Comparative Study of ACR TI-RADS and ATA 2015 for Ultrasound Risk Stratification of Thyroid Nodules

Objective To study the adoption rate of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) scoring system over a 3-year period in a community setting and compare its performance with that of the American Thyroid Association 2015 (ATA 2015) ultrasound risk scoring system. Study Design Case series with prospective data collection and retrospective chart review. Setting Large community-based practice with multiple satellite offices and a dedicated thyroid ultrasound clinic. Methods All patients referred to the thyroid clinic between January 2018 and December 2020 for ultrasound-guided fine-needle biopsy were assigned an ATA 2015 risk score in a prospective manner immediately prior to biopsy. ACR TI-RADS scores were recorded through retrospective chart review of the radiologist report. Performance of the 2 systems was compared with cytology as the gold standard. Results A total of 949 nodules underwent biopsy, of which 236 had available data for both scoring systems. There was a 33.8% increase in adoption of the ACR TI-RADS over the 3-year study period. The ATA 2015 guidelines yielded sensitivity and specificity of 81.6% and 54.5%, respectively, as opposed to 73.7% and 27.0% for the ACR TI-RADS. Conclusion In our community, there has been a gradual increase in adoption of the ACR TI-RADS, although the ATA 2015 risk scoring system has performed better.

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Development of a Sex-Specific Risk Scoring System for Predicting Cognitive Normal to Mild Cognitive Impairment (SRSS-CNMCI)

10.21203/rs.3.rs-517520/v1 ◽

2021 ◽

Author(s):

Wen Luo ◽

Hao Wen ◽

Shuqi Ge ◽

Chunzhi Tang ◽

Xiufeng Liu ◽

...

Keyword(s):

Risk Factors ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

High Risk ◽

Scoring System ◽

External Validation ◽

Scoring Systems ◽

Cutoff Point ◽

Risk Scoring ◽

Risk Scoring System

Abstract Objective: We aim to develop a sex-specific risk scoring system for predicting cognitive normal (CN) to mild cognitive impairment (MCI), abbreviated SRSS-CNMCI, to provide a reliable tool for the prevention of MCI.Methods: Participants aged 61-90 years old with a baseline diagnosis of CN and an endpoint diagnosis of MCI were screened from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with at least one follow-up. Multivariable Cox proportional hazards models were used to identify risk factors associated with conversion from CN to MCI and to build risk scoring systems for male and female groups. Receiver operating characteristic (ROC) curve analysis was applied to determine the risk probability cutoff point corresponding to the optimal prediction effect. We ran an external validation of the discrimination and calibration based on the Harvard Aging Brain Study (HABS) database.Results: A total of 471 participants, including 240 women (51%) and 231 men (49%), aged 61 to 90 years, were included in the study cohort for subsequent primary analysis. The final multivariable models and the risk scoring systems for females and males included age, APOE ε4, Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). The scoring systems for females and males revealed C statistics of 0.902 (95% CI 0.840-0.963) and 0.911 (95% CI 0.863-0.959), respectively, as measures of discrimination. The cutoff point of high and low risk was 33% in females, and more than 33% was considered high risk, while more than 9% was considered high risk for males. The external validation effect of the scoring systems was good: C statistic 0.950 for the females and C statistic 0.965 for the males. Conclusions: Our parsimonious model accurately predicts conversion from CN to MCI with four risk factors and can be used as a predictive tool for the prevention of MCI.

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Practical Risk Scoring System for Predicting Severity of COVID-19 Disease

Clinical Pathology ◽

10.1177/2632010x211068427 ◽

2022 ◽

Vol 15 ◽

pp. 2632010X2110684

Author(s):

Jeffrey Petersen ◽

Darshana Jhala

Keyword(s):

Scoring System ◽

Clinical Laboratory ◽

Disease Risk ◽

Medical Center ◽

Statistical Significance ◽

Scoring Systems ◽

Urgent Care ◽

Smoking History ◽

Risk Scoring ◽

Risk Scoring System

Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19 disease, has become an international pandemic with numerous casualties. It had been noted that the severity of the COVID-19 disease course depends on several clinical, laboratory, and radiological factors. This has led to risk scoring systems in various populations such as in China, but similar risk scoring systems based on the American veteran population are sparse, particularly with the vulnerable Veteran population. As a simple risk scoring system would be very useful, we propose a simple Jhala Risk Scoring System (JRSS) to assess the severity of disease risk. Methods: A retrospective review of all SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) tests collected and performed at the regional Veterans Administration Medical Center (VAMC) serving the Philadelphia and surrounding areas from March 17th, 2020 to May 20th, 2020. Data was collected and analyzed in the same year. These tests were reviewed within the computerized medical record system for demographic, medical history, laboratory test history, and clinical course. Information from the medical records were then scored based on the criteria of the Jhala Risk Scoring System (JRSS). Results: The JRSS, based on age, ethnicity, presence of any lung disease, presence of cardiovascular disease, smoking history, and diabetes history with laboratory parameters correlated and predicted (with statistical significance) which patients would be hospitalized. Conclusion: The JRSS may play a role in informing which COVID-19 positive patients in the emergency room/urgent care for risk stratification.

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Usefulness of Easy-to-Use Risk Scoring Systems Rated in the Emergency Department to Predict Major Adverse Outcomes in Hospitalized COVID-19 Patients

Journal of Clinical Medicine ◽

10.3390/jcm10163657 ◽

2021 ◽

Vol 10 (16) ◽

pp. 3657

Author(s):

Julieta González-Flores ◽

Carlos García-Ávila ◽

Rashidi Springall ◽

Malinalli Brianza-Padilla ◽

Yaneli Juárez-Vicuña ◽

...

Keyword(s):

Emergency Department ◽

Mechanical Ventilation ◽

Scoring System ◽

Positive Likelihood Ratio ◽

Scoring Systems ◽

Adverse Outcomes ◽

Obesity And Diabetes ◽

Diagnostic Characteristics ◽

Risk Scoring ◽

Risk Scoring System

Background: Several easy-to-use risk scoring systems have been built to identify patients at risk of developing complications associated with COVID-19. However, information about the ability of each score to early predict major adverse outcomes during hospitalization of severe COVID-19 patients is still scarce. Methods: Eight risk scoring systems were rated upon arrival at the Emergency Department, and the occurrence of thrombosis, need for mechanical ventilation, death, and a composite that included all major adverse outcomes were assessed during the hospital stay. The clinical performance of each risk scoring system was evaluated to predict each major outcome. Finally, the diagnostic characteristics of the risk scoring system that showed the best performance for each major outcome were obtained. Results: One hundred and fifty-seven adult patients (55 ± 12 years, 66% men) were assessed at admission to the Emergency Department and included in the study. A total of 96 patients (61%) had at least one major outcome during hospitalization; 32 had thrombosis (20%), 80 required mechanical ventilation (50%), and 52 eventually died (33%). Of all the scores, Obesity and Diabetes (based on a history of comorbid conditions) showed the best performance for predicting mechanical ventilation (area under the ROC curve (AUC), 0.96; positive likelihood ratio (LR+), 23.7), death (AUC, 0.86; LR+, 4.6), and the composite outcome (AUC, 0.89; LR+, 15.6). Meanwhile, the inflammation-based risk scoring system (including leukocyte count, albumin, and C-reactive protein levels) was the best at predicting thrombosis (AUC, 0.63; LR+, 2.0). Conclusions: Both the Obesity and Diabetes score and the inflammation-based risk scoring system appeared to be efficient enough to be integrated into the evaluation of COVID-19 patients upon arrival at the Emergency Department.

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Validation of the Rockall risk scoring system in upper gastrointestinal bleeding

Gut ◽

10.1136/gut.44.3.331 ◽

1999 ◽

Vol 44 (3) ◽

pp. 331-335 ◽

Cited By ~ 151

Author(s):

E M Vreeburg ◽

C B Terwee ◽

P Snel ◽

E A J Rauws ◽

J F W M Bartelsman ◽

...

Keyword(s):

Gastrointestinal Bleeding ◽

External Validity ◽

Scoring System ◽

Upper Gastrointestinal Bleeding ◽

Scoring Systems ◽

Validation Sample ◽

Prediction Of Mortality ◽

Risk Scoring ◽

Risk Scoring System ◽

Upper Gastrointestinal

BACKGROUNDSeveral scoring systems have been developed to predict the risk of rebleeding or death in patients with upper gastrointestinal bleeding (UGIB). These risk scoring systems have not been validated in a new patient population outside the clinical context of the original study.AIMSTo assess internal and external validity of a simple risk scoring system recently developed by Rockall and coworkers.METHODSCalibration and discrimination were assessed as measures of validity of the scoring system. Internal validity was assessed using an independent, but similar patient sample studied by Rockall and coworkers, after developing the scoring system (Rockall’s validation sample). External validity was assessed using patients admitted to several hospitals in Amsterdam (Vreeburg’s validation sample). Calibration was evaluated by a χ2 goodness of fit test, and discrimination was evaluated by calculating the area under the receiver operating characteristic (ROC) curve.RESULTSCalibration indicated a poor fit in both validation samples for the prediction of rebleeding (p<0.0001, Vreeburg; p=0.007, Rockall), but a better fit for the prediction of mortality in both validation samples (p=0.2, Vreeburg; p=0.3, Rockall). The areas under the ROC curves were rather low in both validation samples for the prediction of rebleeding (0.61, Vreeburg; 0.70, Rockall), but higher for the prediction of mortality (0.73, Vreeburg; 0.81, Rockall).CONCLUSIONSThe risk scoring system developed by Rockall and coworkers is a clinically useful scoring system for stratifying patients with acute UGIB into high and low risk categories for mortality. For the prediction of rebleeding, however, the performance of this scoring system was unsatisfactory.

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Practical Risk Scoring System for Predicting Severity of COVID-19 Disease

American Journal of Clinical Pathology ◽

10.1093/ajcp/aqab191.284 ◽

2021 ◽

Vol 156 (Supplement_1) ◽

pp. S133-S133

Author(s):

J M Petersen ◽

D Jhala

Keyword(s):

Scoring System ◽

Clinical Laboratory ◽

Disease Risk ◽

Rapid Development ◽

Statistical Significance ◽

Scoring Systems ◽

Smoking History ◽

Risk Scoring ◽

Risk Scoring System ◽

Similar Risk

Abstract Introduction/Objective SARS-CoV-2 has become an international pandemic with numerous casualties. The severity of the COVID-19 disease course depends on several clinical, laboratory, and radiological factors. This has led to risk scoring systems in various populations such as in China, but similar risk scoring systems developed based on the American veteran population are sparse. As a risk scoring system (RSS) would be very useful for future reference in similar pandemics, we share a simple Jhala Risk Scoring System (JRSS) developed early in the pandemic to assess the severity of disease risk. Methods/Case Report A retrospective review of all SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) tests collected and performed at the regional Veterans Administration Medical Center (VAMC) serving the Philadelphia and surrounding areas from March 17th, 2020 to May 20th, 2020. Data was collected and analyzed in the same year. These tests were reviewed within the computerized medical record system for demographic, medical history, laboratory test history, and clinical course. Information from the medical records were then scored based on the criteria of the JRSS. Results (if a Case Study enter NA) The JRSS, based on age, ethnicity, presence of any lung disease, presence of cardiovascular disease, smoking history, and diabetes history with laboratory parameters correlated and predicted (with statistical significance) which patients would be hospitalized. Conclusion The JRSS reached statistical significance in its predictions on informing risk stratification for COVID-19 positive patients. Similar risk scoring systems may play a role in the rapid development of risk scoring in future pandemics of similar nature and thus provide a useful reference point. A simple RSS based on clinical parameters is a highly practical, cost effective and simple system to evaluate need for hospitalization, which is critical for operations in the intensive care unit and simultaneously the use of ventilators.

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