Predictive value of clinical risk factors for bladder dysfunction in Syrian patients with type 2 diabetes mellitus
© 2024. The Author(s)..
Diabetes mellitus (DM) is a prevalent disorder that affects the endocrine and metabolic systems. Among the various complications associated with DM, diabetic bladder dysfunction (DBD) is the most frequently occurring genitourinary complication. The presence of DBD can lead to complications that affect the upper urinary tract, significantly impacting the quality of life for individuals with DM. Therefore, it is crucial to identify early risk factors for DBD and predict its onset. Given the absence of studies involving bladder dysfunction in patients with type 2 diabetes mellitus (T2DM) in Syria, this study aims to examine the risk factors associated with bladder dysfunction in T2DM patients and develop a predictive model to identify DBD early. Patients diagnosed with T2DM were enrolled in six endocrinology centers spread across four Syrian provinces between January 2018 and December 2023. Factors that showed an association with DBD in the bivariate analysis, with a significance level of p < 0.05, were included in a multiple logistic regression analysis. The logistic regression analysis was used to identify independent risk factors and develop a prediction model. The receiver operating characteristic (ROC) curve was used to assess the predictive performance of the identified risk factors and the prediction model for DBD. One hundred and eighty-four patients were included in this study, and they were divided into the DBD group (n = 88) and the non-DBD group (n = 96). Seven variables showed significance in the bivariate analysis. Furthermore, the multiple logistic regression analysis revealed that age (OR [95% CI]: 0.981 [0.614 - 1.337]), p < 0.007; diabetic peripheral neuropathy (DPN) (OR [95% CI]: 1.421 [1.027 - 3.308]), p = 0.03; glycated hemoglobin (HbA1c) (OR [95% CI]: 0.942 [0.821 - 1.141]), p = 0.042; and percentage of monocyte (Mono%) (OR [95% CI]: 1.109 [0.812 - 1.258]), p = 0.031 were independent risk factors for DBD. Analysis of the ROC curve revealed that the area under the curve (AUC) for age, DPN, HbA1c, and Mono were 0.703, 0.541, 0.613, and 0.836, respectively. Age, DPN, HbA1c, and Mono% were risk factors for DBD. The prediction model constructed based on the four risk factors had a good predictive value for predicting the occurrence of DBD.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Scientific reports - 14(2024), 1 vom: 26. März, Seite 7142 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Khadour, Younes A [VerfasserIn] |
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| Links: |
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| Themen: |
Bladder dysfunction |
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| Anmerkungen: |
Date Completed 28.03.2024 Date Revised 29.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-024-57050-w |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Diabetes mellitus (DM) is a prevalent disorder that affects the endocrine and metabolic systems. Among the various complications associated with DM, diabetic bladder dysfunction (DBD) is the most frequently occurring genitourinary complication. The presence of DBD can lead to complications that affect the upper urinary tract, significantly impacting the quality of life for individuals with DM. Therefore, it is crucial to identify early risk factors for DBD and predict its onset. Given the absence of studies involving bladder dysfunction in patients with type 2 diabetes mellitus (T2DM) in Syria, this study aims to examine the risk factors associated with bladder dysfunction in T2DM patients and develop a predictive model to identify DBD early. Patients diagnosed with T2DM were enrolled in six endocrinology centers spread across four Syrian provinces between January 2018 and December 2023. Factors that showed an association with DBD in the bivariate analysis, with a significance level of p < 0.05, were included in a multiple logistic regression analysis. The logistic regression analysis was used to identify independent risk factors and develop a prediction model. The receiver operating characteristic (ROC) curve was used to assess the predictive performance of the identified risk factors and the prediction model for DBD. One hundred and eighty-four patients were included in this study, and they were divided into the DBD group (n = 88) and the non-DBD group (n = 96). Seven variables showed significance in the bivariate analysis. Furthermore, the multiple logistic regression analysis revealed that age (OR [95% CI]: 0.981 [0.614 - 1.337]), p < 0.007; diabetic peripheral neuropathy (DPN) (OR [95% CI]: 1.421 [1.027 - 3.308]), p = 0.03; glycated hemoglobin (HbA1c) (OR [95% CI]: 0.942 [0.821 - 1.141]), p = 0.042; and percentage of monocyte (Mono%) (OR [95% CI]: 1.109 [0.812 - 1.258]), p = 0.031 were independent risk factors for DBD. Analysis of the ROC curve revealed that the area under the curve (AUC) for age, DPN, HbA1c, and Mono were 0.703, 0.541, 0.613, and 0.836, respectively. Age, DPN, HbA1c, and Mono% were risk factors for DBD. The prediction model constructed based on the four risk factors had a good predictive value for predicting the occurrence of DBD | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Khadour, Fater A |e verfasserin |4 aut | |
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