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    The Optimal Cut-off of BIPSS in Differential Diagnosis of ACTH-dependent Cushing's Syndrome: Is Stimulation Necessary? Chen Shi,Chen Kang,Wang Shirui,Zhu Huijuan,Lu Lin,Zhang Xiaobo,Tong Anli,Pan Hui,Wang Renzhi,Lu Zhaolin The Journal of clinical endocrinology and metabolism CONTEXTS:Bilateral inferior petrosal sinus sampling (BIPSS) can differentiate Cushing's disease (CD) and ectopic adrenocorticotropin (ACTH) syndrome (EAS). The traditional cutoff of inferior petrosal sinus to peripheral (IPS:P) ACTH gradient was 2 before stimulation and 3 after stimulation, which yielded unsatisfactory sensitivity in some studies. OBJECTIVES:To determine the optimal cutoff in BIPSS before or after desmopressin stimulation and to evaluate the necessity of stimulation. DESIGN AND SETTING:Single-center retrospective study (2011-2018) along with meta-analysis. PATIENTS:226 CD and 24 EAS patients with confirmed diagnosis who underwent BIPSS with desmopressin stimulation. RESULTS:In the meta-analysis of 25 studies with 1249 CD and 152 EAS patients, the traditional cutoff yielded sensitivity of 86% and 97% and specificity of 98% and 100% before and after stimulation, respectively. We then analyzed the data from our center. With the traditional cutoff, the sensitivity was 87.2% (197/226) and 96.5% (218/226) before and after stimulation, and specificity was both 100% (25/25), which were close to the results of meta-analysis. Receiver operating characteristic analysis revealed that the optimal cutoff was 1.4 before stimulation and 2.8 after stimulation. With the new cutoff, the sensitivity was 94.7% (214/226) and 97.8% (221/226) while the specificity remained 100% (25/25) before and after stimulation. Among the 7 CD patients (7/226; 3.1%) for whom stimulation was necessary to get correct diagnosis, none has a pituitary lesion >6 mm by magnetic resonance imaging, and their sampling lateralization rate (P = .007) and peak ACTH level at dominant inferior petrosal sinus (P = .011) were lower than those among CD patients with IPS:P >1.4 before stimulation. CONCLUSIONS:The optimal cutoff for IPS:P in BIPSS is different from the commonly-used one. The optimal cutoff value can yield satisfactory accuracy even without stimulation, and stimulation may be unnecessary for those with pituitary adenoma >6 mm. 10.1210/clinem/dgz194
    Development of machine learning models for predicting postoperative delayed remission of patients with Cushing's disease. Fan Yanghua,Li Yichao,Bao Xinjie,Zhu Huijuan,Lu Lin,Yao Yong,Li Yansheng,Su Mingliang,Feng Feng,Feng Shanshan,Feng Ming,Wang Renzhi The Journal of clinical endocrinology and metabolism CONTEXT:Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease (CD). Delayed remission (DR) is defined as not achieving postoperative immediate remission (IR), but having spontaneous remission during long-term follow-up. OBJECTIVE:We aimed to develop and validate machine learning (ML) models for predicting DR in non-IR patients with CD. METHODS:We enrolled 201 CD patients, and randomly divided them into training and test datasets. We then used the recursive feature elimination (RFE) algorithm to select features, and applied five ML algorithms to construct DR prediction models. We used permutation importance and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. RESULTS:Eighty-eight (43.8 %) of the 201 CD patients met the criteria for DR. Overall, patients who were younger, had low body-mass index, Knosp grade III-IV and a tumor not found by pathological examination tended to achieve a lower rate of DR. After RFE feature selection, the Adaboost model, which comprised 18 features, had the greatest discriminatory ability, and its predictive ability was significantly better than using Knosp grade and postoperative immediate morning serum cortisol (PoC). The results obtained from permutation importance and LIME algorithms showed that preoperative 24-hour urine free cortisol, PoC and age were the most important features, and showed the reliability and clinical practicability of Adaboost model in DC prediction. CONCLUSIONS:ML-based models could serve as an effective non-invasive approach to predicting DR, and could aid in determining individual treatment and follow-up strategies for CD patients. 10.1210/clinem/dgaa698
    Accuracy of Laboratory Tests for the Diagnosis of Cushing Syndrome. Galm Brandon P,Qiao Nidan,Klibanski Anne,Biller Beverly M K,Tritos Nicholas A The Journal of clinical endocrinology and metabolism CONTEXT:The diagnosis of Cushing syndrome (CS) can be challenging. It remains to be determined which diagnostic tests are the most accurate. OBJECTIVE:To summarize the accuracy of diagnostic tests for CS using contemporary meta-analytic techniques (hierarchical models). DATA SOURCES:PubMed, Embase, Scopus, Web of Science, and the Cochrane Database of Systemic Reviews (inception until August 3, 2018). STUDY SELECTION:Studies performed in adults that determined the accuracy of one or more diagnostic tests: overnight 1-mg dexamethasone suppression test (DST), 2-day low-dose DST (2d DST), 24-hour urinary free cortisol (UFC), late-night salivary cortisol (LNSC), midnight serum cortisol (MSC), and the dexamethasone-suppressed CRH (dex-CRH) and desmopressin (dex-DDAVP) tests. DATA EXTRACTION:Two authors independently extracted data and performed methodological assessments. DATA SYNTHESIS:One hundred thirty-nine studies (14 140 participants) were included in the analysis. The respective sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio (95% confidence interval [CI]) estimates include the following: DST 98.6% (96.9%-99.4%), 90.6% (86.4%-93.6%), 10.5 (7.2-15.3), and 0.016 (0.007-0.035); 2d DST 95.3% (91.3%-97.5%), 92.8% (85.7%-96.5%), 13.2 (6.47-27.1), and 0.051 (0.027-0.095); UFC 94.0% (91.6%-95.7%), 93.0% (89.0%-95.5%), 13.3 (8.47-21.0), and 0.065 (0.046-0.092); LNSC 95.8% (93.%-97.2%), 93.4% (90.7%-95.4%), 14.6 (10.3-20.7), and 0.045 (0.030-0.066); MSC 96.1% (93.5%-97.6%), 93.2% (88.1%-96.3%), 14.2 (7.96-25.2), and 0.042 (0.026-0.069); and dex-CRH 98.6% (90.4%-99.8%), 85.9% (67.6%-94.7%), 7.0 (2.80-17.6), and 0.016 (0.002-0.118). A single study evaluated dex-DDAVP. Meta-regression and a novel network meta-analytic approach suggest that DST is the most sensitive while UFC is the least sensitive. CONCLUSIONS:All of the included diagnostic tests for CS are highly sensitive and specific. It appears that the DST is the most sensitive while the UFC is less sensitive. The specificity of all first-line tests appears comparable. 10.1210/clinem/dgaa105