The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer
Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomar...
Ausführliche Beschreibung
Autor*in: |
Hailun Xie [verfasserIn] Guotian Ruan [verfasserIn] Lishuang Wei [verfasserIn] Li Deng [verfasserIn] Qi Zhang [verfasserIn] Yizhong Ge [verfasserIn] Mengmeng Song [verfasserIn] Xi Zhang [verfasserIn] Shiqi Lin [verfasserIn] Xiaoyue Liu [verfasserIn] Ming Yang [verfasserIn] Chunhua Song [verfasserIn] Xiaowei Zhang [verfasserIn] Hanping Shi [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2023 |
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In: Journal of Cachexia, Sarcopenia and Muscle - Wiley, 2016, 14(2023), 2, Seite 869-878 |
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volume:14 ; year:2023 ; number:2 ; pages:869-878 |
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DOI / URN: |
10.1002/jcsm.13199 |
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DOAJ088806766 |
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245 | 1 | 4 | |a The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer |
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520 | |a Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. | ||
650 | 4 | |a Systemic inflammation | |
650 | 4 | |a Biomarker | |
650 | 4 | |a Prognosis | |
650 | 4 | |a Cachexia | |
650 | 4 | |a Expenses | |
650 | 4 | |a Non‐small cell lung cancer | |
653 | 0 | |a Diseases of the musculoskeletal system | |
653 | 0 | |a Human anatomy | |
700 | 0 | |a Guotian Ruan |e verfasserin |4 aut | |
700 | 0 | |a Lishuang Wei |e verfasserin |4 aut | |
700 | 0 | |a Li Deng |e verfasserin |4 aut | |
700 | 0 | |a Qi Zhang |e verfasserin |4 aut | |
700 | 0 | |a Yizhong Ge |e verfasserin |4 aut | |
700 | 0 | |a Mengmeng Song |e verfasserin |4 aut | |
700 | 0 | |a Xi Zhang |e verfasserin |4 aut | |
700 | 0 | |a Shiqi Lin |e verfasserin |4 aut | |
700 | 0 | |a Xiaoyue Liu |e verfasserin |4 aut | |
700 | 0 | |a Ming Yang |e verfasserin |4 aut | |
700 | 0 | |a Chunhua Song |e verfasserin |4 aut | |
700 | 0 | |a Xiaowei Zhang |e verfasserin |4 aut | |
700 | 0 | |a Hanping Shi |e verfasserin |4 aut | |
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10.1002/jcsm.13199 doi (DE-627)DOAJ088806766 (DE-599)DOAJf96d5e80478f448daec801b3b93a03fa DE-627 ger DE-627 rakwb eng RC925-935 QM1-695 Hailun Xie verfasserin aut The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. Systemic inflammation Biomarker Prognosis Cachexia Expenses Non‐small cell lung cancer Diseases of the musculoskeletal system Human anatomy Guotian Ruan verfasserin aut Lishuang Wei verfasserin aut Li Deng verfasserin aut Qi Zhang verfasserin aut Yizhong Ge verfasserin aut Mengmeng Song verfasserin aut Xi Zhang verfasserin aut Shiqi Lin verfasserin aut Xiaoyue Liu verfasserin aut Ming Yang verfasserin aut Chunhua Song verfasserin aut Xiaowei Zhang verfasserin aut Hanping Shi verfasserin aut In Journal of Cachexia, Sarcopenia and Muscle Wiley, 2016 14(2023), 2, Seite 869-878 (DE-627)642886121 (DE-600)2586864-0 21906009 nnns volume:14 year:2023 number:2 pages:869-878 https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/article/f96d5e80478f448daec801b3b93a03fa kostenfrei https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/toc/2190-5991 Journal toc kostenfrei https://doaj.org/toc/2190-6009 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 2 869-878 |
spelling |
10.1002/jcsm.13199 doi (DE-627)DOAJ088806766 (DE-599)DOAJf96d5e80478f448daec801b3b93a03fa DE-627 ger DE-627 rakwb eng RC925-935 QM1-695 Hailun Xie verfasserin aut The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. Systemic inflammation Biomarker Prognosis Cachexia Expenses Non‐small cell lung cancer Diseases of the musculoskeletal system Human anatomy Guotian Ruan verfasserin aut Lishuang Wei verfasserin aut Li Deng verfasserin aut Qi Zhang verfasserin aut Yizhong Ge verfasserin aut Mengmeng Song verfasserin aut Xi Zhang verfasserin aut Shiqi Lin verfasserin aut Xiaoyue Liu verfasserin aut Ming Yang verfasserin aut Chunhua Song verfasserin aut Xiaowei Zhang verfasserin aut Hanping Shi verfasserin aut In Journal of Cachexia, Sarcopenia and Muscle Wiley, 2016 14(2023), 2, Seite 869-878 (DE-627)642886121 (DE-600)2586864-0 21906009 nnns volume:14 year:2023 number:2 pages:869-878 https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/article/f96d5e80478f448daec801b3b93a03fa kostenfrei https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/toc/2190-5991 Journal toc kostenfrei https://doaj.org/toc/2190-6009 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 2 869-878 |
allfields_unstemmed |
10.1002/jcsm.13199 doi (DE-627)DOAJ088806766 (DE-599)DOAJf96d5e80478f448daec801b3b93a03fa DE-627 ger DE-627 rakwb eng RC925-935 QM1-695 Hailun Xie verfasserin aut The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. Systemic inflammation Biomarker Prognosis Cachexia Expenses Non‐small cell lung cancer Diseases of the musculoskeletal system Human anatomy Guotian Ruan verfasserin aut Lishuang Wei verfasserin aut Li Deng verfasserin aut Qi Zhang verfasserin aut Yizhong Ge verfasserin aut Mengmeng Song verfasserin aut Xi Zhang verfasserin aut Shiqi Lin verfasserin aut Xiaoyue Liu verfasserin aut Ming Yang verfasserin aut Chunhua Song verfasserin aut Xiaowei Zhang verfasserin aut Hanping Shi verfasserin aut In Journal of Cachexia, Sarcopenia and Muscle Wiley, 2016 14(2023), 2, Seite 869-878 (DE-627)642886121 (DE-600)2586864-0 21906009 nnns volume:14 year:2023 number:2 pages:869-878 https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/article/f96d5e80478f448daec801b3b93a03fa kostenfrei https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/toc/2190-5991 Journal toc kostenfrei https://doaj.org/toc/2190-6009 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 2 869-878 |
allfieldsGer |
10.1002/jcsm.13199 doi (DE-627)DOAJ088806766 (DE-599)DOAJf96d5e80478f448daec801b3b93a03fa DE-627 ger DE-627 rakwb eng RC925-935 QM1-695 Hailun Xie verfasserin aut The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. Systemic inflammation Biomarker Prognosis Cachexia Expenses Non‐small cell lung cancer Diseases of the musculoskeletal system Human anatomy Guotian Ruan verfasserin aut Lishuang Wei verfasserin aut Li Deng verfasserin aut Qi Zhang verfasserin aut Yizhong Ge verfasserin aut Mengmeng Song verfasserin aut Xi Zhang verfasserin aut Shiqi Lin verfasserin aut Xiaoyue Liu verfasserin aut Ming Yang verfasserin aut Chunhua Song verfasserin aut Xiaowei Zhang verfasserin aut Hanping Shi verfasserin aut In Journal of Cachexia, Sarcopenia and Muscle Wiley, 2016 14(2023), 2, Seite 869-878 (DE-627)642886121 (DE-600)2586864-0 21906009 nnns volume:14 year:2023 number:2 pages:869-878 https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/article/f96d5e80478f448daec801b3b93a03fa kostenfrei https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/toc/2190-5991 Journal toc kostenfrei https://doaj.org/toc/2190-6009 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 2 869-878 |
allfieldsSound |
10.1002/jcsm.13199 doi (DE-627)DOAJ088806766 (DE-599)DOAJf96d5e80478f448daec801b3b93a03fa DE-627 ger DE-627 rakwb eng RC925-935 QM1-695 Hailun Xie verfasserin aut The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. Systemic inflammation Biomarker Prognosis Cachexia Expenses Non‐small cell lung cancer Diseases of the musculoskeletal system Human anatomy Guotian Ruan verfasserin aut Lishuang Wei verfasserin aut Li Deng verfasserin aut Qi Zhang verfasserin aut Yizhong Ge verfasserin aut Mengmeng Song verfasserin aut Xi Zhang verfasserin aut Shiqi Lin verfasserin aut Xiaoyue Liu verfasserin aut Ming Yang verfasserin aut Chunhua Song verfasserin aut Xiaowei Zhang verfasserin aut Hanping Shi verfasserin aut In Journal of Cachexia, Sarcopenia and Muscle Wiley, 2016 14(2023), 2, Seite 869-878 (DE-627)642886121 (DE-600)2586864-0 21906009 nnns volume:14 year:2023 number:2 pages:869-878 https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/article/f96d5e80478f448daec801b3b93a03fa kostenfrei https://doi.org/10.1002/jcsm.13199 kostenfrei https://doaj.org/toc/2190-5991 Journal toc kostenfrei https://doaj.org/toc/2190-6009 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023 2 869-878 |
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Hailun Xie @@aut@@ Guotian Ruan @@aut@@ Lishuang Wei @@aut@@ Li Deng @@aut@@ Qi Zhang @@aut@@ Yizhong Ge @@aut@@ Mengmeng Song @@aut@@ Xi Zhang @@aut@@ Shiqi Lin @@aut@@ Xiaoyue Liu @@aut@@ Ming Yang @@aut@@ Chunhua Song @@aut@@ Xiaowei Zhang @@aut@@ Hanping Shi @@aut@@ |
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Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. 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Hailun Xie misc RC925-935 misc QM1-695 misc Systemic inflammation misc Biomarker misc Prognosis misc Cachexia misc Expenses misc Non‐small cell lung cancer misc Diseases of the musculoskeletal system misc Human anatomy The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer |
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RC925-935 QM1-695 The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer Systemic inflammation Biomarker Prognosis Cachexia Expenses Non‐small cell lung cancer |
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Hailun Xie Guotian Ruan Lishuang Wei Li Deng Qi Zhang Yizhong Ge Mengmeng Song Xi Zhang Shiqi Lin Xiaoyue Liu Ming Yang Chunhua Song Xiaowei Zhang Hanping Shi |
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inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer |
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The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer |
abstract |
Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. |
abstractGer |
Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. |
abstract_unstemmed |
Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC. |
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The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer |
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https://doi.org/10.1002/jcsm.13199 https://doaj.org/article/f96d5e80478f448daec801b3b93a03fa https://doaj.org/toc/2190-5991 https://doaj.org/toc/2190-6009 |
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Guotian Ruan Lishuang Wei Li Deng Qi Zhang Yizhong Ge Mengmeng Song Xi Zhang Shiqi Lin Xiaoyue Liu Ming Yang Chunhua Song Xiaowei Zhang Hanping Shi |
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Guotian Ruan Lishuang Wei Li Deng Qi Zhang Yizhong Ge Mengmeng Song Xi Zhang Shiqi Lin Xiaoyue Liu Ming Yang Chunhua Song Xiaowei Zhang Hanping Shi |
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642886121 |
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RC - Internal Medicine |
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doi_str |
10.1002/jcsm.13199 |
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RC925-935 |
up_date |
2024-07-03T19:36:41.220Z |
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Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. 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