Validation of ICD-10 Codes for Gestational and Pregestational Diabetes During Pregnancy in a Large, Public Hospital.

BACKGROUND: The use of billing codes (ICD-10) to identify and track cases of gestational and pregestational diabetes during pregnancy is common in clinical quality improvement, research, and surveillance. However, specific diagnoses may be misclassified using ICD-10 codes, potentially biasing estimates. The goal of this study is to provide estimates of validation parameters (sensitivity, specificity, positive predictive value, and negative predictive value) for pregestational and gestational diabetes diagnosis using ICD-10 diagnosis codes compared with medical record abstraction at a large public hospital in Atlanta, Georgia. METHODS: This study includes 3,654 deliveries to Emory physicians at Grady Memorial Hospital in Atlanta, Georgia, between 2016 and 2018. We linked information abstracted from the medical record to ICD-10 diagnosis codes for gestational and pregestational diabetes during the delivery hospitalization. Using the medical record as the gold standard, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for each. RESULTS: For both pregestational and gestational diabetes, ICD-10 codes had a high-negative predictive value (>99%, Table 3) and specificity (>99%). For pregestational diabetes, the sensitivity was 85.9% (95% CI = 78.8, 93.0) and positive predictive value 90.8% (95% CI = 85, 97). For gestational diabetes, the sensitivity was 95% (95% CI = 92, 98) and positive predictive value 86% (95% CI = 81, 90). CONCLUSIONS: In a large public hospital, ICD-10 codes accurately identified cases of pregestational and gestational diabetes with low numbers of false positives.

Formation and phenotypic characterization of CD49a, CD49b and CD103 expressing CD8 T cell populations in human metastatic melanoma.

Integrins α1ß1 (CD49a), α2ß1 (CD49b) and αEß7 (CD103) mediate retention of lymphocytes in peripheral tissues, and their expression is upregulated on tumor infiltrating lymphocytes (TIL) compared to circulating lymphocytes. Little is known about what induces expression of these retention integrins (RI) nor whether RI define subsets in the tumor microenvironment (TME) with a specific phenotype. Human metastatic melanoma-derived CD8 TIL could be grouped into five subpopulations based on RI expression patterns: RIneg, CD49a+ only, CD49a+CD49b+, CD49a+CD103+, or positive for all three RI. A significantly larger fraction of the CD49a+ only subpopulation expressed multiple effector cytokines, whereas CD49a+CD103+ and CD49a+CD49b+ cells expressed IFNγ only. RIneg and CD49a+CD49b+CD103+ CD8 TIL subsets expressed significantly less effector cytokines overall. Interestingly, however, CD49a+CD49b+CD103+ CD8 expressed lowest CD127, and highest levels of perforin and exhaustion markers PD-1 and Tim3, suggesting selective exhaustion rather than conversion to memory. To gain insight into RI expression induction, normal donor PBMC were cultured with T cell receptor (TCR) stimulation and/or cytokines. TCR stimulation alone induced two RI+ cell populations: CD49a single positive and CD49a+CD49b+ cells. TNFα and IL-2 each were capable of inducing these populations. Addition of TGFß to TCR stimulation generated two additional populations; CD49a+CD49bnegCD103+ and CD49a+CD49b+CD103+. Taken together, our findings identify opportunities to modulate RI expression in the TME by cytokine therapies and to generate subsets with a specific RI repertoire in the interest of augmenting immune therapies for cancer or for modulating other immune-related diseases such as autoimmune diseases.