Influence of pharmacists and infection control teams or antimicrobial stewardship teams on the safety and efficacy of vancomycin: A Japanese administrative claims database study.

INTRODUCTION: Methicillin-resistant Staphylococcus aureus (MRSA) has a high mortality and requires effective treatment with anti-MRSA agents such as vancomycin (VCM). Management of the efficacy and safety of VCM has been implemented with the assignment of pharmacists in hospital wards and the establishment of teams related to infectious diseases. However, there are no reports evaluating the association between these factors and the efficacy and safety of VCM in large populations.
METHODS: This study used the Japanese administrative claims database accumulated from 2010 to 2019. The population was divided into two groups, therapeutic drug monitoring (TDM) group and non-TDM group, and adjusted by propensity score matching. We performed multivariate logistic regression analysis to determine the influence of pharmacists and infection control teams or antimicrobial stewardship teams on acute kidney injury (AKI) and 30-day mortality.
RESULTS: The total number of patients was 73 478 (TDM group, n = 55 269; non-TDM group, n = 18 209). After propensity score matching, 18 196 patients were matched in each group. Multivariate logistic regression analysis showed that pharmacological management for each patient contributed to the reduction of AKI (odds ratio [OR]: 0.812, 95% confidence interval [CI]: 0.723‒0.912) and 30-day mortality (OR: 0.538, 95% CI: 0.503‒0.575). However, the establishment of infectious disease associated team in facilities and the assignment of pharmacists in the hospital wards had no effect on AKI and 30-day mortality. In addition, TDM did not affect the reduction in AKI (OR: 1.061, 95% CI: 0.948‒1.187), but reduced 30-day mortality (OR: 0.873, 95% CI: 0.821‒0.929).
CONCLUSION: Pharmacologic management for individual patients, rather than assignment systems at facilities, is effective to reduce AKI and 30-day mortality with VCM administration.

Introduction

It is estimated that more than 10 million people worldwide will die from infections caused by drug-resistant bacteria by 2050 [1]. Among them, methicillin-resistant Staphylococcus aureus (MRSA) is the most frequent antibiotic-resistant bacterium isolated in hospitals [2]. In a Japanese report, MRSA accounted for 24.6% of deaths due to Staphylococcus aureus bacteremia [3]. Therefore, it is necessary to promote the appropriate treatment for MRSA to reduce the mortality.

We previously studied the trends of anti-MRSA agents utilized in Japan using sales data and clarified that vancomycin (VCM) accounted for 50% of the total usage from 2006 to 2015 [4]. It is important to enhance the efficacy and safety of VCM because it is administered in higher proportions than other anti-MRSA agents. VCM has acute kidney injury (AKI) as a side effect [5], and AKI has been reported to be associated with increased mortality [6]. Accordingly, the guidelines for implementation of antimicrobial stewardship programs published by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America recommend pharmacokinetic monitoring and adjustment programs for VCM in hospitals to avoid the AKI [7]. Therefore, therapeutic drug monitoring (TDM) is recommended to measure the drug concentration in the blood and analyze dosing plans for each patient [8].

In Japan, the assignment of pharmacists to hospital wards and the establishment of infection control teams (ICT) and antimicrobial stewardship teams (AST) are being promoted to manage the efficacy and safety of drugs including VCM. A previous single institutional study reported that pharmacist intervention reduced AKI or mortality in patients treated with VCM [9]. However, there is a lack of large-scale reporting on pharmacist interventions. In addition, the influence of ICT and AST interventions on AKI and mortality with VCM has not yet been clarified.

These issues can be clarified using information on the progress of drug treatment and reimbursement (S1 Table). In Japan, reimbursement is established for the medical system and prescribing behavior due to the public insurance system [10]. The claims database, which comprises real-world data, includes information obtained from multiple institutions over a long period of time [11]. Therefore, it is possible to evaluate the outcomes of various interventions for a large population. Nevertheless, a study using this approach has not been reported previously. In this study, we examined the factors affecting the efficacy and safety of VCM, while considering the interventions of pharmacists, ICTs and ASTs.

Methods

Study design

This retrospective study was conducted using the Japanese administrative claims data obtained from the Medical Data Vision database between 2010 and 2019. This database consists of information from hospitals where the diagnosis procedure combination (DPC) system, mainly for acute inpatient care, has been introduced. As of 2019, it included the data from approximately 22% of all the hospitals in Japan that implement DPC systems. This database has been used previously for various studies in Japan [12,13].

In this study, the following four types of data files were analyzed: patient data containing information on the facility where the patient was treated, disease data comprising diagnostic information, medical practice data including information on treatments and procedures, and discharge summary data constituting information on injuries and sickness and various scores at the time of admission and discharge.

Study population

The patients were classified into two groups depending on TDM implementation (TDM and non-TDM groups). Considering TDM is generally performed in hospitalized patients, only these patients who received VCM injections were included in this study. We identified the first time each patient was hospitalized for at least three consecutive days while VCM treatment was initiated between 2010 and 2019. Patients were excluded if any of the required data, such as weight or discharge information, were missing or if AKI was recorded in the month prior to VCM initiation.

The treatment and management fee for specific drugs is calculated in cases where drugs that are recommended for TDM, such as glycopeptides including VCM, some immunosuppressants, or antiepileptic drugs, are administered. Therefore, when several drugs requiring TDM were administered, it was impossible to identify the fee for which drug. Patients who received drugs requiring TDM other than VCM within 7 days of the index date (the date of initiation of VCM treatment) were excluded (S2 Table).

Definitions and clinical variables

Based on the number of beds, facilities were categorized into the following groups: ≤ 199, 200–499, and ≥ 500. Comorbidity was assessed using the Charlson Comorbidity Index (CCI). We identified claims for reimbursement related to infections and drug treatment (S1 Table). Information on TDM implementation is not included in the claims database. Therefore, similar to the method applied in another study [14], we substituted it with the treatment and management fee for specific drugs, calculated once a month when TDM was conducted for the target drug. As the previous study described, nephrotoxic drugs were classified into the following groups: angiotensin Ⅱ receptor blockers/angiotensin-converting enzyme inhibitors, diuretics, liposomal amphotericin B, nonsteroidal anti-inflammatory drugs, steroids, and piperacillin/tazobactam [15], and were extracted when administered on the same day as that of VCM. The site of infection was classified based on the indications related to the infection in the diagnostic information (S3 Table).

AKI was defined based on the International Classification of Diseases 10th version (ICD-10) codes (N17X: acute renal failure) and five Japanese disease codes (5839017: kidney injury, 8835584: reduced renal function, 8840719/9952036: drug-induced kidney injury, 8849597: acute kidney injury [AKI]). A previous study in elderly subjects had shown that ICD-10 code N17X for AKI had moderate sensitivity [16]. Therefore, defining AKI using ICD-10 codes alone may not be sufficient, and the five Japanese disease codes mentioned above were also defined in conjunction with N17X in this study. The data of only the patients with a confirmed diagnosis of AKI was extracted, and patients with only a suspected diagnosis were not included.

Finally, the study period for AKI incidence was defined as the period from the index date to 7 days after completion of administration, and the 30-day mortality was defined as death recorded within 30 days of the index date.

Statistical analysis

Propensity scores were calculated by logistic regression analysis and 1:1 matching was performed with a caliper distance of 0.2. In this study, propensity score matching was performed using the following variables as factors related to the implementation of TDM; number of beds, CCI, infectious disease-associated teams fee, inpatient pharmaceutical service premium, respiratory infection, bacteremia/sepsis, urinary tract infections, intra-abdominal infections, skin and soft tissue infections, bone and joint infections, central nervous system infections, febrile neutropenia, infective endocarditis, and MRCNS infection. The variable balance between the two groups after propensity score matching was evaluated by standardized mean difference (SMD). A SMD < 0.10 suggests an appropriate variable balance. In addition, univariate and multivariate logistic regression analyses were performed with each as the objective variable in order to clarify the risk factors for AKI and 30-day mortality.

Statistical analysis was performed using Stata software, version 17.0 (Stata Corp., College Station, TX) and EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan) [17], with a significance level set at p < 0.05.

This study was approved by the ethics committee of the Kyoto Pharmaceutical University (approval number: E21-014). The requirement for informed consent was waived owing to the anonymized nature of the study data.

Results

Patient characteristics

Fig 1 illustrates the patient-selection process. The overall study population comprised 146 542 patients who received VCM at the time of admission; 73 478 patients were included in the study according to the patient-selection criteria.

Fig 1

Flow chart showing patient-selection process.

* Specific medication corresponds to drugs considered to be clinically eligible for TDM among those for whom treatment and management fees for specific drugs were calculated. Details are presented in S2 Table. VCM, vancomycin; TDM, therapeutic drug monitoring.

Table 1 shows the characteristics of the study participants before and after propensity score matching. The data of 73 478 patients (TDM group, n = 55 269; non-TDM group, n = 18 209) were examined in this study. Before propensity score matching, the population differed in number of beds, infectious disease-associated teams fee, bacteremia/sepsis, and inpatient pharmaceutical service premium (SMD > 0.100). After propensity score matching, 18 196 patients were matched in each group. As a result, patient characteristic balanced the populations (SMD < 0.100), except for bed size (SMD = 0.200).

Table 1

Baseline characteristics of patients.

	Before propensity score matching			After propensity score matching
	TDM(55 269)	Non-TDM(18 209)	SMD	TDM(18 196)	Non-TDM(18 196)	SMD
Number of beds a
≤ 199	2 761 (5.0)	1 588 (8.7)	0.158	1 242 (6.8)	1 575 (8.7)	0.200
200–499	30 839 (55.8)	10 228 (56.2)		11 991 (65.9)	10 228 (56.2)
≥ 500	21 669 (39.2)	6 395 (35.1)		4 963 (27.3)	6 393 (35.1)
CCI b	2 [1, 4]	2 [1, 4]	0.003	2 [1, 4]	2 [1, 4]	0.058
Reimbursement 1 a
Infectious disease-associated teams fee	45 947 (83.1)	14 219 (78.1)	0.128	14 289 (78.5)	14 219 (78.1)	0.009
Inpatient pharmaceutical service premium	28 865 (52.2)	7 288 (40.0)	0.247	6 917 (38.0)	7 288 (40.1)	0.042
Site of infection a
Respiratory infection	22 535 (40.8)	7 087 (38.9)	0.038	7 256 (39.9)	7 087 (38.9)	0.019
Bacteremia/sepsis	19 784 (35.8)	5 628 (30.9)	0.104	6 111 (33.6)	5 628 (30.9)	0.057
Urinary tract infections	6 307 (11.4)	1 872 (10.3)	0.036	2 090 (11.5)	1 871 (10.3)	0.039
Intra-abdominal infections	3 594 (6.5)	954 (5.2)	0.054	963 (5.3)	954 (5.2)	0.002
Skin and soft tissue infections	3 438 (6.2)	860 (4.7)	0.066	1 016 (5.6)	860 (4.7)	0.039
Bone and joint infections	3 192 (5.8)	816 (4.5)	0.059	978 (5.4)	816 (4.5)	0.041
Central nervous system infections	2 921 (5.3)	911 (5.0)	0.013	877 (4.8)	911 (5.0)	0.009
Febrile neutropenia	2 419 (4.4)	788 (4.3)	0.002	785 (4.3)	787 (4.3)	0.001
Infective endocarditis	1 212 (2.2)	344 (1.9)	0.021	398 (2.2)	343 (1.9)	0.021
MRCNS infection	999 (1.8)	161 (0.9)	0.080	311 (1.7)	161 (0.9)	0.073

CCI, Charlson comorbidity index; MRCNS, methicillin-resistant coagulase-negative staphylococci; SMD, standardized mean difference.

1 Infectious disease-associated teams fee indicates patients for whom infection prevention and control premium or antimicrobial stewardship support premium was calculated. Also, details including claimed requirements for each reimbursement are shown in S1 Table.

a Data are expressed n (%).

b Data are expressed median [interquartile rate].

Risk factors for AKI with VCM administration

Risk factors for AKI were examined by multivariate logistic regression analysis (Table 2). Clinical variables that increased the incidence of AKI may include men (OR: 1.219, 95% CI: 1.084‒1.371) and bacteremia/sepsis (OR: 1.863, 95% CI: 1.664‒2.086). On the other hand, the use of VCM for febrile neutropenia (FN) (OR: 0.691, 95% CI: 0.513‒0.931) decreased the risk of AKI. In terms of reimbursement, only the drug management and guidance fee which is calculated by pharmacists intervening with individual patients reduced the risk of AKI (OR: 0.812, 95%CI: 0.723–0.912). In terms of nephrotoxic drugs used during VCM administration, the use of diuretics (OR: 2.014, 95% CI: 1.795‒2.258), steroids (OR: 1.295, 95% CI: 1.143‒1.467), and PIPC/TAZ (OR: 1.286, 95% CI: 1.122‒1.474) significantly affected AKI.

Table 2

Clinical variables associated with AKI in the adjusted population.

	Univariate Analysis			Multivariate Analysis
	Crude OR	95% CI	p value	Adjusted OR	95% CI	p value
Patient Information
Sex    [female vs. male]	0.839	0.748‒0.940	0.003	1.219	1.084‒1.371	0.001
Age (years)	0.998	0.995‒1.001	0.149	0.993	0.990‒0.997	< 0.001
Number of hospital beds
[> 500 vs. < 200]	0.837	0.670‒1.046	0.118	0.889	0.704‒1.121	0.320
[> 500 vs. 200‒499]	0.865	0.770‒0.973	0.015	0.900	0.796‒1.018	0.093
CCI	1.001	0.983‒1.020	0.889	0.986	0.967‒1.006	0.167
Treatment details
Treatment category 1    [VCM only vs. medication changes]	1.556	1.303‒1.858	< 0.001	1.460	1.217‒1.751	< 0.001
Treatment duration (day)	0.995	0.986‒1.003	0.225	0.990	0.981‒0.999	0.032
Initial dose (mg/kg/day)	0.994	0.990‒0.998	0.003	0.993	0.989‒0.997	< 0.001
Reimbursement 2
Treatment and management fee for specific drugs	1.042	0.935‒1.162	0.454	1.061	0.948‒1.187	0.301
Inpatient pharmaceutical services premium	1.015	0.908‒1.135	0.791	1.017	0.905‒1.144	0.772
Drug management and guidance fee	0.774	0.693‒0.864	< 0.001	0.812	0.723‒0.912	< 0.001
Infectious disease-associated team fee	1.050	0.919‒1.200	0.472	1.044	0.910‒1.197	0.538
Site of infection
Respiratory infection	1.068	0.956‒1.193	0.244	1.002	0.890‒1.128	0.976
Bacteremia/Sepsis	2.066	1.852‒2.304	< 0.001	1.863	1.664‒2.086	< 0.001
Infective endocarditis	1.293	0.917‒1.824	0.142	1.070	0.754‒1.517	0.707
Skin and soft tissue infections	1.069	0.842‒1.358	0.585	1.260	0.986‒1.609	0.065
Bone and joint infections	0.718	0.538‒0.959	0.025	0.845	0.628‒1.136	0.265
Intra-abdominal infections	1.613	1.319‒1.973	< 0.001	1.486	1.207‒1.829	< 0.001
Central nervous system infections	0.832	0.634‒1.091	0.184	0.942	0.712‒1.247	0.677
Urinary tract infections	1.383	1.183‒1.617	< 0.001	1.318	1.120‒1.551	0.001
Febrile neutropenia	0.846	0.635‒1.128	0.255	0.691	0.513‒0.931	0.015
MRCNS infection	0.909	0.551‒1.501	0.709	0.974	0.587‒1.616	0.919
Nephrotoxic drugs used during VCM administration
ACE-I/ARB	0.853	0.718‒1.014	0.071	0.852	0.714‒1.016	0.075
Diuretics	2.053	1.841‒2.290	< 0.001	2.014	1.795‒2.258	< 0.001
L-AMB	1.623	1.104‒2.387	0.014	1.193	0.802‒1.777	0.384
NSAIDs	0.712	0.617‒0.822	< 0.001	0.789	0.680‒0.914	0.002
Steroids	1.521	1.350‒1.714	< 0.001	1.295	1.143‒1.467	< 0.001
PIPC/TAZ	1.414	1.238‒1.615	< 0.001	1.286	1.122‒1.474	< 0.001

OR, odds ratio; CI, confidence interval; CCI, Charlson comorbidity index; VCM, vancomycin; MRCNS, methicillin-resistant coagulase-negative staphylococci.

1 Medication change indicates patients switched from VCM to other anti-MRSA drugs and switched from other anti-MRSA drugs to VCM.

2 Infectious disease-associated teams fee indicates patients for whom infection prevention and control premium or antimicrobial stewardship support premium was calculated. Also, details including claimed requirements for each reimbursement are shown in S1 Table.

Identifying factors affecting 30-day mortality

Multivariate logistic regression analysis was conducted to identify the factors affecting 30-day mortality (Table 3). Patient information such as men (OR: 1.239, 95% CI: 1.161‒1.322), a small number of hospital beds (OR: 1.194, 95% CI: 1.059‒1.346; OR: 1.236, 95% CI: 1.149‒1.329), CCI (OR: 1.048, 95% CI: 1.037‒1.059), AKI (OR: 1.659, 95% CI: 1.443‒1.908), and administration of several anti-MRSA agents (OR: 1.255, 95% CI: 1.116‒1.412), were identified as clinical variables that increased 30-day mortality. Meanwhile, treatment and management fee for specific drugs indicating TDM (OR: 0.873, 95% CI: 0.821‒0.929) and drug management and guidance fee indicating pharmacist intervention (OR: 0.538, 95% CI: 0.504‒0.575) contributed to a reduction in the 30-day mortality. However, calculation of the inpatient pharmaceutical services premium which assign a pharmacist to a hospital ward or infectious disease-associated team fee which establish an ICTs or ASTs in a medical facility was not affected. In addition, respiratory infections (OR: 1.820, 95% CI: 1.707‒1.941) and bacteremia/sepsis (OR: 1.551, 95% CI: 1.454‒1.655) increased the 30-day mortality, while skin and soft tissue infections (OR: 0.430, 95% CI: 0.347‒0.533) and bone and joint infections (OR: 0.301, 95% CI: 0.233‒0.390) were identified as factors that decreased the 30-day mortality.

Table 3

Clinical variables associated with 30-day mortality in the adjusted population.

	Univariate Analysis			Multivariate Analysis
	Crude OR	95% CI	p value	Adjusted OR	95% CI	p value
Patient Information
Sex [female vs. male]	0.826	0.778‒0.878	< 0.001	1.239	1.161‒1.322	< 0.001
Age (years)	1.035	1.032‒1.037	< 0.001	1.029	1.027‒1.032	< 0.001
Number of hospital beds
[> 500 vs. < 200]	1.743	1.561‒1.946	< 0.001	1.194	1.059‒1.346	0.004
[> 500 vs. 200‒499]	1.434	1.340‒1.534	< 0.001	1.236	1.149‒1.329	< 0.001
CCI	1.040	1.030‒1.050	< 0.001	1.048	1.037‒1.059	< 0.001
Treatment details
Treatment category 1[VCM only vs. medication changes]	1.040	0.932‒1.160	0.486	1.255	1.116‒1.412	< 0.001
Treatment duration (day)	0.924	0.918‒0.930	< 0.001	0.915	0.908‒0.921	<0.001
Initial dose (mg/kg/day)	0.992	0.989‒0.994	< 0.001	1.000	0.998‒1.003	0.685
Reimbursement 2
Treatment and management fee for specific drugs	0.779	0.735‒0.826	< 0.001	0.873	0.821‒0.929	< 0.001
Inpatient pharmaceutical services premium	0.899	0.847‒0.954	< 0.001	1.047	0.981‒1.118	0.170
Drug management and guidance fee	0.445	0.419‒0.473	< 0.001	0.538	0.504‒0.575	< 0.001
Infectious disease-associated team fee	1.007	0.939‒1.080	0.838	0.934	0.867‒1.007	0.076
Site of infection
Respiratory infection	2.423	2.285‒2.569	< 0.001	1.820	1.707‒1.941	< 0.001
Bacteremia/Sepsis	1.410	1.329‒1.497	< 0.001	1.551	1.454‒1.655	< 0.001
Infective endocarditis	0.823	0.662‒1.024	0.081	0.933	0.741‒1.176	0.558
Skin and soft tissue infections	0.294	0.239‒0.362	< 0.001	0.430	0.347‒0.533	< 0.001
Bone and joint infections	0.201	0.156‒0.258	< 0.001	0.301	0.233‒0.390	< 0.001
Intra-abdominal infections	0.778	0.676‒0.894	< 0.001	0.866	0.747‒1.004	0.056
Central nervous system infections	0.396	0.328‒0.478	< 0.001	0.627	0.515‒0.762	< 0.001
Urinary tract infections	1.033	0.943‒1.132	0.485	0.743	0.674‒0.820	< 0.001
Febrile neutropenia	0.833	0.716‒0.968	0.017	1.032	0.878‒1.212	0.705
MRCNS infection	0.732	0.551‒0.974	0.032	0.835	0.620‒1.124	0.234
AKI	1.728	1.517‒1.969	< 0.001	1.659	1.443‒1.908	< 0.001

OR, odds ratio; CI, confidence interval; CCI, Charlson comorbidity index; VCM, vancomycin; MRCNS, methicillin-resistant coagulase-negative staphylococci; AKI, acute kidney injury.

1 Medication change indicates patients switched from VCM to other anti-MRSA drugs and switched from other anti-MRSA drugs to VCM.

2 Infectious disease-associated teams fee indicates patients for whom infection prevention and control premium or antimicrobial stewardship support premium was calculated. Also, details including claimed requirements for each reimbursement are shown in S1 Table.

Discussion

In this study, we evaluated the factors for AKI and mortality associated with VCM using claim data, considering the institutional background, the assignment of pharmacists in the hospital wards, and the establishment of ICTs and ASTs, which have not been examined previously.

In previous reports, TDM was evaluated for appropriate management using VCM blood concentration values [18]. On the other hand, in this study using reimbursement, although it was clear that blood concentration management was measured, the control status of the VCM concentration values was unknown. Therefore, it is possible that a decrease in the incidence of AKI due to TDM was not identified in this study.

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [19] and is known to cause renal damage. In the guidelines, sepsis is also described to affect AKI [20], which may reflect similar results in this patient population. On the other hand, AKI may be less likely to occur in FN. Previous reports have shown that VCM clearance is enhanced in neutropenic patients [21]. This may have resulted in a significant reduction in the risk of AKI.

In this study, a smaller number of hospital beds increased the 30-day mortality. The treatment of infectious diseases is inadequate in facilities with a small number of hospital beds [22], which may be a factor in increasing mortality. In addition, CCI and replacement treatment were identified as factors increasing the 30-day mortality, suggesting that the disease severity affects the prognosis. However, there are reports of improved prognosis with early switching from VCM [23], and this needs to be further investigated.

As for the sites of infection, respiratory infections and bacteremia/sepsis were recognized as clinical variables related to increased mortality. Among respiratory infections, pneumonia is reported to be more common in older adults [24]. In addition, it is the fifth most common cause of death in Japan [24]. Since most patients in this study were older individuals (median age, 75 years), this might have affected the mortality. Bacteremia/sepsis is a serious infection with a mortality proportion of over 25‒30% [25] and may be a factor associated with increased 30-day mortality.

The drug management and guidance fee indicating that the pharmacist intervened with each patient contributed to the reduction in the incidence of AKI and 30-day mortality. On the other hand, no association with incidence of AKI and 30-day mortality was found for inpatient pharmaceutical services premium, which is calculated in a system with a pharmacist stationed in the hospital ward, or for infectious disease-associated team fee, which is calculated for establishing ICTs and ASTs in the hospital. The drug management and guidance fee can be claimed through participation in the treatment of individual patients by performing pharmacological management such as confirmation of drug interactions and monitoring of side effects. In contrast, inpatient pharmaceutical services premium and infectious disease-associated team fee are indicative of the facility’s practices and do not indicate pharmacological management for a specific individual. Therefore, in treatment with VCM, it may be more important to intervene with individual patients than to create a facility system to reduce AKI and 30-day mortality.

There are several limitations to this study. The first is the items in the database study. In some cases, data may be missing or the data entry format may not be consistent across facilities. Also, as we are unable to identify post-discharge outcomes, there is a possibility of underestimating 30-day mortality. In addition, this population may not be representative due to sampling bias because it does not cover all patients in Japan. The data obtained is prescription information and may not reflect actual dosing. The second issue concerns definitions in this study. There is a possibility of misclassification bias due to the substitution of reimbursement for TDM and selection bias due to the exclusion of patients receiving drugs that require TDM other than VCM. These definitions may underestimate TDM implementation. Similarly, AKI was substituted for ICD-10 code and Japanese disease code. The incidence of AKI may also have been underestimated. In addition, the timeline of AKI diagnosis and VCM administration may have been reversed. Finally, there were unmeasured confounders including severity of infection and surgical history.

Despite these limitations, it is important to use large claim data to evaluate the impact of reimbursement on AKI and 30-day mortality with VCM administration. However, we were unable to evaluate the utility of reimbursements that do not target individuals, such as inpatient pharmaceutical services premiums and infectious disease-associated team fees. Therefore, a new reimbursement system is required for the treatment participation for individual patients. In addition, while the utilization of real-world data is being promoted, problems had been highlighted, such as the existence of receipt disease names and the lack of detailed diagnosis dates or laboratory data. In the future, medical data such as claims data should be collected with consideration for its use in research.

Conclusion

Drug management and guidance fees for each patient reduced AKI and 30-day mortality with VCM. On the other hand, inpatient pharmaceutical services premiums and infectious disease-associated team fees by system at the facility did not affect AKI and 30-day mortality. In order to decrease AKI and 30-day mortality, pharmacist interventions for individual patients need to be further promoted.

Reimbursement and claimed requirements.

TDM, therapeutic drug monitoring; AEDs, antiepileptic drugs; ICT, infection control team; AST, antimicrobial stewardship team.

(DOCX)

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Number of patients who claimed treatment and management fee for specific drugs excluding vancomycin.

* “Number” indicates the number of patients who used the drug concerned concomitantly within 7 days from the index date, defined as the date of vancomycin initiation.

(DOCX)

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Codes and diagnostic information for classification of infection sites.

These codes were defined based on the Various Information of Medical Fee operated by the Ministry of Health, Labour and Welfare (https://shinryohoshu.mhlw.go.jp/). MRCNS, methicillin-resistant coagulase-negative staphylococci.

(DOCX)

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2 Jun 2022

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Dear Dr. Muraki,

Thank you very much for submitting your manuscript to Plos One. The reviewers recommend reconsideration of your manuscript following major revision. I invite you to resubmit your manuscript after addressing the three reviewers' comments. When revising your manuscript, please consider carefully all issues mentioned in the comments. Please respond to the reviewers' comments in a point by point manner.

Reviewers' comments:

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Comments to the Author

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Reviewer #1: Partly

Reviewer #2: No

Reviewer #3: No

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

Reviewer #3: N/A

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

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Reviewer #1: Major comments

1. The retrospective study investigated the implementation of TDM over time and the effect of TDM on AKI and mortality. However, there has been relative solid evidence that therapeutic drug monitoring of vancomycin is associated with decreased risk of acute kidney injury. The rationale to perform this study requires further clarification.

The Introduction section stressed that the impact of pharmacist and ICT/AST intervention on VCM’s prognosis and side effects hasn’t been clarified. However, the study didn’t clarify this issue either, as I didn’t find that the study considered the intervention of pharmacists and infectious disease-associated teams adequately (only table 1 and post-hoc analysis on 30-day mortality mentioned intervention of pharmacists and infectious diseases-associated teams).

2. Line 102-105: The rationale of substitution TDM with treatment and management fee is not justified adequately, which may be vulnerable from misclassification bias. I was wondering could you provide more details on this issue, including how pharmacist and infectious disease – associated team intervention was identified.

3. Figure 1: Please check the accuracy of number in Fig 1 again, especially in the first 2 steps.

4. The language of this manuscript requires major improvement, especially in Abstract section.

Minor comments:

Abstract:

1. The Introduction was overlong, and I don’t think that the contents of this study took account of interventions of pharmacists and infectious disease-associated teams adequately.

2. Methods: “The Japanese administrative claims database was used to evaluate the incidence of acute kidney injury (AKI) and 30-day mortality after VCM administration from 2010 to 2019 based on TDM implementation” I was wondering why the trend of acute kidney injury incidence and 30-day mortality from 2010 to 2019 was set as the primary analysis, which didn’t match to the title of this manuscript.

3. Results: More details (number, rate, ratio) of quantitative analysis should be added, rather than narrative description alone.

Introduction

4. Line 84-86: I get it that the study at first investigated the implementation of vancomycin TDM over time, and then evaluated the effect of TDM on mortality and AKI. However, the consideration of the interventions of pharmacists and infectious disease-associated teams was inadequate. Could you please rephrase this part?

Methods:

Statistical Analysis

5. Line 152-157: The primary analysis didn’t match with the objective/title of this study. I was wondering why the linear regression was performed on the trend of AKI incidence and 30-day mortality in both TDM and non-TDM group.

Rather, the multivariable logistic regression was used as post-hoc analysis. I am not sure whether the authors adjust confounders properly.

I suggest to add the post-hoc multivariable analysis on AKI.

Results:

6. Table 1: I was wondering could you please add the baseline characteristics of patients in the TDM group and non-TDM group, which may help interpret the results.

7. Line 207: I didn’t get it why the initial VCM dose was analyzed, as initial VCM dose may be less relevant to TDM implementation. The change and maintain dose following TDM may be more clinically relevant.

8. Line 238: “increased” should be removed from the table title.

9. The results section should be re-organized and make it more logically.

Discussion:

9. Line 261-262: the unit should be μg/mL

10. Line 284-285: I am not sure whether the study assessed “TDM and interventions by pharmacists and infectious disease-associated teams” as a whole.

11. Line 298-300:

Did the study adjust for measured confounders properly? Also, pleased provide examples of important unmeasured confounders

Please add potential selection bias (Patients who received drugs requiring TDM other than VCM within 7 days of the index date were excluded)

I am not sure whether the method of using reimbursement to identify TDM-related intervention would be vulnerable from misclassification bias.

One of the strengths of claim data may be long follow-up, which allow us to assess TDM on 3-day mortality.

Reviewer #2: The aim of this study is important, but its scientific validity is not high. I would like to re-read the resubmitted content, as I think considerable revisions are necessary.

Major comments

Are the diagnosis names of AKI (e.g., ICD-10 codes) validated? Do these AKI diagnostic names reflect true AKI? The percentage of AKI in this study is clearly lower than in previous reports.

How did the authors handle suspected diagnosis? Clinicians often make a suspected diagnosis to levy medical fees for performing blood tests and prescriptions in Japan.

Why did the authors exclude CKD patients (N18X)?

Usually, there is a difference in patient background between patients with TDM and those without. The authors should confirm this difference and then evaluate outcomes after adjusting for patient background (e.g., propensity score matching).

Minor comments

Figure 1 has two calculation errors.

The limitation is inadequate. For example, a discussion of sampling bias resulting from the employing DPC data is needed. The authors are only evaluating prescriptions, not the actual dosing situation.

The authors should recognize the difference between rate, proportion, and ratio and use them appropriately.

Reviewer #3: I read with interest of your manuscript entitled “Effect of therapeutic drug monitoring on the incidence of acute kidney injury and 30-day mortality after vancomycin administration: a Japanese administrative claims database study”. I understand TDM is important for adequate dose change for vancomycin, However, authors may not conclude relation between TDM implementation and AKI using administrative data.

Major

1. Authors defined for AKI as primally endpoint such as “AKI was diagnosed based on the ICD-144 10 code (N17X) and five Japanese disease codes (5839017: kidney injury, 8835584:145 reduced renal function, 8840719/ 9952036: drug-induced kidney injury, 8849597: AKI).”

Definition for AKI using N17X is reported for inadequate by the results of validation analysis in renal transplantation patients 1, 2) In addition, Japanese disease codes for authors definition may not be adequate to detect AKI.

Ref 1) JSPE committee, reports available from “https://www.jspe.jp/committee/pdf/

validationtrr120180523.pdf” (see p.27, line 16)

Ref 2) Amber O. Molnar etal. Validation of administrative database codes for acute kidney injury in kidney transplant recipients. Can J Kidney Health Dis. 2016; 3: 18.

2. Detailed date for AKI is able to define by using DPC data? Is the time axis reversed between AKI diagnosis and VCM administration?

3. How to analyze “30-day mortality after VCM administration.”? Authors can assess death after the discharge? Administrative data from Medical Data Vision can provide DPC (in-hospital) and the after discharge?

Minor

1. Authors calculated “rate of TDM implementation” Is it correct? Is it proportion? (line 149)

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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1 Aug 2022

To editor and reviewers

Thank you very much for taking the time to review our paper.

We appreciate your helpful suggestions on many issues, including adjustments to the patient background,

various definitions and biases, and the organization of the manuscript.

In response to your comments, we have substantially revised the title and content of the paper.

Our corrections and comments are as follows documents.

We would appreciate if you could confirm them.

Submitted filename: Response to Reviewers_revise.docx

Click here for additional data file.

26 Aug 2022

Influence of pharmacists and infection control teams or antimicrobial stewardship teams on the safety and efficacy of vancomycin: a Japanese administrative claims database study​

PONE-D-22-09316R1

Dear Dr. Yuichi Muraki,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Tze Shien Lo, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dr. Muraki and his co-workers have responded to the reviewers' comments well and have made appropriately changes recommended by the reviewers. Therefore, the revised manuscript has met PLos ONE's Publication Criteria.

Reviewers' comments:

30 Aug 2022

PONE-D-22-09316R1

Influence of pharmacists and infection control teams or antimicrobial stewardship teams on the safety and efficacy of vancomycin: a Japanese administrative claims database study

Dear Dr. Muraki:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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