[Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data].
Looking for a noninvasive method to predict liver histologic alterations in alcoholic patients without clinical signs of liver failure, we studied 187 chronic alcoholics recently abstinent, divided in 2 series. In the model series (n = 94) several clinical variables and results of common laboratory...
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| Lenguaje: | inglés |
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1993
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| author | Iturriaga, H Hirsch, Steffen Bunout, Daniel Díaz, Miguel Ángel Kelly, Matt Silva, G la Maza, de Petermann, Marcus Vizcarra Ugarte, German Alex |
| author_browse | Bunout, Daniel Díaz, Miguel Ángel Hirsch, Steffen Iturriaga, H Kelly, Matt Petermann, Marcus Silva, G Vizcarra Ugarte, German Alex la Maza, de |
| author_facet | Iturriaga, H Hirsch, Steffen Bunout, Daniel Díaz, Miguel Ángel Kelly, Matt Silva, G la Maza, de Petermann, Marcus Vizcarra Ugarte, German Alex |
| author_sort | Iturriaga, H |
| collection | Producción científica |
| description | Looking for a noninvasive method to predict liver histologic alterations in alcoholic patients without clinical signs of liver failure, we studied 187 chronic alcoholics recently abstinent, divided in 2 series. In the model series (n = 94) several clinical variables and results of common laboratory tests were confronted to the findings of liver biopsies. These were classified in 3 groups: 1. Normal liver; 2. Moderate alterations; 3. Marked alterations, including alcoholic hepatitis and cirrhosis. Multivariate methods used were logistic regression analysis and a classification and regression tree (CART). Both methods entered gamma-glutamyltransferase (GGT), aspartate-aminotransferase (AST), weight and age as significant and independent variables. Univariate analysis with GGT and AST at different cutoffs were also performed. To predict the presence of any kind of damage (Groups 2 and 3), CART and AST > 30 IU showed the higher sensitivity, specificity and correct prediction, both in the model and validation series. For prediction of marked liver damage, a score based on logistic regression and GGT > 110 IU had the higher efficiencies. It is concluded that GGT and AST are good markers of alcoholic liver damage and that, using sample cutoffs, histologic diagnosis can be correctly predicted in 80% of recently abstinent asymptomatic alcoholics. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/1663 |
| institution | UPeU |
| language | English |
| publishDate | 1993 |
| publishDateRange | 1993 |
| publishDateSort | 1993 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/16632026-10-30T16:16:24Z[Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data].Iturriaga, HHirsch, SteffenBunout, DanielDíaz, Miguel ÁngelKelly, MattSilva, Gla Maza, dePetermann, MarcusVizcarra Ugarte, German AlexInternal medicineMedicineGastroenterologyLogistic regressionAsymptomaticCirrhosisAlcoholic hepatitisGamma-glutamyltransferaseUnivariate analysisLiver damageAlcoholic liver diseaseMultivariate analysisPathologyBiologyBiochemistryEnzymeAdultAlcoholism complicationsAspartate Aminotransferases bloodgamma-Glutamyltransferase bloodHumansLiver pathologyLiver Diseases, Alcoholic pathologyMaleMiddle AgedPrognosisRegression AnalysisSensitivity and SpecificityHealth Sciences Medicine Epidemiology Liver Disease Diagnosis and TreatmentHealth Sciences Medicine Pathology and Forensic Medicine Alcohol Consumption and Health EffectsLooking for a noninvasive method to predict liver histologic alterations in alcoholic patients without clinical signs of liver failure, we studied 187 chronic alcoholics recently abstinent, divided in 2 series. In the model series (n = 94) several clinical variables and results of common laboratory tests were confronted to the findings of liver biopsies. These were classified in 3 groups: 1. Normal liver; 2. Moderate alterations; 3. Marked alterations, including alcoholic hepatitis and cirrhosis. Multivariate methods used were logistic regression analysis and a classification and regression tree (CART). Both methods entered gamma-glutamyltransferase (GGT), aspartate-aminotransferase (AST), weight and age as significant and independent variables. Univariate analysis with GGT and AST at different cutoffs were also performed. To predict the presence of any kind of damage (Groups 2 and 3), CART and AST > 30 IU showed the higher sensitivity, specificity and correct prediction, both in the model and validation series. For prediction of marked liver damage, a score based on logistic regression and GGT > 110 IU had the higher efficiencies. It is concluded that GGT and AST are good markers of alcoholic liver damage and that, using sample cutoffs, histologic diagnosis can be correctly predicted in 80% of recently abstinent asymptomatic alcoholics.Articlehttps://pubmed.ncbi.nlm.nih.gov/7903815https://hdl.handle.net/20.500.15037/1663W2413366353enPubMed1993-04-012026-06-13T14:18:09Z2026-06-13T14:18:09Z |
| spellingShingle | Internal medicine Medicine Gastroenterology Logistic regression Asymptomatic Cirrhosis Alcoholic hepatitis Gamma-glutamyltransferase Univariate analysis Liver damage Alcoholic liver disease Multivariate analysis Pathology Biology Biochemistry Enzyme Adult Alcoholism complications Aspartate Aminotransferases blood gamma-Glutamyltransferase blood Humans Liver pathology Liver Diseases, Alcoholic pathology Male Middle Aged Prognosis Regression Analysis Sensitivity and Specificity Health Sciences Medicine Epidemiology Liver Disease Diagnosis and Treatment Health Sciences Medicine Pathology and Forensic Medicine Alcohol Consumption and Health Effects Iturriaga, H Hirsch, Steffen Bunout, Daniel Díaz, Miguel Ángel Kelly, Matt Silva, G la Maza, de Petermann, Marcus Vizcarra Ugarte, German Alex [Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data]. |
| title | [Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data]. |
| title_full | [Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data]. |
| title_fullStr | [Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data]. |
| title_full_unstemmed | [Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data]. |
| title_short | [Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data]. |
| title_sort | prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data |
| topic | Internal medicine Medicine Gastroenterology Logistic regression Asymptomatic Cirrhosis Alcoholic hepatitis Gamma-glutamyltransferase Univariate analysis Liver damage Alcoholic liver disease Multivariate analysis Pathology Biology Biochemistry Enzyme Adult Alcoholism complications Aspartate Aminotransferases blood gamma-Glutamyltransferase blood Humans Liver pathology Liver Diseases, Alcoholic pathology Male Middle Aged Prognosis Regression Analysis Sensitivity and Specificity Health Sciences Medicine Epidemiology Liver Disease Diagnosis and Treatment Health Sciences Medicine Pathology and Forensic Medicine Alcohol Consumption and Health Effects |
| topic_facet | Internal medicine Medicine Gastroenterology Logistic regression Asymptomatic Cirrhosis Alcoholic hepatitis Gamma-glutamyltransferase Univariate analysis Liver damage Alcoholic liver disease Multivariate analysis Pathology Biology Biochemistry Enzyme Adult Alcoholism complications Aspartate Aminotransferases blood gamma-Glutamyltransferase blood Humans Liver pathology Liver Diseases, Alcoholic pathology Male Middle Aged Prognosis Regression Analysis Sensitivity and Specificity Health Sciences Medicine Epidemiology Liver Disease Diagnosis and Treatment Health Sciences Medicine Pathology and Forensic Medicine Alcohol Consumption and Health Effects |
| url | https://pubmed.ncbi.nlm.nih.gov/7903815 https://hdl.handle.net/20.500.15037/1663 |
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