scholarly journals A Simple Scoring Scale for Predicting the Risk of Diabetic Retinopathy over 50 Years Old in China

2021 ◽  
Author(s):  
Qiwei Ge ◽  
Min Li ◽  
Yunjuan Gu ◽  
Bihong Liu ◽  
Dajun Sun ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Reference Value ◽  
Coincidence Rate ◽  
Family History Of Diabetes ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Purpose: To construct and evaluate a simple scoring scale for predicting the risk of diabetic retinopathy (DR).Methods: Based on the chronic disease management database of Yancheng City, Jiangsu Province, China, 1896 diagnosed patients over the age of 50 with diabetes were randomly selected and subjected to the self-designed epidemiological questionnaire survey and ocular clinical examination. Single-factor and multi-factor logistic regression analysis was used to screen the relevant influencing factors of DR and then according to the reference value principle, the weights were assigned, and a simple scoring scale was constructed. A receiver operating characteristic curve (ROC) was developed and the accuracy and validity of the simple scale were evaluated.Results: The DR detection rate was 34.8 %. Multivariate analysis showed that family history of diabetes (odds ratio [OR]: 1.322, 95 % confidence interval [CI]: 1.012-1.727), diabetes treatment method (OR: 2.074, 95 % CI: 1.696-2.537), diabetes duration (OR: 1.113, 95 % CI: 1.089-1.138), and hemoglobin (Hb)A1c (OR: 1.276, 95 % CI: 1.099-1.482) were independent DR risk factors. After excluding confounding factors and based on the β coefficient of the multivariate logistic analysis, the scale had a maximum score of 12 points. The area under the receiver operating characteristic curve was 0.753, the cut-off value was 4, the sensitivity was 66.5 %, and the coincidence rate was 70.9 %. To improve sensitivity, the cut-off value was lowered to 3; the sensitivity was 79.7 %, and the coincidence rate was 65.0 %.Conclusion: The simple scale had good sensitivity and the ability to identify DR patients.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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