Modeling the underlying biological processes in Alzheimer's disease using a multivariate competing risk joint model

Association between urea trajectory and protein dose in critically ill adults: a secondary exploratory analysis of the effort protein trial (RE-EFFORT)

BACKGROUND

Delivering higher doses of protein to mechanically ventilated critically ill patients did not improve patient outcomes and may have caused harm. Longitudinal urea measurements could provide additional information about the treatment effect of higher protein doses. We hypothesised that higher urea values over time could explain the potential harmful treatment effects of higher doses of protein.

METHODS

We conducted a reanalysis of a randomised controlled trial of higher protein doses in critical illness (EFFORT Protein). We applied Bayesian joint models to estimate the strength of association of urea with 30-day survival and understand the treatment effect of higher protein doses.

RESULTS

Of the 1301 patients included in EFFORT Protein, 1277 were included in this analysis. There were 344 deaths at 30 days post-randomisation. By day 6, median urea was 2.1 mmol/L higher in the high protein group (95% CI 1.1-3.2), increasing to 3.0 mmol/L (95% CI 1.3-4.7) by day 12. A twofold rise in urea was associated with an increased risk of death at 30 days (hazard ratio 1.34, 95% credible interval 1.21-1.48), following adjustment of baseline characteristics including age, illness severity, renal replacement therapy, and presence of AKI. This association persisted over the duration of 30-day follow-up and in models adjusting for evolution of organ failure over time.

CONCLUSIONS

The increased risk of death in patients randomised to a higher protein dose in the EFFORT Protein trial was estimated to be mediated by increased urea cycle activity, of which serum urea is a biological signature. Serum urea should be taken into consideration when initiating and continuing protein delivery in critically ill patients.

CLINICALTRIALS

gov Identifier: NCT03160547 (2017-05-17).

Dynamics in Liver Stiffness Measurements Predict Outcomes in Advanced Chronic Liver Disease

BACKGROUND & AIMS

Liver stiffness measurements (LSMs) provide an opportunity to monitor liver disease progression and regression noninvasively. We aimed to determine the prognostic relevance of LSM dynamics over time for liver-related events and death in patients with chronic liver disease.

METHODS

Patients with chronic liver disease undergoing 2 or more reliable LSMs at least 180 days apart were included in this retrospective cohort study and stratified at baseline (BL) as nonadvanced chronic liver disease (non-ACLD, BL-LSM < 10 kPa), compensated ACLD (cACLD; BL-LSM ≥ 10 kPa), and decompensated ACLD. Data on all consecutive LSMs and clinical outcomes were collected.

RESULTS

There were 2508 patients with 8561 reliable LSMs (3 per patient; interquartile range, 2-4) included: 1647 (65.7%) with non-ACLD, 757 (30.2%) with cACLD, and 104 (4.1%) with decompensated ACLD. Seven non-ACLD patients (0.4%) and 83 patients with cACLD (10.9%) developed hepatic decompensation (median follow-up, 71 months). A 20% increase in LSM at any time was associated with an approximately 50% increased risk of hepatic decompensation (hazard ratio, 1.58; 95% CI, 1.41-1.79; P < .001) and liver-related death (hazard ratio, 1.45; 95% CI, 1.28-1.68; P < .001) in patients with cACLD. LSM dynamics yielded a high accuracy to predict hepatic decompensation in the following 12 months (area under the receiver operating characteristics curve = 0.933). The performance of LSM dynamics was numerically better than dynamics in Fibrosis-4 score (0.873), Model for End-Stage Liver Disease (0.835), and single time-point LSM (BL-LSM: 0.846; second LSM: 0.880). Any LSM decrease to <20 kPa identified patients with cACLD with a substantially lower risk of hepatic decompensation (hazard ratio, 0.13; 95% CI, 0.07-0.24). If reliable, LSM also confers prognostic information in decompensated ACLD.

CONCLUSIONS

Repeating LSM enables an individual and updated risk assessment for decompensation and liver-related mortality in ACLD.

Exploring causal mechanisms and quantifying direct and indirect effects using a joint modeling approach for recurrent and terminal events

Recurrent events are commonly encountered in biomedical studies. In many situations, there exist terminal events, such as death, which are potentially related to the recurrent events. Joint models of recurrent and terminal events have been proposed to address the correlation between recurrent events and terminal events. However, there is a dearth of suitable methods to rigorously investigate the causal mechanisms between specific exposures, recurrent events, and terminal events. For example, it is of interest to know how much of the total effect of the primary exposure of interest on the terminal event is through the recurrent events, and whether preventing recurrent event occurrences could lead to better overall survival. In this work, we propose a formal causal mediation analysis method to compute the natural direct and indirect effects. A novel joint modeling approach is used to take the recurrent event process as the mediator and the survival endpoint as the outcome. This new joint modeling approach allows us to relax the commonly used "sequential ignorability" assumption. Simulation studies show that our new model has good finite sample performance in estimating both model parameters and mediation effects. We apply our method to an AIDS study to evaluate how much of the comparative effectiveness of the two treatments and the effect of CD4 counts on the overall survival are mediated by recurrent opportunistic infections.