A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis
Non-cardioembolic ischemic stroke (IS) is the predominant subtype of IS. This study aimed to construct a nomogram for recurrence risks in patients with non-cardioembolic IS in order to maximize clinical benefits. From April 2015 to December 2019, data from consecutive patients who were diagnosed wit...
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MDPI AG
2023-07-01
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| author | Kangmei Shao Fan Zhang Yongnan Li Hongbin Cai Ewetse Paul Maswikiti Mingming Li Xueyang Shen Longde Wang Zhaoming Ge |
| author_facet | Kangmei Shao Fan Zhang Yongnan Li Hongbin Cai Ewetse Paul Maswikiti Mingming Li Xueyang Shen Longde Wang Zhaoming Ge |
| author_sort | Kangmei Shao |
| collection | DOAJ |
| description | Non-cardioembolic ischemic stroke (IS) is the predominant subtype of IS. This study aimed to construct a nomogram for recurrence risks in patients with non-cardioembolic IS in order to maximize clinical benefits. From April 2015 to December 2019, data from consecutive patients who were diagnosed with non-cardioembolic IS were collected from Lanzhou University Second Hospital. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. Multivariable Cox regression analyses were used to identify the independent risk factors. A nomogram model was constructed using the “rms” package in R software via multifactor Cox regression. The accuracy of the model was evaluated using the receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA). A total of 729 non-cardioembolic IS patients were enrolled, including 498 (68.3%) male patients and 231 (31.7%) female patients. Among them, there were 137 patients (18.8%) with recurrence. The patients were randomly divided into training and testing sets. The Kaplan–Meier survival analysis of the training and testing sets consistently revealed that the recurrence rates in the high-risk group were significantly higher than those in the low-risk group (<i>p</i> < 0.01). Moreover, the receiver operating characteristic curve analysis of the risk score demonstrated that the area under the curve was 0.778 and 0.760 in the training and testing sets, respectively. The nomogram comprised independent risk factors, including age, diabetes, platelet–lymphocyte ratio, leukoencephalopathy, neutrophil, monocytes, total protein, platelet, albumin, indirect bilirubin, and high-density lipoprotein. The C-index of the nomogram was 0.752 (95% CI: 0.705~0.799) in the training set and 0.749 (95% CI: 0.663~0.835) in the testing set. The nomogram model can be used as an effective tool for carrying out individualized recurrence predictions for non-cardioembolic IS. |
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| spelling | doaj.art-62d1e0b9ab2347da9ed984032b2fca132023-11-18T18:34:40ZengMDPI AGBrain Sciences2076-34252023-07-01137105110.3390/brainsci13071051A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort AnalysisKangmei Shao0Fan Zhang1Yongnan Li2Hongbin Cai3Ewetse Paul Maswikiti4Mingming Li5Xueyang Shen6Longde Wang7Zhaoming Ge8Department of Neurology, Lanzhou University Second Hospital, Lanzhou 730030, ChinaDepartment of Oncology Surgery, Lanzhou University Second Hospital, Lanzhou 730030, ChinaDepartment of Cardiac Surgery, Lanzhou University Second Hospital, Lanzhou 730030, ChinaDepartment of Neurology, Lanzhou University Second Hospital, Lanzhou 730030, ChinaDepartment of Oncology Surgery, Lanzhou University Second Hospital, Lanzhou 730030, ChinaDepartment of Neurology, Lanzhou University Second Hospital, Lanzhou 730030, ChinaDepartment of Neurology, Lanzhou University Second Hospital, Lanzhou 730030, ChinaExpert Workstation of Academician Wang Longde, Lanzhou University Second Hospital, Lanzhou 730030, ChinaDepartment of Neurology, Lanzhou University Second Hospital, Lanzhou 730030, ChinaNon-cardioembolic ischemic stroke (IS) is the predominant subtype of IS. This study aimed to construct a nomogram for recurrence risks in patients with non-cardioembolic IS in order to maximize clinical benefits. From April 2015 to December 2019, data from consecutive patients who were diagnosed with non-cardioembolic IS were collected from Lanzhou University Second Hospital. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. Multivariable Cox regression analyses were used to identify the independent risk factors. A nomogram model was constructed using the “rms” package in R software via multifactor Cox regression. The accuracy of the model was evaluated using the receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA). A total of 729 non-cardioembolic IS patients were enrolled, including 498 (68.3%) male patients and 231 (31.7%) female patients. Among them, there were 137 patients (18.8%) with recurrence. The patients were randomly divided into training and testing sets. The Kaplan–Meier survival analysis of the training and testing sets consistently revealed that the recurrence rates in the high-risk group were significantly higher than those in the low-risk group (<i>p</i> < 0.01). Moreover, the receiver operating characteristic curve analysis of the risk score demonstrated that the area under the curve was 0.778 and 0.760 in the training and testing sets, respectively. The nomogram comprised independent risk factors, including age, diabetes, platelet–lymphocyte ratio, leukoencephalopathy, neutrophil, monocytes, total protein, platelet, albumin, indirect bilirubin, and high-density lipoprotein. The C-index of the nomogram was 0.752 (95% CI: 0.705~0.799) in the training set and 0.749 (95% CI: 0.663~0.835) in the testing set. The nomogram model can be used as an effective tool for carrying out individualized recurrence predictions for non-cardioembolic IS.https://www.mdpi.com/2076-3425/13/7/1051non-cardiogenic ischemic strokerecurrencerisk factorsnomogramprediction model |
| spellingShingle | Kangmei Shao Fan Zhang Yongnan Li Hongbin Cai Ewetse Paul Maswikiti Mingming Li Xueyang Shen Longde Wang Zhaoming Ge A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis Brain Sciences non-cardiogenic ischemic stroke recurrence risk factors nomogram prediction model |
| title | A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis |
| title_full | A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis |
| title_fullStr | A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis |
| title_full_unstemmed | A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis |
| title_short | A Nomogram for Predicting the Recurrence of Acute Non-Cardioembolic Ischemic Stroke: A Retrospective Hospital-Based Cohort Analysis |
| title_sort | nomogram for predicting the recurrence of acute non cardioembolic ischemic stroke a retrospective hospital based cohort analysis |
| topic | non-cardiogenic ischemic stroke recurrence risk factors nomogram prediction model |
| url | https://www.mdpi.com/2076-3425/13/7/1051 |
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