Editor: Deborah Sesok-Pizzini, MD, MBA, professor, Department of Clinical Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, and chief, Division of Transfusion Medicine, Children’s Hospital of Philadelphia.
Preventing genetic testing order errors via a lab utilization management program
Diagnostic errors, or failure to provide an accurate and timely diagnosis, impact an estimated 12 million outpatient care visits annually in the United States. These errors can often be attributed to the testing process, including test selection, ordering, retrieval, and interpretation. Literature about diagnostic errors has primarily focused on the outpatient setting; study of diagnostic error in the inpatient setting has been limited. The growth of genetic test menus has made the test order process more complex. Furthermore, genetic testing has moved into medical specialties with less experience diagnosing genetic disorders. At many institutions, a substantial proportion of laboratory send-out budgets is committed to genetic testing requests. This has prompted many institutions to establish a utilization management program to decrease and optimize genetic testing. The authors of this study sought to characterize error rates from genetic test orders between medical specialties and in outpatient and inpatient settings. They performed a retrospective analysis of a detailed utilization management database comprising 2.5 years of data and almost 1,400 genetic test orders. Multiple reviewers categorized order modifications and cancellations, qualified rates of positive results and order errors, and compared genetics with nongenetics providers and inpatient with outpatient orders. The results showed that high cost or problems with preauthorization were the most common reasons for modification and cancellation, respectively. Moreover, the cancellation rate for nongenetics providers was three times the rate for geneticists, but abnormal results rates were similar between the two groups. No differences between inpatient and outpatient approval rates were found. Fifteen percent of modified or cancelled orders, or three percent of the genetic test orders overall, were cancelled because the test was not clinically appropriate or modified because the testing was indicated but the wrong test was ordered. In summary, this study demonstrates the high risk for order-entry errors, which may lead to diagnostic errors in genetic testing. A structured utilization committee can help prevent errors associated with genetic testing. Increased laboratory involvement in the diagnostic workup is important in providing the highest value care to patients.
Mathias PC, Conta JH, Konnick EQ, et al. Preventing genetic testing order errors with a laboratory utilization management program. Am J Clin Pathol. 2016;146:221–226.
Correspondence: Dr. Jane A. Dickerson at firstname.lastname@example.org
Tools for early antenatal prediction of gestational diabetes in obese women
All obese pregnant women are categorized as being equally high risk for gestational diabetes, even though the majority of them do not develop the disorder. Women with gestational diabetes mellitus (GDM) require more intensive antenatal care to achieve optimal blood glucose control and to identify other common complications, including fetal macrosomia and large-for-gestational-age (LGA) infants. A prediction tool to help stratify disease risk would allow clinicians to identify women at risk for GDM early in pregnancy so they can receive targeted intervention. The authors conducted a study to develop a simple, robust, and easily accessible GDM prediction tool to facilitate early intervention for obese women with the highest risk. They measured 21 biomarkers of biological relevance to GDM and a targeted metabolome of 158 metabolites in early pregnancy from 1,303 obese women as part of their prediction models. This prospective cohort was from the UPBEAT trial (UK Better Eating and Activity Trial), a multi-center trial of a complex dietary and physical intervention strategy designed to prevent GDM in obese women and LGA in their offspring. Twenty-six percent of women in the UPBEAT trial developed GDM. The authors used statistical modeling to combine clinical variables and biomarkers to develop prediction tools. A stepwise logistic regression model based on the clinical and anthropometric variables of age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, and waist:height and neck:thigh ratios, provided an area under the curve of 0.71 (95 percent confidence interval [CI], 0.68–0.74). This increased to 0.77 (95 percent CI, 0.73–0.80) when the authors added the candidate biomarkers HbA1c, random glucose, fructosamine, triglycerides, adiponectin, and sex hormone binding globulin. Of interest, the addition of targeted nuclear magnetic resonance metabolites did not improve the model’s accuracy. The authors concluded that their model will help identify women at low risk for developing GDM and improve intervention in high-risk women most likely to benefit from treatment.
White SL, Lawlor DA, Briley AL, et al. Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention. PLoS ONE. 2016;11(12):e0167846. doi:10.1371/journal.pone.0167846.
Correspondence: Dharmintra Pasupathy at Dharmintra.Pasupathy@kcl.ac.uk