{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T15:16:17Z","timestamp":1778944577913,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T00:00:00Z","timestamp":1726790400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background and Aims:<\/jats:title>\n                    <jats:p>Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of \u22647%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of \u22647%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol\/L), sex, no ketones in urine, baseline albumin (g\/dL), baseline low-density lipoprotein cholesterol (mmol\/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756].<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968241280096","type":"journal-article","created":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T01:58:47Z","timestamp":1726883927000},"page":"1554-1560","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data"],"prefix":"10.1177","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2779-5635","authenticated-orcid":false,"given":"Carsten Wridt","family":"Stoltenberg","sequence":"first","affiliation":[{"name":"Aalborg University, Gistrup, Denmark"},{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0395-3563","authenticated-orcid":false,"given":"Stine","family":"Hangaard","sequence":"additional","affiliation":[{"name":"Aalborg University, Gistrup, Denmark"},{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ole","family":"Hejlesen","sequence":"additional","affiliation":[{"name":"Aalborg University, Gistrup, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2693-0527","authenticated-orcid":false,"given":"Thomas","family":"Kronborg","sequence":"additional","affiliation":[{"name":"Aalborg University, Gistrup, Denmark"},{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Vestergaard","sequence":"additional","affiliation":[{"name":"Steno Diabetes Center North Denmark, Aalborg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6649-8644","authenticated-orcid":false,"given":"Morten Hasselstr\u00f8m","family":"Jensen","sequence":"additional","affiliation":[{"name":"Aalborg University, Gistrup, Denmark"},{"name":"Data Science, Novo Nordisk A\/S, S\u00f8borg, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-46766-9"},{"key":"e_1_3_3_3_2","article-title":"9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes\u20142023","volume":"46","author":"Elsayed NA","year":"2022","unstructured":"Elsayed NA, Aleppo G, Aroda VR, et al. 9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes\u20142023. Diabetes Care. 2022;46(suppl 1):S140-S157.","journal-title":"Diabetes Care"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.2337\/dci22-0034"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc13-0331"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-015-0260-x"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.l5887"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13300-023-01399-0"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eprac.2023.02.001"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12325-018-0819-5"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2020.108053"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc12-2258"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2010.08.006"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1177\/20552076231203879"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/dom.13860"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1177\/19322968221098057"},{"key":"e_1_3_3_17_2","article-title":"Evaluation of clinical prediction models (part 1): from development to external validation","volume":"384","author":"Collins GS","year":"2024","unstructured":"Collins GS, Dhiman P, Ma J, et al. 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