Positive Predictive Value of the Thumb-Palm Test for General Population Screening of Ascending Aortic Aneurysm

2021 ◽  
Vol 161 ◽  
pp. 116-117 ◽  
Author(s):  
James A. Diao ◽  
Gloria J. Wu ◽  
Arjun K. Manrai
Keyword(s):  
Aortic Aneurysm ◽  
General Population ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Population Screening ◽  
Ascending Aortic Aneurysm

scholarly journals Positive predictive value highlights four novel candidates for actionable genetic screening from analysis of 220,000 clinicogenomic records

2021 ◽  
Author(s):  
Kelly M. Schiabor Barrett ◽  
Alexandre Bolze ◽  
Yunyun Ni ◽  
Simon White ◽  
Magnus Isaksson ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Genetic Screening ◽  
Predictive Value ◽  
Rare Variants ◽  
Large Population ◽  
Population Screening ◽  
Cystic Kidney Disease ◽  
Cystic Kidney ◽  
Screening Programs ◽  
Thalassemia Minor

Abstract Purpose To identify conditions that are candidates for population genetic screening based on population prevalence, penetrance of rare variants, and actionability. Methods We analyzed exome and medical record data from >220,000 participants across two large population health cohorts with different demographics. We performed a gene-based collapsing analysis of rare variants to identify genes significantly associated with disease status. Results We identify 74 statistically significant gene–disease associations across 27 genes. Seven of these conditions have a positive predictive value (PPV) of at least 30% in both cohorts. Three are already used in population screening programs (BRCA1, BRCA2, LDLR), and we also identify four new candidates for population screening: GCK with diabetes mellitus, HBB with β-thalassemia minor and intermedia, PKD1 with cystic kidney disease, and MIP with cataracts. Importantly, the associations are actionable in that early genetic screening of each of these conditions is expected to improve outcomes. Conclusion We identify seven genetic conditions where rare variation appears appropriate to assess in population screening, four of which are not yet used in screening programs. The addition of GCK, HBB, PKD1, and MIP rare variants into genetic screening programs would reach an additional 0.21% of participants with actionable disease risk, depending on the population.

How well do ACPA discriminate and predict RA in the general population: a study based on 12 590 population-representative Swedish twins

2016 ◽  
Vol 76 (1) ◽  
pp. 119-125 ◽  
Author(s):  
Aase Haj Hensvold ◽  
Thomas Frisell ◽  
Patrik K E Magnusson ◽  
Rikard Holmdahl ◽  
Johan Askling ◽  
...  
Keyword(s):  
General Population ◽  
Positive Predictive Value ◽  
Diagnostic Accuracy ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Blood Donation ◽  
Large Population ◽  
Serum Samples ◽  
Protein Levels

ObjectiveAnti-citrullinated protein antibodies (ACPA) are highly specific for rheumatoid arthritis (RA), but the diagnostic accuracy of ACPA in the general population has not been thoroughly assessed. We aimed to assess the diagnostic accuracy of ACPA for RA in the general population and to further characterise the citrullinated peptide recognition pattern.MethodsSerum samples from a large population-representative twin cohort consisting of 12 590 individuals were analysed for the presence of ACPA using anti-CCP2 ELISA. All ACPA-positive samples were further tested on ELISAs for four peptide-specific ACPA. RA cases were identified by linkage to the Swedish National Patient Register at inclusion and after a median follow-up of 37 months (IQR 31–49).Results350 out of 12 590 individuals had a positive anti-CCP2 test, measuring ACPA. Of these, 103 had an RA diagnosis at the time of blood donation and inclusion. During a median follow-up of 3 years, an additional 21 of the remaining 247 ACPA-positive individuals developed RA. Overall, a positive anti-CCP2 test had a positive predictive value of 29% for prevalent RA at inclusion (negative predictive value of 99.6%). High titres (>3× cut-off) of anti-CCP2 increased the positive predictive value to 48% (negative predictive value of 99.5%). ACPA-positive individuals without RA had lower anti-CCP2 titres and fewer peptide-specific ACPA than ACPA-positive patients with RA and higher C reactive protein levels than ACPA-negative individuals without RA.ConclusionPresence of ACPA and especially high titres of anti-CCP2 have a high diagnostic accuracy for an RA diagnosis in a population setting.

scholarly journals Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Irene Bellini ◽  
Valentina Barletta ◽  
Francesco Profili ◽  
Alessandro Bussotti ◽  
Irene Severi ◽  
...  
Keyword(s):  
General Population ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Incidence Rates ◽  
Adverse Outcomes ◽  
Administrative Databases ◽  
Administrative Database ◽  
Complex Patients ◽  
Risk Patients ◽  
Health Registry

Objective. (1) Assessing the performance of the algorithm in terms of sensitivity and positive predictive value, considering General Practitioners’ (GPs) judgement as benchmark, and (2) describing adverse events (hospitalisation, death, and health services’ consumption) of complex patients compared to the general population. Data Sources. (i) Tuscany administrative database containing health data (2013-5); (ii) lists of complex patients indicated by GPs; and (iii) annual health registry of Tuscany. Study Design. The present study is a validation study. It compares a list of complex patients extracted through an administrative algorithm (criteria of high health consumption) to a gold standard list of patients indicated by GPs. GPs’ decision was subjective but fairly well reasoned. The study compares also adverse outcomes (Emergency Room visits, hospitalisation, and death) between identified complex patients and general population. Principal Findings. Considering GPs’ judgement, the algorithm showed a sensitivity of 72.8% and a positive predictive value of 64.4%. The complex cases presented here have higher incidence rates/100,000 (death 46.8; ER visits 223.2, hospitalisations 110.87, laboratory tests 1284.01, and specialist examinations 870.37) compared to the general population. Conclusions. The final validated algorithm showed acceptable sensitivity and positive predictive value.

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