Subtype Diagnosis Of Primary Aldosteronism Research Articles

Clinical prediction model for primary aldosteronism subtyping and special focus on adrenal volumetric assessment.

Our aim was to develop a prediction model based on a simple score with clinical, laboratory, and imaging findings for the subtype diagnosis of primary aldosteronism (PA). The contribution of adrenal volumetric assessment to PA subtyping was also investigated. Thirty-five patients with adequate cannulation in adrenal venous sampling (AVS) were included. Laboratory data, the saline infusion test (SIT), and the AVS results of patients with PA were retrospectively evaluated. Volumetric assessment was performed using magnetic resonance imaging (MRI) and the ratio of adrenal volumes was calculated after adjusting for gender- and side-specific mean reference values of both adrenal glands. The AVS was consistent with unilateral PA in 49% and bilateral in 51% of the patients. Hypertension as a reason for work-up, the highest aldosterone/lowest potassium value higher than 12, the percentage of plasma aldosterone concentration (PAC) reduction after SIT by equal or less than 43.5%, the use of oral potassium replacement, unilateral disease at pre-AVS imaging, and a ratio of adjusted adrenal volumes equal to or below 1.7 were indicative of unilateral disease in univariate logistic regression analysis concerning the distinction of PA subtyping (p < 0.05). Multivariate logistic regression analysis also revealed that adrenal volumetric assessment has an impact on PA subtyping (p < 0.05). In the prediction model, when each of the six parameters that were significant in the univariate logistic regression analysis was assigned one point, < 4 predicted bilateral PA, whereas ≥ 4 predicted unilateral PA (AUC:0.92, p < 0.001). This prediction model before AVS may serve as a convenient and practical approach, while an adjusted adrenal volumetric assessment can make a positive contribution to PA subtyping.

A comparison of the performance of 68Ga-Pentixafor PET/CT versus adrenal vein sampling for subtype diagnosis in primary aldosteronism.

To investigate the diagnostic efficiency and prognostic value of 68Ga-Pentixafor PET/CT in comparison with adrenal vein sampling (AVS) for functional lateralization in primary aldosteronism (PA). Histology and long-term clinical follow-up normally serve as the gold standard for such diagnosis. We prospectively recruited 26 patients diagnosed with PA. All patients underwent 68Ga-Pentixafor PET/CT and AVS. Postsurgical biochemical and clinical outcomes of patients with unilateral primary aldosteronism (UPA), as diagnosed by PET/CT or AVS, were assessed by applying standardized Primary Aldosteronism Surgical Outcome (PASO) criteria. Immunohistochemistry (IHC) was performed to detect the expression of aldosterone synthase (CYP11B2) and CXCR4. On total, 19 patients were diagnosed with UPA; of these, 13 patients were lateralized by both PET/CT and AVS, four patients were lateralized by PET-only, and two by AVS-only. Seven subjects with no lateralization on AVS and PET received medical therapy. All patients achieved complete biochemical success except one with nodular hyperplasia lateralized by AVS alone. The consistency between PET/CT and AVS outcomes was 77% (20/26). Moreover, CYP11B2-positive nodules were all CXCR4-positive and showed positive findings on PET. Patients who achieved complete biochemical and clinical success had a higher uptake on PET as well as stronger expression levels of CXCR4 and CYP11B2. Our analysis showed that 68Ga-Pentixafor PET/CT could enable non-invasive diagnosis in most patients with PA and identify additional cases of unilateral and surgically curable PA which could not be classified by AVS. 68Ga-Pentixafor PET/CT should be considered as a first-line test for the future classification of PA.

Clinical Value of 68Ga-Pentixafor PET/CT in Subtype Diagnosis of Primary Aldosteronism Patients with Adrenal Micronodules.

Our objective was to investigate the clinical value of 68Ga-pentixafor PET/CT in subtype diagnosis of primary aldosteronism (PA) patients with adrenal micronodules less than 1 cm in diameter and compare it with the routine clinical methods. Methods: We used prospective enrollment of PA patients with adrenal micronodules identified by adrenal CT scans to undergo 68Ga-pentixafor PET/CT. Patients were divided into surgically eligible and ineligible groups based on surgical pathology and postoperative follow-up or adrenal venous sampling (AVS) results. Patient management was discussed by a multidisciplinary team. The semiquantitative parameters of PET/CT included SUVmax for adrenal lesion and SUV ratios for lesion to liver and lesion to normal adrenal gland. Results: In total, 123 PA patients with adrenal micronodules were examined using 68Ga-pentixafor PET/CT, and 104 patients who underwent surgery or successful AVS were included in the analysis (48 ± 10 y old). The sensitivity, specificity, and accuracy of visual analysis using 68Ga-pentixafor PET/CT to identify surgically eligible patients were 90.2%, 72.7%, and 86.5%, respectively, which were significantly higher than those of adrenal CT (73.1%, 53.8%, and 68.3%, respectively) and yielded consistent results in different CT morphologic or age subgroups. In 36 patients who had both AVS and 68Ga-pentixafor PET/CT, the tests showed a 66.7% concordance rate. However, PET/CT was significantly more concordant with surgical outcomes than was AVS in 17 patients who underwent adrenalectomy (82.4% vs. 68.86%). Among the 183 adrenal micronodules included in the study, the semiquantitative diagnostic thresholds for 92 lesions eligible for surgical treatment were an SUVmax of at least 4.55, an SUV ratio of at least 2.17 for lesion to liver, and an SUV ratio of at least 1.90 for lesion to normal adrenal gland. All patients benefited from surgical removal of 68Ga-pentixafor-avid microlesions. Conclusion: In PA patients with adrenal micronodules, 68Ga-pentixafor PET/CT demonstrated promising diagnostic accuracy in classification and appeared to perform better than adrenal CT. Furthermore, there was also a suggestion of some potential in predicting postoperative efficacy compared with AVS, although these observations require further investigation and verification in larger cohorts.

PS-C21-7: IMPACT OF THE TIME OF DAY PERFORMING AVS ON THE SUCCESS RATE AND SUBTYPE DIAGNOSIS IN PRIMARY ALDOSTERONISM

Purpose: Adrenal vein sampling (AVS) is the gold-standard technique for the subtype diagnosis of primary aldosteronism (PA). Measurements of aldosterone and cortisol in adrenal venous blood are used to determine the success or failure of AVS and to diagnose the PA subtype, but these hormone secretions show diurnal change. In this study, we examined the effect of the time of AVS on the results. Design: Multicenter, retrospective, observational study Setting: Three centers in Japan Patients: 591 patients diagnosed with PA and undergoing AVS at 3 centers were used. All patients underwent basal AVS and ACTH-stimulated AVS on the same day. 382 patients underwent AVS in the morning (AM-AVS) and 209 patients in the afternoon (PM-AVS). Results: In terms of Selectivity Index (SI) in AVS, there was no significant difference in the right adrenal vein in basal AVS (22 vs. 23, p = 0.26), but SI of AM-AVS group was significantly higher in the left adrenal vein (19 vs. 17, p = 0.03). 03) than PM-AVS. However, in AVS after ACTH loading, SIs of both right and left adrenal veins were significantly higher in the PM group (Right: 42 vs. 44, p = 0.03; Left: 48 vs. 50, p = 0.01). However, there was no significant difference in AVS success rates between the two groups for basal AVS (81% vs. 74%, p = 0.08) and ACTH-stimulated AVS (92% vs. 88%, p = 0.19). There was also no significant difference in the proportion of patients diagnosed with unilateral PA in basal AVS (57% vs. 48%, p = 0.09) and ACTH-stimulated AVS (17% vs. 14%, p = 0.41). Conclusions: In a multicenter observational study, there was no significant difference in AVS success rate or subtype diagnosis of AVS between the different AVS times.

PS-C22-6: PERFORMANCE OF PREDICTION ALGORITHM FOR THE SUBTYPE DIAGNOSIS OF PRIMARY ALDOSTERONISM WHICH WAS ADVOCATED IN THE JAPAN PRIMARY ALDOSTERONISM STUDY

Objective: In the part of the Japan Primary Aldosteronism Study (JPAS), the prediction algorithm for the subtype diagnosis of primary aldosteronism (PA) was advocated and published in 2018 (Umakoshi.H et.al, J Clin Endocrinol Metab, 2018, 103(3):900–908). In the algorithm, patients with PA with bilateral normal results on CT and normokalemia likely have a low prior probability of a lateral form of PA in AVS (6.2%) and could be treated medically, whereas those with unilateral visible mass on CT and presence of hypokalemia have a high probability of a lateralized form of PA in AVS (70.6%). To validate the efficacy of the algorithm, we applied the algorithm for recent PA cases in our institute, Keio University hospital. Patients: A total of 77 patients with PA who underwent adrenal venous sampling (AVS) between November 2018 and October 2019 were enrolled in this study. They were classified into four groups according to CT findings and potassium status. Subtype diagnosis of PA was determined by AVS. Results: In our case, the prevalence for unilateral hyperaldosteronism on AVS was as follows: CT-negative with normokalemia, 7.9%; CT-positive with normokalemia, 15.4%; CT-negative with hypokalemia 22.2%, and CT-positive, 71.4%. The results were quite similar to the results in the previous report. In the algorithm, it was stated that AVS could be spared in patients with unilateral lesion on CT and hypokalemia if they were younger than 35 years, however, in our cases, there was one patient whose age was 30 with unilateral lesion on CT and hypokalemia and the AVS result of whom indicated bilateral PA. In the algorithm, it was also stated that AVS could be spared in patients with bilateral lesions on CT and normokalemia if plasma aldosterone concentration was less than 8 ng/dl and it was consistent in our cases. Conclusion: Overall, the prediction algorithm for the subtype diagnosis of PA developed in JPAS could be applied well in our recent PA cases.

A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism.

Unilateral primary aldosteronism (UPA) and bilateral primary aldosteronism (BPA) are the two subtypes of PA. Discriminating UPA from BPA is of great significance. Although adrenal venous sampling (AVS) is the gold standard for diagnosis, it has shortcomings. Thus, improved methods are needed. The original data were extracted from the public database "Dryad". Ten parameters were included to develop prediction models for PA subtype diagnosis using machine learning technology. Moreover, the optimal model was chose and validated in an external dataset. In the modeling dataset, 165 patients (71 UPA, 94 BPA) were included, while in the external dataset, 43 consecutive patients (20 UPA, 23 BPA) were included. The ten parameters utilized in the prediction model include age, sex, systolic and diastolic blood pressure, aldosterone to renin ratio (ARR), serum potassium, ARR after 50 mg captopril challenge test (CCT), primary aldosterone concentration (PAC) after saline infusion test (SIT), PAC reduction rate after SIT, and number of types of antihypertensive agents at diagnosis. The accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model using the random forest classifier were 90.0%, 81.8%, 96.4%, 0.878, and 0.938, respectively, in the testing dataset and 81.4%, 90.0%, 73.9%, 0.818 and 0.887, respectively, in the validating external dataset. The most important variables contributing to the prediction model were PAC after SIT, ARR, and ARR after CCT. We developed a machine learning-based predictive model for PA subtype diagnosis based on ten clinical parameters without CT imaging. In the future, artificial intelligence-based prediction models might become a robust prediction tool for PA subtype diagnosis, thereby, might reducing at least some of the requests for CT or AVS and assisting clinical decision-making.

ODP611 Combination Of Seated Saline Suppression Test And Computed Tomography In Predicting Subtype Diagnosis Of Primary Aldosteronism In Chinese Population

Abstract Objective The study aimed to evaluate the accuracy of the seated saline suppression test (SSST) combined with adrenal computed tomography (CT) in determining the primary aldosteronism (PA) subtypes. Methods Each patient underwent a SSST and adrenal CT. Logistic diagnostic model (CT finding + post-SSST plasma aldosterone concentration (PAC)) were built, which evaluated the accuracy of the SSST combined with adrenal CT for a subtype classification of PA. Subtype diagnosis of PA was determined by adrenal venous sampling (AVS), pathology and clinical outcomes (primary aldosteronism surgical outcome(PASO)). Results A total of 72 patients with PA were classified into two groups, including 45 aldosterone-producing adenoma (APA) and 27 idiopathic hyperaldosteronism (IHA) patients. By binary logistic regression analysis, adrenal CT (odds ratio (OR)=16.28, 95% confidence interval (CI) 4.18-63.42, P&amp;lt;0. 05) and 4-hours PAC after SSST (OR =1.13, 95% CI 1. 03 to 1.23, P&amp;lt;0. 05) were significantly independent predictors of APA patients. We have successfully developed a diagnostic model based on logistic regression that is able to predict the probability of APA. The area under the receiver operating characteristic (ROC) curve of logistic diagnostic models(AUC=0.907, 95% CI 0.815 to 0.963) was significantly greater either than that for the adrenal CT (AUC=0.807,95% CI 0.696 to 0.890,respectively,P&amp;lt;0. 05) or SSST (AUC=0.799,95% CI 0.688 to 0.884,respectively,P&amp;lt;0. 05). Positive CT finding combined with a post-SSST PAC≥18.2 ng/dL provided sensitivity of 88.89%,specificity of 74. 07%,and positive predictive value (PPV) of 93.9%(31/33) for predicting APA, that determined patients who required AVS prior to surgery. Conclusion The model combining CT finding with post-SSST PAC has superior accuracy in subtype diagnosis of PA compared with SSST or adrenal CT alone. The current study was the first study to investigate the accuracy of the new method on the subtype classification of PA. We are fortunate to find it may be a safe, convenient, sensitive and noninvasive approach in predicting subtype diagnosis of PA in Chinese individuals, especially in primary care where AVS may be unavailable. The results require further confirmation by RCT clinical trials. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m.

The accuracy of adjusted unconventional indices for the assessment of selectivity and lateralization of adrenal vein sampling in the subtype diagnosis of primary aldosteronism

The accuracy of adjusted unconventional indices for the assessment of selectivity and lateralization of adrenal vein sampling in the subtype diagnosis of primary aldosteronism

The Accuracy of Simple and Adjusted Aldosterone Indices for Assessing Selectivity and Lateralization of Adrenal Vein Sampling in the Diagnosis of Primary Aldosteronism Subtypes.

ObjectiveThis study aimed to evaluate the reliability of simple and corrected aldosterone indices for assessing the selectivity and lateralization of adrenal vein sampling (AVS) in patients with primary aldosteronism.MethodsData of all consecutive patients with primary aldosteronism who underwent AVS for subtype diagnosis, followed at two Italian referral centers, were analyzed retrospectively.ResultsAVS achieved bilateral selectivity in 112/144 patients. Unilateral disease was diagnosed in 60 cases (53.6%) and idiopathic hyperaldosteronism in 52 individuals (46.4%). The aldosterone index (aldosterone ratio between an adrenal vein and the inferior vena cava) showed a high accuracy in predicting selectivity, compared to a cortisol selectivity index of 1.1, and a moderate accuracy, compared to cortisol cut-offs of 2 and 3. The simple aldosterone index showed a moderate accuracy in predicting ipsi/contralateral aldosterone hypersecretion, while lesion side- and hypokalemia-corrected aldosterone index revealed a significant improvement in predicting ipsi/contralateral disease. Moreover, the comparative aldosterone index (aldosterone ratio in the dominant vs the non-dominant adrenal vein) revealed a high accuracy in predicting unilateral primary aldosteronism. For an immediate clinical application of our results, the adjusted cut-offs were calculated, according to the Youden’s criterion and to a pre-established specificity of 90%, for all possible combinations of lesion side at imaging and presence/absence of hypokalemia.ConclusionsThis study demonstrated the diagnostic accuracy of simple and clinical-/imaging-corrected aldosterone indices for adrenal vein sampling in subtype diagnosis of primary aldosteronism and suggests the potential application of these tools to select patients for adrenalectomy when standard indices cannot be performed.

The analysis of related factors on the unilateral aldosterone secretion in the functional diagnosis of primary aldosteronism and establishment of a predictive model

Objective: To explore related factors associated with unilateral aldosterone secretion of patients with primary aldosteronism (PA) and construct a predictive model. Methods: The clinical data of patients who were diagnosed as PA in West China Hospital from April 2016 to September 2020 was analyzed retrospectively. According to the results of the adrenal enhancement CT, patients were divided into 3 groups, namely non-nodular group with no bilateral adrenal glands lesions, only with unilateral adrenal hyperplasia or bilateral adrenal hyperplasia, unilateral nodule group with unilateral adrenal nodules and the contralateral adrenal glands with hyperplasia or not, and bilateral nodules group with nodules in both adrenal glands. Regarding the related factors of dominant side of aldosterone secretion, univariate analysis and binary logistic regression were used. Receiver operating characteristic curve and nomogram were used to evaluate the diagnostic performance of regression models. Results: A total of 237 patients with PA were included, of which, 118 males and 119 females, the median age was 39 years, and the body mass index (BMI) was (25.2±3.5) kg/m2. There were 157 (66.2%) of 237 patients with typical imaging findings. There were 32 cases in no-nodular group, 183 cases in unilateral nodule group, and 22 cases in bilateral nodules group. Multivariate analysis showed that age (OR=0.876, P<0.001), blood potassium concentration (OR=0.430, P=0.004), and typical imaging findings (OR=2.202, P=0.035) were associated with unilateral aldosterone secretion. As for unilateral nodule group, multivariate analysis showed that age (OR=0.900, P<0.001), plasma aldosteronism concentration (PAC) (OR=1.050, P=0.018), and typical imaging findings (OR=2.637, P=0.025) were associated with unilateral aldosterone secretion. The agreement rate between the dominant side of the adrenal CT and AVS was only 50.2%. Multivariate analysis showed that age (OR=0.954, P=0.001), BMI (OR=0.893, P=0.024) and PAC (OR=1.043, P=0.011) were independently associated with concordance between AVS and CT. The cut-off value of the ROC curve was 0.43; the model sensitivity was 56.3%; the specificity was 86.7% and the area under the ROC curve was 0.742. Conclusions: Age is an important predictor in the diagnosis of PA subtypes. It is recommended to refer to subgroup based on imaging results for clinical decision. For patients with no obvious lesions or bilateral lesions on CT, AVS should be performed as far as possible to confirm the subtypes in diagnosis of PA.

Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test

Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Overall, 91 patients with unilateral and 138 patients with bilateral PA were randomly assigned to the training and test cohorts. Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop predictive models from 21 clinical variables. The accuracy and the area under the receiver operating characteristic curve (AUC) for predicting of subtype diagnosis of PA in the test cohort were compared among the optimized classifiers. Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium, plasma aldosterone, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF (accuracy: 85.1%). Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.

Significance of Discordant Results: between Confirmatory Tests in Diagnosis of Primary Aldosteronism

Context: Current clinical guidelines recommend confirmation of positive result in at least one confirmatory test in the diagnosis of primary aldosteronism (PA). Clinical implication of multiple confirmatory tests has not been established, especially when patients show discordant results. Objective: The aim of the present study was to explore the role of two confirmatory tests in subtype diagnosis of PA. Design: Retrospective cross-sectional study. Setting: The study was conducted at two referral centers. Participants and Method: We identified 360 hypertensive patients who underwent both captopril challenge test (CCT) and saline infusion test (SIT) and exhibited at least one positive result. Among them, we studied 193 patients with PA whose data were available for subtype diagnosis based on adrenal vein sampling (AVS). Main Outcome Measure: The prevalence of bilateral subtype on AVS according to the results of the confirmatory tests. Results: Of patients studied, 127 were positive for both CCT and SIT (double-positive), while 66 were positive for either CCT or SIT (single-positive) (n = 34 and n = 32, respectively). Altogether, 135 were diagnosed with bilateral subtype on AVS. The single-positive patients had milder clinical features of PA than the double-positive patients. The prevalence of bilateral subtype on AVS was significantly higher in the single-positive patients than in the double-positive patients. (63/66 [95.5%] vs. 72/127 [56.7%], P < 0.01). Several clinical parameters were different between CCT single-positive and SIT single-positive patients. Conclusion: Patients with discordant results between CCT and SIT have a high probability of bilateral subtype of PA on AVS.

Machine Learning-Based Models for Prediction of Subtype Diagnosis of Primary Aldosteronism Using Blood Test

Abstract Context: Primary aldosteronism (PA) is a common cause of secondary hypertension and is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who can be cured by unilateral adrenalectomy and should be referred to specialized centers. The use of machine learning has been introduced in some fields of clinical prediction. Combining machine learning with clinical data could lead to the development of new models for predicting unilateral subtype of PA. Objective: The aim of this study was to develop a predictive model of subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Design and setting: This was a retrospective cross-sectional study in referral centers. Patients: We retrospectively analyzed 91 patients with unilateral and 138 patients with bilateral subtype of PA diagnosed according to adrenal venous sampling findings, and stratified randomly split to the training (80%) and test cohorts (20%). Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop prediction models from 21 clinical variables. Classifiers were trained using stratified 10 fold cross-validation of the training cohort and hyperparameters of each classifier were adjusted using grid search to optimize the accuracy in the training cohort. Main Outcome Measures: The accuracy and the area under the receiver operating characteristic curve (AUC) for subtype prediction in the test cohort were compared among the optimized classifiers. Results: Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium levels, plasma aldosterone concentrations, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF. Conclusions: Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.

Diagnostic accuracy of adrenal imaging for subtype diagnosis in primary aldosteronism: systematic review and meta-analysis

ObjectivesAccurate subtype classification in primary aldosteronism (PA) is critical in assessing the optimal treatment options. This study aimed to evaluate the diagnostic accuracy of adrenal imaging for unilateral PA classification.MethodsSystematic...

Functional imaging with 11C-metomidate PET for subtype diagnosis in primary aldosteronism.

Endocrine Society guidelines recommend adrenal venous sampling (AVS) in primary aldosteronism (PA) if adrenalectomy is considered. We tested whether functional imaging of adrenal cortex with 11C-metomidate (11C-MTO) could offer a noninvasive alternative to AVS in the subtype classification of PA. We prospectively recruited 58 patients with confirmed PA who were eligible for adrenal surgery. Subjects underwent AVS and 11C-MTO-PET without dexamethasone pretreatment in random order. The lateralization of 11C-MTO-PET and adrenal CT were compared with AVS in all subjects and in a prespecified adrenalectomy subgroup in which the diagnosis was confirmed with immunohistochemical staining for CYP11B2. In the whole study population, the concordance of AVS and 11C-MTO-PET was 51% and did not differ from that of AVS and adrenal CT (53%). The concordance of AVS and 11C-MTO-PET was 55% in unilateral and 44% in bilateral PA. In receiver operating characteristics analysis, the maximum standardized uptake value ratio of 1.16 in 11C-MTO-PET had an AUC of 0.507 (P = n.s.) to predict allocation to adrenalectomy or medical therapy with sensitivity of 55% and specificity of 44%. In the prespecified adrenalectomy subgroup, AVS and 11C-MTO-PET were concordant in 10 of 19 subjects with CYP11B2-positive adenoma and in 6 of 10 with CYP11B2-positivity without an adenoma. The concordance of 11C-MTO-PET with AVS was clinically suboptimal, and did not outperform adrenal CT. In a subgroup with CYP11B2-positive adenoma, 11C-MTO-PET identified 53% of cases. 11C-MTO-PET appeared to be inferior to AVS for subtype classification of PA.

The diagnostic accuracy of adjusted unconventional indices for adrenal vein sampling in the diagnosis of primary aldosteronism subtypes.

Simple unconventional indices did not demonstrate a satisfactory accuracy for diagnosing unilateral primary aldosteronism when adrenal vein sampling is not bilaterally selective. This study aimed to evaluate the reliability of clinical/imaging-corrected unconventional indices for adrenal vein sampling in predicting unilateral primary aldosteronism. Data of all consecutive patients with primary aldosteronism subtyped with adrenal vein sampling, referred to two Italian centers, were analyzed retrospectively. All patients with proved unilateral aldosterone hypersecretion underwent adrenalectomy. Unilateral disease was diagnosed in 58 cases (54.2%) and idiopathic hyperaldosteronism in 49 individuals (45.8%). The monoadrenal index (aldosterone-to-cortisol ratio in the adrenal vein) showed high accuracy in predicting ipsilateral disease and moderate accuracy in predicting contralateral aldosterone hypersecretion. The monolateral index (aldosterone-to-cortisol ratio in the adrenal vein vs. peripheral blood) revealed moderate accuracy in predicting ipsilateral disease and high accuracy in predicting contralateral aldosterone hypersecretion. Lesion side- and hypokalemia-corrected ROC curves for these unconventional indices revealed a significant improvement in the reliability of predicting ipsilateral/contralateral disease, reaching high accuracy in all models. For an immediate clinical application of our results, the adjusted cut-offs were calculated, according to the Youden's criterion and to a pre-established specificity of 95%, for all possible combinations of lesion side at imaging and presence/absence of hypokalemia. This study demonstrated the high diagnostic accuracy of clinical-/imaging-corrected unconventional indices for adrenal vein sampling in the diagnosis of primary aldosteronism subtypes and suggests the use of these adjusted indices to select patients for adrenalectomy when adrenal vein sampling is not bilaterally selective.

Subtype diagnosis, treatment, complications and outcomes of primary aldosteronism and future direction of research: a position statement and consensus of the Working Group on Endocrine Hypertension of the European Society of Hypertension.

: Primary aldosteronism is a frequent cause of secondary hypertension requiring a specific pharmacological treatment with mineralocorticoid receptor antagonist or with unilateral adrenalectomy. These treatments have shown to reduce the excess of cardiovascular risk characteristically associated with this disease. In part I of this consensus, we discussed the procedures for the diagnosis of primary aldosteronism. In the present part II, we address the strategies for the differential diagnosis of primary aldosteronism subtypes and therapy. We also discuss the evaluation of outcomes and provide suggestions for follow-up as well as cardiovascular and metabolic complications specifically associated with primary aldosteronism. Finally, we analyse the principal gaps of knowledge and future challenges for research in this field.

SAT-548 Significance of Adrenal Vein Aldosterone Gradient in the Diagnosis of Unilateral Subtype of Primary Aldosteronism

Context: Adrenal vein sampling (AVS) is the standard method for subtype diagnosis in primary aldosteronism (PA), while success rate of its procedures, especially in right adrenal vein (AV), has limited. However, unilateral subtype of PA can be completely cured by surgical treatment and hence diagnostic approach for PA should be established especially when patients with apparent adrenal disease on computed tomography (CT) lack complete AVS result. In recent years, segmental AVS demonstrates that apparent aldosterone gradient in each adrenal vein branch is specific finding in aldosterone producing adenoma. Since we have reported that plasma aldosterone concentrations different in the proximal and distal regions of the left AV in some of the patients with PA, we hypothesized that solo finding of adrenal aldosterone gradient in the left AV (left AV gradient) should be the clue for unilateral subtype of PA. Objective: The aim was to investigate whether left AV gradient indicate the left unilateral subtype of PA. Design and Setting This study was a part of the Kyushu Adrenal Network Database for Advanced medicine (Q-AND-A) study, a cross-sectional and retrospective study in a single referral center. Participants and AVS procedure: Of 194 PA patients who underwent AVS with cosyntropin stimulation between January 2007 and April 2019, 111 patients had available and successful AVS data. Blood samples in left adrenal veins were obtained from following two positions; the common trunk and the central adrenal vein. We calculated left AV gradient by the ratio of aldosterone-to-cortisol ratio on the higher position in left AV to that on the lower position. We determined the presence of left AV gradient if it was greater than four. Main outcome measures: Prevalence of unilateral subtype in patients with left AV gradient who had unilateral disease on CT. Results: Of 111 patients with complete AVS data, 43 who had left unilateral disease on CT were analyzed in present study. Twenty-nine patients were diagnosed as left unilateral subtype on AVS and 14 were bilateral subtype. Aldosterone gradient was observed only in patients with unilateral subtype (41% [12/29]), not in patients with bilateral subtype (0% [0/14]). Of 29 patients with unilateral subtype on AVS, clinical parameters, including plasma aldosterone concentrations, plasma renin activity, and serum potassium levels, were not different in those with and without left AV gradient. The receiver operative curve analysis for the diagnosis of unilateral subtype on AVS showed that the optimal cutoff value of left AV gradient was 3.3 with a sensitivity of 45% and specificity of 100%. Conclusion: The presence of left AV gradient is the reliable finding for the diagnosis of left unilateral subtype in patients with PA who have unilateral disease on CT. However, further study in a larger number of cases is required to validate this finding.

OR34-04 Efficiency of Adrenal Venous Sampling in the Treatment Choice of Primary Aldosteronism (AVSTAT Study)

BACKGROUND: Adrenal venous sampling (AVS) is strongly recommended for a subtype diagnosis of primary aldosteronism (PA) if adrenalectomy (ADX) is desired by the patient. Given various issues related to AVS such as technical demand, invasive nature, expensive cost and radiation exposure, AVS is expected to lead efficiently to the subtype diagnosis and ADX. Aim: Primary objective was to assess the performance of AVS to determine treatment of PA by investigating the ratio of unilateral disease and rate of ADX following AVS in patients with unilateral disease. Methods: Sixteen major referral centers in ENS@T (n=10) and Japan (n=6) participated in the study. Study period was from 2006 to 2018. Data on total number of PA patients, AVS (total number and number of successful procedures), number of patients with unilateral diseases, and number of patients that underwent ADX were collected by a questionnaire-based survey. In addition, reasons for not proceeding to ADX in patients with a unilateral diagnosis were investigated. The diagnosis of PA was based on the positive case detection and at least one positive result in confirmatory testing. Results: Total number of confirmed PA patients and conducted AVS showed a dramatic increase during the past decade (PA: 1061 pts/ 2006–2011 to 3718 pts/ 2012–2018; AVS: 720/ 2006–2011 to 2448/ 2012–2018). Success rate of AVS was improved from 79.0% (2006–2011) to 92.5% (2012–2018). Both rate of unilateral PA and ADX of successful procedures decreased from 42.7% (2006–2011) to 37.3% (2012–2018) and from 40.8% (2006–2011) to 34.9% (2012–2018), respectively. Of the patients with successful AVS, bilateral disease was diagnosed in 63.5% (1812/2854 pts). Of the unilateral PA patients, 11.9% (125/ 1054 pts) were not subjected to ADX. The rate of the patients not subjected to ADX was significantly higher in Japan than in ENS@T centers both in patients with successful AVS (75.8% vs. 53.4%) and with unilateral disease (19.9% vs. 8.6%). Clinical decision against ADX in unilateral disease was made by the physicians in 33.3%, the patients in 33.3%, and both in 33.3%. Medical factors for Dr.’s decision against ADX in unilateral disease included good blood pressure control, normokalemia, comorbidities (e.g. DM, CKD), non-lateralized CT findings (e.g. no tumor, contralateral tumor), and discordant results among different criteria of AVS.Conclusions: High prevalence of bilateral disease and change of treatment policy after implementation affected the efficiency of AVS as an essential diagnostic procedure prior to ADX. Development of non-invasive procedures to exclude bilateral PA and more strict indication of AVS are warranted.

Seated saline infusion test in predicting subtype diagnosis of primary aldosteronism.

Although saline infusion test is widely used as a confirmatory test for primary aldosteronism (PA), it is reportedly less sensitive in patients in whom aldosterone is responsive to the upright position by performing it in recumbent position. Based on a single-centre experience, seated saline infusion test (SSIT) has been reported to be highly sensitive and superior to recumbent testing in identifying both unilateral and bilateral forms of PA. However, due to limited participants number, the utility of SSIT needs to be validated in other series. This study aimed to evaluate the accuracy of SSIT in determining the PA subtypes compared with adrenocorticotropic hormone stimulation test under dexamethasone suppression (Dex-AT). Sixty-four patients with PA who underwent both SSIT and Dex-AT were included. Subtype diagnosis of PA was determined by adrenal venous sampling (AVS) (16 unilateral and 48 bilateral forms). Plasma aldosterone concentrations (PACs) were measured after SSIT and Dex-AT. The area under the receiver operating characteristic (ROC) curve for diagnosing unilateral PA was greater in SSIT than that in Dex-AT (0.907 vs. 0.755; P=.023). ROC curve analysis predicted optimal cut-off PACs of 13.1ng/dL (sensitivity, 93.8%; specificity, 79.2%) for SSIT and 34.2ng/dL (sensitivity, 75.0%; specificity, 68.8%) for Dex-AT. Seated saline infusion test has superior accuracy in subtype diagnosis of PA compared with Dex-AT. SSIT can be a sensitive test for determining patients who require AVS prior to surgery.