Unexpected Term Neonatal Intensive Care Unit Admissions and a Potential Role for Centralized Remote Fetal Monitoring

OBJECTIVE

Centralized remote fetal monitoring (CRFM) has been proposed as a method to improve the performance of intrapartum fetal heart rate (FHR) monitoring and perinatal outcomes. The purpose of this study is to determine whether CRFM was associated with a reduction in unexpected term neonatal intensive care unit (NICU) admissions.

STUDY DESIGN

A pre-post design was used to examine the effectiveness of CRFM which was implemented in stages across five hospitals. The exposure group was all women who underwent intrapartum monitoring via CRFM. The unexposed group was of women who delivered at the same hospitals prior to implementation of CRFM. Pregnancies with expected NICU admissions, gestational age <37 weeks, birth weight <2,500 g, or major fetal anomalies detected prenatally were excluded. The primary outcome was unexpected term NICU admission; secondary outcomes were cesarean and operative vaginal delivery (OVD), and 5-minute Apgar's score of <7 rates. Maternal and delivery characteristics were examined with Student's t, Wilcoxon's, Chi-square, and Fisher's exact tests. Multivariable logistic regression was performed to control for potential confounders.

RESULTS

There were 19,392 live births included in this analysis. In the univariable analysis, the odds of unexpected term NICU admission was lower among the CRFM exposed group compared with the unexposed group (odds ratio [OR] = 0.86, 95% confidence interval [CI]: 0.75-0.99; p = 0.038). In multivariable analysis, this did not reach statistical significance (OR = 0.92, 95% CI: 0.79-1.06; p = 0.24). Cesarean and OVD were less likely in the exposed group (OR = 0.91, 95% CI: 0.85-0.97; p = 0.008) and (OR = 0.70, 95% CI: 0.59-0.83, p < 0.001), respectively, in univariable analysis. When adjusted for potential confounders, the effect remained statistically significant for cesarean delivery (OR = 0.92, 95% CI: 0.85-0.98; p = 0.012). When adjusted for hospital, OVD rate was lower at the highest volume and highest acuity site (OR = 0.48, 95% CI: 0.36-0.65, p < 0.001).

CONCLUSION

In some practice settings, utilization of a CRFM system may decrease the risk of unexpected term NICU admission, cesarean, and OVD rate.

KEY POINTS

· CRFM may decrease unexpected term NICU admissions in some clinical settings.. · CRFM may decrease cesarean delivery rates in some clinical settings.. · CRFM may decrease OVD rates in some clinical settings..

Clopidogrel resistance is common in patients undergoing vascular and coronary interventions

OBJECTIVES

"Clopidogrel resistance," also defined as heightened platelet reactivity (HPR) while on clopidogrel therapy, may lead to a sub-optimal antiplatelet effect and a potential thrombotic event. There is limited literature addressing the prevalence of HPR in a large cohort of patients receiving either coronary or endovascular interventions.

METHODS

In a large integrated healthcare system, patients with a P2Y12 reaction units (PRU) test were identified. HPR was defined as a PRU ≥ 200 during clopidogrel therapy. Vascular and coronary interventions were identified utilizing CPT codes, HPR prevalence was calculated, and Fischer's exact test was used to determine significance.

RESULTS

From an initial cohort of 2,405,957 patients (October 2014 to January 2020), we identified 3301 patients with PRU tests administered. Of these, 1789 tests had a PRU ≥ 200 (HPR overall prevalence, 54%). We then identified 1195 patients who underwent either an endovascular or coronary procedure and had a PRU measurement. This corresponded to 935 coronary and 260 endovascular interventions. In the coronary cohort, the HPR prevalence was 54% (503/935). In the vascular cohort, the HPR prevalence was 53% (137/260); there was no difference between cohorts in HPR prevalence (p = 0.78).

CONCLUSION

"Clopidogrel resistance" or HPR was found to be present in nearly half of patients with cardiovascular disease undergoing intervention. Our data suggest HPR is more common in the cardiovascular patient population than previously appreciated. Evaluating patients for HPR is both inexpensive ($25) and rapid (< 10 min). Future randomized studies are warranted to determine whether HPR has a clinically detectable effect on revascularization outcomes.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative

OBJECTIVE

In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.

MATERIALS AND METHODS

We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements.

RESULTS

Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback.

DISCUSSION

We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate.

CONCLUSION

By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories

A major challenge in using electronic health record repositories for research is the difficulty matching subject eligibility criteria to query capabilities of the repositories. We propose categories for study criteria corresponding to the effort needed for querying those criteria: "easy" (supporting automated queries), mixed (initial automated querying with manual review), "hard" (fully manual record review), and "impossible" or "point of enrollment" (not typically in health repositories). We obtained a sample of 292 criteria from 20 studies from ClinicalTrials.gov. Six independent reviewers, three each from two academic research institutions, rated criteria according to our four types. We observed high interrater reliability both within and between institutions. The analysis demonstrated typical features of criteria that map with varying levels of difficulty to repositories. We propose using these features to improve enrollment workflow through more standardized study criteria, self-service repository queries, and analyst-mediated retrievals.

Validity of Cardiovascular Data From Electronic Sources: The Multi-Ethnic Study of Atherosclerosis and HealthLNK

BACKGROUND

Understanding the validity of data from electronic data research networks is critical to national research initiatives and learning healthcare systems for cardiovascular care. Our goal was to evaluate the degree of agreement of electronic data research networks in comparison with data collected by standardized research approaches in a cohort study.

METHODS

We linked individual-level data from MESA (Multi-Ethnic Study of Atherosclerosis), a community-based cohort, with HealthLNK, a 2006 to 2012 database of electronic health records from 6 Chicago health systems. To evaluate the correlation and agreement of blood pressure in HealthLNK in comparison with in-person MESA examinations, and body mass index in HealthLNK in comparison with MESA, we used Pearson correlation coefficients and Bland-Altman plots. Using diagnoses in MESA as the criterion standard, we calculated the performance of HealthLNK for hypertension, obesity, and diabetes mellitus diagnosis by using International Classification of Diseases, Ninth Revision codes and clinical data. We also identified potential myocardial infarctions, strokes, and heart failure events in HealthLNK and compared them with adjudicated events in MESA.

RESULTS

Of the 1164 MESA participants enrolled at the Chicago Field Center, 802 (68.9%) participants had data in HealthLNK. The correlation was low for systolic blood pressure (0.39; P<0.0001). In comparison with MESA, HealthLNK overestimated systolic blood pressure by 6.5 mm Hg (95% confidence interval, 4.2-7.8). There was a high correlation between body mass index in MESA and HealthLNK (0.94; P<0.0001). HealthLNK underestimated body mass index by 0.3 kg/m² (95% confidence interval, -0.4 to -0.1). With the use of International Classification of Diseases, Ninth Revision codes and clinical data, the sensitivity and specificity of HealthLNK queries for hypertension were 82.4% and 59.4%, for obesity were 73.0% and 89.8%, and for diabetes mellitus were 79.8% and 93.3%. In comparison with adjudicated cardiovascular events in MESA, the concordance rates for myocardial infarction, stroke, and heart failure were, respectively, 41.7% (5/12), 61.5% (8/13), and 62.5% (10/16).

CONCLUSIONS

These findings illustrate the limitations and strengths of electronic data repositories in comparison with information collected by traditional standardized epidemiological approaches for the ascertainment of cardiovascular risk factors and events.

Mapping the evolving definitions of translational research

OBJECTIVE

Systematic review and analysis of definitions of translational research.

MATERIALS AND METHODS

The final corpus was comprised of 33 papers, each read by at least 2 reviewers. Definitions were mapped to a common set of research processes for presentation and analysis. Influence of papers and definitions was further evaluated using citation analysis and agglomerative clustering.

RESULTS

All definitions were mapped to common research processes, revealing most common labels for each process. Agglomerative clustering revealed 3 broad families of definitions. Citation analysis showed that the originating paper of each family has been cited ~10 times more than any other member.

DISCUSSION

Although there is little agreement between definitions, we were able to identify an emerging consensus 5-phase (T0-T4) definition for translational research. T1 involves processes that bring ideas from basic research through early testing in humans. T2 involves the establishment of effectiveness in humans and clinical guidelines. T3 primarily focuses on implementation and dissemination research while T4 focuses on outcomes and effectiveness in populations. T0 involves research such as genome-wide association studies which wrap back around to basic research.

CONCLUSION

We used systematic review and analysis to identify emerging consensus between definitions of translational research phases.

What Is Asked in Clinical Data Request Forms? A Multi-site Thematic Analysis of Forms Towards Better Data Access Support

Many academic medical centers have aggregated data from multiple clinical systems into centralized repositories. These repositories can then be queried by skilled data analysts who act as intermediaries between the data stores and the research teams. To obtain data, researchers are often expected to complete a data request form. Such forms are meant to support record-keeping and, most importantly, provide a means for conveying complex data needs in a clear and understandable manner. Yet little is known about how data request forms are constructed and how effective they are likely to be. We conducted a content analysis of ten data request forms from CTSA-supported institutions. We found that most of the forms over-emphasized the collection of metadata that were not considered germane to the actual data needs. Based on our findings, we provide recommendations to improve the quality of data request forms in support of clinical and translational research.