Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study

Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yao Yu [verfasserIn] Changsheng Zhang [verfasserIn] Faqiang Zhang [verfasserIn] Chang Liu [verfasserIn] Hao Li [verfasserIn] Jingsheng Lou [verfasserIn] Zhipeng Xu [verfasserIn] Yanhong Liu [verfasserIn] Jiangbei Cao [verfasserIn] Weidong Mi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction

Übergeordnetes Werk:	In: BMC Anesthesiology - BMC, 2003, 22(2022), 1, Seite 12
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12871-022-01566-z

Katalog-ID:	DOAJ076114074

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520			\|a Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021.
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allfields	10.1186/s12871-022-01566-z doi (DE-627)DOAJ076114074 (DE-599)DOAJ97e643251ade42438ae07b1320114abf DE-627 ger DE-627 rakwb eng RD78.3-87.3 Yao Yu verfasserin aut Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021. Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction Anesthesiology Changsheng Zhang verfasserin aut Faqiang Zhang verfasserin aut Chang Liu verfasserin aut Hao Li verfasserin aut Jingsheng Lou verfasserin aut Zhipeng Xu verfasserin aut Yanhong Liu verfasserin aut Jiangbei Cao verfasserin aut Weidong Mi verfasserin aut In BMC Anesthesiology BMC, 2003 22(2022), 1, Seite 12 (DE-627)355422115 (DE-600)2091252-3 14712253 nnns volume:22 year:2022 number:1 pages:12 https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/article/97e643251ade42438ae07b1320114abf kostenfrei https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/toc/1471-2253 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 22 2022 1 12
spelling	10.1186/s12871-022-01566-z doi (DE-627)DOAJ076114074 (DE-599)DOAJ97e643251ade42438ae07b1320114abf DE-627 ger DE-627 rakwb eng RD78.3-87.3 Yao Yu verfasserin aut Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021. Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction Anesthesiology Changsheng Zhang verfasserin aut Faqiang Zhang verfasserin aut Chang Liu verfasserin aut Hao Li verfasserin aut Jingsheng Lou verfasserin aut Zhipeng Xu verfasserin aut Yanhong Liu verfasserin aut Jiangbei Cao verfasserin aut Weidong Mi verfasserin aut In BMC Anesthesiology BMC, 2003 22(2022), 1, Seite 12 (DE-627)355422115 (DE-600)2091252-3 14712253 nnns volume:22 year:2022 number:1 pages:12 https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/article/97e643251ade42438ae07b1320114abf kostenfrei https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/toc/1471-2253 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 22 2022 1 12
allfields_unstemmed	10.1186/s12871-022-01566-z doi (DE-627)DOAJ076114074 (DE-599)DOAJ97e643251ade42438ae07b1320114abf DE-627 ger DE-627 rakwb eng RD78.3-87.3 Yao Yu verfasserin aut Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021. Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction Anesthesiology Changsheng Zhang verfasserin aut Faqiang Zhang verfasserin aut Chang Liu verfasserin aut Hao Li verfasserin aut Jingsheng Lou verfasserin aut Zhipeng Xu verfasserin aut Yanhong Liu verfasserin aut Jiangbei Cao verfasserin aut Weidong Mi verfasserin aut In BMC Anesthesiology BMC, 2003 22(2022), 1, Seite 12 (DE-627)355422115 (DE-600)2091252-3 14712253 nnns volume:22 year:2022 number:1 pages:12 https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/article/97e643251ade42438ae07b1320114abf kostenfrei https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/toc/1471-2253 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 22 2022 1 12
allfieldsGer	10.1186/s12871-022-01566-z doi (DE-627)DOAJ076114074 (DE-599)DOAJ97e643251ade42438ae07b1320114abf DE-627 ger DE-627 rakwb eng RD78.3-87.3 Yao Yu verfasserin aut Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021. Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction Anesthesiology Changsheng Zhang verfasserin aut Faqiang Zhang verfasserin aut Chang Liu verfasserin aut Hao Li verfasserin aut Jingsheng Lou verfasserin aut Zhipeng Xu verfasserin aut Yanhong Liu verfasserin aut Jiangbei Cao verfasserin aut Weidong Mi verfasserin aut In BMC Anesthesiology BMC, 2003 22(2022), 1, Seite 12 (DE-627)355422115 (DE-600)2091252-3 14712253 nnns volume:22 year:2022 number:1 pages:12 https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/article/97e643251ade42438ae07b1320114abf kostenfrei https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/toc/1471-2253 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 22 2022 1 12
allfieldsSound	10.1186/s12871-022-01566-z doi (DE-627)DOAJ076114074 (DE-599)DOAJ97e643251ade42438ae07b1320114abf DE-627 ger DE-627 rakwb eng RD78.3-87.3 Yao Yu verfasserin aut Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021. Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction Anesthesiology Changsheng Zhang verfasserin aut Faqiang Zhang verfasserin aut Chang Liu verfasserin aut Hao Li verfasserin aut Jingsheng Lou verfasserin aut Zhipeng Xu verfasserin aut Yanhong Liu verfasserin aut Jiangbei Cao verfasserin aut Weidong Mi verfasserin aut In BMC Anesthesiology BMC, 2003 22(2022), 1, Seite 12 (DE-627)355422115 (DE-600)2091252-3 14712253 nnns volume:22 year:2022 number:1 pages:12 https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/article/97e643251ade42438ae07b1320114abf kostenfrei https://doi.org/10.1186/s12871-022-01566-z kostenfrei https://doaj.org/toc/1471-2253 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_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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 22 2022 1 12
language	English
source	In BMC Anesthesiology 22(2022), 1, Seite 12 volume:22 year:2022 number:1 pages:12
sourceStr	In BMC Anesthesiology 22(2022), 1, Seite 12 volume:22 year:2022 number:1 pages:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction Anesthesiology
isfreeaccess_bool	true
container_title	BMC Anesthesiology
authorswithroles_txt_mv	Yao Yu @@aut@@ Changsheng Zhang @@aut@@ Faqiang Zhang @@aut@@ Chang Liu @@aut@@ Hao Li @@aut@@ Jingsheng Lou @@aut@@ Zhipeng Xu @@aut@@ Yanhong Liu @@aut@@ Jiangbei Cao @@aut@@ Weidong Mi @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	355422115
id	DOAJ076114074
language_de	englisch
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Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. 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callnumber-first	R - Medicine
author	Yao Yu
spellingShingle	Yao Yu misc RD78.3-87.3 misc Acute kidney injury misc Hepatectomy misc Elderly patients misc Renal injury misc Risk score misc Prediction misc Anesthesiology Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study
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topic_title	RD78.3-87.3 Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study Acute kidney injury Hepatectomy Elderly patients Renal injury Risk score Prediction
topic	misc RD78.3-87.3 misc Acute kidney injury misc Hepatectomy misc Elderly patients misc Renal injury misc Risk score misc Prediction misc Anesthesiology
topic_unstemmed	misc RD78.3-87.3 misc Acute kidney injury misc Hepatectomy misc Elderly patients misc Renal injury misc Risk score misc Prediction misc Anesthesiology
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title	Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study
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title_full	Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study
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author_browse	Yao Yu Changsheng Zhang Faqiang Zhang Chang Liu Hao Li Jingsheng Lou Zhipeng Xu Yanhong Liu Jiangbei Cao Weidong Mi
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title_sort	development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study
callnumber	RD78.3-87.3
title_auth	Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study
abstract	Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021.
abstractGer	Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021.
abstract_unstemmed	Abstract Background Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Early identification of high-risk patients of developing postoperative AKI can optimize perioperative renal management and facilitate patient survival. The present study aims to develop and validate a nomogram to predict postoperative AKI after liver resection in older patients. Methods A retrospective observational study was conducted involving data from 843 older patients scheduled for liver resection at a single tertiary high caseload general hospital between 2012 and 2019. The data were randomly divided into training (70%, n = 599) and validation (30%, n = 244) datasets. The training cohort was used to construct a predictive nomogram for postoperative AKI with the logistic regression model which was confirmed by a validation cohort. The model was evaluated by receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis in the validation cohort. A summary risk score was also constructed for identifying postoperative AKI patients. Results Postoperative AKI occurred in 155 (18.4%) patients and was highly associated with in-hospital mortality (5.2% vs. 0.7%, P < 0.001). The six predictors selected and assembled into the nomogram included age, preexisting chronic kidney disease (CKD), non-steroidal anti-inflammatory drugs (NSAIDs) usage, intraoperative hepatic inflow occlusion, blood loss, and transfusion. The predictive nomogram performed well in terms of discrimination with area under ROC curve (AUC) in training (0.73, 95% confidence interval (CI): 0.68–0.78) and validation (0.71, 95% CI: 0.63–0.80) datasets. The nomogram was well-calibrated with the Hosmer-Lemeshow chi-square value of 9.68 (P = 0.47). Decision curve analysis demonstrated a significant clinical benefit. The summary risk score calculated as the sum of points from the six variables (one point for each variable) performed as well as the nomogram in identifying the risk of AKI (AUC 0.71, 95% CI: 0.66–0.76). Conclusion This nomogram and summary risk score accurately predicted postoperative AKI using six clinically accessible variables, with potential application in facilitating the optimized perioperative renal management in older patients undergoing liver resection. Trial registration NCT04922866 , retrospectively registered on clinicaltrials.gov on June 11, 2021.
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title_short	Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study
url	https://doi.org/10.1186/s12871-022-01566-z https://doaj.org/article/97e643251ade42438ae07b1320114abf https://doaj.org/toc/1471-2253
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Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study

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