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Ding J, Ma X, Huang W, Yue C, Xu G, Wang Y, Sheng S, Liu M, Ren Y. Validation and refinement of a predictive nomogram using artificial intelligence: assessing in-hospital mortality in patients with large hemispheric cerebral infarction. Front Neurol 2024; 15:1398142. [PMID: 38984035 PMCID: PMC11231922 DOI: 10.3389/fneur.2024.1398142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024] Open
Abstract
Background Large Hemispheric Infarction (LHI) poses significant mortality and morbidity risks, necessitating predictive models for in-hospital mortality. Previous studies have explored LHI progression to malignant cerebral edema (MCE) but have not comprehensively addressed in-hospital mortality risk, especially in non-decompressive hemicraniectomy (DHC) patients. Methods Demographic, clinical, risk factor, and laboratory data were gathered. The population was randomly divided into Development and Validation Groups at a 3:1 ratio, with no statistically significant differences observed. Variable selection utilized the Bonferroni-corrected Boruta technique (p < 0.01). Logistic Regression retained essential variables, leading to the development of a nomogram. ROC and DCA curves were generated, and calibration was conducted based on the Validation Group. Results This study included 314 patients with acute anterior-circulating LHI, with 29.6% in the Death group (n = 93). Significant variables, including Glasgow Coma Score, Collateral Score, NLR, Ventilation, Non-MCA territorial involvement, and Midline Shift, were identified through the Boruta algorithm. The final Logistic Regression model led to a nomogram creation, exhibiting excellent discriminative capacity. Calibration curves in the Validation Group showed a high degree of conformity with actual observations. DCA curve analysis indicated substantial clinical net benefit within the 5 to 85% threshold range. Conclusion We have utilized NIHSS score, Collateral Score, NLR, mechanical ventilation, non-MCA territorial involvement, and midline shift to develop a highly accurate, user-friendly nomogram for predicting in-hospital mortality in LHI patients. This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations.
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Affiliation(s)
- Jian Ding
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Xiaoming Ma
- Department of Neurology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China
| | - Wendie Huang
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Chunxian Yue
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Geman Xu
- Department of Neurology, Affiliated Fuyang People's Hospital of Anhui Medical University, Fuyang, China
| | - Yumei Wang
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Shiying Sheng
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Meng Liu
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Yi Ren
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
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Cao L, Ma X, Huang W, Xu G, Wang Y, Liu M, Sheng S, Mao K. An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction. Eur Neurol 2024; 87:54-66. [PMID: 38565087 DOI: 10.1159/000538424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/16/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Malignant cerebral edema (MCE) is a serious complication and the main cause of poor prognosis in patients with large-hemisphere infarction (LHI). Therefore, the rapid and accurate identification of potential patients with MCE is essential for timely therapy. This study utilized an artificial intelligence-based machine learning approach to establish an interpretable model for predicting MCE in patients with LHI. METHODS This study included 314 patients with LHI not undergoing recanalization therapy. The patients were divided into MCE and non-MCE groups, and the eXtreme Gradient Boosting (XGBoost) model was developed. A confusion matrix was used to measure the prediction performance of the XGBoost model. We also utilized the SHapley Additive exPlanations (SHAP) method to explain the XGBoost model. Decision curve and receiver operating characteristic curve analyses were performed to evaluate the net benefits of the model. RESULTS MCE was observed in 121 (38.5%) of the 314 patients with LHI. The model showed excellent predictive performance, with an area under the curve of 0.916. The SHAP method revealed the top 10 predictive variables of the MCE such as ASPECTS score, NIHSS score, CS score, APACHE II score, HbA1c, AF, NLR, PLT, GCS, and age based on their importance ranking. CONCLUSION An interpretable predictive model can increase transparency and help doctors accurately predict the occurrence of MCE in LHI patients not undergoing recanalization therapy within 48 h of onset, providing patients with better treatment strategies and enabling optimal resource allocation.
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Affiliation(s)
- Liping Cao
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Xiaoming Ma
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, China,
| | - Wendie Huang
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Geman Xu
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Yumei Wang
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Meng Liu
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Shiying Sheng
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Keshi Mao
- Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
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Wu S, Wang Y, Yuan R, Guo F, Yang D, Li Z, Wu B, Wang C, Duan J, Ling T, Zhang H, Zhang S, Wu B, Anderson CS, Liu M. Predicting the emergence of malignant brain oedema in acute ischaemic stroke: a prospective multicentre study with development and validation of predictive modelling. EClinicalMedicine 2023; 59:101977. [PMID: 37152361 PMCID: PMC10154953 DOI: 10.1016/j.eclinm.2023.101977] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/06/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023] Open
Abstract
Background We aimed to develop and validate a prognostic model for predicting malignant brain oedema in patients with acute ischaemic stroke in a real-world setting of practice. Methods A prospective multicentre study enrolled adult patients with acute ischaemic stroke with brain CT < 24 h of onset of symptoms admitted to nine tertiary-level hospitals in China between September 2017 and December 2019. Malignant brain oedema was defined as any patient who had decompressive craniectomy, discharge in coma, or in-hospital death attributed to symptomatic brain swelling. The derivation cohort was a consecutive cohort of patients from one centre and the validation cohort was non-consecutive patients from the other centres. Multivariable logistic regression was used to define independent predictors from baseline clinical characteristics, imaging features, complications, and management. A web-based nomogram and a risk score were developed based on the final model. Model performance was assessed for discrimination and calibration in both derivation and validation cohorts. The study is registered, NCT03222024. Findings Based on the derivation cohort (n = 1627), the model was developed with seven variables including large infarct (adjusted odds ratio [OR] 40.90, 95% CI 20.20-82.80), National Institutes of Health Stroke Scale (NIHSS) score (OR 1.09, 1.06-1.12), thrombolysis (OR 2.11, 1.18-3.78), endovascular treatment (OR 2.87, 1.47-5.59), pneumonia (OR 2.47, 1.53-3.97), brain atrophy (OR 0.57, 0.37-0.86), and recanalisation (OR 0.36, 0.17-0.75). The classification threshold of a predicted probability ≥0.14 showed good discrimination and calibration in both derivation cohort (area under the receiver-operating curve [AUC] 0.90, 0.87-0.92; sensitivity 0.95, 0.92-0.98) and validation cohort (n = 556, AUC 0.88, 0.82-0.95; sensitivity 0.84, 0.73-0.95). The risk score based on this model had a total point that ranged from -1 to 20, with an optimal score of ≥10 showing good discrimination and calibration in both derivation (AUC 0.89, 0.87-0.92; sensitivity 0.95, 0.92-0.98) and validation (AUC 0.88, 0.82-0.95; sensitivity 0.84, 0.73-0.95) cohorts. Interpretation The INTEP-AR model (i.e. large Infarct, NIHSS score, Thrombolysis, Endovascular treatment, Pneumonia, brain Atrophy, and Recanalisation) incorporating multiple clinical and radiological characteristics has shown good prognostic value for predicting malignant brain oedema after acute ischaemic stroke. Funding National Natural Science Foundation of China; Science and Technology Department of Sichuan Province; West China Hospital.
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Affiliation(s)
- Simiao Wu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Corresponding author.
| | - Yanan Wang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Ruozhen Yuan
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Neurology, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Fuqiang Guo
- Department of Neurology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China
| | - Dongdong Yang
- Department of Neurology, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, China
| | - Zuoxiao Li
- Department of Neurology, Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Bihua Wu
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 634700, China
| | - Chun Wang
- Department of Neurology, People's Hospital of Deyang City, Deyang, 618000, China
| | - Jingfeng Duan
- Department of Neurology, Mianyang Central Hospital, Mianyang, 621000, China
| | - Tianjin Ling
- Department of Neurology, The First People's Hospital of Ziyang, Ziyang, 641300, China
| | - Hao Zhang
- Department of Neurology, Jiangyou People's Hospital, Jiangyou, 621000, China
| | - Shihong Zhang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Bo Wu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Craig S. Anderson
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, 2050, Australia
- The George Institute for Global Health China, Beijing, 100600, China
| | - Ming Liu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Corresponding author.
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Lin T, Peng S, Lu S, Fu S, Zeng D, Li J, Chen T, Fan T, Lang C, Feng S, Ma J, Zhao C, Antony B, Cicuttini F, Quan X, Zhu Z, Ding C. Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study. Osteoarthritis Cartilage 2023; 31:267-278. [PMID: 36334697 DOI: 10.1016/j.joca.2022.10.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/26/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVES To develop and validate a nomogram to detect improved knee pain in osteoarthritis (OA) by integrating magnetic resonance imaging (MRI) radiomics signature of subchondral bone and clinical characteristics. METHODS Participants were selected from the Vitamin D Effects on Osteoarthritis (VIDEO) study. The primary outcome was 20% improvement of knee pain score over 2 years in participants administrated either vitamin D or placebo. Radiomics features of subchondral bone and clinical characteristics from 216 participants were extracted and analyzed. The participants were randomly split into the training and validation cohorts at a ratio of 8:2. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate radiomics signatures. The optimal radiomics signature and clinical indicators were fitted into a nomogram using multivariable logistic regression model. RESULTS The nomogram showed favorable discrimination performance [AUCtraining, 0.79 (95% CI: 0.72-0.79), AUCvalidation, 0.83 (95% CI: 0.70-0.96)] as well as a good calibration. Additional contributing value of fusion radiomics signature to the nomogram was statistically significant (NRI, 0.23; IDI, 0.14, P < 0.001 in training cohort and NRI, 0.29; IDI, 0.18, P < 0.05 in validating cohort). Decision curve analysis confirmed the clinical usefulness of nomogram. CONCLUSION The radiomics-based nomogram comprising the MR radiomics signature and clinical variables achieves a favorable predictive efficacy and accuracy in differentiating improvement in knee pain among OA patients. This proof-of-concept study provides a promising way to predict clinically meaningful outcomes.
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Affiliation(s)
- T Lin
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - S Peng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - S Lu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - S Fu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - D Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - J Li
- Division of Orthopaedic Surgery, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - T Chen
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - T Fan
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - C Lang
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - S Feng
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, 999077, Hong Kong, China.
| | - J Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - C Zhao
- Philips China, Beijing, 100000, China.
| | - B Antony
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, 7000, Australia.
| | - F Cicuttini
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, 3800, Australia.
| | - X Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - Z Zhu
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - C Ding
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, 7000, Australia.
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Wang J, Zhang W, Hou W, Zhao E, Li X. Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma. Front Cell Dev Biol 2022; 10:837919. [PMID: 35386197 PMCID: PMC8978676 DOI: 10.3389/fcell.2022.837919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating the gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction. In this study, 5,198 differentially expressed genes (DEGs) and 270 DNA methylation-driven genes were selected to obtain 146 differentially expressed DNA methylation-driven genes (DEMDGs). Two clusters were distinguished by consensus clustering using 146 DEMDGs. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Cluster-based immune status analysis and functional enrichment analysis of 106 DEGs provide new insights for the development of RCC. To predict the prognosis of patients with RCC, a prognostic model based on eight DEMDGs was constructed. The patients were divided into high-risk groups and low-risk groups based on their risk scores. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:3496) were built to validate the predictive accuracy of the prognostic model. Gene set enrichment analysis was performed to annotate the signaling pathways in which the genes are enriched. The correlation of the risk score with clinical features, immune status, and drug susceptibility was also evaluated. These results suggested that the prognostic model might be a promising prognostic tool for RCC and might facilitate the management of patients with RCC.
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Affiliation(s)
- Jinpeng Wang
- Department of Urology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wei Zhang
- Department of Urology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wenbin Hou
- Department of Urology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Enyang Zhao
- Department of Urology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xuedong Li
- Department of Urology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
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Wang Y, Wang H, Sun W, Miao J, Liang W, Qiu X, Lan Y, Pan C, Li G, Zhao X, Zhu Z, Zhu S. Higher Concentration of Adrenocorticotropic Hormone Predicts Post-Stroke Depression. Clin Interv Aging 2022; 17:417-427. [PMID: 35411137 PMCID: PMC8994598 DOI: 10.2147/cia.s356361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/30/2022] [Indexed: 11/23/2022] Open
Abstract
Background Post-stroke depression (PSD) is the most common neuropsychiatric complication after stroke, seriously affecting the quality of survivors’ life. As one of the important causes of PSD, neuroendocrine mechanism has been widely studied in recent years. The main objective of this study was to investigate the relationship between adrenocorticotropic hormone (ACTH) on admission and PSD at 3 months. Methods This is a hospital-based prospective cohort study, which was conducted at three independent hospitals (Tongji Hospital, Wuhan First Hospital and Wuhan Central Hospital) between August 2018 and June 2019. A total of 768 ischemic stroke patients were finally eligible for analysis and categorized into equal tertiles according to the distribution of ACTH and the number of patients. The χ2-test, Mann–Whitney U-test and Kruskal–Wallis test were used to check for statistical significance. And restricted cubic spline (RCS) regression model was used to explore the non-linear relationship between continuous ACTH levels and PSD at 3 months. Results The optimal cut-off points of ACTH were as follows: (T1) 0.32–20.55 pg/mL, (T2) 20.56–39.79 pg/mL, (T3) 39.80–143.40 pg/mL. A total of 305 patients (39.7%) were diagnosed as PSD at 3 months follow-up. Significant differences were found between the PSD and non-PSD groups in ACTH concentration (P = 0.001). After adjustment for all conventional confounders, the odds ratios of PSD were 1.735 (95% CI = 1.176–2.560, P = 0.005) for the highest tertile of ACTH and 1.496 (95% CI = 1.019–2.194, P = 0.040) for the middle tertile of ACTH, as compared with the lowest tertile. In multiple-adjusted RCS regression, continuous ACTH showed saturation effect relation with PSD risk after 31.02 pg/mL (P for nonlinear = 0.0143). Conclusion Higher ACTH level on admission is a significant and independent biomarker to predict the development of PSD at 3 months follow-up. Besides, saturation effect was revealed even if the underlying mechanism is unclear. For stroke patients, doctors should pay attention to the baseline ACTH for screening high-risk PSD in clinical practice.
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Affiliation(s)
- Yanyan Wang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - He Wang
- Department of Medical Affairs, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Wenzhe Sun
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Jinfeng Miao
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Wenwen Liang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Xiuli Qiu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Yan Lan
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Chensheng Pan
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Guo Li
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Xin Zhao
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
| | - Zhou Zhu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
- Correspondence: Zhou Zhu; Suiqiang Zhu, Tel +86 18171081029; +86 13035101141, Fax +86 27-83663337, Email ;
| | - Suiqiang Zhu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China
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Hu P, Li Y, Zhang H, Su Z, Xu S, Li X, Gao X, Liu Y, Deng G, Xu Y, Ye L, Chen Q. Development and external validation of a dynamic nomogram for delayed cerebral ischaemia after aneurysmal subarachnoid hemorrhage: a study protocol for a multicentre retrospective cohort study. BMJ Open 2021; 11:e051956. [PMID: 34949617 PMCID: PMC8712981 DOI: 10.1136/bmjopen-2021-051956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
INTRODUCTION Delayed cerebral ischaemia (DCI) caused by aneurysmal subarachnoid haemorrhage (aSAH) is the most frequent complication and typically contributes to poor neurological outcome or deterioration of patients' condition. Therefore, early accurate and effective prediction of DCI is urgently needed. This study aims to construct a dynamic nomogram for precisely calculating the risk of DCI in patients with aSAH. Internal validation of this tool is conducted using the training cohort, and independent external validation is completed by using other medical centre datasets. METHODS AND ANALYSIS This study is a multicentre, retrospective, observational cohort study using data from patients with aSAH. The participants include all adult patients who received surgical treatment in neurosurgery of multiple medical centres from 1 September 2019 to 1 April 2021, including Renmin Hospital of Wuhan University, Huzhou Central Hospital, First Affiliated Hospital of Harbin Medical University, General Hospital of Northern Theatre Command and Affiliated Hospital of Panzhihua University. Clinical information is collected via the electronic medical record system, including demographic data, clinical state on admission and serum laboratory tests. Modified Fisher grade at admission, admission subarachnoid clot and cerebral oedema density, and residual postoperative subarachnoid clot density are determined using the electronic imagine record software. The primary outcome is DCI. ETHICS AND DISSEMINATION This study protocol was reviewed and approved by the Medical Ethics Committee of Renmin Hospital of Wuhan University, which is the principal affiliation of this study (approval number: WDRM2021-K022). The other Ethics Committees, including Huzhou Central Hospital (approval number: 202108005-01), First Affiliated Hospital of Harbin Medical University (approval number: H202156), General Hospital of Northern Theater Command (approval number: Y2021060) and Affiliated Hospital of Panzhihua University (approval number: 202105002), also approved the protocol. The results of this research will be published in a peer-reviewed medical journal. TRIAL REGISTRATION NUMBER ChiCTR2100044448.
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Affiliation(s)
- Ping Hu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuntao Li
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
- Department of Neurosurgery, Huzhou Central Hospital, Huzhou, China
| | - Hongbo Zhang
- Department of Neurosurgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhongzhou Su
- Department of Neurosurgery, Huzhou Central Hospital, Huzhou, China
| | - Shancai Xu
- Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xuesong Li
- Department of Neurosurgery, Huizhou Third People's Hospital, Huizhou, China
| | - Xu Gao
- Department of Neurosurgery, General Hospital of Northern Theatre Command, Shenyang, China
| | - Yangfan Liu
- Department of Neurosurgery, Affiliated Hospital of Panzhihua University, Panzhihua, China
| | - Gang Deng
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yang Xu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liguo Ye
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qianxue Chen
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, China
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McLeod G, Kennedy I, Simpson E, Joss J, Goldmann K. A pilot project informing the design of a web-based dynamic nomogram in order to predict survival one year after hip fracture surgery (Preprint). Interact J Med Res 2021; 11:e34096. [PMID: 35238320 PMCID: PMC9008534 DOI: 10.2196/34096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/18/2022] [Accepted: 02/13/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Graeme McLeod
- Department of Anaesthesia, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom
- School of Medicine, University of Dundee, Ninewells Hospital, Dundee, United Kingdom
| | - Iain Kennedy
- Department of Anaesthesia, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom
| | - Eilidh Simpson
- Crosshouse Hospital, National Health Service Ayrshire and Arran, Kilmarnock, United Kingdom
| | - Judith Joss
- Department of Anaesthesia, Ninewells Hospital, National Health Service Tayside, Dundee, United Kingdom
| | - Katriona Goldmann
- William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, London, United Kingdom
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