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Background: Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD)., Objective: The study objective was to develop a prediction model to assist with this diagnostic dilemma., Methods: Data from a retrospective cohort of children hospitalized with KD before the coronavirus disease 2019 pandemic were compared to a prospective cohort of children hospitalized with MIS-C. A bootstrapped backwards selection process was used to develop a logistic regression model predicting the probability of MIS-C diagnosis. A nomogram was created for application to individual patients., Results: Compared to children with incomplete and complete KD (N = 602), children with MIS-C (N = 105) were older and had longer hospitalizations; more frequent intensive care unit admissions and vasopressor use; lower white blood cell count, lymphocyte count, erythrocyte sedimentation rate, platelet count, sodium, and alanine aminotransferase; and higher hemoglobin and C-reactive protein (CRP) at admission. Left ventricular dysfunction was more frequent in patients with MIS-C, whereas coronary abnormalities were more common in those with KD. The final prediction model included age, sodium, platelet count, alanine aminotransferase, reduction in left ventricular ejection fraction, and CRP. The model exhibited good discrimination with AUC 0.96 (95% confidence interval: [0.94-0.98]) and was well calibrated (optimism-corrected intercept of -0.020 and slope of 0.99)., Conclusions: A diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation., (© 2024 Society of Hospital Medicine.)

To make informed health policy decisions regarding a treatment, we must consider both its cost and its clinical effectiveness. In past work, we introduced the net benefit separation (NBS) as a novel measure of cost-effectiveness. The NBS is a probabilistic measure that characterizes the extent to which a treated patient will be more likely to experience benefit as compared to an untreated patient. Due to variation in treatment response across patients, uncovering factors that influence cost-effectiveness can assist policy makers in population-level decisions regarding resource allocation. In this paper, we introduce a regression framework for NBS in order to estimate covariate-specific NBS and find determinants of variation in NBS. Our approach is able to accommodate informative cost censoring through inverse probability weighting techniques, and addresses confounding through a semiparametric standardization procedure. Through simulations, we show that NBS regression performs well in a variety of common scenarios. We apply our proposed regression procedure to a realistic simulated data set as an illustration of how our approach could be used to investigate the association between cancer stage, comorbidities and cost-effectiveness when comparing adjuvant radiation therapy and chemotherapy in post-hysterectomy endometrial cancer patients., (© 2022 John Wiley & Sons Ltd.)

Background: Antiviral treatment is recommended for all hospitalized children with suspected or confirmed influenza, regardless of their risk profile. Few data exist on adherence to these recommendations, so we sought to determine factors associated with influenza testing and antiviral treatment in children., Methods: Hospitalized children <18 years of age with acute respiratory illness (ARI) were enrolled through active surveillance at pediatric medical centers in seven cities between 11/1/2015 and 6/30/2016; clinical information was obtained from parent interview and chart review. We used generalized linear mixed-effects models to identify factors associated with influenza testing and antiviral treatment., Results: Of the 2299 hospitalized children with ARI enrolled during one influenza season, 51% (n = 1183) were tested clinically for influenza. Clinicians provided antiviral treatment for 61 of 117 (52%) patients with a positive influenza test versus 66 of 1066 (6%) with a negative or unknown test result. In multivariable analyses, factors associated with testing included neuromuscular disease (aOR = 5.35, 95% CI [3.58-8.01]), immunocompromised status (aOR = 2.88, 95% CI [1.66-5.01]), age (aOR = 0.93, 95% CI [0.91-0.96]), private only versus public only insurance (aOR = 0.78, 95% CI [0.63-0.98]), and chronic lung disease (aOR = 0.64, 95% CI [0.51-0.81]). Factors associated with antiviral treatment included neuromuscular disease (aOR = 1.86, 95% CI [1.04, 3.31]), immunocompromised state (aOR = 2.63, 95% CI [1.38, 4.99]), duration of illness (aOR = 0.92, 95% CI [0.84, 0.99]), and chronic lung disease (aOR = 0.60, 95% CI [0.38, 0.95])., Conclusion: Approximately half of children hospitalized with influenza during the 2015-2016 influenza season were treated with antivirals. Because antiviral treatment for influenza is associated with better health outcomes, further studies of subsequent seasons would help evaluate current use of antivirals among children and better understand barriers for treatment., (© 2021 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

In patients with chronic kidney disease (CKD), clinical interest often centers on determining treatments and exposures that are causally related to renal progression. Analyses of longitudinal clinical data in this population are often complicated by clinical competing events, such as end-stage renal disease (ESRD) and death, and time-dependent confounding, where patient factors that are predictive of later exposures and outcomes are affected by past exposures. We developed multistate marginal structural models (MS-MSMs) to assess the effect of time-varying systolic blood pressure on disease progression in subjects with CKD. The multistate nature of the model allows us to jointly model disease progression characterized by changes in the estimated glomerular filtration rate (eGFR), the onset of ESRD, and death, and thereby avoid unnatural assumptions of death and ESRD as noninformative censoring events for subsequent changes in eGFR. We model the causal effect of systolic blood pressure on the probability of transitioning into 1 of 6 disease states given the current state. We use inverse probability weights with stabilization to account for potential time-varying confounders, including past eGFR, total protein, serum creatinine, and hemoglobin. We apply the model to data from the Chronic Renal Insufficiency Cohort Study, a multisite observational study of patients with CKD., (Copyright © 2017 John Wiley & Sons, Ltd.)

Repeated low-dose challenge designs in nonhuman primate studies have recently received attention in the literature as a means of evaluating vaccines for HIV prevention and identifying immune surrogates for their protective effects. Existing methods for surrogate identification in this type of study design rely on the assumption of homogeneity across subjects (namely, independent infection risks after each challenge within each subject and conditional on covariates). In practice, random variation across subjects is likely to occur because of unmeasured biologic factors. Failure to account for this heterogeneity or within-subject correlation can result in biased inference regarding the surrogate value of immune biomarkers and underpowered study designs for detecting surrogate endpoints. In this paper, we adopt a discrete-time survival model with random effects to account for between-subject heterogeneity, and we develop estimators and testing procedures for evaluating principal surrogacy of immune biomarkers. Simulation studies reveal that the heterogeneous model achieves substantial bias reduction compared to the homogeneous model, with little cost of efficiency. We recommend the use of this heterogeneous model as a complementary tool to existing methods when designing and analyzing repeated low-dose challenge studies for evaluating surrogate endpoints., (Copyright © 2017 John Wiley & Sons, Ltd.)

Purpose: In cross-sectional observational data, evaluation of biomarker-to-exposure associations is often complicated by nonrandom medication use. Traditional approaches often lead to biased estimates, consistent with known results involving confounding by indication. More sophisticated, yet easy to implement approaches such as inverse probability weighting and censored normal regression can address medication use in certain settings but have poor performance when medication use depends on off-medication biomarker values. More sophisticated approaches are necessary., Methods: Heckman's treatment effects model resembles the process that gives rise to cross-sectional data. In this study, we conduct a variety of simulation studies to illustrate why traditional approaches are inappropriate when medication use depends on underlying biomarker values. We illustrate how Heckman's model can accommodate this feature. We also apply the models to data from the Multi-Ethnic Study of Atherosclerosis., Results: Inverse probability weighting and censored normal regression are sensitive to how strongly medication use is associated with untreated biomarker values (the untreated value acts as an unmeasured predictor of medication use in this context). Heckman's model can often adequately remove bias and is robust to certain forms of model misspecification but relies on knowing important predictors of medication use, even when they are independent of the biomarker. The advantages of Heckman's model can be negated if the effect of medication on biomarker values is proportionate to the underlying biomarker., Conclusions: If predictors of medication use are measured, data are cross-sectional, and effects are approximately additive, then Heckman's model is more accurate relative to alternative approaches., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2015 John Wiley & Sons, Ltd.)