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BACKGROUND: The study aimed to compare the growth responses to 3 years of growth hormone (GH) treatment in children and adolescents with GH deficiency (GHD) according to idiopathic, organic, isolated (IGHD), and multiple pituitary hormone deficiency (MPHD). METHODS: Total 163 patients aged 2-18 years (100 males and 63 females; 131 idiopathic and 32 organic GHD; 129 IGHD and 34 MPHD) were included from data obtained from the LG Growth Study. Parameters of growth responses and biochemical results were compared during the 3-year GH treatment. RESULTS: The baseline age, bone age (BA), height (Ht) standard deviation score (SDS), weight SDS, mid-parental Ht SDS, predicted adult Ht (PAH) SDS, and insulin like growth factor-1 (IGF-1) SDS were significantly higher in the organic GHD patients than in the idiopathic GHD patients, but peak GH on the GH-stimulation test, baseline GH dose, and mean 3-year-GH dosage were higher in the idiopathic GHD patients than in the organic GHD patients. The prevalence of MPHD was higher in the organic GHD patients than in the idiopathic GHD patients. Idiopathic MPHD subgroup showed the largest increase for the ΔHt SDS and ΔPAH SDS during GH treatment, and organic MPHD subgroup had the smallest mean increase after GH treatment, depending on ΔIGF-1 SDS and ΔIGF binding protein-3 (IGFBP-3) SDS. The growth velocity and the parental-adjusted Ht gain were greater in the idiopathic GHD patients than the organic GHD patients during the 3-year GH treatment, which may have been related to the different GH dose, ΔIGF-1 SDS, and ΔIGFBP-3 SDS between two groups. Multiple linear regression analysis revealed that baseline IGF-1 SDS, BA, and MPH SDS in idiopathic group and baseline HT SDS in organic group are the most predictable parameters for favorable 3-year-GH treatment. CONCLUSION: The 3-year-GH treatment was effective in both idiopathic and organic GHD patients regardless of the presence of MPHD or underlying causes, but their growth outcomes were not constant with each other. Close monitoring along with appropriate dosage of GH and annual growth responses, not specific at baseline, are more important in children and adolescents with GHD for long-term treatment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01604395.

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The associations between covariates and the outcomes often vary over time, regardless of whether the covariate is time-varying or time-invariant. For example, we hypothesize that the impact of chronic diseases, such as diabetes and heart disease, on people's physical functions differ with aging. However, the age-varying effect would be missed if one models the covariate simply as a time-invariant covariate (yes/no) with a time-constant coefficient. We propose a fused lasso-based time-varying linear mixed effect (FTLME) model and an efficient two-stage parameter estimation algorithm to estimate the longitudinal trajectories of fixed-effect coefficients. Simulation studies are presented to demonstrate the efficacy of the method and its computational efficiency in estimating smooth time-varying effects in high dimensional settings. A real data example on the Health and Retirement Study (HRS) analysis is used to demonstrate the practical usage of our method to infer age-varying impact of chronic disease on older people's physical functions.

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Dynamic survival analysis is a variant of traditional survival analysis where time-to-event predictions are updated as new information arrives about an individual over time. In this paper we propose a new approach to dynamic survival analysis based on learning a global parametric distribution, followed by individualization via truncating and renormalizing that distribution at different locations over time. We combine this approach with a likelihood-based loss that includes predictions at every time step within an individual's history, rather than just including one term per individual. The combination of this loss and model results in an interpretable approach to dynamic survival, requiring less fine tuning than existing methods, while still achieving good predictive performance. We evaluate the approach on the problem of predicting hospital mortality for a dataset with over 6900 COVID-19 patients.

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BACKGROUND: Although most patients with cutaneous melanoma are non-Hispanic whites (NHWs), minorities consistently suffer worse melanoma-specific survival (MSS). Much of the literature comes from analyses of registries from the 1990s and 2000s. OBJECTIVE: We sought to evaluate whether and to what degree racial disparity in MSS persists since 2010. METHODS: We analyzed 381,035 patients from the Surveillance, Epidemiology, and End Results registry. Race categories included Hispanic, NHW, non-Hispanic black (NHB), non-Hispanic Asian or Pacific Islander (NHAPI), and non-Hispanic American Indian/Alaska Native (NHAIAN). We evaluated the association between MSS and race in 3 time periods: before the year 2000, 2000 to 2009, and 2010 or later. NHW was the reference group for all analyses. RESULTS: Racial disparity worsened from before the year 2000 to 2010 or later for Hispanic (P < .001), NHB (P = .024), and NHAPI (P < .001) patients. Across all minority groups, patients with localized disease suffered increasing disparity (P = .010 for Hispanic, P < .001 for NHB, P = .023 for NHAPI, and P = .042 for NHAIAN patients). Among those with regional and distant disease, Hispanic patients were the only minority to experience worsening disparity (P = .001 and P = .019, respectively). LIMITATIONS: Lack of immunotherapy and targeted treatment information. CONCLUSIONS: Racial disparity in MSS is worsening. Improving postdiagnosis management for minorities with localized disease is imperative to mitigate disparity and improve survival.

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OBJECTIVE: Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks. MATERIALS AND METHODS: This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009-2017) and from the Health and Retirement Survey (HRS, 1995-2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores. RESULTS: The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR. DISCUSSION AND CONCLUSION: EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.

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Despite major improvements in combatting metastatic melanoma since the advent of immunotherapy, the overall survival for patients with advanced disease remains low. Recently, there is a growing number of reports supporting an "obesity paradox," in which patients who are overweight or mildly obese may exhibit a survival benefit in patients who received immune checkpoint inhibitors. We studied the relationship between body mass index and progression-free survival and overall survival in a cohort of 423 metastatic melanoma patients receiving immunotherapy, enrolled and prospectively followed up in the NYU Interdisciplinary Melanoma Cooperative Group database. We analyzed this association stratified by first vs. second or greater-line of treatment and treatment type adjusting for age, gender, stage, lactate dehydrogenase, Eastern Cooperative Oncology Group performance status, number of metastatic sites, and body mass index classification changes. In our cohort, the patients who were overweight or obese did not have different progression-free survival than patients with normal body mass index. Stratifying this cohort by first vs. non-first line immunotherapy revealed a moderate but insignificant association between being overweight or obese and better progression-free survival in patients who received first line. Conversely, an association with worse progression-free survival was observed in patients who received non-first line immune checkpoint inhibitors. Specifically, overweight and obese patients receiving combination immunotherapy had a statistically significant survival benefit, whereas patients receiving the other treatment types showed heterogeneous trends. We caution the scientific community to consider several important points prior to drawing conclusions that could potentially influence patient care, including preclinical data associating obesity with aggressive tumor biology, the lack of congruence amongst several investigations, and the limited reproduced comprehensiveness of these studies.

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