Qpack-a Python package for QASAT-quantitative scale for grading cerebral blood flow, autonomic testing, and skin...

Area of Science:

• Neurology
• Biomedical Engineering
• Computational Biology

Background:

• Quantitative grading of autonomic tests and small fiber densities is crucial for research and clinical applications.
• Current methods for grading dysautonomia, small fiber neuropathy, and cerebral blood flow using the QASAT scale involve complex calculations.
• There is a need for accessible and reproducible tools to streamline these assessments.

Purpose of the Study:

• To introduce qpack, an open-source software package designed to implement the Quantitative Scale for Autonomic Function Testing (QASAT).
• To automate the scoring of autonomic tests and skin biopsies for small fiber assessment, thereby reducing calculation errors and enhancing reproducibility.
• To facilitate the availability and quality of autonomic studies and small fiber density assessments.

Main Methods:

• Development of qpack, an open-source software package using the Python programming language.
• Implementation of the QASAT scoring algorithms within the qpack software.
• Inclusion of verification datasets to ensure the accuracy and reliability of qpack's calculations.

Main Results:

• Qpack automatically generates reproducible scores for autonomic reflex tests, transcranial Doppler, sudomotor testing, and small fiber densities.
• The software reduces the complexity and potential for errors in QASAT score calculations.
• Provided datasets allow for verification of qpack's accurate implementation.

Conclusions:

• Qpack offers an easy-to-use, open-source solution for quantitative grading of autonomic function and small fiber neuropathy.
• The software enhances the reproducibility, availability, and quality of autonomic studies and skin biopsy analyses.
• Qpack can be readily integrated into routine clinical and research settings for improved patient assessment.