{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:13:07Z","timestamp":1787026387156,"version":"build-2736575974"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The original data were extracted from the public database \u201cDryad\u201d. 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2022.1005934","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:14:37Z","timestamp":1669162477000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A new machine learning-based prediction model for subtype diagnosis in primary aldosteronism"],"prefix":"10.3389","volume":"13","author":[{"given":"Shaomin","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing\u2019an","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luhong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingzhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoyong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcm.2021.03.005","article-title":"Primary aldosteronism","volume":"32","author":"Funder","year":"2022","journal-title":"Trends Cardiovasc Med"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0043-122602","article-title":"Subtyping of patients with primary aldosteronism: An update","volume":"49","author":"Lenders","year":"2017","journal-title":"Hormone Metab Res = Hormon- und Stoffwechselforschung = Hormones Metabolisme"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2020.02.052","article-title":"Primary aldosteronism in patients in China with recently detected hypertension","volume":"75","author":"Xu","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"B4","doi-asserted-by":"publisher","first-page":":848","DOI":"10.3390\/ijms18040848","article-title":"Subtype diagnosis of primary aldosteronism: Is adrenal vein sampling always necessary","volume":"18","author":"Buffolo","year":"2017","journal-title":"Int J Mol Sci"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2015-4061","article-title":"The management of primary aldosteronism: Case detection, diagnosis, and treatment: An endocrine society clinical practice guideline","volume":"101","author":"Funder","year":"2016","journal-title":"J Clin Endocrinol Metab"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/s2213-8587(21)00210-2","article-title":"Diagnosis and treatment of primary aldosteronism","volume":"9","author":"Reincke","year":"2021","journal-title":"Lancet Diabetes Endocrinology"},{"key":"B7","doi-asserted-by":"publisher","first-page":":dgz092","DOI":"10.1210\/clinem\/dgz092","article-title":"Aldosterone reduction rate after saline infusion test may be a novel prediction in patients with primary aldosteronism","volume":"105","author":"Nagano","year":"2020","journal-title":"J Clin Endocrinol Metab"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.14051","article-title":"Trends and focus of machine learning applications for health research","volume":"2","author":"Beaulieu-Jones","year":"2019","journal-title":"JAMA Network Open"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2019.4917","article-title":"Artificial intelligence and surgical decision-making","volume":"155","author":"Loftus","year":"2020","journal-title":"JAMA Surgery"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1161\/hypertensionaha.121.17444","article-title":"Clinical score and machine learning-based model to predict diagnosis of primary aldosteronism in arterial hypertension","volume":"78","author":"Buffolo","year":"2021","journal-title":"Hypertension"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1530\/eje-20-0656","article-title":"Prediction of hyperaldosteronism subtypes when adrenal vein sampling is unilaterally successful","volume":"183","author":"Burrello","year":"2020","journal-title":"Eur J Endocrinol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.16209","article-title":"Use of steroid profiling combined with machine learning for identification and subtype classification in primary aldosteronism","volume":"3","author":"Eisenhofer","year":"2020","journal-title":"JAMA Network Open"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ando.2016.01.008","article-title":"SFE\/SFHTA\/AFCE consensus on primary aldosteronism, part 4: Subtype diagnosis","volume":"77","author":"Bardet","year":"2016","journal-title":"Annales d\u2019Endocrinologie"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1161\/hypertensionaha.117.10197","article-title":"Confirmatory tests for the diagnosis of primary aldosteronism: A prospective diagnostic accuracy study","volume":"71","author":"Song","year":"2018","journal-title":"Hypertension (Dallas Tex: 1979)"},{"key":"B15","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/s2213-8587(17)30319-4","article-title":"Cardiovascular events and target organ damage in primary aldosteronism compared with essential hypertension: a systematic review and meta-analysis","volume":"6","author":"Monticone","year":"2018","journal-title":"Lancet Diabetes Endocrinology"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-038489","article-title":"Diagnostic accuracy of adrenal imaging for subtype diagnosis in primary aldosteronism: systematic review and meta-analysis","volume":"10","author":"Zhou","year":"2020","journal-title":"BMJ Open"},{"key":"B17","doi-asserted-by":"publisher","first-page":":dgaa379","DOI":"10.1210\/clinem\/dgaa379","article-title":"Development and validation of prediction models for subtype diagnosis of patients with primary aldosteronism","volume":"105","author":"Burrello","year":"2020","journal-title":"JMIR Med Informatics"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2011-2384","article-title":"18-hydroxycorticosterone, 18-hydroxycortisol, and 18-oxocortisol in the diagnosis of primary aldosteronism and its subtypes","volume":"97","author":"Mulatero","year":"2012","journal-title":"J Clin Endocrinol Metab"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1161\/hypertensionaha.114.04453","article-title":"Measurement of peripheral plasma 18-oxocortisol can discriminate unilateral adenoma from bilateral diseases in patients with primary aldosteronism","volume":"65","author":"Satoh","year":"2015","journal-title":"Hypertension (Dallas Tex: 1979)"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2011-0573","article-title":"Significance of adrenocorticotropin stimulation test in the diagnosis of an aldosterone-producing adenoma","volume":"96","author":"Sonoyama","year":"2011","journal-title":"J Clin Endocrinol Metab"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2011-1537","article-title":"Evaluation of the sensitivity and specificity of (11)C-metomidate positron emission tomography (PET)-CT for lateralizing aldosterone secretion by conn\u2019s adenomas","volume":"97","author":"Burton","year":"2012","journal-title":"J Clin Endocrinol Metab"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2012-1917","article-title":"A clinical prediction score to diagnose unilateral primary aldosteronism","volume":"97","author":"K\u00fcpers","year":"2012","journal-title":"J Clin Endocrinol Metab"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1038\/jhh.2014.20","article-title":"A subtype prediction score for primary aldosteronism","volume":"28","author":"Nanba","year":"2014","journal-title":"J Hum Hypertension"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1111\/cen.13278","article-title":"Scoring system for the diagnosis of bilateral primary aldosteronism in the outpatient setting before adrenal venous sampling","volume":"86","author":"Kobayashi","year":"2017","journal-title":"Clin Endocrinology"},{"key":"B25","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1507\/endocrj.EJ20-0395","article-title":"Computed tomography combined with confirmatory tests for the diagnosis of aldosterone-producing adenoma","volume":"68","author":"Chen","year":"2021","journal-title":"Endocrine J"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.634879","article-title":"A clinical-radiomic nomogram based on unenhanced computed tomography for predicting the risk of aldosterone-producing adenoma","volume":"11","author":"He","year":"2021","journal-title":"Front Oncol"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1093\/ajh\/hpab195","article-title":"External validation of clinical prediction models in unilateral primary aldosteronism","volume":"35","author":"Sam","year":"2022","journal-title":"Am J Hypertens"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1007\/s11906-009-0038-1","article-title":"Differential diagnosis of primary aldosteronism subtypes","volume":"11","author":"Mulatero","year":"2009","journal-title":"Curr Hypertension Rep"},{"key":"B29","doi-asserted-by":"publisher","first-page":"5781","DOI":"10.1038\/s41598-022-09706-8","article-title":"Machine learning-based models for predicting clinical outcomes after surgery in unilateral primary aldosteronism","volume":"12","author":"Kaneko","year":"2022","journal-title":"Hypertension (Dallas Tex: 1979)"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2020.06.023","article-title":"Minimally invasive partial versus total adrenalectomy for the treatment of primary aldosteronism: Results of a multicenter series according to the PASO criteria","volume":"7","author":"Anceschi","year":"2021","journal-title":"Eur Urol Focus"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2018.07.030","article-title":"Robot-assisted partial adrenalectomy for the treatment of conn\u2019s syndrome: Surgical technique, and perioperative and functional outcomes","volume":"75","author":"Simone","year":"2019","journal-title":"Eur Urology"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2022.1005934\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:14:40Z","timestamp":1669162480000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2022.1005934\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,23]]},"references-count":31,"alternative-id":["10.3389\/fendo.2022.1005934"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2022.1005934","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,23]]},"article-number":"1005934"}}