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A simple clinical model for planning transfusion quantities in heart surgery

BACKGROUND: Patients undergoing heart surgery continue to be the largest demand on blood transfusions. The need for transfusion is based on the risk of complications due to poor cell oxygenation, however large transfusions are associated with increased morbidity and risk of mortality in heart surger...

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Autores principales: Simeone, Felicetta, Franchi, Federico, Cevenini, Gabriele, Marullo, Antonino, Fossombroni, Vittorio, Scolletta, Sabino, Biagioli, Bonizella, Giomarelli, Pierpaolo, Barbini, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3141374/
https://www.ncbi.nlm.nih.gov/pubmed/21693020
http://dx.doi.org/10.1186/1472-6947-11-44
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author Simeone, Felicetta
Franchi, Federico
Cevenini, Gabriele
Marullo, Antonino
Fossombroni, Vittorio
Scolletta, Sabino
Biagioli, Bonizella
Giomarelli, Pierpaolo
Barbini, Paolo
author_facet Simeone, Felicetta
Franchi, Federico
Cevenini, Gabriele
Marullo, Antonino
Fossombroni, Vittorio
Scolletta, Sabino
Biagioli, Bonizella
Giomarelli, Pierpaolo
Barbini, Paolo
author_sort Simeone, Felicetta
collection PubMed
description BACKGROUND: Patients undergoing heart surgery continue to be the largest demand on blood transfusions. The need for transfusion is based on the risk of complications due to poor cell oxygenation, however large transfusions are associated with increased morbidity and risk of mortality in heart surgery patients. The aim of this study was to identify preoperative and intraoperative risk factors for transfusion and create a reliable model for planning transfusion quantities in heart surgery procedures. METHODS: We performed an observational study on 3315 consecutive patients who underwent cardiac surgery between January 2000 and December 2007. To estimate the number of packs of red blood cells (PRBC) transfused during heart surgery, we developed a multivariate regression model with discrete coefficients by selecting dummy variables as regressors in a stepwise manner. Model performance was assessed statistically by splitting cases into training and testing sets of the same size, and clinically by investigating the clinical course details of about one quarter of the patients in whom the difference between model estimates and actual number of PRBC transfused was higher than the root mean squared error. RESULTS: Ten preoperative and intraoperative dichotomous variables were entered in the model. Approximating the regression coefficients to the nearest half unit, each dummy regressor equal to one gave a number of half PRBC. The model assigned 4 units for kidney failure requiring preoperative dialysis, 2.5 units for cardiogenic shock, 2 units for minimum hematocrit at cardiopulmonary bypass less than or equal to 20%, 1.5 units for emergency operation, 1 unit for preoperative hematocrit less than or equal to 40%, cardiopulmonary bypass time greater than 130 minutes and type of surgery different from isolated artery bypass grafting, and 0.5 units for urgent operation, age over 70 years and systemic arterial hypertension. CONCLUSIONS: The regression model proved reliable for quantitative planning of number of PRBC in patients undergoing heart surgery. Besides enabling more rational resource allocation of costly blood-conservation strategies and blood bank resources, the results indicated a strong association between some essential postoperative variables and differences between the model estimate and the actual number of packs transfused.
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spelling pubmed-31413742011-07-23 A simple clinical model for planning transfusion quantities in heart surgery Simeone, Felicetta Franchi, Federico Cevenini, Gabriele Marullo, Antonino Fossombroni, Vittorio Scolletta, Sabino Biagioli, Bonizella Giomarelli, Pierpaolo Barbini, Paolo BMC Med Inform Decis Mak Research Article BACKGROUND: Patients undergoing heart surgery continue to be the largest demand on blood transfusions. The need for transfusion is based on the risk of complications due to poor cell oxygenation, however large transfusions are associated with increased morbidity and risk of mortality in heart surgery patients. The aim of this study was to identify preoperative and intraoperative risk factors for transfusion and create a reliable model for planning transfusion quantities in heart surgery procedures. METHODS: We performed an observational study on 3315 consecutive patients who underwent cardiac surgery between January 2000 and December 2007. To estimate the number of packs of red blood cells (PRBC) transfused during heart surgery, we developed a multivariate regression model with discrete coefficients by selecting dummy variables as regressors in a stepwise manner. Model performance was assessed statistically by splitting cases into training and testing sets of the same size, and clinically by investigating the clinical course details of about one quarter of the patients in whom the difference between model estimates and actual number of PRBC transfused was higher than the root mean squared error. RESULTS: Ten preoperative and intraoperative dichotomous variables were entered in the model. Approximating the regression coefficients to the nearest half unit, each dummy regressor equal to one gave a number of half PRBC. The model assigned 4 units for kidney failure requiring preoperative dialysis, 2.5 units for cardiogenic shock, 2 units for minimum hematocrit at cardiopulmonary bypass less than or equal to 20%, 1.5 units for emergency operation, 1 unit for preoperative hematocrit less than or equal to 40%, cardiopulmonary bypass time greater than 130 minutes and type of surgery different from isolated artery bypass grafting, and 0.5 units for urgent operation, age over 70 years and systemic arterial hypertension. CONCLUSIONS: The regression model proved reliable for quantitative planning of number of PRBC in patients undergoing heart surgery. Besides enabling more rational resource allocation of costly blood-conservation strategies and blood bank resources, the results indicated a strong association between some essential postoperative variables and differences between the model estimate and the actual number of packs transfused. BioMed Central 2011-06-21 /pmc/articles/PMC3141374/ /pubmed/21693020 http://dx.doi.org/10.1186/1472-6947-11-44 Text en Copyright ©2011 Simeone et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Simeone, Felicetta
Franchi, Federico
Cevenini, Gabriele
Marullo, Antonino
Fossombroni, Vittorio
Scolletta, Sabino
Biagioli, Bonizella
Giomarelli, Pierpaolo
Barbini, Paolo
A simple clinical model for planning transfusion quantities in heart surgery
title A simple clinical model for planning transfusion quantities in heart surgery
title_full A simple clinical model for planning transfusion quantities in heart surgery
title_fullStr A simple clinical model for planning transfusion quantities in heart surgery
title_full_unstemmed A simple clinical model for planning transfusion quantities in heart surgery
title_short A simple clinical model for planning transfusion quantities in heart surgery
title_sort simple clinical model for planning transfusion quantities in heart surgery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3141374/
https://www.ncbi.nlm.nih.gov/pubmed/21693020
http://dx.doi.org/10.1186/1472-6947-11-44
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