Lactate indices as predictors of in-hospital mortality or 90-day survival after admission to an intensive care unit in unselected critically ill patients
We performed an exclusive study to investigate the associations between a total of 23 lactate-related indices during the first 24h in an intensive care unit (ICU) and in-hospital mortality. Nine static and 14 dynamic lactate indices, including changes in lactate concentrations (Δ Lac) and slope (lin...
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| creator | Hayashi, Yusuke Endoh, Hiroshi Kamimura, Natuo Tamakawa, Taro Nitta, Masakazu |
| description | We performed an exclusive study to investigate the associations between a total of 23 lactate-related indices during the first 24h in an intensive care unit (ICU) and in-hospital mortality.
Nine static and 14 dynamic lactate indices, including changes in lactate concentrations (Δ Lac) and slope (linear regression coefficient), were calculated from individual critically ill patient data extracted from the Multiparameter Intelligent Monitoring for Intensive Care (MIMIC) III database.
Data from a total of 781 ICU patients were extracted, consisted of 523 survivors and 258 non-survivors. The in-hospital mortality rate for this cohort was 33.0%. A multivariate logistic regression model identified maximal lactate concentration at 24h after ICU admission (max lactate at T24) as a significant predictor of in-hospital mortality (odds ratio = 1.431, 95% confidence interval [CI] = 1.278-1.604, p |
| doi_str_mv | 10.1371/journal.pone.0229135 |
| format | Article |
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Nine static and 14 dynamic lactate indices, including changes in lactate concentrations (Δ Lac) and slope (linear regression coefficient), were calculated from individual critically ill patient data extracted from the Multiparameter Intelligent Monitoring for Intensive Care (MIMIC) III database.
Data from a total of 781 ICU patients were extracted, consisted of 523 survivors and 258 non-survivors. The in-hospital mortality rate for this cohort was 33.0%. A multivariate logistic regression model identified maximal lactate concentration at 24h after ICU admission (max lactate at T24) as a significant predictor of in-hospital mortality (odds ratio = 1.431, 95% confidence interval [CI] = 1.278-1.604, p<0.001) after adjusting for predefined confounders (age, gender, sepsis, Elixhauser comorbidity score, mechanical ventilation, renal replacement therapy, vasopressors, ICU severity scores). Area under curve (AUC) for max lactate at T24 was larger (AUC = 0.776, 95% CI = 0.740-0.812) than other indices (p<0.001), comparable to an APACHE III score of 0.771. When combining max lactate at T24 with APACHE III, the AUC was increased to 0.815 (95% CI:0.783-0.847). The sensitivity, specificity, and positive and negative predictive values for the cut-off value of 3.05 mmol/L were 64.3%, 77.4%, 58.5%, and 81.5%, respectively. Kaplan-Myer survival curves of the max lactate at T24 for 90-day survival after admission to ICU demonstrated a significant difference according to the cut-off value (p<0.001).
These data indicate that the maximal arterial lactate concentration at T24 is a robust predictor of in-hospital mortality as well as 90-day survival in unselected ICU patients with predictive ability as comparable with APACHE III score.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0229135</identifier><identifier>PMID: 32150560</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Biology and Life Sciences ; Comorbidity ; Computer and Information Sciences ; Confidence intervals ; Critical care ; Hospital patients ; Hospitals ; Intensive care ; Lactates ; Lactic acid ; Mechanical ventilation ; Medicine ; Medicine and Health Sciences ; Mortality ; Normal distribution ; Patient outcomes ; Patients ; Physical Sciences ; Physiology ; Regression analysis ; Regression coefficients ; Regression models ; Renal replacement therapy ; Research and Analysis Methods ; Sepsis ; Statistical analysis ; Structured Query Language-SQL ; Studies ; Survival ; Taiwan ; University faculty ; Ventilation ; Ventilators</subject><ispartof>PloS one, 2020-03, Vol.15 (3), p.e0229135-e0229135</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Hayashi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Hayashi et al 2020 Hayashi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-29f1b21e9e95e1bf8a0d8d73779bd3606339c7972cf3c59e8ae79dc6b88e55193</citedby><cites>FETCH-LOGICAL-c692t-29f1b21e9e95e1bf8a0d8d73779bd3606339c7972cf3c59e8ae79dc6b88e55193</cites><orcidid>0000-0003-2832-013X ; 0000-0002-7195-5547</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062275/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062275/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23865,27923,27924,53790,53792,79371,79372</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32150560$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Brakenridge, Scott</contributor><creatorcontrib>Hayashi, Yusuke</creatorcontrib><creatorcontrib>Endoh, Hiroshi</creatorcontrib><creatorcontrib>Kamimura, Natuo</creatorcontrib><creatorcontrib>Tamakawa, Taro</creatorcontrib><creatorcontrib>Nitta, Masakazu</creatorcontrib><title>Lactate indices as predictors of in-hospital mortality or 90-day survival after admission to an intensive care unit in unselected critically ill patients</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>We performed an exclusive study to investigate the associations between a total of 23 lactate-related indices during the first 24h in an intensive care unit (ICU) and in-hospital mortality.
Nine static and 14 dynamic lactate indices, including changes in lactate concentrations (Δ Lac) and slope (linear regression coefficient), were calculated from individual critically ill patient data extracted from the Multiparameter Intelligent Monitoring for Intensive Care (MIMIC) III database.
Data from a total of 781 ICU patients were extracted, consisted of 523 survivors and 258 non-survivors. The in-hospital mortality rate for this cohort was 33.0%. A multivariate logistic regression model identified maximal lactate concentration at 24h after ICU admission (max lactate at T24) as a significant predictor of in-hospital mortality (odds ratio = 1.431, 95% confidence interval [CI] = 1.278-1.604, p<0.001) after adjusting for predefined confounders (age, gender, sepsis, Elixhauser comorbidity score, mechanical ventilation, renal replacement therapy, vasopressors, ICU severity scores). Area under curve (AUC) for max lactate at T24 was larger (AUC = 0.776, 95% CI = 0.740-0.812) than other indices (p<0.001), comparable to an APACHE III score of 0.771. When combining max lactate at T24 with APACHE III, the AUC was increased to 0.815 (95% CI:0.783-0.847). The sensitivity, specificity, and positive and negative predictive values for the cut-off value of 3.05 mmol/L were 64.3%, 77.4%, 58.5%, and 81.5%, respectively. Kaplan-Myer survival curves of the max lactate at T24 for 90-day survival after admission to ICU demonstrated a significant difference according to the cut-off value (p<0.001).
These data indicate that the maximal arterial lactate concentration at T24 is a robust predictor of in-hospital mortality as well as 90-day survival in unselected ICU patients with predictive ability as comparable with APACHE III score.</description><subject>Biology and Life Sciences</subject><subject>Comorbidity</subject><subject>Computer and Information Sciences</subject><subject>Confidence intervals</subject><subject>Critical care</subject><subject>Hospital patients</subject><subject>Hospitals</subject><subject>Intensive care</subject><subject>Lactates</subject><subject>Lactic acid</subject><subject>Mechanical ventilation</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Mortality</subject><subject>Normal distribution</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Physiology</subject><subject>Regression analysis</subject><subject>Regression coefficients</subject><subject>Regression models</subject><subject>Renal replacement therapy</subject><subject>Research and Analysis Methods</subject><subject>Sepsis</subject><subject>Statistical analysis</subject><subject>Structured Query Language-SQL</subject><subject>Studies</subject><subject>Survival</subject><subject>Taiwan</subject><subject>University 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indices as predictors of in-hospital mortality or 90-day survival after admission to an intensive care unit in unselected critically ill patients</title><author>Hayashi, Yusuke ; Endoh, Hiroshi ; Kamimura, Natuo ; Tamakawa, Taro ; Nitta, Masakazu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-29f1b21e9e95e1bf8a0d8d73779bd3606339c7972cf3c59e8ae79dc6b88e55193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biology and Life Sciences</topic><topic>Comorbidity</topic><topic>Computer and Information Sciences</topic><topic>Confidence intervals</topic><topic>Critical care</topic><topic>Hospital patients</topic><topic>Hospitals</topic><topic>Intensive care</topic><topic>Lactates</topic><topic>Lactic acid</topic><topic>Mechanical ventilation</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Mortality</topic><topic>Normal distribution</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Physical Sciences</topic><topic>Physiology</topic><topic>Regression analysis</topic><topic>Regression coefficients</topic><topic>Regression models</topic><topic>Renal replacement therapy</topic><topic>Research and Analysis Methods</topic><topic>Sepsis</topic><topic>Statistical analysis</topic><topic>Structured Query Language-SQL</topic><topic>Studies</topic><topic>Survival</topic><topic>Taiwan</topic><topic>University faculty</topic><topic>Ventilation</topic><topic>Ventilators</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hayashi, Yusuke</creatorcontrib><creatorcontrib>Endoh, Hiroshi</creatorcontrib><creatorcontrib>Kamimura, Natuo</creatorcontrib><creatorcontrib>Tamakawa, Taro</creatorcontrib><creatorcontrib>Nitta, Masakazu</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing 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one</jtitle><addtitle>PLoS One</addtitle><date>2020-03-09</date><risdate>2020</risdate><volume>15</volume><issue>3</issue><spage>e0229135</spage><epage>e0229135</epage><pages>e0229135-e0229135</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We performed an exclusive study to investigate the associations between a total of 23 lactate-related indices during the first 24h in an intensive care unit (ICU) and in-hospital mortality.
Nine static and 14 dynamic lactate indices, including changes in lactate concentrations (Δ Lac) and slope (linear regression coefficient), were calculated from individual critically ill patient data extracted from the Multiparameter Intelligent Monitoring for Intensive Care (MIMIC) III database.
Data from a total of 781 ICU patients were extracted, consisted of 523 survivors and 258 non-survivors. The in-hospital mortality rate for this cohort was 33.0%. A multivariate logistic regression model identified maximal lactate concentration at 24h after ICU admission (max lactate at T24) as a significant predictor of in-hospital mortality (odds ratio = 1.431, 95% confidence interval [CI] = 1.278-1.604, p<0.001) after adjusting for predefined confounders (age, gender, sepsis, Elixhauser comorbidity score, mechanical ventilation, renal replacement therapy, vasopressors, ICU severity scores). Area under curve (AUC) for max lactate at T24 was larger (AUC = 0.776, 95% CI = 0.740-0.812) than other indices (p<0.001), comparable to an APACHE III score of 0.771. When combining max lactate at T24 with APACHE III, the AUC was increased to 0.815 (95% CI:0.783-0.847). The sensitivity, specificity, and positive and negative predictive values for the cut-off value of 3.05 mmol/L were 64.3%, 77.4%, 58.5%, and 81.5%, respectively. Kaplan-Myer survival curves of the max lactate at T24 for 90-day survival after admission to ICU demonstrated a significant difference according to the cut-off value (p<0.001).
These data indicate that the maximal arterial lactate concentration at T24 is a robust predictor of in-hospital mortality as well as 90-day survival in unselected ICU patients with predictive ability as comparable with APACHE III score.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32150560</pmid><doi>10.1371/journal.pone.0229135</doi><tpages>e0229135</tpages><orcidid>https://orcid.org/0000-0003-2832-013X</orcidid><orcidid>https://orcid.org/0000-0002-7195-5547</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Biology and Life Sciences Comorbidity Computer and Information Sciences Confidence intervals Critical care Hospital patients Hospitals Intensive care Lactates Lactic acid Mechanical ventilation Medicine Medicine and Health Sciences Mortality Normal distribution Patient outcomes Patients Physical Sciences Physiology Regression analysis Regression coefficients Regression models Renal replacement therapy Research and Analysis Methods Sepsis Statistical analysis Structured Query Language-SQL Studies Survival Taiwan University faculty Ventilation Ventilators |
| title | Lactate indices as predictors of in-hospital mortality or 90-day survival after admission to an intensive care unit in unselected critically ill patients |
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