTD-proposed indication and the IHSI-estimated indication. The processes for generating an estimated indication will involve both NLP-generation and human co-curation. The current (curator-selected) elements are being used to train the NLP engine. Thereafter, the engine will process clinical trial data to surface tags for human selection to generate the structured inputs.

To describe the cumulative seroprevalence of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) antibodies during the coronavirus disease 2019 (COVID-19) pandemic among employees of a large pediatric healthcare system.

Prospective observational cohort study open to adult employees at the Children’s Hospital of Philadelphia, conducted April 20–December 17, 2020.

Employees were recruited starting with high-risk exposure groups, utilizing e-mails, flyers, and announcements at virtual town hall meetings. At baseline, 1 month, 2 months, and 6 months, participants reported occupational and community exposures and gave a blood sample for SARS-CoV-2 antibody measurement by enzyme-linked immunosorbent assays (ELISAs). A post hoc Cox proportional hazards regression model was performed to identify factors associated with increased risk for seropositivity.

In total, 1,740 employees were enrolled. At 6 months, the cumulative seroprevalence was 5.3%, which was below estimated community point seroprevalence. Seroprevalence was 5.8% among employees who provided direct care and was 3.4% among employees who did not perform direct patient care. Most participants who were seropositive at baseline remained positive at follow-up assessments. In a post hoc analysis, direct patient care (hazard ratio [HR], 1.95; 95% confidence interval [CI], 1.03–3.68), Black race (HR, 2.70; 95% CI, 1.24–5.87), and exposure to a confirmed case in a nonhealthcare setting (HR, 4.32; 95% CI, 2.71–6.88) were associated with statistically significant increased risk for seropositivity.

Employee SARS-CoV-2 seroprevalence rates remained below the point-prevalence rates of the surrounding community. Provision of direct patient care, Black race, and exposure to a confirmed case in a nonhealthcare setting conferred increased risk. These data can inform occupational protection measures to maximize protection of employees within the workplace during future COVID-19 waves or other epidemics.