Time-dependent Cox proportional hazards regression is a popular statistical method used in kidney disease research to evaluate associations between biomarkers collected serially over time with progression to kidney failure. Typically, biomarkers of interest are considered time-dependent covariates being updated at each new measurement using last observation carried forward (LOCF). Recently, joint modeling has emerged as a flexible alternative for multivariate longitudinal and time-to-event data. This study describes and demonstrates multivariate joint modeling using as an example the association of serial biomarkers (plasma oxalate [POX] and urinary oxalate [UOX]) and kidney function among patients with primary hyperoxaluria in the Rare Kidney Stone Consortium Registry.

Time-to-kidney failure was regressed on serially measured biomarkers in two ways: time-dependent LOCF Cox proportional hazards regression and multivariate joint models.

In time-dependent LOCF Cox regression, higher POX was associated with increased risk of kidney failure (HR = 2.20 per doubling, 95% CI = [1.38-3.51], p < 0.001) whereas UOX was not (HR = 1.08 per doubling, [0.66–1.77], p = 0.77). In multivariate joint models, estimates suggest higher UOX may be associated with lower risk of kidney failure (HR = 0.42 per doubling [0.15–1.04], p = 0.066), though not statistically significant, since impaired urinary excretion of oxalate may reflect worsening kidney function.

Multivariate joint modeling is more flexible than LOCF and may better reflect biological plausibility since biomarkers are not steady-state values between measurements. While LOCF is preferred to naïve methods not accounting for changes in biomarkers over time, results may not accurately reflect flexible relationships that can be captured with multivariate joint modeling.

Despite knowing for many decades that depressive psychopathology is common in first-episode schizophrenia spectrum disorders (FES), there is limited knowledge regarding the extent and nature of such psychopathology (degree of comorbidity, caseness, severity) and its demographic, clinical, functional and treatment correlates. This study aimed to determine the pooled prevalence of depressive disorder and caseness, and the pooled mean severity of depressive symptoms, as well as the demographic, illness, functional and treatment correlates of depressive psychopathology in FES.

This systematic review, meta-analysis and meta-regression was prospectively registered (CRD42018084856) and conducted in accordance with PRISMA and MOOSE guidelines.

Forty studies comprising 4041 participants were included. The pooled prevalence of depressive disorder and caseness was 26.0% (seven samples, N = 855, 95% CI 22.1–30.3) and 43.9% (11 samples, N = 1312, 95% CI 30.3–58.4), respectively. The pooled mean percentage of maximum depressive symptom severity was 25.1 (38 samples, N = 3180, 95% CI 21.49–28.68). Correlates of depressive psychopathology were also found.

At least one-quarter of individuals with FES will experience, and therefore require treatment for, a full-threshold depressive disorder. Nearly half will experience levels of depressive symptoms that are severe enough to warrant diagnostic investigation and therefore clinical intervention – regardless of whether they actually fulfil diagnostic criteria for a depressive disorder. Depressive psychopathology is prominent in FES, manifesting not only as superimposed comorbidity, but also as an inextricable symptom domain.

The success of central line-associated bloodstream infection (CLABSI) prevention programs in intensive care units (ICUs) has led to the expansion of surveillance at many hospitals. We sought to compare non-ICU CLABSI (nCLABSI) rates with national reports and describe methods of surveillance at several participating US institutions.

An electronic survey of several medical centers about infection surveillance practices and rate data for non-ICU Patients.

Ten tertiary care hospitals.

In March 2011, a survey was sent to 10 medical centers. The survey consisted of 12 questions regarding demographics and CLABSI surveillance methodology for non-ICU patients at each center. Participants were also asked to provide available rate and device utilization data.

Hospitals ranged in size from 238 to 1,400 total beds (median, 815). All hospitals reported using Centers for Disease Control and Prevention (CDC) definitions. Denominators were collected by different means: counting patients with central lines every day (5 hospitals), indirectly estimating on the basis of electronic orders (n = 4), or another automated method (n = 1). Rates of nCLABSI ranged from 0.2 to 4.2 infections per 1,000 catheter-days (median, 2.5). The national rate reported by the CDC using 2009 data from the National Healthcare Surveillance Network was 1.14 infections per 1,000 catheter-days.

Only 2 hospitals were below the pooled CLABSI rate for inpatient wards; all others exceeded this rate. Possible explanations include differences in average central line utilization or hospital size in the impact of certain clinical risk factors notably absent from the definition and in interpretation and reporting practices. Further investigation is necessary to determine whether the national benchmarks are low or whether the hospitals surveyed here represent a selection of outliers.