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 Impact of Newborn Screening on Clinical Presentation of Congenital Adrenal Hyperplasia

Medicina ◽  
2021 ◽  
Vol 57 (10) ◽  
pp. 1035
Author(s):  
Rūta Navardauskaitė ◽  
Kornelija Banevičiūtė ◽  
Jurgita Songailienė ◽  
Kristina Grigalionienė ◽  
Darius Čereškevičius ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Congenital Adrenal Hyperplasia ◽  
Newborn Screening ◽  
Sensitivity And Specificity ◽  
Serum Potassium ◽  
Predictive Value ◽  
Clinical Presentation ◽  
Adrenal Hyperplasia ◽  
Salt Wasting ◽  
Screening Programs

Background and Objectives: The main reason for Newborn screening (NBS) for congenital adrenal hyperplasia (CAH) is to prevent adrenal insufficiency that can lead to life-threatening conditions. On the other hand, screening programs are not always sensitive and effective enough to detect the disease. We aimed to evaluate impact of the national NBS on the clinical presentation of patients with CAH in Lithuania. Materials and Methods: A retrospective study was performed on data of 88 patients with CAH from 1989 to 2020. Patients with confirmed CAH were divided into two groups: (1) 75 patients diagnosed before NBS: 52 cases with salt-wasting (SW), 21 with simple virilising (SV) and two with non-classical (NC) form; (2) 13 patients diagnosed with NBS: 12 cases with SW and 1 case with SV form. For the evaluation of NBS effectiveness, data of only male infants with salt-wasting CAH were analysed (n = 36, 25 unscreened and nine screened). Data on gestational age, birth weight, weight, symptoms, and laboratory tests (serum potassium and sodium levels) on the day of diagnosis, were analysed. Results: A total of 158,486 neonates were screened for CAH from 2015 to 2020 in Lithuania and CAH was confirmed in 13 patients (12 SW, one–SV form), no false negative cases were found. The sensitivity and specificity of NBS program for classical CAH forms were 100%; however, positive predictive value was only 4%. There were no significant differences between unscreened and screened male infant groups in terms of age at diagnosis, serum potassium, and serum sodium levels. Significant differences were found in weight at diagnosis between the groups (−1.67 ± 1.12 SDS versus 0.046 ± 1.01 SDS of unscreened and screened patients respectively, p = 0.001). Conclusions: The sensitivity and specificity of NBS for CAH program were 100%, but positive predictive value—only 4%. Weight loss was significantly lower and the weight SDS at diagnosis was significantly higher in the group of screened patients.

scholarly journals Novel genotyping algorithms for rare variants significantly improve the accuracy of Applied Biosystems™ Axiom™ array genotyping calls

2021 ◽  
Author(s):  
Orna Mizrahi Man ◽  
Marcos H Woehrmann ◽  
Teresa A Webster ◽  
Jeremy Gollub ◽  
Adrian Bivol ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Exome Sequencing ◽  
Predictive Value ◽  
Rare Variants ◽  
Uk Biobank ◽  
Sequencing Data ◽  
Data Set ◽  
Array Data ◽  
Exome Sequencing Data ◽  
The Uk

Objective: To significantly improve the positive predictive value (PPV) and sensitivity of Applied Biosystems™ Axiom™ array variant calling, by means of novel improvement to genotyping algorithms and careful quality control of array probesets. The improvement makes array genotyping more suitable for very rare variants. Design: Retrospective evaluation of UK Biobank array data re-genotyped with improved algorithms for rare variants. Participant: 488,359 people recruited to the UK Biobank with Axiom array genotyping data including 200,630 with exome sequencing data. Main Outcome Measures: A comparison of genotyping calls from array data to genotyping calls on a subset of variants with exome sequencing data. Results: Axiom genotyping [18] performed well, based on comparison to sequencing data, for over 100,000 common variants directly genotyped on the Axiom UK Biobank array and also exome sequenced by the UK Biobank Exome Sequencing Consortium. However, in a comparison to the initial exome sequencing results of the first 50K individuals, Weedon et al. [1] observed that when grouping these variants by the minor allele frequency (MAF) observed in UK Biobank, the concordance with sequencing and resulting positive predictive value (PPV) decreased with the number of heterozygous (Het) array calls per variant. An improved genotyping algorithm, Rare Heterozygous Adjustment (RHA) [16], released mid-2020 for genotyping on Axiom arrays, significantly improves PPV in all MAF ranges for the 50K data as well as when compared to the exome sequencing of 200K individuals, released after Weedon et al. [1] performed their comparison. The RHA algorithm improved PPVs in the 200K data in the lowest three frequency groups [0, 0.001%), [0.001%, 0.005%) and [0.005%, 0.01%) to 83%, 82% and 88%; respectively. PPV was above 95% for higher MAF ranges without algorithm improvement. PPVs are somewhat higher in the 200K dataset, due to a different "truth set" from exome sequencing and because monomorphic exome loci are not included in the joint genotyping calls for the 200K data set, as explained in the methods section. Sensitivity was higher in the 200K data set than in the original 50K data as well, especially for low MAF ranges. This increase is in part due to the larger data set over which sensitivity could be computed and in part due to the different WES algorithms used for the 200K data [7]. Filtering of a relatively small number of non-performing probesets (determined without reference to the exome sequencing data) significantly improved sensitivities for all MAF ranges, resulting in 70%, 88% and 94% respectively in the three lowest MAF ranges and greater than 98% and 99.9% for the two higher MAF ranges ([0.01%, 1%), [1%, 50%]). Conclusions: Improved algorithms for genotyping along with enhanced quality control of array probesets, significantly improve the positive predictive value and the sensitivity of array data, making it suitable for the detection of very rare variants. The probeset filtering methods developed have resulted in better probe designs for arrays and the new genotyping algorithm is part of the standard algorithm for all Axiom arrays since early 2020.

scholarly journals Artificial Intelligence Algorithms and Natural Language Processing for the Recognition of Syncope Patients on Emergency Department Medical Records

2019 ◽  
Vol 8 (10) ◽  
pp. 1677 ◽  
Author(s):  
Franca Dipaola ◽  
Mauro Gatti ◽  
Veronica Pacetti ◽  
Anna Giulia Bottaccioli ◽  
Dana Shiffer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Emergency Department ◽  
Natural Language Processing ◽  
Positive Predictive Value ◽  
Natural Language ◽  
Language Processing ◽  
Predictive Value ◽  
Medical Records ◽  
Large Population ◽  
Support Vector

Background: Enrollment of large cohorts of syncope patients from administrative data is crucial for proper risk stratification but is limited by the enormous amount of time required for manual revision of medical records. Aim: To develop a Natural Language Processing (NLP) algorithm to automatically identify syncope from Emergency Department (ED) electronic medical records (EMRs). Methods: De-identified EMRs of all consecutive patients evaluated at Humanitas Research Hospital ED from 1 December 2013 to 31 March 2014 and from 1 December 2015 to 31 March 2016 were manually annotated to identify syncope. Records were combined in a single dataset and classified. The performance of combined multiple NLP feature selectors and classifiers was tested. Primary Outcomes: NLP algorithms’ accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F3 score. Results: 15,098 and 15,222 records from 2013 and 2015 datasets were analyzed. Syncope was present in 571 records. Normalized Gini Index feature selector combined with Support Vector Machines classifier obtained the best F3 value (84.0%), with 92.2% sensitivity and 47.4% positive predictive value. A 96% analysis time reduction was computed, compared with EMRs manual review. Conclusions: This artificial intelligence algorithm enabled the automatic identification of a large population of syncope patients using EMRs.

Diagnosis of cerebral hemorrhage before the era of computed tomography

2013 ◽  
Vol 154 (44) ◽  
pp. 1743-1746
Author(s):  
Gergely Hofgárt ◽  
Rita Szepesi ◽  
Bertalan Vámosi ◽  
László Csiba
Keyword(s):  
Computed Tomography ◽  
Cerebrospinal Fluid ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Hemorrhagic Stroke ◽  
Cranial Computed Tomography ◽  
Cerebral Stroke ◽  
Daily Routine ◽  
Stroke Patients ◽  
The Difference

Introduction: During the past decades there has been a great progress in neuroimaging methods. Cranial computed tomography is part of the daily routine now and its use allows a fast diagnosis of parenchymal hemorrhage. However, before the availability of computed tomography the differentiation between ischemic and hemorrhagic stroke was based on patient history, physical examination, percutan angiography and cerebrospinal fluid sampling, and the clinical utility could be evaluated by autopsy of deceased patients. Aim: The authors explored the diagnostic performance of cerebrospinal fluid examination for the diagnosis of ischemic and hemorrhagic stroke. Method: Data of 200 deceased stroke patients were retrospectively evaluated. All patients had liquor sampling at admission and all of them had brain autopsy. Results: Bloody or yellowish cerebrospinal fluid at admission had a positive predictive value of 87.5% for hemorrhagic stroke confirmed by autopsy, while clear cerebrospinal fluid had positive predictive value of 90.7% for ischemic stroke. Patients who had clear liquor, but autopsy revealed hemorrhagic stroke had higher protein level in the cerebrospinal fluid, but the difference was not statistically significant (p = 0.09). Conclusions: The results confirm the importance of pathological evaluation of the brain in cases deceased from cerebral stroke. With this article the authors wanted to salute for those who contributed to the development of the Hungarian neuropathology. In this year we remember the 110th anniversary of the birth, and the 60th anniversary of the death of professor Kálmán Sántha. Professor László Molnár would be 90 years old in 2013. Orv. Hetil., 154 (44), 1743–1746.

VALUE OF COMBINATION OF HAP SCORE AND BISAP SCORE IN PREDICTING SEVERITY IN PATIENTS OF ACUTE PANCREATITIS

2019 ◽  
pp. 96-100
Author(s):  
Thi Ngoc Suong Le ◽  
Pham Chi Tran ◽  
Van Huy Tran
Keyword(s):  
Acute Pancreatitis ◽  
Positive Predictive Value ◽  
Roc Curve ◽  
Predictive Value ◽  
Prognostic Value ◽  
Acute Inflammation ◽  
Clinical Symptoms ◽  
Mortality Rates ◽  
Multiple Organ ◽  
Central Hospital

Acute pancreatitis (AP) is an acute inflammation of the pancreas, usually occurs suddenly with a variety of clinical symptoms, complications of multiple organ failure and high mortality rates. Objectives: To determine the value of combination of HAP score and BISAP score in predicting the severity of acute pancreatitis of the Atlanta 2012 Classification. Patients and Methods: 75 patients of acute pancreatitis hospitalized at Hue Central Hospital between March 2017 and July 2018; HAP and BISHAP score is calculated within the first 24 hours. The severity of AP was classified by the revised Atlanta criteria 2012. Results: When combining the HAP and BISAP scores in predicting the severity of acute pancreatitis, the area under the ROC curve was 0,923 with sensitivity value was 66.7%, specificity value was 97.1%; positive predictive value was 66.7%, negative predictive value was 97.1%. Conclusion: The combination of HAP and BISAP scores increased the sensitivity, predictive value, and prognostic value in predicting the severity of acute pancreatitis of the revised Atlanta 2012 classification in compare to each single scores. Key words: HAPscore, BiSAP score, acute pancreatitis, predicting severity

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