Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram

Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion gr...
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Autor*in:	Liyi Chen [verfasserIn] Zhaoping Gan [verfasserIn] Shengsheng Huang [verfasserIn] Tuo Liang [verfasserIn] Xuhua Sun [verfasserIn] Ming Yi [verfasserIn] Shaofeng Wu [verfasserIn] Binguang Fan [verfasserIn] Jiarui Chen [verfasserIn] Tianyou Chen [verfasserIn] Zhen Ye [verfasserIn] Wuhua Chen [verfasserIn] Hao Li [verfasserIn] Jie Jiang [verfasserIn] Hao Guo [verfasserIn] Yuanlin Yao [verfasserIn] Shian Liao [verfasserIn] Chaojie Yu [verfasserIn] Chong Liu [verfasserIn] Xinli Zhan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Transfusion Spinal tuberculosis Nomogram Surgery

Übergeordnetes Werk:	In: BMC Musculoskeletal Disorders - BMC, 2003, 23(2022), 1, Seite 15
Übergeordnetes Werk:	volume:23 ; year:2022 ; number:1 ; pages:15

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12891-022-05132-z

Katalog-ID:	DOAJ075011301

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520			\|a Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.
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allfields	10.1186/s12891-022-05132-z doi (DE-627)DOAJ075011301 (DE-599)DOAJ34b3b0aed460437a8c1405cddad18a56 DE-627 ger DE-627 rakwb eng RC925-935 Liyi Chen verfasserin aut Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery. Transfusion Spinal tuberculosis Nomogram Surgery Diseases of the musculoskeletal system Zhaoping Gan verfasserin aut Shengsheng Huang verfasserin aut Tuo Liang verfasserin aut Xuhua Sun verfasserin aut Ming Yi verfasserin aut Shaofeng Wu verfasserin aut Binguang Fan verfasserin aut Jiarui Chen verfasserin aut Tianyou Chen verfasserin aut Zhen Ye verfasserin aut Wuhua Chen verfasserin aut Hao Li verfasserin aut Jie Jiang verfasserin aut Hao Guo verfasserin aut Yuanlin Yao verfasserin aut Shian Liao verfasserin aut Chaojie Yu verfasserin aut Chong Liu verfasserin aut Xinli Zhan verfasserin aut In BMC Musculoskeletal Disorders BMC, 2003 23(2022), 1, Seite 15 (DE-627)326643745 (DE-600)2041355-5 14712474 nnns volume:23 year:2022 number:1 pages:15 https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/article/34b3b0aed460437a8c1405cddad18a56 kostenfrei https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/toc/1471-2474 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 15
spelling	10.1186/s12891-022-05132-z doi (DE-627)DOAJ075011301 (DE-599)DOAJ34b3b0aed460437a8c1405cddad18a56 DE-627 ger DE-627 rakwb eng RC925-935 Liyi Chen verfasserin aut Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery. Transfusion Spinal tuberculosis Nomogram Surgery Diseases of the musculoskeletal system Zhaoping Gan verfasserin aut Shengsheng Huang verfasserin aut Tuo Liang verfasserin aut Xuhua Sun verfasserin aut Ming Yi verfasserin aut Shaofeng Wu verfasserin aut Binguang Fan verfasserin aut Jiarui Chen verfasserin aut Tianyou Chen verfasserin aut Zhen Ye verfasserin aut Wuhua Chen verfasserin aut Hao Li verfasserin aut Jie Jiang verfasserin aut Hao Guo verfasserin aut Yuanlin Yao verfasserin aut Shian Liao verfasserin aut Chaojie Yu verfasserin aut Chong Liu verfasserin aut Xinli Zhan verfasserin aut In BMC Musculoskeletal Disorders BMC, 2003 23(2022), 1, Seite 15 (DE-627)326643745 (DE-600)2041355-5 14712474 nnns volume:23 year:2022 number:1 pages:15 https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/article/34b3b0aed460437a8c1405cddad18a56 kostenfrei https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/toc/1471-2474 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 15
allfields_unstemmed	10.1186/s12891-022-05132-z doi (DE-627)DOAJ075011301 (DE-599)DOAJ34b3b0aed460437a8c1405cddad18a56 DE-627 ger DE-627 rakwb eng RC925-935 Liyi Chen verfasserin aut Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery. Transfusion Spinal tuberculosis Nomogram Surgery Diseases of the musculoskeletal system Zhaoping Gan verfasserin aut Shengsheng Huang verfasserin aut Tuo Liang verfasserin aut Xuhua Sun verfasserin aut Ming Yi verfasserin aut Shaofeng Wu verfasserin aut Binguang Fan verfasserin aut Jiarui Chen verfasserin aut Tianyou Chen verfasserin aut Zhen Ye verfasserin aut Wuhua Chen verfasserin aut Hao Li verfasserin aut Jie Jiang verfasserin aut Hao Guo verfasserin aut Yuanlin Yao verfasserin aut Shian Liao verfasserin aut Chaojie Yu verfasserin aut Chong Liu verfasserin aut Xinli Zhan verfasserin aut In BMC Musculoskeletal Disorders BMC, 2003 23(2022), 1, Seite 15 (DE-627)326643745 (DE-600)2041355-5 14712474 nnns volume:23 year:2022 number:1 pages:15 https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/article/34b3b0aed460437a8c1405cddad18a56 kostenfrei https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/toc/1471-2474 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 15
allfieldsGer	10.1186/s12891-022-05132-z doi (DE-627)DOAJ075011301 (DE-599)DOAJ34b3b0aed460437a8c1405cddad18a56 DE-627 ger DE-627 rakwb eng RC925-935 Liyi Chen verfasserin aut Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery. Transfusion Spinal tuberculosis Nomogram Surgery Diseases of the musculoskeletal system Zhaoping Gan verfasserin aut Shengsheng Huang verfasserin aut Tuo Liang verfasserin aut Xuhua Sun verfasserin aut Ming Yi verfasserin aut Shaofeng Wu verfasserin aut Binguang Fan verfasserin aut Jiarui Chen verfasserin aut Tianyou Chen verfasserin aut Zhen Ye verfasserin aut Wuhua Chen verfasserin aut Hao Li verfasserin aut Jie Jiang verfasserin aut Hao Guo verfasserin aut Yuanlin Yao verfasserin aut Shian Liao verfasserin aut Chaojie Yu verfasserin aut Chong Liu verfasserin aut Xinli Zhan verfasserin aut In BMC Musculoskeletal Disorders BMC, 2003 23(2022), 1, Seite 15 (DE-627)326643745 (DE-600)2041355-5 14712474 nnns volume:23 year:2022 number:1 pages:15 https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/article/34b3b0aed460437a8c1405cddad18a56 kostenfrei https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/toc/1471-2474 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 15
allfieldsSound	10.1186/s12891-022-05132-z doi (DE-627)DOAJ075011301 (DE-599)DOAJ34b3b0aed460437a8c1405cddad18a56 DE-627 ger DE-627 rakwb eng RC925-935 Liyi Chen verfasserin aut Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery. Transfusion Spinal tuberculosis Nomogram Surgery Diseases of the musculoskeletal system Zhaoping Gan verfasserin aut Shengsheng Huang verfasserin aut Tuo Liang verfasserin aut Xuhua Sun verfasserin aut Ming Yi verfasserin aut Shaofeng Wu verfasserin aut Binguang Fan verfasserin aut Jiarui Chen verfasserin aut Tianyou Chen verfasserin aut Zhen Ye verfasserin aut Wuhua Chen verfasserin aut Hao Li verfasserin aut Jie Jiang verfasserin aut Hao Guo verfasserin aut Yuanlin Yao verfasserin aut Shian Liao verfasserin aut Chaojie Yu verfasserin aut Chong Liu verfasserin aut Xinli Zhan verfasserin aut In BMC Musculoskeletal Disorders BMC, 2003 23(2022), 1, Seite 15 (DE-627)326643745 (DE-600)2041355-5 14712474 nnns volume:23 year:2022 number:1 pages:15 https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/article/34b3b0aed460437a8c1405cddad18a56 kostenfrei https://doi.org/10.1186/s12891-022-05132-z kostenfrei https://doaj.org/toc/1471-2474 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 15
language	English
source	In BMC Musculoskeletal Disorders 23(2022), 1, Seite 15 volume:23 year:2022 number:1 pages:15
sourceStr	In BMC Musculoskeletal Disorders 23(2022), 1, Seite 15 volume:23 year:2022 number:1 pages:15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Transfusion Spinal tuberculosis Nomogram Surgery Diseases of the musculoskeletal system
isfreeaccess_bool	true
container_title	BMC Musculoskeletal Disorders
authorswithroles_txt_mv	Liyi Chen @@aut@@ Zhaoping Gan @@aut@@ Shengsheng Huang @@aut@@ Tuo Liang @@aut@@ Xuhua Sun @@aut@@ Ming Yi @@aut@@ Shaofeng Wu @@aut@@ Binguang Fan @@aut@@ Jiarui Chen @@aut@@ Tianyou Chen @@aut@@ Zhen Ye @@aut@@ Wuhua Chen @@aut@@ Hao Li @@aut@@ Jie Jiang @@aut@@ Hao Guo @@aut@@ Yuanlin Yao @@aut@@ Shian Liao @@aut@@ Chaojie Yu @@aut@@ Chong Liu @@aut@@ Xinli Zhan @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	326643745
id	DOAJ075011301
language_de	englisch
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Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. 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callnumber-first	R - Medicine
author	Liyi Chen
spellingShingle	Liyi Chen misc RC925-935 misc Transfusion misc Spinal tuberculosis misc Nomogram misc Surgery misc Diseases of the musculoskeletal system Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram
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topic_title	RC925-935 Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram Transfusion Spinal tuberculosis Nomogram Surgery
topic	misc RC925-935 misc Transfusion misc Spinal tuberculosis misc Nomogram misc Surgery misc Diseases of the musculoskeletal system
topic_unstemmed	misc RC925-935 misc Transfusion misc Spinal tuberculosis misc Nomogram misc Surgery misc Diseases of the musculoskeletal system
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title	Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram
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title_full	Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram
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author_browse	Liyi Chen Zhaoping Gan Shengsheng Huang Tuo Liang Xuhua Sun Ming Yi Shaofeng Wu Binguang Fan Jiarui Chen Tianyou Chen Zhen Ye Wuhua Chen Hao Li Jie Jiang Hao Guo Yuanlin Yao Shian Liao Chaojie Yu Chong Liu Xinli Zhan
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title_sort	blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram
callnumber	RC925-935
title_auth	Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram
abstract	Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.
abstractGer	Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.
abstract_unstemmed	Abstract Objective The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. Methods The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. Results The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%–.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01–0.79. Conclusion A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.
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title_short	Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram
url	https://doi.org/10.1186/s12891-022-05132-z https://doaj.org/article/34b3b0aed460437a8c1405cddad18a56 https://doaj.org/toc/1471-2474
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author2	Zhaoping Gan Shengsheng Huang Tuo Liang Xuhua Sun Ming Yi Shaofeng Wu Binguang Fan Jiarui Chen Tianyou Chen Zhen Ye Wuhua Chen Hao Li Jie Jiang Hao Guo Yuanlin Yao Shian Liao Chaojie Yu Chong Liu Xinli Zhan
author2Str	Zhaoping Gan Shengsheng Huang Tuo Liang Xuhua Sun Ming Yi Shaofeng Wu Binguang Fan Jiarui Chen Tianyou Chen Zhen Ye Wuhua Chen Hao Li Jie Jiang Hao Guo Yuanlin Yao Shian Liao Chaojie Yu Chong Liu Xinli Zhan
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