Establishment and validation a relapse prediction model for bipolar disorder
BackgroundThe recurrence rate of bipolar disorder (BD) is relatively high. Assessing the risk of relapse in patients with BD can assist in identifying populations at high risk for recurrence, and early feasible interventions can improve patient’ prognoses. Therefore, it is important to establish and...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1500892/full |
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| author | Xiaoqian Zhang Xiaoqian Zhang Xiaoqian Zhang Xiaoqian Zhang Minghao Wu Daojin Wang Long Wang Long Wang Long Wang Wen Xie Wen Xie Wen Xie |
| author_facet | Xiaoqian Zhang Xiaoqian Zhang Xiaoqian Zhang Xiaoqian Zhang Minghao Wu Daojin Wang Long Wang Long Wang Long Wang Wen Xie Wen Xie Wen Xie |
| author_sort | Xiaoqian Zhang |
| collection | DOAJ |
| description | BackgroundThe recurrence rate of bipolar disorder (BD) is relatively high. Assessing the risk of relapse in patients with BD can assist in identifying populations at high risk for recurrence, and early feasible interventions can improve patient’ prognoses. Therefore, it is important to establish and validate predictive models for relapse risk in patients with BD.MethodWe used 303 patients with BD admitted to the Anhui Mental Health Center as a retrospective training cohort and 81 patients from the Wuhu Fourth People’s Hospital as an external validation cohort. We collected a multidimensional assessment of the characteristics of patients eligible for enrollment, including general demographic characteristics, medical history, treatment, and assessment of selected scales. At the same time, they were followed up for 1 year after reaching the recovery standard after treatment. Depending on whether their symptoms returned within a year, patients with BD were divided into recurrent and non-recurrent groups. Recurrence risk factors for BD were selected using univariate and binary logistic regression analyses based on the clinical data of the patients and other pertinent information. A nomogram model was developed to predict the incidence of BD relapse. To further assess the model fit and dependability, calibration curves, working curves of subject attributes, and decision curves were also employed.ResultA total of 384 patients with BD were enrolled in this study, of whom 250(65.1%) had non-recurrent episodes and 134(34.9%) had recurrent episodes. Of these, 96 (31.7%) had relapses at the Anhui Mental Health Centre and 38 (46.9%) at the Fourth People’s Hospital of Wuhu City. According to the results of univariate and multivariate logistic regression analyses, the number of prior episodes (odds ratio [OR]: 1.38, 95% confidence interval [CI]: 1.179–1.615), Social Disability Screening Schedule (SDSS) score (OR: 1.303, 95% CI: 1.027-1.652), Pittsburgh Sleep Quality Index (PSQI) (OR: 1.476, 95% CI: 1.29-1.689), Number of visits(OR: 0.768, 95% CI: 0.684-0.863), suicidal behaviors (OR: 5.54, 95% CI: 1.818-16.881) and the electroconvulsive therapy (ECT) (OR: 0.382, 95% CI: 0.156-0.94) were independent risk factors for relapse in patients with BD. An analysis of the receiver operating characteristic curve, calibration curve, and clinical decision curve further revealed that the predictive efficiency and degree of fit between the predicted value of the nomogram and the actual observed value were better.ConclusionThis study found that the number of previous episodes, SDSS score, PSQI score and suicidal behaviors were independent risk factors for relapse of BD, while the number of visits and ECT were protective factor. Based on these factors, we developed and validated a nomogram for predicting relapse in patients with BD; that has clinical reference values. |
| format | Article |
| id | doaj-art-98710c6c59e04e84bf52e25208edc37b |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj-art-98710c6c59e04e84bf52e25208edc37b2025-01-16T06:10:18ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-01-011510.3389/fpsyt.2024.15008921500892Establishment and validation a relapse prediction model for bipolar disorderXiaoqian Zhang0Xiaoqian Zhang1Xiaoqian Zhang2Xiaoqian Zhang3Minghao Wu4Daojin Wang5Long Wang6Long Wang7Long Wang8Wen Xie9Wen Xie10Wen Xie11School of Mental Health and Psychological Science, Anhui Medical University, Hefei, ChinaDepartment of Mood Disorder, Affiliated Psychological Hospital of Anhui Medical University, Hefei, ChinaDepartment of Mood Disorder, Hefei Fourth People’s Hospital, Hefei, ChinaDepartment of Mood Disorder, Anhui Mental Health Center, Hefei, ChinaDepartment of Psychiatry, Wuhu Hospital of Beijing Anding Hospital, Capital Medical University (Wuhu Fourth People’s Hospital), Wuhu, ChinaDepartment of Psychiatry, Wuhu Hospital of Beijing Anding Hospital, Capital Medical University (Wuhu Fourth People’s Hospital), Wuhu, ChinaDepartment of Mood Disorder, Affiliated Psychological Hospital of Anhui Medical University, Hefei, ChinaDepartment of Mood Disorder, Hefei Fourth People’s Hospital, Hefei, ChinaDepartment of Mood Disorder, Anhui Mental Health Center, Hefei, ChinaDepartment of Mood Disorder, Affiliated Psychological Hospital of Anhui Medical University, Hefei, ChinaDepartment of Mood Disorder, Hefei Fourth People’s Hospital, Hefei, ChinaDepartment of Mood Disorder, Anhui Mental Health Center, Hefei, ChinaBackgroundThe recurrence rate of bipolar disorder (BD) is relatively high. Assessing the risk of relapse in patients with BD can assist in identifying populations at high risk for recurrence, and early feasible interventions can improve patient’ prognoses. Therefore, it is important to establish and validate predictive models for relapse risk in patients with BD.MethodWe used 303 patients with BD admitted to the Anhui Mental Health Center as a retrospective training cohort and 81 patients from the Wuhu Fourth People’s Hospital as an external validation cohort. We collected a multidimensional assessment of the characteristics of patients eligible for enrollment, including general demographic characteristics, medical history, treatment, and assessment of selected scales. At the same time, they were followed up for 1 year after reaching the recovery standard after treatment. Depending on whether their symptoms returned within a year, patients with BD were divided into recurrent and non-recurrent groups. Recurrence risk factors for BD were selected using univariate and binary logistic regression analyses based on the clinical data of the patients and other pertinent information. A nomogram model was developed to predict the incidence of BD relapse. To further assess the model fit and dependability, calibration curves, working curves of subject attributes, and decision curves were also employed.ResultA total of 384 patients with BD were enrolled in this study, of whom 250(65.1%) had non-recurrent episodes and 134(34.9%) had recurrent episodes. Of these, 96 (31.7%) had relapses at the Anhui Mental Health Centre and 38 (46.9%) at the Fourth People’s Hospital of Wuhu City. According to the results of univariate and multivariate logistic regression analyses, the number of prior episodes (odds ratio [OR]: 1.38, 95% confidence interval [CI]: 1.179–1.615), Social Disability Screening Schedule (SDSS) score (OR: 1.303, 95% CI: 1.027-1.652), Pittsburgh Sleep Quality Index (PSQI) (OR: 1.476, 95% CI: 1.29-1.689), Number of visits(OR: 0.768, 95% CI: 0.684-0.863), suicidal behaviors (OR: 5.54, 95% CI: 1.818-16.881) and the electroconvulsive therapy (ECT) (OR: 0.382, 95% CI: 0.156-0.94) were independent risk factors for relapse in patients with BD. An analysis of the receiver operating characteristic curve, calibration curve, and clinical decision curve further revealed that the predictive efficiency and degree of fit between the predicted value of the nomogram and the actual observed value were better.ConclusionThis study found that the number of previous episodes, SDSS score, PSQI score and suicidal behaviors were independent risk factors for relapse of BD, while the number of visits and ECT were protective factor. Based on these factors, we developed and validated a nomogram for predicting relapse in patients with BD; that has clinical reference values.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1500892/fullbipolar disorderrecurrencerisk factorsnomogramprediction model |
| spellingShingle | Xiaoqian Zhang Xiaoqian Zhang Xiaoqian Zhang Xiaoqian Zhang Minghao Wu Daojin Wang Long Wang Long Wang Long Wang Wen Xie Wen Xie Wen Xie Establishment and validation a relapse prediction model for bipolar disorder Frontiers in Psychiatry bipolar disorder recurrence risk factors nomogram prediction model |
| title | Establishment and validation a relapse prediction model for bipolar disorder |
| title_full | Establishment and validation a relapse prediction model for bipolar disorder |
| title_fullStr | Establishment and validation a relapse prediction model for bipolar disorder |
| title_full_unstemmed | Establishment and validation a relapse prediction model for bipolar disorder |
| title_short | Establishment and validation a relapse prediction model for bipolar disorder |
| title_sort | establishment and validation a relapse prediction model for bipolar disorder |
| topic | bipolar disorder recurrence risk factors nomogram prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1500892/full |
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