Laboratory Testing for Endocrine Hypertension: Current and Future Perspectives

BACKGROUND

Secondary hypertension (SH) is a form of high blood pressure caused by an identifiable underlying condition. Although, it accounts for a small fraction of the overall hypertensive population, detection and management of SH is of utmost importance, because SH phenotypes carry a high cardiovascular risk and can possibly be cured by timely treatment.

CONTENT

This review focuses on the endocrine causes of SH, such as primary aldosteronism, Cushing syndrome, thyroid disease, pheochromocytoma and paraganglioma, acromegaly, and rare monogenic forms. It discusses current biomarkers, analytical methods, and diagnostic strategies, highlighting advantages and limitations of each approach. It also explores the emerging -omics technologies that can provide a comprehensive and multidimensional assessment of SH and its underlying mechanisms.

SUMMARY

Endocrine SH is a heterogeneous and complex condition that requires proper screening and confirmatory tests to avoid diagnostic delays and improve patient outcomes. Careful biomarker interpretation is essential due to potential interferences, variability, and method-dependent differences. Liquid chromatography-tandem mass spectrometry is a superior method for measuring low-concentration hormones and metabolites involved in SH, but it requires expertise. Omics approaches have great potential to identify novel biomarkers, pathways, and targets for SH diagnosis and treatment, especially considering its multifactorial nature.

Machine learning-based clinical decision support using laboratory data

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

Treating Primary Aldosteronism-Induced Hypertension: Novel Approaches and Future Outlooks

Primary aldosteronism (PA) is the most common cause of secondary hypertension and is associated with increased morbidity and mortality when compared with blood pressure-matched cases of primary hypertension. Current limitations in patient care stem from delayed recognition of the condition, limited access to key diagnostic procedures, and lack of a definitive therapy option for nonsurgical candidates. However, several recent advances have the potential to address these barriers to optimal care. From a diagnostic perspective, machine-learning algorithms have shown promise in the prediction of PA subtypes, while the development of noninvasive alternatives to adrenal vein sampling (including molecular positron emission tomography imaging) has made accurate localization of functioning adrenal nodules possible. In parallel, more selective approaches to targeting the causative aldosterone-producing adrenal adenoma/nodule (APA/APN) have emerged with the advent of partial adrenalectomy or precision ablation. Additionally, the development of novel pharmacological agents may help to mitigate off-target effects of aldosterone and improve clinical efficacy and outcomes. Here, we consider how each of these innovations might change our approach to the patient with PA, to allow more tailored investigation and treatment plans, with corresponding improvement in clinical outcomes and resource utilization, for this highly prevalent disorder.

Biochemical Assessment of Pheochromocytoma and Paraganglioma

Pheochromocytoma and paraganglioma (PPGL) require prompt consideration and efficient diagnosis and treatment to minimize associated morbidity and mortality. Once considered, appropriate biochemical testing is key to diagnosis. Advances in understanding catecholamine metabolism have clarified why measurements of the O-methylated catecholamine metabolites rather than the catecholamines themselves are important for effective diagnosis. These metabolites, normetanephrine and metanephrine, produced respectively from norepinephrine and epinephrine, can be measured in plasma or urine, with choice according to available methods or presentation of patients. For patients with signs and symptoms of catecholamine excess, either test will invariably establish the diagnosis, whereas the plasma test provides higher sensitivity than urinary metanephrines for patients screened due to an incidentaloma or genetic predisposition, particularly for small tumors or in patients with an asymptomatic presentation. Additional measurements of plasma methoxytyramine can be important for some tumors, such as paragangliomas, and for surveillance of patients at risk of metastatic disease. Avoidance of false-positive test results is best achieved by plasma measurements with appropriate reference intervals and preanalytical precautions, including sampling blood in the fully supine position. Follow-up of positive results, including optimization of preanalytics for repeat tests or whether to proceed directly to anatomic imaging or confirmatory clonidine tests, depends on the test results, which can also suggest likely size, adrenal vs extra-adrenal location, underlying biology, or even metastatic involvement of a suspected tumor. Modern biochemical testing now makes diagnosis of PPGL relatively simple. Integration of artificial intelligence into the process should make it possible to fine-tune these advances.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

BACKGROUND

Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing.

METHODS

A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations.

RESULTS

The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems.

CONCLUSIONS

A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.