The utility of preoperative level of erythrocytosis in the prediction of postoperative blood loss and 30-day mortality in patients with tetralogy of fallot

Abstract

Background:Postoperative major bleeding is a relatively common complication of patients undergoing corrective surgery of tetralogy of Fallot (TOF). Life-threatening blood losses can lead to aggressive transfusions or reoperation. Little is known about the risk factors associated with a bleeding tendency in TOF patients. This study aimed to establish predictive models for postoperative blood loss and mortality in TOF patients.Methods:We conducted a retrospective observational study involving patients with TOF who were posted for corrective cardiac surgery in a single hospital between 2010 and 2015. Hospital records including sociodemographic, pre- and intra-operative characteristics were extracted. Postoperative blood loss (within 24 and 48 h) and 30-day mortality were the primary and secondary outcomes, respectively. Multivariate linear and logistic regression models were used to identify determinants of outcomes.Results:A total of 60 patients were included in this study. The median age was 1 year (interquartile range = 0.62–5) and the male to female ratio of 1.7:1. Mean postoperative blood loss within 24 h was 283 ± 212 mL. In multivariate linear regression, preoperative hematocrit (β = 6.63, P = 0.042) and duration of intraoperative oxygenator with CPB (β = 5.16, P = 0.025) were significantly correlated with postoperative blood loss within 24 h. After adjusting for sociodemographic, intra- and post-operative characteristics, preoperative hematocrit (odds ratio [OR] = 1.10, 95% confidence interval [CI] = 1.01–1.21), and postoperative red blood cell transfusions (OR = 3.88, 95% CI = 1.16–12.9) showed statistically significant association with 30-day mortality. The area under the receiver operating characteristic curve of the multivariable model was 0.863.Conclusions:Preoperative levels of erythrocytosis appear to predict postoperative blood loss and short-term mortality in TOF patients undergoing corrective surgery.

Use of artificial intelligence in paediatric anaesthesia: a systematic review

“…Machine learning was the most represented with 38 included studies describing its use, 13 23 28 29 31 32 , 34 , 35 , 36 , 38 , 39 , 40 , 42 44 46 47 , 49 , 50 , 51 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 70 whereas two applications incorporated the use of fuzzy logic 21 , 22 and only one study described the use of each of natural language processing 23 and computer vision. 13 Many branches of machine learning were discussed, including regression models, 29 45 46 49 50 51 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 decision trees, 23 , 33 , 34 , 35 expert systems, …”

“… 13 Many branches of machine learning were discussed, including regression models, 29 45 46 49 50 51 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 decision trees, 23 , 33 , 34 , 35 expert systems, 59 K-means classifiers, 35 K-nearest neighbours, 29 , 44 Bayesian approaches, 29 neural networks, 23 , 25 , 34 , 45 random forest models, 23 29 31 , 45 , 46 , 47 and support vector machines. 34 , 45 , 70 Regression models were the most commonly described, with one study describing a linear regression model, 50 23 studies describing logistic regression models, 29 36 46 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , …”

“…33 Shim and colleagues 45 Jiao and colleagues 23 Chiesa and colleagues 46 Safranek and colleagues 47 Hu and colleagues 29 Ward and colleagues 31 Regression: Linear, Logistic, Elastic Net, LASSO Characterising the strength of the relationship between a dependent variable and one or more explanatory variables. 48 Shim and colleagues 45 Zhang and colleagues 49 Guevara and colleagues 50 Agostoni and colleagues 51 Alassaf and Reitsma 52 Ali and colleagues 53 Ammer and colleagues 54 Ariza and colleagues 55 Cheon and colleagues 56 Chiesa and colleagues 46 Fishman and colleagues 57 Hino and colleagues 58 Hu and colleagues 29 Kawaguchi and colleagues 59 Kim and colleagues 60 Lee and colleagues 61 Lin and colleagues 62 Nafiu and colleagues 63 Nasr and colleagues 64 Packiasabapathy and colleagues 65 Spencer and colleagues 66 Tao and colleagues 67 Vlasov 68 Ward and colleagues 31 Support vector machines Classifies data by creating a decision boundary, known as the hyperplane, that is orientated as far as possible from the closest data points from each observed class of data. 69 Galvez and colleagues 34 Shim and colleagues 45 Chini and colleagues 70 …”

Investigation of associated factors with post-operative outcomes in patients undergoing Tetralogy of Fallot correction

“…Likewise, the percentage of HCT before surgery in cases with death outcome was higher than those survived (0.047). In a same way, Guevara et al showed that preoperative haematocrit showed statistically significant association with 30-day mortality [ 33 ]. Actually haematocrit increases in TOF patients as a response to hypoxia [ 34 ].…”

Advances in pediatric perioperative care using artificial intelligence

“…Guevara et al [26] developed a predictive model for 30-day mortality in children undergoing corrective surgery of tetralogy of Fallot. Additionally, the authors found that preoperative hematocrit and duration of intraoperative oxygenator with cardiopulmonary bypass were significantly correlated with postoperative blood loss within 24 h postop [26].…”