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Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation.

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Summary

This study introduces the average of deltas (AoD) quality control (QC) strategy, combining delta checks and moving averages to enhance systematic error detection in laboratory testing. AoD effectively monitors intrapatient results, improving accuracy with fewer samples.

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Quality Control

Background:

  • Traditional quality control (QC) methods are often discontinuous, necessitating supplementary strategies for systematic error detection.
  • Delta checks are effective for large systematic errors, while moving averages (MA) struggle with skewed data distributions.
  • The proposed average of deltas (AoD) strategy integrates delta checks and MA to monitor intrapatient result variations.

Purpose of the Study:

  • To develop and evaluate a novel patient-based quality control strategy, the average of deltas (AoD).
  • To assess the efficacy of AoD in detecting systematic analytical errors across various laboratory assays.
  • To compare the performance of AoD against traditional QC methods.

Main Methods:

  • Historical patient data were used to generate arrays of differences (deltas) between consecutive, intrapatient results (20-28h intervals).
  • A simulated annealing algorithm optimized AoD protocols by selecting the number of deltas to average and truncation limits.
  • Systematic error was simulated by introducing bias to analytes including albumin, calcium, creatinine, and electrolytes, with average number of deltas to detection (ANDED) calculated.

Main Results:

  • The average number of deltas to detection (ANDED) varied significantly across different assays and AoD protocols.
  • Systematic errors for albumin, lipase, and total protein were detected with a mean of 6 delta pairs.
  • Calcium shift detection showed variability, with a positive 0.6-mg/dL shift detected at ANDED 75 and a negative shift at ANDED 25.

Conclusions:

  • The average of deltas (AoD) strategy demonstrates effectiveness in detecting systematic laboratory errors.
  • AoD requires relatively few paired patient samples for error detection, making it an efficient patient-based QC technique.
  • This novel approach enhances overall error detection capabilities in clinical laboratories.