Adrenal Venous Sampling Could Be Omitted before Surgery in Patients with Conn’s Adenoma Confirmed by Computed Tomography and Higher Normal Aldosterone Concentration after Saline Infusion Test

Is adrenal venous sampling always necessary to differentiate between unilateral and bilateral primary aldosteronism? Lesson from the SPAIN-ALDO register

PURPOSE

To evaluate whether the clinical, biochemical and radiological features of patients with primary aldosteronism (PA) can predict both main subtypes of PA.

METHODS

A retrospective multicenter study of PA patients followed in 27 Spanish tertiary hospitals (SPAIN-ALDO Register). Only patients with confirmed unilateral or bilateral PA based on adrenal venous sampling (AVS) and/or postsurgical biochemical cure after adrenalectomy were included. Supervised regression techniques were used for model development.

RESULTS

328 patients [270 unilateral PA (UPA), 58 bilateral PA (BPA)] were included. The area under the curve (AUC) for aldosterone/potassium ratio and aldosterone responses following saline infusion test were 0.602 [95%CI 0.520 to 0.684] and 0.574 [95% CI 0.446-0.701], respectively, to differentiate UPA from BPA. The AUC was 0.825 [95% 0.764-0.886] when the prediction model with seven parameters - comorbidities (dyslipidemia, cerebrovascular disease, sleep apnea syndrome [SAS]), systolic blood pressure (SBP), plasma aldosterone levels (PAC), hypokalemia and unilateral adrenal nodule >1 cm and normal contralateral adrenal gland on CT/MRI - was used. In patients without comorbidities, hypokalemia, SBP > 160 mmHg, PAC > 40 ng/dL, and unilateral adrenal lesions were associated with a likelihood of having a UPA of 98.5%. The chance of BPA was higher in individuals with comorbidities, SBP < 140 mmHg, normokalemia, low PAC levels, and no adrenal tumors on the CT/MRI (91.5%).

CONCLUSION

A combination of high PAC, SBP > 160 mmHg, low serum potassium, a unilateral adrenal nodule>1 cm and no comorbidities could predict a UPA with a 98.5% accuracy.

Predicting Bilateral Subtypes of Primary Aldosteronism Without Adrenal Vein Sampling: A Systematic Review and Meta-analysis

CONTEXT

Primary aldosteronism (PA) is the most common endocrine cause of hypertension. The final diagnostic step involves subtyping, using adrenal vein sampling (AVS), to determine if PA is unilateral or bilateral. The complete PA diagnostic process is time and resource intensive, which can impact rates of diagnosis and treatment. Previous studies have developed tools to predict bilateral PA before AVS.

OBJECTIVE

Evaluate the sensitivity and specificity of published tools that aim to identify bilateral subtypes of PA.

METHODS

Medline and Embase databases were searched to identify published models that sought to subtype PA, and algorithms to predict bilateral PA are reported. Meta-analysis and meta-regression were then performed.

RESULTS

There were 35 studies included, evaluating 55 unique algorithms to predict bilateral PA. The algorithms were grouped into 6 categories: those combining biochemical, radiological, and demographic characteristics (A); confirmatory testing alone or combined with biochemical, radiological, and demographic characteristics (B); biochemistry results alone (C); adrenocorticotropic hormone stimulation testing (D); anatomical imaging (E); and functional imaging (F). Across the identified algorithms, sensitivity and specificity ranged from 5% to 100% and 36% to 100%, respectively. Meta-analysis of 30 unique predictive tools from 32 studies showed that the group A algorithms had the highest specificity for predicting bilateral PA, while group F had the highest sensitivity.

CONCLUSIONS

Despite the variability in published predictive algorithms, they are likely important for decision-making regarding the value of AVS. Prospective validation may enable medical treatment upfront for people with a high likelihood of bilateral PA without the need for an invasive and resource-intensive test.

A clinical assessment of portable point-of-care testing for quick cortisol assay during adrenal vein sampling

This study assessed the clinical performance of point-of-care testing (POCT) for quick cortisol assay (QCA) during adrenal vein sampling (AVS) using a newly invented portable quantitative assay instrument. An observational study was conducted prospectively at two centres in Japan. Forty-eight patients with primary aldosteronism considered for adrenalectomy were enrolled in this study and underwent AVS. Three basal adrenal vein samples from each adrenal vein and two from the inferior vena cava were collected sequentially. The cortisol concentration of adrenal vein samples was measured by routine method and QCA. A total of 338 adrenal vein samples were analysed from 250 sites to determine AVS success or failure. The distribution of turnaround time of the QCA for AVS success or failure followed a normal distribution with an average of 20.5 min. A positive correlation between the routine method and QCA was observed regarding cortisol concentration or selectivity index. No significant difference between the two methods was observed regarding the success rate of AVS. Using the routine method as a reference, the sensitivity and specificity of AVS success or failure were 99.1% (210/212) and 81.6% (31/38), respectively. Easy, quick, portable, and precise POCT-QCA demonstrated its compatibility with routine methods regarding clinical performance.

Identifying primary aldosteronism patients who require adrenal venous sampling: a multi-center study

Adrenal venous sampling (AVS) is crucial for subtyping primary aldosteronism (PA) to explore the possibility of curing hypertension. Because AVS availability is limited, efforts have been made to develop strategies to bypass it. However, it has so far proven unsuccessful in applying clinical practice, partly due to heterogeneity and missing values of the cohorts. For this purpose, we retrospectively assessed 210 PA cases from three institutions where segment-selective AVS, which is more accurate and sensitive for detecting PA cases with surgical indications, was available. A machine learning-based classification model featuring a new cross-center domain adaptation capability was developed. The model identified 102 patients with PA who benefited from surgery in the present cohort. A new data imputation technique was used to address cross-center heterogeneity, making a common prediction model applicable across multiple cohorts. Logistic regression demonstrated higher accuracy than Random Forest and Deep Learning [(0.89, 0.86) vs. (0.84, 0.84), (0.82, 0.84) for surgical or medical indications in terms of f-score]. A derived integrated flowchart revealed that 35.2% of PA cases required AVS with 94.1% accuracy. The present model enabled us to reduce the burden of AVS on patients who would benefit the most.

Validation of Existing Clinical Prediction Tools for Primary Aldosteronism Subtyping

The new clinical prediction score (SCORE) has been recently proposed for primary aldosteronism (PA) subtyping prior to adrenal vein sampling (AVS). This study aimed to compare that SCORE with previously published scores and their validation using a cohort of patients at our center who had had positive SIT confirming PA and had been diagnosed with either bilateral PA according to AVS or unilateral PA if biochemically cured after an adrenalectomy. Final diagnoses were used to evaluate the diagnostic performance of the proposed clinical prediction tools. Only Kamemura's model (with a maximum score of 4 points) and Kobayashi's score (with a maximum score of 12 points) reached 100% reliability for prediction of bilateral PA; however, with sensitivity of only 3%. On the other hand, the values of SCORE = 3 (with sensitivity of 48%), the SPACE score ≥18 (with sensitivity of 35%), the Kobayashi's score ≤2 (with sensitivity of 28%), and the Kocjan's score = 3 (with sensitivity of 28%) were able to predict unilateral PA with 100% probability. Furthermore, Umakoshi's and Young's models both reached 100% reliability for a unilateral PA with score = 4 and both predictive factors together respectively; however, the sensitivity was lower compared with previous models; 4% and 14%, respectively. None of the clinical prediction tools applied to our cohort predicted unilateral and bilateral subtypes together with the expected high diagnostic performance, and therefore can only be used for precisely defined cases.