Hostname: page-component-74656f9884-npj9w Total loading time: 0 Render date: 2026-11-07T00:53:54.968Z Has data issue: false hasContentIssue false
  • English
  • Français

Red cell transfusion practices after stage 1 palliation: a survey of practitioners from the Pediatric Cardiac Intensive Care Society

Published online by Cambridge University Press:  14 November 2019

Aditya Badheka Open the ORCID record for Aditya Badheka [Opens in a new window] ,
Priscilla Yu ,
Felina Mille ,
Dennis Durbin ,
Okan Elci  and
Joshua Blinder
Aditya Badheka*
Affiliation:
Division of Critical Care, Department of Pediatrics, Stead Family Children’s Hospital, University of Iowa, Iowa City, IA, USA
Priscilla Yu
Affiliation:
Division of Critical Care, Department of Pediatrics, University of Texas Southwestern, Dallas, TX, USA
Felina Mille
Affiliation:
Division of Cardiac Critical Care, Department of Anesthesia/Critical Care, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Dennis Durbin
Affiliation:
Department of Pediatrics and the Research Institute, Nationwide Children’s Hospital, Columbus, OH, USA
Okan Elci
Affiliation:
Westat, Rockville, MD, USA Biostatistics and Data Management Core, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Joshua Blinder
Affiliation:
Division of Cardiac Critical Care, Department of Anesthesia/Critical Care, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
*
Author for correspondence: Aditya Badheka, MD MS, Assistant Clinical Professor, Division of Pediatric Critical Care, Department of Pediatrics, 200 Hawkins Dr., Iowa City, IA 52242, USA. Tel: +1 319 356 1615; Fax: +1 319 356 8443; E-mail: aditya-badheka@uiowa.edu
Rights & Permissions [Opens in a new window]

Abstract

Introduction:

Neonates may require increased red cell mass to optimise oxygen content after stage 1 palliation; however, data informing transfusion practices are limited. We hypothesise there is a patient-, provider-, and institution-based heterogeneity in red cell transfusion decision-making after stage 1 palliation.

Methods:

We conducted an online survey of Pediatric Cardiac Intensive Care Society practitioners in 2016. Respondents answered scenario-based questions that defined transfusion indications and identified haematocrit transfusion thresholds. Respondents were divided into restrictive and liberal groups based on a haematocrit score. Fisher’s exact test was used to determine the associations between transfusion likelihood and patient, provider, and institutional characteristics. Bonferroni correction was applied to adjust the p-value to 0.004 for multiple comparisons.

Results:

There was a 21% response rate (116 responses). Most were male (58.6%), attending physicians (85.3%) with >5 year of intensive care experience (88.7%) and subspeciality training in critical care medicine (47.4%). The majority of institutions were academic (96.6%), with a separate cardiac ICU (86.2%), and performed >10 stage 1 palliation cases annually (68.1%). After Bonferroni correction, there were no significant patient, respondent, or institutional differences between the restrictive and liberal groups. No respondent or institutional characteristics influenced transfusion decision-making after stage 1 palliation.

Conclusions:

Decision-making around red cell transfusion after stage 1 palliation is heterogeneous. We found no clear relationships between patient, respondent, or institutional characteristics and transfusion decision-making among surveyed respondents. Given the lack of existing data informing red cell transfusion after stage 1 palliation, further studies are necessary to inform evidence-based guidelines.

Keywords

Single ventricle palliationred cell transfusionsurveypost-operative

Information

Type
Original Article
Information
Cardiology in the Young , Volume 29 , Issue 12 , December 2019 , pp. 1452 - 1458
Copyright
© Cambridge University Press 2019 

Neonates with hypoplastic left heart syndrome and its variants typically undergo the first of three staged surgical palliations in the first week of life. This operation carries the highest mortality of all neonatal cardiac surgeries.Reference Jacobs, Mayer and Pasquali1 Stage 1 palliation establishes parallel systemic and pulmonary circulations, necessitating meticulous post-operative management to balance these circulations and maximise tissue oxygen delivery in the setting of lower ventricular mass. Circulatory imbalance may result in post-operative shock, with impaired tissue oxygenation and end-organ dysfunction.Reference Hornik, He and Jacobs2 Neonates with impaired tissue oxygen delivery are at high risk of post-operative cardiac arrest, in-hospital mortality and major ICU morbidity.Reference Hornik, He and Jacobs2, Reference Arnold3 Immature neonatal coagulation and exposure to cardiopulmonary bypass results in higher risk of post-operative bleeding in neonates.Reference Arnold3 For these reasons, ICU providers routinely use red blood cell transfusion to support neonates after stage 1 palliation.Reference Armano, Gauvin, Ducruet and Lacroix4, Reference Kwiatkowski and Manno5

While red cell transfusion increases red cell mass and intravascular oxygen content, data indicating improved tissue oxygen delivery after red cell transfusion are limited. In a single centre retrospective study, 617 red cell transfusions were administered to 45 children on extracorporeal membrane oxygenation with an overall median haematocrit of 37%.Reference Fiser, Irby and Ward6 Only 9% of transfusions resulted in pre-defined improvement in markers of tissue oxygen (either 5% improvement in mixed venous saturation or cerebral near infrared spectroscopy).Reference Fiser, Irby and Ward6 Notably, this lack of tissue oxygen delivery improvement was independent of extracorporeal membrane oxygenation indication (cardiac versus respiratory) and haematocrit pre-transfusion threshold (25–45%).Reference Fiser, Irby and Ward6

In addition, red cell transfusions are associated with many risks including haemolytic reactions, transfusion-related acute lung injury, transfusion-associated circulatory overload, thrombotic complications, multi-organ dysfunction syndrome, and mortality.Reference Armano, Gauvin, Ducruet and Lacroix4, Reference Kwiatkowski and Manno5, Reference Lavoie7–Reference Manlhiot, Menjak and Brandao9 In adult cardiac surgery, higher rates of infection and mortality are reported with increased post-operative red cell transfusion.Reference Bracey, Radovancevic and Riggs10–Reference Horvath, Acker and Chang13

In an effort to identify optimal red cell mass for tissue oxygen delivery while avoiding unnecessary transfusion-related risks, several recent trials evaluated restrictive (~21%) versus liberal (~30%) haematocrit threshold for transfusion.Reference Lacroix, Hebert and Hutchison14, Reference Willems, Harrington and Lacroix15 Restrictive transfusion thresholds have been trialed in a broad range of critically ill patients including those with sepsis, trauma, stroke, haemorrhagic shock, respiratory failure, and after cardiac surgery for acyanotic lesions.Reference Horvath, Acker and Chang13, Reference Lacroix, Hebert and Hutchison14, Reference Willems, Harrington and Lacroix15 These data indicate that a restrictive transfusion threshold is non-inferior and, in some cases, superior to more a liberal red cell transfusion threshold.Reference Lacroix, Hebert and Hutchison14, Reference Hajjar, Vincent and Galas16–Reference Jairath, Hearnshaw and Brunskill18

Current literature informing transfusion practice in cyanotic and single ventricle subjects is very limited. Small single centre studies demonstrated increased cerebral near infrared spectroscopy or central venous oxygen saturation with red cell transfusion but failed to show improvement in clinical outcomes.Reference Kuo, Maher, Kirshbom and Mahle19–Reference Cholette, Swartz and Rubenstein22 Transfusion decision-making is multi-factorial. A recent survey of Canadian ICU providers attempted to understand factors affecting red cell transfusion decision-making after congenital heart surgery; haemodynamic instability and cyanotic heart disease were identified as influencing transfusion decision-making.Reference Tremblay-Roy, Poirier, Ducruet, Lacroix and Harrington23 Of note, providers chose a widely ranging haemoglobin threshold (ranging from <7 to 14 g/dl) to transfuse after surgery, illustrating the complete lack of consensus around red cell transfusion decision-making.Reference Tremblay-Roy, Poirier, Ducruet, Lacroix and Harrington23

In clinical practice, the decision to transfuse after stage 1 palliation depends on a complex interplay of institutional, provider, and patient factors. In an effort to characterise decision-making around red cell transfusion after stage 1 palliation, we sought to explore ICU providers’ red cell transfusion practices and to identify factors affecting the decision with a case-based survey instrument. We hypothesised that red cell transfusion practices after stage 1 palliation are variable and that this variability is driven by institutional, provider, and patient factors.

Materials and methods

Survey design

We conducted an online survey through the Pediatric Cardiac Intensive Care Society membership (n = 542 members in 12 nations) from August until October 2016. The Pediatric Cardiac Intensive Care Society is an international forum of healthcare professionals providing care for neonates, children, and adults with CHD. Patients answered demographic questions, including patient and institutional variables, followed by case-based questions about red cell transfusion after stage 1 palliation (Supplemental figure 1). All questions were composed by the authors (PY, AB).

To better assess the influence of the respondent and respondent’s institution, we included six provider characteristics and six institutional characteristics. Respondent characteristics included age, gender, subspecialty (cardiology versus critical care), role (attending versus non-attending clinician), practice setting (i.e. academic hospital), and years of ICU experience. Institutional characteristics included presence of a separate cardiac ICU, number of cardiothoracic surgeons, surgical volume and annual stage 1 palliation volume, blood transfusion protocol, and laboratory testing protocol. Eight specific transfusion indications were surveyed: bleeding, delayed sternal closure, low cardiac output syndrome, haemodynamic instability, need for inotropes, mechanical ventilation, worsening ventricular function, and pulmonary vein desaturation. We did not include a scenario involving mechanical circulatory support due to the existence of institutional protocolisation around blood product transfusion.

Each question had relevant clinical details, including a haematocrit value; respondents were required to decide whether or not to transfuse at the given haematocrit (Supplemental table 1). If respondents chose not to transfuse at the pre-defined haematocrit, they were asked to provide the haematocrit threshold at which would transfusion would occur. Consent occurred at the time of the survey completion. The survey instrument took approximately 10 minutes to complete. No identifying information was collected from survey respondents, and the study was determined to be exempt from review by the Institutional Review Board at the Children’s Hospital of Philadelphia. This survey attempted to simulate clinical situations. Mutiple transfusion indications may occur contemporaneously and are interdependent (e.g. neonate with bleeding after stage 1 palliation may also have high inotropic requirement and haemodynamic instability). A pilot survey was conducted among the authors’ local cardiac ICU attending physician and nurse practitioner group to assess the fidelity of the proposed scenarios.

Survey administration

Study data were collected and managed using Research Electronic Data Capture supported by the Children’s Hospital of Philadelphia.Reference Harris, Taylor, Thielke, Payne, Gonzalez and Conde24 Research Electronic Data Capture is a secure, web-based application designed to support data capture for research studies. Reminder emails were sent out after 1 and 2 months.

Statistical analysis

We used frequency counts and percentages for categorical variables to summarise the data. Each scenario was categorised into a transfusion indication, with some scenarios carrying multiple indications. Haematocrit in the survey scenarios ranged from 35 to 45% (35, 36, 37, 38, 40, 41, 42, and 45%). If a respondent would not transfuse at the threshold listed in the scenario, respondents chose one of the six options provided (<33, <36, <39, <42, <45, and <48%). If the listed haematocrit was between two values, it was counted as the higher value (i.e. haematocrit of 35% is <36% and haematocrit of 45% is <48%). A haematocrit score was assigned to each scenario and ranged from a possible score of 1–6, with 1 indicating transfusion threshold at a low haematocrit (<33%) and 6 indicating transfusion threshold at a high haematocrit (<48%). An overall haemoatcrit score was calculated as the average score summed from 19 scenarios. The overall scores were truncated to correspond to existing categories only in figures, not in the analysis (Supplemental figure 2). Only one respondent had an overall score of 4 or greater, corresponding to an average cut-off for a haematocrit of 39%. We divided the overall score into two groups: low ≤ 2.5 and high > 2.5. For example, if the respondent selected a haematocrit 48% to transfuse for a given scenario, the respondent was cohorted into the high haematocrit score group. Lower Hematocrit (HCT) score suggest lower threshold for transfusion. Similarly, higher HCT score suggest higher threshold for transfusion. In other words, lower HCT score means higher likelihood for transfusion and higher HCT score means lower likelihood for transfusion.

Scenarios were designed to reflect real-world conditions with multiple potential transfusion indications within a single scenario (Supplemental figure 1). Characteristics (respondent, institutional, and patient) were compared between the low and high HCT transfusion groups using Fisher’s exact test. For individual comparisons, the statistical significance level was set at p < 0.05. Bonferroni correction was performed to adjust the p-value for multiple comparisons with an adjusted significance level of p < 0.004. We used Stata 15.0 (Stata Corporation, College Station, TX, USA) to generate the summary statistics and statistical analyses.

Results

Characteristics of respondents and respondents’ institutions are outlined in Tables 1 and 2, respectively. We had 116 responses (21% response rate). The majority were male (59%) and most were attending physicians (85%) with >5 years of ICU experience (89%). Patients most often had subspeciality training in critical care (47%) or combined training in critical care and cardiology (34%). Most had dedicated cardiac ICUs (86%) and performed >5 Stage 1 palliation (S1P) cases annually (91%). Nearly half of institutions (47%) had laboratory testing protocols after S1P, but few had blood transfusion protocols (23%).

Table 1.

Characteristics of respondents

Table 2.

Characteristics of institutions

More than one respondent may have come from a single institution

Overall responses by transfusion indications showed wide variation in respondents’ decisions to transfuse. Respondent and institutional characteristics did not vary between the low and high HCT transfusion cohorts (Figs 1a–f and 2a–f and Supplemental tables 2 and 3). We assessed the impact of six provider and six institutional characteristics against all eight transfusion indications. Some comparisions were initially significant, such as the respondent’s role affected their likelihood to transfuse in case of failure to wean from the mechanical ventilation. Attending practioners reported higher likelihood to transfuse, while nurse practioners reported lower likelihood to transfuse in case of failure to wean from the mechanical ventilation (attending physician 95.2 versus 81.1% and nurse practitioner 4.8 versus 18.9%, lower versus higher HCT scores, p = 0.048). We applied Bonferonni correction, as we compared several respondent, and institutional factors with eight indications. After applying Bonferonni correction due to multiple comparisons, none of the provider or institutional characteristics remained statistically significant for any transfusion indication (Figs 1a–f and 2a–f and Supplemental tables 2 and 3).

Figure 1.

(a–f) Respondent’s characteristics – gender, age in years, sub-specialty, academic status of the respondent’s hospital, job title, and intensive care experience impact on haematocrit score by the eight transfusion indications, respectively. Of note, none of these differences were statistically significant.

Figure 2.

(a–f) Institutional characteristics – number of cardio-pulmonary bypass cases annually, number of stage 1 palliation cases annually, number of cardio-thoracic surgeons, presence of separate cardiac ICU, blood transfusion, and laboratory protocol impact on haematocrit score by the eight transfusion indications, respectively. Of note, none of these differences were statistically significant.

Discussion

In this study, we are adding to the existing body of literature seeking to understand transfusion-related decision-making after congenital heart surgery. We found significant variability among providers’ transfusion practice following stage 1 palliation. None of the respondent-, patient-, and institution-related factors we tested appeared to influence red cell transfusion decision-making.

Literature regarding red cell transfusion practices after congenital heart surgery is limited. A survey-based study by Tremblay-Roy et al examined red cell transfusion practices of Canadian providers in a broad cohort of children after congenital heart surgery. The survey used 4 case-based scenarios and 11 transfusion indicators.Reference Tremblay-Roy, Poirier, Ducruet, Lacroix and Harrington23 Providers were more likely to transfuse cyanotic and unstable patients.Reference Tremblay-Roy, Poirier, Ducruet, Lacroix and Harrington23 Among cyanotic patients, providers exhibited substantial transfusion threshold variability, with treatment thresholds ranging from 7 to 14 g/dl.Reference Tremblay-Roy, Poirier, Ducruet, Lacroix and Harrington23

In comparison, our survey was focused on a narrower cohort of neonates undergoing stage 1 palliation. It is well known that neonates after stage 1 palliation receive substantial post-operative blood product transfusions.Reference Dasgupta, Parsons and McClelland20, Reference Gupta, King and Benjamin25 In clinical practice, the transfusion indications are interdependent and can occur simultaneously during the post-operative period (e.g. a neonate with an open sternum may bleed and have high inotrope need). In our survery, we presented scenarios to simulate clinical practice and compared several provider and institutional factors with multiple transfusion indications to better understand the complex decision-making process. Our results showed similar lack of consensus around transfusion decision-making. This may explain the significant practice variability.

Wide variability in transfusion thresholds may be explained by provider and institutional biases, as well as the inherent complexity of critical care decision-making. Complex decision-making and the impact of potential unmeasured biases have long been recognised in the aviation industry and economics.Reference Duignan, Ryan, O’Keeffe, Kenny and McMahon26 Increasingly, investigators have identified the impact of heuristics, biases, and cognitive traps on medical decision-making.Reference Ryan, Duignan, Kenny and McMahon27 Heuristics are unconscious routines that enable providers to cope complex decision-making when evidence is limited.Reference Hammond, Keeney and Raiffa28 Heuristics introduce unconscious biases that prevent nimble decision-making when clinicians are presented with conflicting data. Tversky et al described three types of heuristics.Reference Tversky and Kahneman29 “Representativeness” describes a total or partial similarity of a scenario to its parent population. Using representativeness in decision-making may increase risk of an error as a scenario being more representative does not make it more likely.Reference Duignan, Ryan, O’Keeffe, Kenny and McMahon26, Reference Tversky and Kahneman29 “Anchoring” is a second type of heuristic by which the first bit of data the provider receives disproportionately affects decision-making.Reference Duignan, Ryan, O’Keeffe, Kenny and McMahon26, Reference Tversky and Kahneman29 The third type of heuristic is “availability.” Availability highlights providers’ bias of relying on the most available knowledge while making a decision.Reference Duignan, Ryan, O’Keeffe, Kenny and McMahon26, Reference Tversky and Kahneman29 Providers remember the most recent bad outcome after an intervention which influences decision-making the next time a provider encounters a similar scenario. As there are few data informing red cell transfusion decision-making after stage 1, a “knowledge gap” exists. The lack of data-based guidelines results in the use of the heuristics and likely result in the heterogeneity found in our study.

There are several important limitations to this study. Although we made considerable efforts to reach every member of Pediatric Cardiac Intensive Care Society, our survey response rate was only 21%, potentially limiting the generalisability of our findings. Surveys are self-reported instruments and may not represent what providers actually do when making clinical decisions. To minimise this potential reporting bias, we structured the survey into clinical scenarios, pilot tested the survey at our institution, and assessed multiple transfusion indicators to better understand the transfusion decision-making process. However, creating realistic case-based scenarios that capture the complexity of post-operative care after stage 1 palliation is challenging. We provided haematocrit value for all scenarios to represent the clinical situation well. Unfortunately, a given value of haematocrit in the scenario may influence the respondent due to anchoring. In an effort to mitigate this, we provided free text option to provide HCT threshold for each scenario for the respondent to minimise this limitation; however, the degree to which anchoring influenced the study results is unknown As a result, our ability to understand the nuances of decision-making may be limited. At this level of investigation, it is notable that we could not detect any consistent factors associated with a decision to transfuse, highlighting the overall variability that exists in practice. We did not include scenarios describing a neonate requiring post-operative mechanical circulatory support because transfusion thresholds may be more protocolised when compared to neonates not requiring mechanical circulatory support. Finally, we did not specify the use of whole blood as opposed to packed red blood cells because the post-operative utilisation of whole blood is limited.Reference Tremblay-Roy, Poirier, Ducruet, Lacroix and Harrington23

Conclusions

Transfusion practices in the post-operative period after stage 1 palliation vary widely. We identified no demographic, institutional, or patient factors that consistently influence decision-making around red cell transfusion after stage 1 palliation. Our study highlights the existing knowledge gap around the care of neonates after stage 1 palliation. Red cell transfusion decision-making is complicated and likely involves cognitive biases and heuristics. Lack of evidence-based guidelines may increase cognitive biases. Further investigation aimed at understanding red cell transfusion decision-making after stage 1 palliation is necessary to inform evidence-based guidelines and care standardisation to limit unnecessary transfusion exposure.

Supplementary Material

To view supplementary material for this article, please visit https://doi.org/10.1017/S1047951119002385

Acknowledgements

We are thankful to the Pediatric Cardiac Intensive Care Society for their support.

Author Contributions

Conceptualisation: AB, PY, DD, JB.

Data curation: AB, PY, FM.

Formal analysis: OE.

Methodology: AB, PY, DD, OE, XZ, FM, JB.

Project administration: AB, PY, FM, JB

Resources: JB, DD.

Software: OE, XZ.

Supervision: DD, JB.

Validation: OE, XZ, JB.

Writing – original draft: AB, PY, OE, FM, JB.

Writing – review and editing: AB, PY, DD, OE, FM, JB.

Financial Support

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

Conflicts of Interest

None for all authors pertaining to this study.

References

Jacobs, JP, Mayer, JE Jr., Pasquali, SK, et al. The society of thoracic surgeons congenital heart surgery database: 2018 update on outcomes and quality. Ann Thorac Surg 2018; 105: 680–689.CrossRefGoogle ScholarPubMed
Hornik, CP, He, X, Jacobs, JP, et al. Complications after the Norwood operation: an analysis of the society of thoracic surgeons congenital heart surgery database. Ann Thorac Surg 2011; 92: 1734–1740.CrossRefGoogle ScholarPubMed
Arnold, PD. Coagulation and the surgical neonate. Paediatr Anaesth 2014; 24: 89–97.10.1111/pan.12296CrossRefGoogle ScholarPubMed
Armano, R, Gauvin, F, Ducruet, T, Lacroix, J. Determinants of red blood cell transfusions in a pediatric critical care unit: a prospective, descriptive epidemiological study. Crit Care Med 2005; 33: 2637–2644.CrossRefGoogle Scholar
Kwiatkowski, JL, Manno, CS. Blood transfusion support in pediatric cardiovascular surgery. Transfus Sci 1999; 21: 63–72.CrossRefGoogle ScholarPubMed
Fiser, RT, Irby, K, Ward, RM, et al. RBC transfusion in pediatric patients supported with extracorporeal membrane oxygenation: is there an impact on tissue oxygenation? Pediatr Crit Care Med 2014; 15: 806–813.CrossRefGoogle ScholarPubMed
Lavoie, J. Blood transfusion risks and alternative strategies in pediatric patients. Paediatr Anaesth 2011; 21: 14–24.CrossRefGoogle ScholarPubMed
Marik, PE, Corwin, HL. Efficacy of red blood cell transfusion in the critically ill: a systematic review of the literature. Crit Care Med 2008; 36: 2667–2674.10.1097/CCM.0b013e3181844677CrossRefGoogle ScholarPubMed
Manlhiot, C, Menjak, IB, Brandao, LR, et al. Risk, clinical features, and outcomes of thrombosis associated with pediatric cardiac surgery. Circulation 2011; 124: 1511–1519.CrossRefGoogle ScholarPubMed
Bracey, AW, Radovancevic, R, Riggs, SA, et al. Lowering the hemoglobin threshold for transfusion in coronary artery bypass procedures: effect on patient outcome. Transfusion 1999; 39: 1070–1077.10.1046/j.1537-2995.1999.39101070.xCrossRefGoogle ScholarPubMed
Kuduvalli, M, Oo, AY, Newall, N, et al. Effect of peri-operative red blood cell transfusion on 30-day and 1-year mortality following coronary artery bypass surgery. Eur J Cardiothorac Surg 2005; 27: 592–598.CrossRefGoogle ScholarPubMed
Patel, NN, Avlonitis, VS, Jones, HE, Reeves, BC, Sterne, JA, Murphy, GJ. Indications for red blood cell transfusion in cardiac surgery: a systematic review and meta-analysis. Lancet Haematol 2015; 2: e543–553.CrossRefGoogle ScholarPubMed
Horvath, KA, Acker, MA, Chang, H, et al. Blood transfusion and infection after cardiac surgery. Ann Thorac Surg 2013; 95: 2194–2201.CrossRefGoogle ScholarPubMed
Lacroix, J, Hebert, PC, Hutchison, JS, et al. Transfusion strategies for patients in pediatric intensive care units. N Engl J Med 2007; 356: 1609–1619.CrossRefGoogle ScholarPubMed
Willems, A, Harrington, K, Lacroix, J, et al. Comparison of two red-cell transfusion strategies after pediatric cardiac surgery: a subgroup analysis. Crit Care Med 2010; 38: 649–656.CrossRefGoogle ScholarPubMed
Hajjar, LA, Vincent, JL, Galas, FR, et al. Transfusion requirements after cardiac surgery: the TRACS randomized controlled trial. JAMA 2010; 304: 1559–1567.CrossRefGoogle ScholarPubMed
McIntyre, LA, Fergusson, DA, Hutchison, JS, et al. Effect of a liberal versus restrictive transfusion strategy on mortality in patients with moderate to severe head injury. Neurocrit Care 2006; 5: 4–9.CrossRefGoogle ScholarPubMed
Jairath, V, Hearnshaw, S, Brunskill, SJ, et al. Red cell transfusion for the management of upper gastrointestinal haemorrhage. Cochrane Database Syst Rev 2010:CD006613.CrossRefGoogle Scholar
Kuo, JA, Maher, KO, Kirshbom, PM, Mahle, WT. Red blood cell transfusion for infants with single-ventricle physiology. Pediatr Cardiol 2011; 32: 461–468.CrossRefGoogle ScholarPubMed
Dasgupta, R, Parsons, A, McClelland, S, et al. Association of haematocrit and red blood cell transfusion with outcomes in infants with shunt-dependent pulmonary blood flow and univentricular physiology. Blood Transfus 2015; 13: 417–422.Google ScholarPubMed
Cholette, JM, Rubenstein, JS, Alfieris, GM, Powers, KS, Eaton, M, Lerner, NB. Children with single-ventricle physiology do not benefit from higher hemoglobin levels post cavopulmonary connection: results of a prospective, randomized, controlled trial of a restrictive versus liberal red-cell transfusion strategy. Pediatr Crit Care Med 2011; 12: 39–45.CrossRefGoogle Scholar
Cholette, JM, Swartz, MF, Rubenstein, J, et al. Outcomes using a conservative versus liberal red blood cell transfusion strategy in infants requiring cardiac operation. Ann Thorac Surg 2017; 103: 206–214.CrossRefGoogle ScholarPubMed
Tremblay-Roy, JS, Poirier, N, Ducruet, T, Lacroix, J, Harrington, K. Red blood cell transfusion in the postoperative care of pediatric cardiac surgery: survey on stated practice. Pediatr Cardiol 2016; 37: 1266–1273.CrossRefGoogle ScholarPubMed
Harris, PA, Taylor, R, Thielke, R, Payne, J, Gonzalez, N, Conde, JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42: 377–381.CrossRefGoogle ScholarPubMed
Gupta, P, King, C, Benjamin, L, et al. Association of hematocrit and red blood cell transfusion with outcomes in infants undergoing Norwood operation. Pediatr Cardiol 2015; 36: 1212–1218.CrossRefGoogle ScholarPubMed
Duignan, S, Ryan, A, O’Keeffe, D, Kenny, D, McMahon, CJ. Prospective analysis of decision making during joint cardiology cardiothoracic conference in treatment of 107 consecutive children with congenital heart disease. Pediatr Cardiol 2018; 39: 1330–1338.CrossRefGoogle ScholarPubMed
Ryan, A, Duignan, S, Kenny, D, McMahon, CJ. Decision making in paediatric cardiology. Are we prone to heuristics, biases and traps? Pediatr Cardiol 2018; 39: 160–167.CrossRefGoogle ScholarPubMed
Hammond, JS, Keeney, RL, Raiffa, H. The hidden traps in decision making. Clin Lab Manage Rev 1999; 13: 39–47.Google ScholarPubMed
Tversky, A, Kahneman, D. Judgment under uncertainty: heuristics and biases. Science 1974; 185: 1124–1131.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Characteristics of respondents

Figure 1

Table 2. Characteristics of institutions

Figure 2

Figure 1. (a–f) Respondent’s characteristics – gender, age in years, sub-specialty, academic status of the respondent’s hospital, job title, and intensive care experience impact on haematocrit score by the eight transfusion indications, respectively. Of note, none of these differences were statistically significant.

Figure 3

Figure 2. (a–f) Institutional characteristics – number of cardio-pulmonary bypass cases annually, number of stage 1 palliation cases annually, number of cardio-thoracic surgeons, presence of separate cardiac ICU, blood transfusion, and laboratory protocol impact on haematocrit score by the eight transfusion indications, respectively. Of note, none of these differences were statistically significant.

Supplementary material: File

Badheka et al. supplementary material

Badheka et al. supplementary material

Download Badheka et al. supplementary material(File)
File 364.6 KB