Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China
PurposeThe study aimed to assess factors associated with early infection and identify patients at high risk of developing infection in multiple myeloma.MethodsThe study retrospectively analyzed patients with MM seen at two medical centers between January 2013 and June 2019. One medical center report...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.772015/full |
| _version_ | 1828894970912702464 |
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| author | Yufeng Shang Weida Wang Weida Wang Yuxing Liang Natasha Mupeta Kaweme Qian Wang Minghui Liu Xiaoqin Chen Zhongjun Xia Zhongjun Xia Fuling Zhou |
| author_facet | Yufeng Shang Weida Wang Weida Wang Yuxing Liang Natasha Mupeta Kaweme Qian Wang Minghui Liu Xiaoqin Chen Zhongjun Xia Zhongjun Xia Fuling Zhou |
| author_sort | Yufeng Shang |
| collection | DOAJ |
| description | PurposeThe study aimed to assess factors associated with early infection and identify patients at high risk of developing infection in multiple myeloma.MethodsThe study retrospectively analyzed patients with MM seen at two medical centers between January 2013 and June 2019. One medical center reported 745 cases, of which 540 of the cases were available for analysis and were further subdivided into training cohort and internal validation cohort. 169 cases from the other medical center served as an external validation cohort. The least absolute shrinkage and selection operator (Lasso) regression model was used for data dimension reduction, feature selection, and model building.ResultsBacteria and the respiratory tract were the most common pathogen and localization of infection, respectively. In the training cohort, PS≥2, HGB<35g/L of the lower limit of normal range, β2MG≥6.0mg/L, and GLB≥2.1 times the upper limit of normal range were identified as factors associated with early grade ≥ 3 infections by Lasso regression. An infection risk model of MM (IRMM) was established to define high-, moderate- and low-risk groups, which showed significantly different rates of infection in the training cohort (46.5% vs. 22.1% vs. 8.8%, p<0.0001), internal validation cohort (37.9% vs. 24.1% vs. 13.0%, p=0.009) and external validation cohort (40.0% vs. 29.2% vs. 8.5%, p=0.0003). IRMM displayed good calibration (p<0.05) and discrimination with AUC values of 0.76, 0.67 and 0.71 in the three cohorts, respectively. Furthermore, IRMM still showed good classification ability in immunomodulatory (IMiD) based regimens, proteasome-inhibitors (PI) based regimens and combined IMiD and PI regimens.ConclusionIn this study, we determined risk factors for early grade ≥ 3 infection and established a predictive model to help clinicians identify MM patients with high-risk infection. |
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| format | Article |
| id | doaj.art-59ccca9a52224a5798356115b3b41cfa |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-13T14:21:03Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-59ccca9a52224a5798356115b3b41cfa2022-12-21T23:42:06ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-03-011210.3389/fonc.2022.772015772015Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in ChinaYufeng Shang0Weida Wang1Weida Wang2Yuxing Liang3Natasha Mupeta Kaweme4Qian Wang5Minghui Liu6Xiaoqin Chen7Zhongjun Xia8Zhongjun Xia9Fuling Zhou10Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaDepartment of Hematologic Oncology, State Key Laboratory of Oncology in South China/Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, ChinaDepartment of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaDepartment of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, ChinaDepartment of Hematologic Oncology, State Key Laboratory of Oncology in South China/Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou, ChinaDepartment of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaPurposeThe study aimed to assess factors associated with early infection and identify patients at high risk of developing infection in multiple myeloma.MethodsThe study retrospectively analyzed patients with MM seen at two medical centers between January 2013 and June 2019. One medical center reported 745 cases, of which 540 of the cases were available for analysis and were further subdivided into training cohort and internal validation cohort. 169 cases from the other medical center served as an external validation cohort. The least absolute shrinkage and selection operator (Lasso) regression model was used for data dimension reduction, feature selection, and model building.ResultsBacteria and the respiratory tract were the most common pathogen and localization of infection, respectively. In the training cohort, PS≥2, HGB<35g/L of the lower limit of normal range, β2MG≥6.0mg/L, and GLB≥2.1 times the upper limit of normal range were identified as factors associated with early grade ≥ 3 infections by Lasso regression. An infection risk model of MM (IRMM) was established to define high-, moderate- and low-risk groups, which showed significantly different rates of infection in the training cohort (46.5% vs. 22.1% vs. 8.8%, p<0.0001), internal validation cohort (37.9% vs. 24.1% vs. 13.0%, p=0.009) and external validation cohort (40.0% vs. 29.2% vs. 8.5%, p=0.0003). IRMM displayed good calibration (p<0.05) and discrimination with AUC values of 0.76, 0.67 and 0.71 in the three cohorts, respectively. Furthermore, IRMM still showed good classification ability in immunomodulatory (IMiD) based regimens, proteasome-inhibitors (PI) based regimens and combined IMiD and PI regimens.ConclusionIn this study, we determined risk factors for early grade ≥ 3 infection and established a predictive model to help clinicians identify MM patients with high-risk infection.https://www.frontiersin.org/articles/10.3389/fonc.2022.772015/fullmultiple myelomainfectionrisk factorsinfection modelnovel drug |
| spellingShingle | Yufeng Shang Weida Wang Weida Wang Yuxing Liang Natasha Mupeta Kaweme Qian Wang Minghui Liu Xiaoqin Chen Zhongjun Xia Zhongjun Xia Fuling Zhou Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China Frontiers in Oncology multiple myeloma infection risk factors infection model novel drug |
| title | Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China |
| title_full | Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China |
| title_fullStr | Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China |
| title_full_unstemmed | Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China |
| title_short | Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China |
| title_sort | development of a risk assessment model for early grade ≥ 3 infection during the first 3 months in patients newly diagnosed with multiple myeloma based on a multicenter real world analysis in china |
| topic | multiple myeloma infection risk factors infection model novel drug |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.772015/full |
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