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arxiv: 2604.12740 · v1 · submitted 2026-04-14 · 📊 stat.AP

Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study

Qendresa Selimi , Christiana Charalambous , Taban Baghfalaki , John Booth , Stephen D Marks This is my paper

Pith reviewed 2026-05-10 14:20 UTC · model grok-4.3

classification 📊 stat.AP
keywords joint modelingcreatinine trajectoriespediatric kidney diseaseautoimmune disordersBayesian analysisdynamic risk predictionlongitudinal datatime-to-event analysis
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The pith

Joint Bayesian models link rising creatinine trajectories to higher risk of kidney events in children with autoimmune disorders.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper applies a joint modeling framework to repeated serum creatinine measurements and time-to-event data in children with autoimmune disorders at one hospital. It establishes that evolving creatinine profiles are strongly associated with the composite outcome of death, acute kidney injury, or chronic kidney disease. Certain treatments raise that risk while others lower it, and creatinine levels themselves shift with age and body-mass index. The model produces updated risk estimates for each patient as new creatinine readings arrive, aiming to support real-time clinical decisions in pediatric nephrology.

Core claim

The paper claims a strong association between evolving creatinine profiles and the risk of the composite event. Treatment with corticosteroids and calcium channel blockers was associated with an increased event risk, whereas immunosuppressive therapy was associated with a reduced risk. Creatinine trajectories were significantly influenced by age and BMI z-score. Dynamic risk predictions generated from patients' observed creatinine trajectories illustrate the framework's utility for personalized risk assessment.

What carries the argument

The joint modelling framework that simultaneously models repeated creatinine measurements and the time to the composite event, letting the biomarker trajectory directly inform the hazard of the event.

If this is right

  • Corticosteroid and calcium channel blocker use increases the risk of the composite kidney event.
  • Immunosuppressive therapy decreases the risk of death, acute kidney injury, or chronic kidney disease.
  • Creatinine trajectories vary significantly with patient age and BMI z-score.
  • Dynamic risk predictions can be updated for individual patients using their ongoing creatinine measurements.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If validated, the approach could allow earlier treatment changes based on a child's creatinine path before an event occurs.
  • Extending the same joint-model structure to other repeated biomarkers could improve outcome forecasting in related pediatric conditions.
  • Multi-center data would be required to test whether the observed treatment associations generalize beyond the original hospital population.

Load-bearing premise

The joint model accurately captures the true relationship between creatinine changes and event risk without unmeasured confounding, model misspecification, or selection bias from single-center data.

What would settle it

In an external group of similar children, the model's predicted risks from their creatinine trajectories fail to match the actual rates of death, acute kidney injury, or chronic kidney disease.

Figures

Figures reproduced from arXiv: 2604.12740 by Christiana Charalambous, John Booth, Qendresa Selimi, Stephen D Marks, Taban Baghfalaki.

Figure 1
Figure 1. Figure 1: Kaplan–Meier survival curve estimating the probability of remaining free from acute kidney [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of serum creatinine and its log-transformed values in the GPKC dataset. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Log-transformed serum creatinine trajectories for a randomly selected subset of patients from [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Dynamic predictions of survival probability for three patients (38, 313, and 415), evaluated at [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
read the original abstract

This study investigates the relationship between longitudinal serum creatinine measurements and the risk of adverse kidney outcomes in paediatric patients with auto-immune disorders at Great Ormond Street Hospital for Children NHS Foundation Trust, London. To jointly analyse repeated biomarker measurements and time-to-event outcomes, we employed a joint modelling framework that combines the creatinine trajectories with the time to death or diagnosis of acute kidney injury or chronic kidney disease. Covariates considered in analysis included demographic and clinical characteristics. The results demonstrate a strong association between evolving creatinine profiles and the risk of the composite event. Specifically, treatment with corticosteroids and calcium channel blockers was associated with an increased event risk, whereas immunosuppressive therapy was associated with a reduced risk. The longitudinal component showed that creatinine trajectories were significantly influenced by age and BMI z-score. To demonstrate the practical utility of the proposed framework, dynamic risk predictions were generated using patients' observed creatinine trajectories. Model performance was compared using model selection criteria, alongside area under the curve and Brier score to evaluate the accuracy of dynamic risk predictions. These predictions illustrate the potential of joint models to support personalised medicine and clinical decision making in paediatric nephrology through real-time risk assessment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents a Bayesian joint model combining longitudinal serum creatinine trajectories with a time-to-event submodel for a composite outcome (death, AKI or CKD) in a single-center cohort of children with auto-immune disorders. It reports that age and BMI z-score influence creatinine trajectories, that corticosteroids and calcium-channel blockers are associated with increased event risk while immunosuppressive therapy is associated with reduced risk, and that dynamic risk predictions derived from observed trajectories achieve useful AUC and Brier-score performance.

Significance. If the joint-model assumptions hold and treatment associations are robust to confounding, the work offers a practical demonstration of how longitudinal biomarker data can be used for real-time, individualized risk assessment in pediatric nephrology. The Bayesian framework and emphasis on dynamic predictions are appropriate strengths for this setting.

major comments (3)
  1. [Methods] Methods section: the joint-model specification (longitudinal submodel form, random-effects structure, association parameter linking creatinine process to hazard, priors, and software implementation) is not described in sufficient detail to allow reproduction or evaluation of the reported associations and predictions.
  2. [Results] Results / Discussion: the directional treatment associations (corticosteroids and calcium-channel blockers increasing composite-event risk; immunosuppressives decreasing it) are presented without addressing confounding by indication, time-varying treatment effects, or sensitivity analyses, which is load-bearing for the highlighted clinical claims in an observational single-center study.
  3. [Abstract/Methods] Abstract and Methods: no information is given on missing-data mechanisms for the longitudinal creatinine measurements, model diagnostics, or any form of internal or external validation of the dynamic predictions beyond AUC/Brier scores on the same data.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'model selection criteria' is used without naming the specific criteria (DIC, WAIC, etc.) employed.
  2. [Discussion] The single-center design and lack of external validation should be more explicitly acknowledged when discussing implications for 'personalised medicine'.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We have addressed each major comment below and agree that the suggested additions will improve clarity, reproducibility, and appropriate interpretation of the findings. We plan to submit a revised version incorporating these changes.

read point-by-point responses

  1. Referee: [Methods] Methods section: the joint-model specification (longitudinal submodel form, random-effects structure, association parameter linking creatinine process to hazard, priors, and software implementation) is not described in sufficient detail to allow reproduction or evaluation of the reported associations and predictions.

    Authors: We agree that the Methods section requires greater detail to support reproducibility. In the revised manuscript we will expand this section to specify: the longitudinal submodel as a linear mixed-effects model on log-creatinine with fixed effects for age and BMI z-score plus random intercepts and slopes; the random-effects covariance structure; the association structure linking the current value of the longitudinal process to the hazard; the prior distributions employed in the Bayesian estimation; and the software implementation (including package and version). These additions will allow readers to fully evaluate the model and reported results. revision: yes

  2. Referee: [Results] Results / Discussion: the directional treatment associations (corticosteroids and calcium-channel blockers increasing composite-event risk; immunosuppressives decreasing it) are presented without addressing confounding by indication, time-varying treatment effects, or sensitivity analyses, which is load-bearing for the highlighted clinical claims in an observational single-center study.

    Authors: We acknowledge that confounding by indication is a valid concern in this observational single-center study and that the reported treatment associations should not be interpreted as causal. We will revise the Results and Discussion to explicitly note the observational nature of the data, discuss the potential for confounding by indication (e.g., more severely affected patients receiving corticosteroids), and highlight the absence of granular time-varying treatment information that would permit sensitivity analyses such as propensity-score methods. These clarifications will temper the clinical interpretation while retaining the reported associations as adjusted model outputs. revision: partial

  3. Referee: [Abstract/Methods] Abstract and Methods: no information is given on missing-data mechanisms for the longitudinal creatinine measurements, model diagnostics, or any form of internal or external validation of the dynamic predictions beyond AUC/Brier scores on the same data.

    Authors: We will add the requested information to the Methods section of the revised manuscript. We will state that the joint model assumes a missing-at-random mechanism and naturally incorporates all observed creatinine measurements; describe model diagnostics including MCMC convergence diagnostics and posterior predictive checks; and clarify that the AUC and Brier scores were obtained via an internal landmarking approach on the full dataset. We will also note as a limitation that external validation is not feasible with the current single-center cohort. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the joint modeling derivation

full rationale

The paper applies a standard Bayesian joint model to link longitudinal creatinine trajectories with a composite time-to-event outcome, estimating associations and generating dynamic predictions from the fitted parameters and observed patient trajectories. This is conventional practice in joint modeling and does not reduce any claimed result to a tautology or self-definition by construction. No equations or steps in the provided description show fitted inputs relabeled as independent predictions, self-citation load-bearing premises, uniqueness theorems imported from the authors' prior work, or ansatzes smuggled via citation. The reported treatment associations and trajectory effects are direct outputs of the model fit on the single-center data; while external validity may be limited by design, the derivation chain itself remains independent of its inputs and self-contained.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard joint modeling assumptions and data-specific parameter estimates from a single-center cohort; no new entities are postulated.

free parameters (2)
  • association parameter linking longitudinal creatinine process to event hazard
    Estimated from data to quantify how creatinine trajectories influence event risk.
  • random effects variances and covariance structure
    Fitted to capture individual variability in creatinine trajectories.
axioms (2)
  • domain assumption The joint model correctly specifies the dependence between the longitudinal biomarker process and the time-to-event process.
    Core assumption required for the association and dynamic prediction claims.
  • domain assumption The single-center cohort is representative of the broader pediatric autoimmune population.
    Invoked implicitly for generalizability of risk predictions.

pith-pipeline@v0.9.0 · 5530 in / 1376 out tokens · 43611 ms · 2026-05-10T14:20:35.373548+00:00 · methodology

discussion (0)

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