Flow cytometry-based peripheral blood analysis as an easily friendly tool for prognostic monitoring of acute ischemic stroke: a multicenter study
Acute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis o...
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| Published in | Frontiers in immunology Vol. 15; p. 1402724 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Switzerland
Frontiers Media S.A
21.05.2024
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| Online Access | Get full text |
| ISSN | 1664-3224 1664-3224 |
| DOI | 10.3389/fimmu.2024.1402724 |
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| Abstract | Acute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis of AIS. We aimed to identify the most promising prognostic indicators and establish prognostic models for AIS.
484 subjects enrolled from four centers were analyzed immunophenotypic indicators of peripheral blood by flow cytometry. Least absolute shrinkage and selection operator (LASSO) regression was applied to minimize the potential collinearity and over-fitting of variables measured from the same subject and over-fitting of variables. Univariate and multivariable Cox survival analysis of differences between and within cohorts was performed by log-rank test. The areas under the receiving operating characteristic (ROC) curves were used to evaluate the selection accuracy of immunophenotypic indicators in identifying AIS subjects with survival risk. The prognostic model was constructed using a multivariate Cox model, consisting of 402 subjects as a training cohort and 82 subjects as a testing cohort.
In the prospective study, 7 immunophenotypic indicators of distinct significance were screened out of 72 peripheral blood immunophenotypic indicators by LASSO. In multivariate cox regression, CTL (%) [HR: 1.18, 95% CI: 1.03-1.33], monocytes/μl [HR: 1.13, 95% CI: 1.05-1.21], non-classical monocytes/μl [HR: 1.09, 95% CI: 1.02-1.16] and CD56
NK cells/μl [HR: 1.13, 95% CI: 1.05-1.21] were detected to decrease the survival probability of AIS, while Tregs/μl [HR:0.97, 95% CI: 0.95-0.99, p=0.004], B
/μl [HR:0.90, 95% CI: 0.85-0.95, p=0.023] and CD16
NK cells/μl [HR:0.93, 95% CI: 0.88-0.98, p=0.034] may have the protective effect. As for indicators' discriminative ability, the AUC for CD56
NK cells/μl attained the highest of 0.912. In stratification analysis, the survival probability for AIS subjects with a higher level of Tregs/μl, B
/μl, CD16
NK cells/μl, or lower levels of CD56
NK cells/μl, CTL (%), non-classical monocytes/μl, Monocytes/μl were more likely to survive after AIS. The multivariate Cox model showed an area under the curve (AUC) of 0.805, 0.781 and 0.819 and 0.961, 0.924 and 0.982 in the training and testing cohort, respectively.
Our study identified 7 immunophenotypic indicators in peripheral blood may have great clinical significance in monitoring the prognosis of AIS and provide a convenient and valuable predictive model for AIS. |
|---|---|
| AbstractList | Background and objectiveAcute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis of AIS. We aimed to identify the most promising prognostic indicators and establish prognostic models for AIS.Methods484 subjects enrolled from four centers were analyzed immunophenotypic indicators of peripheral blood by flow cytometry. Least absolute shrinkage and selection operator (LASSO) regression was applied to minimize the potential collinearity and over-fitting of variables measured from the same subject and over-fitting of variables. Univariate and multivariable Cox survival analysis of differences between and within cohorts was performed by log-rank test. The areas under the receiving operating characteristic (ROC) curves were used to evaluate the selection accuracy of immunophenotypic indicators in identifying AIS subjects with survival risk. The prognostic model was constructed using a multivariate Cox model, consisting of 402 subjects as a training cohort and 82 subjects as a testing cohort.ResultsIn the prospective study, 7 immunophenotypic indicators of distinct significance were screened out of 72 peripheral blood immunophenotypic indicators by LASSO. In multivariate cox regression, CTL (%) [HR: 1.18, 95% CI: 1.03–1.33], monocytes/μl [HR: 1.13, 95% CI: 1.05–1.21], non-classical monocytes/μl [HR: 1.09, 95% CI: 1.02–1.16] and CD56high NK cells/μl [HR: 1.13, 95% CI: 1.05–1.21] were detected to decrease the survival probability of AIS, while Tregs/μl [HR:0.97, 95% CI: 0.95–0.99, p=0.004], BM/μl [HR:0.90, 95% CI: 0.85–0.95, p=0.023] and CD16+NK cells/μl [HR:0.93, 95% CI: 0.88–0.98, p=0.034] may have the protective effect. As for indicators’ discriminative ability, the AUC for CD56highNK cells/μl attained the highest of 0.912. In stratification analysis, the survival probability for AIS subjects with a higher level of Tregs/μl, BM/μl, CD16+NK cells/μl, or lower levels of CD56highNK cells/μl, CTL (%), non-classical monocytes/μl, Monocytes/μl were more likely to survive after AIS. The multivariate Cox model showed an area under the curve (AUC) of 0.805, 0.781 and 0.819 and 0.961, 0.924 and 0.982 in the training and testing cohort, respectively.ConclusionOur study identified 7 immunophenotypic indicators in peripheral blood may have great clinical significance in monitoring the prognosis of AIS and provide a convenient and valuable predictive model for AIS. Acute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis of AIS. We aimed to identify the most promising prognostic indicators and establish prognostic models for AIS.Background and objectiveAcute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis of AIS. We aimed to identify the most promising prognostic indicators and establish prognostic models for AIS.484 subjects enrolled from four centers were analyzed immunophenotypic indicators of peripheral blood by flow cytometry. Least absolute shrinkage and selection operator (LASSO) regression was applied to minimize the potential collinearity and over-fitting of variables measured from the same subject and over-fitting of variables. Univariate and multivariable Cox survival analysis of differences between and within cohorts was performed by log-rank test. The areas under the receiving operating characteristic (ROC) curves were used to evaluate the selection accuracy of immunophenotypic indicators in identifying AIS subjects with survival risk. The prognostic model was constructed using a multivariate Cox model, consisting of 402 subjects as a training cohort and 82 subjects as a testing cohort.Methods484 subjects enrolled from four centers were analyzed immunophenotypic indicators of peripheral blood by flow cytometry. Least absolute shrinkage and selection operator (LASSO) regression was applied to minimize the potential collinearity and over-fitting of variables measured from the same subject and over-fitting of variables. Univariate and multivariable Cox survival analysis of differences between and within cohorts was performed by log-rank test. The areas under the receiving operating characteristic (ROC) curves were used to evaluate the selection accuracy of immunophenotypic indicators in identifying AIS subjects with survival risk. The prognostic model was constructed using a multivariate Cox model, consisting of 402 subjects as a training cohort and 82 subjects as a testing cohort.In the prospective study, 7 immunophenotypic indicators of distinct significance were screened out of 72 peripheral blood immunophenotypic indicators by LASSO. In multivariate cox regression, CTL (%) [HR: 1.18, 95% CI: 1.03-1.33], monocytes/μl [HR: 1.13, 95% CI: 1.05-1.21], non-classical monocytes/μl [HR: 1.09, 95% CI: 1.02-1.16] and CD56high NK cells/μl [HR: 1.13, 95% CI: 1.05-1.21] were detected to decrease the survival probability of AIS, while Tregs/μl [HR:0.97, 95% CI: 0.95-0.99, p=0.004], BM/μl [HR:0.90, 95% CI: 0.85-0.95, p=0.023] and CD16+NK cells/μl [HR:0.93, 95% CI: 0.88-0.98, p=0.034] may have the protective effect. As for indicators' discriminative ability, the AUC for CD56highNK cells/μl attained the highest of 0.912. In stratification analysis, the survival probability for AIS subjects with a higher level of Tregs/μl, BM/μl, CD16+NK cells/μl, or lower levels of CD56highNK cells/μl, CTL (%), non-classical monocytes/μl, Monocytes/μl were more likely to survive after AIS. The multivariate Cox model showed an area under the curve (AUC) of 0.805, 0.781 and 0.819 and 0.961, 0.924 and 0.982 in the training and testing cohort, respectively.ResultsIn the prospective study, 7 immunophenotypic indicators of distinct significance were screened out of 72 peripheral blood immunophenotypic indicators by LASSO. In multivariate cox regression, CTL (%) [HR: 1.18, 95% CI: 1.03-1.33], monocytes/μl [HR: 1.13, 95% CI: 1.05-1.21], non-classical monocytes/μl [HR: 1.09, 95% CI: 1.02-1.16] and CD56high NK cells/μl [HR: 1.13, 95% CI: 1.05-1.21] were detected to decrease the survival probability of AIS, while Tregs/μl [HR:0.97, 95% CI: 0.95-0.99, p=0.004], BM/μl [HR:0.90, 95% CI: 0.85-0.95, p=0.023] and CD16+NK cells/μl [HR:0.93, 95% CI: 0.88-0.98, p=0.034] may have the protective effect. As for indicators' discriminative ability, the AUC for CD56highNK cells/μl attained the highest of 0.912. In stratification analysis, the survival probability for AIS subjects with a higher level of Tregs/μl, BM/μl, CD16+NK cells/μl, or lower levels of CD56highNK cells/μl, CTL (%), non-classical monocytes/μl, Monocytes/μl were more likely to survive after AIS. The multivariate Cox model showed an area under the curve (AUC) of 0.805, 0.781 and 0.819 and 0.961, 0.924 and 0.982 in the training and testing cohort, respectively.Our study identified 7 immunophenotypic indicators in peripheral blood may have great clinical significance in monitoring the prognosis of AIS and provide a convenient and valuable predictive model for AIS.ConclusionOur study identified 7 immunophenotypic indicators in peripheral blood may have great clinical significance in monitoring the prognosis of AIS and provide a convenient and valuable predictive model for AIS. Acute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis of AIS. We aimed to identify the most promising prognostic indicators and establish prognostic models for AIS. 484 subjects enrolled from four centers were analyzed immunophenotypic indicators of peripheral blood by flow cytometry. Least absolute shrinkage and selection operator (LASSO) regression was applied to minimize the potential collinearity and over-fitting of variables measured from the same subject and over-fitting of variables. Univariate and multivariable Cox survival analysis of differences between and within cohorts was performed by log-rank test. The areas under the receiving operating characteristic (ROC) curves were used to evaluate the selection accuracy of immunophenotypic indicators in identifying AIS subjects with survival risk. The prognostic model was constructed using a multivariate Cox model, consisting of 402 subjects as a training cohort and 82 subjects as a testing cohort. In the prospective study, 7 immunophenotypic indicators of distinct significance were screened out of 72 peripheral blood immunophenotypic indicators by LASSO. In multivariate cox regression, CTL (%) [HR: 1.18, 95% CI: 1.03-1.33], monocytes/μl [HR: 1.13, 95% CI: 1.05-1.21], non-classical monocytes/μl [HR: 1.09, 95% CI: 1.02-1.16] and CD56 NK cells/μl [HR: 1.13, 95% CI: 1.05-1.21] were detected to decrease the survival probability of AIS, while Tregs/μl [HR:0.97, 95% CI: 0.95-0.99, p=0.004], B /μl [HR:0.90, 95% CI: 0.85-0.95, p=0.023] and CD16 NK cells/μl [HR:0.93, 95% CI: 0.88-0.98, p=0.034] may have the protective effect. As for indicators' discriminative ability, the AUC for CD56 NK cells/μl attained the highest of 0.912. In stratification analysis, the survival probability for AIS subjects with a higher level of Tregs/μl, B /μl, CD16 NK cells/μl, or lower levels of CD56 NK cells/μl, CTL (%), non-classical monocytes/μl, Monocytes/μl were more likely to survive after AIS. The multivariate Cox model showed an area under the curve (AUC) of 0.805, 0.781 and 0.819 and 0.961, 0.924 and 0.982 in the training and testing cohort, respectively. Our study identified 7 immunophenotypic indicators in peripheral blood may have great clinical significance in monitoring the prognosis of AIS and provide a convenient and valuable predictive model for AIS. |
| Author | Lu, Kang Chen, Guo-Bo Tong, Xiangmin Yue, Juanqing Wang, Ying Du, Jing Li, Yanchun Cheng, Yongran Ni, Wanmao |
| AuthorAffiliation | 5 Laboratory Medicine Center, Department of Laboratory Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College , Hangzhou, Zhejiang , China 2 Clinical Research Institute, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College , Hangzhou, Zhejiang , China 3 Department of Pathology, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University , Hangzhou, Zhejiang , China 1 Department of Medical Laboratory Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine) , Hangzhou, Zhejiang , China 7 Department of Hematology, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University , Hangzhou, Zhejiang , China 4 School of Public Health, Hangzhou Medical College , Hangzhou , China 6 Clinical Research Center, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University , Hangzhou, |
| AuthorAffiliation_xml | – name: 6 Clinical Research Center, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University , Hangzhou, Zhejiang , China – name: 5 Laboratory Medicine Center, Department of Laboratory Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College , Hangzhou, Zhejiang , China – name: 1 Department of Medical Laboratory Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine) , Hangzhou, Zhejiang , China – name: 3 Department of Pathology, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University , Hangzhou, Zhejiang , China – name: 7 Department of Hematology, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University , Hangzhou, Zhejiang , China – name: 4 School of Public Health, Hangzhou Medical College , Hangzhou , China – name: 2 Clinical Research Institute, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College , Hangzhou, Zhejiang , China |
| Author_xml | – sequence: 1 givenname: Kang surname: Lu fullname: Lu, Kang – sequence: 2 givenname: Wanmao surname: Ni fullname: Ni, Wanmao – sequence: 3 givenname: Juanqing surname: Yue fullname: Yue, Juanqing – sequence: 4 givenname: Yongran surname: Cheng fullname: Cheng, Yongran – sequence: 5 givenname: Jing surname: Du fullname: Du, Jing – sequence: 6 givenname: Yanchun surname: Li fullname: Li, Yanchun – sequence: 7 givenname: Xiangmin surname: Tong fullname: Tong, Xiangmin – sequence: 8 givenname: Guo-Bo surname: Chen fullname: Chen, Guo-Bo – sequence: 9 givenname: Ying surname: Wang fullname: Wang, Ying |
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| Keywords | immunophenotypic indicators prognostic model acute ischemic stroke LASSO survival |
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| Snippet | Acute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide... Background and objectiveAcute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based... |
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| SubjectTerms | acute ischemic stroke Aged Aged, 80 and over Biomarkers - blood Female Flow Cytometry - methods Humans Immunology immunophenotypic indicators Immunophenotyping Ischemic Stroke - blood Ischemic Stroke - diagnosis Ischemic Stroke - immunology Ischemic Stroke - mortality LASSO Male Middle Aged Prognosis prognostic model Prospective Studies survival |
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| Title | Flow cytometry-based peripheral blood analysis as an easily friendly tool for prognostic monitoring of acute ischemic stroke: a multicenter study |
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