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The 6-hour lactate clearance rate in predicting 30-day mortality in cardiogenic shock

Early evaluation of prognosis in cardiogenic shock (CS) is crucial for tailored treatment selection. Both lactate clearance and lactate levels are considered useful prognostic biomarkers in patients with CS. However, there is yet no literature comparing the 6-hour lactate clearance rate (Δ6Lac) with...

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Published in:Journal of intensive medicine 2024-07, Vol.4 (3), p.393-399
Main Authors: Wang, Junfeng, Ji, Mingxia
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description Early evaluation of prognosis in cardiogenic shock (CS) is crucial for tailored treatment selection. Both lactate clearance and lactate levels are considered useful prognostic biomarkers in patients with CS. However, there is yet no literature comparing the 6-hour lactate clearance rate (Δ6Lac) with lactate levels measured at admission (L1) and after 6 h (L2) to predict 30-day mortality in CS. In this observational cohort study, 95 patients with CS were treated at Department of Intensive Care Unit, Yiwu Central Hospital between January 2020 and December 2022. Of these, 88 patients met the eligibility criteria. The lactate levels were measured after admission (L1) as the baseline lactate value, and were measured after 6 h (L2) following admission. The primary endpoint of the study was survival rate at 30 days. A receiver operating characteristic curve was used for data analysis. Univariate and multivariate Cox regression analyses were performed based on Δ6Lac. Kaplan–Meier (KM) survival curves were generated to compare the 30-day survival rates among L1, L2, and Δ6Lac. The Δ6Lac model showed the highest area under the curve value (0.839), followed by the L2 (0.805) and L1 (0.668) models. The Δ6Lac model showed a sensitivity of 84.2% and specificity of 75.4%. The L1 and L2 models had sensitivities of 57.9% each and specificities of 89.9% and 98.6%, respectively. The cut-off values for Δ6Lac, L1, and L2 were 18.2%, 6.7 mmol/L, and 6.1 mmol/L, respectively. Univariate Cox regression analysis revealed a significant association between Δ6Lac and 30-day mortality. After adjusting for five models in multivariate Cox regression, Δ6Lac remained a significant risk factor for 30-day mortality in patients with CS. In our fifth multivariate Cox regression model, Δ6Lac remained a risk factor associated with 30-day mortality (hazard ratio [HR]=5.14, 95% confidence interval [CI]: 1.48 to 17.89, P=0.010) as well as L2 (HR=8.42, 95% CI: 1.26 to 56.22, P=0.028). The KM survival curve analysis revealed that L1 >6.7 mmol/L (HR=8.08, 95% CI: 3.23 to 20.20, P 6.1 mmol/L (HR=25.97, 95% CI: 9.76 to 69.15, P
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Both lactate clearance and lactate levels are considered useful prognostic biomarkers in patients with CS. However, there is yet no literature comparing the 6-hour lactate clearance rate (Δ6Lac) with lactate levels measured at admission (L1) and after 6 h (L2) to predict 30-day mortality in CS. In this observational cohort study, 95 patients with CS were treated at Department of Intensive Care Unit, Yiwu Central Hospital between January 2020 and December 2022. Of these, 88 patients met the eligibility criteria. The lactate levels were measured after admission (L1) as the baseline lactate value, and were measured after 6 h (L2) following admission. The primary endpoint of the study was survival rate at 30 days. A receiver operating characteristic curve was used for data analysis. Univariate and multivariate Cox regression analyses were performed based on Δ6Lac. Kaplan–Meier (KM) survival curves were generated to compare the 30-day survival rates among L1, L2, and Δ6Lac. The Δ6Lac model showed the highest area under the curve value (0.839), followed by the L2 (0.805) and L1 (0.668) models. The Δ6Lac model showed a sensitivity of 84.2% and specificity of 75.4%. The L1 and L2 models had sensitivities of 57.9% each and specificities of 89.9% and 98.6%, respectively. The cut-off values for Δ6Lac, L1, and L2 were 18.2%, 6.7 mmol/L, and 6.1 mmol/L, respectively. Univariate Cox regression analysis revealed a significant association between Δ6Lac and 30-day mortality. After adjusting for five models in multivariate Cox regression, Δ6Lac remained a significant risk factor for 30-day mortality in patients with CS. In our fifth multivariate Cox regression model, Δ6Lac remained a risk factor associated with 30-day mortality (hazard ratio [HR]=5.14, 95% confidence interval [CI]: 1.48 to 17.89, P=0.010) as well as L2 (HR=8.42, 95% CI: 1.26 to 56.22, P=0.028). The KM survival curve analysis revealed that L1 &gt;6.7 mmol/L (HR=8.08, 95% CI: 3.23 to 20.20, P &lt;0.001), L2 &gt;6.1 mmol/L (HR=25.97, 95% CI: 9.76 to 69.15, P &lt;0.001), and Δ6Lac ≤18.2% (HR=8.92, 95% CI: 2.95 to 26.95, P &lt;0.001) were associated with a higher risk of 30-day mortality. Δ6Lac is a better predictor for 30-day mortality in CS than lactate levels at admission. It has a predictive value equivalent to that of lactate level at 6 h after admission, making it an important surrogate indicator for evaluating the suitability as well as poor prognosis after CS treatment. We found that a cut-off value of 18.2% for Δ6Lac provided the most accurate assessment of early prognosis in CS.</description><identifier>ISSN: 2667-100X</identifier><identifier>EISSN: 2667-100X</identifier><identifier>DOI: 10.1016/j.jointm.2024.01.003</identifier><identifier>PMID: 39035609</identifier><language>eng</language><publisher>China: Elsevier B.V</publisher><subject>Cardiogenic shock ; Cox regression ; Lactates ; Mortality ; The 6-h lactate clearance rate</subject><ispartof>Journal of intensive medicine, 2024-07, Vol.4 (3), p.393-399</ispartof><rights>2024</rights><rights>2024 The Authors. 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Both lactate clearance and lactate levels are considered useful prognostic biomarkers in patients with CS. However, there is yet no literature comparing the 6-hour lactate clearance rate (Δ6Lac) with lactate levels measured at admission (L1) and after 6 h (L2) to predict 30-day mortality in CS. In this observational cohort study, 95 patients with CS were treated at Department of Intensive Care Unit, Yiwu Central Hospital between January 2020 and December 2022. Of these, 88 patients met the eligibility criteria. The lactate levels were measured after admission (L1) as the baseline lactate value, and were measured after 6 h (L2) following admission. The primary endpoint of the study was survival rate at 30 days. A receiver operating characteristic curve was used for data analysis. Univariate and multivariate Cox regression analyses were performed based on Δ6Lac. Kaplan–Meier (KM) survival curves were generated to compare the 30-day survival rates among L1, L2, and Δ6Lac. The Δ6Lac model showed the highest area under the curve value (0.839), followed by the L2 (0.805) and L1 (0.668) models. The Δ6Lac model showed a sensitivity of 84.2% and specificity of 75.4%. The L1 and L2 models had sensitivities of 57.9% each and specificities of 89.9% and 98.6%, respectively. The cut-off values for Δ6Lac, L1, and L2 were 18.2%, 6.7 mmol/L, and 6.1 mmol/L, respectively. Univariate Cox regression analysis revealed a significant association between Δ6Lac and 30-day mortality. After adjusting for five models in multivariate Cox regression, Δ6Lac remained a significant risk factor for 30-day mortality in patients with CS. In our fifth multivariate Cox regression model, Δ6Lac remained a risk factor associated with 30-day mortality (hazard ratio [HR]=5.14, 95% confidence interval [CI]: 1.48 to 17.89, P=0.010) as well as L2 (HR=8.42, 95% CI: 1.26 to 56.22, P=0.028). The KM survival curve analysis revealed that L1 &gt;6.7 mmol/L (HR=8.08, 95% CI: 3.23 to 20.20, P &lt;0.001), L2 &gt;6.1 mmol/L (HR=25.97, 95% CI: 9.76 to 69.15, P &lt;0.001), and Δ6Lac ≤18.2% (HR=8.92, 95% CI: 2.95 to 26.95, P &lt;0.001) were associated with a higher risk of 30-day mortality. Δ6Lac is a better predictor for 30-day mortality in CS than lactate levels at admission. It has a predictive value equivalent to that of lactate level at 6 h after admission, making it an important surrogate indicator for evaluating the suitability as well as poor prognosis after CS treatment. We found that a cut-off value of 18.2% for Δ6Lac provided the most accurate assessment of early prognosis in CS.</description><subject>Cardiogenic shock</subject><subject>Cox regression</subject><subject>Lactates</subject><subject>Mortality</subject><subject>The 6-h lactate clearance rate</subject><issn>2667-100X</issn><issn>2667-100X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kUFv1DAQhSMEolXpP0AoRy4JYzu2kwsSqihUqsSllbhZE3uy65DEi51F2n-Pl5SKEyd7xm_e0_grircMagZMfRjrMfhlnWsOvKmB1QDiRXHJldIVA_j-8p_7RXGd0ggAvM2l7F4XF6IDIRV0l8Xjw55KVe3DMZYT2hVXKu1EGHGxVMZz6ZfyEMl5u_plVwqoHJ7KOcQVJ7-ezs8Wo_NhR4u3ZdoH--NN8WrAKdH103lVPN5-frj5Wt1_-3J38-m-skKKtVK27QHIITmnhp4joYPeoR4EH0CpTiFqPWjuWtlQL4A0tK5VjVSD1EKLq-Ju83UBR3OIfsZ4MgG9-dMIcWcwrj4vZCQ47jR2vca26TqZQ2AQEhgHy6Qbstf7zesQw88jpdXMPlmaJlwoHJMR0ArOVCdlljab1MaQUqThOZqBOfMxo9n4mDMfA8xkPnns3VPCsZ_JPQ_9pZEFHzcB5T_75SmaZD1lEM5Hsmteyv8_4TeVkKJt</recordid><startdate>202407</startdate><enddate>202407</enddate><creator>Wang, Junfeng</creator><creator>Ji, Mingxia</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7753-3436</orcidid></search><sort><creationdate>202407</creationdate><title>The 6-hour lactate clearance rate in predicting 30-day mortality in cardiogenic shock</title><author>Wang, Junfeng ; Ji, Mingxia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-6c8b00edaedd6fb2aead0bda7f32f06696aa77f72d854eb30e708d86456f57373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Cardiogenic shock</topic><topic>Cox regression</topic><topic>Lactates</topic><topic>Mortality</topic><topic>The 6-h lactate clearance rate</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Junfeng</creatorcontrib><creatorcontrib>Ji, Mingxia</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of intensive medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Junfeng</au><au>Ji, Mingxia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The 6-hour lactate clearance rate in predicting 30-day mortality in cardiogenic shock</atitle><jtitle>Journal of intensive medicine</jtitle><addtitle>J Intensive Med</addtitle><date>2024-07</date><risdate>2024</risdate><volume>4</volume><issue>3</issue><spage>393</spage><epage>399</epage><pages>393-399</pages><issn>2667-100X</issn><eissn>2667-100X</eissn><abstract>Early evaluation of prognosis in cardiogenic shock (CS) is crucial for tailored treatment selection. Both lactate clearance and lactate levels are considered useful prognostic biomarkers in patients with CS. However, there is yet no literature comparing the 6-hour lactate clearance rate (Δ6Lac) with lactate levels measured at admission (L1) and after 6 h (L2) to predict 30-day mortality in CS. In this observational cohort study, 95 patients with CS were treated at Department of Intensive Care Unit, Yiwu Central Hospital between January 2020 and December 2022. Of these, 88 patients met the eligibility criteria. The lactate levels were measured after admission (L1) as the baseline lactate value, and were measured after 6 h (L2) following admission. The primary endpoint of the study was survival rate at 30 days. A receiver operating characteristic curve was used for data analysis. Univariate and multivariate Cox regression analyses were performed based on Δ6Lac. Kaplan–Meier (KM) survival curves were generated to compare the 30-day survival rates among L1, L2, and Δ6Lac. The Δ6Lac model showed the highest area under the curve value (0.839), followed by the L2 (0.805) and L1 (0.668) models. The Δ6Lac model showed a sensitivity of 84.2% and specificity of 75.4%. The L1 and L2 models had sensitivities of 57.9% each and specificities of 89.9% and 98.6%, respectively. The cut-off values for Δ6Lac, L1, and L2 were 18.2%, 6.7 mmol/L, and 6.1 mmol/L, respectively. Univariate Cox regression analysis revealed a significant association between Δ6Lac and 30-day mortality. After adjusting for five models in multivariate Cox regression, Δ6Lac remained a significant risk factor for 30-day mortality in patients with CS. In our fifth multivariate Cox regression model, Δ6Lac remained a risk factor associated with 30-day mortality (hazard ratio [HR]=5.14, 95% confidence interval [CI]: 1.48 to 17.89, P=0.010) as well as L2 (HR=8.42, 95% CI: 1.26 to 56.22, P=0.028). The KM survival curve analysis revealed that L1 &gt;6.7 mmol/L (HR=8.08, 95% CI: 3.23 to 20.20, P &lt;0.001), L2 &gt;6.1 mmol/L (HR=25.97, 95% CI: 9.76 to 69.15, P &lt;0.001), and Δ6Lac ≤18.2% (HR=8.92, 95% CI: 2.95 to 26.95, P &lt;0.001) were associated with a higher risk of 30-day mortality. Δ6Lac is a better predictor for 30-day mortality in CS than lactate levels at admission. It has a predictive value equivalent to that of lactate level at 6 h after admission, making it an important surrogate indicator for evaluating the suitability as well as poor prognosis after CS treatment. We found that a cut-off value of 18.2% for Δ6Lac provided the most accurate assessment of early prognosis in CS.</abstract><cop>China</cop><pub>Elsevier B.V</pub><pmid>39035609</pmid><doi>10.1016/j.jointm.2024.01.003</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0001-7753-3436</orcidid><oa>free_for_read</oa></addata></record>
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subjects Cardiogenic shock
Cox regression
Lactates
Mortality
The 6-h lactate clearance rate
title The 6-hour lactate clearance rate in predicting 30-day mortality in cardiogenic shock
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