scholarly journals Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Sonali Sethi ◽  
Scott Oh ◽  
Alexander Chen ◽  
Christina Bellinger ◽  
Lori Lofaro ◽  
...  
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Diagnostic Procedures ◽  
Lung Nodules ◽  
Genomic Sequencing ◽  
Curative Therapy ◽  
Risk Of Malignancy ◽  
Very High ◽  
Independent Cohort

Abstract Background Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (> 60%) to “very high risk” with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence. Methods Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (> 60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision. Results One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p < 0.001) In the pre-post cross-over cohort, the rate increased from 17 to 56% (p < 0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists’ confidence in decision-making following a nondiagnostic bronchoscopy. Conclusions Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.

scholarly journals Percepta Genomic Sequencing Classifier And Decision-Making In Patients With High-Risk Lung Nodules: A Decision Impact Study

2021 ◽  
Author(s):  
Sethi Sonali ◽  
Scott Oh ◽  
Alexander Chen ◽  
Christina Bellinger ◽  
Lori Lofaro ◽  
...  
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Diagnostic Procedures ◽  
Lung Nodules ◽  
Genomic Sequencing ◽  
Curative Therapy ◽  
Risk Of Malignancy ◽  
Very High ◽  
Independent Cohort

Abstract Background: Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (>60%) to “very high risk” with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence.Methods: Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (>60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision.Results: One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p<0.001) In the pre-post cross-over cohort, the rate increased from 17% to 56% (p<0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists’ confidence in decision-making following a nondiagnostic bronchoscopy.Conclusions: Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.Trial registration: not applicable

Classification of cutaneous leishmaniasis patients based on its risk factors using latent class analysis

2020 ◽  
pp. 004947552097159
Author(s):  
Mojtaba Salimi ◽  
Ebrahim Shakiba ◽  
Eslam Moradi-Asl ◽  
Abbas Abbasi-Ghahramanloo ◽  
Keyvan Khassi
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Latent Class Analysis ◽  
Cutaneous Leishmaniasis ◽  
Latent Class ◽  
Preventive Intervention ◽  
Class Analysis ◽  
Risk Class ◽  
Very High

Cutaneous leishmaniasis is one of the main health-economic problems around the world. Data were collected from all patients with cutaneous leishmaniasis referred to the health centres of Kermanshah province between 2013 and 2019. Latent class analysis was conducted by PROC LCA in SAS 9.2 and a significant level was set at 0.05. Four latent classes were identified: low (33.8%), moderate (9.8%), high (22.4%) and very high risk (34.0%). The probability of having a travel history was high in the third class. Our study indicated that having history of an eschar has no role in the classification of patients. On the other hand, a positive smear test is important in classifying subjects. Our results indicate that more than half of all patients fell under high risk or very high-risk class. This emphasises the importance of planning preventive intervention by considering different risk factors of cutaneous leishmaniasis simultaneously.

The impact of a genomic sequencing classifier (GSC) on clinical decision making in patients with a high-risk lung nodule.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8549-8549
Author(s):  
Sonali Sethi ◽  
Scott Oh ◽  
Alexander Chen ◽  
Christina Bellinger ◽  
Lori Lofaro ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Decision Making ◽  
Lung Nodule ◽  
Clinical Decision ◽  
Lung Nodules ◽  
P Value ◽  
Ablative Therapies ◽  
Risk Patients ◽  
The Impact ◽  
Very High

8549 Background: Current guidelines recommend that patients who have lung nodules with high risk of malignancy (ROM) ( > 65%) should undergo surgical and other ablative therapies. However, prior studies have shown that clinicians may opt for more conservative management in these high-risk patients. Percepta Genomic Sequencing Classifier (GSC), a RNA-seq based classifier derived from bronchial epithelial cells to assess risk of lung cancer, was designed to risk stratify lung nodules by both down classifying ROM as a “rule -out“ test with high sensitivity as well as up-classifying ROM as a “rule- in” test with high specificity for malignancy. This study assesses the impact of up-classification of high ROM to very high- risk (ROM > 90%) by Percepta GSC in increasing the number of ablative therapies recommended for high-risk lung nodules. Methods: This prospective randomized decision impact survey included 37 patients from the AEGIS I/ II cohorts and the Percepta Registry who were undergoing work up of a lung nodule and had a high ROM that was up-classified to very high ROM by Percepta GSC. 97 physicians assessed 10 randomly assigned patient cases. They then responded to a survey designed to test the hypothesis that including a Percepta GSC result will increase the recommendation for surgical or other ablative therapy in very high- risk patients as well as their level of confidence of this recommendation. Physicians were first presented with the patient’s clinical information without Percepta GSC and then with Percepta GSC. Results: 97 physicians provided a total of 682 evaluations of 37 patients. In this study, the recommendation for surgical or other ablative therapy increased from 19/341 (5.6%) prior to the Percepta GSC result to 157/341 (46%) after the Percepta GSC result (odds ratio of 4.76, p-value < 0.001). The number of extremely confident recommendations increased from 72/341 (21%) without Percepta GSC to 106/341 (31%) with Percepta GSC. Significantly more physicians had increased confidence in their recommended next step post-Percepta GSC when collapsing the confidence level responses into increased confidence (n = 93) and decreased confidence (n = 44) (p-value = 0.002). Conclusions: Percepta GSC had a quantifiable impact on clinical decision making. It increased the number of surgical and other ablative therapies recommended when patients were re-classified from high to very high- risk of lung cancer with a higher confidence in the recommended next step. By up-classifying nodules from high to very high ROM, Percepta GSC will improve the likelihood and timeliness of appropriate therapies and assist clinicians more effectively manage patients to improve patient outcomes.

scholarly journals EROSIVITY INDICATORS BASED ON RAINFALL IN NORTHWESTERN MEXICO

2016 ◽  
Vol 24 (2) ◽  
pp. 133-142 ◽  
Author(s):  
Mariano NORZAGARAY- CAMPOS ◽  
Patricia MUÑOZ- SEVILLA ◽  
Leticia ESPINOSA- CARREÓN ◽  
Rosario RUÍZ- GUERRERO ◽  
Héctor GONZÁLEZ- OCAMPO ◽  
...  
Keyword(s):  
High Risk ◽  
Statistical Tests ◽  
Correlation Coefficients ◽  
Test Statistic ◽  
Weather Stations ◽  
Northwestern Mexico ◽  
Agricultural Areas ◽  
Rain Erosion ◽  
Very High

This study is motivated by the problem of erosivity (R), exacervated in semiarid zones by intense seasonal storms. The purpose was to estimate the spatial variation of R in a coastal area covering 37500 km2 which is one of the most important agricultural areas in northwestern Mexico. Four methods were used. Rainfall data from 11 SMNCONAGUA weather stations (from 1966 to 2013) were used to calculate R. The annual average R1 was 1181.08, and R2 was 1084.51 MJ mm ha–1 h–1 with ranges of 2.35–5220.55 and 2.93–4711.38 MJ mm ha–1 h–1. Statistical tests showed that a transformation of the data of the form y = log (x), was appropriate for an ANOVA analysis of the data. The value of the test statistic was F = 1.77 with p = 0.149, showing interdependence between the indicators P (α = 0.05). The values of the correlation coefficients for the data were P vs. R1 = 0.96, P vs. R2 = 0.99, P vs. AIm = 0.98, P vs. MFI = 0.99. The classification of risk in this region showed that 2017.5 km2 of the study area was at a very high risk of rain erosion, 2407.5 km2 under high risk, 5662.5 km2 under medium to high risk, and 14250 km2 under low risk. The results are shown on 1:10,000 maps. Results are a set of useful information for soil management programs and for cultivation planning that takes the seasonal variation of R into account in this region where large volumes of extensive crops are grown.

scholarly journals Estimation of fat cover of bovine carcases by means of computer vision system (CVS)

2021 ◽  
Vol 854 (1) ◽  
pp. 012087
Author(s):  
S Simunovic ◽  
S Rajic ◽  
V Djordjevic ◽  
V Tomovic ◽  
D Vujadinovic ◽  
...  
Keyword(s):  
Decision Making ◽  
Computer Vision ◽  
Classification System ◽  
Vision System ◽  
Fat Content ◽  
Computer Vision System ◽  
The Future ◽  
Reference Images ◽  
Very High

Abstract The aims of this study were to obtain percentages of meat and fat cover for SEUROP classification system reference images using a computer vision system (CVS) and to calculate classification intervals which could be used in the future for construction of cheap and easy to use classification devices for small slaughterhouses. Lowest percentages of fat cover were found for the first class marked as “low” (the lowest fat content) and they gradually increased to the last class marked as “very high” (the highest fat content). Based on the obtained results, decision making intervals were proposed. In the present study, classification only refers to classification of adult bovine animals based on fat cover.

Genomic validation of three-tiered sub-classification of high-risk prostate cancer.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 17-17
Author(s):  
Vinayak Muralidhar ◽  
Jingbin Zhang ◽  
Daniel Eidelberg Spratt ◽  
Felix Y Feng ◽  
Elai Davicioni ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Intermediate Risk ◽  
High Risk Prostate Cancer ◽  
High Risk Disease ◽  
Genomic Markers ◽  
Risk Prostate Cancer ◽  
Clinical Genomic ◽  
Very High

17 Background: Recent data and National Comprehensive Cancer Network (NCCN) guidelines suggest that high-risk prostate cancer (cT3-4, Gleason score ≥ 8, or prostate-specific antigen [PSA] > 20 ng/mL) is a heterogenous group in terms of long-term patient outcomes. We sought to determine whether sub-classification of high-risk prostate cancer based on clinical factors correlates with genomic markers of risk. Methods: We identified 3,220 patients with NCCN unfavorable intermediate-risk (n=2,000) or high-risk (n=1,220) prostate cancer. We defined the following sub-classification of high-risk prostate cancer based on previously published data: favorable high-risk (cT1c, Gleason 6, and PSA > 20 ng/mL or cT1c, Gleason 4+4=8, PSA < 10 ng/mL); very high-risk (cT3b-T4 or primary Gleason pattern 5); and standard high-risk (all others with cT3a, Gleason score ≥ 8, or PSA > 20 ng/mL). We used a set of 37 previously published genomic classifiers, including the 22-gene Decipher assay, to determine whether high-risk genomic features correlated with the clinical sub-classification of high-risk prostate cancer. Results: Among those with favorable high-risk, standard high-risk, and very high-risk prostate cancer, 50.4%, 64.2%, 81.6% had a high-risk Decipher score, respectively (p < 0.001). Among 36 other genomic signatures, 33 had a similar increasing trend across the three sub-classes of high-risk (p < 0.05 after correction for multiple hypothesis testing). Patients in the three sub-classes of high-risk disease were positive for a median number of 5, 7, and 14 high-risk signatures. Under a novel clinical-genomic risk group classification (Spratt et al., 2017), 27.5%, 19.7%, and 7.0% of patients with favorable, standard, or very high-risk disease would be re-classified as intermediate-risk, respectively. In comparison, among those with unfavorable intermediate-risk prostate cancer, 38.2% had a high-risk Decipher score and would be re-classified as clinical-genomic high-risk. Conclusions: Genomic markers of risk correlate with the clinical sub-classification of high-risk prostate cancer into favorable high-risk, standard high-risk, and very high-risk disease, validating the prognostic utility of this stratification.

scholarly journals Geospatial mapping of COVID-19 cases, risk and agriculture hotspots in decision-making of lockdown relaxation in Nepal

2020 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Bijaya Maharjan ◽  
Alina Maharjan ◽  
Shanker Dhakal ◽  
Manash Gadtaula ◽  
Sunil B. Shrestha ◽  
...  
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Population Density ◽  
Social Distance ◽  
Entry Points ◽  
Risk Zones ◽  
The Government ◽  
Risk Scenario ◽  
Very High ◽  
Geospatial Mapping

The study has explored the risk scenario via geospatial mapping of temporal transmission trend of COVID-19 in 77 districts of Nepal focusing on the district-wise risk analyses based on present active cases, population density and land entry points from neighboring countries. In overall, low to very high risk zones have been identified. Jhapa, Morang and Sunsari districts of Province 1; Dhanusa, Mahottari, Sarlahi, Rautahat, Bara and Parsa districts of Province 2; Kathmandu district of Bagmati Province, Nawalparasi West, Rupandehi, Kapilbastu and Banke districts of Province 5, as well as Kailali of Sudurpanchim Province are identified to have very high risk for COVID-19 spread. The rapid growth in the number of cases has made many districts remarkably susceptible to the infection. The vulnerability analysis has been then followed by identification of agriculture hotspots across the country in terms of major crops. 42 districts with moderate to high crop productivities have been recognized as being not in very high risk zones where the government should allow farmers to do their agriculture activities with well-maintained social distance and other safety precautions. The results when combined would suggest an urgent decision by the Government for gradual lockdown relaxation for agro-economic reinstatement what is commonly called the latent comparative advantage for Nepalese economy after tourism.

Prediction of Survival in Patients with Myelodysplastic Syndrome According to WHO Classification-Based Prognostic Scoring System (WPSS).

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 4617-4617
Author(s):  
Jun Ho Jang ◽  
Kihyun Kim ◽  
Chul W. Jung ◽  
Keon Woo Park
Keyword(s):  
High Risk ◽  
Scoring System ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
The Mean ◽  
Very High

Abstract Background Based upon the classification of FAB criteria, International Prognostic Scoring System(IPSS) has been a standard prognostic model to predict survival and progression in MDS. In 2000, the WHO has formulated a new classification of myelodysplastic syndrome(MDS). The aim of this study was to evaluate the prognostic value of WHO classification-based prognostic scoring system(WPSS) in MDS. Patients and methods One hundred forty-nine patients who were diagnosed as having de novo MDS at the Division of Hematology-Oncology, Samsung medical center, Seoul, Korea, between Dec. 1994 and Feb. 2007, were evaluated retrospectively for clinical and hamatologic features at diagnosis, transfusion dependence, overall survival(OS), and progression to leukemia(LFS). Risk group stratifications in MDS patients were done according to IPSS and WPSS. Results 18 patients(12.1%), 93 patients (62.4%), 29 patients(29%) and 9 patients(6%) had IPSS risk scores of low, intermediate-1(Int-1), intermediate-2(Int-2) and high, respectively. According to WPSS risk scores, 8 patients(5.4%), 30 patients(20.1%), 41 patients(27.5%), 57 patients(38.3%) and 13 patients(8.7%) were classified to very low, low, intermediate, high and very high risk group, respectively. In IPSS, median OSs of low, Int-1, Int-2 and high subgroup were 65.2, 32.9, 14.3 and 9.1 months respectively (p<0.001). According to WPSS, median OSs of very low, low, intermediate, high and very high risk subgroup were not reached, 55.4, 27.4, 19.0 and 6.2 months respectively (p<0.001). Between subgroups classified according to WPSS, significant differences in OS were noted in low vs. intermediate risk group (p=0.047), in intermediate vs. high risk group (p=0.046) and in high vs. very high risk group(p=0.003) but statistically not significant difference in OS was observed between very low and low risk group (p=0.08). The mean and median OS of the lowest risk group(low risk) in IPSS are 65.33 and 55.43 months, respectively. The mean and median OS of the lowest risk group(very low risk) in WPSS are 102.8 months and not reached, respectively. Conclusion These data show that WPSS with five risk groups might provide more refined prognostic stratifications of MDS than IPSS with four risk groups. Especially, new prognostic system appears to discriminate a subset of patients with very low risk, who could have long term survival.

scholarly journals Kajian Sanitasi Sekolah Dasar Di Kabupaten Kayong Utara Provinsi Kalimantan Barat

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Reza Wahyudi ◽  
Nia Febrianti ◽  
Murti Juliandari
Keyword(s):  
Elementary Schools ◽  
High Risk ◽  
Primary School ◽  
Drainage Channel ◽  
Risk Area ◽  
Self Assessment ◽  
Questionnaire Method ◽  
Very High ◽  
Risk Categories

<p>The sanitation study of the Primary School in Kayong Utara District demands that the Sanitation Risk  area be established in the EHRA study. Based on the EHRA study, the classification of risk categories is divided into three which are namely No, Medium, and Very High Risk. The scope of the study area covers the elementary schools and this encompasses 18 high-risk villages from 4 sub-districts. And the total number of primary and secondary schools in the study conducted was thirty-three. This study used a questionnaire method that was provided by the school for self-assessment. The result stated that sanitation was in an unpleasant category and  it can be broken down to the provision of handwashing (35.52%), trash temporary shelter (45.44%), drainage  channel (43.42) and PHBS education and practice (47.32%).</p>

Target LDL Under 70 mg/dL for Very High Risk

2012 ◽  
Vol 45 (7) ◽  
pp. 14
Author(s):  
JENNIE SMITH
Keyword(s):  
High Risk ◽  
Very High
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