{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:04:40Z","timestamp":1775138680892,"version":"3.50.1"},"reference-count":30,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Hypertension"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Ambulatory blood pressure monitoring is indispensable for diagnosing nocturnal hypertension (NH) among patients with chronic kidney disease, but it is costly and time-consuming. Screening tools for predicting high-risk NH are urgently needed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>A large cohort of 5769 patients with nondialysis chronic kidney disease were enrolled (hospital A: 4565; hospital B: 1204). Patients from hospital A were split 8:2 into training\/internal test sets; hospital B served as the external test set. A total of 1006 patients with at least 2 valid ambulatory blood pressure monitoring recordings were used for secondary validation. We proposed a table-value diffusion model by incorporating generative modeling concepts to map distributions between clinical variables and predict NH. The predicted probabilities for NH were stratified and validated according to patients\u2019 true adverse renal\/cardiovascular prognoses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      Through a rigorous selection process combining univariate ranking and clinical expertise, we selected 7 core predictors: age, body mass index, clinic systolic\/diastolic blood pressure, estimated glomerular filtration rate, hypertension history, and use of non\u2013renin-angiotensin-aldosterone system antihypertensive agents. In the test sets, the 7-variable diffusion model achieved a significantly higher area under the curve than the 7-variable logistic model (internal: 0.870 versus 0.807,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001; external: 0.854 versus 0.792,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001). Secondary validation confirmed the 7-variable diffusion model\u2019s stability (area under the curve=0.869, Cohen kappa index=0.560). Risk stratification was established (high-risk &gt;0.692, intermediate-risk 0.692\u20130.515, and low-risk &lt;0.515). Patients in the high-risk group had a higher incidence of adverse renal\/cardiovascular events than those in the low-risk group.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>We developed a concise 7-variable diffusion model for NH using a table-value diffusion model, supporting targeted ambulatory blood pressure monitoring screening in patients with chronic kidney disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/hypertensionaha.126.26706","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:00:40Z","timestamp":1772100040000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Nocturnal Hypertension in CKD Through Table-Value Diffusion Model"],"prefix":"10.1161","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8919-492X","authenticated-orcid":true,"given":"Xiaoyu","family":"Cai","sequence":"first","affiliation":[{"name":"Division of Nephrology, Department of Medicine, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China (X.C., Z.L., X.J., S.G., C.W.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0635-0549","authenticated-orcid":true,"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China (R.L., X.Z., Y.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3611-2503","authenticated-orcid":true,"given":"Zhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China (X.C., Z.L., X.J., S.G., C.W.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, City University of Hong Kong, Hong Kong, China (S.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China (R.L., X.Z., Y.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinying","family":"Jiang","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China (X.C., Z.L., X.J., S.G., C.W.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengnan","family":"Ge","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China (X.C., Z.L., X.J., S.G., C.W.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China (J.Z., H.P.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Man","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai, China (M.L., C.W.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0220-3616","authenticated-orcid":true,"given":"Hui","family":"Peng","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China (J.Z., H.P.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9004-9569","authenticated-orcid":true,"given":"Yao","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China (R.L., X.Z., Y.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1590-7954","authenticated-orcid":true,"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China (X.C., Z.L., X.J., S.G., C.W.)."},{"name":"Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital Sun Yat-Sen University, Zhuhai, China (M.L., 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