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46 (); 112-116
doi:
10.1016/j.jor.2023.10.016

Risk of blood transfusion following total hip arthroplasty: A national database study from 367,894 patients

, ,
University of Texas Southwestern, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA

∗Corresponding author: Felipe Gonzalez Gutierrez. felipe.gonzalezgutierrez@utsouthwestern.edu

Received: , Accepted: ,
Professor P K Surendran Memorial Education Foundation
Disclaimer:
This article was originally published by Reed Elsevier India Pvt. Ltd. and was migrated to Scientific Scholar after the change of Publisher.

Abstract

Abstract

A growing elderly population in the United States coupled with improvement in surgical techniques have resulted in more elderly individuals undergoing total hip arthroplasty (THA). As such, risk factors associated with increased risk of blood transfusion following THA, which has been linked to various detrimental outcomes, must be better understood. This study aims to identify co-morbidities associated with blood transfusion following THA.

Using the Nationwide Inpatient Sample (NIS) database, we selected patients that received a THA from 2016 to 2019 using ICD-10CMP codes. Patients were classified into a “blood transfusion” or “no transfusion” groups and data pertaining to demographics, co-morbidities, and events during hospital stays were compared between the groups.

Our study dataset included 367,894 patients from the NIS database that underwent a THA from 2016 to 2019. 12,900 (3.5 %) patients received a blood transfusion after their THA and were classified as “blood transfusion group.” The remaining 354,994 patients were classified as the “no transfusion group.” Elective admission was found to decrease the odds of a blood transfusion following a THA (compared to nonelective THA: odd's ratio 0.283; p value < 0.001). Multivariate analysis demonstrated sickle cell disease, liver cirrhosis, and dialysis exhibited the greatest increase in odds of blood transfusion after a THA by 4.81- (p < 0.001), 3.02- (p < 0.001), and 2.22-fold (p < 0.001), respectively. Looking at patient demographics, male sex increased odds of postoperative transfusion by 1.99 (p < 0.001) while Caucasian ethnicity decreased odds of postoperative transfusion by 0.65 (p < 0.001).

Blood transfusion has a low occurrence in the early post-operative period following THA (3.6 % of patients). Sickle cell disease, liver cirrhosis, dialysis, SLE, and heart pathologies were the comorbidities found to be most significantly associated with an increased risk of blood transfusion after a THA. Additionally, both mortality and non-elective admissions were significantly more prevalent in the “blood transfusion” group.

Keywords

Blood transfusion
Total hip arthroplasty
Risk factors
National inpatient sample
1

1 Introduction

Total Hip Arthroplasty (THA) is a surgical reconstruction of the hip joint with proven efficacy in treating hip arthritis.1 The growing population of elderly patients in the United States has contributed to an increased demand for THA.2 With advances in surgical techniques and prosthetic designs, THA procedures have seen a decrease in mortality and are now being offered to older individuals.3 However, despite advances in the perioperative management of patients undergoing THA, there remains a positive correlation between patient age, comorbidities, and postoperative complications.4

An important aspect of perioperative management for THA patients is the requirement for blood transfusion.5 While historical rates of blood transfusions for THA patients have exceeded 30 %, current practices have decreased this rate to as little as 9 %.6 Multiple other studies have found rates of blood transfusion after THA between 2.3 and 12.9 %.7,8 A major reason for this push has been the modification in clinical practice guidelines due to the known morbidity associated with receiving blood transfusions.9–12 For example, patients who receive blood transfusions are at increased risk of surgical-site infections.13 Therefore, the identification and treatment of anemia preoperatively is a reliable method to decrease postoperative transfusion requirements in THA patients.14–17

Previous studies have identified risk factors in THA patients such as anemia and history of bleeding diathesis, which predict increased requirement for postoperative transfusion.6,18 Through utilization of the National Inpatient Sample (NIS), this study aims to build upon previous findings by identifying additional risk factors that correlate with increased transfusion requirements along with the complications may arise in patients undergoing primary THA.

2

2 Methods

2.1

2.1 NIS database and acquisition of data

The patient data for primary THA was collected from the NIS database and includes patients that underwent THA from 2016 to 2019 in the United States. The NIS database contains clinical and nonclinical information such as patient demographics and comorbidities, complications or adverse events related to surgery, hospital admissions, and costs related to care.

2.2

2.2 NIS database- setting

The NIS database is the largest inpatient dataset available in the United States. The database includes patient information from 4573 hospitals, covering over 97 % of the U.S. population across 46 states as well as the District of Columbia.19 In order to ensure a proper representation of the US population the NIS uses a self-weighted sample design for the descriptive factors of each hospital.19 Diagnoses in the database are based on the International Classification of Disease-Tenth Revision, Clinical Modification/Procedure coding System (ICD-10-CM/PCS)].4

2.3

2.3 Data extraction - variables

The publicly available and de-identified nature of the NIS database exempted this study from the Institutional Review Board (IRB) process for approval. We identified patients that underwent THA using the ICD-10-CM/PCS codes. For each patient, collected data included patient demographics (e.g., sex, age, and race), medical comorbidities (e.g., diabetes, CKD, and sickle cell anemia), and complications. Additionally, information related to the hospital admission and stay was collected. This information included length of hospital stay, charges incurred, mortality during hospitalization, and classification of admission. Admissions were classified as “elective” versus “non-elective” based on the entries in the database. Non-elective admissions were defined as instances in which patients were admitted to the hospital due to a serious decline in either mental or physical health.

Our study dataset included 367,894 patients from the NIS database that underwent a THA from 2016 to 2019. 12,900 (3.5 %) patients received a blood transfusion after their THA and were classified as “blood transfusion group.” The remaining 354,994 patients were classified as the “no transfusion group.”

2.4

2.4 Statistical method

We performed analysis of statistical significance to identify comorbidities that demonstrated an association with blood transfusion after a THA. Our variables were first analyzed on univariate analysis using a T test for numerical variables and a Chi-square test for categorical variables, with Fischer's exact test implemented when a categorical variable had an incidence less than 5. Variables with a significant association were included in a multivariate analysis using a logistic regression. Odds ratios, the relative incidence in the “blood transfusion” and “no transfusion” groups, and 95 % confidence intervals were calculated for each variable. A p value less than 0.05 was used as the cut-off for statistical significance.

3

3 Results

3.1

3.1 Patient demographics and mortality

Looking at sex and ethnicity, we found that males, African Americans, Hispanics, and Asians were significantly associated with higher incidence of blood transfusion after a THA on the univariate analysis (Table 1). Caucasians had significantly decreased prevalence in the “no transfusion” group (Odds ratio of 0.67). Based on the multivariate analysis, male sex increased odds of postoperative transfusion by 1.99 while Caucasian ethnicity decreased odds of postoperative transfusion by 0.65 (Table 2).

Table 1 Distribution of patient sex and ethnicity between the “blood transfusion” and “no transfusion” groups in the univariate analysis.
Sex/Ethnicity Total prevalence in “blood transfusion” group [N (%)] N = 12,900 Total prevalence in “no transfusion” group [N (%)] N = 354,994 Odds ratio 95 % confidence interval p value
Male 3623 (28.1 %) 158,491 (44.6 %) 2.07 1.99–2.15 <0.001
African American 1369 (10.6 %) 26,193 (7.4 %) 1.49 1.41–1.58 <0.001
Hispanic 560 (4.3 %) 12,474 (3.5 %) 1.25 1.14–1.36 <0.001
Asian 211 (1.6 %) 3199 (0.9 %) 1.83 1.59–2.10 <0.001
Caucasian 10,045 (77.9 %) 293,057 (82.6 %) 0.67 0.64–0.70 <0.001
Native American 47 (0.4 %) 1075 (0.3 %) 1.20 0.90–1.61 0.214
Table 2 Distribution of patient sex and ethnicity between the “blood transfusion” and “no transfusion” groups in the multivariate analysis.
Sex/Ethnicity Exp (B) Odds ratio 95 % confidence interval p value
Male 1.99 1.91–2.07 <0.001
African American 1.04 0.92–1.18 0.510
Hispanic 0.83 0.72–0.96 0.010
Asian 1.08 0.90–1.29 0.401
Caucasian 0.65 0.58–0.72 <0.001

We evaluated the incidence of death and non-elective admissions from our patients in the NIS dataset. Based on our univariate analysis, the rate of mortality was higher in the “blood transfusion” group [“blood transfusion” (0.4 %) vs. “no transfusion” (<0.1 %)] (Table 3). Non-elective admission was more prevalent in the “blood transfusion” group (27.3 %) compared to the “no transfusion” group (8.0 %). A multivariate analysis was used to determine how non-elective admission affected the odds of blood transfusion following a THA (Table 4). We found that an elective admission decreased the odds of a blood transfusion following a THA by 0.283 (95 % CI: 0.271–0.230; p value < 0.001).

Table 3 Distribution of events between the “blood transfusion” and “no transfusion” groups in the univariate analysis.
Event Total prevalence in “blood transfusion” group [N (%)] N = 12,900 Total prevalence in “no transfusion” group [N (%)] N = 354,994 Odds ratio 95 % confidence interval p value
Death 57 (0.4 %) 275 (<0.1 %) 5.72 4.30–7.62 <0.001
Non-elective admission 3489 (27.3 %) 28,345 (8.0 %) 0.23 0.22–0.24 <0.001
Table 4 Distribution of events between the “blood transfusion” and “no transfusion” groups in the multivariate analysis.
Event Exp (B) Odds ratio 95 % confidence interval p value
Non-elective admission 0.28 0.27–0.30 <0.001
3.2

3.2 Comorbidities associated with a blood transfusion after a THA

From our patients in the NIS dataset, we evaluated the presence of comorbidities that included chronic kidney disease (CKD), diabetes mellitus (DM), sickle cell disease, obesity, liver cirrhosis, and heart problems [including coronary artery bypass grafting (CABG), pacemaker, heart valve, stent, or implantable cardioverter-defibrillator (ICD) placement] among other co-morbidities.

In our univariate analysis, we found tobacco-related disorders, sickle cell disease, SLE, organ transplant, dialysis, CKD, Parkinson's disease, obesity, liver cirrhosis, colostomy, legally blind status, prior CABG, and cardiac pacemakers, stents, and valves to be significantly associated with higher incidence of blood transfusion after a THA (Table 5). Tobacco-related disorders (17.6 % vs. 9.7 %) and obesity (21.9 % vs. 17.7 %) had a significantly greater prevalence in the “no transfusion” group compared to the “blood transfusion” group. Multivariate analysis demonstrated sickle cell disease, liver cirrhosis, and dialysis exhibited the greatest increase in odds of blood transfusion after a THA by 4.81-, 3.02-, and 2.22-fold, respectively (Table 6). By contrast, tobacco-related disorders and obesity decreased the odds of blood transfusion after a THA by 0.60- and 0.88-fold, respectively.

Table 5 Distribution of comorbidities between the “blood transfusion” and “no transfusion” groups in the univariate analysis.
Comorbidities Total prevalence in “blood transfusion” group [N (%)] N = 12,900 Total prevalence in “no transfusion” group [N (%)] N = 354,994 Odds ratio 95 % confidence interval p value
Uncomplicated diabetes mellitus (DM) 1253 (9.7 %) 35,575 (10.0 %) 0.97 0.91–1.03 0.250
Complicated diabetes mellitus (DM) 30 (0.2 %) 683 (0.2 %) 1.21 0.84–1.74 0.309
Tobacco-related disorder 1245 (9.7 %) 62,463 (17.6 %) 0.50 0.47–0.53 <0.001
Sickle cell disease 115 (0.9 %) 540 (0.2 %) 5.90 4.82–7.23 <0.001
SLE 146 (1.1 %) 1538 (0.4 %) 2.63 2.22–3.12 <0.001
Organ Transplant 62 (0.5 %) 751 (0.2 %) 2.28 1.76–2.95 <0.001
Dialysis 56 (0.5 %) 324 (0.1 %) 5.03 3.81–6.64 <0.001
CKD 1450 (11.2 %) 20,176 (5.7 %) 2.10 1.99–2.22 <0.001
Parkinson's Disease 146 (1.1 %) 1781 (0.5 %) 2.27 1.92–2.69 <0.001
Obesity 2287 (17.7 %) 77,632 (21.9 %) 0.77 0.74–0.81 <0.001
Morbid obesity 963 (7.5 %) 27,115 (7.6 %) 0.98 0.91–1.04 0.464
Super obesity 90 (0.7 %) 1556 (0.4 %) 1.60 1.29–1.98 <0.001
HIV 25 (0.2 %) 479 (0.1 %) 1.44 0.96–2.15 0.76
Liver cirrhosis 128 (1.0 %) 1006 (0.3 %) 3.53 2.93–4.24 <0.001
Down Syndrome 5 (<0.1 %) 124 ((<0.1 %) 1.11 0.45–2.71 0.82
Cardiac Pacemaker 392 (3.0 %) 5165 (1.5 %) 2.12 1.91–2.36 <0.001
CABG 557 (4.3 %) 8534 (2.4 %) 1.83 1.68–2.00 <0.001
Cardiac Stent 696 (5.4 %) 12,568 (3.5 %) 1.55 1.44–1.68 <0.001
Heart Valve 273 (2.1 %) 2848 (0.8 %) 2.67 2.36–3.03 <0.001
Legally Blind 24 (0.2 %) 298 (0.1 %) 2.218 1.46–3.36 <0.001
Colostomy 36 (0.3 %) 409 (0.1 %) 2.43 1.73–3.41 <0.001
Ankylosing Spondylitis 16 (0.1 %) 485 (0.1 %) 0.91 0.55–1.49 0.703
Table 6 Distribution of comorbidities between the “blood transfusion” and “no transfusion” groups in the multivariate analysis.
Comorbidities Exp (B) Odds ratio 95 % confidence interval p value
Tobacco-related disorders 0.60 0.56–0.63 <0.001
Sickle cell disease 4.81 3.87–6.98 <0.001
SLE 1.97 1.65–2.36 <0.001
Organ Transplant 1.81 1.37–2.38 <0.001
Dialysis 2.22 1.65–3.00 <0.001
CKD 1.56 1.48–1.68 <0.001
Parkinson's Disease 1.52 1.27–1.82 <0.001
Obesity 0.88 0.83–0.92 <0.001
Super Obesity 1.87 1.50–2.35 <0.001
Liver cirrhosis 3.02 2.48–3.68 <0.001
Cardiac pacemaker 1.38 1.24–1.55 <0.001
CABG 1.55 1.41–1.70 <0.001
Heart valve 1.95 1.70–2.23 <0.001
Cardiac stent 1.48 1.36–1.61 <0.001
Legally Blind 1.44 0.93–2.222 0.099
Colostomy 1.90 1.32–2.73 <0.001
4

4 Discussion

THA is proven to successfully treat patients with arthritic hip pathologies.1 Developments in prosthetic materials and designs paired with improved surgical techniques have resulted in improved functional outcomes and lower rates of mortality.3 A growing elderly population in the United States has seen a corresponding rise in THA offered to these individuals.3 Older individuals tend to present with more comorbidities and suffer from more postoperative complications.4 Identification of comorbidities that may predispose patients to postoperative complications is imperative for improving perioperative management.

One important aspect of perioperative management in THA patients is the requirement for blood transfusions. Prior to advances in techniques and equipment for THA, rates of blood transfusion were as high as 30 %. However, new advancements have dropped this rate to as low as 12.9–2.34 %.6–8 Changes to clinical guidelines are largely responsible, as awareness to morbidities associated with blood transfusions has increased.9–12 Management of anemia and preoperative optimization of hemoglobin levels are prime examples of new strategies to mitigate the risk of blood transfusions following THA.14–17 While postoperative blood transfusions are crucial in acute resuscitation, they have been implicated in increased rates of infection, morbidity, and mortality.13,20,21

Trends of blood transfusion have been on the decline across joint arthroplasty procedures. From 2011 to 2019, primary total knee arthroplasty (TKA) and THA have seen a decrease in blood transfusion rate of 21.4 %–2.5 % and 17.6 %–0.7 %, respectively, while revision TKA and THA rates have decreased from 33.5 % to 12.0 % and 19.4 %–2.6 %, respectively.6 The rate of blood transfusion after a THA in our patient population from the NIS database was 3.5 % (N = 12,900). This rate compares to reported rates of blood transfusion (2.34–12.9 %) following THA in the current literature.7,8 In a large retrospective analysis carried out in Argentina, researchers found that patients undergoing blood transfusion after THA spent more time in the hospital compared to patients that did not receive a blood transfusion (8 vs. 5 days; p = 0.007).8 These patients were also found to have higher rates of post-operative complications (22.2 % vs. 3.9 %; p = 0.017).8 Taking this into consideration, reducing the rates of blood transfusions after THA is key to improving the outlook of patients after THA.

Previous research has been carried out on identifying intra- and perioperative considerations associated with THA postoperative blood transfusions. In a large retrospective cohort study based in China, researchers found that the use of tranexamic acid, intraoperative blood transfusion from a closed suction drainage system, and higher preoperative hemoglobin levels had a protective association with blood transfusion following THA.7 We did not have access to the use of preoperative labs nor intraoperative procedures from the NIS dataset. As such, we did not have the opportunity to analyze these variables in our study.

A cohort retrospective study from Browne et al.22 evaluated patient risk factors that contributed to blood transfusions. The Charlson Comorbidity Index, a predictor of mortality within 1 year of hospitalization for patients with different comorbidities, and having comorbidities were found to significantly increase rates of blood transfusion. Our study supports this association. Specifically, we found that sickle cell disease (odds ratio: 4.81), liver cirrhosis (odds ratio:3.02), dialysis (odds ratio:2.22), SLE (odds ratio: 1.97), and prosthetic heart valve (odds ratio: 1.95) had the highest risk of blood transfusion following primary THA. A prior retrospective study by DeMik et al.6 found significant associations between heart, liver, and kidney comorbidities with blood transfusions after THA. Another retrospective study by Jeschke et al.23 found that heart complications and renal failure, among other comorbidities, increased the risk of blood transfusion after THA.

Conversely, we found that diabetes mellitus (both complicated and uncomplicated), morbid obesity, down syndrome, ankylosing spondylitis, and legally blind status each did not have a significant association with postoperative blood transfusion. While some of these findings may be associated with sampling bias, such as morbid obesity and legally blind status, improvements in the management and pre-operative care of the other conditions may help to explain the lack of significance.

Finally, we found lower rates of blood transfusion in patients with self-reported tobacco-related disorders and obesity (BMI >30). Previous research has implicated obesity with both lower rates of blood transfusion and mortality after THA.4,6,23 For the focus of this paper, higher BMI may confer protection from blood transfusion due to increased blood volume in obese individuals or may simply be a finding attributed to the size of the “blood transfusion” group.

The perioperative events evaluated in this study were non-elective admission and mortality. Non-elective admission was more prevalent in the “blood transfusion” group (27.3 %) compared to the “no transfusion” group (8.0 %; odds ratio = 0.23; p < 0.001). Additionally, elective admission was found to decrease the risk of blood transfusion in our multivariate analysis (odds ratio = 0.28; p < 0.001). Conversely, mortality was found to be significantly more prevalent in the “blood transfusion” group (odds ratio = 5.72; p < 0.001). Previous research looking at mortality after THA has shown that it shares many of the same risk factors as blood transfusion, such as CKD, liver cirrhosis, and a history of organ transplant.1

Other research has sought to develop models to estimate the risk of blood transfusion after THA. Buddhiraju et al.24 developed a machine learning algorithm for predicting blood transfusion after a THA, finding that a preoperative hematocrit less than 39.4 % and operation time greater than 157 min served as a cut off point for greater risk of transfusion. Another study by Trevisan et al.25 developed a separate algorithm that found a preoperative Hb greater than or equal to 13 g/dl to be the cutoff point for low vs. high risk of transfusion following a THA. While this study focused on the effect of comorbidities on the risk of blood transfusion following a THA, clearly many factors play a significant role. Future research should focus on managing preoperative risk factors, such as patient hemoglobin or hematocrit, as well as comorbidities, such as sickle cell disease, liver cirrhosis, dialysis, SLE, and heart pathologies, that can be considered to limit blood transfusions and improve patient outcomes post-surgery.

4.1

4.1 Limitations and strength

This study has limitations that need to be considered. The retrospective analysis from the NIS database contains only information obtained during the patient's hospital stay. As such, any potential factors that can contribute to blood transfusion after a THA, such as comorbidities and intraoperative events, that were not documented would be missed. As mentioned in the discussion, we did not have access to the use of preoperative labs nor intraoperative procedures from the NIS dataset, so we did not have the opportunity to analyze these variables in our study. The large sample size from the NIS database (367,894 patients); however, provides ample data to analyze and identify risk factors associated with blood transfusion after THA.

5

5 Conclusion

Based on the analysis of the NIS dataset, blood transfusion seems to have a low occurrence rate following THA (3.6 % of patients). We found that sickle cell disease, liver cirrhosis, dialysis, SLE, and heart pathologies were the comorbidities most significantly associated with an increased risk of blood transfusion after a THA. The analysis of events prior to or post THA found that both mortality and non-elective admissions were significantly more prevalent in the “blood transfusion” group. Taking these findings, and the current literature, into consideration, preoperative screening and optimization of aforementioned comorbidities has the potential to further decrease rates of postoperative blood transfusion in patients undergoing primary THA.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Consent to participate

Not applicable since database is commercially available and HIPAA compliant.

Consent for publication

All authors consent for publication of this manuscript.

Ethical approval and informed consent

The publicly available and de-identified nature of the NIS database exempted this study from the Institutional Review Board (IRB) process for approval and no informed consent was obtained.

Ethical compliance statement

We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.

Patient consent

The patient data for primary THA was collected from the NIS database and includes patients that underwent THA from 2016 to 2019 in the United States. The NIS database contains clinical and nonclinical information such as patient demographics and comorbidities, complications or adverse events related to surgery, hospital admissions, and costs related to care. The publicly available and de-identified nature of the NIS database exempted this study from the Institutional Review Board (IRB) process for approval and no informed consent was obtained.

CRediT authorship contribution statement

Felipe Gonzalez Gutierrez: Writing – original draft, preparation, Data curation, Visualization. Joshua Sun: Writing – review & editing. Senthil Sambandam: Supervision, Conceptualization.

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