Positive predictive value of the International Classification of Diseases, 10th edition diagnosis codes for anemia caused by bleeding in the Danish National Registry of Patients

Jihen Zalfani, Trine Frøslev, Morten Olsen, Inès Ben Ghezala, Henrik Gammelager, Johan FB Arendt, Rune Erichsen

Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark

Objective: Valid data on anemia caused by bleeding are needed for epidemiological research and monitoring health care. The Danish National Registry of Patients (DNRP) is a nationwide medical database with information on all Danish residents' hospital history. We aimed to assess the positive predictive value (PPV) of the diagnostic coding of anemia caused by bleeding in the DNRP.
Methods: In the DNRP, we identified all patients with International Classification of Disease, 10th edition codes for anemia caused by bleeding (acute: D50.0; chronic: D62.6) at three Danish hospitals from 2000 through 2009. For these patients we computed the PPV using hemoglobin level data, from Aarhus University laboratory database, as reference standard. Anemia was defined by a hemoglobin level less than 7.0 mmol/L for women and less than 8.0 mmol/L for men.
Results: We identified 3391 patients in the DNRP with a diagnosis of anemia caused by bleeding. The overall PPV was 95.4% (95% confidence interval [CI]: 94.6%–96.0%). The PPV was 97.6% (95% CI: 96.6%–98.3%) for men and 94.0% (95% CI: 92.9%–94.9%) for women, and the PPV increased with age at diagnosis. The PPV varied according to type of discharging departments, from 89.2% (95% CI: 83.4%–93.4%) in gynecology to 96.8% (95% CI: 94.9%–98.2%) in surgery, and was lower for outpatients compared with inpatients.
Conclusion: We found a high PPV of the coding for anemia caused by bleeding in the DNRP. The registry is a valid source of data on anemia caused by bleeding for various purposes including research and monitoring health care.

Keywords: anemia, Danish National Registry of Patients, International Classification of Diseases, predictive value, laboratory database, validation