Validation of algorithms to identify adverse perinatal outcomes in the Medicaid Analytic Extract database.

Autor:	He M; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Huybrechts KF; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Dejene SZ; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Straub L; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Bartels D; Department of Anesthesia and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Burns S; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Combs DJ; Department of Anesthesia and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Cottral J; Department of Anesthesia and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Gray KJ; Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Manning-Geist BL; Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Mogun H; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Reimers RM; Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA., Hernandez-Diaz S; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Bateman BT; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Jazyk:	angličtina
Zdroj:	Pharmacoepidemiology and drug safety [Pharmacoepidemiol Drug Saf] 2020 Apr; Vol. 29 (4), pp. 419-426. Date of Electronic Publication: 2020 Mar 02.
DOI:	10.1002/pds.4967
Abstrakt:	Background: The Medicaid Analytic eXtract (MAX) is a health care utilization database from publicly insured individuals that has been used for studies of drug safety in pregnancy. Claims-based algorithms for defining many important maternal and neonatal outcomes have not been validated. Objective: To validate claims-based algorithms for identifying selected pregnancy outcomes in MAX using hospital medical records. Methods: The medical records of mothers who delivered between 2000 and 2010 within a single large healthcare system were linked to their claims in MAX. Claims-based algorithms for placental abruption, preeclampsia, postpartum hemorrhage, small for gestational age, and noncardiac congenital malformation were defined. Fifty randomly sampled cases for each outcome identified using these algorithms were selected, and their medical records were independently reviewed by two physicians to confirm the presence of the diagnosis of interest; disagreements were resolved by a third physician reviewer. Positive predictive values (PPVs) and 95% confidence intervals (CIs) of the claims-based algorithms were calculated using medical records as the gold standard. Results: The linked cohort included 10,899 live-birth pregnancies. The PPV was 92% (95% CI, 82%-97%) for placental abruption, 82% (95% CI, 70%-91%) for preeclampsia, 74% (95% CI, 61%-85%) for postpartum hemorrhage, 92% (95% CI, 82%-97%) for small for gestational age, and 86% (95% CI, 74%-94%) for noncardiac congenital malformation. Conclusions: Across the perinatal outcomes considered, PPVs ranged between 74% and 92%. These PPVs can inform bias analyses that correct for outcome misclassification. (© 2020 John Wiley & Sons Ltd.)
Databáze:	MEDLINE
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