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Wen R, Wang M, Bian W, Zhu H, Xiao Y, He Q, Wang Y, Liu X, Shi Y, Hong Z, Xu B. Nomogram to predict 6-month mortality in acute ischemic stroke patients treated with endovascular treatment. Front Neurol 2024; 14:1330959. [PMID: 38249750 PMCID: PMC10796830 DOI: 10.3389/fneur.2023.1330959] [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: 10/31/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
Background Acute Ischemic Stroke (AIS) presents significant challenges in evaluating the effectiveness of Endovascular Treatment (EVT). This study develops a novel prognostic model to predict 6-month mortality post-EVT, aiding in identifying patients likely to benefit less from this intervention, thus enhancing therapeutic decision-making. Methods We employed a cohort of AIS patients from Shenyang First People's Hospital, serving as the Validation set, to develop our model. LASSO regression was used for feature selection, followed by logistic regression to create a prognostic nomogram for predicting 6-month mortality post-EVT. The model's performance was validated using a dataset from PLA Northern Theater Command General Hospital, assessing discriminative ability (C-index), calibration (calibration plot), and clinical utility (decision curve analysis). Statistical significance was set at p < 0.05. Results The development cohort consisted of 219 patients. Six key predictors of 6-month mortality were identified: "Lack of Exercise" (OR, 4.792; 95% CI, 1.731-13.269), "Initial TICI Score 1" (OR, 1.334; 95% CI, 0.628-2.836), "MRS Score 5" (OR, 1.688; 95% CI, 0.754-3.78), "Neutrophil Percentage" (OR, 1.08; 95% CI, 1.042-1.121), "Onset Blood Sugar" (OR, 1.119; 95% CI, 1.007-1.245), and "Onset NIHSS Score" (OR, 1.074; 95% CI, 1.029-1.121). The nomogram demonstrated a high predictive capability with a C-index of 0.872 (95% CI, 0.830-0.911) in the development set and 0.830 (95% CI, 0.726-0.920) in the validation set. Conclusion Our nomogram, incorporating factors such as Lack of Exercise, Initial TICI Score 1, MRS Score 5, Neutrophil Percentage, Onset Blood Sugar, and Onset NIHSS Score, provides a valuable tool for predicting 6-month mortality in AIS patients post-EVT. It offers potential to refine early clinical decision-making and optimize patient outcomes, reflecting a shift toward more individualized patient care.
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Affiliation(s)
- Rui Wen
- Shenyang Tenth People’s Hospital, Shenyang, China
| | - Miaoran Wang
- Affiliated Central Hospital of Shenyang Medical College, Shenyang Medical College, Shenyang, China
| | - Wei Bian
- Shenyang First People’s Hospital, Shenyang Medical College, Shenyang, China
| | - Haoyue Zhu
- Shenyang First People’s Hospital, Shenyang Medical College, Shenyang, China
| | - Ying Xiao
- Shenyang First People’s Hospital, Shenyang Medical College, Shenyang, China
| | - Qian He
- Shenyang Tenth People’s Hospital, Shenyang, China
| | - Yu Wang
- Shenyang Tenth People’s Hospital, Shenyang, China
| | - Xiaoqing Liu
- Shenyang Tenth People’s Hospital, Shenyang, China
| | - Yangdi Shi
- Shenyang Tenth People’s Hospital, Shenyang, China
| | - Zhe Hong
- Shenyang First People’s Hospital, Shenyang Medical College, Shenyang, China
| | - Bing Xu
- Shenyang Tenth People’s Hospital, Shenyang, China
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Chalos V, Venema E, Mulder MJHL, Roozenbeek B, Steyerberg EW, Wermer MJH, Lycklama à Nijeholt GJ, van der Worp HB, Goyal M, Campbell BCV, Muir KW, Guillemin F, Bracard S, White P, Dávalos A, Jovin TG, Hill MD, Mitchell PJ, Demchuk AM, Saver JL, van der Lugt A, Brown S, Dippel DWJ, Lingsma HF. Development and Validation of a Postprocedural Model to Predict Outcome After Endovascular Treatment for Ischemic Stroke. JAMA Neurol 2023; 80:2807606. [PMID: 37523199 PMCID: PMC10391355 DOI: 10.1001/jamaneurol.2023.2392] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/19/2023] [Indexed: 08/01/2023]
Abstract
Importance Outcome prediction after endovascular treatment (EVT) for ischemic stroke is important to patients, family members, and physicians. Objective To develop and validate a model based on preprocedural and postprocedural characteristics to predict functional outcome for individual patients after EVT. Design, Setting, and Participants A prediction model was developed using individual patient data from 7 randomized clinical trials, performed between December 2010 and December 2014. The model was developed within the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials (HERMES) collaboration and external validation in data from the Dutch Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry of patients treated in clinical practice between March 2014 and November 2017. Participants included patients from multiple centers throughout different countries in Europe, North America, East Asia, and Oceania (derivation cohort), and multiple centers in the Netherlands (validation cohort). Included were adult patients with a history of ischemic stroke from an intracranial large vessel occlusion in the anterior circulation who underwent EVT within 12 hours of symptom onset or last seen well. Data were last analyzed in July 2022. Main Outcome(s) and Measure(s) A total of 19 variables were assessed by multivariable ordinal regression to predict functional outcome (modified Rankin Scale [mRS] score) 90 days after EVT. Variables were routinely available 1 day after EVT. Akaike information criterion (AIC) was used to optimize model fit vs model complexity. Probabilities for functional independence (mRS 0-2) and survival (mRS 0-5) were derived from the ordinal model. Model performance was expressed with discrimination (C statistic) and calibration. Results A total of 781 patients (median [IQR] age, 67 [57-76] years; 414 men [53%]) constituted the derivation cohort, and 3260 patients (median [IQR] age, 72 [61-80] years; 1684 men [52%]) composed the validation cohort. Nine variables were included in the model: age, baseline National Institutes of Health Stroke Scale (NIHSS) score, prestroke mRS score, history of diabetes, occlusion location, collateral score, reperfusion grade, NIHSS score at 24 hours, and symptomatic intracranial hemorrhage 24 hours after EVT. External validation in the MR CLEAN Registry showed excellent discriminative ability for functional independence (C statistic, 0.91; 95% CI, 0.90-0.92) and survival (0.89; 95% CI, 0.88-0.90). The proportion of functional independence in the MR CLEAN Registry was systematically higher than predicted by the model (41% vs 34%), whereas observed and predicted survival were similar (72% vs 75%). The model was updated and implemented for clinical use. Conclusion and relevance The prognostic tool MR PREDICTS@24H can be applied 1 day after EVT to accurately predict functional outcome for individual patients at 90 days and to provide reliable outcome expectations and personalize follow-up and rehabilitation plans. It will need further validation and updating for contemporary patients.
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Affiliation(s)
- Vicky Chalos
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Esmee Venema
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Emergency Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Maxim J. H. L. Mulder
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Bob Roozenbeek
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Ewout W. Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Marieke J. H. Wermer
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - H. Bart van der Worp
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Mayank Goyal
- Departments of Clinical Neuroscience and Radiology, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Bruce C. V. Campbell
- Department of Medicine and Neurology, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Keith W. Muir
- Institute of Neuroscience & Psychology, University of Glasgow, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Francis Guillemin
- CHRU Nancy, Inserm, Université de Lorraine, CIC Clinical Epidemiology, Nancy, France
| | - Serge Bracard
- Department of Diagnostic and Interventional Neuroradiology, University of Lorraine and University Hospital of Nancy, Nancy, France
| | - Philip White
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Antoni Dávalos
- Department of Neuroscience, Hospital Germans Trias y Pujol, Barcelona, Spain
| | - Tudor G. Jovin
- Stroke Institute, Department of Neurology, University of Pittsburgh Medical Center Stroke Institute, Presbyterian University Hospital, Pittsburgh, Pennsylvania
| | - Michael D. Hill
- Departments of Clinical Neuroscience and Radiology, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Peter J. Mitchell
- Department of Radiology, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew M. Demchuk
- Departments of Clinical Neuroscience and Radiology, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Jeffrey L. Saver
- Department of Neurology and Comprehensive Stroke Center, David Geffen School of Medicine, University of Los Angeles, Los Angeles, California
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Scott Brown
- Altair Biostatistics, Mooresville, North Carolina
| | - Diederik W. J. Dippel
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Hester F. Lingsma
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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Li Q, Chi L, Zhao W, Wu L, Jiao C, Zheng X, Zhang K, Li X. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis. Front Neurol 2023; 14:1039794. [PMID: 37388543 PMCID: PMC10299899 DOI: 10.3389/fneur.2023.1039794] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/25/2023] [Indexed: 07/01/2023] Open
Abstract
Background Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke. Methods A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters. Results A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively. Conclusion ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
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Affiliation(s)
- Qinglin Li
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Chi
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Weiying Zhao
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Lei Wu
- Department of Acupuncture, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Chuanxu Jiao
- Department of Neurorehabilitation, Taizhou Enze Medical Center Luqiao Hospital, Taizhou, Zhejiang, China
| | - Xue Zheng
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Kaiyue Zhang
- Second Clinical Medical School, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
| | - Xiaoning Li
- Department of Acupuncture, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China
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Development and Validation of a Nomogram to Predict Hemiplegic Shoulder Pain in Patients With Stroke: A Retrospective Cohort Study. Arch Rehabil Res Clin Transl 2022; 4:100213. [PMID: 36123984 PMCID: PMC9482044 DOI: 10.1016/j.arrct.2022.100213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
We developed a nomogram for the individualized prediction of hemiplegic shoulder pain in patients with stroke during inpatient rehabilitation. The nomogram demonstrated good performance in terms of discrimination, calibration, and clinical utility. The variables incorporated in the nomogram were readily available from clinical characteristics, making the nomogram a simple and practical tool for clinicians. A web application of the nomogram was developed to facilitate its use for clinicians.
Objective The development and validation of a nomogram for the individualized prediction of hemiplegic shoulder pain (HSP) during the inpatient rehabilitation of patients with stroke. Design Retrospective cohort study. Setting The rehabilitation department at a tertiary hospital. Participants A total of 376 patients (N=376) with stroke admitted to inpatient rehabilitation from January 2018 to April 2021 were included in this study. Interventions Not applicable. Main Outcome Measures The outcome measure was shoulder pain on the patients’ hemiplegic side occurring at rest or with movement during hospitalization. Results Among the 376 patients with stroke, 113 (30.05%) developed HSP. Five independent predictors were included in the nomogram: subluxation, Brunnstrom stage, hand edema, spasticity, and sensory disturbance. The nomogram was a good predictor, with a C-index of 0.85 (95% confidence interval, 0.81-0.89) and corrected C-index of 0.84. The Homer-Lemeshow test (χ2=13.854, P=.086) and calibration plot suggested good calibration ability of the nomogram. The optimal cutoff value for the predicted probability of HSP was 0.30 (sensitivity, 0.73; specificity, 0.83). Moreover, the decision curve analysis revealed that the nomogram would add net clinical benefits if the threshold possibility of HSP risk was from 5%-88%. Conclusions Our nomogram could accurately predict HSP, which may help clinicians accurately quantify the HSP risk in individuals and implement early interventions.
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