A clinical prediction score using age at diagnosis and saline infusion test parameters can predict aldosterone-producing adenoma from idiopathic adrenal hyperplasia

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 hyperaldosteronism subtypes predictive model using ensemble learning

This study aimed to develop a machine-learning algorithm to diagnose aldosterone-producing adenoma (APA) for predicting APA probabilities. A retrospective cross-sectional analysis of the Japan Rare/Intractable Adrenal Diseases Study dataset was performed using the nationwide PA registry in Japan comprised of 41 centers. Patients treated between January 2006 and December 2019 were included. Forty-six features at screening and 13 features at confirmatory test were used for model development to calculate APA probability. Seven machine-learning programs were combined to develop the ensemble-learning model (ELM), which was externally validated. The strongest predictive factors for APA were serum potassium (s-K) at first visit, s-K after medication, plasma aldosterone concentration, aldosterone-to-renin ratio, and potassium supplementation dose. The average performance of the screening model had an AUC of 0.899; the confirmatory test model had an AUC of 0.913. In the external validation, the AUC was 0.964 in the screening model using an APA probability of 0.17. The clinical findings at screening predicted the diagnosis of APA with high accuracy. This novel algorithm can support the PA practice in primary care settings and prevent potentially curable APA patients from falling outside the PA diagnostic flowchart.

CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study

BACKGROUND

To establish and verify a radiomics nomogram for differentiating isolated micronodular adrenal hyperplasia (iMAD) from lipid-poor adenoma (LPA) based on computed tomography (CT)-extracted radiomic features.

METHODS

A total of 148 patients with iMAD or LPA were divided into three cohorts: a training cohort (n = 72; 37 iMAD and 35 LPA), a validation cohort (n = 36; 22 iMAD and 14 LPA), and an external validation cohort (n = 40; 20 iMAD and 20 LPA). Radiomics features were extracted from contrast-enhanced and non-contrast CT images. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a triphasic radiomics model and unenhanced radiomics model using reproducible radiomics features. A clinical model was constructed using certain laboratory variables and CT findings. Radiomics nomogram was established by selected radiomics signature and clinical factors. Nomogram performance was assessed by calibration curve, the areas under receiver operating characteristic curves (AUC), and decision curve analysis (DCA).

RESULTS

Eleven and eight extracted features were finally selected to construct an unenhanced radiomics model and a triphasic radiomics model, respectively. There was no significant difference in AUC between the two models in the external validation cohort (0.838 vs. 0.843, p = 0.949). The radiomics nomogram inclusive of the unenhanced model, maximum diameter, and aldosterone showed the AUC of 0.951, 0.938, and 0.893 for the training, validation, and external validation cohorts, respectively. The nomogram showed good calibration, and the DCA demonstrated the superiority of the nomogram compared with the clinical factors model alone in terms of clinical usefulness.

CONCLUSIONS

A radiomics nomogram based on unenhanced CT images and clinical variables showed favorable performance for distinguishing iMAD from LPA. In addition, an efficient unenhanced model can help avoid extra contrast-enhanced scanning and radiation risk.

Validation of three novel clinical prediction tools for primary aldosteronism subtyping

The 20-point clinical prediction SPACE score, the aldosterone-to-lowest potassium ratio (APR), aldosterone concentration (AC) and the AC relative reduction rate after saline infusion test (SIT) have recently been proposed for primary aldosteronism (PA) subtyping prior to adrenal vein sampling (AVS). To validate those claims, we performed a retrospective cross-sectional study that included all patients at our center who had positive SIT to confirm PA and were diagnosed with either bilateral disease (BPA) according to AVS or with lateralized disease (LPA) if biochemically cured after adrenalectomy from November 2004 to the end of 2019. Final diagnoses were used to evaluate the diagnostic performance of proposed clinical prediction tools. Our cohort included 144 patients (40 females), aged 32-72 years (mean 54 years); 59 with LPA and 85 with BPA. The originally suggested SPACE score ≤8 and SPACE score >16 rules yielded about 80% positive predictive value (PPV) for BPA and LPA, respectively. Multivariate analyses with the predictors constituting the SPACE score highlighted post-SIT AC as the most important predictor of PA subtype for our cohort. APR-based tool of <5 for BPA and >15 for LPA yielded about 75% PPV for LPA and BPA. The proposed post-SIT AC <8.79 ng/dL criterion yielded 41% sensitivity and 90% specificity, while the relative post-SIT AC reduction rate of >33.8% criterion yielded 80% sensitivity and 51% specificity for BPA prediction. The application of any of the validated clinical prediction tools to our cohort did not predict the PA subtype with the high diagnostic performance originally reported.

Outcome-Based Decision-Making Algorithm for Treating Patients with Primary Aldosteronism

BACKGROUND

Optimal management of primary aldosteronism (PA) is crucial due to the increased risk of cardiovascular and cerebrovascular diseases. Adrenal venous sampling (AVS) is the gold standard method for determining subtype but is technically challenging and invasive. Some PA patients do not benefit clinically from surgery. We sought to develop an algorithm to improve decision- making before engaging in AVS and surgery in clinical practice.

METHODS

We conducted the ongoing Korean Primary Aldosteronism Study at two tertiary centers. Study A involved PA patients with successful catheterization and a unilateral nodule on computed tomography and aimed to predict unilateral aldosterone-producing adenoma (n=367). Study B involved similar patients who underwent adrenalectomy and aimed to predict postoperative outcome (n=330). In study A, we implemented important feature selection using the least absolute shrinkage and selection operator regression.

RESULTS

We developed a unilateral PA prediction model using logistic regression analysis: lowest serum potassium level ≤3.4 mEq/L, aldosterone-to-renin ratio ≥150, plasma aldosterone concentration ≥30 ng/mL, and body mass index <25 kg/m2 (area under the curve, 0.819; 95% confidence interval, 0.774 to 0.865; sensitivity, 97.6%; specificity, 25.5%). In study B, we identified female, hypertension duration <5 years, anti-hypertension medication <2.5 daily defined dose, and the absence of coronary artery disease as predictors of clinical success, using stepwise logistic regression models (sensitivity, 94.2%; specificity, 49.3%). We validated our algorithm in the independent validation dataset (n=53).

CONCLUSION

We propose this new outcome-driven diagnostic algorithm, simultaneously considering unilateral aldosterone excess and clinical surgical benefits in PA patients.

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.

Recent Development toward the Next Clinical Practice of Primary Aldosteronism: A Literature Review

For the last seven decades, primary aldosteronism (PA) has been gradually recognized as a leading cause of secondary hypertension harboring increased risks of cardiovascular incidents compared to essential hypertension. Clinically, PA consists of two major subtypes, surgically curable and uncurable phenotypes, determined as unilateral or bilateral PA by adrenal venous sampling. In order to further optimize the treatment, surgery or medications, diagnostic procedures from screening to subtype differentiation is indispensable, while in the general clinical practice, the work-up rate is extremely low even in the patients with refractory hypertension because of the time-consuming and labor-intensive nature of the procedures. Therefore, a novel tool to simplify the diagnostic flow has been recently in enormous demand. In this review, we focus on recent progress in the following clinically important topics of PA: prevalence of PA and its subtypes, newly revealed histopathological classification of aldosterone-producing lesions, novel diagnostic biomarkers and prediction scores. More effective strategy to diagnose PA based on better understanding of its epidemiology and pathology should lead to early detection of PA and could decrease the cardiovascular and renal complications of the patients.

Development and Validation of Prediction Models for Subtype Diagnosis of Patients With Primary Aldosteronism

CONTEXT

Primary aldosteronism (PA) comprises unilateral (lateralized [LPA]) and bilateral disease (BPA). The identification of LPA is important to recommend potentially curative adrenalectomy. Adrenal venous sampling (AVS) is considered the gold standard for PA subtyping, but the procedure is available in few referral centers.

OBJECTIVE

To develop prediction models for subtype diagnosis of PA using patient clinical and biochemical characteristics.

DESIGN, PATIENTS AND SETTING

Patients referred to a tertiary hypertension unit. Diagnostic algorithms were built and tested in a training (N = 150) and in an internal validation cohort (N = 65), respectively. The models were validated in an external independent cohort (N = 118).

MAIN OUTCOME MEASURE

Regression analyses and supervised machine learning algorithms were used to develop and validate 2 diagnostic models and a 20-point score to classify patients with PA according to subtype diagnosis.

RESULTS

Six parameters were associated with a diagnosis of LPA (aldosterone at screening and after confirmatory testing, lowest potassium value, presence/absence of nodules, nodule diameter, and computed tomography results) and were included in the diagnostic models. Machine learning algorithms displayed high accuracy at training and internal validation (79.1%-93%), whereas a 20-point score reached an area under the curve of 0.896, and a sensitivity/specificity of 91.7/79.3%. An integrated flowchart correctly addressed 96.3% of patients to surgery and would have avoided AVS in 43.7% of patients. The external validation on an independent cohort confirmed a similar diagnostic performance.

CONCLUSIONS

Diagnostic modelling techniques can be used for subtype diagnosis and guide surgical decision in patients with PA in centers where AVS is unavailable.