Background

The Kids Intracranial Injury Decision Support tool for Traumatic Brain Injury (KIIDS-TBI) tool is a validated risk prediction model for managing children with mild traumatic brain injuries (mTBI) and intracranial injuries. Electronic clinical decision support (CDS) may facilitate the clinical implementation of this evidence-based guidance.

Objective

Our objective was to evaluate the acceptability and usability of an electronic CDS tool for managing children with mTBI and intracranial injuries.

Methods

Emergency medicine and neurosurgery physicians (10 each) from 10 hospitals in the United States were recruited to participate in usability testing of a novel CDS prototype in a simulated electronic health record environment. Testing included a think-aloud protocol, an acceptability and usability survey, and a semi-structured interview. The prototype was updated twice during testing to reflect user feedback. Usability problems recorded in the videos were categorized using content analysis. Interview transcripts were analyzed using thematic analysis.

Results

Among the 20 participants, most worked at teaching hospitals (80%), freestanding children's hospitals (95%), and level-1 trauma centers (75%). During the two prototype updates, problems with clarity of terminology and navigating through the CDS interface were identified and corrected. Corresponding to these changes, the number of usability problems decreased from 35 in phase 1 to 8 in phase 3 and the number of mistakes made decreased from 18 (phase 1) to 2 (phase 3). Through the survey, participants found the tool easy to use (90%), useful for determining a patient's level of care (95%), and likely to improve resource use (90%) and patient safety (79%). Interview themes related to the CDS's ability to support evidence-based decision-making and improve clinical workflow proposed implementation strategies and potential pitfalls.

Conclusion

After iterative evaluation and refinement, the KIIDS-TBI CDS tool was found to be highly usable and useful for aiding the management of children with mTBI and intracranial injuries.

Background

Current management of children with minor head trauma (MHT) and intracranial injuries is not evidence-based and may place some children at risk of harm. Evidence-based electronic clinical decision support (CDS) for management of these children may improve patient safety and decrease resource use. To guide these efforts, we evaluated the sociotechnical environment impacting the implementation of electronic CDS, including workflow and communication, institutional culture, and hardware and software infrastructure, among other factors.

Methods

Between March and May, 2020 semi-structured qualitative focus group interviews were conducted to identify sociotechnical influences on CDS implementation. Physicians from neurosurgery, emergency medicine, critical care, and pediatric general surgery were included, along with information technology specialists. Participants were recruited from nine health centers in the United States. Focus group transcripts were coded and analyzed using thematic analysis. The final themes were then cross-referenced with previously defined sociotechnical dimensions.

Results

We included 28 physicians and four information technology specialists in seven focus groups (median five participants per group). Five physicians were trainees and 10 had administrative leadership positions. Through inductive thematic analysis, we identified five primary themes: (1) clinical impact; (2) stakeholders and users; (3) tool content; (4) clinical practice integration; and (5) post-implementation evaluation measures. Participants generally supported using CDS to determine an appropriate level-of-care for these children. However, some had mixed feelings regarding how the tool could best be used by different specialties (e.g. use by neurosurgeons versus non-neurosurgeons). Feedback from the interviews helped refine the tool content and also highlighted potential technical and workflow barriers to address prior to implementation.

Conclusions

We identified key factors impacting the implementation of electronic CDS for children with MHT and intracranial injuries. These results have informed our implementation strategy and may also serve as a template for future efforts to implement health information technology in a multidisciplinary, emergency setting.

Implementation of prevention policies has often been impeded or delayed due to the lack of randomized controlled trials (RCTs) with hard clinical outcomes (eg, incident disease, mortality). Despite the prominent role of RCTs in health care, it may not always be feasible to conduct RCTs of public health interventions with hard outcomes due to logistical and ethical considerations. RCTs may also lack external validity and have limited generalizability. Currently, there is insufficient guidance for policymakers charged with establishing evidence-based policy to determine whether an RCT with hard outcomes is needed before policy recommendations. In this context, the purpose of this article is to assess, in a case study, the feasibility of conducting an RCT of the oft-cited issue of sodium reduction on cardiovascular outcomes and then propose a framework for decision-making, which includes an assessment of the feasibility of conducting an RCT with hard clinical outcomes when such trials are unavailable. We designed and assessed the feasibility of potential individual- and cluster-randomized trials of sodium reduction on cardiovascular outcomes. Based on our assumptions, a trial using any of the designs considered would require tens of thousands of participants and cost hundreds of millions of dollars, which is prohibitively expensive. Our estimates may be conservative given several key challenges, such as the unknown costs of sustaining a long-term difference in sodium intake, the effect of differential cotreatment with antihypertensive medications, and long lag time to clinical outcomes. Thus, it would be extraordinarily difficult to conduct such a trial, and despite the high costs, would still be at substantial risk for a spuriously null result. A robust framework, such as the one we developed, should be used to guide policymakers when establishing evidence-based public health interventions in the absence of trials with hard clinical outcomes.

Background

The higher risk of heart failure (HF) in African-American and Hispanic women compared with white women is related to the higher burden of risk factors (RFs) in minorities. However, it is unclear if there are differences in the association between the number of RFs for HF and the risk of development of HF and death within racial/ethnic groups.

Methods and results

In the WHI (Women's Health Initiative; 1993-2010), African-American (n=11 996), white (n=18 479), and Hispanic (n=5096) women with 1, 2, or 3+ baseline RFs were compared with women with 0 RF within their respective racial/ethnic groups to assess risk of developing HF or all-cause mortality before and after HF, using survival analyses. After adjusting for age, socioeconomic status, and hormone therapy, the subdistribution hazard ratio (95% confidence interval) of developing HF increased as number of RFs increased (P<0.0001, interaction of race/ethnicity and RF number P=0.18)-African-Americans 1 RF: 1.80 (1.01-3.20), 2 RFs: 3.19 (1.84-5.54), 3+ RFs: 7.31 (4.26-12.56); Whites 1 RF: 1.27 (1.04-1.54), 2 RFs: 1.95 (1.60-2.36), 3+ RFs: 4.07 (3.36-4.93); Hispanics 1 RF: 1.72 (0.68-4.34), 2 RFs: 3.87 (1.60-9.37), 3+ RFs: 8.80 (3.62-21.42). Risk of death before developing HF increased with subsequent RFs (P<0.0001) but differed by racial/ethnic group (interaction P=0.001). The number of RFs was not associated with the risk of death after developing HF in any group (P=0.25; interaction P=0.48).

Conclusions

Among diverse racial/ethnic groups, an increase in the number of baseline RFs was associated with higher risk of HF and death before HF but was not associated with death after HF. Early RF prevention may reduce the burden of HF across multiple racial/ethnic groups.

Background

Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI).

Methods

We used 2 machine-learning methods-Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)-to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years.

Results

LASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76).

Conclusions

Machine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.

Background

When evaluating children with mild traumatic brain injuries (mTBIs) and intracranial injuries (ICIs), neurosurgeons intuitively consider injury size. However, the extent to which such measures (eg, hematoma size) improve risk prediction compared with the kids intracranial injury decision support tool for traumatic brain injury (KIIDS-TBI) model, which only includes the presence/absence of imaging findings, remains unknown.

Objective

To determine the extent to which measures of injury size improve risk prediction for children with mild traumatic brain injuries and ICIs.

Methods

We included children ≤18 years who presented to 1 of the 5 centers within 24 hours of TBI, had Glasgow Coma Scale scores of 13 to 15, and had ICI on neuroimaging. The data set was split into training (n = 1126) and testing (n = 374) cohorts. We used generalized linear modeling (GLM) and recursive partitioning (RP) to predict the composite of neurosurgery, intubation >24 hours, or death because of TBI. Each model's sensitivity/specificity was compared with the validated KIIDS-TBI model across 3 decision-making risk cutoffs (<1%, <3%, and <5% predicted risk).

Results

The GLM and RP models included similar imaging variables (eg, epidural hematoma size) while the GLM model incorporated additional clinical predictors (eg, Glasgow Coma Scale score). The GLM (76%-90%) and RP (79%-87%) models showed similar specificity across all risk cutoffs, but the GLM model had higher sensitivity (89%-96% for GLM; 89% for RP). By comparison, the KIIDS-TBI model had slightly higher sensitivity (93%-100%) but lower specificity (27%-82%).

Conclusion

Although measures of ICI size have clear intuitive value, the tradeoff between higher specificity and lower sensitivity does not support the addition of such information to the KIIDS-TBI model.

Objective

Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.

Materials and methods

The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.

Results

Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.

Conclusions

The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.