To optimize delivery of health care services in clinical practice, the use of unnecessary interventions should be reduced. Although recommendations for this reduction have been accepted worldwide, recent studies have revealed that the use of such procedures continues to increase. We conducted a retrospective cohort study using a nationwide claim-based database to evaluate factors influencing preoperative blood testing prior to low-risk surgery, via a Bayesian generalized linear mixed approach. The study period was set from April 1, 2012, to March 31, 2016, and 69,252 surgeries performed at 9,922 institutions were included in the analysis. Mean patient age was 44.3 ± 11.3 years (57% female). Preoperative blood tests were performed for 59.0% of procedures. Among institutional factors, the number of beds was strongly associated with preoperative blood testing (odds ratio [95% highest posterior density interval (HPD interval)], 2.64 [2.53 to 2.75]). The difference (95% credible interval) in the rate of preoperative blood testing between institutions with <100 beds and ≥100 beds was 0.315 [0.309 to 0.322], and the Bayesian index
To optimize the delivery of health care services in clinical practice, the use of unnecessary and/or non-evidence-based tests, treatments, and procedures should be reduced [
Although these campaigns and recommendations have been accepted worldwide, recent studies have revealed that the use of unnecessary tests continues to increase [
We conducted a retrospective cohort study using an insurance claim-based database covering approximately five million insured individuals in Japan since 2005. Individuals who had undergone low-risk surgery were included in the analysis. Factors influencing the use of preoperative blood tests were explored, following which institutions were divided into two groups based on the number of beds. The differences between the two groups and the probability of hypothesis truth were then evaluated.
The insurance claim-based database was provided by the Japan Medical Data Center Co., Ltd. (JMDC; Tokyo, Japan), and the study period was set from April 1, 2012, to March 31, 2016. The database included the following information: sex, age, medical and pharmacy claim data (outpatient as well as inpatient data), clinical diagnostic codes (International Classification of Disease 10th revision [ICD-10]), drug prescription information codes (World Health Organization Anatomical Therapeutic Chemical classification codes [ATC codes]), and standardized procedure codes (Japan-specific standardized procedure codes [K codes]).
Low-risk surgery was defined according to K codes for ophthalmologic, superficial, breast, thyroid, minor gynecological, orthopedic [arthroscopy], and minor urological procedures, based on the findings of previous studies [
The primary outcome measure of the present study was the presence or absence of preoperative blood tests prior to low-risk surgery. We used a Bayesian generalized linear mixed approach to estimate the coefficients of each variable (patient variables and institutional factors) for preoperative blood tests. Preoperative tests were defined as those ordered within 60 days of the index procedure [
Continuous variables are summarized as mean and standard deviations (SD), while categorical variables are summarized as frequencies and proportions (%).
In the present study, we evaluated differences in preoperative testing among institutions using a Bayesian generalized linear mixed model [
In this model,
We calculated the number of preoperative blood tests and compared the rate of testing between institutions with <100 beds and ≥100 beds. We calculated the 95% credible interval (CrI) of the difference in the rate of preoperative blood testing using an exact method. We then compared rates of preoperative blood testing between the two institution groups using the Bayesian index proposed by Kawasaki and Miyaoka [
In the present study, we set
All statistical analyses were performed using SAS version 9.4 for Windows (SAS Institute Inc., Cary, NC, USA). When performing the analysis using the Bayesian generalized linear mixed model, we used the MCMC procedure of SAS. A general SAS code for this analysis is included in Appendix
The present study was approved by the Ethics Committee of Kyoto University Graduate School and Faculty of Medicine (number R0800, September 8, 2016). This study was conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan [
The flow diagram for the present study is shown in Figure
Patient characteristics.
Characteristics | |
---|---|
|
69,252 |
Age, yr | |
<25 | 3,070 (4.4) |
25–34 | 13,172 (19.0) |
35–44 | 19,816 (28.6) |
45–54 | 18,281 (26.4) |
55–64 | 14,913 (21.5) |
Sex, female | 39,489 (57.0) |
CCI score | |
0-1 | 57,106 (82.5) |
2 | 6,742 (9.7) |
≥3 | 5,404 (7.8) |
Medication | |
Antiplatelet | 1,759 (2.5) |
Anticoagulant | 330 (0.5) |
ACEI/ARB | 5,399 (7.8) |
Diuretics | 1,075 (1.6) |
Chemotherapy | 792 (1.1) |
Type of anesthesia | |
General anesthesia | 8,824 (12.7) |
Regional anesthesia | 7,291 (10.5) |
Sedation | 7,802 (11.3) |
Local anesthesia | 38,874 (56.1) |
Unknown | 6,461 (9.3) |
Surgical setting | |
Inpatient | 21,496 (31.0) |
Outpatient | 47,756 (69.0) |
Teaching hospital | 5,372 (7.8) |
Number of beds | |
<100 | 41,157 (59.4) |
≥100 | 28,095 (40.6) |
ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-receptor blocker; CCI, Charlson comorbidity index.
Study flow diagram.
During the study period, 3,543,575 individuals from 65,371 institutions were included in the JMDC database. Among them, 70,244 individuals had undergone a total of 87,858 low-risk surgical procedures. Following the exclusion of 18,606 cases that met exclusion criteria, 69,252 surgeries performed at 9,922 institutions were included in the final analysis. Mean age (SD) in this cohort was 44.3 years (11.3 years), and 57.0% of patients were female. Local anesthesia was most frequently performed. Inpatient procedures accounted for 31.0% of surgical cases, and 40.6% of institutions had ≥100 beds.
Preoperative blood tests were performed for 59.0% of procedures. The prevalence of each preoperative blood test was as follows: CBC, 57.8%; basic metabolic panel, 49.6%; LFTs, 48.0%; coagulation test, 35.6%. The odds ratio of each variable (patient variables and institutional factors) with 95% HPD intervals for preoperative blood testing is presented in Table
Odds ratio for patient and institutional factors.
Variables | OR [95% HPD interval] |
---|---|
|
|
Age | 1.09 [1.07 to 1.11] |
Sex (female) | 1.03 [0.99 to 1.07] |
CCI | 1.84 [1.77 to 1.92] |
Antiplatelet | 1.40 [1.23 to 1.63] |
Anticoagulant | 3.57 [2.22 to 5.61] |
ACEI/ARB | 1.53 [1.42 to 1.66] |
Diuretics | 1.40 [1.15 to 1.69] |
Chemotherapy | 1.55 [1.16 to 2.09] |
Type of anesthesia | |
General anesthesia | 5.42 [4.85 to 6.03] |
Regional anesthesia | 3.14 [2.89 to 3.44] |
Sedation | 2.19 [2.05 to 2.34] |
Unknown | 0.63 [0.58 to 0.66] |
Ophthalmologic procedure | 1.58 [1.47 to 1.69] |
Outpatient | 0.37 [0.35 to 0.39] |
|
|
|
|
Hospital with ≥100 beds | 2.64 [2.53 to 2.75] |
Teaching hospital | 0.71 [0.66 to 0.77] |
Number of operations | 1.15 [1.13 to 1.17] |
CEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-receptor blocker; CCI, Charlson comorbidity index; HPD, highest posterior density; OR; odds ratio.
The posterior beta distribution parameters were
Differences in the rate of preoperative blood testing based on institution size.
|
Preoperative blood test | Total | |
---|---|---|---|
|
+ | ||
Number of beds |
|||
<100 | 22164 (53.9) | 18993 (46.1) | 41157 |
≥100 | 6266 (22.3) | 21829 (77.7) | 28095 |
|
|||
Difference (95% CrI) |
| ||
|
|||
0.315 [0.309 to 0.322] | 1.00 |
CrI, credible interval;
The present study aimed to evaluate factors influencing preoperative blood testing prior to low-risk surgery among individuals in a nationwide claim-based database using Bayesian approaches. Our results indicated that the rate of preoperative blood testing is strongly influenced by institutional factors, such as institution size. Furthermore, our results suggested that patient factors were also associated with preoperative blood tests. However, the influence of institutional factors remained after adjusting for these variables, indicating that modification of practices at the institutional level is necessary to reduce unnecessary preoperative blood testing.
In this study, we utilized a nationwide claim-based database covering 4.7 million insured individuals treated at more than 9,000 institutions in Japan [
The 95% CrI for differences between institutions with <100 beds and ≥100 beds was very narrow (approximately 1%). The use of Bayesian approaches for the calculation of the CrI is advantageous in that the true parameter is contained within the interval [
The present study possesses several limitations of note. The main limitation of this study was the use of a database with limited information, as the database did not include information regarding symptoms or physical examination results. In addition, the number of operations could be only analyzed as quantile-categories. Therefore, we were unable to strictly evaluate the suitability of preoperative blood tests for each patient in the present study. In addition, the database contained information from a limited population of participants. Because the database contained only insurance claim-based data accumulated from medium-to-large scale companies in Japan, it only included data for employees under the age of 75 and their families. Therefore, we were unable to examine the influence of patient age on preoperative blood testing. Indeed, previous studies have reported that advanced age is a risk factor for perioperative events and complications, even in low-risk surgeries [
In conclusion, our findings indicate that preoperative blood testing prior to low-risk surgery is influenced by institutional factors, such as institution size, suggesting that Bayesian approaches can be used to develop guidelines aimed at reducing excessive preoperative testing. Future studies should investigate the influence of additional patient characteristics (e.g., age) in a more varied population in order to establish the most appropriate guidelines.
Koji Kawakami received honoraria from Astellas, Taisho Pharmaceutical, AbbVie, Eisai, Mitsubishi Tanabe Pharma, Takeda Pharmaceutical Company Limited, Sanofi K. K., and consulting fees from Olympus, Kyowa Hakko Kirin, Kaken Pharmaceutical, and Otsuka Pharmaceuticals. There are no patents, products under development, or marketed products relevant to these companies to declare. These companies had no role in the study design, collection, analysis, or interpretation of the data; writing of the manuscript; or the decision to submit the paper for publication. The other authors declare no conflicts of interest.
Kazuki Ide, Hiroshi Yonekura, and Yohei Kawasaki had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design were performed by Kazuki Ide and Yohei Kawasaki. Acquisition of data was performed by Hiroshi Yonekura and Koji Kawakami. Statistical analysis and interpretation of data were performed by Kazuki Ide, Yohei Kawasaki, and Hiroshi Yonekura. Drafting and revising of the manuscript were performed by Kazuki Ide and Yohei Kawasaki. All authors have read and approved the final manuscript.
The authors would like to acknowledge the staff members at Japan Medical Data Center Co., Ltd., Tokyo, Japan, for assistance with data preparation.
Appendix 1. MCMC trace and MCMC autocorrelation function plots. Appendix 2. SAS code for analysis. Appendix 3. Univariate analysis for patient and institutional factors.