Outcomes of critically ill children with pre-existing mental health conditions.

Importance: Critically ill children with pre-existing mental health conditions may have an increased risk of poor health outcomes. Objective: We aimed to evaluate if pre-existing mental health conditions in critically ill pediatric patients would be associated with worse clinical outcomes, compared to children with no documented mental health conditions. Methods: This retrospective observational cohort study utilized the TriNetX electronic health record database of critically ill subjects aged 12-18 years. Data were analyzed for demographics, pre-existing conditions, diagnostic, medication, procedural codes, and mortality. Results: From a dataset of 102 027 critically ill children, we analyzed 1999 subjects (284 [14.2%] with a pre-existing mental health condition and 1715 [85.8%] with no pre-existing mental health condition). Multivariable analysis demonstrated that death within one year was associated with the presence of pre-existing mental health conditions (odds ratio 8.97 [3.48-23.15], P < 0.001), even after controlling for the presence of a complex chronic condition. Interpretation: The present study demonstrates that the presence of pre-existing mental health conditions was associated with higher odds of death within 1 year after receiving critical care. However, the confidence interval was wide and hence, the findings are inconclusive. Future studies with a larger sample size may be necessary to evaluate the true long-term impact of children with pre-existing mental health conditions who require critical care services.

Estimating the risk of brain metastasis for patients newly diagnosed with cancer.

BACKGROUND: Brain metastases (BM) affect clinical management and prognosis but limited resources exist to estimate BM risk in newly diagnosed cancer patients. Additionally, guidelines for brain MRI screening are limited. We aimed to develop and validate models to predict risk of BM at diagnosis for the most common cancer types that spread to the brain. METHODS: Breast cancer, melanoma, kidney cancer, colorectal cancer (CRC), small cell lung cancer (SCLC), and non-small cell lung cancer (NSCLC) data were extracted from the National Cancer Database to evaluate for the variables associated with the presence of BM at diagnosis. Multivariable logistic regression (LR) models were developed and performance was evaluated with Area Under the Receiver Operating Characteristic Curve (AUC) and random-split training and testing datasets. Nomograms and a Webtool were created for each cancer type. RESULTS: We identify 4,828,305 patients from 2010-2018 (2,095,339 breast cancer, 472,611 melanoma, 407,627 kidney cancer, 627,090 CRC, 164,864 SCLC, and 1,060,774 NSCLC). The proportion of patients with BM at diagnosis is 0.3%, 1.5%, 1.3%, 0.3%, 16.0%, and 10.3% for breast cancer, melanoma, kidney cancer, CRC, SCLC, and NSCLC, respectively. The average AUC over 100 random splitting for the LR models is 0.9534 for breast cancer, 0.9420 for melanoma, 0.8785 for CRC, 0.9054 for kidney cancer, 0.7759 for NSCLC, and 0.6180 for SCLC. CONCLUSIONS: We develop accurate models that predict the BM risk at diagnosis for multiple cancer types. The nomograms and Webtool may aid clinicians in considering brain MRI at the time of initial cancer diagnosis.

When patients are diagnosed with cancer, it is unknown which patients have a significant risk of cancer spread to the brain. Cancer spread to the brain is important to diagnose since it changes how patients are treated and affects their prognosis. This study used a large national database of patients diagnosed with cancer and studied the characteristics that were associated with cancer spread to the brain. The results can be used by doctors to assess the risk of cancer spread to the brain and determine which patients with cancer may benefit most from brain imaging.

Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.

Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.

A retrospective analysis of complications associated with postpartum hemorrhage up to 1 year postpartum in mothers with and without a pre-existing mental health diagnosis.

BACKGROUND/OBJECTIVES: There is limited research on the associated immediate and long-term outcomes of postpartum hemorrhage. Mothers with a pre-existing psychiatric disease prior to delivery may be especially vulnerable to postpartum hemorrhage outcomes but little is known on this topic. Barriers to studying this population exist and add to knowledge gaps. The goal of this study is to determine the clinical characteristics and frequency of complications within 1 year of a postpartum hemorrhage diagnosis and the psychiatric sequelae within 7 days of a postpartum hemorrhage diagnosis in mothers with a pre-existing mental health diagnosis prior to delivery versus those without. METHODS/DESIGN: This is a multicenter retrospective observational cohort study using TriNetX, a de-identified electronic health record database. The following electronic health record data were collected and evaluated in postpartum females who were billed for either a vaginal or cesarean delivery: age, race, ethnicity, diagnostic codes, medication codes, and number of deaths. RESULTS: We included 10,649 subjects (6994 (65.7%) no mental health diagnosis and 3655 (34.3%) pre-existing mental health diagnosis). Haloperidol administration (118 (3.2%) versus 129 (1.8%), p < 0.001) was more prevalent in subjects with a pre-existing mental health diagnosis. Adjusting for demographics, pre-existing mental health diagnoses were associated with complications within 1 year after postpartum hemorrhage diagnosis (OR = 1.39, 95% CI: 1.26-1.52, p < 0.001). CONCLUSION: Having a mental health disorder history is associated with a higher odds of developing subsequent complications within 1 year of postpartum hemorrhage diagnosis. Mothers with a pre-existing mental health disorder have a significantly higher frequency of certain severe postpartum hemorrhage sequelae, including acute respiratory distress syndrome, retained placenta, sickle cell crisis, and need for mechanical ventilation/tracheostomy up to 1 year after delivery. Medications such as haloperidol were ordered more frequently within 7 days of a postpartum hemorrhage diagnosis in these mothers as well. Further research is needed to understand and manage the unique consequences of postpartum hemorrhage in this vulnerable maternal population.

A Multicenter Retrospective Database Evaluation of Pediatric Patients Diagnosed With Tinea Capitis.

PURPOSE: Tinea capitis is a common pediatric superficial dermatophyte infection associated with lower socioeconomic status, overcrowded environments, and poor hygiene internationally. Nevertheless, to the authors' knowledge, no studies in the United States have reported an association between tinea capitis diagnoses and diagnostic codes for social determinants of health (SDOH). The objectives of the present study were to analyze the diagnostic and treatment approach and frequency of SDOH diagnostic codes in order to assess the presence of racial disparities in the treatment of pediatric patients aged 0 to 18 years diagnosed with tinea capitis. METHODS: This study comprised a retrospective analysis using the TriNetX electronic health record database of de-identified pediatric tinea capitis data in ambulatory and emergency settings. The data evaluated demographics, SDOH diagnostic codes, medication codes, and procedure codes. RESULTS: Analysis of 19,677 patients (17,471 [88.8%] ambulatory and 2206 [11.2%] emergency encounters) demonstrated that a low frequency of patients had a confirmatory test for tinea capitis (ie, potassium hydroxide prep or fungal culture; 5.5%), prescription for dual therapy (25.2%), or SDOH diagnostic codes (5.5%). Patients with races classified as Black (odds ratio = 0.48, 95% confidence interval = 0.41-0.57, p < 0.001) and "other" (odds ratio = 0.52, 95% confidence interval = 0.33-0.81, p = 0.004) had a lower likelihood of having an ambulatory encounter, but a higher likelihood of receiving dual therapy. CONCLUSIONS: This study found that diagnostic testing, dual therapy, and SDOH diagnostic codes were underutilized for pediatric patients diagnosed with tinea capitis. In addition, patients of races classified as Black and "other" were more likely to be diagnosed in emergency encounters, but had a higher likelihood of receiving dual therapy regardless of encounter type. Further research is needed to determine how to improve the management of tinea capitis and better understand its relationship with SDOH.

Multicenter retrospective database evaluation of Takotsubo syndrome in subjects undergoing catheter ablation for atrial fibrillation.

Background: Intracardiac catheter ablation for atrial fibrillation with pulmonary vein isolation may result in Takotsubo syndrome (TS), but the frequency, predisposing factors (age, sex, mental health disorders), and outcomes are currently unknown. This study sought to assess the frequency, predisposing factors, and outcomes of subjects who underwent intracardiac catheter ablation for atrial fibrillation with pulmonary vein isolation and were diagnosed with TS. Methods: This was a retrospective observational cohort study utilizing TriNetX® electronic health record (EHR) data. We included subjects aged older than 18 years who underwent intracardiac catheter ablation for atrial fibrillation with pulmonary vein isolation. The study population was divided into two groups (no TS diagnostic code presence and TS diagnostic code presence). We analyzed the distributions of age, sex, race, diagnostic codes, common terminology procedures (CPT), and vasoactive medication codes and examined mortality rate within 30 days. Results: We included 69,116 subjects. Of these, 27 (0.04%) had a TS diagnostic code, the cohort was comprised mostly of females [17 (63.0%)], and 1 (3.7%) death within 30 days was reported. There were no significant differences in age and frequency of mental health disorders between those patients in TS and non-TS cohorts. Adjusting for age, sex, race, ethnicity, patient regionality, and mental health disorder diagnostic code, those patients who developed TS had a significantly higher odds of dying in 30 days after catheter ablation compared to those without TS (OR = 15.97, 95% CI: 2.10-121.55, p = .007). Conclusions: Approximately 0.04% of subjects who underwent intracardiac catheter ablation of atrial fibrillation by pulmonary vein isolation had a subsequent diagnostic code of TS. Further study is needed to determine whether there are predisposing factors associated with the development of TS in subjects who undergo catheter ablation of atrial fibrillation by pulmonary vein isolation.

Designing three-level cluster randomized trials to assess treatment effect heterogeneity.

Cluster randomized trials often exhibit a three-level structure with participants nested in subclusters such as health care providers, and subclusters nested in clusters such as clinics. While the average treatment effect has been the primary focus in planning three-level randomized trials, interest is growing in understanding whether the treatment effect varies among prespecified patient subpopulations, such as those defined by demographics or baseline clinical characteristics. In this article, we derive novel analytical design formulas based on the asymptotic covariance matrix for powering confirmatory analyses of treatment effect heterogeneity in three-level trials, that are broadly applicable to the evaluation of cluster-level, subcluster-level, and participant-level effect modifiers and to designs where randomization can be carried out at any level. We characterize a nested exchangeable correlation structure for both the effect modifier and the outcome conditional on the effect modifier, and generate new insights from a study design perspective for conducting analyses of treatment effect heterogeneity based on a linear mixed analysis of covariance model. A simulation study is conducted to validate our new methods and two real-world trial examples are used for illustrations.

Value of CTA/MRA in the setting of intraparenchymal hemorrhage in the emergency department.

PURPOSE: To assess the diagnostic yield of computed tomography angiography (CTA)/magnetic resonance angiography (MRA) brain and neck ordered in the emergency department (ED) for patients who have intraparenchymal hemorrhage (IPH) on their initial noncontrast CT (NCCT) of the head. METHODS: In this retrospective study, we reviewed 156 patients presenting to the ED with nontraumatic IPH, documented on NCCT. We assessed if the subsequent CTA/MRA was positive, and collected data regarding the location of the bleed, patients' age group, and presence/absence of associated SAH/IVH. Two neuroradiologists were asked to predict, based on age and NCCT appearance, whether the CTA/MRA would be positive or negative for demonstrating a vascular etiology of the hemorrhage. RESULTS: The yield of CTA/MRA head for patients above 75 years old was 2% (1/49), as the etiology for IPH in such age group was more commonly related to hypertensive bleed or amyloid angiopathy. The concomitant presence of subarachnoid hemorrhage (SAH) and intraventricular hemorrhage (IVH), particularly in patients younger than 75 years, correlated with a higher likelihood of a positive CTA. Statistically, the neuroradiologists were able to exclude a vascular source of the IPH based on CT appearance, bleed location, and patient's age in over 92% of cases. CONCLUSION: CTA/MRA for IPH has a lower yield in patients at older age and with deep gray matter distribution without SAH. Neuroradiologists were accurate at excluding a vascular source of the IPH in most cases. This study suggests that CTA/MRA can be omitted in certain scenarios, thereby preventing overutilization, and leading to optimal use of health care resources.

COVID-19 Contact Tracing Highlights Disparities: Household Size and Low-English Proficiency.

Although it is known that coronavirus disease 2019 (COVID-19) disproportionately affects racial and ethnic minorities, our study characterizes the connection between COVID-19 susceptibility and both limited English proficiency (LEP) and large household size. We examined demographic and social data for 1130 individuals who tested positive for or were exposed to COVID-19. Analysis revealed that LEP persons were 3.2 times as likely to report difficulty obtaining supplies for quarantine. Individuals in large households were 1.9 times as likely to report difficulty obtaining supplies for quarantine and 2.0 times as likely to report inability to quarantine. This study, therefore, informs interventions targeted to these populations.

Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes.

Motivated by a suicide prevention trial with hierarchical treatment allocation (cluster-level and individual-level treatments), we address the sample size requirements for testing the treatment effects as well as their interaction. We assume a linear mixed model, within which two types of treatment effect estimands (controlled effect and marginal effect) are defined. For each null hypothesis corresponding to an estimand, we derive sample size formulas based on large-sample z-approximation, and provide finite-sample modifications based on a t-approximation. We relax the equal cluster size assumption and express the sample size formulas as functions of the mean and coefficient of variation of cluster sizes. We show that the sample size requirement for testing the controlled effect of the cluster-level treatment is more sensitive to cluster size variability than that for testing the controlled effect of the individual-level treatment; the same observation holds for testing the marginal effects. In addition, we show that the sample size for testing the interaction effect is proportional to that for testing the controlled or the marginal effect of the individual-level treatment. We conduct extensive simulations to validate the proposed sample size formulas, and find the empirical power agrees well with the predicted power for each test. Furthermore, the t-approximations often provide better control of type I error rate with a small number of clusters. Finally, we illustrate our sample size formulas to design the motivating suicide prevention factorial trial. The proposed methods are implemented in the R package H2x2Factorial.

Clarifying selection bias in cluster randomized trials.

BACKGROUND: In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads to differential recruitment and consequently systematic differences in baseline characteristics of the recruited patients between intervention and control arms, inducing post-randomization selection bias. We aim to rigorously define causal estimands in the presence of selection bias. We elucidate the conditions under which standard covariate adjustment methods can validly estimate these estimands. We further discuss the additional data and assumptions necessary for estimating causal effects when such conditions are not met. METHODS: Adopting the principal stratification framework in causal inference, we clarify there are two average treatment effect (ATE) estimands in cluster randomized trials: one for the overall population and one for the recruited population. We derive analytical formula of the two estimands in terms of principal-stratum-specific causal effects. Furthermore, using simulation studies, we assess the empirical performance of the multivariable regression adjustment method under different data generating processes leading to selection bias. RESULTS: When treatment effects are heterogeneous across principal strata, the average treatment effect on the overall population generally differs from the average treatment effect on the recruited population. A naïve intention-to-treat analysis of the recruited sample leads to biased estimates of both average treatment effects. In the presence of post-randomization selection and without additional data on the non-recruited subjects, the average treatment effect on the recruited population is estimable only when the treatment effects are homogeneous between principal strata, and the average treatment effect on the overall population is generally not estimable. The extent to which covariate adjustment can remove selection bias depends on the degree of effect heterogeneity across principal strata. CONCLUSION: There is a need and opportunity to improve the analysis of cluster randomized trials that are subject to post-randomization selection bias. For studies prone to selection bias, it is important to explicitly specify the target population that the causal estimands are defined on and adopt design and estimation strategies accordingly. To draw valid inferences about treatment effects, investigators should (1) assess the possibility of heterogeneous treatment effects, and (2) consider collecting data on covariates that are predictive of the recruitment process, and on the non-recruited population from external sources such as electronic health records.