A Nomogram to Predict Postoperative Complications in Elderly with Total Hip Replacement

A Nomogram to Predict Postoperative Complications in Elderly with Total Hip Replacement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article A Nomogram to Predict Postoperative Complications in Elderly with Total Hip Replacement Xiujuan Tan, Fengmin Ge, Guixi Mo, Zhiyi Li, Xiaoxia Gu, Liangqing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-79448/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2022 Read the published version in World Journal of Clinical Cases → Version 1 posted You are reading this latest preprint version Abstract Background : By analyzing the risk factors of postoperative complications in elderly patients with hip replacement, We aimed to develop a nomogram model based on preoperative and intraoperative variables and verified the sensitivity and specificity for risk stratification of postoperative complications in elderly with total hip replacement patients. Methods : A total of 414 elderly patients who underwent surgical treatment for total hip replacement hospitalized at the Affiliated Hospital of Guangdong Medical University from March 1, 2017 to August 31, 2019 were included into this study. Univariate and multivariate logistic regression were conducted to identify independent risk factors of postoperative complication in the 414 patients. A nomogram was developed by R software and validated to predict the risk of postoperative complications. Results : Multivariate logistic regression analysis revealed that age (OR=1.05, 95%CI: 1.00-1.09) , renal failure(OR=0.90, 95% CI: 0.83～0.97) , Type2 diabetes (OR=1.05, 95% CI: 1.00～1.09) , ALB (OR=0.91, 95% CI: 0.83～0.99) were independent risk factors of postoperative complication in elderly patients with hip replacement (P＜0.05) . For validation of the nomogram, ROC curve revealed that the model predicting postoperative complication in elderly patients with hip replacement was the area under the curve of 0.8254(95% CI: 0.78～0.87) , the slope of the calibration plot was close to 1 and the model passed Hosmer-Lemeshow goodness of fit test (x 2 = 10.16, P=0.4264), calibration in R Emax=0.176, Eavg=0.027, which all demonstrated that the model was of good accuracy. Conclusion : The nomogram predicting postoperative complications in patients with total hip replacement constructed based on age, type 2 diabetes, renal failure and ALB is of good discrimination and accuracy, which was of clinical significance. Anesthesiology & Pain Medicine elderly total hip replacement postoperative complication nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Hip replacement is a frequently done and highly successful surgical intervention [ 1 ]. More than one million hip arthroplasties are performed annually worldwide [ 2 ]. Kurtz and colleagues [ 3 ] projected the demand for primary THA to grow 174% to 572,000 procedures per year by 2030. These procedures carry a complication rate estimated to be between 2% and 14%; higher complication rates are associated with more elderly and comorbid patient populations [ 4 ]. Numerous clinical tools have been developed to predict a variety of THA patient outcomes [ 5 ], but fewer to predict complications after surgery and no one is modelling by Chinese. As an anesthetist, when we go to the inpatient ward for preoperative evaluation, patient often ask: what is the proportion of risk in my surgery༟The answer is perhaps or we don’t know. So we want to create a tool not only can predict the risk precisely, but also guide the clinical work. Therefore this study will analyze clinical data, explored the independent risk factors for postoperative complications in elderly patients undergoing total hip replacement, develop a nomogram for accurate risk stratification of postoperative complications based on preoperative and intraoperative variables, and verify whether this tool would have good predictive for patients undergoing total hip replacement in our hospital. Methods Patients Approved by the Ethics Committee of the Affiliated Hospital of Guangdong Medical University committee (PJ2020-022), we were collected from all patients undergoing total hip replacement from March 1, 2017 to August 31, 2019 at the Affiliated Hospital of Guangdong Medical University. Inclusion criteria were age > 65y, elective surgical treatment with total hip replacement. Non-inclusion criteria were age < 65y, electronic medical records incomplete, patients or family members disagree with the study. Research Methods Data on general patient information, comorbidities, laboratory test results, intraoperative variables, and postoperative complications during hospitalization were collected from electronic medical records and electronic Anesthesia Information Management System. Known patient-related factors of complications are age, gender, fractures or not and comorbidity such as renal failure、hypertension、diabetes、coronary heart disease、stroke. Laboratory test results of white blood cell (WBC), red blood cell (RBC), hemoglobin (HB), platelets (PLT), albumin (ALB) and blood urea nitrogen (BUN). Intraoperative variables included estimated blood loss (EBL), lowest heart rate, the type of anesthesia, preoperative ASA score (American Society of Anesthesiologists Score) and operation time. The primary outcome measure was the incidence of a postoperative complication or death during hospitalization. Complications were identified from diagnoses in discharge summaries, operative reports, and ICD-9 codes by a single investigator. Included: cardiac arrest, deep venous thrombosis, myocardial infarction, pneumonia, pulmonary embolism, systemic inflammatory response syndrome, infection, dislocation, delirium, according to definitions from the NSQIP [ 6 ]. Statistical analysis All statistical analyses were performed using the STATA14.0 statistical software package and R software (R3.2.3) with rms package added. Differences between patients with and without complications were compared with t-test or Wilcoxon rank-sum test using the mean ± SD and the median (range) for quantitative variables and chi-squared test using n (%) for qualitative variables. Univariate logistic regression for patients clinical data were analyzed to identify the independent risk factors for postoperative complications after surgery. A multivariate logistic regression model was built using the factors associated with p values < 0.05 by univariate analysis. Selection values of p < 0.05 variables by multivariate analysis to create a nomogram model by R software (R3.2.3) rms package, evaluated the nomogram by assessing discrimination and calibration. Results In our study 414 elderly total hip replacement patients of 59 cases with postoperative complications after surgery, the incidence was 14.3%. The demographics and descriptive statistics for our patient cohort are given in Table 1 . Multivariable logistic regression of each clinical variable of patients related factors for postoperative complications include patients age(OR = 1.05, 95%CI: 1.00 ~ 1.09), renal failure(OR = 0.90, 95%CI: 0.83 ~ 0.97),diabetes(OR = 2.37, 95%CI: 1.04 ~ 5.40) and ALB(OR = 0.91, 95%CI:0.83 ~ 0.99) (Table 2 ). According to the above multivariable logistic regression analysis results, choose p < 0.05 variable apply R Software to build a nomogram model of postoperative complications in elderly total hip replacement patients (Fig. 1 ). Using nomogram model to predict postoperative complications in elderly total hip replacement patients risk ROC curve, AUC is 0.8254(95% CI: 0.78ཞ0.87), indicating that the nomogram model has a good bootstrap-corrected concordance(Fig. 2 ). Perform Hosmer-Lemeshow goodness-of-fit test and evaluate nomogram Model accuracy, Hosmer-Lemeshow fit goodness test x 2 = 10.16, P = 0.4264(Fig. 3 ), calibration in rms package by R software Emax = 0.176, Eavg = 0.027(Fig. 4 ), all shows that the model appears to be well-calibrated, with predicted outcome rates closely reflecting the observed rates. Table 1 Comparison of clinical data between No Complications and Complications. Patient Characteristics No Complications (N = 355) Complications (N = 59) t/x 2 /Z Value P Value Age(y) a 74.06 ± 7.25 81.29 ± 8.19 6.96 0.000 Male gender (%) b 149(41.9) 24(40.7) 0.03 0.852 Weight(kg) a 58.93 ± 10.23 57.2 ± 9.93 1.2 0.226 Comorbidities Hypertension (%) b 114(32.1) 30(50.8) 7.83 0.006 Diabetes (%) b 50(14.1) 16(27.1) 6.41 0.017 Coronary heart disease (%) b 30(5) 10(16.9) 4.19 0.041 Stroke (%) b 16(4.5) 12(20.3) 20.11 0.000 Renal failure (%) b 13(3.7) 7(11.9) 7.404 0.017 Fractures (%) b 123((34.6) 45(76.3) 36.35 0.000 Laboratory data WBC (10 9 /L) a 7.6 ± 2.46 8.69 ± 2.93 2.7 0.003 RBC (10 12 /L) a 4.21 ± 0.59 3.89 ± 0.69 3.81 0.000 HB(g/L) a 123.37 ± 20.99 111.89 ± 22.64 3.85 0.000 PLT (10 9 /L) a 235.66 ± 71.56 247.91 ± 115.27 0.79 0.272 ALB(g/L) a 39.37 ± 4.51 35.75 ± 4.41 5.72 0.000 BUN (mmol/L) a 6.05 ± 4.02 7.71 ± 5.08 2.39 0.019 Intraoperative characteristics EBL (ml) c 203.79(50-1500) 229.8(50-1000) 1.5 0.134 Lowest heart rate(bpm) a 59.39 ± 10.49 65.37 ± 13.88 3.16 0.001 Operation time(min) a 100.85 ± 40.26 89.41 ± 37.24 2.04 0.505 General Anesthesia (%) b 159(44.8) 24(40.7) 0.35 0.556 ASA Class Ⅲ or Ⅳ (%) b 159(44.8) 518(86.4) 39.35 0.000 Preoperative patient characteristics for 414 total hip replacement procedures between March 1, 2017 to August 31, 2019. Results are presented as number (percentage) of patients, or as mean ± standard deviation. The P values were obtained from chi-square tests, t-tests, or Wilcoxon rank-sum tests, as indicated (a t-test; b chi-square test; c Wilcoxon rank-sum test). Table 2 Multivariable logistic regression analysis results Variables OR SE Z 95%CI P Value Age 1.05 0.02 2.04 1.00 ~ 1.09 0.041 Renal failure 3.96 2.44 2.23 1.18 ~ 13.27 0.026 Hypertension 1.31 0.47 0.74 0.65 ~ 2.64 0.457 Coronary heart disease 0.84 0.41 0.36 0.32 ~ 2.18 0.722 Diabetes 2.37 1.00 2.05 1.04 ~ 5.40 0.040 Stroke 2.41 1.15 1.84 0.94 ~ 6.16 0.066 Fractures 2.21 0.95 1.86 0.96 ~ 5.12 0.063 ASA Class Ⅲ or Ⅳ 1.85 0.68 1.69 0.91 ~ 3.79 0.090 Lowest heart rate 1.02 0.01 1.10 0.99 ~ 1.05 0.272 RBC 1.05 0.34 0.16 0.56 ~ 1.99 0.869 WBC HB ALB 1.03 1.00 0.90 0.64 0.01 0.04 0.44 0.10 2.73 0.91 ~ 1.16 0.98 ~ 1.02 0.83 ~ 0.97 0.657 0.922 0.006 BUN 1.00 0.04 0.13 0.94 ~ 1.08 0.893 Discussion China is the most populated country in the world, and now has the second-largest economy in the world [ 7 ]. As nearly 166 million Chinese are aged more than 65 years [ 8 ]. The demand for healthcare, including hip arthroplasty is increasing [ 9 , 10 ]. There are several predict complications model have be reported [ 11 , 12 , 13 ], but on one is modeling by Chinese. In this study, 59 of the 414 elderly total hip replacement patients who underwent surgical treatment were developed postoperative complication, the incidence is 14.3%, morbidity is much higher than 3.9% [ 14 ]. Probably because our definition of complications not only included dislocation, pulmonary embolism, and infection as reported previously, but included systemic inflammatory response syndrome, delirium which are common in elderly. Among 566 older patients (mean age, 76.7 years) undergoing a variety of elective operations (including orthopedic, general, and vascular), 23.9% patients developed postoperative delirium [ 15 ]. The incidence of postoperative delirium was reported as 7.0%-30.2% in hip arthroplasty [ 16 , 17 ]. In this study the average age is 75.09 ± 7.8. As a result, the morbidity 14.3% is considered to be reasonable. Age is a recognized risk factor for postoperative complications. The results of this study indicate that elderly patients with renal failure and diabetes are more likely to have postoperative complications after total hip replacement. It is Consistent with the research results of Robert K [ 18 ] et al. One possible reason is that elderly patients have more comorbidities will make them less able to withstand the stresses of anesthesia and surgery [ 19 , 20 ]. Diabetes have been reported to be significant predictors for complications such as surgical site infections [ 21 ]. The available data suggest that diabetes may promote the development of osteoarthritis [ 22 ]. Our results for the outcome measure indicate that elderly patients with renal failure and diabetes increase the weight of the nomogram model score by 25 points and 24 points, respectively. Interestingly, our data suggest that low preoperative albumin levels can predict the incidence of postoperative complications following surgery for total hip replacement. Since ALB is a biomarker of visceral protein and immune-competence status, it is commonly used for nutritional assessment [ 23 ]. Preoperative albumin bears strong potential as a practical metric to assess a patient’s overall health [ 24 ]. Recent studies even show that low ALB rather reflects a state of persistent inflammation [ 25 ]. Our results pointed out that the weight of 12.7 points in the nomogram model score will be increased for every 5 g/L decrease in ALB. We recommend surgeons and anesthetists should ideally attempt to optimize patient nutritional status before total hip replacement in elderly in order to avoid a greater likelihood of postoperative complications or mortality. However our study has several limitations. First, our data were limited only 414 patients, it only represents an elective patient population. Second, complications were only collected while in hospital, some of these complications could have occurred after discharge. Third, the predict model quality checks only with internal validation, so external validation will have to be included in future studies in order to promote use. Fourth, this was a retrospective study that relied on 9th edition coding, which can lead to errors and/or incomplete coding. Risk calculators should serve as a tool to help clinical decision-making, promote individualized medicine, and aid in the shared decision-making process [ 26 ]. Many of the studies report poor discrimination and calibration of the investigated risk calculators. In our study, founded that age, diabetes, renal failure, and Albumin value are independent risk factors for postoperative complications in elderly patients with total hip replacement, ROC curve shows the AUC is 0.8254, indicating that the nomogram model has a good discrimination. The Hosmer-Lemeshow fit goodness test x 2 = 10.16, P = 0.4264 and calibration curve is a straight line with a slope close to 1, indicating that the nomogram model has good accuracy in predicting the risk of postoperative complications in elderly patients with total hip replacements surgery and has clinical application value. Conclusions This study created a nomogram model based on age, diabetes, renal failure, and Albumin value independent risk factors for postoperative complications, has g good indexing and accuracy can provide scientific guidance for individualized clinical prevention and treatment of postoperative complications in elderly patients with total hip replacements surgery in our hospital. This four variables are easy to get in clinical practice, has clinical application value especially for Basic-level hospital. Abbreviations ASA: American Society of Anesthesiologists; THA: Total hip arthroplasty; NSQIP: National Surgical Quality Improvement Program; SD: Standard error; ROC: Receiver operating characteristic curve; AUC: Area under curve Declarations Ethics approval and consent to participate This study was approved by the ethics committee of the Affiliated Hospital of Guangdong Medical University with the reference number PJ2020-022. All participants were informed and asked for written informed consent. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study will be available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No-funding. XJ T and XX G are designer of the study, collecting data, statistical analysis, interpretation of results and writing the manuscript together. Authors' contributions XJ T is first Author, participated in protocol writing, collecting data, statistical analysis, interpretation of results and manuscript writing. FM G helped collection of cases. ZY L participated in protocol writing, essay writing. XX G helped interpretation of results and manuscript writing. GX M and LQ Z did the statistical analysis and reviewed the manuscript. All authors have read and approved the final submitted manuscript. Acknowledgements We thank Dr. JY W of the Affiliated Hospital of Guangdong Medical University Department of Scientific Research for his excellent assistance in the data analysis. Authors' information XX G and LQ Z is Co-Corresponding author. 1, Department of Anesthesiology, the First Affiliated Hospital, Jinan University, No.601 West Huangpu Avenue, Tianhe District Guangzhou City 510632, Guangdong Province, China. 2, Department of Anesthesiology, the Affiliated Hospital of Guangdong Medical University, No.57 South People’s Avenue, Xiashan District, Zhanjiang City 524001, Guangdong Province, China. References Ferguson RJ, Palmer AJ, Taylor A, et al. Hip replacement Lancet. 2018;392(10158):1662–71. Pivec R, Johnson AJ, Mears SC, Mont MA. Hip arthroplasty. Lancet. 2012;380:1768–77. Kurtz S, Ong K, Lau E, et al. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am. 2007;89:780–5. Nanjayan SK, Swamy GN, Yellu S, Yallappa S, Abuzakuk T, Straw R. In-hospital complications following primary total hip and knee arthroplasty in octogenarian and nonagenarian patients. J Orthop Traumatol. 2014;15(1):29–33. Joseph F, Konopka MD. MS, et al. Risk Assessment Tools Used to Predict Outcomes of Total Hip and T otal Knee Arthroplasty. Orthop Clin N Am. 2015;46:351–62. Khuri SF, Daley J, Henderson W, et al. The National Veterans Administration Surgical Risk Study: risk adjustment for the comparative assessment of the quality of surgical care. J Am Coll Surg. 1995;180:519. No authors listed. Open Knowledge Repository (OKR). World Bank Group. World Development Indicators 2017. https://openknowledge.worldbank.org/han-dle/10986/26447 (date last accessed 8 July 2019). No authors listed. National Bureau of Statistics of China. China Statistics Press. China Statistical Yearbook 2017. http://www.stats.gov.cn/tjsj/ndsj/2019/indexeh.htm) . Wang K. Brief discussion on present status and future of joint replacement in China. Chin J Joint Surg (Electronic Edition). 2015;9:12–4. http://www.cnki.com.cn/Article/CJFDTotal-ZHGJ201506004.htm . (date last accessed 8 August 2019) (In Chinese). Pei FX. The current status and future perspective of hip and knee arthroplasty in China. Chinese Journal of Bone Joint. 2012;1:4–8. http://www.cnki.com.cn/Article/CJFDTotal-GZGL201201019.htm . (date last accessed 8 August 2019) (In Chinese). Wuerz TH, Kent DM, Malchau H, et al. A nomogram to predict major complications after hip and knee arthroplasty. J Arthroplasty. 2014;29:1457–62. Wuerz TH, Regenbogen SE, Ehrenfeld JM, et al. The Surgical Apgar Score in hip and knee arthroplasty. Clin Orthop Relat Res. 2011;469:1119–26. Inneh IA, Lewis CG, Schutzer SF. Focused risk analysis: regression model based on 5,314 total hip andknee arthroplasty patients from a single institution. J Arthroplasty. 2014;29(10):2031–5. Phillips CB, Barrett JA, Losina E, et al. Incidence rates of dislocation, pulmonary embolism, and deep infection during the first six months after elective total hip replacement. J Bone Joint Surg Am. 2003;85-A:20. Zenilman ME. MD. Delirium An Important Postoperative Complication. JAMA 2017 Jan 3;317(1):77–78. Chung KS, Lee JK, Park JS, Choi CH. Risk factors of delirium in patients undergoing total knee arthroplasty. Arch Gerontol Geriatr. 2015;60:443–7. Scott JE, Mathias JL, Kneebone AC. Incidence of delirium following total joint replacement in older adults: a meta-analysis. Gen Hosp Psychiatry. 2015;37(3):223–9. Robert K. Merrill1& John M. Ibrahim, el at. Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery.Current Reviews in Musculoskeletal Medicine (2020) 13:298–308. Monk TG, Saini V, Weldon BC, et al. Anesthetic management and one-year mortality after noncardiac surgery. Anesth Analg. 2005;100:4. Higuera CA, Elsharkawy K, Klika AK, et al. 2010 Mid-America Orthopaedic Association Physician in Training Award: predictors of early adverse outcomes after knee and hip arthroplasty in geriatric patients. Clin Orthop Relat Res 2011; 469:1391. 21 Eymard F, Parsons C, Edwards MH, Petit-Dop F, Reginster JY, et al. Diabetes is a risk factor for knee osteoarthritis progression. Osteoarthritis Cartilage 2015;23: 851–9. Saucedo JM, Marecek GS, Wanke TR, Lee J, Stulberg SD, Puri L. Understanding Readmission After Primary Total Hip and Knee Arthroplasty: Who’s At Risk? Journal of Arthroplasty. 2014;29(2):256–60. Illingworth KD, El Bitar YF, Banerjee D, Scaife SL, Saleh KJ. Inpatient mortality after primary total hip arthroplasty: analysis from the National Inpatient Sample database. J Arthroplasty. 2015;30:369–73. Seltzer MH, Bastidas JA, Cooper DM, Engler P, Slocum B, Fletcher HS. Instant nutritional assessment. JPEN J Parenter Enteral Nutr. 1979;3:157–9. Anmol Gupta MBA, et al. Serum albumin levels predict which patients are at increased risk for complications following surgical management of acute osteoporotic vertebral compression fractures. The Spine Journal. 2019;19:1796–802. de Mutsert R, Grootendorst D, Indemans F, Boeschoten E, Krediet R, Dekker F. Association between serum albumin and mortality in dialysis patients is partly explained by inflammation, and not by malnutrition. J Ren Nutr. 2009;19:127–35. Mansmann U, Rieger A, Strahwald B, Crispin A. Risk calculators-methods, development, implementation, and validation. Int J Colorectal Dis. 2016;31(6):1111–6. Cite Share Download PDF Status: Published Journal Publication published 26 Apr, 2022 Read the published version in World Journal of Clinical Cases → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-79448","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":3005601,"identity":"53212b03-79ea-4d66-86c2-1cae97d40487","order_by":0,"name":"Xiujuan Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACeWb+zw8SKmzq+9kbiNRi2N5gZvDgTBrjzJ4DxFpz5oCB5MO2Q4wbbiQQqYNxRkKCQWLbAWaDm4833mCosYkmqIVdIuHAg4Rzd9gkb6cVWzAcS8ttIGxLYoNBQtkzHr7bOWYSjA2HCWthuJHMIJHAdliC4eYZYrWcOQbU0nbYQOAGD5FaDNt72AwSzqQlSPYA/ZJAjF/kmXmYH/6osEngZz+88caHGhsiHIYEDCQSSFEO0UKqjlEwCkbBKBgZAABLa0XHVOHfyAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0608-9045","institution":"The First Affiliated Hospital, Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Xiujuan","middleName":"","lastName":"Tan","suffix":""},{"id":3005602,"identity":"6ee1244f-b9b9-4251-bfed-29828c483ffc","order_by":1,"name":"Fengmin Ge","email":"","orcid":"","institution":"The Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fengmin","middleName":"","lastName":"Ge","suffix":""},{"id":3005603,"identity":"9cbe6d31-4fe2-49d4-86b0-3c93136154e6","order_by":2,"name":"Guixi Mo","email":"","orcid":"","institution":"The Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guixi","middleName":"","lastName":"Mo","suffix":""},{"id":3005604,"identity":"7844537d-37bc-473e-a474-546d9fee08f1","order_by":3,"name":"Zhiyi Li","email":"","orcid":"","institution":"The Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyi","middleName":"","lastName":"Li","suffix":""},{"id":3005605,"identity":"33370a68-ffb6-407e-8801-9e1766c2b2c3","order_by":4,"name":"Xiaoxia Gu","email":"","orcid":"","institution":"The Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Gu","suffix":""},{"id":3005606,"identity":"df88959c-d1e8-4860-9306-6a95f2c81023","order_by":5,"name":"Liangqing Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liangqing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2020-09-17 11:21:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-79448/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-79448/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.12998/wjcc.v10.i12.3720","type":"published","date":"2022-04-26T07:44:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2811734,"identity":"7dc86d2d-6233-4ba6-a197-c12f0fff9816","added_by":"auto","created_at":"2020-10-06 18:53:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15740,"visible":true,"origin":"","legend":"Nomogram. The Morbidity and Mortality Acute Predictor (arthro-MAP). The nomogram computes the probability of having a postoperative complication. In order to compute the predicted complication probability, a vertical line is to be drawn from the values of the individual variables to the scale for points on the top. Then a vertical line from the total points to the corresponding predicted complication probability.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-79448/v1/e59ced22de2b8a4e32126abb.png"},{"id":2811735,"identity":"94231ac4-06da-4841-8381-e32bf73613d2","added_by":"auto","created_at":"2020-10-06 18:53:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12619,"visible":true,"origin":"","legend":"ROC curve. It also name sensitivity curve, the AUC (Area under curve) can evaluate the nomogram model discrimination degree. AUC\u003e0.6: may make sence; AUC\u003e0.7: Not bad; AUC\u003e0.8: Excellent.","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-79448/v1/284f891ee81e8cf4f1c802f7.png"},{"id":2811736,"identity":"66121162-17ab-4157-ab21-03c08ad6c6b7","added_by":"auto","created_at":"2020-10-06 18:53:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31458,"visible":true,"origin":"","legend":"Hosmer -Lemeshow test. The null hypothesis is that the fitting probability pi is grouped by 10 decile, and the difference between the fitted value and the observed value in each group, p\u003c0.05 shows that the scatter separation is significantly deviated from the reference line, the predicted value is not equal to the actual value. Otherwise, the test passes, the predicted value is equal to the actual value.","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-79448/v1/a0165fd0772ce30ec9358bec.png"},{"id":2811737,"identity":"cd899017-29e1-4894-ad39-70cdf4784511","added_by":"auto","created_at":"2020-10-06 18:53:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11720,"visible":true,"origin":"","legend":"Calibration plot. A calibration plot compares the model’s predicted probabilities and observed proportions. The diagonal line reflects the ideal situation (predicted probability = observed proportion). The curve represents the relation nonparametrically. The calibration curve is a straight line with a slope close to 1, indicating that this model predicts postoperative complication risk in elderly total hip replacement patients consistent with the actual risk.","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-79448/v1/acad6d6824630f244f0f79ec.png"},{"id":20488356,"identity":"65c066c5-1755-45da-8ffe-50bfde4b677a","added_by":"auto","created_at":"2022-04-19 07:44:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":346588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-79448/v1/200544a7-b6d9-4af7-9b88-8360e7b53145.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eA Nomogram to Predict Postoperative Complications in Elderly with Total Hip Replacement\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eHip replacement is a frequently done and highly successful surgical intervention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. More than one million hip arthroplasties are performed annually worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Kurtz and colleagues [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] projected the demand for primary THA to grow 174% to 572,000 procedures per year by 2030. These procedures carry a complication rate estimated to be between 2% and 14%; higher complication rates are associated with more elderly and comorbid patient populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Numerous clinical tools have been developed to predict a variety of THA patient outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], but fewer to predict complications after surgery and no one is modelling by Chinese. As an anesthetist, when we go to the inpatient ward for preoperative evaluation, patient often ask: what is the proportion of risk in my surgery༟The answer is perhaps or we don\u0026rsquo;t know. So we want to create a tool not only can predict the risk precisely, but also guide the clinical work. Therefore this study will analyze clinical data, explored the independent risk factors for postoperative complications in elderly patients undergoing total hip replacement, develop a nomogram for accurate risk stratification of postoperative complications based on preoperative and intraoperative variables, and verify whether this tool would have good predictive for patients undergoing total hip replacement in our hospital.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eApproved by the Ethics Committee of the Affiliated Hospital of Guangdong Medical University committee (PJ2020-022), we were collected from all patients undergoing total hip replacement from March 1, 2017 to August 31, 2019\u0026nbsp;at the Affiliated Hospital of Guangdong Medical University. Inclusion criteria were age\u0026thinsp;\u0026gt;\u0026thinsp;65y, elective surgical treatment with total hip replacement. Non-inclusion criteria were age\u0026thinsp;\u0026lt;\u0026thinsp;65y, electronic medical records incomplete, patients or family members disagree with the study.\u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eResearch Methods\u003c/h2\u003e\n \u003cp\u003eData on general patient information, comorbidities, laboratory test results, intraoperative variables, and postoperative complications during hospitalization were collected from electronic medical records and electronic Anesthesia Information Management System. Known patient-related factors of complications are age, gender, fractures or not and comorbidity such as renal failure、hypertension、diabetes、coronary heart disease、stroke. Laboratory test results of white blood cell (WBC), red blood cell (RBC), hemoglobin (HB), platelets (PLT), albumin (ALB) and blood urea nitrogen (BUN). Intraoperative variables included estimated blood loss (EBL), lowest heart rate, the type of anesthesia, preoperative ASA score (American Society of Anesthesiologists Score) and operation time.\u003c/p\u003e \u003cp\u003eThe primary outcome measure was the incidence of a postoperative complication or death during hospitalization. Complications were identified from diagnoses in discharge summaries, operative reports, and ICD-9 codes by a single investigator. Included: cardiac arrest, deep venous thrombosis, myocardial infarction, pneumonia, pulmonary embolism, systemic inflammatory response syndrome, infection, dislocation, delirium, according to definitions from the NSQIP [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using the STATA14.0 statistical software package and R software (R3.2.3) with rms package added. Differences between patients with and without complications were compared with t-test or Wilcoxon rank-sum test using the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and the median (range) for quantitative variables and chi-squared test using n (%) for qualitative variables. Univariate logistic regression for patients clinical data were analyzed to identify the independent risk factors for postoperative complications after surgery. A multivariate logistic regression model was built using the factors associated with p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 by univariate analysis. Selection values of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 variables by multivariate analysis to create a nomogram model by R software (R3.2.3) rms package, evaluated the nomogram by assessing discrimination and calibration.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003eIn our study 414 elderly total hip replacement patients of 59 cases with postoperative complications after surgery, the incidence was 14.3%. The demographics and descriptive statistics for our patient cohort are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Multivariable logistic regression of each clinical variable of patients related factors for postoperative complications include patients age(OR\u0026thinsp;=\u0026thinsp;1.05, 95%CI: 1.00\u0026thinsp;~\u0026thinsp;1.09), renal failure(OR\u0026thinsp;=\u0026thinsp;0.90, 95%CI: 0.83\u0026thinsp;~\u0026thinsp;0.97),diabetes(OR\u0026thinsp;=\u0026thinsp;2.37, 95%CI: 1.04\u0026thinsp;~\u0026thinsp;5.40) and ALB(OR\u0026thinsp;=\u0026thinsp;0.91, 95%CI:0.83\u0026thinsp;~\u0026thinsp;0.99) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the above multivariable logistic regression analysis results, choose p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 variable apply R Software to build a nomogram model of postoperative complications in elderly total hip replacement patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing nomogram model to predict postoperative complications in elderly total hip replacement patients risk ROC curve, AUC is 0.8254(95% CI: 0.78ཞ0.87), indicating that the nomogram model has a good bootstrap-corrected concordance(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePerform Hosmer-Lemeshow goodness-of-fit test and evaluate nomogram Model accuracy, Hosmer-Lemeshow fit goodness test x\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.16, P\u0026thinsp;=\u0026thinsp;0.4264(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), calibration in rms package by R software Emax\u0026thinsp;=\u0026thinsp;0.176, Eavg\u0026thinsp;=\u0026thinsp;0.027(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), all shows that the model appears to be well-calibrated, with predicted outcome rates closely reflecting the observed rates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical data between No Complications and Complications.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Complications\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;355)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplications\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et/x\u003csup\u003e2\u003c/sup\u003e/Z Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.06\u0026thinsp;\u0026plusmn;\u0026thinsp;7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.29\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale gender (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149(41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.93\u0026thinsp;\u0026plusmn;\u0026thinsp;10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal failure (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFractures (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123((34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (10\u003csup\u003e12\u003c/sup\u003e/L) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB(g/L) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.37\u0026thinsp;\u0026plusmn;\u0026thinsp;20.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.89\u0026thinsp;\u0026plusmn;\u0026thinsp;22.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235.66\u0026thinsp;\u0026plusmn;\u0026thinsp;71.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247.91\u0026thinsp;\u0026plusmn;\u0026thinsp;115.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB(g/L) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.37\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBL (ml)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203.79(50-1500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229.8(50-1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest heart rate(bpm)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.39\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.37\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation time(min)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.85\u0026thinsp;\u0026plusmn;\u0026thinsp;40.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.41\u0026thinsp;\u0026plusmn;\u0026thinsp;37.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Anesthesia (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159(44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA Class Ⅲ or Ⅳ (%) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159(44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e518(86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePreoperative patient characteristics for 414 total hip replacement procedures between March 1, 2017 to August 31, 2019. Results are presented as number (percentage) of patients, or as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The P values were obtained from chi-square tests, t-tests, or Wilcoxon rank-sum tests, as indicated (a t-test; b chi-square test; c Wilcoxon rank-sum test).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026thinsp;~\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u0026thinsp;~\u0026thinsp;13.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u0026thinsp;~\u0026thinsp;2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u0026thinsp;~\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\u0026thinsp;~\u0026thinsp;5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u0026thinsp;~\u0026thinsp;6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFractures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u0026thinsp;~\u0026thinsp;5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA Class Ⅲ or Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u0026thinsp;~\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest heart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u0026thinsp;~\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u0026thinsp;~\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003cp\u003eHB\u003c/p\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003cp\u003e0.10\u003c/p\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u0026thinsp;~\u0026thinsp;1.16\u003c/p\u003e \u003cp\u003e0.98\u0026thinsp;~\u0026thinsp;1.02\u003c/p\u003e \u003cp\u003e0.83\u0026thinsp;~\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003cp\u003e0.922\u003c/p\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u0026thinsp;~\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eChina is the most populated country in the world, and now has the second-largest economy in the world [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As nearly 166\u0026nbsp;million Chinese are aged more than 65\u0026nbsp;years [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The demand for healthcare, including hip arthroplasty is increasing [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There are several predict complications model have be reported [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but on one is modeling by Chinese. In this study, 59 of the 414 elderly total hip replacement patients who underwent surgical treatment were developed postoperative complication, the incidence is 14.3%, morbidity is much higher than 3.9% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Probably because our definition of complications not only included dislocation, pulmonary embolism, and infection as reported previously, but included systemic inflammatory response syndrome, delirium which are common in elderly. Among 566 older patients (mean age, 76.7\u0026nbsp;years) undergoing a variety of elective operations (including orthopedic, general, and vascular), 23.9% patients developed postoperative delirium [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The incidence of postoperative delirium was reported as 7.0%-30.2% in hip arthroplasty [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study the average age is 75.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8. As a result, the morbidity 14.3% is considered to be reasonable.\u003c/p\u003e \u003cp\u003eAge is a recognized risk factor for postoperative complications. The results of this study indicate that elderly patients with renal failure and diabetes are more likely to have postoperative complications after total hip replacement. It is Consistent with the research results of Robert K [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] et al. One possible reason is that elderly patients have more comorbidities will make them less able to withstand the stresses of anesthesia and surgery [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Diabetes have been reported to be significant predictors for complications such as surgical site infections [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The available data suggest that diabetes may promote the development of osteoarthritis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our results for the outcome measure indicate that elderly patients with renal failure and diabetes increase the weight of the nomogram model score by 25 points and 24 points, respectively.\u003c/p\u003e \u003cp\u003eInterestingly, our data suggest that low preoperative albumin levels can predict the incidence of postoperative complications following surgery for total hip replacement. Since ALB is a biomarker of visceral protein and immune-competence status, it is commonly used for nutritional assessment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Preoperative albumin bears strong potential as a practical metric to assess a patient\u0026rsquo;s overall health [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Recent studies even show that low ALB rather reflects a state of persistent inflammation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our results pointed out that the weight of 12.7 points in the nomogram model score will be increased for every 5\u0026nbsp;g/L decrease in ALB. We recommend surgeons and anesthetists should ideally attempt to optimize patient nutritional status before total hip replacement in elderly in order to avoid a greater likelihood of postoperative complications or mortality.\u003c/p\u003e \u003cp\u003eHowever our study has several limitations. First, our data were limited only 414 patients, it only represents an elective patient population. Second, complications were only collected while in hospital, some of these complications could have occurred after discharge. Third, the predict model quality checks only with internal validation, so external validation will have to be included in future studies in order to promote use. Fourth, this was a retrospective study that relied on 9th edition coding, which can lead to errors and/or incomplete coding.\u003c/p\u003e \u003cp\u003eRisk calculators should serve as a tool to help clinical decision-making, promote individualized medicine, and aid in the shared decision-making process [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Many of the studies report poor discrimination and calibration of the investigated risk calculators. In our study, founded that age, diabetes, renal failure, and Albumin value are independent risk factors for postoperative complications in elderly patients with total hip replacement, ROC curve shows the AUC is 0.8254, indicating that the nomogram model has a good discrimination. The Hosmer-Lemeshow fit goodness test x\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.16, P\u0026thinsp;=\u0026thinsp;0.4264 and calibration curve is a straight line with a slope close to 1, indicating that the nomogram model has good accuracy in predicting the risk of postoperative complications in elderly patients with total hip replacements surgery and has clinical application value.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThis study created a nomogram model based on age, diabetes, renal failure, and Albumin value independent risk factors for postoperative complications, has g good indexing and accuracy can provide scientific guidance for individualized clinical prevention and treatment of postoperative complications in elderly patients with total hip replacements surgery in our hospital. This four variables are easy to get in clinical practice, has clinical application value especially for Basic-level hospital.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eASA: American Society of Anesthesiologists; THA: Total hip arthroplasty; NSQIP: National Surgical Quality Improvement Program; SD: Standard error; ROC: Receiver operating characteristic curve; AUC: Area under curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of the Affiliated Hospital of Guangdong Medical University with the reference number PJ2020-022. All participants were informed and asked for written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study will be available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo-funding. XJ T and XX G are designer of the study, collecting data, statistical analysis, interpretation of results and writing the manuscript together.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXJ T is first Author, participated in protocol writing, collecting data, statistical analysis, interpretation of results and manuscript writing. FM G helped collection of cases. ZY L participated in protocol writing, essay writing. XX G helped interpretation of results and manuscript writing. GX M and LQ Z did the statistical analysis and reviewed the manuscript. All authors have read and approved the final submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. JY W of the Affiliated Hospital of Guangdong Medical University Department of Scientific Research for his excellent assistance in the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXX G and LQ Z is Co-Corresponding author.\u003c/p\u003e\n\u003cp\u003e1, Department of Anesthesiology, the First Affiliated Hospital, Jinan University, No.601 West Huangpu Avenue, Tianhe District Guangzhou City 510632, Guangdong Province, China.\u003c/p\u003e\n\u003cp\u003e2, Department of Anesthesiology, the Affiliated Hospital of Guangdong Medical University, No.57 South People\u0026rsquo;s Avenue, Xiashan District, Zhanjiang City 524001, Guangdong Province, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eFerguson RJ, Palmer AJ, Taylor A, et al. Hip replacement Lancet. 2018;392(10158):1662\u0026ndash;71.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003ePivec R, Johnson AJ, Mears SC, Mont MA. Hip arthroplasty. Lancet. 2012;380:1768\u0026ndash;77.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKurtz S, Ong K, Lau E, et al. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am. 2007;89:780\u0026ndash;5.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNanjayan SK, Swamy GN, Yellu S, Yallappa S, Abuzakuk T, Straw R. In-hospital complications following primary total hip and knee arthroplasty in octogenarian and nonagenarian patients. J Orthop Traumatol. 2014;15(1):29\u0026ndash;33.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eJoseph F, Konopka MD. MS, et al. Risk Assessment Tools Used to Predict Outcomes of Total Hip and T otal Knee Arthroplasty. Orthop Clin N Am. 2015;46:351\u0026ndash;62.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKhuri SF, Daley J, Henderson W, et al. The National Veterans Administration Surgical Risk Study: risk adjustment for the comparative assessment of the quality of surgical care. J Am Coll Surg. 1995;180:519.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNo authors listed. Open Knowledge Repository (OKR). World Bank Group. World Development Indicators 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openknowledge.worldbank.org/han-dle/10986/26447\u003c/span\u003e\u003c/span\u003e (date last accessed 8 July 2019).\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNo authors listed. National Bureau of Statistics of China. China Statistics Press. China Statistical Yearbook 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.stats.gov.cn/tjsj/ndsj/2019/indexeh.htm)\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWang K. Brief discussion on present status and future of joint replacement in China. Chin J Joint Surg (Electronic Edition). 2015;9:12\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cnki.com.cn/Article/CJFDTotal-ZHGJ201506004.htm\u003c/span\u003e\u003c/span\u003e. (date last accessed 8 August 2019) (In Chinese).\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003ePei FX. The current status and future perspective of hip and knee arthroplasty in China. Chinese Journal of Bone Joint. 2012;1:4\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cnki.com.cn/Article/CJFDTotal-GZGL201201019.htm\u003c/span\u003e\u003c/span\u003e. (date last accessed 8 August 2019) (In Chinese).\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWuerz TH, Kent DM, Malchau H, et al. A nomogram to predict major complications after hip and knee arthroplasty. J Arthroplasty. 2014;29:1457\u0026ndash;62.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWuerz TH, Regenbogen SE, Ehrenfeld JM, et al. The Surgical Apgar Score in hip and knee arthroplasty. Clin Orthop Relat Res. 2011;469:1119\u0026ndash;26.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eInneh IA, Lewis CG, Schutzer SF. Focused risk analysis: regression model based on 5,314 total hip andknee arthroplasty patients from a single institution. J Arthroplasty. 2014;29(10):2031\u0026ndash;5.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003ePhillips CB, Barrett JA, Losina E, et al. Incidence rates of dislocation, pulmonary embolism, and deep infection during the first six months after elective total hip replacement. J Bone Joint Surg Am. 2003;85-A:20.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZenilman ME. MD. Delirium An Important Postoperative Complication. JAMA 2017 Jan 3;317(1):77\u0026ndash;78.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChung KS, Lee JK, Park JS, Choi CH. Risk factors of delirium in patients undergoing total knee arthroplasty. Arch Gerontol Geriatr. 2015;60:443\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eScott JE, Mathias JL, Kneebone AC. Incidence of delirium following total joint replacement in older adults: a meta-analysis. Gen Hosp Psychiatry. 2015;37(3):223\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRobert K. Merrill1\u0026amp; John M. Ibrahim, el at. Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery.Current Reviews in Musculoskeletal Medicine (2020) 13:298\u0026ndash;308.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMonk TG, Saini V, Weldon BC, et al. Anesthetic management and one-year mortality after noncardiac surgery. Anesth Analg. 2005;100:4.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eHiguera CA, Elsharkawy K, Klika AK, et al. 2010 Mid-America Orthopaedic Association Physician in Training Award: predictors of early adverse outcomes after knee and hip arthroplasty in geriatric patients. Clin Orthop Relat Res 2011; 469:1391. 21 Eymard F, Parsons C, Edwards MH, Petit-Dop F, Reginster JY, et al. Diabetes is a risk factor for knee osteoarthritis progression. Osteoarthritis Cartilage 2015;23: 851\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSaucedo JM, Marecek GS, Wanke TR, Lee J, Stulberg SD, Puri L. Understanding Readmission After Primary Total Hip and Knee Arthroplasty: Who\u0026rsquo;s At Risk? Journal of Arthroplasty. 2014;29(2):256\u0026ndash;60.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eIllingworth KD, El Bitar YF, Banerjee D, Scaife SL, Saleh KJ. Inpatient mortality after primary total hip arthroplasty: analysis from the National Inpatient Sample database. J Arthroplasty. 2015;30:369\u0026ndash;73.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSeltzer MH, Bastidas JA, Cooper DM, Engler P, Slocum B, Fletcher HS. Instant nutritional assessment. JPEN J Parenter Enteral Nutr. 1979;3:157\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAnmol Gupta MBA, et al. Serum albumin levels predict which patients are at increased risk for complications following surgical management of acute osteoporotic vertebral compression fractures. The Spine Journal. 2019;19:1796\u0026ndash;802.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003ede Mutsert R, Grootendorst D, Indemans F, Boeschoten E, Krediet R, Dekker F. Association between serum albumin and mortality in dialysis patients is partly explained by inflammation, and not by malnutrition. J Ren Nutr. 2009;19:127\u0026ndash;35.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMansmann U, Rieger A, Strahwald B, Crispin A. Risk calculators-methods, development, implementation, and validation. Int J Colorectal Dis. 2016;31(6):1111\u0026ndash;6.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"elderly, total hip replacement, postoperative complication, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-79448/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-79448/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: By analyzing the risk factors of postoperative complications in elderly patients with hip replacement, We aimed to develop a nomogram model based on preoperative and intraoperative variables and verified the sensitivity and specificity for risk stratification of postoperative complications in elderly with total hip replacement patients.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 414 elderly patients who underwent surgical treatment for total hip replacement hospitalized at the Affiliated Hospital of Guangdong Medical University from March 1, 2017 to August 31, 2019 were included into this study. Univariate and multivariate logistic regression were conducted to identify independent risk factors of postoperative complication in the 414 patients. A nomogram was developed by R software and validated to predict the risk of postoperative complications.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Multivariate logistic regression analysis revealed that age (OR=1.05, 95%CI: 1.00-1.09) , renal failure(OR=0.90, 95% CI: 0.83～0.97) , Type2 diabetes (OR=1.05, 95% CI: 1.00～1.09) , ALB (OR=0.91, 95% CI: 0.83～0.99) were independent risk factors of postoperative complication in elderly patients with hip replacement (P＜0.05) . For validation of the nomogram, ROC curve revealed that the model predicting postoperative complication in elderly patients with hip replacement was the area under the curve of 0.8254(95% CI: 0.78～0.87) , the slope of the calibration plot was close to 1 and the model passed Hosmer-Lemeshow goodness of fit test (x\u003csup\u003e2\u003c/sup\u003e = 10.16, P=0.4264), calibration in R Emax=0.176, Eavg=0.027, which all demonstrated that the model was of good accuracy. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The nomogram predicting postoperative complications in patients with total hip replacement constructed based on age, type 2 diabetes, renal failure and ALB is of good discrimination and accuracy, which was of clinical significance.\u003c/p\u003e","manuscriptTitle":"A Nomogram to Predict Postoperative Complications in Elderly with Total Hip Replacement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-06 18:53:13","doi":"10.21203/rs.3.rs-79448/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d22e94b-177d-437f-8de0-ce4225ec65e9","owner":[],"postedDate":"October 6th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":704836,"name":"Anesthesiology \u0026 Pain Medicine"}],"tags":[],"updatedAt":"2022-04-19T07:44:25+00:00","versionOfRecord":{"articleIdentity":"rs-79448","link":"https://doi.org/10.12998/wjcc.v10.i12.3720","journal":{"identity":"world-journal-of-clinical-cases","isVorOnly":true,"title":"World Journal of Clinical Cases"},"publishedOn":"2022-04-26 07:44:25","publishedOnDateReadable":"April 26th, 2022"},"versionCreatedAt":"2020-10-06 18:53:13","video":"","vorDoi":"10.12998/wjcc.v10.i12.3720","vorDoiUrl":"https://doi.org/10.12998/wjcc.v10.i12.3720","workflowStages":[]},"version":"v1","identity":"rs-79448","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-79448","identity":"rs-79448","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}