A risk score for predicting hospitalization for community-acquired pneumonia in ITP using nationally representative data
© 2020 by The American Society of Hematology..
Infection is one of the primary causes of death from immune thrombocytopenia (ITP), and the lungs are the most common site of infection. We identified the factors associated with hospitalization for community-acquired pneumonia (CAP) in nonsplenectomized adults with ITP and established the [corrected] (ACPA) prediction model to predict the incidence of hospitalization for CAP. This was a retrospective study of nonsplenectomized adult patients with ITP from 10 large medical centers in China. The derivation cohort included 145 ITP inpatients with CAP and 1360 inpatients without CAP from 5 medical centers, and the validation cohort included the remaining 63 ITP inpatients with CAP and 526 inpatients without CAP from the other 5 centers. The 4-item ACPA model, which included age, Charlson Comorbidity Index score, initial platelet count, and initial absolute lymphocyte count, was established by multivariable analysis of the derivation cohort. Internal and external validation were conducted to assess the performance of the model. The ACPA model had an area under the curve of 0.853 (95% confidence interval [CI], 0.818-0.889) in the derivation cohort and 0.862 (95% CI, 0.807-0.916) in the validation cohort, which indicated the good discrimination power of the model. Calibration plots showed high agreement between the estimated and observed probabilities. Decision curve analysis indicated that ITP patients could benefit from the clinical application of the ACPA model. To summarize, the ACPA model was developed and validated to predict the occurrence of hospitalization for CAP, which might help identify ITP patients with a high risk of hospitalization for CAP.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:4 |
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| Enthalten in: |
Blood advances - 4(2020), 22 vom: 24. Nov., Seite 5846-5857 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wu, Ye-Jun [VerfasserIn] |
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Date Completed 14.05.2021 Date Revised 25.09.2021 published: Print ErratumIn: Blood Adv. 2021 Jan 26;5(2):365. - PMID 33496730 Citation Status MEDLINE |
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| doi: |
10.1182/bloodadvances.2020003074 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM317991973 |
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| 100 | 1 | |a Wu, Ye-Jun |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A risk score for predicting hospitalization for community-acquired pneumonia in ITP using nationally representative data |
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| 500 | |a Date Revised 25.09.2021 | ||
| 500 | |a published: Print | ||
| 500 | |a ErratumIn: Blood Adv. 2021 Jan 26;5(2):365. - PMID 33496730 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2020 by The American Society of Hematology. | ||
| 520 | |a Infection is one of the primary causes of death from immune thrombocytopenia (ITP), and the lungs are the most common site of infection. We identified the factors associated with hospitalization for community-acquired pneumonia (CAP) in nonsplenectomized adults with ITP and established the [corrected] (ACPA) prediction model to predict the incidence of hospitalization for CAP. This was a retrospective study of nonsplenectomized adult patients with ITP from 10 large medical centers in China. The derivation cohort included 145 ITP inpatients with CAP and 1360 inpatients without CAP from 5 medical centers, and the validation cohort included the remaining 63 ITP inpatients with CAP and 526 inpatients without CAP from the other 5 centers. The 4-item ACPA model, which included age, Charlson Comorbidity Index score, initial platelet count, and initial absolute lymphocyte count, was established by multivariable analysis of the derivation cohort. Internal and external validation were conducted to assess the performance of the model. The ACPA model had an area under the curve of 0.853 (95% confidence interval [CI], 0.818-0.889) in the derivation cohort and 0.862 (95% CI, 0.807-0.916) in the validation cohort, which indicated the good discrimination power of the model. Calibration plots showed high agreement between the estimated and observed probabilities. Decision curve analysis indicated that ITP patients could benefit from the clinical application of the ACPA model. To summarize, the ACPA model was developed and validated to predict the occurrence of hospitalization for CAP, which might help identify ITP patients with a high risk of hospitalization for CAP | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Hou, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hui-Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Liang-Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Lin-Hua |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Ru |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Hong-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Ze-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wen-Sheng |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Xu-Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Hai-Xia |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Qiao-Zhu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xing-Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Qiu-Sha |e verfasserin |4 aut | |
| 700 | 1 | |a He, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xiang-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xiao-Su |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Ying-Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Lan-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yue-Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Qian-Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiao-Hui |e verfasserin |4 aut | |
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