Systematic review of validated case definitions to identify hypertensive disorders of pregnancy in administrative healthcare databases

被引:4
作者
Johnston, Amy [1 ,2 ]
Dancey, Sonia R. [3 ]
Tseung, Victrine [2 ]
Skidmore, Becky
Tanuseputro, Peter [4 ,5 ,6 ]
Smith, Graeme N. [7 ]
Coutinho, Thais [1 ,8 ,9 ,10 ]
Edwards, Jodi D. [1 ,2 ,5 ]
机构
[1] Univ Ottawa, Sch Epidemiol & Publ Hlth, Ottawa, ON, Canada
[2] Univ Ottawa, Brain & Heart Nexus Res Program, Heart Inst, Ottawa, ON, Canada
[3] Univ Ottawa, Sch Med, Ottawa, ON, Canada
[4] Ottawa Hosp Res Inst, Ottawa, ON, Canada
[5] ICES, Ottawa, ON, Canada
[6] Univ Ottawa, Dept Med, Div Palliat Care, Ottawa, ON, Canada
[7] Kingston Hlth Sci Ctr, Dept Obstet & Gynecol, Div Maternal Fetal Med, Kingston, ON, Canada
[8] Univ Ottawa, Div Cardiol, Heart Inst, Ottawa, ON, Canada
[9] Univ Ottawa, Canadian Womens Heart Hlth Ctr, Heart Inst, Ottawa, ON, Canada
[10] Univ Ottawa, Div Cardiac Prevent & Rehabil, Heart Inst, Ottawa, ON, Canada
关键词
Systematic Reviews as Topic; Epidemiology; Hypertension; Pregnancy; MATERNAL MEDICAL CONDITIONS; PERINATAL DATA; ACCURACY; DIAGNOSES; VALIDITY; PREECLAMPSIA; CODES; EPIDEMIOLOGY; MORBIDITY; DISEASES;
D O I
10.1136/openhrt-2022-002151
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundAdministrative data are frequently used to study cardiovascular disease (CVD) risk in women with hypertensive disorders of pregnancy (HDP). Little is known about the validity of case-finding definitions (CFDs, eg, disease classification codes/algorithms) designed to identify HDP in administrative databases. MethodsA systematic review of the literature. We searched MEDLINE, Embase, CINAHL, Web of Science and grey literature sources for eligible studies. Two independent reviewers screened articles for eligibility and extracted data. Quality of reporting was assessed using checklists; risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, adapted for administrative studies. Findings were summarised descriptively. ResultsTwenty-six studies were included; most (62%) validated CFDs for a variety of maternal and/or neonatal outcomes. Six studies (24%) reported reference standard definitions for all HDP definitions validated; seven reported all 2x2 table values for & GE;1 CFD or they were calculable. Most CFDs (n=83; 58%) identified HDP with high specificity (ie, & GE;98%); however, sensitivity varied widely (3%-100%). CFDs validated for any maternal hypertensive disorder had the highest median sensitivity (91%, range: 15%-97%). Quality of reporting was generally poor, and all studies were at unclear or high risk of bias on & GE;1 QUADAS-2 domain. ConclusionsEven validated CFDs are subject to bias. Researchers should choose the CFD(s) that best align with their research objective, while considering the relative importance of high sensitivity, specificity, negative predictive value and/or positive predictive value, and important characteristics of the validation studies from which they were derived (eg, study prevalence of HDP, spectrum of disease studied, methodological rigour, quality of reporting and risk of bias). Higher quality validation studies on this topic are urgently needed. PROSPERO registration numberCRD42021239113.
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页数:14
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共 65 条
[1]  
[Anonymous], 2003, An evaluation of the quality of obstetric/neonatal discharge abstract data by reabstraction of medical charts
[2]  
[Anonymous], 2022, Diagnostic test evaluation Calculator, V20
[3]   Epidemiology and Health Administrative Data: Focus on Methodology and Transparency [J].
Benchimol, Eric I. .
INFLAMMATORY BOWEL DISEASES, 2014, 20 (10) :1780-1781
[4]   Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data [J].
Benchimol, Eric I. ;
Manuel, Douglas G. ;
To, Teresa ;
Griffiths, Anne M. ;
Rabeneck, Linda ;
Guttmann, Astrid .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2011, 64 (08) :821-829
[5]   Hypertension Canada's 2018 Guidelines for the Management of Hypertension in Pregnancy [J].
Butalia, Sonia ;
Audibert, Francois ;
Cote, Anne-Marie ;
Firoz, Tabassum ;
Logan, Alexander G. ;
Magee, Laura A. ;
Mundle, William ;
Rey, Evelyne ;
Rabi, Doreen M. ;
Daskalopoulou, Stella S. ;
Nerenberg, Kara A. .
CANADIAN JOURNAL OF CARDIOLOGY, 2018, 34 (05) :526-531
[6]   Validation of an algorithm to identify children with biopsy-proven celiac disease from within health administrative data: An assessment of health services utilization patterns in Ontario, Canada [J].
Chan, Jason ;
Mack, David R. ;
Manuel, Douglas G. ;
Mojaverian, Nassim ;
de Nanassy, Joseph ;
Benchimol, Eric I. .
PLOS ONE, 2017, 12 (06)
[7]   French hospital discharge database: Data production, validity, and origins of errors in the field of severe maternal morbidity [J].
Chantry, A. A. ;
Deneux-Tharaux, C. ;
Bal, G. ;
Zeitlin, J. ;
Quantin, C. ;
Bouvier-Colle, M. -H. .
REVUE D EPIDEMIOLOGIE ET DE SANTE PUBLIQUE, 2012, 60 (03) :177-188
[8]   Hospital discharge data can be used for monitoring procedures and intensive care related to severe maternal morbidity [J].
Chantry, Anne A. ;
Deneux-Tharaux, Catherine ;
Cans, Christine ;
Ego, Anne ;
Quantin, Catherine ;
Bouvier-Colle, Marie-Helene .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2011, 64 (09) :1014-1022
[9]   Identifying hypertension in pregnancy using electronic medical records: The importance of blood pressure values [J].
Chen, Lu ;
Shortreed, Susan M. ;
Easterling, Thomas ;
Cheetham, T. Craig ;
Reynolds, Kristi ;
Avalos, Lyndsay A. ;
Kamineni, Aruna ;
Holt, Victoria ;
Neugebauer, Romain ;
Akosile, Mary ;
Nance, Nerissa ;
Bider-Canfield, Zoe ;
Walker, Rod L. ;
Badon, Sylvia E. ;
Dublin, Sascha .
PREGNANCY HYPERTENSION-AN INTERNATIONAL JOURNAL OF WOMENS CARDIOVASCULAR HEALTH, 2020, 19 :112-118
[10]   Development and Validation of ICD-10-CM-based Algorithms for Date of Last Menstrual Period, Pregnancy Outcomes, and Infant Outcomes (vol 46, pg 209, 2023) [J].
Chomistek, Andrea K. ;
Phiri, Kelesitse ;
Doherty, Michael C. ;
Calderbank, Jenna F. ;
Chiuve, Stephanie E. ;
McIlroy, Brenda Hinman ;
Snabes, Michael C. ;
Enger, Cheryl ;
Seeger, John D. .
DRUG SAFETY, 2023, 46 (05) :515-515