Patrick C. Mathias

Evaluation of matrix effects using a spike recovery approach in a dilute-and-inject liquid chromatography-tandem mass spectrometry opioid monitoring assay.

BACKGROUND Liquid chromatography-tandem mass spectrometry has become the gold standard for quantitative analysis of compounds in human matrices. Introduction of these assays into clinical practice, where false positive and false negative results have substantial implications, requires careful attention to matrix effects. We describe an evaluation of matrix effects in human urine from a dilute-and-inject liquid chromatography-tandem mass spectrometric assay for the quantitative analysis of opioids and metabolites. METHODS A spike-recovery approach was employed for each analyte in each sample. We examined the impact of spike-recovery for the 6 glucuronides measured in this assay and compared the analytes for which conventional stable isotope-labeled internal standards were used with the analytes for which analog internal standards were used. RESULTS For analytes that had analog internal standards, up to 1.5% of negative samples failed our requirement of recovering at least 80% of the expected spiked concentration while passing all other quality control parameters. CONCLUSIONS Using spike-recovery as a quality control parameter decreases the rate of false negatives for compounds using analog internal standards, but does not have benefit for compounds with conventional stable isotope-labeled internal standards.

Preventing Genetic Testing Order Errors With a Laboratory Utilization Management Program

OBJECTIVES To characterize error rates for genetic test orders between medical specialties and in different settings by examining detailed order information. METHODS We performed a retrospective analysis of a detailed utilization management case database, comprising 2.5 years of data and almost 1,400 genetic test orders. After review by multiple reviewers, we categorized order modifications and cancellations, quantified rates of positive results and order errors, and compared genetics with nongenetics providers and inpatient with outpatient orders. RESULTS High cost or problems with preauthorization were the most common reasons for modification and cancellation, respectively. The cancellation rate for nongenetics providers was three times the rate for geneticists, but abnormal result rates were similar between the two groups. The approval rate for inpatient orders was not significantly lower than outpatient orders, and abnormal result rates were similar for these two groups as well. Order error rates were approximately 8% among tests recommended by genetics providers in the inpatient setting, and tests ordered or recommended by nongeneticists had error rates near 5% in both inpatient and outpatient settings. CONCLUSIONS Clinicians without specialty training in genetics make genetic test order errors at a significantly higher rate than geneticists. A laboratory utilization management program prevents these order errors from becoming diagnostic errors and reaching the patient.

Establishing evidence-based thresholds and laboratory practices to reduce inappropriate treatment of pseudohyperkalemia

BACKGROUND Unrecognized pseudohyperkalemia (PHK), defined as an artificial increase in measured potassium concentration, due to thrombocytosis and leukocytosis can lead to inappropriate patient treatment. Understanding the laboratory and patient characteristics that increase risk of PHK is key to preventing diagnostic errors. METHODS Serum/plasma potassium results collected at 2 laboratories over 4years were selected based on blood cell counts collected within 24h and whole blood potassium concentrations determined within 2h of the serum/plasma sample. Differences between whole blood and serum or plasma potassium were compared as functions of platelet or leukocyte count, fit to linear models, and stratified based on leukemia diagnosis codes. Patients having a serum/plasma potassium concentration that was at least 1mEq/mL higher than the whole blood concentration were defined as having PHK. Based on this analysis, high-risk patients were prospectively identified and PHK risk was communicated to providers. Medication administration records were queried to compare rates of kayexalate use pre- and post-intervention. RESULTS Approximately 14% of serum samples with platelet counts >500×109/L had a>1mEq/L increase relative to whole blood potassium. >25% of serum and plasma samples showed a>1mEq/L increase relative to whole blood potassium when leukocyte counts were >50×109/L. Patients with chronic lymphocytic leukemia and high WBC count demonstrated the highest rates of PHK. The rate of kayexalate administration prior to confirmatory testing decreased from 37% to 16% after the laboratory started verbally communicating the possibility of PHK to treating providers. CONCLUSIONS According to our data, a leukocyte count threshold for plasma samples of 50×109/L is appropriate for indicating a high risk of PHK. Direct communication by the laboratory to the care team reduces inappropriate potassium lowering treatment in populations at high risk.

Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing

OBJECTIVES To apply techniques for ancestry and sex computation from next-generation sequencing (NGS) data as an approach to confirm sample identity and detect sample processing errors. METHODS We combined a principal component analysis method with k-nearest neighbors classification to compute the ancestry of patients undergoing NGS testing. By combining this calculation with X chromosome copy number data, we determined the sex and ancestry of patients for comparison with self-report. We also modeled the sensitivity of this technique in detecting sample processing errors. RESULTS We applied this technique to 859 patient samples with reliable self-report data. Our k-nearest neighbors ancestry screen had an accuracy of 98.7% for patients reporting a single ancestry. Visual inspection of principal component plots was consistent with self-report in 99.6% of single-ancestry and mixed-ancestry patients. Our model demonstrates that approximately two-thirds of potential sample swaps could be detected in our patient population using this technique. CONCLUSIONS Patient ancestry can be estimated from NGS data incidentally sequenced in targeted panels, enabling an inexpensive quality control method when coupled with patient self-report.

A Tincture of Time—Latent Crystal Formation and Clinical Decision-Making in Acute Gout: A Teachable Moment

Story From the Front Lines A 51-year-old man without a history of gout presented 4 days after abrupt onset of a painful right elbow without trauma. He had no fevers or constitutional symptoms. He was monogamous and denied drug use. On examination, the patient appeared uncomfortable and resisted right elbow movement. The joint was swollen, erythematous, warm, and tender. Other joints were unaffected. Findings from the rest of his examination were normal. His white blood cell (WBC) count was within reference range (4500-11 000/μL; to convert WBC to 109/L, multiply by 0.001), and his serum glucose level was 147 mg/dL (to convert glucose to millimoles per liter, multiply by 0.0555). His uric acid level was not checked. A radiograph of the elbow revealed an effusion without erosions. Arthrocentesis produced straw-colored synovial fluid containing a WBC count of 9696/μL (91% neutrophils), and a glucose level of 133 mg/dL. Initial polarized light microscopy showed no crystal formation; results from Gram stain and bacterial culture were negative. The patient was treated for suspected culturenegative septic arthritis, and, on hospital day 3, was discharged home with oral antibiotics. Four days later, the patient returned for follow-up. His primary care clinician noted that the final fluid analysis report in the electronic health record (EHR) identified copious monosodium urate crystals, confirming a diagnosis of gout.