# Vancomycin Renal Feedback 100k

> Vancomycin clears through the kidneys — and damages them Vancomycin is excreted by the kidneys and injures the tubules, so exposure acts on its own elimination: dose → exposure → tubular damage →...

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**Tags:** synthetic-data, pharmacokinetics, vancomycin, therapeutic-drug-monitoring, TDM, nephrotoxicity, acute-kidney-injury, AKI, renal-function, precision-dosing, ODE-model, in-silico, clinical-pharmacology, pharmacometrics, AUC-estimation, causal-inference, parquet, closed-loop-simulation, dose-response, KDIGO
**License:** https://ai.market/licenses/standard/1.0/ai-training
**Published:** 2026-10-07 | **Updated:** 2026-10-07
**Canonical URL:** https://ai.market/listings/seller-4678e3cc-6247-48a3-acd7-4e61fb11ee0d

## Description
Vancomycin clears through the kidneys — and damages them
Vancomycin is excreted by the kidneys and injures the tubules, so exposure acts on its own elimination: dose → exposure → tubular damage → falling clearance → rising exposure. In every published population PK model and dosing program reviewed, renal function is a covariate fed in from outside. Here it is a state variable: the loop is closed inside the equations.

The cohort is 100,000 virtual adult inpatients whose baseline creatinine, ICU share, age and treatment duration match those of 128,993 real vancomycin-treated patients (Yu et al. 2021, AJHP). Each patient is simulated for up to 20 days of therapy and 45 days of follow-up. Two calibrated scenarios bracket the strength of vancomycin nephrotoxicity: scenario A is conservative, scenario B pushes the mechanism to its upper limit. The same patients, the same random streams — only the damage rate differs.

Every patient is recorded twice
Observed (what the hospital sees)	Truth (what actually happened)
Noisy creatinine samples at 12-, 24-, 48- or 72-hour intervals, four laboratory regimes	True hourly filtration (GFR) and vancomycin concentration
Vancomycin trough and peak levels within the registered immunoassay range (4–80 mg/L)	True daily and course AUC — the number no assay can measure
Doses actually administered, KDIGO injury flag from observed creatinine	Cause of each injury: vancomycin or competing. Detection lag of the clinical flag
Creatinine-based clearance estimate used by the dose controller	Error of three dosing estimators, patient by patient, in three time windows
No real hospital archive contains the right column. Here it exists because every value was computed from a mechanistic model with 242 constants, each traced to a published source.

The same patients re-simulated under other conditions
Without dose adjustment — what would have happened if the controller never reduced the dose.
Without vancomycin toxicity — isolates the loop-induced bias in the dosing estimator.
With intact kidneys — the exposure each patient would have had without any injury at all.
Two monitoring regimes on the same patients — trough-guided vs. AUC-guided dosing, three emulated cohorts of 20,000 each, same noise.
Nine tables per scenario
Table	Side	Rows (A / B)	Cols	What it holds
cohort	observed	100,000 / 100,000	15	Baseline covariates and assigned regimen
observed	observed	1,802,987 / 1,333,673	10	Creatinine and vancomycin samples, dose, clearance estimate, injury flag
observed_outcomes	observed	100,000 / 100,000	15	Per-patient outcomes from observed data only
truth	truth	100,000 / 100,000	59	True parameters, exposure, estimator error, injury cause
truth_traj	truth	2,405,000 / 2,405,000	7	Hourly true states for 5,000 patients
truth_counterfactual	truth	100,000 / 100,000	17	Same patients without dose adjustment
truth_noloop	truth	100,000 / 100,000	9	Same patients without vancomycin damage
truth_exposure_daily	truth	100,000 / 100,000	41	True daily AUC with injury and with intact kidneys
regime_arms	truth	60,000 / 60,000	20	Paired trough/AUC arms, three emulated studies
The model
State variables: vancomycin mass in central and peripheral compartments, vancomycin-attributable damage Dv, competing damage Dc, serum creatinine. Integration: fourth-order Runge–Kutta, step 0.2 h. Eight physical invariants checked at every step.

C1 = A1 / V1
GFR(t) = max( GFR_0(t) · (1 − Dv) · (1 − Dc), 1 mL/min )
CL(t)  = CL_nr + fr · GFR(t) · 0.06

dA1/dt = R_inf(t) − CL·C1 − Q·(C1 − A2/V2)
dA2/dt = Q·(C1 − A2/V2)
dDv/dt = k_dam · γ·C1 / (IC50 + γ·C1) · (1 − Dv) − k_rec · Dv
dDc/dt = k_dam_c · (1 − Dc) − k_rec · Dc
dCr/dt = ( G − GFR(t) · 0.06 · Cr ) / (0.6 · weight)
Non-renal clearance 1.93 L/h, renal fraction 0.40 of filtration; central volume 36.3 L per 82 kg; peripheral 36.8 L, inter-compartmental clearance 2.66 L/h; damage follows Emax by tubular concentration (40–50× plasma), IC50 6.06–8.8 mg/mL from HK-2 cell studies; creatinine balance with Vd = 0.6 × body weight.

Key findings
Quantity	Scenario A	Scenario B
Injury flagged, % of patients	19.8	20.6
Vancomycin as primary cause / competing cause	4,570 / 15,188	18,452 / 2,198
Injury without vancomycin channel, %	14.3	3.4
Naive slope inflated vs. causal, ×	5.0	3.1
Reverse arm share (kidney → drug), %	35.5	27.3
Detection lag, median (IQR), hours	14 (3–35)	12 (1–28)
Causal slope, random dose as instrument (95% CI)	1.44 (1.15–1.82)	3.17 (2.82–3.51)
The exposure-mediated injury difference between monitoring regimes is bounded at 0.8–1.9 pp (scenario A) to 1.8–4.1 pp (scenario B) and at most 5.5 pp — against published observational differences of 13.7 and 17.2 pp.

Validation
113 structural checks, 0 failed — nulls, schema, key integrity, flag recomputation, exposure sums, observed/truth column separation.
Bit-for-bit reproducibility — all 18 tables regenerated from the published generator, SHA-256 match confirmed.
Calibration bands passed on the reference arm (therapy ≥ 5 days): injury rate 17.8% / 19.1%, creatinine rise ratio 1.86 / 2.10, time to injury 4.0 / 4.0 days, recovery 11.0 / 12.0 days.
External validation against three published regimen comparisons the model was never fitted to — six criteria recorded before any run. Scenario A passes 3/6, scenario B passes 5/6. The null study is reproduced in both. The long-therapy study is not reproduced by either — that is the ceiling result.
Benchmark — injury prediction at 48 h, 5-fold CV, observed tables only: logistic regression AUROC 0.745 (A) / 0.773 (B); XGBoost 0.767 / 0.797. Published on real patients: 0.735 (0.638–0.833) for logistic regression.
Companion products and preprint
An accompanying preprint describes the model, calibration, both scenarios, the three main findings and the external validation in full — it is the scientific paper behind this dataset and will be posted to a preprint server. The dataset is the companion data release of that preprint.

A generator — the complete Python source code of the model (17 modules) — is available as a separate product. It reproduces every table bit for bit with one command (python run.py --scenario both --verify) and can build new cohorts of any size from the same 36 calibrated parameter sets. ~25 minutes for both scenarios on a single CPU core, 4 GB RAM. The generator comes with its own passport, constants audit and full documentation.

Limitations
Data are synthetic. No dosing recommendations follow, and no AUC value in the dataset is a diagnostic threshold for injury.
Tubular concentration is a fixed multiple of plasma: no renal cortical tissue compartment.
Non-creatinine chromogen interference with the Jaffe reaction is not modeled.
The dose controller acts on the creatinine-based clearance estimate; it does not use measured vancomycin concentrations.
The competing channel does not switch off once started.
Scenario B is an upper bound: it overstates the published dose-response increment and the vancomycin share in the randomized comparison.
Provenance
57 peer-reviewed sources, 242 audited constants. Every constant is listed in constants_audit.csv with author, year, DOI/PMID, search queries and verification date. Every source is listed in literature_registry.csv with a working link. Specification hash (SHA-256): 08d02bab628c0b1f93b41088c5a11e725709a717d68c09fb1ca9f40ca2be29a8. Seed 20260929.

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