{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T02:36:27Z","timestamp":1777516587164,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clin Nurs Res"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Maintenance hemodialysis is a common alternative therapy for patients with end-stage renal disease (ESRD). We designed a prediction model of hypoproteinemia among the patients based on machine learning algorithms. The \u201chypoproteinemia risk factor data\u201d were obtained, and univariate analysis was used to screen independent risk factors as prediction variables. A total of 468 patients were recruited. The incidence of hypoproteinemia in total was 30.8%. A difference between the hypoproteinemia and non-hypoproteinemia groups was significant in 18 aspects, including age, weight, dialysis duration, and dialysis frequency. In the training set, after hyper-parameter adjustment by k-fold (\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    \u2009=\u20095) cross-validation and grid search, random forest (RF), support vector machine, and logistic regression (LR) prediction models were greater than 0.8. The RF model had the highest value (0.924). The specificities of the LR and RF models were similar (0.846 and 0.839), whereas the RF model had the best accuracy (0.924). The prediction model provided by the results of this study is likely to recognize the characteristics related to hypoproteinemia. The clinical significance of the findings is a prediction of the risk of hypoproteinemia in ESRD, thus helping risk observation to nurses and improving accurate screening, primary prevention, and early intervention.\n                  <\/jats:p>","DOI":"10.1177\/10547738251403950","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T07:58:05Z","timestamp":1765871885000},"page":"27-36","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Hypoproteinemia Among Patients Undergoing Maintenance Hemodialysis: A Development and Validation Study Based on Machine Learning Algorithms"],"prefix":"10.1177","volume":"35","author":[{"given":"Yao","family":"Wang","sequence":"first","affiliation":[{"name":"The First Bethune Hospital of Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8365-7541","authenticated-orcid":false,"given":"Jingshu","family":"Yang","sequence":"additional","affiliation":[{"name":"The First Bethune Hospital of Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Wang","sequence":"additional","affiliation":[{"name":"The First Bethune Hospital of Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiru","family":"Zhang","sequence":"additional","affiliation":[{"name":"The First Bethune Hospital of Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotian","family":"Duan","sequence":"additional","affiliation":[{"name":"The First Bethune Hospital of Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songyu","family":"Wang","sequence":"additional","affiliation":[{"name":"The First Bethune Hospital of Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongshi","family":"Cao","sequence":"additional","affiliation":[{"name":"The First Bethune Hospital of Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.jrn.2019.06.003"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/imj.14962"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfz005"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.2147\/CIA.S349978"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1159\/000504955"},{"issue":"57","key":"e_1_3_3_7_1","first-page":"863","article-title":"The shared decision-making experience of elderly patients with end-stage renal disease: A systematic review and meta synthesis of qualitative studies","volume":"7","author":"Cui J.","year":"2022","unstructured":"Cui J., Xiao Q., Ke J., Yan J., Zeng T. (2022). The shared decision-making experience of elderly patients with end-stage renal disease: A systematic review and meta synthesis of qualitative studies. Chinese Journal of Nursing, 7(57), 863\u2013871.","journal-title":"Chinese Journal of Nursing"},{"issue":"18","key":"e_1_3_3_8_1","first-page":"122","article-title":"Determination of sample size in logistic regression analysis","volume":"2","author":"Gao Y. X.","year":"2018","unstructured":"Gao Y. X., Zhang J. X. (2018). Determination of sample size in logistic regression analysis. The Journal of Evidence-Based Medicine, 2(18), 122\u2013124.","journal-title":"The Journal of Evidence-Based Medicine"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/1744-9987.13753"},{"issue":"40","key":"e_1_3_3_10_1","first-page":"161","article-title":"Relationship between microinflammatory state related-factors and hypoalbuminemia in maintenance hemodialysis patients","volume":"2","author":"Jin J.","year":"2006","unstructured":"Jin J., Zheng X., Zhang M., Ye H., Lin Q., Li P. (2006). Relationship between microinflammatory state related-factors and hypoalbuminemia in maintenance hemodialysis patients. Journal of Fujian Medical University, 2(40), 161\u2013163.","journal-title":"Journal of Fujian Medical University"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.14712\/fb2019065050212"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11255-012-0170-8"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/nep.14025"},{"issue":"7","key":"e_1_3_3_14_1","first-page":"586","article-title":"Relationship between serum albumin level and prognosis of maintenance hemodialysis patients","volume":"6","author":"Li J.","year":"2012","unstructured":"Li J., Wei Y., Wen Q., Wu C., Zeng Q., Tang N. (2012). Relationship between serum albumin level and prognosis of maintenance hemodialysis patients. Internal Medicine, 6(7), 586\u2013588.","journal-title":"Internal Medicine"},{"issue":"21","key":"e_1_3_3_15_1","first-page":"478","article-title":"Predictive value of bioelectrical impedance phase angle for hypoalbuminemia in maintenance hemodialysis patients","volume":"7","author":"Liang W. F.","year":"2022","unstructured":"Liang W. F., Liu Y., Xu S.-L., Chen W.-X., Tan R.-S., Liu Y., Zhong X.-S. (2022). Predictive value of bioelectrical impedance phase angle for hypoalbuminemia in maintenance hemodialysis patients. Chinese Journal of Blood Purification, 7(21), 478\u2013482.","journal-title":"Chinese Journal of Blood Purification"},{"issue":"20","key":"e_1_3_3_16_1","first-page":"373","article-title":"The value of albumin to C-reactive protein ratio, red blood cell distribution width, and serum uric acid in evaluating the prognosis of maintenance hemodialysis patients","volume":"6","author":"Ma L.","year":"2021","unstructured":"Ma L., Feng S. D., Zhang Y. (2021). The value of albumin to C-reactive protein ratio, red blood cell distribution width, and serum uric acid in evaluating the prognosis of maintenance hemodialysis patients. Chinese Journal of Blood Purification, 6(20), 373\u2013377.","journal-title":"Chinese Journal of Blood Purification"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.09410721"},{"issue":"5","key":"e_1_3_3_18_1","article-title":"Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review","volume":"159","author":"Nwanosike E. M.","year":"2022","unstructured":"Nwanosike E. M., Conway B. R., Merchant H. A., Hasan S. S. (2022). Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. 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Chinese Journal of Postgraduate Medicine, 44(5), 411\u2013415.","journal-title":"Chinese Journal of Postgraduate Medicine"},{"issue":"4","key":"e_1_3_3_24_1","article-title":"Clinical prediction of heart failure in hemodialysis patients: Based on the extreme gradient boosting method","volume":"13","author":"Wang Y.","year":"2022","unstructured":"Wang Y., Miao X., Xiao G., Huang C., Sun J., Wang Y., Li P., You X. (2022). Clinical prediction of heart failure in hemodialysis patients: Based on the extreme gradient boosting method. Frontiers in Genetics, 13(4), Article 889378.","journal-title":"Frontiers in Genetics"},{"issue":"4","key":"e_1_3_3_25_1","first-page":"1","article-title":"Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure","volume":"15","author":"Zhang H.","year":"2023","unstructured":"Zhang H., Wang L. C., Chaudhuri S., Pickering A., Usvyat L., Larkin J., Waguespack P., Kuang Z., Kooman J. P., Maddux F. W., Kotanko P. (2023). Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure. Nephrology Dialysis Transplantation, 15(4), 1\u201328.","journal-title":"Nephrology Dialysis Transplantation"}],"container-title":["Clinical Nursing Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10547738251403950","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/10547738251403950","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10547738251403950","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:59:11Z","timestamp":1777391951000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/10547738251403950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1177\/10547738251403950"],"URL":"https:\/\/doi.org\/10.1177\/10547738251403950","relation":{},"ISSN":["1054-7738","1552-3799"],"issn-type":[{"value":"1054-7738","type":"print"},{"value":"1552-3799","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,15]]}}}