BACKGROUND: The aim of this study was to generate a score based on preoperative characteristics and predictive of the individual probability of allogeneic erythrocyte transfusion in patients undergoing elective thoracolumbar spine surgery. METHODS: Two hundred thirty consecutive patients were retrospectively included over a 15-month period (derivation set). Preoperative independent predictors of erythrocyte transfusion from the day of surgery until postoperative day 5 were determined by multivariable analysis, from which a model of individual probability of transfusion was derived and prospectively validated in 125 additional patients (validation set). RESULTS: Four preoperative independent predictors were associated with transfusion: age older than 50 yr (adjusted odds ratio = 4.9 [2-13.5]), preoperative hemoglobin level less than 12 g/dl (adjusted odds ratio = 6.9 [3.1-17.2]), fusion of more than two levels (adjusted odds ratio = 6.7 [3.1-15.2]), and transpedicular osteotomy (adjusted odds ratio = 19.9 [5.6-98.2]). A 0-4 score (0 = no risk, 4 = maximum risk) predictive of allogeneic transfusion was derived by weighting estimate parameters for each variable in a multivariable logistic regression model. Discriminating capacity of the score was 0.86 [0.81-0.92] in the receiver operating characteristics in the derivation sample and 0.83 [0.75-0.91] in the validation sample. The observed transfusion rates in the validation set and the individual probabilities of erythrocyte transfusion from the score were well correlated (y = 0.98x + 0.04; P < 0.0001), and the observed differences were not statistically different (goodness-of-fit chi-square, P = 0.125). The score was also correlated with the number of erythrocyte units transfused (Spearman rho = 0.61; P < 0.0001). CONCLUSION: The Predictive Model of Transfusion in Spine Surgery may be useful in clinical practice to identify patients undergoing spine surgery at risk of massive bleeding and encourage erythrocyte-saving strategies in these patients.
BACKGROUND: The aim of this study was to generate a score based on preoperative characteristics and predictive of the individual probability of allogeneic erythrocyte transfusion in patients undergoing elective thoracolumbar spine surgery. METHODS: Two hundred thirty consecutive patients were retrospectively included over a 15-month period (derivation set). Preoperative independent predictors of erythrocyte transfusion from the day of surgery until postoperative day 5 were determined by multivariable analysis, from which a model of individual probability of transfusion was derived and prospectively validated in 125 additional patients (validation set). RESULTS: Four preoperative independent predictors were associated with transfusion: age older than 50 yr (adjusted odds ratio = 4.9 [2-13.5]), preoperative hemoglobin level less than 12 g/dl (adjusted odds ratio = 6.9 [3.1-17.2]), fusion of more than two levels (adjusted odds ratio = 6.7 [3.1-15.2]), and transpedicular osteotomy (adjusted odds ratio = 19.9 [5.6-98.2]). A 0-4 score (0 = no risk, 4 = maximum risk) predictive of allogeneic transfusion was derived by weighting estimate parameters for each variable in a multivariable logistic regression model. Discriminating capacity of the score was 0.86 [0.81-0.92] in the receiver operating characteristics in the derivation sample and 0.83 [0.75-0.91] in the validation sample. The observed transfusion rates in the validation set and the individual probabilities of erythrocyte transfusion from the score were well correlated (y = 0.98x + 0.04; P < 0.0001), and the observed differences were not statistically different (goodness-of-fit chi-square, P = 0.125). The score was also correlated with the number of erythrocyte units transfused (Spearman rho = 0.61; P < 0.0001). CONCLUSION: The Predictive Model of Transfusion in Spine Surgery may be useful in clinical practice to identify patients undergoing spine surgery at risk of massive bleeding and encourage erythrocyte-saving strategies in these patients.
Authors: Joseph S Butler; John P Burke; Roisin T Dolan; Philip Fitzpatrick; John M O'Byrne; Damian McCormack; Keith Synnott; Ashley R Poynton Journal: Eur Spine J Date: 2010-06-27 Impact factor: 3.134
Authors: Michael P Kelly; Lukas P Zebala; Han Jo Kim; Daniel M Sciubba; Justin S Smith; Christopher I Shaffrey; Shay Bess; Eric Klineberg; Gregory Mundis; Douglas Burton; Robert Hart; Alex Soroceanu; Frank Schwab; Virginie Lafage Journal: J Neurosurg Spine Date: 2015-09-25
Authors: Giuseppe Ristagno; Simonetta Beluffi; Dario Tanzi; Federica Belloli; Paola Carmagnini; Massimo Croci; Giuseppe D'Aviri; Guido Menasce; Juan C Pastore; Armando Pellanda; Alberto Pollini; Giorgio Savoia Journal: J Clin Med Date: 2018-01-30 Impact factor: 4.241