scholarly journals OC154: Prospective external validation of an ultrasound scoring system to differentiate benign from malignant masses in specific subgroups of adnexal tumors

2008 ◽  
Vol 32 (3) ◽  
pp. 293-293
Author(s):  
L. Valentin ◽  
L. Ameye ◽  
R. Fruscio ◽  
C. Van Holsbeke ◽  
A. Czekierdowski ◽  
...  
Keyword(s):  
Scoring System ◽  
External Validation ◽  
Adnexal Tumors

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

scholarly journals External validation of the R.I.R.S. scoring system to predict stone-free rate after retrograde intrarenal surgery

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Cong Wang ◽  
ShouTong Wang ◽  
Xuemei Wang ◽  
Jun Lu
Keyword(s):  
Scoring System ◽  
Kidney Stone ◽  
Medical Center ◽  
External Validation ◽  
Retrograde Intrarenal Surgery ◽  
Stone Burden ◽  
Stone Free Rate ◽  
Stone Density ◽  
Free Rate ◽  
Free Status

Abstract Background The R.I.R.S. scoring system is defined as a novel and straightforward scoring system that uses the main parameters (kidney stone density, inferior pole stones, stone burden, and renal infundibular length) to identify most appropriate patients for retrograde intrarenal surgery (RIRS). We strived to evaluate the accuracy of the R.I.R.S. scoring system in predicting the stone-free rate (SFR) after RIRS. Methods In our medical center, we retrospectively analyzed charts of patients who had, between September 2018 and December 2019, been treated by RIRS for kidney stones. A total of 147 patients were enrolled in the study. Parameters were measured for each of the four specified variables. Results Stone-free status was achieved in 105 patients (71.43%), and 42 patients had one or more residual fragments (28.57%). Differences in stone characteristics, including renal infundibulopelvic angle, renal infundibular length, lower pole stone, kidney stone density, and stone burden were statistically significant in patients whether RIRS achieved stone-free status or not (P < 0.001, P: 0.005, P < 0.001, P < 0.001, P: 0.003, respectively). R.I.R.S. scores were significantly lower in patients treated successfully with RIRS than patients in which RIRS failed (P < 0.001). Binary logistic regression analyses revealed that R.I.R.S. scores were independent factors affecting RIRS success (P = 0.033). The area under the curve of the R.I.R.S. scoring system was 0.737. Conclusions Our study retrospectively validates that the R.I.R.S. scoring system is associated with SFR after RIRS in the treatment of renal stones, and can predict accurately.

External Validation of the Renal Ablation-Specific (MC)2 Risk Scoring System in Predicting Complications from Percutaneous Renal Cryoablation

2021 ◽  
Author(s):  
Benjamin J. McCafferty ◽  
Junjian J. Huang ◽  
Husameddin El Khudari ◽  
Venkata Macha ◽  
Eric Bready ◽  
...  
Keyword(s):  
Scoring System ◽  
External Validation ◽  
Renal Ablation ◽  
Risk Scoring ◽  
Risk Scoring System

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

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.

scholarly journals Development and validation of a prognostic model to predict clinical deterioration in hospitalized patients with COVID-19

2021 ◽  
Author(s):  
Javid Azadbakht ◽  
Sina Rashedi ◽  
Soheil Kooraki ◽  
Hamed Kowsari ◽  
Elnaz Tabibian
Keyword(s):  
Roc Curve ◽  
Scoring System ◽  
Prognostic Model ◽  
Operating Characteristic ◽  
Clinical Laboratory ◽  
External Validation ◽  
Clinical Deterioration ◽  
Roc Curve Analysis ◽  
Prospective Multicenter Study ◽  
Development And Validation

Abstract Objectives We aimed to develop and validate a prognostic model to predict clinical deterioration defined as either death or intensive care unit admission of hospitalized COVID-19 patients.Methods This prospective, multicenter study investigated 172 consecutive hospitalized COVID-19 patients who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort), as well as an independent sample of 40 consecutive patients for external validation (validation cohort). The clinical, laboratory, and radiologic data were gathered, and logistic regression along with receiver operating characteristic (ROC) curve analysis was performed.Results The overall clinical deterioration rates of the development and validation cohorts were 28.4% (49 of 172) and 30% (12 of 40), respectively. Seven predictors were included in the scoring system with a total score of 15: CT severity score\(\ge\)15 (Odds Ratio (OR)=6.34, 4 points), pleural effusion (OR = 6.80, 2 points), symptom onset to admission ≤ 6 days (OR = 2.44, 2 points), age\(\ge\)70 years (OR = 2.44, 2 points), diabetes mellitus (OR = 2.24, 2 points), dyspnea (OR = 2.17, 1.5 points), and abnormal leukocyte count (OR = 1.89, 1.5 points). The area under the ROC curve for the scoring system in the development and validation cohorts was 0.823 (CI [0.751–0.895]) and 0.558 (CI [0.340–0.775]), respectively.Conclusion This study provided a new easy-to-calculate scoring system with external validation for hospitalized COVID-19 patients to predict clinical deterioration based on a combination of seven clinical, laboratory, and radiologic parameters.

External validation of the Arterial Based Complexity (ABC) scoring system in renal tumors treated by minimally invasive partial nephrectomy

2017 ◽  
Vol 116 (4) ◽  
pp. 507-514 ◽  
Author(s):  
Liangyou Gu ◽  
Xin Ma ◽  
Hongzhao Li ◽  
Yuanxin Yao ◽  
Yongpeng Xie ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Partial Nephrectomy ◽  
Scoring System ◽  
External Validation ◽  
Renal Tumors

scholarly journals OC-024 Predicting 30-day Mortality Following Peg Insertion: External Validation Of A Previous Scoring System And Analysis For Additional Predictive Factors

Gut ◽  
2014 ◽  
Vol 63 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
CS MacLeod ◽  
R McKay ◽  
D Barber ◽  
AW Mckinlay ◽  
JS Leeds
Keyword(s):  
Predictive Factors ◽  
Scoring System ◽  
External Validation

scholarly journals External validation of Tzanakis scoring system in acute appendicitis in a rural hospital

2019 ◽  
Vol 7 (5) ◽  
pp. 1536
Author(s):  
Mohd Riyaz Lattoo ◽  
Shabir Ahmad Mir ◽  
Nayeemul Hassan Ganie ◽  
Shabir Hussain Rather
Keyword(s):  
Retrospective Study ◽  
Acute Appendicitis ◽  
Predictive Value ◽  
Scoring System ◽  
Histopathological Examination ◽  
External Validation ◽  
Scoring Systems ◽  
Common Cause ◽  
Negative Laparotomy ◽  
Negative Appendicectomy

Background: Acute appendicitis is one of the most common cause of acute abdomen surgery. Several scoring systems have been adopted by physicians to aid in the diagnosis and decrease the negative appendicectomy rate. Tzanakis scoring system is one such score. Objective of present study was the validation of this scoring system in our population and compare its accuracy with histopathological examination (HPE).Methods: A retrospective study was carried out at the Department of Surgery at Mohammad Afzal Beigh Memorial Hospital Anantnag India. Tzanakis score was calculated in 288 patients who underwent appendicectomy from September 2016-2018 and HPE results were analysed.Results: 276 patients were eligible for the study. The sensitivity and specificity of Tzanakis score in diagnosing appendicitis was 90.66% and 73.68% respectively. The overall diagnostic accuracy was 86.23% with positive predictive value of 97.89% and negative predictive value of 36.84%.Conclusions: Tzanakis scoring system is an accurate modality in establishing the diagnosis of acute appendicitis and preventing a negative laparotomy.

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