RESUMO
Background: Despite monogenic and polygenic contributions to cardiovascular disease (CVD), genetic testing is not widely adopted, and current tests are limited by the breadth of surveyed conditions and interpretation burden. Methods: We developed a comprehensive clinical genome CVD test with semi-automated interpretation. Monogenic conditions and risk alleles were selected based on the strength of disease association and evidence for increased disease risk, respectively. Non-CVD secondary findings genes, pharmacogenomic (PGx) variants and CVD polygenic risk scores (PRS) were assessed for inclusion. Test performance was modeled using 2,594 genomes from the 1000 Genomes Project, and further investigated in 20 previously tested individuals. Results: The CVD genome test is composed of a panel of 215 CVD gene-disease pairs, 35 non-CVD secondary findings genes, 4 risk alleles or genotypes, 10 PGx genes and a PRS for coronary artery disease. Modeling of test performance using samples from the 1000 Genomes Project revealed ~6% of individuals with a monogenic finding in a CVD-associated gene, 6% with a risk allele finding, ~1% with a non-CVD secondary finding, and 93% with CVD-associated PGx variants. Assessment of blinded clinical samples showed complete concordance with prior testing. An average of 4 variants were reviewed per case, with interpretation and reporting time ranging from 9-96 min. Conclusions: A genome sequencing based CVD genetic risk assessment can provide comprehensive genetic disease and genetic risk information to patients with CVD. The semi-automated and limited interpretation burden suggest that this testing approach could be scaled to support population-level initiatives.
RESUMO
PURPOSE: Measurement and monitoring of the quality of care using a core set of quality measures are increasing in health service research. Although administrative databases include limited clinical data, they offer an attractive source for quality measurement. The purpose of this study, therefore, was to evaluate the completeness of different administrative data sources compared to a clinical survey in evaluating rectal cancer cases. METHODS: Between May 2012 and November 2014, a clinical survey was done on 498 Lombardy patients who had rectal cancer and underwent surgical resection. These collected data were compared with the information extracted from administrative sources including Hospital Discharge Dataset, drug database, daycare activity data, fee-exemption database, and regional screening program database. The agreement evaluation was performed using a set of 12 quality indicators. RESULTS: Patient complexity was a difficult indicator to measure for lack of clinical data. Preoperative staging was another suboptimal indicator due to the frequent missing administrative registration of tests performed. The agreement between the 2 data sources regarding chemoradiotherapy treatments was high. Screening detection, minimally invasive techniques, length of stay, and unpreventable readmissions were detected as reliable quality indicators. Postoperative morbidity could be a useful indicator but its agreement was lower, as expected. CONCLUSIONS: Healthcare administrative databases are large and real-time collected repositories of data useful in measuring quality in a healthcare system. Our investigation reveals that the reliability of indicators varies between them. Ideally, a combination of data from both sources could be used in order to improve usefulness of less reliable indicators.