Bioimpedance vector analysis predicts hospital length of stay in acute heart failure
•Congestion in acute heart failure affects survival curves and length of stay.•Our comparisons revealed that the higher the hydration status, the longer the LOS.•BIVA measurements are independent predictor of length of stay in acute HF patients. Congestion in acute heart failure (AHF) affects surviv...
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| Veröffentlicht in: | Nutrition (Burbank, Los Angeles County, Calif.) Los Angeles County, Calif.), 2019-05, Vol.61, p.56-60 |
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| creator | Massari, Francesco Scicchitano, Pietro Ciccone, Marco Matteo Caldarola, Pasquale Aspromonte, Nadia Iacoviello, Massimo Barro, Sabrina Pantano, Ivan Valle, Roberto |
| description | •Congestion in acute heart failure affects survival curves and length of stay.•Our comparisons revealed that the higher the hydration status, the longer the LOS.•BIVA measurements are independent predictor of length of stay in acute HF patients.
Congestion in acute heart failure (AHF) affects survival curves and hospital length of stay (LOS). The evaluation of congestion, however, is not totally objective. The aim of this study was to verify the accuracy of bioelectrical impedance vector analysis (BIVA) in predicting the LOS in AHF patients.
This is a retrospective study. A total of 706 patients (367 male; mean age: 78 ± 10 y) who had been admitted to hospital with an AHF event were enrolled. All underwent anthropometric and clinical evaluation, baseline transthoracic echocardiography, and biochemical and BIVA evaluations.
The comparison among the clinical characteristics of congestion, LOS, and hyperhydration status revealed that the higher the hydration status, the longer the LOS (from 7.36 d [interquartile range: 7.34–7.39 d] in normohydrated patients to 9.04 d [interquartile range: 8.85– 9.19 d] in severe hyperhydrated patients; P < 0.05). At univariate analysis, brain natriuretic peptide, blood urea nitrogen, New York Heart Association class, hemoglobin, hydration index, and peripheral edema all had a statistically significant influence on LOS. At multivariate analysis, only brain natriuretic peptide (P < 0.0001), blood urea nitrogen (P = 0.011), and hydration index (P < 0.0001) were significantly associated to LOS.
Congestion evaluated by BIVA is an independent predictor of length of total hospital stay in HF patients with acute decompensation. The quick and reliable detection of congestion permits the administration of target therapy for AHF, thus reducing LOS and treatment costs. |
| doi_str_mv | 10.1016/j.nut.2018.10.028 |
| format | Article |
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Congestion in acute heart failure (AHF) affects survival curves and hospital length of stay (LOS). The evaluation of congestion, however, is not totally objective. The aim of this study was to verify the accuracy of bioelectrical impedance vector analysis (BIVA) in predicting the LOS in AHF patients.
This is a retrospective study. A total of 706 patients (367 male; mean age: 78 ± 10 y) who had been admitted to hospital with an AHF event were enrolled. All underwent anthropometric and clinical evaluation, baseline transthoracic echocardiography, and biochemical and BIVA evaluations.
The comparison among the clinical characteristics of congestion, LOS, and hyperhydration status revealed that the higher the hydration status, the longer the LOS (from 7.36 d [interquartile range: 7.34–7.39 d] in normohydrated patients to 9.04 d [interquartile range: 8.85– 9.19 d] in severe hyperhydrated patients; P < 0.05). At univariate analysis, brain natriuretic peptide, blood urea nitrogen, New York Heart Association class, hemoglobin, hydration index, and peripheral edema all had a statistically significant influence on LOS. At multivariate analysis, only brain natriuretic peptide (P < 0.0001), blood urea nitrogen (P = 0.011), and hydration index (P < 0.0001) were significantly associated to LOS.
Congestion evaluated by BIVA is an independent predictor of length of total hospital stay in HF patients with acute decompensation. The quick and reliable detection of congestion permits the administration of target therapy for AHF, thus reducing LOS and treatment costs.</description><identifier>ISSN: 0899-9007</identifier><identifier>EISSN: 1873-1244</identifier><identifier>DOI: 10.1016/j.nut.2018.10.028</identifier><identifier>PMID: 30703569</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Acute Disease ; Acute heart failure ; Age ; Aged ; Aged, 80 and over ; Anthropometry ; Bioelectricity ; Bioimpedance vector analysis ; Blood ; Blood Urea Nitrogen ; Brain ; Brain natriuretic peptide ; Cardiology ; Cardiovascular disease ; Congestion ; Congestive heart failure ; Echocardiography ; Edema ; Electric Impedance ; Family medical history ; Female ; Heart ; Heart failure ; Heart Failure - diagnosis ; Heart Failure - physiopathology ; Hemoglobin ; Humans ; Hydration ; Identification ; Length of stay ; Length of Stay - statistics & numerical data ; Male ; Medical prognosis ; Multivariate Analysis ; Natriuretic Peptide, Brain - analysis ; Nitrogen ; Organism Hydration Status ; Patients ; Prediction ; Predictive Value of Tests ; Retrospective Studies ; Severity of Illness Index ; Standard deviation ; Statistical analysis ; Urea ; Values ; Vector analysis</subject><ispartof>Nutrition (Burbank, Los Angeles County, Calif.), 2019-05, Vol.61, p.56-60</ispartof><rights>2018 Elsevier Inc.</rights><rights>Copyright © 2018 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited May 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c447t-5d1e4e2b05d50099aa49d3e67c59f1c86b9a35696144cad35d4b55dc6e2c20b43</citedby><cites>FETCH-LOGICAL-c447t-5d1e4e2b05d50099aa49d3e67c59f1c86b9a35696144cad35d4b55dc6e2c20b43</cites><orcidid>0000-0003-0471-0053</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2191338844?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30703569$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Massari, Francesco</creatorcontrib><creatorcontrib>Scicchitano, Pietro</creatorcontrib><creatorcontrib>Ciccone, Marco Matteo</creatorcontrib><creatorcontrib>Caldarola, Pasquale</creatorcontrib><creatorcontrib>Aspromonte, Nadia</creatorcontrib><creatorcontrib>Iacoviello, Massimo</creatorcontrib><creatorcontrib>Barro, Sabrina</creatorcontrib><creatorcontrib>Pantano, Ivan</creatorcontrib><creatorcontrib>Valle, Roberto</creatorcontrib><title>Bioimpedance vector analysis predicts hospital length of stay in acute heart failure</title><title>Nutrition (Burbank, Los Angeles County, Calif.)</title><addtitle>Nutrition</addtitle><description>•Congestion in acute heart failure affects survival curves and length of stay.•Our comparisons revealed that the higher the hydration status, the longer the LOS.•BIVA measurements are independent predictor of length of stay in acute HF patients.
Congestion in acute heart failure (AHF) affects survival curves and hospital length of stay (LOS). The evaluation of congestion, however, is not totally objective. The aim of this study was to verify the accuracy of bioelectrical impedance vector analysis (BIVA) in predicting the LOS in AHF patients.
This is a retrospective study. A total of 706 patients (367 male; mean age: 78 ± 10 y) who had been admitted to hospital with an AHF event were enrolled. All underwent anthropometric and clinical evaluation, baseline transthoracic echocardiography, and biochemical and BIVA evaluations.
The comparison among the clinical characteristics of congestion, LOS, and hyperhydration status revealed that the higher the hydration status, the longer the LOS (from 7.36 d [interquartile range: 7.34–7.39 d] in normohydrated patients to 9.04 d [interquartile range: 8.85– 9.19 d] in severe hyperhydrated patients; P < 0.05). At univariate analysis, brain natriuretic peptide, blood urea nitrogen, New York Heart Association class, hemoglobin, hydration index, and peripheral edema all had a statistically significant influence on LOS. At multivariate analysis, only brain natriuretic peptide (P < 0.0001), blood urea nitrogen (P = 0.011), and hydration index (P < 0.0001) were significantly associated to LOS.
Congestion evaluated by BIVA is an independent predictor of length of total hospital stay in HF patients with acute decompensation. The quick and reliable detection of congestion permits the administration of target therapy for AHF, thus reducing LOS and treatment costs.</description><subject>Acute Disease</subject><subject>Acute heart failure</subject><subject>Age</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Anthropometry</subject><subject>Bioelectricity</subject><subject>Bioimpedance vector analysis</subject><subject>Blood</subject><subject>Blood Urea Nitrogen</subject><subject>Brain</subject><subject>Brain natriuretic peptide</subject><subject>Cardiology</subject><subject>Cardiovascular disease</subject><subject>Congestion</subject><subject>Congestive heart failure</subject><subject>Echocardiography</subject><subject>Edema</subject><subject>Electric Impedance</subject><subject>Family medical history</subject><subject>Female</subject><subject>Heart</subject><subject>Heart failure</subject><subject>Heart Failure - diagnosis</subject><subject>Heart Failure - physiopathology</subject><subject>Hemoglobin</subject><subject>Humans</subject><subject>Hydration</subject><subject>Identification</subject><subject>Length of stay</subject><subject>Length of Stay - statistics & numerical data</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Multivariate Analysis</subject><subject>Natriuretic Peptide, Brain - analysis</subject><subject>Nitrogen</subject><subject>Organism Hydration Status</subject><subject>Patients</subject><subject>Prediction</subject><subject>Predictive Value of Tests</subject><subject>Retrospective Studies</subject><subject>Severity of Illness Index</subject><subject>Standard deviation</subject><subject>Statistical analysis</subject><subject>Urea</subject><subject>Values</subject><subject>Vector analysis</subject><issn>0899-9007</issn><issn>1873-1244</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kE1P3DAQhq2KqmxpfwAXZIkLl2zHsfNhcQJECxJSL_RsOfaE9SobB9tB2n-PowUOHHoazeiZVzMPIacM1gxY_Wu7Hue0LoG1uV9D2X4hK9Y2vGClEEdkBa2UhQRojsn3GLcAwGQtv5FjDg3wqpYr8njtvNtNaPVokL6gST5QPephH12kU0DrTIp04-Pkkh7ogONT2lDf05j0nrqRajMnpBvUIdFeu2EO-IN87fUQ8edbPSH_ft8-3twVD3__3N9cPRRGiCYVlWUosOygshWAlFoLaTnWjalkz0xbd1IvV9ZMCKMtr6zoqsqaGktTQif4Cbk45E7BP88Yk9q5aHAY9Ih-jqpkjRSC1ZJn9PwTuvVzyH8ulGSct61YAtmBMsHHGLBXU3A7HfaKgVqUq63KytWifBll5Xnn7C157nZoPzbeHWfg8gBgVvHiMKhoHGbd1oXsW1nv_hP_Cp-xkVw</recordid><startdate>201905</startdate><enddate>201905</enddate><creator>Massari, Francesco</creator><creator>Scicchitano, Pietro</creator><creator>Ciccone, Marco Matteo</creator><creator>Caldarola, Pasquale</creator><creator>Aspromonte, Nadia</creator><creator>Iacoviello, Massimo</creator><creator>Barro, Sabrina</creator><creator>Pantano, Ivan</creator><creator>Valle, Roberto</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RQ</scope><scope>7RV</scope><scope>7TS</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>ASE</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FPQ</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K6X</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0471-0053</orcidid></search><sort><creationdate>201905</creationdate><title>Bioimpedance vector analysis predicts hospital length of stay in acute heart failure</title><author>Massari, Francesco ; Scicchitano, Pietro ; Ciccone, Marco Matteo ; Caldarola, Pasquale ; Aspromonte, Nadia ; Iacoviello, Massimo ; Barro, Sabrina ; Pantano, Ivan ; Valle, Roberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-5d1e4e2b05d50099aa49d3e67c59f1c86b9a35696144cad35d4b55dc6e2c20b43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Acute Disease</topic><topic>Acute heart failure</topic><topic>Age</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Anthropometry</topic><topic>Bioelectricity</topic><topic>Bioimpedance vector analysis</topic><topic>Blood</topic><topic>Blood Urea Nitrogen</topic><topic>Brain</topic><topic>Brain natriuretic peptide</topic><topic>Cardiology</topic><topic>Cardiovascular disease</topic><topic>Congestion</topic><topic>Congestive heart failure</topic><topic>Echocardiography</topic><topic>Edema</topic><topic>Electric Impedance</topic><topic>Family medical history</topic><topic>Female</topic><topic>Heart</topic><topic>Heart failure</topic><topic>Heart Failure - diagnosis</topic><topic>Heart Failure - physiopathology</topic><topic>Hemoglobin</topic><topic>Humans</topic><topic>Hydration</topic><topic>Identification</topic><topic>Length of stay</topic><topic>Length of Stay - statistics & numerical data</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Multivariate Analysis</topic><topic>Natriuretic Peptide, Brain - analysis</topic><topic>Nitrogen</topic><topic>Organism Hydration Status</topic><topic>Patients</topic><topic>Prediction</topic><topic>Predictive Value of Tests</topic><topic>Retrospective Studies</topic><topic>Severity of Illness Index</topic><topic>Standard deviation</topic><topic>Statistical analysis</topic><topic>Urea</topic><topic>Values</topic><topic>Vector analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Massari, Francesco</creatorcontrib><creatorcontrib>Scicchitano, Pietro</creatorcontrib><creatorcontrib>Ciccone, Marco Matteo</creatorcontrib><creatorcontrib>Caldarola, Pasquale</creatorcontrib><creatorcontrib>Aspromonte, Nadia</creatorcontrib><creatorcontrib>Iacoviello, Massimo</creatorcontrib><creatorcontrib>Barro, Sabrina</creatorcontrib><creatorcontrib>Pantano, Ivan</creatorcontrib><creatorcontrib>Valle, Roberto</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Career & Technical Education Database</collection><collection>Nursing & Allied Health Database</collection><collection>Physical Education Index</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>British Nursing Database</collection><collection>British Nursing Index</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>British Nursing Index (BNI) (1985 to Present)</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>British Nursing Index</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Nutrition (Burbank, Los Angeles County, Calif.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Massari, Francesco</au><au>Scicchitano, Pietro</au><au>Ciccone, Marco Matteo</au><au>Caldarola, Pasquale</au><au>Aspromonte, Nadia</au><au>Iacoviello, Massimo</au><au>Barro, Sabrina</au><au>Pantano, Ivan</au><au>Valle, Roberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bioimpedance vector analysis predicts hospital length of stay in acute heart failure</atitle><jtitle>Nutrition (Burbank, Los Angeles County, Calif.)</jtitle><addtitle>Nutrition</addtitle><date>2019-05</date><risdate>2019</risdate><volume>61</volume><spage>56</spage><epage>60</epage><pages>56-60</pages><issn>0899-9007</issn><eissn>1873-1244</eissn><abstract>•Congestion in acute heart failure affects survival curves and length of stay.•Our comparisons revealed that the higher the hydration status, the longer the LOS.•BIVA measurements are independent predictor of length of stay in acute HF patients.
Congestion in acute heart failure (AHF) affects survival curves and hospital length of stay (LOS). The evaluation of congestion, however, is not totally objective. The aim of this study was to verify the accuracy of bioelectrical impedance vector analysis (BIVA) in predicting the LOS in AHF patients.
This is a retrospective study. A total of 706 patients (367 male; mean age: 78 ± 10 y) who had been admitted to hospital with an AHF event were enrolled. All underwent anthropometric and clinical evaluation, baseline transthoracic echocardiography, and biochemical and BIVA evaluations.
The comparison among the clinical characteristics of congestion, LOS, and hyperhydration status revealed that the higher the hydration status, the longer the LOS (from 7.36 d [interquartile range: 7.34–7.39 d] in normohydrated patients to 9.04 d [interquartile range: 8.85– 9.19 d] in severe hyperhydrated patients; P < 0.05). At univariate analysis, brain natriuretic peptide, blood urea nitrogen, New York Heart Association class, hemoglobin, hydration index, and peripheral edema all had a statistically significant influence on LOS. At multivariate analysis, only brain natriuretic peptide (P < 0.0001), blood urea nitrogen (P = 0.011), and hydration index (P < 0.0001) were significantly associated to LOS.
Congestion evaluated by BIVA is an independent predictor of length of total hospital stay in HF patients with acute decompensation. The quick and reliable detection of congestion permits the administration of target therapy for AHF, thus reducing LOS and treatment costs.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>30703569</pmid><doi>10.1016/j.nut.2018.10.028</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-0471-0053</orcidid></addata></record> |
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| subjects | Acute Disease Acute heart failure Age Aged Aged, 80 and over Anthropometry Bioelectricity Bioimpedance vector analysis Blood Blood Urea Nitrogen Brain Brain natriuretic peptide Cardiology Cardiovascular disease Congestion Congestive heart failure Echocardiography Edema Electric Impedance Family medical history Female Heart Heart failure Heart Failure - diagnosis Heart Failure - physiopathology Hemoglobin Humans Hydration Identification Length of stay Length of Stay - statistics & numerical data Male Medical prognosis Multivariate Analysis Natriuretic Peptide, Brain - analysis Nitrogen Organism Hydration Status Patients Prediction Predictive Value of Tests Retrospective Studies Severity of Illness Index Standard deviation Statistical analysis Urea Values Vector analysis |
| title | Bioimpedance vector analysis predicts hospital length of stay in acute heart failure |
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