“A method to measure predictive ability of an injury risk curve using an observation-adjusted area under the receiver operating characteristic curve” by A.M. Baker, F.C. Hsu, F.S. Gayzik (2018)

2020 ◽  
Vol 100 ◽  
pp. 109087
Author(s):  
Anjishnu Banerjee ◽  
Frank A. Pintar ◽  
Narayan Yoganandan
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Injury Risk ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Risk Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Diagnostic Accuracy of Somatosensory Evoked Potential Monitoring in Evaluating Neurological Complications During Endovascular Aneurysm Treatment

2017 ◽  
Vol 14 (2) ◽  
pp. 151-157 ◽  
Author(s):  
William J Ares ◽  
Ramesh M Grandhi ◽  
David M Panczykowski ◽  
Gregory M Weiner ◽  
Parthasarathy Thirumala ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Evoked Potential ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Neurological Complications ◽  
Somatosensory Evoked Potential ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract BACKGROUND Somatosensory evoked potential (SSEP) monitoring is used extensively for early detection and prevention of neurological complications in patients undergoing many different neurosurgical procedures. However, the predictive ability of SSEP monitoring during endovascular treatment of cerebral aneurysms is not well detailed. OBJECTIVE To evaluate the performance of intraoperative SSEP in the prediction postprocedural neurological deficits (PPNDs) after coil embolization of intracranial aneurysms. METHODS This population-based cohort study included patients ≥18 years of age undergoing intracranial aneurysm embolization with concurrent SSEP monitoring between January 2006 and August 2012. The ability of SSEP to predict PPNDs was analyzed by multiple regression analyses and assessed by the area under the receiver operating characteristic curve. RESULTS In a population of 888 patients, SSEP changes occurred in 8.6% (n = 77). Twenty-eight patients (3.1%) suffered PPNDs. A 50% to 99% loss in SSEP waveform was associated with a 20-fold increase in risk of PPND; a total loss of SSEP waveform, regardless of permanence, was associated with a greater than 200-fold risk of PPND. SSEPs displayed very good predictive ability for PPND, with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.76-0.92). CONCLUSION This study supports the predictive ability of SSEPs for the detection of PPNDs. The magnitude and persistence of SSEP changes is clearly associated with the development of PPNDs. The utility of SSEP monitoring in detecting ischemia may provide an opportunity for neurointerventionalists to respond to changes intraoperatively to mitigate the potential for PPNDs.

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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