Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage
Abstract Background We aimed to establish risk factors for stroke-associated pneumonia (SAP) following intracerebral hemorrhage (ICH) and develop an efficient and convenient model to predict SAP in patients with ICH. Methods Our study involved 1333 patients consecutively diagnosed with ICH and admit...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Geriatrics |
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| Online Access: | https://doi.org/10.1186/s12877-023-04310-5 |
| _version_ | 1797451531690180608 |
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| author | Ying Wang Yuting Chen Roumeng Chen Yuchen Xu Han Zheng Jiajun Xu Jinyang Xia Yifan Cai Huiqin Xu Xinshi Wang |
| author_facet | Ying Wang Yuting Chen Roumeng Chen Yuchen Xu Han Zheng Jiajun Xu Jinyang Xia Yifan Cai Huiqin Xu Xinshi Wang |
| author_sort | Ying Wang |
| collection | DOAJ |
| description | Abstract Background We aimed to establish risk factors for stroke-associated pneumonia (SAP) following intracerebral hemorrhage (ICH) and develop an efficient and convenient model to predict SAP in patients with ICH. Methods Our study involved 1333 patients consecutively diagnosed with ICH and admitted to the Neurology Department of the First Affiliated Hospital of Wenzhou Medical University. The 1333 patients were randomly divided (3:1) into the derivation cohort (n = 1000) and validation Cohort (n = 333). Variables were screened from demographics, lifestyle-related factors, comorbidities, clinical symptoms, neuroimaging features, and laboratory tests. In the derivation cohort, we developed a prediction model with multivariable logistic regression analysis. In the validation cohort, we assessed the model performance and compared it to previously reported models. The area under the receiver operating characteristic curve (AUROC), GiViTI calibration belt, net reclassification index (NRI), integrated discrimination index (IDI) and decision curve analysis (DCA) were used to assess the prediction ability and the clinical decision-making ability. Results The incidence of SAP was 19.9% and 19.8% in the derivation (n = 1000) and validation (n = 333) cohorts, respectively. We developed a nomogram prediction model including age (Odds Ratio [OR] 1.037, 95% confidence interval [CI] 1.020–1.054), male sex (OR 1.824, 95% CI 1.206–2.757), multilobar involvement (OR 1.851, 95% CI 1.160–2.954), extension into ventricles (OR 2.164, 95% CI 1.456–3.215), dysphagia (OR 3.626, 95% CI 2.297–5.725), disturbance of consciousness (OR 2.113, 95% CI 1.327–3.362) and total muscle strength of the worse side (OR 0.93, 95% CI 0.876–0.987). Compared with previous models, our model was well calibrated and showed significantly higher AUROC, better reclassification ability (improved NRI and IDI) and a positive net benefit for predicted probability thresholds between 10% and 73% in DCA. Conclusions We developed a simple, valid, and clinically useful model to predict SAP following ICH, with better predictive performance than previous models. It might be a promising tool to assess the individual risk of developing SAP for patients with ICH and optimize decision-making. |
| first_indexed | 2024-03-09T14:55:59Z |
| format | Article |
| id | doaj.art-42408b7c6db74b65ab722534e7c90743 |
| institution | Directory Open Access Journal |
| issn | 1471-2318 |
| language | English |
| last_indexed | 2024-03-09T14:55:59Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
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| series | BMC Geriatrics |
| spelling | doaj.art-42408b7c6db74b65ab722534e7c907432023-11-26T14:11:19ZengBMCBMC Geriatrics1471-23182023-10-0123111110.1186/s12877-023-04310-5Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhageYing Wang0Yuting Chen1Roumeng Chen2Yuchen Xu3Han Zheng4Jiajun Xu5Jinyang Xia6Yifan Cai7Huiqin Xu8Xinshi Wang9Department of Neurology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical UniversityFirst Clinical School of Medicine, Wenzhou Medical UniversityFirst Clinical School of Medicine, Wenzhou Medical UniversityFirst Clinical School of Medicine, Wenzhou Medical UniversityFirst Clinical School of Medicine, Wenzhou Medical UniversityDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical UniversityAbstract Background We aimed to establish risk factors for stroke-associated pneumonia (SAP) following intracerebral hemorrhage (ICH) and develop an efficient and convenient model to predict SAP in patients with ICH. Methods Our study involved 1333 patients consecutively diagnosed with ICH and admitted to the Neurology Department of the First Affiliated Hospital of Wenzhou Medical University. The 1333 patients were randomly divided (3:1) into the derivation cohort (n = 1000) and validation Cohort (n = 333). Variables were screened from demographics, lifestyle-related factors, comorbidities, clinical symptoms, neuroimaging features, and laboratory tests. In the derivation cohort, we developed a prediction model with multivariable logistic regression analysis. In the validation cohort, we assessed the model performance and compared it to previously reported models. The area under the receiver operating characteristic curve (AUROC), GiViTI calibration belt, net reclassification index (NRI), integrated discrimination index (IDI) and decision curve analysis (DCA) were used to assess the prediction ability and the clinical decision-making ability. Results The incidence of SAP was 19.9% and 19.8% in the derivation (n = 1000) and validation (n = 333) cohorts, respectively. We developed a nomogram prediction model including age (Odds Ratio [OR] 1.037, 95% confidence interval [CI] 1.020–1.054), male sex (OR 1.824, 95% CI 1.206–2.757), multilobar involvement (OR 1.851, 95% CI 1.160–2.954), extension into ventricles (OR 2.164, 95% CI 1.456–3.215), dysphagia (OR 3.626, 95% CI 2.297–5.725), disturbance of consciousness (OR 2.113, 95% CI 1.327–3.362) and total muscle strength of the worse side (OR 0.93, 95% CI 0.876–0.987). Compared with previous models, our model was well calibrated and showed significantly higher AUROC, better reclassification ability (improved NRI and IDI) and a positive net benefit for predicted probability thresholds between 10% and 73% in DCA. Conclusions We developed a simple, valid, and clinically useful model to predict SAP following ICH, with better predictive performance than previous models. It might be a promising tool to assess the individual risk of developing SAP for patients with ICH and optimize decision-making.https://doi.org/10.1186/s12877-023-04310-5Intracranial hemorrhageStroke-associated pneumoniaPredictorsNomogramValidation |
| spellingShingle | Ying Wang Yuting Chen Roumeng Chen Yuchen Xu Han Zheng Jiajun Xu Jinyang Xia Yifan Cai Huiqin Xu Xinshi Wang Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage BMC Geriatrics Intracranial hemorrhage Stroke-associated pneumonia Predictors Nomogram Validation |
| title | Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage |
| title_full | Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage |
| title_fullStr | Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage |
| title_full_unstemmed | Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage |
| title_short | Development and validation of a nomogram model for prediction of stroke-associated pneumonia associated with intracerebral hemorrhage |
| title_sort | development and validation of a nomogram model for prediction of stroke associated pneumonia associated with intracerebral hemorrhage |
| topic | Intracranial hemorrhage Stroke-associated pneumonia Predictors Nomogram Validation |
| url | https://doi.org/10.1186/s12877-023-04310-5 |
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