Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis
ObjectivesTo explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction.MethodsWe retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospit...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2022.967249/full |
| _version_ | 1818483631810674688 |
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| author | Ruqian Fu Ruqian Fu Manqiong Yang Zhihui Li Zhihui Li Zhijuan Kang Zhijuan Kang Mai Xun Ying Wang Manzhi Wang Xiangyun Wang |
| author_facet | Ruqian Fu Ruqian Fu Manqiong Yang Zhihui Li Zhihui Li Zhijuan Kang Zhijuan Kang Mai Xun Ying Wang Manzhi Wang Xiangyun Wang |
| author_sort | Ruqian Fu |
| collection | DOAJ |
| description | ObjectivesTo explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction.MethodsWe retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram.ResultsAge, persistent cutaneous purpura, erythrocyte distribution width, complement C3, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15–82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively.ConclusionThe prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability.Clinical trial registration:www.chictr.org.cn, identifier ChiCTR2000033435. |
| first_indexed | 2024-12-10T15:44:27Z |
| format | Article |
| id | doaj.art-e4f834c8b5b24f6f95a2e1a518098a11 |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-12-10T15:44:27Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-e4f834c8b5b24f6f95a2e1a518098a112022-12-22T01:43:00ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602022-08-011010.3389/fped.2022.967249967249Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitisRuqian Fu0Ruqian Fu1Manqiong Yang2Zhihui Li3Zhihui Li4Zhijuan Kang5Zhijuan Kang6Mai Xun7Ying Wang8Manzhi Wang9Xiangyun Wang10Academy of Pediatrics of University of South China, Changsha, ChinaDepartment of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, ChinaDepartment of Pediatrics, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, ChinaAcademy of Pediatrics of University of South China, Changsha, ChinaDepartment of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, ChinaAcademy of Pediatrics of University of South China, Changsha, ChinaDepartment of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, ChinaDepartment of Nephrology and Rheumatology of Hunan Children's Hospital, Changsha, ChinaDepartment of Pediatrics of Changsha Central Hospital, Changsha, ChinaDepartment of Pediatrics of Changsha Central Hospital, Changsha, ChinaDepartment of Pediatrics of Changsha First People's Hospital, Changsha, ChinaObjectivesTo explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction.MethodsWe retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram.ResultsAge, persistent cutaneous purpura, erythrocyte distribution width, complement C3, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15–82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively.ConclusionThe prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability.Clinical trial registration:www.chictr.org.cn, identifier ChiCTR2000033435.https://www.frontiersin.org/articles/10.3389/fped.2022.967249/fullchildrenimmunoglobulin vasculitisrenal damageclinical predictive modelnomogram |
| spellingShingle | Ruqian Fu Ruqian Fu Manqiong Yang Zhihui Li Zhihui Li Zhijuan Kang Zhijuan Kang Mai Xun Ying Wang Manzhi Wang Xiangyun Wang Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis Frontiers in Pediatrics children immunoglobulin vasculitis renal damage clinical predictive model nomogram |
| title | Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis |
| title_full | Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis |
| title_fullStr | Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis |
| title_full_unstemmed | Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis |
| title_short | Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis |
| title_sort | risk assessment and prediction model of renal damage in childhood immunoglobulin a vasculitis |
| topic | children immunoglobulin vasculitis renal damage clinical predictive model nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fped.2022.967249/full |
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