Tamar Lasky

Purpose: To characterize select laboratory tests ordered versus reported for patients diagnosed with COVID‐19 in administrative healthcare and commercial laboratory data.Methods: Among patients with an outpatient COVID‐19 diagnosis claim in HealthVerity data (01/01/2021–12/31/2022), this study described baseline characteristics and descriptively compared SARS‐CoV‐2 diagnostic tests and liver function tests from administrative healthcare (insurance claims and hospital billing data) and commercial laboratories, overall and by code type (e.g., CPT, LOINC). Select liver function tests were also described by method‐specific and methodless LOINC.Results: Among 214 998 patients with COVID‐19, 46.1% had a SARS‐CoV‐2 molecular diagnostic test recorded within 7 days of diagnosis (in either administrative or laboratory data); 44.5% had a corresponding CPT in medical claims, while only 10.0% had a corresponding LOINC in laboratory data. In contrast, the six most common liver function tests (albumin, aspartate aminotransferase, total protein, alkaline phosphatase, alanine aminotransferase, and total bilirubin) were identified in 55.7%–56.6% of patients via LOINC, but only in 3.2%–4.2% via CPT claims. Of the total count of select liver function tests performed in the laboratory data, 99.7% of aspartate aminotransferase, 96.1% of direct bilirubin, and 82.9% of lactate dehydrogenase were reported by methodless LOINC rather than method‐specific LOINC.Conclusions: Important differences were identified between orders for SARS‐CoV‐2 diagnostic tests and liver function tests, as well as missingness of LOINC method, highlighting challenges related to completeness of laboratory data in real‐world data sources. These challenges underscore a need to improve data quality when considering the utility of laboratory data for research.

Unique challenges pertain when studying children, although many research principles are the same as those when studying adult populations. This truism extends to the use of real-world data (RWD). RWD are particularly relevant to pediatrics because they may potentially provide an additional source of data to inform pediatric labeling and practice patterns when clinical trials have not been or cannot be conducted. The purpose of this commentary is to provide a brief overview of the unique issues in using RWD to study the effectiveness or safety of medical therapies in children.

Authors:Alecia Clary , Ph.D., MSW (AC) (Corresponding author)Affiliation: The Reagan-Udall Foundation for the FDA, Washington, DC, USA Address: 1333 New Hampshire Ave, NW Suite 420, Washington DC 20036, USA Email: aclary@reaganudall.org Phone: 202-849-2075 ORCID ID: 0000-0002-7774-9808Nancy D Lin , (NDL) Affiliation: IQVIA Real World Solutions, Bridgewater, NJ, USA Address: 77 Corporate Drive, Bridgewater, NJ 08807 Email: Nancy.lin@iqvia.comTamar Lasky , (TL) Affiliation: The United States Food and Drug Administration, Office of Data, Analytics, & Research, Office of the Commissioner, Washington DC, USA Address: 10903 New Hampshire Avenue, Silver Spring, MD, USA. Email: Tamar.lasky@fda.hhs.govMatthew W Reynolds , (MR) Affiliation: IQVIA Real-World Solutions, Washington, DC, USA Address: 201 Broadway, Cambridge, MA, USA. Email: Matthew.Reynolds@IQVIA.comAnand Chokkalingam , (ACH) Affiliation: Gilead Sciences, Foster City, California, USA Address: 333 Lakeside Drive Email: Anand.Chokkalingam@gilead.comCarla Rodriguez-Watson, (CRW)Affiliation: The Reagan-Udall Foundation for the FDA, Washington, DC, USA Address: 1333 New Hampshire Ave, NW Suite 420, Washington DC 20036, USA Email: crodriguezwatson@reaganudall.orgThis paper has not been previously printed, currently has not been submitted for publication in any other journal and is not pending acceptance in any other journal. We have not had any prior correspondence with the journal about the manuscript.

Purpose: Algorithms for classification of inpatient COVID‐19 severity are necessary for confounding control in studies using real‐world data.Methods: Using Healthverity chargemaster and claims data, we selected patients hospitalized with COVID‐19 between April 2020 and February 2021, and classified them by severity at admission using an algorithm we developed based on respiratory support requirements (supplemental oxygen or non‐invasive ventilation, O2/NIV, invasive mechanical ventilation, IMV, or NEITHER). To evaluate the utility of the algorithm, patients were followed from admission until death, discharge, or a 28‐day maximum to report mortality risks and rates overall and by stratified by severity. Trends for heterogeneity in mortality risk and rate across severity classifications were evaluated using Cochran‐Armitage and Logrank trend tests, respectively.Results: Among 118 117 patients, the algorithm categorized patients in increasing severity as NEITHER (36.7%), O2/NIV (54.3%), and IMV (9.0%). Associated mortality risk (and 95% CI) was 11.8% (11.6–12.0%) overall and increased with severity [3.4% (3.2–3.5%), 11.5% (11.3–11.8%), 47.3% (46.3–48.2%); p < 0.001]. Mortality rate per 1000 person‐days (and 95% CI) was 15.1 (14.9–15.4) overall and increased with severity [5.7 (5.4–6.0), 14.5 (14.2–14.9), 32.7 (31.8–33.6); p < 0.001].Conclusion: As expected, we observed a positive association between the algorithm‐defined severity on admission and 28‐day mortality risk and rate. Although performance remains to be validated, this provides some assurance that this algorithm may be used for confounding control or stratification in treatment effect studies.

Objective: This scoping review aims to map studies using real-world data (RWD) to measure pediatric safety and effectiveness of vaccines administered to pregnant women. Introduction: In the United States, two vaccines are recommended for all pregnant women to prevent illness in the infant: inactivated influenza vaccine (recommended since 2004) and the combined tetanus-diphtheria-acellular pertussis (Tdap) vaccine (recommended since 2013). Because of the ethical constraints in conducting randomized clinical trials to measure the effects on the infant, there is great interest in using electronic health care data or administrative claims data to study the effects of maternal immunization on the infant's health, and it is anticipated that such studies may be submitted to support regulatory decision-making. This scoping review will map the studies conducted to date that address these questions and provide a context for considering the regulatory issues that may arise in the future. Inclusion criteria: Studies that report on pregnant women receiving immunization and the effectiveness or safety outcomes in their infants will be included. Study participants may be from any population or country, of any reproductive age, and with any health status. Studies will be included if they use real-world data (from electronic health records, administrative claims, pharmacy benefit records, or registries). Methods: An electronic search of PubMed and Embase will identify citations for screening. The search will be limited to studies published in English during the preceding 10 years. Two reviewers will screen citations in a two-step process (titles and abstracts, then full-text articles), and two reviewers will extract data for summary and synthesis.

Background The promise of real-world evidence (RWE) is especially relevant to pediatrics, where medicines prescribed for children are often used without evidence derived from randomized clinical trials. Objectives The aim of this systematic review was to describe the state of RWE in pediatrics by identifying observational studies published during 2016 that used RWE to assess medication safety or effectiveness in children. Methods An electronic search of PubMed was combined with an extended search of references within systematic reviews and expert suggestions. Studies were included if they reported on an infant or child under 18 years with exposure to medications; assessed safety or effectiveness; specified a comparison or control group, and were published in English in 2016. Data extraction was conducted by one team member using a standardized form and reviewed by a second team member. Study quality was assessed using the GRACE checklist for rating the quality of observational studies. Results After removing duplicates, 915 citations were screened and 29 studies met the eligibility criteria. Most of the eligible studies relied on primary data collection or chart review at a single institution and did not use the growing number of administrative or electronic health record databases available. One-quarter of the studies did not use well-established statistical methods to control for confounders. No single disease group or medication predominated, and age groups ranged from infants to adolescents. Conclusions A small body of observational studies published in 2016 were categorized by the study team as using real-world data to assess medication safety or effectiveness in children. Studies varied in age groups, diseases or conditions, and methods, and may not have fully met the FDA definition of RWE. Our review indicates that the use of RWE is not fully developed in pediatrics, and suggests an opportunity to further develop capabilities and more fully leverage administrative and electronic health record databases to study medication safety and effectiveness in children. Our systematic review appears generalizable to pediatrics broadly, and documents that the high level of activity in RWE in general has had less of an impact on pediatrics.

In the real world, we lack evidence guiding the use of medications and devices in children. This lack of evidence arose out of the challenges of conducting clinical trials in children and other vulnerable populations and the historical decision (reversed in recent decades) to exclude children from clinical trials. The recent focus on the potential of real-world evidence (RWE) to guide approval and use of new treatments may provide a much-needed solution. A broad definition of RWE includes prospective observational data and data from electronic health records and claims, as well as other sources. For the most part, it is reasonable to expect that considerations around the use of RWE in adult populations will apply to its use in children. However, a number of issues around the use of RWE are unique to studying children. These fall into at least four categories: (1) identification of databases with adequate numbers of children in the age sub-groups of interest, (2) access to critical variables such as birth date, birth weight, and gestational age, (3) linkage to parental records for information about pre-natal exposures, family history, and socio-economic status, and (4) linkage to school records for information about outcomes such as missed school days, academic progress, and behavioral issues. Addressing the needs of children in developing methodologies for use of RWE ensures that ongoing efforts will benefit children as well as other sectors of the population.