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August 2008, Volume 58, Issue 8

Original Article

Predictive Model of blood transfusion during CABG Surgery in Pakistan

Sana Shoukat  ( Medical Officer, Faculty of Health Sciences, Aga Khan University, Karachi )
Saqib A. Gowani  ( Medical Student, Faculty of Health Sciences, Aga Khan University )
Farhad Khimani  ( Medical Student, Faculty of Health Sciences, Aga Khan University, Karachi )
Faraz A. Khan  ( Medical Student, Faculty of Health Sciences, Aga Khan University, Karachi )
Mohammad Zaman  ( Department of Psychiatry, Aga Khan University, Karachi )
Hasanat Sharif  ( Cardiothoracic Section, Department of Surgery )

Abstract

Objective: To determine predictors of need for transfusion of blood and blood products and create a clinical predictive model to reduce indiscriminate use of blood products during surgery.
Method: We conducted a retrospective chart review of 485 patients who underwent coronary artery bypass surgery from January 2004 to December 2004 at a Tertiary Care Hospital in Karachi, Pakistan. Independent predictors associated with transfusion were identified and a clinical prediction model developed.
Results: The transfusion rate was 37.1%. A predictive model was created based on the presence of pulmonary disease, diabetes mellitus, low ejection fraction and recent/ongoing myocardial infarction.
Conclusion: The study identifies some predictors of need for blood transfusion in patients undergoing Coronary Artery Bypass Grafting. However, prospective studies with a larger sample of patients are needed to determine other predictors and their applicability in patient selection across institutions (JPMA 58:421;2008).

Introduction

Coronary artery bypass-graft surgery (CABG) currently is the commonest cardiac operation performed in the adult population.1 There have been great advances in perfecting this surgery leading to reduced number of complications. However, the complications related to blood transfusion are still high. According to international figures, use of blood transfusions for cardiac patients is as high as 10-40%, presumably higher in Pakistan considering the lack of blood conservation strategies.2,3 The patients requiring blood transfusion make up more than one third of those going for elective CABG.4

Transfusion of blood or its products are reported as the most important factor associated with increased risk of postoperative morbidity and mortality following a coronary artery bypass graft surgery.5,6  The risks associated with transfusion include infectious disease transmission, acute and delayed haemolytic reactions, transfusion-related acute lung injury and transfusion related immunomodulation.7,8 These morbidities not only affect the patient's quality of life and risk of death after the procedure but also add a huge cost burden especially if a lifelong infection is acquired.

Blood conservation techniques including preoperative autologous blood donation (PABD), intraoperative use of antifibrinolytic drugs, hypervolaemic haemodilution, preoperative erythropoietin administration and acute normovolaemic haemodilution are expensive and carry their own risks.9,10 It is therefore imperative to have a model that selects patients for such procedures and patients who will eventually need a transfusion.

At the same time, risks related with use of blood in Pakistan are higher than the developed world. The prevalence of Hepatitis B exposure determined by Anti-HBc prevalence in healthy blood donors is reported to be as high as 17% and at the same time, not all blood bank facilities are well equipped for infectious disease detection in donors.11,12An average patient receiving two units of blood has a 10% chance of acquiring hepatitis C infection.11 It is obvious that transfusion related risks are higher in the country than reported in the literature.  To the best of our knowledge, no study has been conducted to explore the predictors for use of blood products in patients undergoing CABG in Pakistan.

Several studies have explored predictors for the use of blood and blood products in patients undergoing CABG. The various predictors include preoperative haemoglobin, red cell mass, body mass index, body surface area, female sex, age, extent of disease, number of diseased vessels, prior CABG, redo procedure, emergent operation, diabetes and renal disease, extent of acute normovolaemic haemodilution, use of cardiopulmonary bypass, ejection fraction albumin, preoperative antiplatelet or antithrombotic drugs and preoperative bleeding and prothrombin times2,13-17 the above predicators cannot be applied to our population , as the demographics and  outcome of surgery may be variable.

We aimed to determine the predictors of transfusion of blood and blood products in patients undergoing CABG in a tertiary care centre in Pakistan and develop a clinical prediction model for use of blood products in patients undergoing CABG.

Patients and Methods

After institutional ethics approval, data on consecutive patients who had undergone CABG surgery at a tertiary care hospital, from January 2004 to December 2004 was retrieved retrospectively. All patients undergoing CABG surgery during this time period at were included. Those with missing record files and unclear or incomplete information were excluded.

The study was conducted in a tertiary care teaching hospital (representing private sector) of Karachi, Pakistan. A total of 510 open-heart surgical procedures were performed during the study year at this institution. Blood-conservation methods used before surgery included discontinuation of anti-platelet and anticoagulant medication 1 week prior to surgery and screening for and correction of coagulation disorders. Blood-conservation methods used during surgery included the administration of antifibrinolytic agents (tranexamic acid and aprotinin). On routine, recycled pumped blood was used in all patients. Blood was transfused in patients with haemoglobin levels less than 10 mg/dl if off-pump and 7 mg/dl if on-pump.

Platelets and fresh frozen plasma was empirically given to any patient who had episode of bleeding post operatively.

The exposure variables were divided into continuous and categorical variables. The continuous variables included age (years), Body Mass Index (kg/m2), preoperative haemoglobin (g/dl), preoperative platelet, preoperative prothrombin time, preoperative activated partial thromboplastin time, preoperative bleeding time, ejection fraction, cardio-pulmonary bypass duration (min) and aortic clamp duration (min).

The categorical variables included gender, use of anti-platelets, anticoagulant or antithrombotic agents (defined as more than 2 weeks of use of the agent prior to the surgery), coagulation defects ( history of any coagulopathy or objective evidence of coagulation defect pre-operatively), smoking (ex and current smokers combined), renal dysfunction ( defined as Cr>1.1 mg/dl), hypertension (history of hypertension and being on antihypertensive therapy at the time of surgery), peripheral vascular disease (defined as a history of claudication, ischaemic resting pain, vascular surgery or a non invasive vascular test showing > 50% obstruction of the lower extremity vasculator), carotid artery disease (Carotid doppler proven > 70% stenosis of carotid artery), diabetes mellitus (as history of diabetes mellitus, Type I or II, and being on medication at the time of surgery), pulmonary disease (known history of pulmonary disease or FEV1 < 1.5 L at the time of surgery), recent/ ongoing myocardial infarction (based on EKG changes and Trop I > 0.5 ng/ml), type of surgery defined as urgent, emergent or elective and catheterization during admission. All variables except gender and type of surgery were binomial variables defined as Yes/No.

The primary outcome measure was blood transfusion given intra-operatively or post-operatively.

Preoperative and peri-operative patient variables previously reported to be associated with the need for blood transfusion during CABG and other cardiac surgeries were determined along with variables which revealed a statistically significant association. Database accuracy was measured by re-abstracting a random sample of 10 percent of the medical records of CABG patients. In addition, all outlying values were compared to patients' records to identify and correct errors in the database.

SPSS 15.0 (copyright © SPSS Inc., 1989-2007) was used for statistical analysis.

Results were expressed as mean ± standard deviation for all continuous variables and frequency and percentage for categorical data. Univariate analysis was performed by using the independent sample t-test, Pearson Chi-square test and Fisher Exact test wherever appropriate to assess the unadjusted relationship between the predictor variables and the outcome. A p-value <0.05 was considered as statistically significant.

The initial model was derived by using all statistically significant (p<0.25, the standard criterion for selection of candidate variables to be used in multivariate analysis) predictor variables that were not confounding with other variables. The best fitting model was identified from different models, each derived from different predictor variables. The Hosmer and Lemeshow goodness-of-fit test was also used to determine the final model.18

Results

Among 485 patients 82.5% (n=400) were male and 17.5% (n=85) female. The mean age of the patients was 58. 5 ± 9.6 years. Mean Body Mass Index of the patient population was 26.6 ± 4.5 kg/m2. Out of 485 CABG surgeries performed during the study period selected 68.9 %( n=334) were elective, 22.3% (n=108) were urgent and 8.9% (n=43) emergency.

Of the 485 patients undergoing CABG, 21.0% (n= 102) patients had acute coronary syndrome with 19.8% (n=96) having a recent or ongoing myocardial infarction at the time of presentation.

Hypertension was a common comorbid seen in 70.7% (n= 343) of the patients while 43.1% (n=209) had diabetes, 39.0% (n= 189) had dyslipidaemia, 4.5% (n=22) had renal disease, 1% (n=5) had carotid artery disease and 6.6 %( n=32) patients had pulmonary disease, whereas 2.1% (n=10) of the patients had a history of previous coronary bypass surgery. Mean preoperative haemoglobin and haematocrit level of the patients were 12.8 ± 1.8mg/dl and 38.6 ± 73.4% respectively. The mean International Normalized Ratio was 1.02 ± 0.13. 73.4% (n= 356) of the patients were exposed to some form of anteplatelet therapy prior to the surgery. Among this group, majority (95.2%) had taken aspirin. These medications were stopped in 68.2% (n= 331) patients at a mean of 5 days before the surgery was performed.

Majority of the procedures were carried out using the conventional technique, (only a small proportion (2.3%; n=11) used the off pump technique.  Mean cardiopulmonary bypass time was 99.1 ± 31.9 minutes. Aortic Cross Clamp time was 59.9 ± 20.7 minutes. Intra-aortic Balloon Pump (IABP) use was found in 9.1% (n=44) cases. Blood or blood products were used in 37.1% (n= 180) of the patients.

The complications of surgery occurred in 9.9% (n=48) patients.  45.8% of these suffered from hemorrhage.

On  univariate analysis presence of diabetes mellitus differed significantly between the patients who required blood transfusions and who did not. Also, ejection fraction, presence of pulmonary disease, prothrombin time, recent/ongoing myocardial infarction differed between the two groups, however, it was not significant. We found no interaction among the variables used in univariate analysis. The comparison of all variables between the two groups is shown in Tables 1 and 2.

We applied multiple logistic regression on all the significant variables to put together a predictive model for transfusion requirement during CABG. Hence, after removing all the confounded variables, a model consisting of diabetes mellitus, pulmonary disease, ejection fraction and recent/ongoing myocardial infarction were found as predicting factors for need of blood transfusion in patients undergoing CABG surgery (Table 3).

[(0)] [(1)][(2)]

Conclusion

Employing a clinical predictive model and following blood conservation guidelines can help to select the right patients who need transfusion during CABG surgery. The prediction rule comprises of presence of pulmonary disease, diabetes mellitus, ongoing/recent myocardial infarction and a low ejection fraction. This rule needs to be validated to assess its applicability. Gender differences in need of transfusion also need to be explored.

References

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