{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:50:06Z","timestamp":1790002206342,"version":"4.0.1"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2022,7,25]]},"abstract":"<jats:p>Objective. A predictive model was established based on logistic regression and XGBoost algorithm to investigate the factors related to postoperative hypocalcemia in patients with secondary hyperparathyroidism (SHPT). Methods. A total of 60 SHPT patients who underwent parathyroidectomy (PTX) in our hospital were retrospectively enrolled. All patients were randomly divided into a training set (<jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>n<\/a:mi>\n                        <a:mo>=<\/a:mo>\n                        <a:mn>42<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula>) and a test set (<jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>n<\/c:mi>\n                        <c:mo>=<\/c:mo>\n                        <c:mn>18<\/c:mn>\n                     <\/c:math>\n                  <\/jats:inline-formula>). The clinical data of the patients were analyzed, including gender, age, dialysis time, body mass, and several preoperative biochemical indicators. The multivariate logistic regression and XGBoost algorithm models were used to analyze the independent risk factors for severe postoperative hypocalcemia (SH). The forecasting efficiency of the two prediction models is analyzed. Results. Multivariate logistic regression analysis showed that body mass (<jats:inline-formula>\n                     <e:math xmlns:e=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\">\n                        <e:mtext>OR<\/e:mtext>\n                        <e:mo>=<\/e:mo>\n                        <e:mn>1.203<\/e:mn>\n                     <\/e:math>\n                  <\/jats:inline-formula>, <jats:inline-formula>\n                     <g:math xmlns:g=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\">\n                        <g:mi>P<\/g:mi>\n                        <g:mo>=<\/g:mo>\n                        <g:mn>0.032<\/g:mn>\n                     <\/g:math>\n                  <\/jats:inline-formula>), age (<jats:inline-formula>\n                     <i:math xmlns:i=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\">\n                        <i:mtext>OR<\/i:mtext>\n                        <i:mo>=<\/i:mo>\n                        <i:mn>1.214<\/i:mn>\n                     <\/i:math>\n                  <\/jats:inline-formula>, <jats:inline-formula>\n                     <k:math xmlns:k=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\">\n                        <k:mi>P<\/k:mi>\n                        <k:mo>=<\/k:mo>\n                        <k:mn>0.035<\/k:mn>\n                     <\/k:math>\n                  <\/jats:inline-formula>), preoperative PTH (<jats:inline-formula>\n                     <m:math xmlns:m=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\">\n                        <m:mtext>OR<\/m:mtext>\n                        <m:mo>=<\/m:mo>\n                        <m:mn>1.026<\/m:mn>\n                     <\/m:math>\n                  <\/jats:inline-formula>, <jats:inline-formula>\n                     <o:math xmlns:o=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M8\">\n                        <o:mi>P<\/o:mi>\n                        <o:mo>=<\/o:mo>\n                        <o:mn>0.043<\/o:mn>\n                     <\/o:math>\n                  <\/jats:inline-formula>), preoperative Ca (<jats:inline-formula>\n                     <q:math xmlns:q=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M9\">\n                        <q:mtext>OR<\/q:mtext>\n                        <q:mo>=<\/q:mo>\n                        <q:mn>1.062<\/q:mn>\n                     <\/q:math>\n                  <\/jats:inline-formula>, <jats:inline-formula>\n                     <s:math xmlns:s=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M10\">\n                        <s:mi>P<\/s:mi>\n                        <s:mo>=<\/s:mo>\n                        <s:mn>0.025<\/s:mn>\n                     <\/s:math>\n                  <\/jats:inline-formula>), and preoperative ALP (<jats:inline-formula>\n                     <u:math xmlns:u=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M11\">\n                        <u:mtext>OR<\/u:mtext>\n                        <u:mo>=<\/u:mo>\n                        <u:mn>1.031<\/u:mn>\n                     <\/u:math>\n                  <\/jats:inline-formula>, <jats:inline-formula>\n                     <w:math xmlns:w=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M12\">\n                        <w:mi>P<\/w:mi>\n                        <w:mo>=<\/w:mo>\n                        <w:mn>0.027<\/w:mn>\n                     <\/w:math>\n                  <\/jats:inline-formula>) were positively correlated with postoperative SH. The top three important features of XGBoost algorithm prediction model were preoperative Ca, preoperative PTH, and preoperative ALP. The area under the curve of the logistic regression and XGBoost algorithm model in the test set was 0.734 (95% CI: 0.595~0.872) and 0.827 (95% CI: 0.722~0.932), respectively. Conclusion. The predictive models based on the logistic regression and XGBoost algorithm model can predict the occurrence of postoperative SH.<\/jats:p>","DOI":"10.1155\/2022\/8752826","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T20:50:07Z","timestamp":1658782207000},"page":"1-7","source":"Crossref","is-referenced-by-count":11,"title":["Prediction Model of Postoperative Severe Hypocalcemia in Patients with Secondary Hyperparathyroidism Based on Logistic Regression and XGBoost Algorithm"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3417-1097","authenticated-orcid":true,"given":"Chao","family":"Ding","sequence":"first","affiliation":[{"name":"Hemodialysis Room, Department of Renal Endocrinology, Anhui Lujiang People\u2019s Hospital, Lujiang 231501, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9877-751X","authenticated-orcid":true,"given":"Yuwen","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Renal Endocrinology, Anhui Lujiang People\u2019s Hospital, Lujiang 231501, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3456-0500","authenticated-orcid":true,"given":"Qinqin","family":"Mo","sequence":"additional","affiliation":[{"name":"Hemodialysis Room, Department of Renal Endocrinology, Anhui Lujiang People\u2019s Hospital, Lujiang 231501, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8178-1923","authenticated-orcid":true,"given":"Jin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Geriatric Medicine, The First People\u2019s Hospital in Hefei, Hefei 230061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1159\/000491041"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.10390917"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.22636.1"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1111\/1744-9987.12772"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.14745"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1111\/sdi.12734"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1097\/TP.0000000000003653"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1097\/MNH.0000000000000365"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1111\/1744-9987.13553"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.08.079"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.numecd.2020.12.029"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/5584_2021_628"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1111\/nep.13854"},{"issue":"12, article e039676","key":"14","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2020-039676","article-title":"Comparison of arima model and xgboost model for prediction of human brucellosis in mainland china: a time-series study","volume":"10","author":"M. 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