Application of statistical process control to qualitative molecular diagnostic assays

Area of Science:

• Molecular biology
• Laboratory medicine
• Biostatistics

Background:

• Modern pathology labs, especially high-throughput clinical chemistry, utilize statistical process control (SPC).
• A significant gap exists in applying SPC to molecular laboratories, primarily limited to quantitative assays.
• The absence of SPC for qualitative assays presents a notable disadvantage in quality control.

Purpose of the Study:

• To develop and evaluate a method for applying SPC to qualitative molecular assays.
• To address the lack of reliable quality control systems in routine molecular diagnostics.
• To determine the feasibility and limitations of using frequency estimates and confidence intervals for detecting deviations in mutation frequencies.

Main Methods:

• Utilized a frequency estimate coupled with confidence interval calculations.
• Applied the method to modeled laboratory data for routine molecular laboratory simulation.
• Analyzed minimum sample number requirements for effective deviation detection.

Main Results:

• Demonstrated the strengths and weaknesses of the proposed SPC approach for qualitative data.
• Highlighted that assays with low mutation frequencies require larger sample sizes for timely detection of small deviations.
• Identified minimum sample number requirements crucial for mitigating protracted detection times.

Conclusions:

• The study successfully describes the first application of SPC to qualitative laboratory data.
• The developed method offers a viable solution for quality control in molecular laboratories.
• Optimizing sample size is critical for the efficacy of this SPC approach, particularly for sensitive mutation detection.