Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes
BackgroundKetosis-prone type 2 diabetes (KPD), as a unique emerging clinical entity, often has no clear inducement or obvious clinical symptoms at the onset of the disease. Failure to determine ketosis in time may lead to more serious consequences and even death. Therefore, our study aimed to develo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1235048/full |
| _version_ | 1797671756173934592 |
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| author | Rui Min Yiqin Liao Bocheng Peng |
| author_facet | Rui Min Yiqin Liao Bocheng Peng |
| author_sort | Rui Min |
| collection | DOAJ |
| description | BackgroundKetosis-prone type 2 diabetes (KPD), as a unique emerging clinical entity, often has no clear inducement or obvious clinical symptoms at the onset of the disease. Failure to determine ketosis in time may lead to more serious consequences and even death. Therefore, our study aimed to develop and validate a novel nomogram to predict KPD.MethodsIn this retrospective study, clinical data of a total of 398 newly diagnosed type 2 diabetes in our hospital who met our research standards with an average age of 48.75 ± 13.86 years years old from January 2019 to December 2022 were collected. According to the occurrence of ketosis, there were divided into T2DM groups(228 cases)with an average age of 52.19 ± 12.97 years, of whom 69.74% were male and KPD groups (170cases)with an average age of 44.13 ± 13.72 years, of whom males account for 80.59%. Univariate and multivariate logistic regression analysis was performed to identify the independent influencing factors of KPD and then a novel prediction nomogram model was established based on these independent predictors visually by using R4.3. Verification and evaluation of predictive model performance comprised receiver-operating characteristic (ROC) curve, corrected calibration curve, and clinical decision curve (DCA).Results4 primary independent predict factors of KPD were identified by univariate and multivariate logistic regression analysis and entered into the nomogram including age, family history, HbA1c and FFA. The model incorporating these 4 predict factors displayed good discrimination to predict KPD with the area under the ROC curve (AUC) of 0.945. The corrected calibration curve of the nomogram showed good fitting ability with an average absolute error =0.006 < 0.05, indicating a good accuracy. The decision analysis curve (DCA) demonstrated that when the risk threshold was between 5% and 99%, the nomogram model was more practical and accurate.ConclusionIn our novel prediction nomogram model, we found that age, family history, HbA1c and FFA were the independent predict factors of KPD. The proposed nomogram built by these 4 predictors was well developed and exhibited powerful predictive performance for KPD with high discrimination, good accuracy, and potential clinical applicability, which may be a useful tool for early screening and identification of high-risk population of KPD and therefore help clinicians in making customized treatment strategy. |
| first_indexed | 2024-03-11T21:20:09Z |
| format | Article |
| id | doaj.art-b25868509ad1464aaa41a44ffc1d4509 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-11T21:20:09Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-b25868509ad1464aaa41a44ffc1d45092023-09-28T09:29:10ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-09-011410.3389/fendo.2023.12350481235048Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetesRui Min0Yiqin Liao1Bocheng Peng2Department of Geriatrics, Wuhan Fourth Hospital, Wuhan, Hubei, ChinaDepartment of Thyroid and Breast Surgery, Xianning Central Hospital, Xianning, Hubei, ChinaDepartment of Pain, Wuhan Fourth Hospital, Wuhan, Hubei, ChinaBackgroundKetosis-prone type 2 diabetes (KPD), as a unique emerging clinical entity, often has no clear inducement or obvious clinical symptoms at the onset of the disease. Failure to determine ketosis in time may lead to more serious consequences and even death. Therefore, our study aimed to develop and validate a novel nomogram to predict KPD.MethodsIn this retrospective study, clinical data of a total of 398 newly diagnosed type 2 diabetes in our hospital who met our research standards with an average age of 48.75 ± 13.86 years years old from January 2019 to December 2022 were collected. According to the occurrence of ketosis, there were divided into T2DM groups(228 cases)with an average age of 52.19 ± 12.97 years, of whom 69.74% were male and KPD groups (170cases)with an average age of 44.13 ± 13.72 years, of whom males account for 80.59%. Univariate and multivariate logistic regression analysis was performed to identify the independent influencing factors of KPD and then a novel prediction nomogram model was established based on these independent predictors visually by using R4.3. Verification and evaluation of predictive model performance comprised receiver-operating characteristic (ROC) curve, corrected calibration curve, and clinical decision curve (DCA).Results4 primary independent predict factors of KPD were identified by univariate and multivariate logistic regression analysis and entered into the nomogram including age, family history, HbA1c and FFA. The model incorporating these 4 predict factors displayed good discrimination to predict KPD with the area under the ROC curve (AUC) of 0.945. The corrected calibration curve of the nomogram showed good fitting ability with an average absolute error =0.006 < 0.05, indicating a good accuracy. The decision analysis curve (DCA) demonstrated that when the risk threshold was between 5% and 99%, the nomogram model was more practical and accurate.ConclusionIn our novel prediction nomogram model, we found that age, family history, HbA1c and FFA were the independent predict factors of KPD. The proposed nomogram built by these 4 predictors was well developed and exhibited powerful predictive performance for KPD with high discrimination, good accuracy, and potential clinical applicability, which may be a useful tool for early screening and identification of high-risk population of KPD and therefore help clinicians in making customized treatment strategy.https://www.frontiersin.org/articles/10.3389/fendo.2023.1235048/fullketosis-prone type 2 diabetesnomogram modelpredictionrisk factorsglycosylated hemoglobin A1cfree fatty acid |
| spellingShingle | Rui Min Yiqin Liao Bocheng Peng Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes Frontiers in Endocrinology ketosis-prone type 2 diabetes nomogram model prediction risk factors glycosylated hemoglobin A1c free fatty acid |
| title | Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes |
| title_full | Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes |
| title_fullStr | Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes |
| title_full_unstemmed | Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes |
| title_short | Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes |
| title_sort | development and validation of a novel nomogram for prediction of ketosis prone type 2 diabetes |
| topic | ketosis-prone type 2 diabetes nomogram model prediction risk factors glycosylated hemoglobin A1c free fatty acid |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1235048/full |
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