A Prediction Model for Detecting Dysthyroid Optic Neuropathy Based on Clinical Factors and Imaging Markers of the Optic Nerve and Cerebrospinal Fluid in the Optic Nerve Sheath

Objective This study aimed to develop and test a model for predicting dysthyroid optic neuropathy(DON)based on clinical factors and imaging markers of the optic nerve and cerebrospinal fluid(CSF)in the optic nerve sheath.Methods This retrospective study included patients with thyroid-associated ophthalmopathy(TAO)without DON and patients with TAO accompanied by DON at our hospital.The imaging markers of the optic nerve and CSF in the optic nerve sheath were measured on the water-fat images of each patient and,together with clinical factors,were screened by Least absolute shrinkage and selection operator.Subsequently,we constructed a prediction model using multivariate logistic regression.The accuracy of the model was verified using receiver operating characteristic curve analysis.Results In total,80 orbits from 44 DON patients and 90 orbits from 45 TAO patients were included in our study.Two variables(optic nerve subarachnoid space and the volume of the CSF in the optic nerve sheath)were found to be independent predictive factors and were included in the prediction model.In the development cohort,the mean area under the curve(AUC)was 0.994,with a sensitivity of 0.944,specificity of 0.967,and accuracy of 0.901.Moreover,in the validation cohort,the AUC was 0.960,the sensitivity was 0.889,the specificity was 0.893,and the accuracy was 0.890.Conclusions A combined model was developed using imaging data of the optic nerve and CSF in the optic nerve sheath,serving as a noninvasive potential tool to predict DON.

Nomogram for predicting transmural bowel infarction in patients with acute superior mesenteric venous thrombosis

BACKGROUND The prognosis of acute mesenteric ischemia(AMI)caused by superior mesenteric venous thrombosis(SMVT)remains undetermined and early detection of transmural bowel infarction(TBI)is crucial.The predisposition to develop TBI is of clinical concern,which can lead to fatal sepsis with hemodynamic instability and multi-organ failure.Early resection of necrotic bowel could improve the prognosis of AMI,however,accurate prediction of TBI remains a challenge for clinicians.When determining the eligibility for explorative laparotomy,the underlying risk factors for bowel infarction should be fully evaluated.AIM To develop and externally validate a nomogram for prediction of TBI in patients with acute SMVT.METHODS Consecutive data from 207 acute SMVT patients at the Wuhan Tongji Hospital and 89 patients at the Guangzhou Nanfang Hospital between July 2005 and December 2018 were included in this study.They were grouped as training and external validation cohort.The 207 cases(training cohort)from Tongji Hospital were divided into TBI and reversible intestinal ischemia groups based on the final therapeutic outcomes.Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for TBI using the training data,and a nomogram was subsequently developed.The performance of the nomogram was evaluated with respect to discrimination,calibration,and clinical usefulness in the training and external validation cohort.RESULTS Univariate and multivariate logistic regression analyses identified the following independent prognostic factors associated with TBI in the training cohort:The decreased bowel wall enhancement(OR=6.37,P<0.001),rebound tenderness(OR=7.14,P<0.001),serum lactate levels>2 mmol/L(OR=3.14,P=0.009)and previous history of deep venous thrombosis(OR=6.37,P<0.001).Incorporating these four factors,the nomogram achieved good calibration in the training set[area under the receiver operator characteristic curve(AUC)0.860;95%CI:0.771-0.925]and the external validation set(AUC 0.851;95%CI:0.796-0.897).The positive and negative predictive values(95%CIs)of the nomogram were calculated,resulting in positive predictive values of 54.55%(40.07%-68.29%)and 53.85%(43.66%-63.72%)and negative predictive values of 93.33%(82.14%-97.71%)and 92.24%(85.91%-95.86%)for the training and validation cohorts,respectively.Based on the nomogram,patients who had a Nomo-score of more than 90 were considered to have high risk for TBI.Decision curve analysis indicated that the nomogram was clinically useful.CONCLUSION The nomogram achieved an optimal prediction of TBI in patients with AMI.Using the model,the risk for an individual patient inclined to TBI can be assessed,thus providing a rational therapeutic choice.