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Validation study: evaluation of the metrological quality of French hospital data for perinatal algorithms. | LitMetric

AI Article Synopsis

Article Abstract

Objective: The aim of our validation study was to assess the metrological quality of hospital data for perinatal algorithms on a national level.

Design: Validation study.

Setting: This was a multicentre study of the French medicoadministrative database on perinatal indicators.

Participants: In each hospital, we selected 150 discharge abstracts for delivery (after 22 weeks of gestation), in 2014, and their corresponding medical records. Overall, 22 hospitals were included.

Interventions: A single investigator performed blind data collection from medical records in order to compare data from discharge abstracts with data from medical records. Finally, 3246 discharge abstracts were studied.

Primary And Secondary Outcome Measures: Seventy items, including maternal and delivery characteristics and maternal morbidity, were collected for each delivery stay.

Results: The concordance rate of maternal age at delivery was 94.8% (95% CI 93.8 to 95.4). Combining the two forms of pre-existing diabetes, the algorithm presented a PPV of 65.9% and a sensitivity of 75.7%. The concordance rate of gestational age at delivery was 91.8% (90.9 to 92.7). Regarding gestational diabetes, the PPV was 80.8% (79.4 to 82.2) and the sensitivity was 79.5% (78.1 to 80.9). Regardless of the algorithm explored, the PPV for vaginal delivery was over 99%. For the diagnosis codes corresponding to immediate postpartum haemorrhage, the PPV was 77.7% (76.3 to 79.1) and the sensitivity was 75.5% (74.0 to 77.0). The algorithm for stillbirth presented a PPV of 89.4% (88.3 to 90.5) and a sensitivity of 95.4% (94.7 to 96.1).

Conclusions: This first national validation study of many perinatal algorithms suggests that the French national hospital database is an appropriate data source for epidemiological studies, except for some indicators which presented low PPV and/or sensitivity.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228531PMC
http://dx.doi.org/10.1136/bmjopen-2019-035218DOI Listing

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