Dataset Information

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

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.

SUBMITTER: Goueslard K

PROVIDER: S-EPMC7228531 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Goueslard Karine K Cottenet Jonathan J Benzenine Eric E Tubert-Bitter Pascale P Quantin Catherine C

BMJ open 20200512 5

<h4>Objective</h4>The aim of our validation study was to assess the metrological quality of hospital data for perinatal algorithms on a national level.<h4>Design</h4>Validation study.<h4>Setting</h4>This was a multicentre study of the French medicoadministrative database on perinatal indicators.<h4>Participants</h4>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 ...[more]

PMID: 32404391

Similar Datasets

Project description:BACKGROUND:Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. RESULTS:In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. CONCLUSIONS:Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results.

Project description:IntroductionWHO has recommended using Robson's Ten Group Classification System (TGCS) to monitor and analyze CD rates. Its failure to take some maternal and organizational factors into account, however, could limit the interpretation of CD rate comparisons, because it may contribute to variations in hospital CD rates.ObjectiveTo study the contribution of maternal socioeconomic and clinical characteristics and hospital organizational factors to the variation in CD rates when using Robson's ten-group classification system for CD rate comparisons.MethodsThis prospective, observational, population-based study included all deliveries at a gestational age > 24 weeks at the 10 hospitals of the French MYPA perinatal network in the Paris area. CD rates were calculated for each TGCS group in each hospital. Interhospital variations in these rates were investigated with hierarchical logistic regression models to quantify the variation explained by differences in patient and hospital characteristics when the TGCS is considered. Variations in CD rates between hospitals were estimated with median odds ratios (MOR) to express interhospital variance on the standard odds ratio scale. The percentage of variation explained by TGCS and maternal and hospital characteristics was also calculated.ResultsThe global CD rate was 24.0% (interhospital range: 17-32%). CD rates within each TGCS group differed significantly between hospitals (P<0.001). CD was significantly associated with maternal age (>40 years), severe preeclampsia, and two organizational factors: hospital status (private maternities) and the deliveries per staff member per 24 hours. The MOR in the empty model was 1.27 and did not change after taking the TGCS into account. Adding maternal characteristics and hospital organizational factors lowered the MOR to 1.14 and reduced the variation between hospital CD rates by 70%.ConclusionMaternal characteristics and hospital factors are needed to address variation in CD rates among the TGCS groups. Therefore, comparisons of these rates that do not consider these factors should be interpreted carefully.

Project description:BackgroundResults of associations between process and mortality indicators, both used for the external assessment of hospital care quality or public reporting, differ strongly across studies. However, most of those studies were conducted in North America or United Kingdom. Providing new evidence based on French data could fuel the international debate on quality of care indicators and help inform French policy-makers. The objective of our study was to explore whether optimal care delivery in French hospitals as assessed by their Hospital Process Indicators (HPIs) is associated with low Hospital Standardized Mortality Ratios (HSMRs).MethodsThe French National Authority for Health (HAS) routinely collects for each hospital located in France, a set of mandatory HPIs. Five HPIs were selected among the process indicators collected by the HAS in 2009. They were measured using random samples of 60 to 80 medical records from inpatients admitted between January 1st, 2009 and December 31, 2009 in respect with some selection criteria. HSMRs were estimated at 30, 60 and 90 days post-admission (dpa) using administrative health data extracted from the national health insurance information system (SNIIR-AM) which covers 77% of the French population. Associations between HPIs and HSMRs were assessed by Poisson regression models corrected for measurement errors with a simulation-extrapolation (SIMEX) method.ResultsMost associations studied were not statistically significant. Only two process indicators were found associated with HSMRs. Completeness and quality of anesthetic records was negatively associated with 30 dpa HSMR (0.72 [0.52-0.99]). Early detection of nutritional disorders was negatively associated with all HSMRs: 30 dpa HSMR (0.71 [0.54-0.95]), 60 dpa HSMR (0.51 [0.39-0.67]) and 90 dpa HSMR (0.52 [0.40-0.68]).ConclusionIn absence of gold standard of quality of care measurement, the limited number of associations suggested to drive in-depth improvements in order to better determine associations between process and mortality indicators. A smart utilization of both process and outcomes indicators is mandatory to capture aspects of the hospital quality of care complexity.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets