Aims To develop and validate empirically a mathematical model for identifying new cannabis use in chronic, daily cannabis smokers.
Design Models were based on urinary creatinine-normalized (CN) cannabinoid excretion in chronic cannabis smokers.
Setting For model development, participants resided on a secure research unit for 30 days. For model validation, participants were abstinent with daily observed urine specimens for 28 days.
Participants A total of 48 (model development) and 67 (model validation) daily cannabis smokers were recruited.
Measurements All voided urine was collected and analyzed for 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THCCOOH) by gas chromatography-mass spectrometry (GCMS; limit of quantification 2.5 ng/ml) and creatinine (mg/ml). Urine THCCOOH was normalized to creatinine, yielding ng/mg CN-THCCOOH concentrations. Urine concentration ratios were determined from 123 513 specimen pairs collected 2–30 days apart.
Findings A mono-exponential model (with two parameters, initial urine specimen CN-THCCOOH concentration and time between specimens), based on the Marquardt–Levenberg algorithm, provided a reasonable data fit. Prediction intervals with varying probability levels (80, 90, 95, 99%) provide upper ratio limits for each urine specimen pair. Ratios above these limits suggest cannabis re-use. Disproportionate numbers of ratios were higher than expected for some participants, prompting development of two additional rules that avoid misidentification of re-use in participants with unusual CN-THCCOOH excretion patterns.
Conclusions For the first time, a validated model is available to aid in the differentiation of new cannabis use from residual creatinine-normalized 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (CN-THCCOOH) excretion in chronic, daily cannabis users. These models are valuable for clinicians, toxicologists and drug treatment staff and work-place, military and criminal justice drug-testing programs.