OBJECTIVE: Determine maternity hospital and lesion-specific prenatal detection rates of major congenital heart disease (mCHD) for hospitals referring prenatally and postnatally to one Congenital Cardiac Centre, and assess interhospital relative performance (relative risk, RR). METHODS: We manually linked maternity data (3 hospitals prospectively and another 16 retrospectively) with admissions, fetal diagnostic and surgical cardiac data from one Congenital Cardiac Centre. This Centre submits verified information to National Institute for Cardiovascular Outcomes Research (NICOR-Congenital), which publishes aggregate antenatal diagnosis data from infant surgical procedures. We included 120 198 unselected women screened prospectively over 11 years in 3 maternity hospitals (A, B, C). Hospital A: colocated with fetal medicine, proactive superintendent, on-site training, case-review and audit, hospital B: on-site training, proactive superintendent, monthly telemedicine clinics, and hospital C: sonographers supported by local obstetrician. We then studied 321 infants undergoing surgery for complete transposition (transposition of the great arteries (TGA), n=157) and isolated aortic coarctation (CoA, n=164) screened in hospitals A, B, C prospectively, and 16 hospitals retrospectively. RESULTS: 385 mCHD recorded prospectively from 120 198 (3.2/1000) screened women in 3 hospitals. Interhospital relative performance (RR) in Hospital A:1.68 (1.4 to 2.0), B:0.70 (0.54 to 0.91), C:0.65 (0.5 to 0.8). Standardised prenatal detection rates (funnel plots) demonstrating inter-hospital variation across 19 hospitals for TGA (37%, 0.00 to 0.81) and CoA (34%, 0.00 to 1.06). CONCLUSIONS: Manually linking data sources produced hospital-specific and lesion-specific prenatal mCHD detection rates. More granular, rather than aggregate, data provides meaningful feedback to improve screening performance. Automatic maternal and infant record linkage on a national scale, requires verified, prospective maternity audit and integration of health information systems.
OBJECTIVE: Determine maternity hospital and lesion-specific prenatal detection rates of major congenital heart disease (mCHD) for hospitals referring prenatally and postnatally to one Congenital Cardiac Centre, and assess interhospital relative performance (relative risk, RR). METHODS: We manually linked maternity data (3 hospitals prospectively and another 16 retrospectively) with admissions, fetal diagnostic and surgical cardiac data from one Congenital Cardiac Centre. This Centre submits verified information to National Institute for Cardiovascular Outcomes Research (NICOR-Congenital), which publishes aggregate antenatal diagnosis data from infant surgical procedures. We included 120 198 unselected women screened prospectively over 11 years in 3 maternity hospitals (A, B, C). Hospital A: colocated with fetal medicine, proactive superintendent, on-site training, case-review and audit, hospital B: on-site training, proactive superintendent, monthly telemedicine clinics, and hospital C: sonographers supported by local obstetrician. We then studied 321 infants undergoing surgery for complete transposition (transposition of the great arteries (TGA), n=157) and isolated aortic coarctation (CoA, n=164) screened in hospitals A, B, C prospectively, and 16 hospitals retrospectively. RESULTS: 385 mCHD recorded prospectively from 120 198 (3.2/1000) screened women in 3 hospitals. Interhospital relative performance (RR) in Hospital A:1.68 (1.4 to 2.0), B:0.70 (0.54 to 0.91), C:0.65 (0.5 to 0.8). Standardised prenatal detection rates (funnel plots) demonstrating inter-hospital variation across 19 hospitals for TGA (37%, 0.00 to 0.81) and CoA (34%, 0.00 to 1.06). CONCLUSIONS: Manually linking data sources produced hospital-specific and lesion-specific prenatal mCHD detection rates. More granular, rather than aggregate, data provides meaningful feedback to improve screening performance. Automatic maternal and infant record linkage on a national scale, requires verified, prospective maternity audit and integration of health information systems.
Authors: Nelangi M Pinto; Kevin A Henry; William A Grobman; Amen Ness; Stephen Miller; Sarah Ellestad; Nina Gotteiner; Theresa Tacy; Guo Wei; L LuAnn Minich; Anita Y Kinney Journal: J Ultrasound Med Date: 2019-12-24 Impact factor: 2.153
Authors: I Durand; G Deverriere; C Thill; A S Lety; C Parrod; N David; E Barre; T Hazelzet Journal: Pediatr Cardiol Date: 2015-04-07 Impact factor: 1.655
Authors: L R Freud; A Moon-Grady; M C Escobar-Diaz; N L Gotteiner; L T Young; D B McElhinney; W Tworetzky Journal: Ultrasound Obstet Gynecol Date: 2015-01-28 Impact factor: 7.299
Authors: Trisha V Vigneswaran; Manish D Sinha; Israel Valverde; John M Simpson; Marietta Charakida Journal: Pediatr Cardiol Date: 2017-10-17 Impact factor: 1.655
Authors: M C Escobar-Diaz; L R Freud; A Bueno; D W Brown; K G Friedman; D Schidlow; S Emani; P J Del Nido; W Tworetzky Journal: Ultrasound Obstet Gynecol Date: 2015-04-30 Impact factor: 7.299