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Comparing five equations to calculate estimated glomerular filtration rate to predict acute kidney injury following total joint arthroplasty

Comparing five equations to calculate estimated glomerular filtration rate to predict acute... Background Acute kidney injury (AKI) following total joint arthroplasty ( TJA) is associated with increased morbidity and mortality. Estimated glomerular filtration rate (eGFR) is used as an indicator of renal function. The purpose of this study was (1) to assess each of the five equations that are used in calculating eGFR, and (2) to evaluate which equation may best predict AKI in patients following TJA. Methods The National Surgical Quality Improvement Program (NSQIP) was queried for all 497,261 cases of TJA performed from 2012 to 2019 with complete data. The Modification of Diet in Renal Disease (MDRD) II, re-expressed MDRD II, Cockcroft-Gault, Mayo quadratic, and Chronic Kidney Disease Epidemiology Collaboration equations were used to calculate preoperative eGFR. Two cohorts were created based on the development of postoperative AKI and were compared based on demographic and preoperative factors. Multivariate regression analysis was used to assess for independent associations between preoperative eGFR and postoperative renal failure for each equation. The Akaike information criterion (AIC) was used to evaluate predictive ability of the five equations. Results Seven hundred seventy-seven (0.16%) patients experienced AKI after TJA. The Cockcroft-Gault equation yielded the highest mean eGFR (98.6 ± 32.7), while the Re-expressed MDRD II equation yielded the lowest mean eGFR (75.1 ± 28.8). Multivariate regression analysis demonstrated that a decrease in preoperative eGFR was independently associated with an increased risk of developing postoperative AKI in all five equations. The AIC was the lowest in the Mayo equation. Conclusions Preoperative decrease in eGFR was independently associated with increased risk of postoperative AKI in all five equations. The Mayo equation was most predictive of the development of postoperative AKI following TJA. The mayo equation best identified patients with the highest risk of postoperative AKI, which may help providers make decisions on perioperative management in these patients. Keywords Total joint arthroplasty, Estimated glomerular filtration rate, Ecute kidney injury *Correspondence: Harpal S. Khanuja khanuja@jhmi.edu Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA Department of Orthopaedic Surgery, Philadelphia College of Osteopathic Medicine, Philadelphia, PA 19131, USA Department of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, CA 94063, USA © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Mekkawy et al. Arthroplasty (2023) 5:14 Page 2 of 6 Background Methods There has been a growing emphasis on preopera- Study population tive medical optimization in total joint arthroplasty A retrospective review was conducted of all 640,880 cases (TJA) patients to improve care and decrease the risk of TJA in the American College of Surgeons National of postoperative complications. It is important to Surgical Quality Improvement Program (ACS-NSQIP) identify renal dysfunction perioperatively to properly from January 1, 2012 to December 31, 2019. TJA cases stratify and modify care to improve outcomes. There were identified with the Current Procedural Terminology are mixed reports on the incidence of acute kidney (CPT) codes 27,447 (total knee arthroplasty) and 27,130 injury (AKI) after joint arthroplasty, ranging anywhere (total hip arthroplasty). Exclusion criteria included from 2% to 15% [1–3]. AKI following surgery has been unknown or not reported race, age 90 or older (as NSQIP shown to increase length of stay and costs related to groups these patients as age 90+), emergency cases, complications, as well as increased mortality rates [4, patients on preoperative dialysis, unknown preoperative 5]. One of the most widely accepted classifications, creatinine, and unknown preoperative height or weight. Kidney Disease: Improving Global Outcomes (KDIGO) A total of 143,619 cases were removed due to these defines AKI as an increase in serum creatinine (sCr) ≥ criteria, resulting in a total of 497,261 cases included for 0.3 mg/dL within 24 h, an increase in sCr ≥ 1.5 times analysis in this study (Fig. 1). baseline, or a urine volume < 0.5 mL/kg/hr for 6 h [6]. Chronic kidney disease (CKD), which is defined as a glomerular filtration rate (GFR) of < 60 mL/min/1.73 , Variables has been demonstrated to be a predictor of AKI fol- Preoperative factors (age, sex, race, height, weight, medi- lowing TJA [7, 8]. The GFR is used to measure renal cal comorbidities, and preoperative laboratory values), function and to identify renal impairment. However, intraoperative factors (surgical duration and procedure direct measurement in clinical practice is complex, type), and complications (progressive renal insufficiency expensive, and impractical [9]. Instead, estimated and acute renal failure) were extracted from NSQIP and GFR (eGFR) is most commonly used to calculate renal included in this study. NSQIP collects data for 30 days function [7]. There are various equations used to cal- postoperatively, therefore all complications including culate eGFR, each factoring in a combination of sCr, AKI are within one month after surgery. Body mass index age, race, sex, and/or height and weight [10]. The (BMI) was calculated using height and weight. major five equations are the Modification of Diet in The eGFR was calculated using the following equa - Renal Disease (MDRD II) [11] equation, re-expressed tions, utilizing the preoperative sCr taken closest to the MDRD II [12], Cockcroft-Gault (CG) [13], the Mayo time before surgery: Quadratic (Mayo) [14], and the Chronic Kidney Dis- ease Epidemiology Collaboration (CKD-EPI) equations 1. MDRD II equation [11]: eGFR = 186 × sCr − 1. [15]. The differing variables and coefficients used in 154 × Age − 0.203 × (0.742  if female) × (1.210  if these equations result in different eGFR values for any African − American) specific sCr. As a worsening GFR leads to unfavorable 2. Re-expressed MDRD II equation [12]: eGFR outcomes [16], it is important to identify a consistent = 175 × sCr − 1.154 × Age − 0.203 × (0.742  if calculation that best predicts postoperative AKI. female) × (1.210 if African − American) According to the 2012 KDIGO clinical practice 3. CG equation [13]: eGFR = [(140 − Age) × Weight/ guidelines, the CKD-EPI equation is recommended (72 × sCr)] × (0.85 if female) to calculate eGFR as it has demonstrated the high- This equation is adjusted for body surface area: est accuracy as compared to the other equations [6]. (1.73  m × CG)/BSA，where BSA = 0.007184 × weig However, a recent study demonstrated the Mayo equa- ht 0.425 × height 0.725 tion was the most predictive in identifying AKI after 4. Mayo equation [14]: eGFR = exp  [1.911 + 5.249/ cardiovascular surgery [17]. The best equation for sCr − 2.114/sCr − 0.00686 × Age − (0.205 if female)], predicting postoperative AKI in TJA has not been if sCr < 0.8 mg/dL then sCr = 0.8 investigated, and that is the purpose of this study. We 5. CKD-EPI Equation [15]: eGFR = 141 × min (sCr/κ, 1) sought to evaluate the eGFRs calculated from the five α × max  (sCr/κ, 1) − 1.209 × 0.993Age × 1.018  [if equations, and to identify which equation may be most female] × 1.159  [if African − American], where κ predictive of postoperative AKI in patients following is 0.9 for males and 0.7 for females, α is –0.411 for TJA. males and –0.329 for females, min demonstrates the minimum of sCr/κ or 1, and max demonstrates the maximum of sCR/κ or 1 [15]. M ekkawy et al. Arthroplasty (2023) 5:14 Page 3 of 6 Fig. 1 Flow diagram indicating study inclusion and exclusion criteria The preoperative eGFRs calculated by the five differ - this study, an alpha value was accepted at 0.01. Statistical ent equations were stratified into categories based on analyses were performed by utilizing Stata software, ver- KDIGO classification: Stage 1: ≥ 90, Stage 2: < 90–60, sion 17.0 (StataCorp LLC, College Station, TX, USA). Stage 3a: < 60–45, Stage 3b: < 45–30, Stage 4: < 30–15, and Stage 5: < 15 mL/min/1.73 m [6]. Results Of the 497,261 cases included in this study, 777 (0.16%) Statistical analysis patients developed AKI. Table  1 shows the baseline and Cases were stratified into two groups based on the devel - perioperative characteristics of the study population. The opment of AKI postoperatively and assessed for differ - mean age of the AKI cohort was 59 ± 14 years old and ences in preoperative factors and intraoperative factors. 349 (45%) were female. The mean BMI of the AKI cohort Descriptive statistics were reported for continuous vari- was 35 ± 8.1 kg/m . ables as mean ± standard deviation and for categori- Regarding preoperative factors, the AKI and non- cal variables as frequencies and percentages. Univariate AKI cohorts differed significantly by sex (P < 0.001), age analysis for continuous and categorical variables was (P < 0.001), race (P < 0.001), BMI (P < 0.001), hematocrit conducted by using analysis of variance and chi-squared (P < 0.001), albumin (P < 0.001), diabetes mellitus (P < 0.001), or Fisher’s exact test, as appropriate. Multivariate logis- hypertension (P < 0.001), congestive heart failure (P < 0.001), tic regression models were used to evaluate the odds of and chronic obstructive pulmonary disease (P < 0.001). developing AKI postoperatively, adjusted for age, sex, The two cohorts did not differ significantly by smoking BMI, preoperative laboratory values (creatinine, albumin, status (P = 0.049). Intraoperatively, the two cohorts also and hematocrit), patient comorbidities (diabetes, conges- differed significantly by surgical duration (P < 0.001) tive heart failure, chronic obstructive pulmonary disease, (Table 1). hypertension, and smoking status), and surgical duration The results of the eGFR for each of the five equations for each of the five equations. Results of the multivari - is summarized in Table  2. The equation with the highest ate regression model were reported as odds ratios (ORs) calculated mean eGFR was CG equation (98.6 ± 32.7), with 95% confidence intervals (CIs). Akaike informa - followed by the Mayo equation (97.3 ± 19.6), CKD-EPI tion criterion (AIC) was used to compare the fit of each equation (91.6 ± 17.1), MDRD II equation (86.6 ± 26.4), model in predicting AKI postoperatively, and receiver and finally the re-expressed MDRD II equation had the operating curves (ROC) were generated for each Logistic lowest calculated mean eGFR (75.1 ± 28.8). regression model, with the area under the curve (AUC) The results of the Logistic regression analysis are calculated for each ROC. Due to the large sample size of outlined in Table  3. Lower preoperative eGFR was Mekkawy et al. Arthroplasty (2023) 5:14 Page 4 of 6 Table 1 Patient demographics and perioperative characteristics Variable n (%) P-Value All Cases: n = 497,261 AKI: n = 777 No AKI: n = 496,484 Female Gender 294,369 (59) 349 (45) 294,369 (59) <0.001 Age (year) 55 ± 13 59 ± 14 55 ± 13 <0.001 Race <0.001 White 435,993 (88) 617 (79) 435,376 (88) Black 46,202 (9.3) 148 (19) 46,054 (9.3) Asian 10,614 (2.1) 8 (1.0) 10,606 (2.1) Native American, Hawaiian, or Pacific Islander 4452 (0.9) 4 (0.5) 4448 (0.0) BMI (kg/m ) 32 ± 6.7 35 ± 8.1 32 ± 6.7 <0.001 Preoperative Serum Creatinine (mg/dL) 0.9 ± 0.3 1.3 ± 0.7 0.9 ± 0.3 <0.001 Hematocrit (%) 41 ± 4.1 39 ± 5.2 41 ± 4.1 <0.001 Albumin (g/dL) 4.1 ± 0.4 3.9 ± 0.5 4.1 ± 0.4 <0.001 Diabetes Mellitus 80,003 (16) 273 (35) 79,730 (16) <0.001 Hypertension 313,447 (63) 699 (90) 312,748 (63) <0.001 Congestive Heart Failure 1753 (0.4) 30 (3.9) 1723 (0.4) <0.001 Chronic Obstructive Pulmonary Disease 18,998 (3.8) 92 (12) 18,906 (3.8) <0.001 Smoker 49,643 (10) 94 (12) 49,549 (10) 0.049 Type of Surgery THA 188,939 (38) 287 (37) 188,652 (38) 0.543 TKA 308,322 (62) 490 (63) 307,832 (62) Surgical Duration (min) 93 ± 38 103 ± 46 93 ± 38 <0.001 Data are expressed as number of patients (%) or mean ± standard deviation Values available for 489,607 total cases Values available for 301,393 total cases Table 2 Distribution of patients by preoperative eGFR based on each of the five equations MDRD II Re-Expressed MDRD II CG Mayo CKD-EPI Mean eGFR 86.6 ± 26.4 75.1 ± 28.8 98.6 ± 32.7 97.3 ± 19.6 91.6 ± 17.1 ≥90 203,028 (41) 115,455 (23) 286,132 (58) 351,417 (71) 283,794 (57) ≥60, <90 233,171 (47) 226,453 (46) 163,614 (33) 122,585 (25) 191,771 (39) ≥45, <60 45,253 (9.1) 107,606 (22) 34,820 (7.0) 14,332 (2.9) 15,663 (3.2) ≥30, <45 13,206 (2.7) 40,494 (8.1) 10,868 (2.2) 6,404 (1.3) 4,864 (1.0) ≥30, <15 2185 (0.4) 6666 (1.3) 1517 (0.3) 2021 (0.4) 959 (0.2) ≤15 418 (0.1) 587 (0.1) 310 (0.1) 502 (0.1) 210 (0.0) Data are expressed as the number of patients (%) MDRD II: Modification of Diet in Renal Disease, CG: Cockcroft-Gault, Mayo: Mayo Clinic Quadratic, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration significantly associated with an increased risk of groups, while the re-expressed MDRD II equation developing AKI following TJA. The Mayo equation had classified more patients in lower eGFR groups. For the best fit of the equations to predict postoperative AKI example, the proportion of patients defined as having (AIC = 6546; AUC = 0.712). eGFR < 60 mL/min/1.73m was 22% when calculated using the re-expressed MDRD II equation, in contrast Discussion to just 2.9% of patients when using the Mayo equation. Although there were similarities in eGFR between The variability seen among these equations in the the five equations, the distribution of patients in the presence of various eGFR categories might be ascribed various KDIGO categories varied significantly. The to differences in the variables and study populations Mayo equation classified more patients in higher eGFR used in their calculation. For instance, the CG equation M ekkawy et al. Arthroplasty (2023) 5:14 Page 5 of 6 Table 3 Logistic regression analysis of odds of developing AKI our cohort was much less than previous studies. This by each of the five equations might be because NSQIPs definition of AKI under - estimates the incidence of AKI [19]. Studies utilizing Equation Acute Kidney Injury P-Value AIC AUC Odds Ratio (95%CI) NSQIP may only capture patients with more severe AKI, giving the perception of low incidence but high MDRD II 0.78 (0.74–0.82) <0.001 6599 0.721 mortality [20]. Therefore, the incidence of AKI fol - Re-Expressed 0.86 (0.82–0.90) <0.001 6660 0.658 lowing TJA is likely to be much higher than what was MDRD II observed in this study. Another limitation is that this CG 0.78 (0.75–0.82) <0.001 6588 0.689 study did not assess patient demographic and comor- Mayo 0.74 (0.70–0.78) <0.001 6546 0.712 bidity characteristics as independent predictors of AKI. CKD-EPI 0.75 (0.71–0.80) <0.001 6628 0.707 Also, the hospitals participating in this database may MDRD II: Modification of Diet in Renal Disease, CG: Cockcroft-Gault, Mayo: Mayo not be reflective of the national population as they tend Clinic Quadratic, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration to be more academic and have more resources [21]. However, NSQIP data have been proven highly reliable adjusts for body surface area, the MDRD equation through the use of internal audits and clinical data col- has demonstrated better performance in those with lector reviews [22]. impaired kidney function, and the Mayo equation has demonstrated superior performance in those with Conclusions preserved renal function [11, 13, 14]. In conclusion, decreasing preoperative eGFR as calcu- The fact that the variability in eGFR calculated in lated by each of the five equations was independently the same patient depends on which equation was used associated with an increased risk of AKI following TJA. may have profound significance. Patients may be strati - Although each equation had significant predictive ability, fied into different KDIGO categories, and therefore the Mayo equation had the most successful model in pre- may or may not be identified as having a preoperative dicting AKI in patients undergoing TJA. The results of renal impairment. This may affect subsequent decisions this study may allow providers to make a more informed about whether these patients receive perioperative opti- decision when identifying patients at risk of postopera- mization treatments. Since the literature shows worse tive AKI and underscores the need for further investiga- outcomes in patients who acquire AKI, it is essential to tion and standardization in assessing AKI in arthroplasty identify the best method of determining which patients surgery. are at the highest risk of developing postoperative AKI and who may benefit from preoperative identification and treatment. A more sensitive rather than specific Abbreviations TJA Total joint arthroplasty approach may be best in order to capture more patients AKI Acute kidney injury with AKI and be able to treat them accordingly. KDIGO Kidney Disease: Improving Global Outcomes According to KDIGO clinical practice guidelines, the sCR Serum creatinine CKD Chronic kidney disease CKD-EPI equation is recommended to calculate eGFR GFR Glomerular filtration rate since it has the highest accuracy as compared to the other eGFR Estimated glomerular filtration rate equations [6]. However, our results demonstrated that MDRD II Modification of Diet in Renal Disease CG Cockcroft-Gault the Mayo equation was a better predictor of the develop- Mayo Mayo Quadratic Equation ment of AKI postoperatively. In a recent study evaluating CKD-EPI Chr onic Kidney Disease Epidemiology Collaboration these five equations in patients undergoing cardiovascu - ACS-NSQIP Amer ican College of Surgeons National Surgical Quality Improve- ment Program lar surgery, Jo et al. similarly found that the Mayo equa- CPT Current Procedural Terminology tion had the greatest accuracy in predicting postoperative BMI Body mass index AKI than the other four equations [17]. However, ortho- BSA Body surface area ORs Odds ratios pedic literature on eGFR equations remains lacking. The CIs Confidence intervals literature shows that AKI following TJA is associated AIC Akaike information criterion with significant morbidity, increased length of stay, and ROC Receiver operating curve AUC Area under the curve possible additional therapy including hemodialysis [8, 16, 18]. u Th s, being able to identify patients at high risk of Acknowledgements developing AKI following TJA may positively affect out - Not applicable. comes and reduce costs. Authors’ contributions There are several limitations to this study. First, the Conceptualization: Y.P.C., S.S.R., M.R., R.M.A.. Data Collection: K.L.M., Y.P.C.. Data number of patients who acquired postoperative AKI in Analysis: K.L.M., Y.P.C.. Validation: R.M.A., H.S.K.. Preparation of Manuscript/ Mekkawy et al. Arthroplasty (2023) 5:14 Page 6 of 6 Review/Revision: K.L. M., Y.P.C., S.S.R., M.R., R.M.A., H.S.K.. All authors have read 13. Cockcroft D, Gault M. Prediction of creatinine clearance from serum and approved the final submission. creatinine. Nephron. 1976;16:31–41. 14. Rule A, et al. Using serum creatinine to estimate glomerular filtration rate: Funding accuracy in good health and in chronic kidney disease. Ann Intern Med. This study received no source of funding. 2004;141:929–37. 15. Levey A, et al. A new equation to estimate glomerular filtration rate. Ann Availability of data and materials Intern Med. 2009;150:604–12. The dataset was extracted from a national database and is available upon 16. Warren JA, George J, Anis HK, Krebs OK, Molloy RM, Higuera CA, et al. request. Eec ff ts of estimated glomerular filatration rate on 30-day mortality and postoperative complications after total hip arthroplasty: a risk stratification instrument. J Arthroplast. 2020;35:786–93. Declarations 17. Jo J, Ryu SA, Kim J. Comparison of five glomerular filtration rate estimating equations as predictors of acute kidney injury after Ethics approval and consent to participate cardiovascular surgery. Sci Rep. 2019. https:// doi. org/ 10. 1038/ This study was deemed exempt by our Institutional Review Board. s41598- 019- 47559-w. 18. Sundaram K, Warren JA, Krebs OK, Anis HK, Klika AK, Molloy RM, et al. Consent for publication Estimated glomerular filtration rate is a prognosticator of adverse Patient information was extracted from a deidentified national database. outcomes after primary total knee arthroplasty among patients with chronic kidney disease and glomerular hyperfiltration. Knee. 2021;28:36– Competing interests 44. https:// doi. org/ 10. 1016/j. knee. 2020. 11. 008. The authors have no competing interests or conflicts of interest to declare. 19. Bihorac A, Brennan M, Ozrazgat-Baslanti T, Bozorgmehri S, Efron PA, Moore FA, et al. National surgical quality improvement program underestimates the risk associated with mild and moderate Received: 18 July 2022 Accepted: 26 December 2022 postoperative acute kidney injury. Crit Care Med. 2013;41:2570–83. https:// doi. org/ 10. 1097/ CCM. 0b013 e3182 9860fc. 20. Kheterpal S, Tremper KK, Heung M, Rosenberg AL, Englesbe M, Shanks AM, et al. Development and validation of an acute kidney injury risk index for patients undergoing general surgery: results from a national data set. References Anesthesiology. 2009;110:505–15. https:// doi. org/ 10. 1097/ ALN. 0b013 1. Jiang EX, Gogineni HC, Mayerson JL, Glassman AH, Magnussen e3181 979440. RA, Scharschmidt TJ. Acute kidney disease after total hip and 21. Sheils CR, Dahlke AR, Kreutzer L, Bilimoria KY, Yang AD. Evaluation of knee arthroplasty: incidence and associated factors. J Arthroplast. hospitals participating in the American College of Surgeons National 2017;32:2381–5. https:// doi. org/ 10. 1016/j. arth. 2017. 03. 009. Surgical Quality Improvement Program. Surgery. 2016;160:1182–8. 2. Weingarten TN, Gurrieri C, Jarett PD, Brown DR, Berntson NJ, Calaro RD, 22. Shiloach M, Frencher SK Jr, Steeger JE, Rowell KS, Bartzokis K, Tomeh et al. Acute kidney injury following total joint arthroplasty: retrospective MG, et al. Toward robust information: data quality and interrater analysis. Can J Anaesth. 2012;59:1111–8. https:// doi. org/ 10. 1007/ reliability in the American College of Surgeons National Surgical Quality s12630- 012- 9797-2. Improvement Program. J Am Coll Surg. 2010;210:6–16. 3. Kimmel LA, Wilson S, Janardan JD, Liew SM, Walker RG. Incidence of acute kidney injury following total joint arthroplasty: a retrospective review by Publisher’s Note RIFLE criteria. Clin Kidney J. 2014;7:546–51. https:// doi. org/ 10. 1093/ ckj/ Springer Nature remains neutral with regard to jurisdictional claims in pub- sfu108. lished maps and institutional affiliations. 4. 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Curr Opin Nephrol Hy. maximum visibility for your research: over 100M website views per year 2001;10:785–92. 12. Levey A, et al. Expressing the Modification of Diet in Renal Disease Study At BMC, research is always in progress. equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem. 2007;53:766–72. Learn more biomedcentral.com/submissions http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Arthroplasty Springer Journals

Comparing five equations to calculate estimated glomerular filtration rate to predict acute kidney injury following total joint arthroplasty

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Abstract

Background Acute kidney injury (AKI) following total joint arthroplasty ( TJA) is associated with increased morbidity and mortality. Estimated glomerular filtration rate (eGFR) is used as an indicator of renal function. The purpose of this study was (1) to assess each of the five equations that are used in calculating eGFR, and (2) to evaluate which equation may best predict AKI in patients following TJA. Methods The National Surgical Quality Improvement Program (NSQIP) was queried for all 497,261 cases of TJA performed from 2012 to 2019 with complete data. The Modification of Diet in Renal Disease (MDRD) II, re-expressed MDRD II, Cockcroft-Gault, Mayo quadratic, and Chronic Kidney Disease Epidemiology Collaboration equations were used to calculate preoperative eGFR. Two cohorts were created based on the development of postoperative AKI and were compared based on demographic and preoperative factors. Multivariate regression analysis was used to assess for independent associations between preoperative eGFR and postoperative renal failure for each equation. The Akaike information criterion (AIC) was used to evaluate predictive ability of the five equations. Results Seven hundred seventy-seven (0.16%) patients experienced AKI after TJA. The Cockcroft-Gault equation yielded the highest mean eGFR (98.6 ± 32.7), while the Re-expressed MDRD II equation yielded the lowest mean eGFR (75.1 ± 28.8). Multivariate regression analysis demonstrated that a decrease in preoperative eGFR was independently associated with an increased risk of developing postoperative AKI in all five equations. The AIC was the lowest in the Mayo equation. Conclusions Preoperative decrease in eGFR was independently associated with increased risk of postoperative AKI in all five equations. The Mayo equation was most predictive of the development of postoperative AKI following TJA. The mayo equation best identified patients with the highest risk of postoperative AKI, which may help providers make decisions on perioperative management in these patients. Keywords Total joint arthroplasty, Estimated glomerular filtration rate, Ecute kidney injury *Correspondence: Harpal S. Khanuja khanuja@jhmi.edu Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA Department of Orthopaedic Surgery, Philadelphia College of Osteopathic Medicine, Philadelphia, PA 19131, USA Department of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, CA 94063, USA © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Mekkawy et al. Arthroplasty (2023) 5:14 Page 2 of 6 Background Methods There has been a growing emphasis on preopera- Study population tive medical optimization in total joint arthroplasty A retrospective review was conducted of all 640,880 cases (TJA) patients to improve care and decrease the risk of TJA in the American College of Surgeons National of postoperative complications. It is important to Surgical Quality Improvement Program (ACS-NSQIP) identify renal dysfunction perioperatively to properly from January 1, 2012 to December 31, 2019. TJA cases stratify and modify care to improve outcomes. There were identified with the Current Procedural Terminology are mixed reports on the incidence of acute kidney (CPT) codes 27,447 (total knee arthroplasty) and 27,130 injury (AKI) after joint arthroplasty, ranging anywhere (total hip arthroplasty). Exclusion criteria included from 2% to 15% [1–3]. AKI following surgery has been unknown or not reported race, age 90 or older (as NSQIP shown to increase length of stay and costs related to groups these patients as age 90+), emergency cases, complications, as well as increased mortality rates [4, patients on preoperative dialysis, unknown preoperative 5]. One of the most widely accepted classifications, creatinine, and unknown preoperative height or weight. Kidney Disease: Improving Global Outcomes (KDIGO) A total of 143,619 cases were removed due to these defines AKI as an increase in serum creatinine (sCr) ≥ criteria, resulting in a total of 497,261 cases included for 0.3 mg/dL within 24 h, an increase in sCr ≥ 1.5 times analysis in this study (Fig. 1). baseline, or a urine volume < 0.5 mL/kg/hr for 6 h [6]. Chronic kidney disease (CKD), which is defined as a glomerular filtration rate (GFR) of < 60 mL/min/1.73 , Variables has been demonstrated to be a predictor of AKI fol- Preoperative factors (age, sex, race, height, weight, medi- lowing TJA [7, 8]. The GFR is used to measure renal cal comorbidities, and preoperative laboratory values), function and to identify renal impairment. However, intraoperative factors (surgical duration and procedure direct measurement in clinical practice is complex, type), and complications (progressive renal insufficiency expensive, and impractical [9]. Instead, estimated and acute renal failure) were extracted from NSQIP and GFR (eGFR) is most commonly used to calculate renal included in this study. NSQIP collects data for 30 days function [7]. There are various equations used to cal- postoperatively, therefore all complications including culate eGFR, each factoring in a combination of sCr, AKI are within one month after surgery. Body mass index age, race, sex, and/or height and weight [10]. The (BMI) was calculated using height and weight. major five equations are the Modification of Diet in The eGFR was calculated using the following equa - Renal Disease (MDRD II) [11] equation, re-expressed tions, utilizing the preoperative sCr taken closest to the MDRD II [12], Cockcroft-Gault (CG) [13], the Mayo time before surgery: Quadratic (Mayo) [14], and the Chronic Kidney Dis- ease Epidemiology Collaboration (CKD-EPI) equations 1. MDRD II equation [11]: eGFR = 186 × sCr − 1. [15]. The differing variables and coefficients used in 154 × Age − 0.203 × (0.742  if female) × (1.210  if these equations result in different eGFR values for any African − American) specific sCr. As a worsening GFR leads to unfavorable 2. Re-expressed MDRD II equation [12]: eGFR outcomes [16], it is important to identify a consistent = 175 × sCr − 1.154 × Age − 0.203 × (0.742  if calculation that best predicts postoperative AKI. female) × (1.210 if African − American) According to the 2012 KDIGO clinical practice 3. CG equation [13]: eGFR = [(140 − Age) × Weight/ guidelines, the CKD-EPI equation is recommended (72 × sCr)] × (0.85 if female) to calculate eGFR as it has demonstrated the high- This equation is adjusted for body surface area: est accuracy as compared to the other equations [6]. (1.73  m × CG)/BSA，where BSA = 0.007184 × weig However, a recent study demonstrated the Mayo equa- ht 0.425 × height 0.725 tion was the most predictive in identifying AKI after 4. Mayo equation [14]: eGFR = exp  [1.911 + 5.249/ cardiovascular surgery [17]. The best equation for sCr − 2.114/sCr − 0.00686 × Age − (0.205 if female)], predicting postoperative AKI in TJA has not been if sCr < 0.8 mg/dL then sCr = 0.8 investigated, and that is the purpose of this study. We 5. CKD-EPI Equation [15]: eGFR = 141 × min (sCr/κ, 1) sought to evaluate the eGFRs calculated from the five α × max  (sCr/κ, 1) − 1.209 × 0.993Age × 1.018  [if equations, and to identify which equation may be most female] × 1.159  [if African − American], where κ predictive of postoperative AKI in patients following is 0.9 for males and 0.7 for females, α is –0.411 for TJA. males and –0.329 for females, min demonstrates the minimum of sCr/κ or 1, and max demonstrates the maximum of sCR/κ or 1 [15]. M ekkawy et al. Arthroplasty (2023) 5:14 Page 3 of 6 Fig. 1 Flow diagram indicating study inclusion and exclusion criteria The preoperative eGFRs calculated by the five differ - this study, an alpha value was accepted at 0.01. Statistical ent equations were stratified into categories based on analyses were performed by utilizing Stata software, ver- KDIGO classification: Stage 1: ≥ 90, Stage 2: < 90–60, sion 17.0 (StataCorp LLC, College Station, TX, USA). Stage 3a: < 60–45, Stage 3b: < 45–30, Stage 4: < 30–15, and Stage 5: < 15 mL/min/1.73 m [6]. Results Of the 497,261 cases included in this study, 777 (0.16%) Statistical analysis patients developed AKI. Table  1 shows the baseline and Cases were stratified into two groups based on the devel - perioperative characteristics of the study population. The opment of AKI postoperatively and assessed for differ - mean age of the AKI cohort was 59 ± 14 years old and ences in preoperative factors and intraoperative factors. 349 (45%) were female. The mean BMI of the AKI cohort Descriptive statistics were reported for continuous vari- was 35 ± 8.1 kg/m . ables as mean ± standard deviation and for categori- Regarding preoperative factors, the AKI and non- cal variables as frequencies and percentages. Univariate AKI cohorts differed significantly by sex (P < 0.001), age analysis for continuous and categorical variables was (P < 0.001), race (P < 0.001), BMI (P < 0.001), hematocrit conducted by using analysis of variance and chi-squared (P < 0.001), albumin (P < 0.001), diabetes mellitus (P < 0.001), or Fisher’s exact test, as appropriate. Multivariate logis- hypertension (P < 0.001), congestive heart failure (P < 0.001), tic regression models were used to evaluate the odds of and chronic obstructive pulmonary disease (P < 0.001). developing AKI postoperatively, adjusted for age, sex, The two cohorts did not differ significantly by smoking BMI, preoperative laboratory values (creatinine, albumin, status (P = 0.049). Intraoperatively, the two cohorts also and hematocrit), patient comorbidities (diabetes, conges- differed significantly by surgical duration (P < 0.001) tive heart failure, chronic obstructive pulmonary disease, (Table 1). hypertension, and smoking status), and surgical duration The results of the eGFR for each of the five equations for each of the five equations. Results of the multivari - is summarized in Table  2. The equation with the highest ate regression model were reported as odds ratios (ORs) calculated mean eGFR was CG equation (98.6 ± 32.7), with 95% confidence intervals (CIs). Akaike informa - followed by the Mayo equation (97.3 ± 19.6), CKD-EPI tion criterion (AIC) was used to compare the fit of each equation (91.6 ± 17.1), MDRD II equation (86.6 ± 26.4), model in predicting AKI postoperatively, and receiver and finally the re-expressed MDRD II equation had the operating curves (ROC) were generated for each Logistic lowest calculated mean eGFR (75.1 ± 28.8). regression model, with the area under the curve (AUC) The results of the Logistic regression analysis are calculated for each ROC. Due to the large sample size of outlined in Table  3. Lower preoperative eGFR was Mekkawy et al. Arthroplasty (2023) 5:14 Page 4 of 6 Table 1 Patient demographics and perioperative characteristics Variable n (%) P-Value All Cases: n = 497,261 AKI: n = 777 No AKI: n = 496,484 Female Gender 294,369 (59) 349 (45) 294,369 (59) <0.001 Age (year) 55 ± 13 59 ± 14 55 ± 13 <0.001 Race <0.001 White 435,993 (88) 617 (79) 435,376 (88) Black 46,202 (9.3) 148 (19) 46,054 (9.3) Asian 10,614 (2.1) 8 (1.0) 10,606 (2.1) Native American, Hawaiian, or Pacific Islander 4452 (0.9) 4 (0.5) 4448 (0.0) BMI (kg/m ) 32 ± 6.7 35 ± 8.1 32 ± 6.7 <0.001 Preoperative Serum Creatinine (mg/dL) 0.9 ± 0.3 1.3 ± 0.7 0.9 ± 0.3 <0.001 Hematocrit (%) 41 ± 4.1 39 ± 5.2 41 ± 4.1 <0.001 Albumin (g/dL) 4.1 ± 0.4 3.9 ± 0.5 4.1 ± 0.4 <0.001 Diabetes Mellitus 80,003 (16) 273 (35) 79,730 (16) <0.001 Hypertension 313,447 (63) 699 (90) 312,748 (63) <0.001 Congestive Heart Failure 1753 (0.4) 30 (3.9) 1723 (0.4) <0.001 Chronic Obstructive Pulmonary Disease 18,998 (3.8) 92 (12) 18,906 (3.8) <0.001 Smoker 49,643 (10) 94 (12) 49,549 (10) 0.049 Type of Surgery THA 188,939 (38) 287 (37) 188,652 (38) 0.543 TKA 308,322 (62) 490 (63) 307,832 (62) Surgical Duration (min) 93 ± 38 103 ± 46 93 ± 38 <0.001 Data are expressed as number of patients (%) or mean ± standard deviation Values available for 489,607 total cases Values available for 301,393 total cases Table 2 Distribution of patients by preoperative eGFR based on each of the five equations MDRD II Re-Expressed MDRD II CG Mayo CKD-EPI Mean eGFR 86.6 ± 26.4 75.1 ± 28.8 98.6 ± 32.7 97.3 ± 19.6 91.6 ± 17.1 ≥90 203,028 (41) 115,455 (23) 286,132 (58) 351,417 (71) 283,794 (57) ≥60, <90 233,171 (47) 226,453 (46) 163,614 (33) 122,585 (25) 191,771 (39) ≥45, <60 45,253 (9.1) 107,606 (22) 34,820 (7.0) 14,332 (2.9) 15,663 (3.2) ≥30, <45 13,206 (2.7) 40,494 (8.1) 10,868 (2.2) 6,404 (1.3) 4,864 (1.0) ≥30, <15 2185 (0.4) 6666 (1.3) 1517 (0.3) 2021 (0.4) 959 (0.2) ≤15 418 (0.1) 587 (0.1) 310 (0.1) 502 (0.1) 210 (0.0) Data are expressed as the number of patients (%) MDRD II: Modification of Diet in Renal Disease, CG: Cockcroft-Gault, Mayo: Mayo Clinic Quadratic, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration significantly associated with an increased risk of groups, while the re-expressed MDRD II equation developing AKI following TJA. The Mayo equation had classified more patients in lower eGFR groups. For the best fit of the equations to predict postoperative AKI example, the proportion of patients defined as having (AIC = 6546; AUC = 0.712). eGFR < 60 mL/min/1.73m was 22% when calculated using the re-expressed MDRD II equation, in contrast Discussion to just 2.9% of patients when using the Mayo equation. Although there were similarities in eGFR between The variability seen among these equations in the the five equations, the distribution of patients in the presence of various eGFR categories might be ascribed various KDIGO categories varied significantly. The to differences in the variables and study populations Mayo equation classified more patients in higher eGFR used in their calculation. For instance, the CG equation M ekkawy et al. Arthroplasty (2023) 5:14 Page 5 of 6 Table 3 Logistic regression analysis of odds of developing AKI our cohort was much less than previous studies. This by each of the five equations might be because NSQIPs definition of AKI under - estimates the incidence of AKI [19]. Studies utilizing Equation Acute Kidney Injury P-Value AIC AUC Odds Ratio (95%CI) NSQIP may only capture patients with more severe AKI, giving the perception of low incidence but high MDRD II 0.78 (0.74–0.82) <0.001 6599 0.721 mortality [20]. Therefore, the incidence of AKI fol - Re-Expressed 0.86 (0.82–0.90) <0.001 6660 0.658 lowing TJA is likely to be much higher than what was MDRD II observed in this study. Another limitation is that this CG 0.78 (0.75–0.82) <0.001 6588 0.689 study did not assess patient demographic and comor- Mayo 0.74 (0.70–0.78) <0.001 6546 0.712 bidity characteristics as independent predictors of AKI. CKD-EPI 0.75 (0.71–0.80) <0.001 6628 0.707 Also, the hospitals participating in this database may MDRD II: Modification of Diet in Renal Disease, CG: Cockcroft-Gault, Mayo: Mayo not be reflective of the national population as they tend Clinic Quadratic, CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration to be more academic and have more resources [21]. However, NSQIP data have been proven highly reliable adjusts for body surface area, the MDRD equation through the use of internal audits and clinical data col- has demonstrated better performance in those with lector reviews [22]. impaired kidney function, and the Mayo equation has demonstrated superior performance in those with Conclusions preserved renal function [11, 13, 14]. In conclusion, decreasing preoperative eGFR as calcu- The fact that the variability in eGFR calculated in lated by each of the five equations was independently the same patient depends on which equation was used associated with an increased risk of AKI following TJA. may have profound significance. Patients may be strati - Although each equation had significant predictive ability, fied into different KDIGO categories, and therefore the Mayo equation had the most successful model in pre- may or may not be identified as having a preoperative dicting AKI in patients undergoing TJA. The results of renal impairment. This may affect subsequent decisions this study may allow providers to make a more informed about whether these patients receive perioperative opti- decision when identifying patients at risk of postopera- mization treatments. Since the literature shows worse tive AKI and underscores the need for further investiga- outcomes in patients who acquire AKI, it is essential to tion and standardization in assessing AKI in arthroplasty identify the best method of determining which patients surgery. are at the highest risk of developing postoperative AKI and who may benefit from preoperative identification and treatment. A more sensitive rather than specific Abbreviations TJA Total joint arthroplasty approach may be best in order to capture more patients AKI Acute kidney injury with AKI and be able to treat them accordingly. KDIGO Kidney Disease: Improving Global Outcomes According to KDIGO clinical practice guidelines, the sCR Serum creatinine CKD Chronic kidney disease CKD-EPI equation is recommended to calculate eGFR GFR Glomerular filtration rate since it has the highest accuracy as compared to the other eGFR Estimated glomerular filtration rate equations [6]. However, our results demonstrated that MDRD II Modification of Diet in Renal Disease CG Cockcroft-Gault the Mayo equation was a better predictor of the develop- Mayo Mayo Quadratic Equation ment of AKI postoperatively. In a recent study evaluating CKD-EPI Chr onic Kidney Disease Epidemiology Collaboration these five equations in patients undergoing cardiovascu - ACS-NSQIP Amer ican College of Surgeons National Surgical Quality Improve- ment Program lar surgery, Jo et al. similarly found that the Mayo equa- CPT Current Procedural Terminology tion had the greatest accuracy in predicting postoperative BMI Body mass index AKI than the other four equations [17]. However, ortho- BSA Body surface area ORs Odds ratios pedic literature on eGFR equations remains lacking. The CIs Confidence intervals literature shows that AKI following TJA is associated AIC Akaike information criterion with significant morbidity, increased length of stay, and ROC Receiver operating curve AUC Area under the curve possible additional therapy including hemodialysis [8, 16, 18]. u Th s, being able to identify patients at high risk of Acknowledgements developing AKI following TJA may positively affect out - Not applicable. comes and reduce costs. Authors’ contributions There are several limitations to this study. First, the Conceptualization: Y.P.C., S.S.R., M.R., R.M.A.. Data Collection: K.L.M., Y.P.C.. Data number of patients who acquired postoperative AKI in Analysis: K.L.M., Y.P.C.. Validation: R.M.A., H.S.K.. Preparation of Manuscript/ Mekkawy et al. Arthroplasty (2023) 5:14 Page 6 of 6 Review/Revision: K.L. M., Y.P.C., S.S.R., M.R., R.M.A., H.S.K.. All authors have read 13. Cockcroft D, Gault M. Prediction of creatinine clearance from serum and approved the final submission. creatinine. Nephron. 1976;16:31–41. 14. Rule A, et al. Using serum creatinine to estimate glomerular filtration rate: Funding accuracy in good health and in chronic kidney disease. Ann Intern Med. This study received no source of funding. 2004;141:929–37. 15. Levey A, et al. A new equation to estimate glomerular filtration rate. Ann Availability of data and materials Intern Med. 2009;150:604–12. The dataset was extracted from a national database and is available upon 16. Warren JA, George J, Anis HK, Krebs OK, Molloy RM, Higuera CA, et al. request. Eec ff ts of estimated glomerular filatration rate on 30-day mortality and postoperative complications after total hip arthroplasty: a risk stratification instrument. J Arthroplast. 2020;35:786–93. Declarations 17. Jo J, Ryu SA, Kim J. 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Journal

Arthroplasty – Springer Journals

Published: Mar 10, 2023

Keywords: Total joint arthroplasty; Estimated glomerular filtration rate; Ecute kidney injury

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