DOI: 10.5281/zenodo.17429833
title: Computational Validation of a Clinical Decision Support Algorithm for Long-Acting Injectable PrEP Bridge Period Navigation at UNAIDS Global Target Scale

description: Clinical decision support tool validated at UNAIDS global scale (21.2M patients) for LAI-PrEP bridge period navigation. Provides population-specific risk stratification, barrier identification, and evidence-based intervention recommendations. Demonstrates 81.6% improvement in bridge period success (4.1M additional transitions globally). 100% unit test pass rate. Open-source Python implementation with external JSON configuration enabling local adaptation.
description: Background: Long-acting injectable cabotegravir (LAI-CAB) and lenacapavir for HIV pre-exposure prophylaxis demonstrate >96% efficacy but face a critical implementation challenge: 47% of patients prescribed LAI-PrEP never receive their first injection during the "bridge period" between prescription and administration. This tool addresses this gap through systematic risk assessment and evidence-based intervention selection.

Tool Components:



Core Algorithm (lai_prep_decision_tool_v2_1.py, 850 lines): Population-specific risk stratification, barrier impact quantification, intervention recommendation with mechanism diversity scoring

External Configuration (lai_prep_config.json, 558 lines): 7 populations, 13 barriers, 21 evidence-based interventions with literature-derived effect sizes

Comprehensive Test Suite (test_edge_cases.py, 18 scenarios): 100% pass rate covering clinical edge cases, mathematical validation, mechanism diversity, data export, and error handling

Validation Scripts: Progressive validation from 1K to 21.2M patients

Documentation: Installation guides, API reference, integration instructions, contributing guidelines

Example Data: Individual patient JSON template, batch CSV with 10 diverse scenarios

Command-Line Interface (cli.py): Single patient assessment, batch processing, configuration validation


Validation Results:



Progressive Scales: Validated on 1K (functional), 1M (large-scale), 10M (ultra-large-scale), and 21.2M patients (UNAIDS global target)

Precision: Achieved ±0.018 percentage point margin of error at 21.2M scale (policy-grade statistical precision)

Convergence: Demonstrated algorithmic stability across all scales

Unit Testing: 100% pass rate (18/18 edge cases)

Population Alignment: All predictions within published clinical trial ranges

Predicted Impact: 81.6% relative improvement (baseline 23.96% → 43.50% with interventions)


Global Health Impact:



4.1 million additional successful LAI-PrEP transitions globally

~100,000 HIV infections prevented annually (assuming 2-5% HIV incidence, 96% LAI-PrEP efficacy)

$40 billion in lifetime treatment costs saved

Health equity focus: Greatest benefits to PWID (+265%) and adolescents (+147%)


Technical Features:



Configuration-driven architecture enabling parameter updates without code modification

Streaming processing supporting millions of patients with <4GB RAM

Mechanism diversity scoring preventing redundant intervention recommendations

JSON export for reproducibility and machine learning integration

Optional logit-space calculations for mathematical soundness

Cross-platform compatibility (Python 3.7+, minimal dependencies)


Evidence Base: Parameters derived from:



Clinical trials: HPTN 083 (n=4,566), HPTN 084 (n=3,224), PURPOSE trials (n=10,761)

Implementation studies: Real-world LAI-PrEP initiation data

Systematic reviews: Patient navigation effectiveness, structural barrier impacts

International guidelines: WHO, CDC, UNAIDS


Supplementary Materials:



S1: Clinician Quick-Reference Card (point-of-care decision support)

S2: Patient Information Handout (accessible patient education guide)

S3: Machine-Readable Data Files (JSON configuration documentation, patient input examples)

S4: Implementation Guide (staged validation protocols, training curriculum)

S5: Clinical Decision Flowchart (6-step systematic workflow)


Publication: Demidont, A.C.; Backus, K.V. Computational Validation of Clinical Decision Support Algorithm for Long-Acting Injectable PrEP Bridge Period Navigation at UNAIDS Global Target Scale. Viruses 2025 (in review).

Repository Contents: This Zenodo archive contains the exact version of the code, configuration, validation data, and supplementary materials used in the published manuscript, ensuring complete reproducibility of all reported results.

Intended Use:



Clinical implementation: Risk stratification and intervention selection at LAI-PrEP prescription

Research: Prospective validation studies, algorithm refinement

Quality improvement: Bridge period navigation program development

Policy: Resource allocation modeling, implementation planning


Important Limitations:



Computational validation only; prospective clinical validation required

Some parameters extrapolated from oral PrEP cascade (PWID, adolescents)

Population categories aggregate substantial heterogeneity

Real-world performance may differ from predictions

Recommended staged implementation with systematic outcome tracking


License:



Software (Python code): MIT License (permissive, commercial use allowed)

Configuration/Data: CC BY 4.0 (attribution required)

Documentation: CC BY 4.0 (attribution required)

Supplementary Materials (LaTeX): CC BY 4.0 (attribution required)


Keywords: HIV prevention, pre-exposure prophylaxis, long-acting injectable, cabotegravir, lenacapavir, implementation science, clinical decision support, health equity, patient navigation, bridge period, algorithm validation, computational validation, UNAIDS targets, global health

Funding: This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflicts of Interest: K.V.B. is an employee of Gilead Sciences, Inc., manufacturer of lenacapavir. The decision support tool is designed to be product-agnostic and applies to all long-acting injectable PrEP formulations. A.C.D. declares no conflicts of interest.

Contact: Adrian C. Demidont, DO Email: acdemidont@nyxdynamics.org Organization: Nyx Dynamics, LLC Location: Fairfield, CT, USA

GitHub Repository: https://github.com/Nyx-Dynamics/lai-prep-bridge-decision-tool

Version: v2.1.0 (manuscript submission version)

Date: October 2024 - January 2025

Citation:

If you use this tool in your work, please cite:

**Software:**```bibtex@software{demidont2025laiprep,  author = {Demidont, Adrian C. and Backus, Kandis V.},  title = {LAI-PrEP Bridge Period Decision Support Tool},  version = {2.1.0},  year = {2025},  publisher = {Zenodo},  doi = {10.5281/zenodo.17429833},  url = {https://github.com/Nyx-Dynamics/lai-prep-bridge-decision-tool}}```

Related Identifiers:



Manuscript DOI: 10.5281/zenodo.17429833

GitHub Release: https://github.com/Nyx-Dynamics/lai-prep-bridge-decision-tool/releases/tag/v2.1.0