Abstract
Aims
While preoperative bloodwork is routinely ordered, its value in determining which patients are at risk of postoperative readmission following total knee arthroplasty (TKA) and total hip arthroplasty (THA) is unclear. The objective of this study was to determine which routinely ordered preoperative blood markers have the strongest association with acute hospital readmission for patients undergoing elective TKA and THA.
Methods
Two population-based retrospective cohorts were assembled for all adult primary elective TKA (n = 137,969) and THA (n = 78,532) patients between 2011 to 2018 across 678 North American hospitals using the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) registry. Six routinely ordered preoperative blood markers - albumin, haematocrit, platelet count, white blood cell count (WBC), estimated glomerular filtration rate (eGFR), and sodium level - were queried. The association between preoperative blood marker values and all-cause readmission within 30 days of surgery was compared using univariable analysis and multivariable logistic regression adjusted for relevant patient and treatment factors.
Results
The mean TKA age was 66.6 years (SD 9.6) with 62% being females (n = 85,163/137,969), while in the THA cohort the mean age was 64.7 years (SD 11.4) with 54% being female (n = 42,637/78,532). In both cohorts, preoperative hypoalbuminemia (< 35 g/l) was associated with a 1.5- and 1.8-times increased odds of 30-day readmission following TKA and THA, respectively. In TKA patients, decreased eGFR demonstrated the strongest association with acute readmission with a standardized odds ratio of 0.75 per two standard deviations increase (p < 0.0001).
Conclusion
In this population level cohort analysis of arthroplasty patients, low albumin demonstrated the strongest association with acute readmission in comparison to five other commonly ordered preoperative blood markers. Identification and optimization of preoperative hypoalbuminemia could help healthcare providers recognize and address at-risk patients undergoing TKA and THA. This is the most comprehensive and rigorous examination of the association between preoperative blood markers and readmission for TKA and THA patients to date.
Cite this article: Bone Jt Open 2021;2(6):388–396.
Take home message
In this retrospective cohort study of 216,501 patients, preoperative lower albumin proved to have the greatest impact on the odds of readmission in comparison to five other commonly ordered preoperative blood tests in both total hip arthroplasty (THA) and total knee arthroplasty (TKA) patients.
Identification and optimization of preoperative hypoalbuminemia could help healthcare providers recognize and address at-risk patients undergoing elective TKA and THA.
Introduction
More than 650,000 total knee arthroplasties (TKAs) and 450,000 total hip arthroplasties (THAs) are performed in the USA annually, with these numbers expected to increase to 3.5 million annually by 2030.1-3 Between 2.4% to 4.6% of arthroplasty patients are readmitted to hospital within the first 30 days as a result of an acute complication, leading to increased patient morbidity as well as additional personal and hospital costs.4-6 This becomes increasingly prudent as more hospitals move towards episode of care bundled funding models for arthroplasty, whereby hospitals and providers are responsible for acute readmission care costs.7,8
Previous studies have identified various factors that are associated with a higher risk of postoperative morbidity following arthroplasty, including advanced age,9-11 male sex,12,13 previous myocardial infarction (MI), liver or renal disease,14,15 and a higher American Society of Anesthesiologists (ASA) classification.16,17 However, some of these patient factors are non-modifiable, limiting the opportunity for preoperative interventions to lower postoperative risks. Schroer et al7 examined a series of modifiable risk factors in arthroplasty patients and determined that anaemia, malnutrition, obesity, diabetic control, narcotic use, and tobacco use were all associated with adverse outcomes and increased healthcare expenditure.
Patients undergoing elective TKA or THA routinely have preoperative bloodwork drawn, though the value of these tests in the arthroplasty population has been called into question.18 The purpose of this study was to determine which commonly ordered preoperative blood markers, as prospectively collected by the American College of Surgeons National Surgical Quality Improvement Programme (ACS-NSQIP) registry, are most associated with acute hospital re-admissions following primary elective unilateral TKA and THA for osteoarthritis (OA).
Methods
Study design and data source
A population-based, retrospective cohort study of patients undergoing primary TKA or THA in North America was conducted using the ACS-NSQIP registry. ACS-NSQIP is a prospective collected and audited registry with over 678 hospitals in North America, whereby patients undergoing common surgical procedures are identified and followed for 30 days postoperatively.19,20 Audited coders review inpatient and outpatient records to track the occurrence of complications. The accuracy and reproducibility of ACS-NSQIP coding has been previously reported on in several surgical sub-specialities including orthopaedics.21-29
Study patients and cohort assembly
Two retrospective cohorts composed of all adult patients (aged > 18 years) who underwent a primary elective unilateral TKA or THA for OA at an ACS-NSQIP affiliated hospital in North America between 2011 and 2018 inclusive were created using appropriate Current Procedural Terminology (CPT) codes (Supplementary Material). We excluded all cases that were bilateral, nonelective, indicated for infectious, traumatic, pathological, or oncological diagnosis, patients with systemic sepsis or disseminated cancer, revision procedures, cases performed by nonorthopaedic primary surgeons, and those who were missing one or more of the six preoperative blood markers routinely collected (albumin, haematocrit, platelets, WBC, eGFR, and sodium) (Figure 1).30
Fig. 1
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)30 diagram of cohort assembly for total knee arthroplasty (TKA) and total hip arthroplasty (THA) patients. NSQIP, National Quality Improvement Programme.
Following the application of inclusion and exclusion criteria, the THA and TKA cohorts were further split into 2011 to 2016 arthroplasty patients and 2017 to 2018 arthroplasty patients inclusive. The 2011 to 2016 cohorts were used as the development cohorts, while the 2017 to 2018 cohorts were used as validation cohorts for the purpose of a temporal external validation of association findings.
Preoperative blood markers
The ACS-NSQIP database records ten commonly ordered preoperative blood markers, of which six were selected a priori given their use in practice and potential association with arthroplasty outcomes and included in our analysis as covariates of interest: albumin,7 haematocrit,31 platelets,32 sodium,33 and white blood cells (WBCs).34 Estimated globular filtration rate was derived using creatinine, age, sex, and race.35 For this purpose, we assumed all elective patients undergoing arthroplasty were in a steady state at the time of preoperative blood collection. All blood markers were required to have been collected within 30 days prior to surgery. Normal ranges for these blood markers are defined in Supplementary Material 2.
Covariates
Covariates for inclusion in our multivariable analysis were identified a priori based on known confounders, variables known to influence outcomes in arthroplasty patients, and those deemed clinically relevant. Patient factors included age,9-11 sex,12,13 race,36 BMI,37 baseline functional status (as per ACS-NSQIP definitions: independent, partially dependent, or totally dependent),4 comorbidities (congestive heart failure (CHF), chronic obstructive pulmonary disorder (COPD), diabetes, bleeding disorder, chronic renal failure requiring dialysis, hypertension requiring medication, current steroid use for chronic condition(s), and active smoking within one year of index surgery),14,15,19 and American Society of Anesthesiologists (ASA) class.16,17 Perioperative and treatment factors included: anaesthesia type,38 operation length, hospital length of stay,36 and discharge destination.36 Missing values for age and BMI were imputed via multiple imputation.39
Outcome
Our primary outcome was acute hospital readmission, defined as within 30 days of index arthroplasty surgery. The ACS-NSQIP database captures any cause readmission within 30 days of surgery at any hospital, neither limited to the institution at which surgery was performed nor ACS-NSQIP participating hospitals.
Statistical analysis
Descriptive statistics were calculated for each blood marker and covariate as appropriate. Unadjusted associations between variables and readmission were assessed using univariable logistic regression and Pearson’s chi-squared tests as appropriate. Multivariable logistic regression modelling was performed on the development cohorts to evaluate the independent association of each blood marker with acute readmission while controlling for the aforementioned a priori selected covariates. We used univariable models to determine the unadjusted association of blood markers with readmission, and multivariable models adjusted for relevant covariates were used to determine the independent association of the blood markers with readmission. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) for included covariates, expressed per two standard deviations change for continuous blood marker data.40 This was done to allow for comparison of the magnitude of effect across included continuous and binary variables on the odds of readmission independent of their respective scales.40 Secondarily, blood markers were categorized based on clinical cut-offs (Supplementary Material 2) and our multivariable models were again applied to produce ORs and 95% CIs for the influence of abnormally high or low blood marker values on acute readmission.
Statistical significance was set conservatively at a two-sided p < 0.0005 using Bonferroni correction to account for multiplicity of testing of the six covariates of interest in development and validation cohorts as either continuous or categorical variables, corresponding to 96 statistical tests. All statistical work was calculated using SAS Software, version 9.4 (SAS Institute, USA).
Validation
Results from statistical analyses within the development cohorts were compared to results following the same analyses in the 2017 to 2018 validation cohorts. This was done to examine the results using a temporal-based validation in an effort to lessen the likelihood of type 1 error. Variables that maintained statistical significance through both cohorts were considered to influence acute readmission.
Results
We identified a total of 137,969 TKA patients (mean age 66.6 years (SD 9.6); 62% female, BMI 33.2 kg/m2 (SD 6.8)) and 78,532 THA patients (mean age 64.7 years (SD 11.4), 54% female, BMI 30.4 kg/m2 (SD 6.3)) that underwent an elective unilateral arthroplasty from 2011 to 2018 with complete blood marker profiles (Figure 1). A comparison of included patients to those who were excluded for not having a full set of blood markers is presented in Supplementary Material 3. On average, patients who were missing one or more blood marker values were slightly younger and healthier preoperatively, with a lower prevalence of comorbidities in comparison to those included in our analysis.
Overall, the vast majority of included patients in both cohorts were functionally independent at baseline and the most common comorbidities were hypertension requiring medication, diabetes, and smoking, which is in keeping with previous studies (Table I).14,41
Table I.
Summary of patients.
| Variable | TKA cohort (2011 to 2018) n = 137,969 |
THA cohort (2011 to 2018) n = 78,532 |
|---|---|---|
| Mean age, yrs (SD) | 66.6 (9.6) | 64.7 (11.4) |
| Female sex, n (%) | 85,163 (61.7) | 42,637 (54.3) |
| Race, n (%) | ||
| White | 109,179 (79.1) | 64,370 (82.0) |
| Black | 10,939 (7.9) | 6,478 (8.3) |
| Other | 17,851 (12.9) | 7,684 (9.7) |
| Independent functional status | 136,484 (98.9) | 77,241 (98.4) |
| Mean BMI, kg/m2 (SD) | 33.2 (6.8) | 30.4 (6.3) |
| Smoker, n (%) | 11,766 (8.5) | 10,392 (13.2) |
| CHF, n (%) | 442 (0.3) | 259 (0.3) |
| COPD, n (%) | 5143 (3.7) | 3,181 (4.1) |
| Diabetes, n (%) | 25,660 (18.6) | 9,673 (12.3) |
| Dialysis, n (%) | 262 (0.2) | 157 (0.2) |
| Hypertension, n (%) | 91,673 (66.4) | 44,953 (57.2) |
| Steroid use, n (%) | 5,487 (4.0) | 3,152 (4.0) |
| Bleeding disorder, n (%) | 3,111 (2.3) | 1,620 (2.1) |
| Dyspnea, n (%) | 8,387 (6.1) | 3,730 (4.8) |
| ASA grade, n (%) | ||
| I/II | 67,731 (49.1) | 44,494 (56.7) |
| III/IV | 70,238 (50.9) | 34,038 (43.3) |
| Anaesthesia type, n (%) | ||
| General | 70,317 (51.0) | 43,515 (55.4) |
| Neuraxial | 67,652 (49.0) | 35,017 (44.6) |
| Mean operation length, mins (SD) | 92.4 (33.6) | 92.5 (38.4) |
| Mean length of stay, days (SD) | 2.6 (2.4) | 2.2 (1.4) |
| Discharge destination, n (%) | ||
| Home | 106,562 (77.2) | 63,529 (80.9) |
| Other | 31,407 (22.8) | 15,003 (19.1) |
-
ASA, American Society of Anesthesiologists; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; SD, standard deviation; THA, total hip arthroplasty; TKA, total knee arthroplasty.
With regards to baseline blood markers, 111,130 TKA patients (80.6%) and 62,701 THA patients (79.8%) had at least one blood marker abnormality; the most common abnormalities in both cohorts were low eGFR, anemia, hypoalbuminemia, and hyponatremia (Table II). The acute readmission rate was 3.2% (n = 4,383) for all TKA patients and 3.5% (n = 2,725) for all THA patients.
Table II.
Preoperative laboratory results.
| Preoperative blood marker | TKA cohort (2011 to 2018) n = 137,969 |
THA cohort (2011 to 2018) n = 78,532 |
|---|---|---|
| Albumin | ||
| Mean albumin g/l (SD) | 41.1 (0.37) | 41.5 (0.39) |
| Low (0 to 34 g/l), n (%) | 5,253 (3.8) | 2,658 (3.4) |
| Normal (35+ g/l), n (%) | 132,716 (96.2) | 75,874 (96.6) |
| HCT | ||
| Mean HCT L/L (SD) | 41.0 (4.1) | 41.2 (4.2) |
| Low (Male < 45 L/L, Female < 37 L/L), n (%) | 43,239 (31.3) | 28,020 (35.7) |
| Normal, n (%) | 94,730 (68.7) | 50,512 (64.3) |
| Platelets | ||
| Mean platelets 109/l (SD) | 245.0 (66.6) | 248.5 (67.7) |
| Low (0 to 139 × 109/l), n (%) | 4,248 (3.1) | 2,142 (2.7) |
| Normal (140+ × 109/l), n (%) | 133,721 (96.9) | 76,390 (97.3) |
| WBC | ||
| Mean WBC 109/l (SD) | 7.0 (2.1) | 7.0 (2.3) |
| Low/Normal (0 to 11 × 109/l), n (%) | 133,222 (96.6) | 75,592 (96.3) |
| High (12+ × 109/l), n (%) | 4,747 (3.4) | 2,940 (3.7) |
| eGFR | ||
| Mean eGFR, ml/min/1.73 m2 (SD) | 82.2 (24.4) | 84.7 (25.4) |
| Severe (< 30 ml/min/1.73 m2), n (%) | 1,022 (0.7) | 574 (0.7) |
| Mild/Moderate (30 to 89 ml/min/1.73 m2), n (%) | 102,066 (66.7) | 49,073 (62.5) |
| Normal (≥ 90 ml/min/1.73 m2), n (%) | 44,881 (32.5) | 28,885 (36.8) |
| Sodium | ||
| Mean sodium, mmol/l (SD) | 139.8 (2.8) | 139.7 (2.8) |
| Low (< 135 mmol/l), n (%) | 4,808 (3.5) | 3,173 (4.0) |
| Normal (135 to 147 mmol/l), n (%) | 132,889 (96.3) | 75,222 (95.8) |
| High (≥ 148 mmol/l), n (%) | 272 (0.2) | 137 (0.2) |
-
eGFR, estimate glomerular filtration rate; HCT, hematocrit; preop, preoperative; SD, standard deviation; THA, total hip arthroplasty; TKA, total knee arthroplasty; WBC, white blood cell count.
Univariable analysis – total knee arthroplasty
Our univariable analysis demonstrated that all six preoperative blood markers were significantly associated with acute readmission following TKA in both the development and validation cohorts when analyzed as continuous variables (p < 0.0001) (Table III). When standardized per two standard deviations increase, albumin (OR 0.69, 95% CI 0.64 to 0.74) and eGFR (OR 0.62, 95% CI 0.56 to 0.67) demonstrated the strongest associations with acute readmission. The association between blood markers and readmission (p < 0.0005) was maintained for all variables except WBC and sodium, when the variables were analyzed using clinically relevant cut-offs to compare normal versus abnormal on a categorical basis (Table IV). This analysis demonstrated that those with severe renal impairment (eGFR < 30) had a 3.32-times increase in their odds of readmission in comparison to normal (95% CI 2.50 to 4.42; p < 0.0001). Similarly, patients with hypoalbuminemia had 1.88-times greater odds of readmission in comparison to those with normal albumin values (95% CI 1.60 to 2.20; p < 0.0001).
Table III.
Univariable association of continuous blood marker results with acute readmission in development and validation.
| TKA | THA | |||||||
|---|---|---|---|---|---|---|---|---|
| Variable | Development (2011 to 2016) | Validation (2017 to 2018) | Development (2011 to 2016) | Validation (2017 to 2018) | ||||
| Patients, n | 82,810 | 55,159 | 46,889 | 31,643 | ||||
| Readmissions, n (%) | 2,635 (3.2) | 1,748 (3.2) | 1,623 (3.5) | 1,102 (3.5) | ||||
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Albumin | 0.69 (0.64 to 0.74) | < 0.0001 | 0.64 (0.59 to 0.70) | < 0.0001 | 0.64 (0.58 to 0.70) | < 0.0001 | 0.55 (0.49 to 0.61) | < 0.001 |
| HCT | 0.79 (0.73 to 0.85) | < 0.0001 | 0.70 (0.64 to 0.77) | < 0.0001 | 0.75 (0.69 to 0.83) | < 0.0001 | 0.56 (0.50 to 0.62) | < 0.0001 |
| Platelets | 0.84 (0.77 to 0.91) | < 0.0001 | 0.81 (0.73 to 0.89) | < 0.0001 | 0.98 (0.89 to 1.08) | 0.6835 | 0.98 (0.87 to 1.10) | 0.6927 |
| WBC | 1.20 (1.13 to 1.28) | < 0.0001 | 1.22 (1.12 to 1.32) | < 0.0001 | 1.19 (1.11 to 1.29) | < 0.0001 | 1.33 (1.21 to 1.46) | < 0.0001 |
| eGFR | 0.62 (0.56 to 0.67) | < 0.0001 | 0.60 (0.54 to 0.66) | < 0.0001 | 0.81 (0.72 to 0.90) | < 0.0001 | 0.65 (0.57 to 0.74) | < 0.0001 |
| Sodium | 0.86 (0.80 to 0.93) | < 0.0001 | 0.83 (0.76 to 0.91) | < 0.0001 | 0.91 (0.82 to 1.00) | 0.0502 | 0.84 (0.75 to 0.94) | 0.0032 |
-
Odds ratios are standardized to demonstrate odds adjustment per two standard deviation incremental increase in each variable, allowing for magnitude comparison between variables. Odds ratios are unadjusted. Variables are considered statistically significant if p < 0.0005 is maintained across both development and validation cohorts.
-
CI, confidence interval; eGFR, estimated glomerular filtration rate; HCT, haematocrit; OR, odds ratio; THA, total hip arthroplasty; TKA, total knee arthroplasty; WBC, white blood cell count.
Table IV.
Univariable association of blood marker results categorized by clinically relevant cutoffs and acute readmission.
| Preoperative blood marker | TKA | THA | ||||||
|---|---|---|---|---|---|---|---|---|
| Development (2011 to 2016) | Validation (2017 to 2018) | Development (2011 to 2016) | Validation (2017 to 2018) | |||||
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Albumin, g/l | ||||||||
| Low (0 to 34) | 1.88 (1.60 to 2.20) | < 0.0001 | 2.43 (2.05 to 2.89) | < 0.0001 | 2.01 (1.63 to 2.48) | < 0.0001 | 2.59 (2.26 to 2.96) | < 0.0001 |
| Normal (35+) | Ref. | Ref. | Ref. | Ref. | ||||
| Haematocrit, l/l | ||||||||
| Low (M: < 45, F: < 37) | 1.48 (1.37 to 1.61) | < 0.0001 | 1.57 (1.42 to 1.73) | < 0.0001 | 1.18 (1.07 to 1.31) | 0.0011 | 1.45 (1.28 to 1.60) | < 0.0001 |
| Normal | Ref. | Ref. | Ref. | Ref. | ||||
| Platelets, 109/l | ||||||||
| Low (0 to 139) | 1.59 (1.33 to 1.91) | < 0.0001 | 1.83 (1.47 to 2.28) | < 0.0001 | 1.73 (1.35 to 2.20) | < 0.0001 | 1.65 (1.23 to 2.22) | 0.0007 |
| Normal (140+) | Ref. | Ref. | Ref. | Ref. | ||||
| WBC, 109/l | ||||||||
| Low/Normal (0 to 11) | Ref. | Ref. | Ref. | Ref. | ||||
| High (12+) | 1.37 (1.14 to 1.65) | < 0.0001 | 1.45 (1.16 to 1.82) | 0.0012 | 1.67 (1.35 to 2.07) | < 0.0001 | 1.67 (1.30 to 2.16) | < 0.0001 |
| eGFR, ml/min/1.73m2 | ||||||||
| Severe (< 30) | 3.32 (2.50 to 4.42) | < 0.0001 | 3.99 (2.78 to 5.72) | < 0.0001 | 2.30 (1.50 to 3.51) | < 0.0001 | 3.99 (2.60 to 6.13) | < 0.0001 |
| Mild/Mod. (30 to 89) | 1.25 (1.15 to 1.37) | 1.34 (1.21 to 1.50) | 1.09 (0.98 to 1.21) | 1.34 (0.17 to 1.53) | ||||
| Normal (≥ 90) | Ref. | Ref. | Ref. | Ref. | ||||
| Sodium, mmol/l | ||||||||
| Low (< 135) | 1.56 (1.32 to 1.85) | < 0.0001 | 1.39 (1.10 to 1.76) | 0.0199 | 1.35 (1.08 to 1.68) | < 0.0001 | 1.67 (1.26 to 2.15) | 0.0004 |
| Normal (135 to 147) | Ref. | Ref. | Ref. | Ref. | ||||
| High (≥ 148) | 1.14 (0.51 to 2.59) | 0.61 (0.15 to 2.48) | 3.94 (1.96 to 7.92) | 0.93 (0.23 to 3.81) | ||||
-
Odds ratios (OR) are unadjusted. Variables are considered statistically significant if p < 0.0005 is maintained across both development and validation cohorts.
-
CI, confidence interval; eGFR, estimated glomerular filtration rate; Mod, moderate; N/A, not applicable; Ref, reference category; THA, total hip arthroplasty; TKA, total knee arthroplasty; WBC, white blood cell count.
Multivariable analysis – total knee arthroplasty
Our multivariable analysis to determine the independent association of preoperative blood markers with odds of acute readmission after adjustment for relevant patient and treatment covariates demonstrated that lower preoperative albumin, haemoglobin, sodium, WBC, and eGFR were all significantly associated with increased rates of readmission in both the development and external validation cohorts (Table V). When analyzed as a continuous variable, per two standard deviations increase in preoperative albumin, the OR for acute readmission was 0.82 (95% CI 0.76 to 0.88; p < 0.0001) in TKA patients. Translated clinically, TKA patients with preoperative hypoalbuminemia (< 35 g/l) had a 1.4-times greater odds of readmission in comparison to those with normal preoperative values (95% CI 1.19 to 1.65; p < 0.0001) (Table VI). These findings were replicated with similar values and maintained statistical significance in our validation cohorts. Preoperative eGFR and haematocrit were significantly associated with the odds of readmission when analyzed as a continuous variables in both the development and validation cohorts (Table V), however when clinically relevant cut-offs were applied for categorical analysis, no significant association was demonstrated (Table VI). Preoperative platelet count, WBC, and sodium were not associated with acute readmission.
Table V.
Multivariable regression of the association between continuous blood marker results and acute readmission.
| Variable | TKA | THA | ||||||
|---|---|---|---|---|---|---|---|---|
| Development (2011 to 2016) | Validation (2017 to 2018) | Development (2011 to 2016) | Validation (2017 to 2018) | |||||
| Patients, n | 82,810 | 55,159 | 46,889 | 31,643 | ||||
| Readmissions, n (%) | 2,635 (3.2) | 1,748 (3.2) | 1,623 (3.5) | 1,102 (3.5) | ||||
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Albumin | 0.82 (0.76 to 0.88) | < 0.0001 | 0.80 (0.73 to 0.88) | < 0.0001 | 0.82 (0.74 to 0.91) | < 0.0001 | 0.78 (0.69 to 0.89) | < 0.0001 |
| HCT | 0.86 (0.80 to 0.94) | 0.0005 | 0.81 (0.73 to 0.89) | < 0.0001 | 0.89 (0.80 to 0.99) | 0.0258 | 0.70 (0.62 to 0.89) | < 0.0001 |
| Platelets | 0.93 (0.85 to 1.01) | 0.0688 | 0.89 (0.89 to 0.98) | 0.0242 | 1.00 (0.90 to 1.10) | 0.9404 | 0.98 (0.86 to 1.11) | 0.7156 |
| WBC | 1.13 (1.05 to 1.21) | 0.0008 | 1.14 (1.04 to 1.25) | 0.0054 | 1.09 (1.01 to 1.17) | 0.0281 | 1.18 (1.06 to 1.32) | 0.0027 |
| eGFR | 0.75 (0.69 to 0.83) | < 0.0001 | 0.78 (0.71 to 0.87) | < 0.0001 | 1.00 (0.90 to 1.11) | 0.9371 | 0.96 (0.84 to 1.09) | 0.4929 |
| Sodium | 0.90 (0.83 to 0.97) | 0.0057 | 0.88 (0.80 to 0.96) | 0.0063 | 0.98 (0.88 to 1.08) | 0.6214 | 0.93 (0.83 to 1.05) | 0.2568 |
-
Odds ratios are standardized to demonstrate odds adjustment per two standard deviation incremental increase in each variable, allowing for magnitude comparison between variables. Odds ratios are adjusted for the patient and treatment factors listed in Table I – adjusted odds ratios for these variables are presented in Supplementary Material 4. Variables are considered statistically significant if p < 0.0005 is maintained across both development and validation cohorts.
-
CI, confidence interval; eGFR, estimated glomerular filtration rate; HCT, haematocrit; OR, odds ratio; WBC, white blood cell count.
Table VI.
Multivariable regression of the association of blood marker results categorized by clinically relevant cutoffs and acute readmission.
| Preoperative blood marker | TKA | THA | ||||||
|---|---|---|---|---|---|---|---|---|
| Development (2011 to 2016) | Validation (2017 to 2018) | Development (2011 to 2016) | Validation (2017 to 2018) | |||||
| OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Albumin | ||||||||
| Low (0 to 34) | 1.40 (1.19 to 1.65) | < 0.001 | 1.77 (1.48 to 2.12) | < 0.001 | 1.39 (1.12 to 1.73) | 0.0029 | 1.53 (1.20 to 1.93) | < 0.001 |
| Normal (35+) | Ref. | Ref. | Ref. | Ref. | ||||
| Haematocrit | ||||||||
| Low (M: < 45, F: < 37) | 1.17 (1.06 to 1.28) | 0.0016 | 1.25 (1.11 to 1.41) | < 0.001 | 1.04 (0.92 to 1.18) | 0.4910 | 1.39 (1.20 to 1.62) | < 0.001 |
| Normal | Ref. | Ref. | Ref. | Ref. | ||||
| Platelets | ||||||||
| Low (0 to 139) | 1.14 (0.94 to 1.37) | 0.1787 | 1.31 (1.04 to 1.64) | 0.0227 | 1.35 (1.05 to 1.74) | 0.0199 | 1.19 (0.87 to 1.62) | 0.2780 |
| Normal (140+) | Ref. | Ref. | Ref. | Ref. | ||||
| WBC | ||||||||
| Low/Normal (0 to 11) | Ref. | Ref. | Ref. | Ref. | ||||
| High (12+) | 1.16 (0.96 to 1.40) | 0.1313 | 1.18 (0.93 to 1.48) | 0.1676 | 1.32 (1.06 to 1.64) | 0.0123 | 1.24 (0.95 to 1.61) | 0.1176 |
| eGFR | ||||||||
| Severe (< 30) | 2.00 (1.45 to 2.76) | < 0.001 | 1.62 (1.04 to 2.50) | < 0.001 | 1.38 (0.86 to 2.21) | 0.5106 | 1.32 (0.75 to 2.30) | 0.1364 |
| Mild/Mod. (30 to 89) | 1.13 ( | 1.21 (1.08 to 1.36) | 0.94 (0.84 to 1.05) | 1.10 (0.96 to 1.27) | ||||
| Normal (≥ 90) | Ref. | Ref. | Ref. | Ref. | ||||
| Sodium | ||||||||
| Low (< 135) | 1.39 (1.17 to 1.65) | < 0.001 | 1.17 (0.91 to 1.49) | 0.1642 | 1.14 (0.91 to 1.43) | 0.6907 | 1.29 (0.99 to 1.68) | 0.0506 |
| Normal (135 to 147) | Ref. | Ref. | Ref. | Ref. | ||||
| High (≥ 148) | 1.06 (0.46 to 2.40) | 0.56 (0.14 to 2.30) | 3.67 (1.80 to 7.49) | 0.75 (0.18 to 3.12) | ||||
-
Odds ratios are adjusted for previously listed covariates in Table I. Variables are considered statistically significant if p < 0.0005 is maintained across both development and validation cohorts.
-
CI, confidence interval; eGFR, estimated glomerular filtration rate; Mod, moderate; OR, odds ratio; Ref, reference category; THA, total hip arthroplasty; TKA, total knee arthroplasty; WBC, white blood cell count.
Univariable analysis – total hip arthroplasty
Our univariable analysis demonstrated that albumin, haematocrit, WBC, and eGFR were significantly associated with acute readmission following THA in both the development and validation cohorts when analyzed as continuous variables (p < 0.0001) (Table III). Platelets and sodium were not associated with readmission. When standardized per two standard deviations increase, albumin (OR 0.64, 95% CI 0.58 to 0.70) and haematocrit (OR 0.75, 95% CI 0.69 to 0.83) demonstrated the strongest associations with acute readmission. When analyzed categorically (Table IV), patients with hypoalbuminemia had 2.01-times greater odds of readmission (95% CI 1.63 to 2.48; p < 0.0001). The association between haematocrit and acute readmission did not maintain significance when analyzed categorically, as those with anaemia (haematocrit < 45 for men, < 37 for women) and those without. THA patients with severe renal impairment had a 2.30-times increase in their odds of readmission in comparison to those with normal eGFR (95% CI 1.50 to 3.51; p < 0.0001).
Multivariable analysis – total hip arthroplasty
In the THA cohort specifically, lower albumin was the only preoperative blood marker to produce statistically significant results in both the development and validation cohorts when analyzed either continuously or with clinically relevant cut-offs (Table V and Table VI). For each two standard deviations increase in preoperative albumin, the OR for acute readmission was 0.82 (95% CI 0.74 to 0.91; p < 0.0001) for THA patients. Preoperative haematocrit, platelets, WBC, eGFR, and sodium levels did not prove to significantly influence the odds of acute readmission. Independent associations for non-laboratory covariates and acute readmission are shown in Supplementary Material 4.
Discussion
Using health-administrated data in the ACS-NSQIP registry, we identified 137,969 patients who underwent an elective unilateral primary TKA and 78,532 patients who underwent an elective unilateral primary THA between 2011 and 2018, and analyzed their risk for acute hospital readmission based on six routinely ordered preoperative blood markers. To our knowledge, this is the only study to examine the association of each of these blood markers with hospital readmission. With over 137,000 TKA patients and 78,000 THA patients identified, 3.2% and 3.5% experienced a hospital readmission within 30 days of their elective arthroplasty surgery. Overall, preoperative albumin was strongly associated with readmission for both TKA and THA patients in univariable and multivariable analysis, while haematocrit and eGFR also demonstrated association with readmission in both univariable and multivariable analyses in TKA patients.
Few studies have investigated the relationship between preoperative blood markers and readmission in the arthroplasty population. Very recently, several of these studies have highlighted the association of hypoalbuminemia and malnutrition in postoperative complication rates, with increased odds of postoperative medical complication, surgical site infection, intensive care unit transfer, and readmission following arthroplasty.7,42-48 In this regard, our study’s findings are consistent with these previous studies, which provide a possible avenue for perioperative intervention. A 2019 comparative observational study by Shroer et al49 demonstrated significantly lower rates of readmission in malnourished arthroplasty patients treated with perioperative nutritional intervention compared with controls. Similar findings have been demonstrated in hip fracture patients.50 While no randomized trials in orthopaedic patients have been performed on nutritional intervention, a recent large scale trial in general internal medicine demonstrated improved survival and clinical outcomes in hospitalized patients at nutritional risk with individualized nutritional support.51 Taken together, the mounting body of evidence for the association of malnutrition and preoperative hypoalbuminemia with postoperative arthroplasty outcomes suggests that albumin has utility as a preoperative marker to identify patients who are at greater odds of postoperative readmission, and suggests an avenue for intervention.
Previous studies performed on THA patients,52,53 and on combined arthroplasty patients,54 have demonstrated increased odds of postoperative complications, mortality, and surgical site infection in patients with preoperative anaemia. While our study similarly found increased odds of readmission in the TKA population with low HCT, these results were not seen in the THA cohort after adjustment for relevant covariates. Similarly, previously literature has demonstrated decreased eGFR and/or elevated creatinine are associated with significantly higher postoperative DVT, MI, readmission, and mortality in arthroplasty patients.35,55-58 In our study, decreased eGFR was associated with increased odds of acute readmission in the elective TKA population, but these results were not replicated in the THA cohort after covariate adjustment.
The pre-specification of development and validation cohorts with temporal validation of initial findings using both continuous and clinically relevant cut-offs, the conservative α adjustment for significance, and the use of a large database from over 650 hospitals strengthens the external validity of our study and the clinical applicability of our findings in comparison to previous published work. Furthermore, our use of both multivariable and univariable models allows for better understanding of the blood markers’ independent association with readmission, as well as the unadjusted change in odds if a patient presents with a particular laboratory derangement. However, despite its strengths, this study has several limitations. First, this is a retrospective study using a registry which is highly dependent on coder validity. However, the NSQIP database and its coding practices are audited, demonstrate an interobserver reliability of 98.4%, and have demonstrated high accuracy of reported outcomes and complications when cross-referenced with other databases.59 The > 700 hospitals who participate in NSQIP do so voluntarily. Accordingly, it is possible that the characteristics of patients and outcomes differ between hospitals that participate in NSQIP and those that do not. However, studies investigating these potential discrepancies have not demonstrated a meaningful difference.60 The NSQIP database includes follow-up for only 30 days postoperatively, and there is no indication from this study as to longer-term outcomes or patient-reported outcome measures and satisfaction. The metric of morbidity used in this study – 30-day readmission – while very relevant given hospital funding and healthcare cost implications, may not accurately capture all specific postoperative complications, as those that happened during initial hospital stay or those that were managed as an outpatient would not be captured in this surrogate outcome. It would also include patients admitted to hospital for another reason which may not have been related to their arthroplasty during the 30-day period. However, under most bundled payment care models, the hospitals would still be responsible for the costs of readmission in this time window, regardless of cause.61-64 Furthermore, we are unable to comment on the aetiologies of blood marker derangement and whether they were a result of comorbid chronic conditions. Finally, because we included only patients with a full set of preoperative blood markers to maintain consistency across our statistical analysis, this cohort represents a sicker, more comorbid arthroplasty population as shown in Supplementary Material 3. Clinicians may be less likely to order preoperative investigations on patients without comorbid conditions, introducing an element of selection bias.
In summary, in this population-level large cohort analysis of arthroplasty patients, albumin demonstrated the strongest association with acute readmission in comparison to five other commonly ordered preoperative blood markers. Identification and optimization of preoperative hypoalbuminemia could help healthcare providers recognize and address at-risk patients undergoing elective TKA and THA, though further studies are needed in this regard.
References
1. Maradit Kremers H , Larson DR , Crowson CS , et al. Prevalence of total hip and knee replacement in the United States . J Bone Joint Surg Am . 2015 ; 97-A ( 17 ): 1386 – 1397 . Crossref PubMed Google Scholar
2. Goltz DE , Baumgartner BT , Politzer CS , DiLallo M , Bolognesi MP , Seyler TM . The American College of surgeons national surgical quality improvement program surgical risk calculator has a role in predicting discharge to post-acute care in total joint arthroplasty . J Arthroplasty . 2018 ; 33 ( 1 ): 25 – 29 . Crossref PubMed Google Scholar
3. Singh JA , Yu S , Chen L , Cleveland JD . Rates of total joint replacement in the United States: future projections to 2020-2040 using the National inpatient sample . J Rheumatol . 2019 ; 46 ( 9 ): 1134 – 1140 . Crossref PubMed Google Scholar
4. Pugely AJ , Callaghan JJ , Martin CT , Cram P , Gao Y . Incidence of and risk factors for 30-day readmission following elective primary total joint arthroplasty: analysis from the ACS-NSQIP . J Arthroplasty . 2013 ; 28 ( 9 ): 1499 – 1504 . Crossref PubMed Google Scholar
5. Sutton JC , Antoniou J , Epure LM , Huk OL , Zukor DJ , Bergeron SG . Hospital discharge within 2 days following total hip or knee arthroplasty does not increase major-complication and readmission rates . J Bone Joint Surg Am . 2016 ; 98-A ( 17 ): 1419 – 1428 . Google Scholar
6. Maldonado-Rodriguez N , Ekhtiari S , Khan MM , et al. Emergency department presentation after total hip and knee arthroplasty: a systematic review . J Arthroplasty . 2020 ; 35 ( 10 ): 3038 – 3045 . Crossref PubMed Google Scholar
7. Schroer WC , Diesfeld PJ , LeMarr AR , Morton DJ , Reedy ME . Modifiable risk factors in primary joint arthroplasty increase 90-day cost of care . J Arthroplasty . 2018 ; 33 ( 9 ): 2740 – 2744 . Crossref PubMed Google Scholar
8. Swenson ER , Bastian ND , Nembhard HB , Davis Iii CM . Reducing cost drivers in total joint arthroplasty: understanding patient readmission risk and supply cost . Health Syst . 2018 ; 7 ( 2 ): 135 – 147 . Crossref PubMed Google Scholar
9. Gill GS , Mills D , Joshi AB . Mortality following primary total knee arthroplasty . J Bone Joint Surg Am . 2003 ; 85-A ( 3 ): 432 – 435 . Crossref PubMed Google Scholar
10. Parvizi J , Sullivan TA , Trousdale RT , Lewallen DG . Thirty-day mortality after total knee arthroplasty . J Bone Joint Surg Am . 2001 ; 83-A ( 8 ): 1157 – 1161 . Crossref PubMed Google Scholar
11. Belmont PJ , Goodman GP , Hamilton W , Waterman BR , Bader JO , Schoenfeld AJ . Morbidity and mortality in the thirty-day period following total hip arthroplasty: risk factors and incidence . J Arthroplasty . 2014 ; 29 ( 10 ): 2025 – 2030 . Crossref PubMed Google Scholar
12. Parvizi J , Johnson BG , Rowland C , Ereth MH , Lewallen DG . Thirty-day mortality after elective total hip arthroplasty . J Bone Joint Surg Am . 2001 ; 83-A ( 10 ): 1524 – 1528 . Crossref PubMed Google Scholar
13. Berstock JR , Beswick AD , Lenguerrand E , Whitehouse MR , Blom AW . Mortality after total hip replacement surgery: a systematic review . Bone Joint Res . 2014 ; 3 ( 6 ): 175 – 182 . Crossref PubMed Google Scholar
14. Hunt LP , Ben-Shlomo Y , Clark EM , et al. 45-day mortality after 467 779 knee replacements for osteoarthritis from the National Joint Registry for England and Wales: an observational study . The Lancet . 2014 ; 384 ( 9952 ): 1429 – 1436 . Google Scholar
15. Murphy B P d'S , Dowsey MM , Spelman T , Choong PFM . The impact of older age on patient outcomes following primary total knee arthroplasty . Bone Joint J . 2018 ; 100-B ( 11 ): 1463 – 1470 . Crossref PubMed Google Scholar
16. Belmont PJ , Goodman GP , Waterman BR , Bader JO , Schoenfeld AJ . Thirty-day postoperative complications and mortality following total knee arthroplasty: incidence and risk factors among a national sample of 15,321 patients . J Bone Joint Surg Am . 2014 ; 96-A ( 1 ): 20 – 26 . Crossref PubMed Google Scholar
17. Saklad M . Grading of patients for surgical procedures . Anesthesiol . 1941 ; 2 ( 5 ): 281 – 284 . Google Scholar
18. Shumborski S , Gooden B , Salmon LJ , et al. Utility of preoperative blood screening before hip and knee arthroplasty . ANZ J Surg . 2020 ; 90 ( 3 ): 350 – 354 . Crossref PubMed Google Scholar
19. American College of Surgeons - National Surgical Quality Improvement Program - User Guide for the 2016 ACS NSQIP Procedure Targeted Participant USE Data File . The ACS national surgical quality improvement program . 2015 . https://www.facs.org/~/media/files/qualityprograms/nsqip/pt_nsqip_puf_userguide_2016.ashx Crossref PubMed Google Scholar
20. ACS-NSQIP . American College of surgeons national surgical quality improvement program . 2017 . https://www.facs.org/~/media/files/quality%20programs/nsqip/nsqip_puf_userguide_2017.ashx Crossref PubMed Google Scholar
21. Chughtai M , Gwam CU , Khlopas A , et al. The incidence of postoperative pneumonia in various surgical subspecialties: a dual database analysis . Surg Technol Int . 2017 ; 30 : 45 – 51 . PubMed Google Scholar
22. Sodhi N , Piuzzi NS , Dalton SE , et al. What influence does the time of year have on postoperative complications following total knee arthroplasty? J Arthroplasty . 2018 ; 33 ( 6 ): 1908 – 1913 . Crossref PubMed Google Scholar
23. Bedard NA , Pugely AJ , McHugh M , et al. Analysis of outcomes after TKA: do all databases produce similar findings? Clin Orthop Relat Res . 2018 ; 476 ( 1 ): 52 – 63 . Crossref PubMed Google Scholar
24. Bedard NA , Pugely AJ , McHugh MA , Lux NR , Bozic KJ , Callaghan JJ . Big data and total hip arthroplasty: how do large databases compare? J Arthroplasty . 2018 ; 33 ( 1 ): 41 – 45 . Crossref PubMed Google Scholar
25. McIsaac DI , Hamilton GM , Abdulla K , et al. Validation of new ICD-10-based patient safety indicators for identification of in-hospital complications in surgical patients: a study of diagnostic accuracy . BMJ Qual Saf . 2020 ; 29 ( 3 ): 209 – 216 . Crossref PubMed Google Scholar
26. Rolston JD , Han SJ , Chang EF . Systemic inaccuracies in the National surgical quality improvement program database: implications for accuracy and validity for neurosurgery outcomes research . J Clin Neurosci . 2017 ; 37 : 44 – 47 . Crossref PubMed Google Scholar
27. Neale J , Reickert C , Swartz A , Reddy S , Abbas MA , Rubinfeld I . Accuracy of national surgery quality improvement program models in predicting postoperative morbidity in patients undergoing colectomy . Perm J . 2014 ; 18 ( 1 ): 14 – 18 . Crossref PubMed Google Scholar
28. Awad MI , Shuman AG , Montero PH , Palmer FL , Shah JP , Patel SG . Accuracy of administrative and clinical registry data in reporting postoperative complications after surgery for oral cavity squamous cell carcinoma . Head Neck . 2015 ; 37 ( 6 ): 851 – 861 . Crossref PubMed Google Scholar
29. Trickey AW , Wright JM , Donovan J , et al. Interrater reliability of hospital readmission evaluations for surgical patients . Am J Med Qual . 2017 ; 32 ( 2 ): 201 – 207 . Crossref PubMed Google Scholar
30. von Elm E , Altman DG , Egger M , et al. Lancet . 2007 ; 370 ( 9596 ): 1453 – 1457 . Google Scholar
31. Viola J , Gomez MM , Restrepo C , Maltenfort MG , Parvizi J . Preoperative anemia increases postoperative complications and mortality following total joint arthroplasty . J Arthroplasty . 2015 ; 30 ( 5 ): 846 – 848 . Crossref PubMed Google Scholar
32. Malpani R , Haynes MS , Clark MG , Galivanche AR , Bovonratwet P , Grauer JN . Abnormally high, as well as low, preoperative platelet counts correlate with adverse outcomes and readmissions after elective total knee arthroplasty . J Arthroplasty . 2019 ; 34 ( 8 ): 1670 – 1676 . Crossref PubMed Google Scholar
33. Ondeck NT , Fu MC , McLynn RP , Bovonratwet P , Malpani R , Grauer JN . Preoperative laboratory testing for total hip arthroplasty: unnecessary tests or a helpful prognosticator . J Orthop Sci . 2020 ; 25 ( 5 ): 854 – 860 . Crossref PubMed Google Scholar
34. Kildow BJ , Howell EP , Karas V , et al. When should complete blood count tests be performed in primary total hip arthroplasty patients? J Arthroplasty . 2018 ; 33 ( 10 ): 3211 – 3214 . Crossref PubMed Google Scholar
35. Krebs OK , Warren JA , Anis HK , et al. Estimated glomerular filtration rate as a risk stratification tool for early complications in revision total hip and knee arthroplasty . J Arthroplasty . 2020 ; 35 ( 5 ): 1315 – 1322 . Crossref PubMed Google Scholar
36. Zmistowski B , Restrepo C , Hess J , Adibi D , Cangoz S , Parvizi J . Unplanned readmission after total joint arthroplasty: rates, reasons, and risk factors . J Bone Joint Surg Am . 2013 ; 95-A ( 20 ): 1869 – 1876 . Crossref PubMed Google Scholar
37. Alvi HM , Mednick RE , Krishnan V , Kwasny MJ , Beal MD , Manning DW . The effect of BMI on 30 day outcomes following total joint arthroplasty . J Arthroplasty . 2015 ; 30 ( 7 ): 1113 – 1117 . Crossref PubMed Google Scholar
38. Mesko NW , Bachmann KR , Kovacevic D , LoGrasso ME , O'Rourke C , Froimson MI . Thirty-day readmission following total hip and knee arthroplasty - a preliminary single institution predictive model . J Arthroplasty . 2014 ; 29 ( 8 ): 1532 – 1538 . Crossref PubMed Google Scholar
39. White IR , Royston P , Wood AM . Multiple imputation using chained equations: issues and guidance for practice . Stat Med . 2011 ; 30 ( 4 ): 377 – 399 . Crossref PubMed Google Scholar
40. Gelman A . Scaling regression inputs by dividing by two standard deviations . Stat Med . 2008 ; 27 ( 15 ): 2865 – 2873 . Crossref PubMed Google Scholar
41. Pugely AJ , Martin CT , Gao Y , Belatti DA , Callaghan JJ . Comorbidities in patients undergoing total knee arthroplasty: do they influence hospital costs and length of stay? Clin Orthop Relat Res . 2014 ; 472 ( 12 ): 3943 – 3950 . Crossref PubMed Google Scholar
42. Bohl DD , Shen MR , Kayupov E , Della Valle CJ . Hypoalbuminemia independently predicts surgical site infection, pneumonia, length of stay, and readmission after total joint arthroplasty . J Arthroplasty . 2016 ; 31 ( 1 ): 15 – 21 . Crossref PubMed Google Scholar
43. Nelson CL , Elkassabany NM , Kamath AF , Liu J . Low albumin levels, more than morbid obesity, are associated with complications after TKA . Clin Orthop Relat Res . 2015 ; 473 ( 10 ): 3163 – 3172 . Crossref PubMed Google Scholar
44. Cross MB , Yi PH , Thomas CF , Garcia J , Della Valle CJ . Evaluation of malnutrition in orthopaedic surgery . J Am Acad Orthop Surg . 2014 ; 22 ( 3 ): 193 – 199 . Crossref PubMed Google Scholar
45. Morey VM , Song YD , Whang JS , Kang YG , Kim TK . Can serum albumin level and total lymphocyte count be surrogates for malnutrition to predict wound complications after total knee arthroplasty? J Arthroplasty . 2016 ; 31 ( 6 ): 1317 – 1321 . Crossref PubMed Google Scholar
46. Kishawi D , Schwarzman G , Mejia A , Hussain AK , Gonzalez MH . Low preoperative albumin levels predict adverse outcomes after total joint arthroplasty . J Bone Joint Surg Am . 2020 ; 102-A ( 10 ): 889 – 895 . Crossref PubMed Google Scholar
47. Mednick RE , Alvi HM , Krishnan V , Lovecchio F , Manning DW . Factors affecting readmission rates following primary total hip arthroplasty . J Bone Joint Surg Am . 2014 ; 96-A ( 14 ): 1201 – 1209 . Crossref PubMed Google Scholar
48. Rudasill SE , Ng A , Kamath AF . Preoperative serum albumin levels predict treatment cost in total hip and knee arthroplasty . Clin Orthop Surg . 2018 ; 10 ( 4 ): 398 – 406 . Crossref PubMed Google Scholar
49. Schroer WC , LeMarr AR , Mills K , Childress AL , Morton DJ , Reedy ME . 2019 Chitranjan S. Ranawat Award: elective joint arthroplasty outcomes improve in malnourished patients with nutritional intervention: a prospective population analysis demonstrates a modifiable risk factor . Bone Joint J . 2019 ; 101-B ( 7_Supple_C ): 17 – 21 . Crossref PubMed Google Scholar
50. He Y , Xiao J , Shi Z , He J , Li T . Supplementation of enteral nutritional powder decreases surgical site infection, prosthetic joint infection, and readmission after hip arthroplasty in geriatric femoral neck fracture with hypoalbuminemia . J Orthop Surg Res . 2019 ; 14 ( 1 ): 292 . Crossref PubMed Google Scholar
51. Schuetz P , Fehr R , Baechli V , et al. Individualised nutritional support in medical inpatients at nutritional risk: a randomised clinical trial . Lancet . 2019 ; 393 ( 10188 ): 2312 – 2321 . Crossref PubMed Google Scholar
52. Grosso MJ , Boddapati V , Cooper HJ , Geller JA , Shah RP , Neuwirth AL . The effect of preoperative anemia on complications after total hip arthroplasty . J Arthroplasty . 2020 ; 35 ( 6S ): S214 – S218 . Crossref PubMed Google Scholar
53. Lu M , Sing DC , Kuo AC , Hansen EN . Preoperative anemia independently predicts 30-day complications after aseptic and septic revision total joint arthroplasty . J Arthroplasty . 2017 ; 32 ( 9S ): S197 – S201 . Crossref PubMed Google Scholar
54. Gu A , Malahias M-A , Selemon NA , et al. Increased severity of anaemia is associated with 30-day complications following total joint replacement . Bone Joint J . 2020 ; 102-B ( 4 ): 485 – 494 . Crossref PubMed Google Scholar
55. Kuo F-C , Lin P-C , Lu Y-D , Lee MS , Wang J-W . Chronic kidney disease is an independent risk factor for transfusion, cardiovascular complication, and Thirty-Day readmission in minimally invasive total knee arthroplasty . J Arthroplasty . 2017 ; 32 ( 5 ): 1630 – 1634 . Crossref PubMed Google Scholar
56. Kuo L-T , Lin S-J , Chen C-L , Yu P-A , Hsu W-H , Chen T-H . Chronic kidney disease is associated with a risk of higher mortality following total knee arthroplasty in diabetic patients: a nationwide population-based study . Oncotarget . 2017 ; 8 ( 59 ): 100288 – 100295 . Crossref PubMed Google Scholar
57. Deegan BF , Richard RD , Bowen TR , Perkins RM , Graham JH , Foltzer MA . Impact of chronic kidney disease stage on lower-extremity arthroplasty . Orthopedics . 2014 ; 37 ( 7 ): e613 – e618 . Crossref PubMed Google Scholar
58. Weaver F , Hynes D , Hopkinson W , et al. Preoperative risks and outcomes of hip and knee arthroplasty in the Veterans health administration . J Arthroplasty . 2003 ; 18 ( 6 ): 693 – 708 . Crossref PubMed Google Scholar
59. Pierce AZ , Menendez ME , Tybor DJ , Salzler MJ , Databases TD . Three different databases, three different complication rates for knee and hip arthroplasty: comparing the National inpatient sample, National hospital discharge survey, and national surgical quality improvement program, 2006 to 2010 . J Am Acad Orthop Surg . 2019 ; 27 ( 12 ): e568 – e576 . Crossref PubMed Google Scholar
60. Osborne NH , Nicholas LH , Ryan AM , Thumma JR , Dimick JB . Association of hospital participation in a quality reporting program with surgical outcomes and expenditures for Medicare beneficiaries . JAMA . 2015 ; 313 ( 5 ): 496 – 504 . Crossref PubMed Google Scholar
61. Courtney PM , Ashley BS , Hume EL , Kamath AF . Are bundled payments a viable reimbursement model for revision total joint arthroplasty? Clin Orthop Relat Res . 2016 ; 474 ( 12 ): 2714 – 2721 . Crossref PubMed Google Scholar
62. Dundon JM , Bosco J , Slover J , Yu S , Sayeed Y , Iorio R . Improvement in total joint replacement quality metrics: year one versus year three of the bundled payments for care improvement initiative . J Bone Joint Surg Am . 2016 ; 98-A ( 23 ): 1949 – 1953 . Crossref PubMed Google Scholar
63. Kiridly DN , Karkenny AJ , Hutzler LH , Slover JD , Iorio R , Bosco JA . The effect of severity of disease on cost burden of 30-day readmissions following total joint arthroplasty (TJA) . J Arthroplasty . 2014 ; 29 ( 8 ): 1545 – 1547 . Crossref PubMed Google Scholar
64. Iorio R , Clair AJ , Inneh IA , Slover JD , Bosco JA , Zuckerman JD . Early results of Medicare’s bundled payment initiative for a 90-day total joint arthroplasty episode of care . J Arthroplasty . 2016 ; 31 ( 2 ): 343 – 350 . Google Scholar
Author contributions
A. Khoshbin: Developed study idea, coordinated study team, drafted manuscript.
G. Hoit: Assisted with study framing and statistical analysis, wrote significant portions of manuscript, created tables and figures, prepared manuscript for submission.
L. L. Nowak: Assisted with data extraction, analysis plan and conducted statistical analysis.
A. Daud: Conducted background research and assisted with manuscript drafting.
M. Steiner: Conducted background research and assisted with manuscript drafting.
P. Juni: Provided assistance with study framing, analysis plan, statistical interpretation and manuscript editing.
B. Ravi: Provided assistance with study framing, analysis plan, statistical interpretation and manuscript editing.
A. Atrey: Provided clinical expertise, assisted with study idea, provided oversight and leadership and assisted with manuscript drafting and editing.
Funding statement
No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.
Acknowledgements
The authors would like to acknowledge Dr. Luana Melo and Samantha White for their assistance in data acquisition and statistical support.
Ethical review statement
Approval was obtained from the Research Ethics Board at St. Michael’s Hospital, Toronto, Ontario. Individual patient informed consent was not required for the use of de-identified, encoded health data housed by ACS-NSQIP.
Supplementary material
A list of Current Procedural Terminology Codes used for patient identification; normal ranges of blood markers examined in this paper; characteristics of excluded patients; and independent associations for non-laboratory covariates with acute readmission.
© 2021 Author(s) et al. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (CC BY-NC-ND 4.0) licence, which permits the copying and redistribution of the work only, and provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc-nd/4.0/
