Assessment of the scope, completeness, and consistency of various drug information resources related to COVID-19 medications in pregnancy and lactation.

BACKGROUND: Drug use in pregnancy and lactation is challenging. It becomes more challenging in pregnant and lactating women with certain critical clinical conditions such as COVID-19, because of inconsistent drug safety data. Therefore, we aimed to evaluate the various drug information resources for the scope, completeness, and consistency of the information related to COVID-19 medications in pregnancy and lactation. METHODS: Data related to COVID-19 medications from various drug information resources such as text references, subscription databases, and free online tools were used for the comparison. The congregated data were analyzed for scope, completeness, and consistency. RESULTS: Scope scores were highest for Portable Electronic Physician Information Database (PEPID), Up-to-date, and drugs.com compared to other resources. The overall completeness scores were higher for Micromedex and drugs.com (p < 0.05 compared to all other resources). The inter-reliability analysis for overall components by Fleiss kappa among all the resources was found to be 'slight' (k < 0.20, p < 0.0001). The information related to the older drugs in most of the resources, provides in-depth details on various components such as pregnancy safety, clinical data related to lactation, the effect of the drug distribution into breast milk, reproductive potential/infertility risk and the pregnancy category/recommendations. However, the information related to these components for newer drugs was superficial and incomplete, with insufficient data and inconclusive evidence, which is a statistically significant observation. The strength of observer agreement for the various COVID-19 medications ranged from poor to fair and moderate for the various recommendation categories studied. CONCLUSION: This study reports discrepancies in the information related to pregnancy, lactation, drug level, reproductive risk, and pregnancy recommendations among the resources directing to refer to more than one resource for information about the safe and quality use of medications in this special population.The present study also emphasizes the need for development of comprehensive, evidence-based, and precise information guide that can promote safe and effective drug use in this special population.

Teenagers' and parental individual needs for side effects information and the influence of nocebo effect education.

OBJECTIVES: When developing a policy on how information about medication and its side effects (SE) should be provided in pediatrics, it is crucial to know individual needs. This paper investigates teenagers' and parental attitudes on information on SE, before and after education on the nocebo effect (NE). METHODS: This multicenter survey study included 226 teenagers (12-18 years) and 525 parents of patients (0-18 years). Questions assessed demographics, clinical characteristics and attitudes towards the amount of SE information before and after the explanation of NE. RESULTS: Before NE education, 679 (93 %) participants preferred to receive SE information: 337 (45 %) about all possible SE and 360 (48 %) desired specific information (i.e., severe, common, visible, or long-term SE). After NE explanation, significantly more participants (58 %) wished to receive information about all possible SE (p < .001). When explaining SE, teenagers preferred positive framing more than parents (64 % vs. 54 %, p = .043). CONCLUSIONS: Most teenagers and parents wish to receive extensive SE information, even after explaining the NE, but variances in individual needs exist. PRACTICE IMPLICATIONS: This study emphasizes the importance of tailor-made communication strategies for providing information on medications to parents and their children.

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.

BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. OBJECTIVE: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). METHODS: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. RESULTS: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. CONCLUSIONS: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.