Analysis of Influencing Factors and Construction of Prediction Models of Artificial Joint Replacement in China
Conditions: Knee Arthropathy, Hip Arthropathy, Shoulder Arthropathy, Ankle Arthropathy, Elbow Arthropathy
Sponsor: Xiangya Hospital of Central South University

Official title: Analysis of influencing factors and construction of prediction models of 
artificial joint replacement in China 
 
 
NCT number: Not yet assigned. 
 
 
Date of the document: December 27, 2017. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Introduction 
Joint arthroplasty (JA) is a safe and cost-effective musculoskeletal surgical procedure 
for relieving pain and improving function and health-related quality of life for patients 
with end-stage joint disease. As one of the most commonly performed procedures, more 
than 1.5 million knee arthroplasties (KA)1, more than 1 million hip arthroplasties (HA)2 
and over 100 thousand shoulder arthroplasties (SA)3 were performed annually 
worldwide. In addition, more than 10 thousand elbow arthroplasties (EA) in Germany 
and has been rapidly increasing4. Moreover, the incidence of ankle osteoarthritis has 
been estimated to be 47.7/100,000, affecting approximately 1% of the world 
population5. The increased demands of JA pose a challenge for the health care system 
from a quality and finance perspective. 
 
 
China is the most populated country in the world, and now has the second-largest 
economy in the world. As nearly 420 million Chinese are aged more than 50 years6, and 
the living standards and health system continue to improve, the demand for healthcare, 
including JA, is increasing7. However, the consequences of increased access to new 
medical technologies and their application to patient care is unknown. Description of 
epidemiological characteristics, risk factors, complications and resource utilization of 
JA8, can provide valuable information for healthcare planners in China and similar 
countries, and can guide care providers to develop effective and targeted preventive 
measures to minimize mortality from this procedure. 
 
 
Therefore, in this study, investigators will use data from a mandatory patient-level 

national database, i.e., the Hospital Quality Monitoring System (HQMS), to study the 
epidemiological characteristics, risk factors, complications and resource utilization of 
JA (i.e., KA, HA, SA, EA and ankle arthroplasty [AA]). 
 
Methods 
Data source 
This nationwide retrospective study will be a review of the HQMS database. The 
HQMS database is a mandatory electronic inpatient database system developed by the 
National Health Commission of the People’s Republic of China. Since 2013, tertiary 
hospitals have been required to upload their inpatient discharge records. By 2019, the 
HQMS database has included about 230 million standardized inpatient discharge 
records of more than 1,000 hospitals across all 31 provincial-level administrative 
regions in mainland China. Patient demographics, clinical diagnosis, procedures and 
operations, costs and complications are all recorded in the HQMS database. The 
diagnosis is coded in the form of International Classification of Diseases, Tenth 
Revision (ICD-10), and the procedures and operations are coded in accordance with 
International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-
9-CM). A more detailed description of this database can be found in previous studies 
7,9,10. This study was authorized by the HQMS Committee Board and approved by the 
institutional review board, with waiver of informed consent. 
 
Study population 

Patients who were recorded in HQMS and underwent KA, HA, SA, EA and AA will 
be included in our study. Investigators will identify KA, HA, SA, EA and AA in 
HQMS based on the ICD-9-CM. To minimize the possibility of miscoding, other 
knee-related procedure, diagnosis codes and free texts will be also identified. 
Investigators will exclude: 1. Patients who didn’t undergo these five types of JA; 2. 
Patients with missing demographics or medical data. 
 
Study variables 
Patient demographics (e.g., age, sex, marital status and comorbidities), clinical 
diagnosis, procedures and operations, costs, complications and hospital characteristics 
will be all extracted from the HQMS database. Medical insurance, including Urban 
Employee-Based Medical Insurance (UEBMI), Urban Resident-Based Medical 
Insurance (URBMI) and New Cooperative Medical Scheme (NCMS), self-pay and 
others (e.g., commercial insurance) will be identified in our study. Preexisting 
comorbidities will be extracted11,12. The Charlson Comorbidity Index (CCI) will be 
calculate13 and categorized7. Investigators will also extract the indication for KA. 
Hospital characteristics, such as region, volume of surgery will be extracted14.  
 
Measurement of outcomes 
Five types of JA of our interest will be identified in HQMS, including KA, HA, SA, 
EA and AA. Epidemiological characteristics, risk factors, complications and resource 
utilization of these five types of JA will be analyzed. 

Statistical analysis 
Line charts will be drawn to describe annual change in trends of JA. Basic 
information in different groups will be compared. For continuous variables, Mann-
Whitney U test will be used for non-normally variables and Student t-test will be used 
for normally variables, while chi-square test or Fisher’s exact test will be used for 
categorical data. Then, multivariable-adjusted logistic regression will be performed to 
examine the risk factors of complications following JA (e.g., year of surgery, patient 
demographics and region of hospitals) with the calculation of odds ratios (ORs) and 
95% confidence interval (CI). To minimize the effect of potential confounders, the 
propensity-score matching or multivariable-adjusted regression will be performed to 
compare the incidence of complications and resource utilization in different groups15.  
 
SAS 9.4 (SAS Institute Inc., Cary, NC, USA) will be used in all statistical 
analyses and P <0.05 will be considered as significant.  
 
 
 
 
 
 
 
 

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