Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records

Alisa Yurovsky; Bryan Zhu; Evan Yang; Kalyani P. Pande; Sandeep K. Mallipattu; Tengfei Ma

arxiv: 2604.24547 · v1 · submitted 2026-04-27 · 💻 cs.LG

Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records

Kalyani P. Pande , Evan Yang , Bryan Zhu , Sandeep K. Mallipattu , Alisa Yurovsky , Tengfei Ma This is my paper

Pith reviewed 2026-05-08 04:21 UTC · model grok-4.3

classification 💻 cs.LG

keywords acute kidney injurydialysis predictioncausal inferencetransformer modelelectronic health recordstreatment effect estimationmedication exposureAKI cohort

0 comments

The pith

A transformer model on longitudinal EHR data predicts dialysis risk and estimates medication treatment effects in AKI patients.

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

The paper develops a model to forecast the rare progression from acute kidney injury to dialysis or end-stage renal disease by processing sequences of diagnoses, procedures, medications, and kidney laboratory trends from electronic health records. It combines standard risk prediction with causal estimation of how specific drug exposures would change that risk through simulated addition or removal of medications under a complete history. The work focuses on common exposures such as ACE inhibitors, ARBs, and loop diuretics to produce directional evidence on their influence on kidney function markers. If the approach succeeds, clinicians would gain both a tool for identifying high-risk patients and initial quantitative guidance on whether altering medication regimens could affect downstream dialysis incidence.

Core claim

The authors assembled a fixed-window cohort of 81,401 AKI patients with 90-day observation periods and 730-day outcome windows, then trained a transformer-based causal multi-head model on full sequences of medical events and lab values. The model delivers dialysis risk predictions at an AUC of 0.694 while estimating average treatment effects via counterfactual removal and insertion of medication exposures. Post-hoc analyses using IPTW, AIPW, and adjusted regression on changes in eGFR, creatinine, and BUN yield partial support for protective-direction effects from ACE/ARB exposures and worsening-direction signals from loop diuretics.

What carries the argument

The transformer-based causal multi-head model that jointly predicts the binary dialysis outcome and computes treatment effects by generating counterfactual medication histories.

If this is right

Patients can be stratified by predicted risk of progressing to dialysis within two years.
Counterfactual medication simulations supply ingredient-level estimates of how exposures alter the probability of the rare outcome.
Lab-based post-hoc checks provide initial clinical directionality for common drugs used in this population.
The low prevalence of the outcome (1.1 percent) is explicitly addressed by the chosen performance metrics and decision threshold.

Where Pith is reading between the lines

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

The same longitudinal modeling strategy could be applied to other infrequent but high-stakes endpoints in chronic disease where randomized trials are logistically difficult.
Incorporating dynamic kidney-function trajectories appears central to both the predictive accuracy and the causal estimates, suggesting similar gains in related renal or cardiovascular settings.
If external validation confirms the directional medication signals, the framework could support decision aids that weigh immediate drug benefits against longer-term dialysis risk.

Load-bearing premise

The causal estimates rest on the premise that no important confounding variables are missing from the records and that altering medication histories in simulation produces valid representations of real-world exposure changes.

What would settle it

A randomized trial that assigns ACE/ARB or loop diuretic exposure to comparable AKI patients and tracks subsequent dialysis rates would contradict the reported directional signals if the trial shows null or reversed effects.

Figures

Figures reproduced from arXiv: 2604.24547 by Alisa Yurovsky, Bryan Zhu, Evan Yang, Kalyani P. Pande, Sandeep K. Mallipattu, Tengfei Ma.

**Figure 1.** Figure 1: Pipeline for dialysis risk prediction and treatment effect estimation from EHR data. Dataset and Cohort Construction We utilized a de-identified electronic health record (EHR) dataset from the TriNetX public research network, which compiles longitudinal clinical data from participating healthcare providers. The dataset includes structured tables on patient encounters, diagnoses, procedures, medication reco… view at source ↗

**Figure 5.** Figure 5: Ingredient-level Average Treatment effects (ATEs) on dialysis risk. Given the potential for confounding by indication, these model-derived ATE estimates are interpreted as hypothesisgenerating signals rather than definitive causal estimates. To further examine the clinical plausibility of these signals, we conducted additional validation using time-anchored kidney marker analyses view at source ↗

read the original abstract

Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures influence downstream risk. We constructed a fixed-window EHR cohort (90-day observation, 730-day prediction; N=81401; dialysis/ESRD prevalence: 1.1%) and modeled sequences of diagnoses, procedures, and medications with kidney laboratory trends (creatinine, BUN, eGFR). A transformer-based causal multi-head model was trained to estimate drug- and ingredient-level average treatment effects (ATEs) using counterfactual exposure removal and insertion under a full medication history setup. On test set, predictive performance reached an AUC of 0.694 and PR-AUC of 0.094. At the selected decision threshold (0.883), the model achieved an F1 score of 0.201 with a Brier score of 0.018. Post-hoc causal analyses of lab changes (eGFR, creatinine, BUN) using IPTW, AIPW, naive, and covariate-adjusted OLS methods assessed clinical directionality. Results showed partial protective-direction support for ACE/ARB exposures and worsening-direction signals for loop diuretics.

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.

Desk Editor's Note private letter to a colleague

This paper pairs a transformer on longitudinal EHR with causal multi-head estimation for dialysis risk and drug ATEs in AKI patients, but delivers modest prediction without baselines and causal claims that rest on untested assumptions.

read the letter

The punchline is that this work uses a transformer on multi-modal longitudinal EHR to predict dialysis risk in AKI patients and estimate drug-specific average treatment effects through counterfactual medication changes in the sequence. It does a reasonable job with a large cohort of 81,401 patients over fixed windows and incorporates kidney lab trends alongside diagnoses, procedures, and meds. The reported test AUC of 0.694 and the post-hoc lab analyses providing directional support for ACE/ARB protection and loop diuretic worsening are concrete outputs from real data. What is new is the causal multi-head extension for ingredient-level ATEs built into the prediction model, rather than separate steps. The soft spots are clear though. There are no baseline models reported for the prediction task, so it's hard to know if the transformer adds much over simpler approaches. The PR-AUC of 0.094 highlights the challenge with rare events but also shows room for improvement. On the causal side, the reliance on IPTW, AIPW, and OLS without any sensitivity analysis or negative controls leaves the estimates vulnerable to unmeasured confounding and incomplete medication histories, which are typical problems in EHR studies. The abstract mentions no error bars or robustness checks. This paper is for readers in clinical machine learning or nephrology who want to see how transformers can be adapted for both forecasting and observational causal questions in kidney disease. Someone looking for a ready-to-use tool might not get it yet, but the setup could spark ideas. I would recommend sending it to peer review. The scale of the data and the attempt to link prediction with treatment effect estimation make it worth a closer look from referees who can probe the methods and suggest the missing validations.

Referee Report

3 major / 1 minor

Summary. The manuscript develops a transformer-based causal multi-head model to predict progression to dialysis or ESRD (1.1% prevalence) in a cohort of 81,401 AKI patients using 90-day longitudinal EHR sequences of diagnoses, procedures, medications, and kidney labs, followed by a 730-day horizon. The model reports test-set AUC 0.694, PR-AUC 0.094, and F1 0.201 at threshold 0.883. It further estimates drug-level ATEs via counterfactual medication-history removal/insertion and post-hoc IPTW/AIPW/OLS analyses on eGFR/creatinine/BUN trajectories, reporting partial protective signals for ACE/ARB exposures and worsening signals for loop diuretics.

Significance. If the causal estimates hold after addressing confounding, the work could inform medication management to reduce dialysis risk in AKI. Combining sequence modeling with counterfactual ATE estimation on large-scale EHR is a relevant direction for clinical ML. The large cohort size and focus on a rare, high-stakes outcome are strengths, but the modest predictive metrics and untested causal assumptions limit immediate clinical significance.

major comments (3)

Abstract: The reported AUC of 0.694 and PR-AUC of 0.094 are presented without any baseline comparisons (e.g., logistic regression or XGBoost on aggregated features), so it is impossible to determine whether the transformer causal multi-head architecture improves upon standard approaches for this rare-event task.
Causal analysis section: The ATE estimates for ACE/ARB (protective) and loop diuretics (worsening) rely on IPTW/AIPW/OLS applied to lab changes after counterfactual exposure modification, but no sensitivity analyses for unmeasured confounding (E-values, negative controls, or placebo tests) are reported despite the observational EHR setting where indication bias and incomplete histories are common.
Methods: The manuscript provides no explicit description of how class imbalance (1.1% prevalence), decision-threshold selection (0.883), or EHR missingness were handled within the transformer training or the counterfactual multi-head setup, which directly affects the reliability of both the F1 score and the reported ATE directions.

minor comments (1)

Abstract: The Brier score of 0.018 is reported but its relationship to the chosen operating threshold and calibration in the presence of imbalance could be clarified for readers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments. We address each major comment below, agreeing where revisions are warranted to enhance the manuscript's clarity and rigor.

read point-by-point responses

Referee: Abstract: The reported AUC of 0.694 and PR-AUC of 0.094 are presented without any baseline comparisons (e.g., logistic regression or XGBoost on aggregated features), so it is impossible to determine whether the transformer causal multi-head architecture improves upon standard approaches for this rare-event task.

Authors: We agree that baseline comparisons are necessary to contextualize our model's performance. In the revised manuscript, we will include results from logistic regression and XGBoost models trained on aggregated EHR features (e.g., summary statistics of labs, event counts for diagnoses, procedures, and medications). This addition will demonstrate the relative contribution of the sequential transformer architecture for the rare-event prediction task. revision: yes
Referee: Causal analysis section: The ATE estimates for ACE/ARB (protective) and loop diuretics (worsening) rely on IPTW/AIPW/OLS applied to lab changes after counterfactual exposure modification, but no sensitivity analyses for unmeasured confounding (E-values, negative controls, or placebo tests) are reported despite the observational EHR setting where indication bias and incomplete histories are common.

Authors: We acknowledge the value of sensitivity analyses for observational causal estimates. Our multi-estimator approach (IPTW, AIPW, OLS) already provides some robustness, but we will add E-value calculations for the key ATEs in the revision to assess the potential impact of unmeasured confounding. Negative controls are challenging given the medication history complexity, but we will include relevant placebo tests where feasible and discuss limitations transparently. revision: yes
Referee: Methods: The manuscript provides no explicit description of how class imbalance (1.1% prevalence), decision-threshold selection (0.883), or EHR missingness were handled within the transformer training or the counterfactual multi-head setup, which directly affects the reliability of both the F1 score and the reported ATE directions.

Authors: We appreciate this observation. The revised Methods section will explicitly detail: (i) use of weighted loss functions to address class imbalance in transformer training; (ii) threshold selection (0.883) via F1 optimization on the validation set; and (iii) missingness handling via forward-fill imputation for labs and binary indicators for absent events. These clarifications will also address implications for the counterfactual ATE estimates. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on held-out evaluation and standard causal methods

full rationale

The paper trains a transformer model on longitudinal EHR sequences for dialysis risk prediction, evaluates it on a held-out test set (AUC 0.694, PR-AUC 0.094), and performs separate post-hoc IPTW/AIPW/OLS analyses on lab trajectories to assess treatment effect directionality. No equations, self-citations, or fitted-parameter renamings are shown that reduce the reported predictions or ATEs to quantities defined by the same inputs by construction. The central claims rest on external assumptions (no unmeasured confounding, valid counterfactuals) rather than internal self-definition, making the chain self-contained against the provided benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 3 axioms · 0 invented entities

The central claims rest on standard supervised learning assumptions plus strong causal identification assumptions required for observational ATE estimation; no new entities are postulated.

free parameters (2)

decision threshold = 0.883
Threshold of 0.883 chosen to maximize F1; this is a post-training tuning choice that directly affects reported F1 and Brier scores.
transformer hyperparameters
All model weights and architectural choices (layers, heads, embedding sizes) are fitted to the training data.

axioms (3)

domain assumption No unmeasured confounding
Required for IPTW and AIPW to recover unbiased average treatment effects from observational EHR.
domain assumption Counterfactual exposure removal and insertion identify ATE
Core modeling assumption of the causal multi-head transformer under full medication history.
domain assumption Fixed 90-day observation and 730-day prediction windows capture relevant dynamics
Cohort construction choice that defines the prediction task and may exclude longer-term effects.

pith-pipeline@v0.9.0 · 5529 in / 1736 out tokens · 85186 ms · 2026-05-08T04:21:31.629056+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Long -term risk of mortality and other adverse outcomes after acute kidney injury: a systematic review and meta-analysis

Coca SG, Yusuf B, Shlipak MG, Garg AX, Parikh CR. Long -term risk of mortality and other adverse outcomes after acute kidney injury: a systematic review and meta-analysis. Am J Kidney Dis. 2009;53(6):961-973

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Empagliflozin in Patients with Chronic Kidney Disease

Herrington WG, Staplin N, Wanner C, et al. Empagliflozin in Patients with Chronic Kidney Disease. N Engl J Med. 2023;388:117-27

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Choi E, Bahadori MT, Sun J, Kulas J, Schuetz A, Stewart WF. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In: Advances in Neural Information Processing Systems (NeurIPS); 2016. p. 3504-12

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BEHRT: Transformer for Electronic Health Records

Li Y, Rao S, Solares JRA, et al. BEHRT: Transformer for Electronic Health Records. Sci Rep. 2020;10:7155

work page 2020

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Med -BERT: pretrained contextualized embeddings on large -scale structured electronic health records for disease prediction

Rasmy L, Wu Y, Wang N, et al. Med -BERT: pretrained contextualized embeddings on large -scale structured electronic health records for disease prediction. NPJ Digit Med. 2021;4:86

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Tangri N, Grams ME, Levey AS, et al. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis. JAMA. 2016;315:164-74

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The kidney failure risk equation for prediction of end -stage renal disease in UK primary care: an external validation and clinical impact projection cohort study

Major RW, Shepherd D, Medcalf JF, et al. The kidney failure risk equation for prediction of end -stage renal disease in UK primary care: an external validation and clinical impact projection cohort study. PLoS Med. 2019;16:e1002955

work page 2019

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Machine learning to predict end -stage kidney disease in chronic kidney disease

Li Y, Tang W, Li Y, et al. Machine learning to predict end -stage kidney disease in chronic kidney disease. Sci Rep. 2022;12:12316

work page 2022