scholarly journals Prospective validation of a novel renal activity index of lupus nephritis

Lupus ◽  
2016 ◽  
Vol 26 (9) ◽  
pp. 927-936 ◽  
Author(s):  
G Gulati ◽  
M R Bennett ◽  
K Abulaban ◽  
H Song ◽  
X Zhang ◽  
...  
Keyword(s):  
Lupus Nephritis ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Activity Index ◽  
Characteristic Curve ◽  
Kidney Injury ◽  
Operating Characteristic Curve ◽  
Kidney Injury Molecule 1 ◽  
Receiver Operating

Objectives The renal activity index for lupus (RAIL) score was developed in children with lupus nephritis as a weighted sum of six urine biomarkers (UBMs) (neutrophil gelatinase-associated lipocalin, monocyte chemotactic protein 1, ceruloplasmin, adiponectin, hemopexin and kidney injury molecule 1) measured in a random urine sample. We aimed at prospectively validating the RAIL in adults with lupus nephritis. Methods Urine from 79 adults was collected at the time of kidney biopsy to assay the RAIL UBMs. Using receiver operating characteristic curve analysis, we evaluated the accuracy of the RAIL to discriminate high lupus nephritis activity status (National Institutes of Health activity index (NIH-AI) score >10), from low/moderate lupus nephritis activity status (NIH-AI score ≤10). Results In this mixed racial cohort, high lupus nephritis activity was present in 15 patients (19%), and 71% had proliferative lupus nephritis. Use of the identical RAIL algorithm developed in children resulted in only fair prediction of lupus nephritis activity status of adults (area under the receiver operating characteristic curve (AUC) 0.62). Alternative weightings of the six RAIL UBMs as suggested by logistic regression yielded excellent accuracy to predict lupus nephritis activity status (AUC 0.88). Accuracy of the model did not improve with adjustment of the UBMs for urine creatinine or albumin, and was little influenced by concurrent kidney damage. Conclusions The RAIL UBMs provide excellent prediction of lupus nephritis activity in adults. Age adaption of the RAIL is warranted to optimize its discriminative validity to predict high lupus nephritis activity status non-invasively.

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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