Estimating original bacterial loads from delayed clinical samples: A methodological Modeling and empirical validation...

Delays in the processing of clinical samples (urine, blood, CSF) distort bacterial colony-forming units (CFUs) due to variable storage/transit, resulting in inaccurate initial load estimates and delayed treatment. This study presents and empirically validates the Al Mahrizi-Mossolem viability correction model (MM-VCM), an inverse logistic growth-decay framework used to back-calculate original loads (N₀) from delayed observations (Nt), accounting for temperature-dependent growth, lag phases, decay, and carrying capacities.

MM-VCM employs a closed-form equation derived from a Gaussian-modulated doubling time (optimal at 37 °C, SD 5 °C) integrated into logistic dynamics. The parameters (lag 0.3-0.6 h, decay 0.01-0.013 h^-1, capacity ∼10⁹ CFU/mL), drawn from the literature, were applied to eight pathogen-matrix pairs (e.g., E. coli in urine/blood and N. meningitidis in CSF). Validation involved 4000 iterations of Monte Carlo and Bayesian simulations in R (v4.3.1) for 0-24 h delays (25 °C mean, SD 2 °C), with Nt ∼10⁸ CFU/mL, plus Sobol sensitivity analysis. Model predictions were compared to a multicenter clinical blood culture storage dataset using actual sample group inocula and positivity rates.

The simulations revealed that N₀ declined exponentially from ∼10⁸ CFU/mL at t = 0 to 1.3-2.0 × 10⁷ CFU/mL at t = 24 h, with 95% confidence intervals widening from ∼10⁷ CFU/mL (t = 2 h) to >4 × 10⁷ CFU/mL (t = 24 h). The probabilities of significant loads (>10⁵ CFU/mL) exceeded 0.99 for ≤6 h delays, whereas the CSF pairs presented greater stability (0.89-0.90 at 24 h). Temperature primarily drove uncertainty (Sobol indices >0.95), and the Bayesian results aligned closely (<5% differences). Empirical results for E. coli, S. aureus, and S. pneumoniae at 25 °C revealed that model predictions broadly corresponded with observed detection trends, with stronger agreement for S. aureus and S. pneumoniae, providing preliminary support for the model's real-world applicability.

MM-VCM enables efficient preanalytic corrections for bacterial loads, supporting diagnostics where resampling is infeasible (e.g., CSF, high-volume labs). Empirical validation against a multicenter clinical blood culture dataset at 25 °C revealed that model predictions broadly corresponded with observed detection patterns for S. aureus and S. pneumoniae, with greater variability observed for E. coli, supporting the model's potential utility pending further validation.