词汇
morbidity 疾病 —— mortality 死亡—— comorbidity 并发症
pre /intra/ post/ perioperative + operative 术前 术中 术后 围手术期
arthroplasty 关节置换术（artho关节 plasty置换术）
retrospective 回顾性
non-elective admission 非选择性住院（病情太重，直接住院，患者没法选）
Nationwide Inpatient Sample (NIS) 美国国家住院患者数据库
有趣观点
近年来THA技术和设备进步，输血率从30%降到了12.9–2.34 %
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 %
贫血管理？和术前优化血红蛋白？（这俩啥技术？）是THA输血风险降低（和输血率应该是两码事？）的主要原因
Management of anemia and preoperative optimization of hemoglobin levels are prime examples of new strategies to mitigate the risk of blood transfusions following THA
2011-2019 原始？TKA和THA以及高级？TKA和THA输血量都下降了（primary / revision TKA有什么区别？）
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.
THA术中输血对患者不太好，要尽量减少
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.
Charlson并发症指数？——用来预测有并发症住院患者1年内死亡率
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.
镰刀细胞、肝硬化、透析、狼疮等病，输血风险更高（这个输血风险是输血率还是输血后出现严重症状率？）
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.
糖尿病、强直性脊柱炎等和输血风险没关系
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.
肥胖、吸烟可以降低输血风险
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.
Buddhiraju等人开发了一种机器学习算法，用于预测THA后的输血，发现术前血细胞比容小于39.4%和手术时间大于157分钟是输血风险更大的分界点。
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 minutes served as a cut off point for greater risk of transfusion.
根据对NIS数据集的分析，THA后的输血发生率似乎较低（3.6%的患者）。镰状细胞病、肝硬化、透析、系统性红斑狼疮和心脏病是与THA后输血风险增加最显着相关的合并症。对THA之前或之后事件的分析发现，死亡率和非选择性入院在“输血”组中明显更为普遍。考虑到这些发现和目前的文献，术前筛查和优化上述合并症有可能进一步降低原发性THA患者的术后输血率。
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.
原文链接
https://www.sciencedirect.com/science/article/pii/S0972978X23002490
原文
Abstract
Purpose
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.
Methods
Using the Nationwide Inpatient Sample (NIS) database, we selected patients that received a THA from 2016-2019 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.
Results
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).
Conclusion
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
- 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 2.3-12.9 %.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, 10, 11, 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, 15, 16, 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.
- Methods
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. 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. 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. 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.
- Results
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. 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 |
- 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, 7, 8 Changes to clinical guidelines are largely responsible, as awareness to morbidities associated with blood transfusions has increased.9, 10, 11, 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, 15, 16, 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 minutes 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. 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.
- 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.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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.
Declaration of competing interest
None of the authors have any competing interest. Ethical (IRB) approval Not applicable since database is commercially available and HIPAA compliant.
Acknowledgements
Felipe Gonzalez Gutierrez: Writing- Original draft preparation, Data curation, Visualization. Joshua Sun: Writing- Reviewing and Editing. Senthil Sambandam: Supervision, Conceptualization. No other individuals were involved in this project.
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，自媒体可以开始断章取义说抽烟、肥胖的好处了