Evaluation of risk scores in predicting perioperative blood transfusions in adult cardiac surgery
N R Madhu Krishna, P S Nagaraja, Naveen G Singh and 4 more
Annals of Cardiac Anaesthesia · 2019
doi:10.4103/aca.aca_18_18 (opens in a new tab)
We email you when a new notice or a question about this paper appears.
Abstract
Objective: Allogeneic blood product transfusions are associated with an increased morbidity and mortality risk in cardiac surgery. At present, a few transfusion risk scores have been proposed for cardiac surgery patients. The present study is aimed to develop a new score and to compare with preexisting scores – Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST) score. Methodology: A total of 1014 adult patients undergoing cardiac surgery were enrolled in the retrospective study. Independent predictors of allogeneic blood transfusions were selected from TRACK and TRUST scores. A predictive score was developed from six variables using logistic regression analysis, and new score was compared to the other existing scores – TRACK and TRUST. Results: The new score had following predictors: age >58 years, weight <63 kg for males and <49 kg for females, gender (female), complex surgery, hemoglobin <13.5 g/dl, and creatinine >1.36 mg/dl. Validation of new score demonstrated an acceptable predictive power (area under the curve [AUC] 0.749) and a good calibration at the Hosmer–Lemeshow test. New score was comparable with TRACK score with P = 0.578 (AUC of TRACK 0.756 and AUC of new score 0.749). There was a significant difference between new score and TRUST score, P = 0.01 (AUC of TRUST 0.72 and AUC of new score 0.749). Conclusion: New score is a simple risk model based on six predictors having a similar accuracy and calibration in predicting the transfusion rate in cardiac surgery as compared to TRACK score.
Abstract licence: CC BY-NC-SA
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.