The impact of hypoalbuminaemia severity on total teicoplanin concentrations after 3 days loading dose: a retrospective observational study

The impact of hypoalbuminaemia severity on total teicoplanin concentrations after 3 days loading dose: a retrospective observational study | 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 The impact of hypoalbuminaemia severity on total teicoplanin concentrations after 3 days loading dose: a retrospective observational study Yuki Shimizu, Kazuhiko Hanada, Takeaki Watanabe, Mari Araki, Keisuke Aoyama, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6607696/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Hypoalbuminaemia is known to decrease total teicoplanin (TEIC) concentration; however, whether total TEIC concentration varies depending on hypoalbuminaemia severity remains unclear. This study investigated the correlation between the severity of hypoalbuminaemia and total TEIC concentrations. Methods A retrospective observational study was conducted on patients (≥ 18 years) administered TEIC between January 2017 and December 2024. Factors influencing total TEIC trough concentrations after 3 days loading dose (C trough ) were determined using multiple linear regression. Serum albumin levels were stratified into three groups. The total and predicted free TEIC C trough of patients administered standard and high loading dose regimens were compared among the groups. Results The total TEIC C trough was negatively correlated with age (p = 0.01) and positively correlated with serum albumin (p < 0.01) and TEIC loading dose for within 3 days (p < 0.01). The median total TEIC C trough for the three groups were significantly different in both dosing regimens: standard, 9.9 [interquartile range, 9.2 − 12.1], 15.2 [12.5 − 17.8], and 17.1 [14.6 − 20.8] µg/mL; high, 13.5 [9.6 − 16.4], 17.6 [16.7 − 19.8], and 20.7 [17.4 − 23.6] µg/mL for albumin < 2.0 g/dL, 2.0 ≤ albumin < 3.0 g/dL, and albumin ≥ 3.0 g/dL, respectively. However, the predicted free TEIC C trough was not significantly different among the albumin groups for either regimen. Conclusions The more severe the hypoalbuminaemia, the lower the total TEIC C trough , but predicted free TEIC C trough was not affected. Clinicians should consider lowering the target total TEIC concentrations in severe hypoalbuminaemia cases. Teicoplanin Serum albumin Hypoalbuminaemia Total concentrations Loading dose Figures Figure 1 Figure 2 Introduction Teicoplanin (TEIC) is a glycopeptide antibiotic used to treat infections caused by Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus [ 1 ]. TEIC not only shows similar efficacy as vancomycin (VCM) but also tends to have fewer adverse effects like nephrotoxicity and cutaneous rash compared with VCM [ 2 , 3 ]. The efficacy of TEIC is related to the ratio of the area under the concentration time curve (AUC) to the minimum inhibitory concentration [ 4 , 5 ]. Because AUC estimation software is unavailable in many institutions, trough concentration is generally recommended as a surrogate marker for therapeutic drug monitoring (TDM) in Japanese clinical practice [ 6 ]. Hanai et al. demonstrated that a total (bound and unbound) TEIC trough concentration within the range of 15–30 µg/mL is a significant independent factor that contributed to successful treatment compared with a concentration of 20 µg/mL were an independent factor for treatment success in cases of severe infection such as bacteraemia and osteomyelitis [ 8 ]. TEIC is excreted mainly from the kidneys and has a long half-life of 83–163 h [ 9 ]. Therefore, a loading dose for 3 days is recommended for TEIC to achieve therapeutic blood levels immediately [ 6 ]. TEIC can bind to serum albumin (Alb) at levels of 90–95% [ 10 ]. However, hypoalbuminaemia has been reported to increase the fraction of free TEIC [ 11 , 12 ]. Previous reports have shown that hypoalbuminaemia is a risk factor that decreases total TEIC concentrations after the loading dose [ 8 , 13 ]. This has been attributed to the increasing total clearance (CL) and volume of distribution (Vd) of TEIC caused by the increasing fraction of free TEIC; however, the underlying mechanism remains poorly understood. Furthermore, there are no reports on whether the effect of hypoalbuminaemia on total TEIC concentrations after the loading dose varies depending on the severity of the condition. Hypoalbuminaemia reduces the total TEIC concentrations but not free TEIC concentrations, which can affect therapeutic efficacy and cause drug side effects [ 14 ]. Therefore, it is necessary to interpret the results for total TEIC concentrations carefully. Although lowering the target total TEIC concentrations in hypoalbuminaemia is recommended in Japanese guidelines [ 6 ], specific total TEIC concentrations have not been defined because the impact of the severity of hypoalbuminaemia on total TEIC concentrations remains unclear. This study aimed to evaluate (1) the impact of the severity of hypoalbuminaemia on total TEIC concentrations after the loading dose and (2) the target total TEIC concentrations in hypoalbuminaemia cases. Methods Study participants and study design This retrospective observational study was conducted at Tokyo Dental College, Ichikawa General Hospital. Patients aged ≥ 18 years and administered TEIC between January 2017 and December 2024 were eligible for this study. Patients treated with TEIC for multiple periods were recruited for the first time only. The exclusion criteria were as follows: (1) patients receiving renal haemodialysis or peritoneal dialysis, (2) patients with incomplete data, and (3) patients whose TEIC concentrations were not measured 84–96 h after the initial TEIC administration. The study protocol was approved by the Institutional Review Board of Tokyo Dental College, Ichikawa General Hospital (approval number: I2502–2504), and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments. Data collection We extracted the following data from electronic medical records: patient background (age, sex, and body weight), comorbidities affecting the fraction of free TEIC and CL (diabetes, heart failure, and haematological malignancy) [ 15 , 16 ], clinical laboratory data (serum creatinine [SCr], estimated glomerular filtration rate [eGFR], and serum Alb), TEIC exposure (TEIC loading dose for within 72 h of initial TEIC administration [D loading ], and total TEIC trough concentrations 84–96 h after initial TEIC administration [C trough ] ), intensive care unit stay, and indication for TEIC. Comorbidities and indications for TEIC use were determined from the information provided by the physician in the electronic medical records. We calculated eGFR using the following prediction equation: eGFR = 194 × SCr − 1.094 × age − 0.287 × 0.739 (if female) [ 17 ]. Serum Alb levels were recruited on the day closest to TEIC C trough measurements and measured using the modified bromocresol purple (BCP) method [ 18 ]. Total TEIC concentrations were measured within 1 h before administration, and the measurement methods were consistent with the latex agglutination turbidimetric immunoassay. Outcomes To assess TEIC concentrations after a loading dose for 3 days (within 72 h of the initial TEIC administration), the primary outcome was the TEIC C trough . Investigation of factors influencing the total TEIC concentrations Multiple linear regression analysis was performed to investigate the factors influencing the total TEIC trough concentrations after the loading dose. The dependent variable was the total TEIC C trough , and the independent variables included patient background, comorbidities, clinical laboratory data, TEIC D loading , intensive care unit stay, and indications for TEIC. Comparison of total and predicted free TEIC C among hypoalbuminaemia severity groups Hypoalbuminaemia severity was stratified into three groups according to common terminology criteria for adverse events version 5.0 ( https://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm#ctc ): Alb < 2.0 g/dL (group 1), 2.0 ≤ Alb 15 µg/mL after 3 days loading dose: 33.4 mg/kg/3 days, 40.0 mg/kg/3 days, and 50.0 mg/kg/3 days for eGFR < 30 mL/min/1.73 m 2 , 30 ≤ eGFR 60 mL/min/1.73 m 2 , respectively [ 19 , 20 ]. Furthermore, recommended high loading dose regimen by renal function to achieve TEIC trough concentrations of > 20 µg/mL is 42.7 mg/kg/3 days, 48.0 mg/kg/3 days, and 60.0 mg/kg/3 days, respectively [ 8 ]. Among the patients included in this study, we selected those who were administered ± 5 mg/kg/3 days of the standard or high loading dose regimens by renal function and compared TEIC C trough among the three Alb groups. In addition, we estimated the predicted free TEIC concentrations using the equation developed by Yano et al.: predicted free TEIC concentrations = total TEIC concentrations/1 + 1.78 × serum Alb [ 12 ]. This estimation equation was derived from the Scatchard equation from a single centre in Japan; the measured free TEIC concentrations were between 0.63 and 1.38 times the predicted concentrations in 95% of the cases [ 12 ]. This estimation equation used serum Alb measured by the modified BCP method [ 12 ]. We also compared the predicted free TEIC C trough among the three Alb groups in patients administered the standard and high TEIC loading dose regimens. Comparison of pharmacokinetic parameters among severity groups of hypoalbuminaemia In the exploratory study, individual pharmacokinetic (PK) parameters were compared for each Alb group. PK parameters were calculated using the Bayesian method from total TEIC C trough measurements according to the population pharmacokinetics (PPK) model equation by Nakayama et al. (Table 1 ) [ 21 ]. The PPK model is a 2-compartment model derived from a single centre in Japan, and CL includes creatinine clearance and body weight as covariates [ 21 ]. Table 1 Pharmacokinetic parameters of teicoplanin reported by Nakayama et al. Population (mean ± SD [range]) Parameter [interindividual variability, %] N = 120 (305 serum samples) CL, L/h = 0.00498 × CCr + 0.00426 × Body weight [22.1] Age, year = 75.5 ± 11.6 [ 18 − 96] V, L = 10.4 [26.7] Body weight, kg = 45.1 ± 8.8 [27.0 − 75.0] K 12 , /h = 0.380 [−] CCr, mL/min = 49.7 ± 23.8 [5.3 − 154.3] K 21 , /h = 0.0485 [24.5] Serum albumin, g/dL = 2.56 ± 0.61 [1.10 − 4.90] CCr, creatinine clearance; CL, clearance of teicoplanin; V, central volume of distribution; K 12 , transfer constant from the central to peripheral compartment; K 21 , transfer constant from the peripheral to central compartment Statistical analysis Statistical analyses were performed using the JMP® Pro software version 17.2 (SAS Institute Inc., Cary, NC, USA). Statistical significance was set at p < 0.05, unless otherwise indicated. Normally distributed continuous data of the three groups were expressed as the mean ± standard deviation and compared using analysis of variance (ANOVA), whereas data not following a normal distribution were expressed as the median and interquartile range (IQR) and compared using the Kruskal–Wallis test. Categorical data were expressed as numbers (%) and compared using Pearson’s χ 2 -test. In the multiple linear regression analysis, potential independent variables were screened using the univariate linear regression method with p < 0.20. A stepwise forward selection method was used to determine the final model based on the Akaike information criterion at the minimum value. The final models described a multiple linear regression equation and the variance inflation factor (VIF). In estimating PK parameters using the Bayesian method, we used BMs-Pod version 8.06 ( https://bmspod.web.fc2.com/ ), which is a freely available software based on Microsoft Excel (Microsoft, Redmond, WA, USA). Results Patient characteristics A flowchart of the patient selection process is shown in Fig. 1 . A total of 512 patients were administered TEIC between January 2017 and December 2024. Among them, 45 patients received renal haemodialysis or peritoneal dialysis, 97 patients had incomplete data, and 209 patients did not have their TEIC concentrations measured at 84–96 h after the initial TEIC administration and were thus excluded from the study. The remaining 161 patients were included in the study. Among them, 58 patients were administered standard loading dose regimen ± 5 mg/kg/3 days and 39 patients were administered high loading dose regimen ± 5 mg/kg/3 days. N indicates the number of patients The clinical data of all patients and the Alb groups are summarised in Table 2 . The characteristics of the 161 patients were as follows: median age, 77 [IQR, 71–83] years; median body weight, 50.2 [44.9 − 61.1] kg; median SCr, 1.0 [0.7–1.7] mg/dL; median eGFR, 48.5 [28.7 − 81.7] mL/min/1.73 m 2 ; and mean serum Alb, 2.4 ± 0.6 g/dL. The mean TEIC D loading was 38.8 ± 10.0 mg/kg/3 days, and the median total TEIC C trough was 14.2 [10.6 − 17.7] µg/mL. Table 2 Patient characteristics All patients (N = 161) Group 1 Alb < 2.0 g/dL (N = 36) Group 2 2.0 ≤ Alb < 3.0 g/dL (N = 93) Group 3 3.0 g/dL ≤ Alb (N = 32) p-value Patient background Age, years 77 [71 − 83] 77.5 ± 10.0 76.1 ± 11.6 71.2 ± 14.4 0.07 b Male, N (%) 98 (60.9) 20 (55.6) 61 (65.6) 17 (53.1) 0.35 a Body weight, kg 50.2 [44.9 − 61.1] 51.8 ± 12.9 53.6 ± 12.8 53.2 ± 9.8 0.74 b Comorbidity Diabetes, N (%) 38 (23.6) 10 (27.8) 17 (18.3) 11 (34.4) 0.14 a Heart failure, N (%) 22 (13.7) 7 (19.4) 12 (12.9) 3 (9.4) 0.46 a Haematological malignancy, N (%) 61 (37.9) 11 (30.6) 32 (34.4) 18 (56.3) 0.05 a Clinical laboratory data Serum creatinine, mg/dL 1.0 [0.7 − 1.7] 1.2 [0.6 − 2.1] 1.0 [0.7 − 1.6] 0.9 [0.7 − 1.6] 0.61 c eGFR, mL/min/1.73 m 2 48.5 [28.7 − 81.7] 41.3 [23.5 − 80.1] 51.6 [28.3 − 83.4] 54.3 [33.0 − 70.3] 0.44 c Serum Alb, g/dL 2.4 ± 0.6 1.7 [1.5 − 1.8] 2.4 [2.1 − 2.7] 3.2 [3.0 − 3.4] < 0.01 c TEIC exposure TEIC loading dose for 3 days, mg/kg/3 days 38.8 ± 10.0 37.8 ± 10.5 38.1 ± 9.9 41.7 ± 9.4 0.19 b TEIC trough concentrations, µg/mL 14.2 [10.6 − 17.7] 11.7 [9.2 − 14.5] 14.1 [11.2 − 17.6] 18.7 [15.1 − 21.1] < 0.01 c Intensive care unit stay, N (%) 25 (15.5) 7 (19.4) 15 (16.1) 3 (9.4) 0.50 a Indication for TEIC Bacteraemia, N (%) 54 (33.5) 15 (41.7) 34 (36.6) 5 (15.6) 0.04 a Catheter-related bloodstream infection, N (%) 13 (8.1) 2 (5.6) 8 (8.6) 3 (9.4) 0.81 a Febrile neutropenia, N (%) 18 (11.2) 3 (8.3) 9 (9.7) 6 (18.8) 0.31 a Pneumonia, N (%) 32 (19.9) 10 (27.8) 16 (17.2) 6 (18.8) 0.40 a Urinary tract infection, N (%) 6 (3.7) 1 (2.8) 3 (3.2) 2 (6.3) 0.70 a Skin and soft tissue infection, N (%) 12 (7.5) 2 (5.6) 9 (9.7) 1 (3.1) 0.42 a Cholangitis/Cholecystitis, N (%) 7 (4.3) 0 (0.0) 5 (5.4) 2 (6.3) 0.34 a Others, N (%) 19 (11.8) 3 (8.3) 9 (9.7) 7 (21.9) Normally distributed data are expressed as mean ± standard deviation, whereas non-normally distributed data are expressed as medians (interquartile ranges). Categorical data are expressed as numbers (%). N, number; eGFR, estimated glomerular filtration rate; Alb, albumin; TEIC, teicoplanin a Pearson’s χ 2 test b ANOVA c Kruskal−Wallis test Although there were no significant differences in eGFR (p = 0.44) and D loading (p = 0.19) among the three Alb groups, significant differences in the total TEIC C trough were observed among the three Alb groups (p < 0.01), with the lower Alb group exhibiting a lower total TEIC C trough . Factors influencing total TEIC concentrations The results of the multiple linear regression analyses are summarised in Table 3 . Univariate linear regression analysis revealed that age, sex, serum Alb, and TEIC D loading were independent variables. Using a stepwise forward selection method, we included age, serum Alb, and TEIC D loading as variables in the final model to assess the factors influencing the total TEIC C trough . The multiple regression equation was as follows: total TEIC C trough = 4.07–0.07 × Age + 2.63 × Serum Alb + 0.25 × TEIC D loading (Adjusted R 2 = 0.37, Age; t = − 2.5, p = 0.01, Serum Alb; t = 4.33, p < 0.01, and TEIC D loading ; t = 7.3, p < 0.01). Table 3 Multiple linear regression analysis for the total TEIC concentrations after 3 days loading dose β Estimate SE 95%CI p-value VIF Intercept 0 4.07 3.14 −2.13 to 10.27 0.20 NA Age, per years −0.16 −0.07 0.03 −0.13 to − 0.02 0.01 1.05 Serum albumin, per g/dL 0.28 2.63 0.61 1.43 to 3.83 < 0.01 1.07 TEIC loading dose for 3 days, per mg/kg/3 days 0.46 0.25 0.03 0.19 to 0.32 < 0.01 1.02 β, standardised partial regression coefficient; SE, standard error; 95%CI, 95% confidence interval; VIF, variance inflation factor; NA, not available; TEIC, teicoplanin We summarised the independent variables with < 0.20 in the univariate linear regression analysis. A total of 161 patients were included in the multiple linear regression analysis. Comparison of total and predicted free TEIC C trough among the three hypoalbuminaemia groups Of the 58 patients administered standard loading dose regimen, there were 15, 27, and 16 patients with eGFR < 30 mL/min/1.73 m 2 , 30 ≤ eGFR 60 mL/min/1.73 m 2 , respectively; there were 10, 35, and 13 patients in groups 1, 2, and 3, respectively. Among the 39 patients administered the high loading dose regimen, 15, 18, and 6 patients had eGFR < 30 mL/min/1.73 m 2 , 30 ≤ eGFR 60 mL/min/1.73 m 2 , respectively, while groups 1, 2, and 3 had 12, 19, and 8 patients, respectively. In the standard loading dose regimen, the median TEIC D loading was 39.5 [33.8 − 45.2], 39.8 [35.4 − 45.8], and 41.1 [35.7 − 46.2] mg/kg/3 days for groups 1, 2, and 3, respectively, with no significant difference (p = 0.82); however, the median total TEIC C trough was significantly different (p < 0.01) among the three groups at 9.9 [9.2 − 12.1], 15.2 [12.5 − 17.8], and 17.1 [14.6 − 20.8] µg/mL, respectively. Similarly, in the high loading dose regimen, although the median total TEIC D loading was not significantly different among the three groups—44.0 [40.9 − 51.6], 44.8 [43.5 − 49.5], and 48.6 [45.7 − 52.5] mg/kg/3 days for groups 1, 2, and 3, respectively (p = 0.38), the median total TEIC C trough was 13.5 [9.6 − 16.4], 17.6 [16.7 − 19.8], and 20.7 [17.4 − 23.6] µg/mL, respectively, showing significant differences (p < 0.01) (Fig. 2 ). As the severity of hypoalbuminaemia increased, the total TEIC C trough decreased. Meanwhile, the median predicted free TEIC C trough for groups 1, 2, and 3 was 2.7 [2.4 − 3.0], 2.9 [2.5 − 3.2], and 2.7 [2.3 − 3.2] µg/mL, respectively, in the standard loading dose regimen, showing no significant differences (p = 0.71), and 3.2 [2.5 − 4.1], 3.3 [2.9 − 4.0], and 3.1 [2.7 − 3.6] µg/mL, respectively, in the high loading dose region, again exhibiting no significant difference (p = 0.64) (Fig. 2 ). Data are presented as the median, IQR, and range. Each albumin groups were compared using the Kruskal − Wallis test. Comparison of pharmacokinetic parameters among hypoalbuminaemia severity groups The individual PK parameters of each Alb group are listed in Table 4 . CL (L/h) was 0.39 [0.31 − 0.60], 0.44 [0.35 − 0.59], and 0.45 [0.36 − 0.52] for groups 1, 2 and 3, with no significant difference (p = 0.67). Meanwhile, the steady state volume of distribution (Vss) (L) was 113.3 [89.9 − 144.1], 93.1 [79.4 − 111.2], and 84.3 [68.4 − 101.1], for groups 1, 2 and 3, respectively (p < 0.01), suggesting that Vss increased with decreasing serum Alb. Similarly, the volume of distribution of central compartment (V 1 ) and volume of distribution of peripheral compartment (V 2 ) tended to increase with decreasing serum Alb (p < 0.01, V 1 and V 2 ). Table 4 Pharmacokinetic parameters of total TEIC for each albumin group Group 1 Alb < 2.0 g/dL (N = 36) Group 2 2.0 ≤ Alb < 3.0 g/dL (N = 93) Group 3 3.0 g/dL ≤ Alb (N = 32) p-value CL, L/h 0.39 [0.31 − 0.60] 0.44 [0.35 − 0.59] 0.45 [0.36 − 0.52] 0.67 Vss, L 113.3 [89.9 − 144.1] 93.1 [79.4 − 111.2] 84.3 [68.4 − 101.1] < 0.01 V 1 , L 11.5 [10.3 − 12.2] 10.5 [9.6 − 11.3] 9.9 [8.6 − 10.9] < 0.01 V 2 , L 101.8 [79.7 − 131.8] 82.6 [69.9 − 99.8] 74.4 [59.8 − 90.2] < 0.01 Data were expressed as medians (interquartile ranges). CL, clearance of teicoplanin; Vss, steady state volume of distribution; V 1 , volume of distribution of central compartment; V 2 , volume of distribution of peripheral compartment. Each albumin groups were compared using the Kruskal–Wallis test. Discussion Our study evaluated the impact of hypoalbuminaemia severity on the total TEIC concentration after the loading dose. We found that hypoalbuminaemia was a significant factor that decreased the total TEIC concentrations after the loading dose, with more severe hypoalbuminaemia leading to lower total TEIC concentrations; however, hypoalbuminaemia did not affect the predicted free TEIC concentrations. Furthermore, the reason for hypoalbuminaemia lowering the total TEIC concentrations was attributed to increasing Vd rather than increasing CL. Clinicians should consider serum Alb levels when performing TDM for total TEIC concentrations and lower the target total TEIC concentrations in cases of severe hypoalbuminaemia. Significant factors that decreased the total TEIC C trough were older age, hypoalbuminaemia, and TEIC D loading . Similar to previous reports [ 8 , 13 ], hypoalbuminaemia was a significant factor in our study. Considering the standardised partial regression coefficient of 0.46 for D loading and 0.28 for serum Alb, it was suggested that serum Alb relatively influenced the toral TEIC C trough (Table 3 ). In general, older patients tend to have lower serum Alb levels [ 22 ], and the patients included in our study were also mostly older adults with lower serum Alb levels (Table 2 ). Although multiple linear regression analysis showed that the VIF for age and serum Alb were less than 10, with no multicollinearity (Table 3 ), they might have been potentially related. In our study, univariate linear regression analysis revealed that serum creatinine and eGFR were not potentially independent variables influencing the total TEIC C trough after 3 days loading dose. Because TEIC is excreted mainly by the kidneys, renal function significantly affects the CL of TEIC [ 9 ]. Additionally, because TEIC has a long half-life of 83–163 h [ 9 ], a steady state was not achieved at 84–96 h after the initial TEIC administration. Total TEIC concentrations after the loading dose might have been substantially influenced by TEIC D loading and Vd but not by renal function. Two previous studies on standard loading dose regimen have reported that the percentage of patients achieving TEIC C trough > 15 µg/mL at day 4 were 76.1% (137/180 patients) and 68.3% (41/60 patients) [ 19 , 20 ]. However, none of these reports described serum Alb levels. In our study, none of patients achieved a total TEIC C trough >15 µg/mL even though TEIC was administered according to the standard loading dose regimen in group 1 (serum Alb < 2.0 g/dL). Regarding the high loading dose regimen, Ueda et al. showed that the severity of hypoalbuminaemia reduced TEIC trough concentrations [ 8 ], consistent with our results. Previous reports have shown that hypoalbuminaemia decreases total TEIC concentrations but not free TEIC concentrations [ 14 ]. In our study as well, the total TEIC C trough decreased with the severity of hypoalbuminaemia, whereas the predicted TEIC C trough showed no significant difference among the Alb groups in either of the regimens. Prediction equations for free TEIC concentrations have been reported by Yano et al. and Byrne et al. [ 12 , 14 ]. Yano et al. developed the prediction equations in Japan using only total TEIC concentrations and serum Alb levels [ 12 ], while the prediction equations proposed by Byrne et al. were developed in Ireland and are more complicated to calculate [ 14 ]. Since both prediction equations had comparable accuracy [ 12 , 14 ], we adopted the Yano et al. equation because the patients in our study were similar to theirs. Our findings suggest that when serum Alb < 2.0 g/dL, target total TEIC concentrations should be reduced to 10 and 13 µg/mL in the standard and high dose regimen, respectively. However, whether lowering the target total TEIC trough concentrations in accordance with the serum Alb level would have an equivalent clinical effect remains unclear and requires further investigation. Although the CL of TEIC has been found to be very low at 11 mL/h/kg in healthy volunteers [ 9 ], hypoalbuminaemia reduces total TEIC trough concentrations after 84 − 96 h, which is not a steady state. This suggests that the Vd variation was more involved in reducing total TEIC concentrations than the CL variation (Table 4 ). TEIC had a large Vd of more than 50 L [ 9 ]. In general, the Vd of a total drug with Vd > 50 L is proportional to fraction unbound in blood (fuB). Yano et al. showed that TEIC fuB was approximately 0.15, 0.18, and 0.23 for serum Alb 3.0, 2.5, and 2.0 g/dL, respectively [ 12 ]. In our study, Vss increased proportionally with fuB. In contrast, Vd of the free drug is generally unaffected by fuB. We calculated the PK parameters using the Bayesian method from the PPK model developed by Nakayama et al. [ 21 ]. This PPK model included creatinine clearance and body weight as covariates of CL [ 21 ]. As there were no significant differences in body weight and SCr in groups 1, 2, and 3 (Table 2 ), we cannot rule out the possibility that no significant differences were found in CL among these three groups (Table 4 ). Further studies on variations in PK parameters in hypoalbuminaemia are required. This study has several limitations. First, this was a single-centre retrospective observational study, and potential bias could not be avoided. Second, we could not assess the severity of sepsis based on the Sequential Organ Failure Assessment score, as data on the level of consciousness could not be collected. To assess severity, our study used intensive care unit stay as a surrogate index, but no significant difference was detected among the three groups (Table 2 ). However, a higher incidence of bacteraemia was reported in groups 1 and 2 (Table 2 ). Therefore, sepsis could have been more frequent and severe in groups 1 and 2 than in group 3. Sepsis may have affected C trough because it increases Vd [ 23 ]. Third, we recruited patients who were administered standard and high loading dose regimens to compare the three serum Alb groups, but the sample size might have been insufficient for each group. Fourth, because the free TEIC concentrations were not measured, we used the values predicted by the prediction equations. Finally, as previously mentioned, we calculated the PK parameters using the Bayesian method according to the PPK model equation of Nakayama et al. [ 21 ]. However, we cannot dismiss the possibility that this was inadequate. Conclusions Hypoalbuminaemia is a significant factor that decreases total TEIC concentrations after the loading dose. The greater the severity of hypoalbuminaemia, the lower the total TEIC concentration after the loading dose, which is possibly caused by the elevated Vd for total TEIC. However, hypoalbuminaemia did not affect the predicted free TEIC concentrations. Clinicians should consider serum Alb levels when performing TDM for total TEIC and aim to reduce the target total TEIC concentration in cases of severe hypoalbuminaemia. Declarations Conflict of interest: The authors have no relevant financial or non-financial interests to disclose. Funding statement: The authors did not receive support from any organisation for the submitted work. Ethics approval statement: This observational study adhered to the principles of the Declaration of Helsinki and its later amendments. The study protocol was approved by the Institutional Review Board of Tokyo Dental College, Ichikawa General Hospital (approval number: I2502–2504), and of the requirement for informed consent was waived because of the retrospective nature of the study. Data availability : Data are provided in the manuscript. Data supporting the results of this study are available from the corresponding authors upon reasonable request. Acknowledgements : We would like to thank Editage (www.editage.com) for English language editing. Author contributions : Yuki Shimizu, Kazuhiko Hanada, Takeaki Watanabe, and Keiko Kadota conceptualised the analysis. Yuki Shimizu was the chief investigator and performed the data analysis. Yuki Shimizu, Mari Araki, Keisuke Aoyama, and Tomoya Sakai collected the data from medical records. Yuki Shimizu, Kazuhiko Hanada, Takeaki Watanabe and Keiko Kadota interpreted the results. The first draft of the manuscript was written by Yuki Shimizu, and all authors commented on previous versions of the manuscript. All the authors have read and approved the final version of the manuscript. References Shea KW, Cunha BA (1995) Teicoplanin. Med Clin North Am 79:833–844. https://doi.org/10.1016/S0025-7125(16)30042-6 Wood MJ (1996) The comparative efficacy and safety of teicoplanin and vancomycin. J Antimicrob Chemother 37:209–222. https://doi.org/10.1093/jac/37.2.209 Cavalcanti AB, Goncalves AR, Almeida CS, Bugano DD, Silva E (2010) Teicoplanin versus vancomycin for proven or suspected infection. Cochrane Database Syst Rev :CD007022. https://doi.org/10.1002/14651858.CD007022.pub2 Watanabe E, Matsumoto K, Ikawa K, Yokoyama Y, Shigemi A, Enoki Y et al (2021) Pharmacokinetic/pharmacodynamic evaluation of teicoplanin against Staphylococcus aureus in a murine thigh infection model. J Glob Antimicrob Resist 24:83–87. https://doi.org/10.1016/j.jgar.2020.11.014 Ramos-Martín V, Johnson A, McEntee L, Farrington N, Padmore K, Cojutti P et al (2017) Pharmacodynamics of teicoplanin against MRSA. J Antimicrob Chemother 72:3382–3389. https://doi.org/10.1093/jac/dkx289 Hanai Y, Takahashi Y, Niwa T, Mayumi T, Hamada Y, Kimura T et al (2022) Clinical practice guidelines for therapeutic drug monitoring of teicoplanin: a consensus review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring. J Antimicrob Chemother 77:869–879. https://doi.org/10.1093/jac/dkab499 Hanai Y, Takahashi Y, Niwa T, Mayumi T, Hamada Y, Kimura T et al (2021) Optimal trough concentration of teicoplanin for the treatment of methicillin-resistant Staphylococcus aureus infection: A systematic review and meta-analysis. J Clin Pharm Ther 46:622–632. https://doi.org/10.1111/jcpt.13366 Ueda T, Takesue Y, Nakajima K, Ichiki K, Ishikawa K, Takai Y et al (2020) Clinical efficacy and safety in patients treated with teicoplanin with a target trough concentration of 20 µg/mL using a regimen of 12 mg/kg for five doses within the initial 3 days. BMC Pharmacol Toxicol 21:4–13. https://doi.org/10.1186/s40360-020-00424-3 Wilson AP (2000) Clinical Pharmacokinetics of Teicoplanin. Clin Pharmacokinet 39:167–183. https://doi.org/10.2165/00003088-200039030-00001 Assandri A, Bernareggi A (1987) Binding of teicoplanin to human serum albumin. Eur J Clin Pharmacol 33:191–195. https://doi.org/10.1007/BF00544566 Byrne CJ, Roberts JA, McWhinney B, Fennell JP, O’Byrne P, Deasy E et al (2017) Variability in trough total and unbound teicoplanin concentrations and achievement of therapeutic drug monitoring targets in adult patients with hematological malignancy. Antimicrob Agents Chemother 61:1–11. https://doi.org/10.1128/AAC.02466-16 Yano R, Nakamura T, Tsukamoto H, Igarashi T, Goto N, Wakiya Y et al (2007) Variability in teicoplanin protein binding and its prediction using serum albumin concentrations. Ther Drug Monit 29:399–403. https://doi.org/10.1097/FTD.0b013e3180690755 Yoshida T, Yoshida S, Okada H, Suzuki A, Niwa T, Suzuki K et al (2019) Risk factors for decreased teicoplanin trough concentrations during initial dosing in critically ill patients. Pharmazie 74:120–124. https://doi.org/10.1619/ph.2019.8731 Byrne CJ, Parton T, McWhinney B, Fennell JP, O’Byrne P, Deasy E et al (2018) Population pharmacokinetics of total and unbound teicoplanin concentrations and dosing simulations in patients with haematological malignancy. J Antimicrob Chemother 73:995–1003. https://doi.org/10.1093/jac/dkx473 Enokiya T, Muraki Y, Iwamoto T, Okuda M (2015) Changes in the pharmacokinetics of teicoplanin in patients with hyperglycaemic hypoalbuminaemia: Impact of albumin glycosylation on the binding of teicoplanin to albumin. Int J Antimicrob Agents 46:164–168. https://doi.org/10.1016/j.ijantimicag.2015.03.010 Sako K-I, Nakamaru Y, Ikawa K, Maeda T, Goto S, Ishihara Y et al (2021) Population Pharmacokinetics of Teicoplanin and Its Dosing Recommendations for Neutropenic Patients With Augmented Renal Clearance for Hematological Malignancies. Ther Drug Monit 43:519–526. https://doi.org/10.1097/FTD.0000000000000906 Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K et al (2009) Revised Equations for Estimated GFR From Serum Creatinine in Japan. Am J Kidney Dis 53:982–992. https://doi.org/10.1053/j.ajkd.2008.12.034 Seimiya M, Ohno S, Asano H, Fujiwara K, Yoshida T, Sawabe Y et al (2014) Change in albumin measurement method affects diagnosis of nephrotic syndrome. Clin Lab 60:1663–1667. https://doi.org/10.7754/clin.lab.2014.131105 Ueda T, Takesue Y, Nakajima K, Ichki K, Wada Y, Komatsu M et al (2014) High-dose regimen to achieve novel target trough concentration in teicoplanin. J Infect Chemother 20:43–47. https://doi.org/10.1016/j.jiac.2013.08.006 Ueda T, Takesue Y, Nakajima K, Ichiki K, Doita A, Wada Y et al (2016) Enhanced loading regimen of teicoplanin is necessary to achieve therapeutic pharmacokinetics levels for the improvement of clinical outcomes in patients with renal dysfunction. Eur J Clin Microbiol Infect Dis 35:1501–1509. https://doi.org/10.1007/s10096-016-2691-z Nakayama K, Gemma H, Kaibara A, Niwa T (2006) Population pharmacokinetics of teicoplanin in adult patients. Jpn J Chemother 54:1–6. https://doi.org/https://doi.org/10.11250/chemotherapy1995.54.1 Gomi I, Fukushima H, Shiraki M, Miwa Y, Ando T, Takai K et al (2007) Relationship between serum albumin level and aging in community-dwelling self-supported elderly population. J Nutr Sci Vitaminol 53:37–42. https://doi.org/10.3177/jnsv.53.37 Roberts JA, Lipman J (2009) Pharmacokinetic issues for antibiotics in the critically ill patient. Crit Care Med 37:840–851 quiz 859. https://doi.org/10.1097/CCM.0b013e3181961bff Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-6607696","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455201425,"identity":"924880b1-2d6d-4bcc-ba90-2bed0c86f900","order_by":0,"name":"Yuki Shimizu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFACHoYDDAYMDPwMIBIIGBuI1SLZRooWMDA4BtVCEBicP3vw0I2CusTN95s3fmCosWNgnk3AGoMbeQmHcwzYErcdYyuWYDiWzMA45wAhLTwGQC08QC08ZgwMbAcYGGckEHLYGZAWicTNbSAt/4jRciAHpMUgcQMbUAtjGxFaJG+AtSQYzziWViyR2JfMQ9AvfOfPGH/O+VMn2998eOOHD9/s5AwJhZgCipFAJ/EYzsCvg0Eew0h5CQJaRsEoGAWjYMQBAE0BRP6NvjzwAAAAAElFTkSuQmCC","orcid":"","institution":"Tokyo Dental College Ichikawa General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Shimizu","suffix":""},{"id":455201426,"identity":"06416565-bf00-4b11-ab99-a05827b3fa0f","order_by":1,"name":"Kazuhiko Hanada","email":"","orcid":"","institution":"Meiji Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Kazuhiko","middleName":"","lastName":"Hanada","suffix":""},{"id":455201427,"identity":"d89e10ca-d8c1-4594-a238-85847bceb1ca","order_by":2,"name":"Takeaki Watanabe","email":"","orcid":"","institution":"Tokyo Dental College Ichikawa General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Takeaki","middleName":"","lastName":"Watanabe","suffix":""},{"id":455201428,"identity":"88574a2a-4ea2-424b-a6b4-80a66d8794fe","order_by":3,"name":"Mari Araki","email":"","orcid":"","institution":"Tokyo Dental College Ichikawa General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mari","middleName":"","lastName":"Araki","suffix":""},{"id":455201429,"identity":"c86fd60c-eb9b-4094-916e-cad59a712547","order_by":4,"name":"Keisuke Aoyama","email":"","orcid":"","institution":"Tokyo Dental College Ichikawa General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Keisuke","middleName":"","lastName":"Aoyama","suffix":""},{"id":455201430,"identity":"b546ccc6-747a-43f9-945a-94e1fb49fbbe","order_by":5,"name":"Tomoya Sakai","email":"","orcid":"","institution":"Tokyo Dental College Ichikawa General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tomoya","middleName":"","lastName":"Sakai","suffix":""},{"id":455201431,"identity":"99bfc062-0a81-4a7d-be79-45595181be04","order_by":6,"name":"Keiko Kadota","email":"","orcid":"","institution":"Tokyo Dental College Ichikawa General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Keiko","middleName":"","lastName":"Kadota","suffix":""}],"badges":[],"createdAt":"2025-05-07 03:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6607696/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6607696/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82758798,"identity":"45bc719b-f6fc-4b14-9da0-91c4c2418a80","added_by":"auto","created_at":"2025-05-15 02:23:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107031,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart illustrating the patient selection process.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6607696/v1/b87036a16885fbe5b85ba26a.png"},{"id":82758795,"identity":"88903b0b-a2f4-44e4-9606-5933444fe2c1","added_by":"auto","created_at":"2025-05-15 02:23:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86827,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the total and predicted free TEIC concentrations after 3 days loading dose for each group of hypoalbuminaemia patients administered TEIC standard and high loading dose regimen.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6607696/v1/0c16cdca93fe6399b39d8a68.png"},{"id":84344868,"identity":"df66f48b-9c8b-496f-8676-b9bfa60ad80f","added_by":"auto","created_at":"2025-06-10 20:01:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1222959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6607696/v1/d03a945d-2214-4624-b9ee-f382f4740374.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of hypoalbuminaemia severity on total teicoplanin concentrations after 3 days loading dose: a retrospective observational study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTeicoplanin (TEIC) is a glycopeptide antibiotic used to treat infections caused by Gram-positive bacteria, including methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. TEIC not only shows similar efficacy as vancomycin (VCM) but also tends to have fewer adverse effects like nephrotoxicity and cutaneous rash compared with VCM [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The efficacy of TEIC is related to the ratio of the area under the concentration time curve (AUC) to the minimum inhibitory concentration [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Because AUC estimation software is unavailable in many institutions, trough concentration is generally recommended as a surrogate marker for therapeutic drug monitoring (TDM) in Japanese clinical practice [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Hanai et al. demonstrated that a total (bound and unbound) TEIC trough concentration within the range of 15\u0026ndash;30 \u0026micro;g/mL is a significant independent factor that contributed to successful treatment compared with a concentration of \u0026lt;\u0026thinsp;15 \u0026micro;g/mL, but adverse events were not significantly different [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, Ueda et al. reported that total TEIC trough concentrations\u0026thinsp;\u0026gt;\u0026thinsp;20 \u0026micro;g/mL were an independent factor for treatment success in cases of severe infection such as bacteraemia and osteomyelitis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. TEIC is excreted mainly from the kidneys and has a long half-life of 83\u0026ndash;163 h [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, a loading dose for 3 days is recommended for TEIC to achieve therapeutic blood levels immediately [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTEIC can bind to serum albumin (Alb) at levels of 90\u0026ndash;95% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, hypoalbuminaemia has been reported to increase the fraction of free TEIC [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous reports have shown that hypoalbuminaemia is a risk factor that decreases total TEIC concentrations after the loading dose [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This has been attributed to the increasing total clearance (CL) and volume of distribution (Vd) of TEIC caused by the increasing fraction of free TEIC; however, the underlying mechanism remains poorly understood. Furthermore, there are no reports on whether the effect of hypoalbuminaemia on total TEIC concentrations after the loading dose varies depending on the severity of the condition. Hypoalbuminaemia reduces the total TEIC concentrations but not free TEIC concentrations, which can affect therapeutic efficacy and cause drug side effects [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, it is necessary to interpret the results for total TEIC concentrations carefully. Although lowering the target total TEIC concentrations in hypoalbuminaemia is recommended in Japanese guidelines [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], specific total TEIC concentrations have not been defined because the impact of the severity of hypoalbuminaemia on total TEIC concentrations remains unclear.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate (1) the impact of the severity of hypoalbuminaemia on total TEIC concentrations after the loading dose and (2) the target total TEIC concentrations in hypoalbuminaemia cases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants and study design\u003c/h2\u003e \u003cp\u003eThis retrospective observational study was conducted at Tokyo Dental College, Ichikawa General Hospital. Patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years and administered TEIC between January 2017 and December 2024 were eligible for this study. Patients treated with TEIC for multiple periods were recruited for the first time only. The exclusion criteria were as follows: (1) patients receiving renal haemodialysis or peritoneal dialysis, (2) patients with incomplete data, and (3) patients whose TEIC concentrations were not measured 84\u0026ndash;96 h after the initial TEIC administration. The study protocol was approved by the Institutional Review Board of Tokyo Dental College, Ichikawa General Hospital (approval number: I2502\u0026ndash;2504), and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eWe extracted the following data from electronic medical records: patient background (age, sex, and body weight), comorbidities affecting the fraction of free TEIC and CL (diabetes, heart failure, and haematological malignancy) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], clinical laboratory data (serum creatinine [SCr], estimated glomerular filtration rate [eGFR], and serum Alb), TEIC exposure (TEIC loading dose for within 72 h of initial TEIC administration [D\u003csub\u003eloading\u003c/sub\u003e], and total TEIC trough concentrations 84\u0026ndash;96 h after initial TEIC administration [C\u003csub\u003etrough\u003c/sub\u003e] ), intensive care unit stay, and indication for TEIC.\u003c/p\u003e \u003cp\u003eComorbidities and indications for TEIC use were determined from the information provided by the physician in the electronic medical records. We calculated eGFR using the following prediction equation: eGFR\u0026thinsp;=\u0026thinsp;194 \u0026times; SCr\u003csup\u003e\u0026minus;\u0026thinsp;1.094\u003c/sup\u003e \u0026times; age\u003csup\u003e\u0026minus;\u0026thinsp;0.287\u003c/sup\u003e \u0026times; 0.739 (if female) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Serum Alb levels were recruited on the day closest to TEIC C\u003csub\u003etrough\u003c/sub\u003e measurements and measured using the modified bromocresol purple (BCP) method [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Total TEIC concentrations were measured within 1 h before administration, and the measurement methods were consistent with the latex agglutination turbidimetric immunoassay.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eTo assess TEIC concentrations after a loading dose for 3 days (within 72 h of the initial TEIC administration), the primary outcome was the TEIC C\u003csub\u003etrough\u003c/sub\u003e.\u003c/p\u003e\n\u003ch3\u003eInvestigation of factors influencing the total TEIC concentrations\u003c/h3\u003e\n\u003cp\u003eMultiple linear regression analysis was performed to investigate the factors influencing the total TEIC trough concentrations after the loading dose. The dependent variable was the total TEIC C\u003csub\u003etrough\u003c/sub\u003e, and the independent variables included patient background, comorbidities, clinical laboratory data, TEIC D\u003csub\u003eloading\u003c/sub\u003e, intensive care unit stay, and indications for TEIC.\u003c/p\u003e\n\u003ch3\u003eComparison of total and predicted free TEIC C among hypoalbuminaemia severity groups\u003c/h3\u003e\n\u003cp\u003eHypoalbuminaemia severity was stratified into three groups according to common terminology criteria for adverse events version 5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm#ctc\u003c/span\u003e\u003cspan address=\"https://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm#ctc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): Alb\u0026thinsp;\u0026lt;\u0026thinsp;2.0 g/dL (group 1), 2.0\u0026thinsp;\u0026le;\u0026thinsp;Alb\u0026thinsp;\u0026lt;\u0026thinsp;3.0 g/dL (group 2), and Alb\u0026thinsp;\u0026ge;\u0026thinsp;3.0 g/dL (group 3). Japanese guidelines recommend the following standard loading dose regimen by renal function to achieve TEIC trough concentrations of \u0026gt;\u0026thinsp;15 \u0026micro;g/mL after 3 days loading dose: 33.4 mg/kg/3 days, 40.0 mg/kg/3 days, and 50.0 mg/kg/3 days for eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, 30\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, and eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, recommended high loading dose regimen by renal function to achieve TEIC trough concentrations of \u0026gt;\u0026thinsp;20 \u0026micro;g/mL is 42.7 mg/kg/3 days, 48.0 mg/kg/3 days, and 60.0 mg/kg/3 days, respectively [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Among the patients included in this study, we selected those who were administered\u0026thinsp;\u0026plusmn;\u0026thinsp;5 mg/kg/3 days of the standard or high loading dose regimens by renal function and compared TEIC C\u003csub\u003etrough\u003c/sub\u003e among the three Alb groups. In addition, we estimated the predicted free TEIC concentrations using the equation developed by Yano et al.: predicted free TEIC concentrations\u0026thinsp;=\u0026thinsp;total TEIC concentrations/1\u0026thinsp;+\u0026thinsp;1.78 \u0026times; serum Alb [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This estimation equation was derived from the Scatchard equation from a single centre in Japan; the measured free TEIC concentrations were between 0.63 and 1.38 times the predicted concentrations in 95% of the cases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This estimation equation used serum Alb measured by the modified BCP method [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We also compared the predicted free TEIC C\u003csub\u003etrough\u003c/sub\u003e among the three Alb groups in patients administered the standard and high TEIC loading dose regimens.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison of pharmacokinetic parameters among severity groups of hypoalbuminaemia\u003c/h2\u003e \u003cp\u003eIn the exploratory study, individual pharmacokinetic (PK) parameters were compared for each Alb group. PK parameters were calculated using the Bayesian method from total TEIC C\u003csub\u003etrough\u003c/sub\u003e measurements according to the population pharmacokinetics (PPK) model equation by Nakayama et al. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The PPK model is a 2-compartment model derived from a single centre in Japan, and CL includes creatinine clearance and body weight as covariates [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\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\u003ePharmacokinetic parameters of teicoplanin reported by Nakayama et al.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD [range])\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter [interindividual variability, %]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;120 (305 serum samples)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL, L/h\u0026thinsp;=\u0026thinsp;0.00498 \u0026times; CCr\u0026thinsp;+\u0026thinsp;0.00426 \u0026times; Body weight [22.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u0026thinsp;=\u0026thinsp;75.5 \u0026plusmn; 11.6 [ 18\u0026thinsp;\u0026minus;\u0026thinsp;96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV, L\u0026thinsp;=\u0026thinsp;10.4 [26.7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight, kg\u0026thinsp;=\u0026thinsp;45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8 [27.0\u0026thinsp;\u0026minus;\u0026thinsp;75.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csub\u003e12\u003c/sub\u003e, /h\u0026thinsp;=\u0026thinsp;0.380 [\u0026minus;]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCr, mL/min\u0026thinsp;=\u0026thinsp;49.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.8 [5.3\u0026thinsp;\u0026minus;\u0026thinsp;154.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csub\u003e21\u003c/sub\u003e, /h\u0026thinsp;=\u0026thinsp;0.0485 [24.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin, g/dL\u0026thinsp;=\u0026thinsp;2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 [1.10\u0026thinsp;\u0026minus;\u0026thinsp;4.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eCCr, creatinine clearance; CL, clearance of teicoplanin; V, central volume of distribution; K\u003csub\u003e12\u003c/sub\u003e, transfer constant from the central to peripheral compartment; K\u003csub\u003e21\u003c/sub\u003e, transfer constant from the peripheral to central compartment\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using the JMP\u0026reg; Pro software version 17.2 (SAS Institute Inc., Cary, NC, USA). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, unless otherwise indicated.\u003c/p\u003e \u003cp\u003eNormally distributed continuous data of the three groups were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using analysis of variance (ANOVA), whereas data not following a normal distribution were expressed as the median and interquartile range (IQR) and compared using the Kruskal\u0026ndash;Wallis test. Categorical data were expressed as numbers (%) and compared using Pearson\u0026rsquo;s χ\u003csup\u003e2\u003c/sup\u003e-test.\u003c/p\u003e \u003cp\u003eIn the multiple linear regression analysis, potential independent variables were screened using the univariate linear regression method with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20. A stepwise forward selection method was used to determine the final model based on the Akaike information criterion at the minimum value. The final models described a multiple linear regression equation and the variance inflation factor (VIF).\u003c/p\u003e \u003cp\u003eIn estimating PK parameters using the Bayesian method, we used BMs-Pod version 8.06 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bmspod.web.fc2.com/\u003c/span\u003e\u003cspan address=\"https://bmspod.web.fc2.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is a freely available software based on Microsoft Excel (Microsoft, Redmond, WA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA flowchart of the patient selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 512 patients were administered TEIC between January 2017 and December 2024. Among them, 45 patients received renal haemodialysis or peritoneal dialysis, 97 patients had incomplete data, and 209 patients did not have their TEIC concentrations measured at 84\u0026ndash;96 h after the initial TEIC administration and were thus excluded from the study. The remaining 161 patients were included in the study. Among them, 58 patients were administered standard loading dose regimen\u0026thinsp;\u0026plusmn;\u0026thinsp;5 mg/kg/3 days and 39 patients were administered high loading dose regimen\u0026thinsp;\u0026plusmn;\u0026thinsp;5 mg/kg/3 days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eN indicates the number of patients\u003c/p\u003e \u003cp\u003eThe clinical data of all patients and the Alb groups are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The characteristics of the 161 patients were as follows: median age, 77 [IQR, 71\u0026ndash;83] years; median body weight, 50.2 [44.9\u0026thinsp;\u0026minus;\u0026thinsp;61.1] kg; median SCr, 1.0 [0.7\u0026ndash;1.7] mg/dL; median eGFR, 48.5 [28.7\u0026thinsp;\u0026minus;\u0026thinsp;81.7] mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e; and mean serum Alb, 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 g/dL. The mean TEIC D\u003csub\u003eloading\u003c/sub\u003e was 38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0 mg/kg/3 days, and the median total TEIC C\u003csub\u003etrough\u003c/sub\u003e was 14.2 [10.6\u0026thinsp;\u0026minus;\u0026thinsp;17.7] \u0026micro;g/mL.\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\u003ePatient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003cp\u003eAlb\u0026thinsp;\u0026lt;\u0026thinsp;2.0 g/dL\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003cp\u003e2.0\u0026thinsp;\u0026le;\u0026thinsp;Alb\u0026thinsp;\u0026lt;\u0026thinsp;3.0 g/dL\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003cp\u003e3.0 g/dL\u0026thinsp;\u0026le;\u0026thinsp;Alb\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003e\u003cb\u003ePatient background\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 [71\u0026thinsp;\u0026minus;\u0026thinsp;83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.2 [44.9\u0026thinsp;\u0026minus;\u0026thinsp;61.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaematological malignancy, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical laboratory data\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 [0.7\u0026thinsp;\u0026minus;\u0026thinsp;1.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 [0.6\u0026thinsp;\u0026minus;\u0026thinsp;2.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 [0.7\u0026thinsp;\u0026minus;\u0026thinsp;1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9 [0.7\u0026thinsp;\u0026minus;\u0026thinsp;1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.5 [28.7\u0026thinsp;\u0026minus;\u0026thinsp;81.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.3 [23.5\u0026thinsp;\u0026minus;\u0026thinsp;80.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.6 [28.3\u0026thinsp;\u0026minus;\u0026thinsp;83.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.3 [33.0\u0026thinsp;\u0026minus;\u0026thinsp;70.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Alb, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 [1.5\u0026thinsp;\u0026minus;\u0026thinsp;1.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 [2.1\u0026thinsp;\u0026minus;\u0026thinsp;2.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2 [3.0\u0026thinsp;\u0026minus;\u0026thinsp;3.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTEIC exposure\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEIC loading dose for 3 days, mg/kg/3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEIC trough concentrations, \u0026micro;g/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2 [10.6\u0026thinsp;\u0026minus;\u0026thinsp;17.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.7 [9.2\u0026thinsp;\u0026minus;\u0026thinsp;14.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.1 [11.2\u0026thinsp;\u0026minus;\u0026thinsp;17.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.7 [15.1\u0026thinsp;\u0026minus;\u0026thinsp;21.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntensive care unit stay, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndication for TEIC\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteraemia, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatheter-related bloodstream infection, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebrile neutropenia, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumonia, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary tract infection, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin and soft tissue infection, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholangitis/Cholecystitis, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNormally distributed data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, whereas non-normally distributed data are expressed as medians (interquartile ranges). Categorical data are expressed as numbers (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eN, number; eGFR, estimated glomerular filtration rate; Alb, albumin; TEIC, teicoplanin\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003ePearson\u0026rsquo;s χ\u003csup\u003e2\u003c/sup\u003e test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eANOVA\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003eKruskal\u0026minus;Wallis test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough there were no significant differences in eGFR (p\u0026thinsp;=\u0026thinsp;0.44) and D\u003csub\u003eloading\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.19) among the three Alb groups, significant differences in the total TEIC C\u003csub\u003etrough\u003c/sub\u003e were observed among the three Alb groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the lower Alb group exhibiting a lower total TEIC C\u003csub\u003etrough\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors influencing total TEIC concentrations\u003c/h2\u003e \u003cp\u003eThe results of the multiple linear regression analyses are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Univariate linear regression analysis revealed that age, sex, serum Alb, and TEIC D\u003csub\u003eloading\u003c/sub\u003e were independent variables. Using a stepwise forward selection method, we included age, serum Alb, and TEIC D\u003csub\u003eloading\u003c/sub\u003e as variables in the final model to assess the factors influencing the total TEIC C\u003csub\u003etrough\u003c/sub\u003e. The multiple regression equation was as follows: total TEIC C\u003csub\u003etrough\u003c/sub\u003e = 4.07\u0026ndash;0.07 \u0026times; Age\u0026thinsp;+\u0026thinsp;2.63 \u0026times; Serum Alb\u0026thinsp;+\u0026thinsp;0.25 \u0026times; TEIC D\u003csub\u003eloading\u003c/sub\u003e (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.37, Age; t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.5, p\u0026thinsp;=\u0026thinsp;0.01, Serum Alb; t\u0026thinsp;=\u0026thinsp;4.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and TEIC D\u003csub\u003eloading\u003c/sub\u003e; t\u0026thinsp;=\u0026thinsp;7.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression analysis for the total TEIC concentrations after 3 days loading dose\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\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 \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.13 to 10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, per years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.13 to \u0026minus;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin, per g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43 to 3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTEIC loading dose for 3 days, per mg/kg/3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19 to 0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eβ, standardised partial regression coefficient; SE, standard error; 95%CI, 95% confidence interval; VIF, variance inflation factor; NA, not available; TEIC, teicoplanin\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eWe summarised the independent variables with \u0026lt;\u0026thinsp;0.20 in the univariate linear regression analysis. A total of 161 patients were included in the multiple linear regression analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of total and predicted free TEIC C\u003csub\u003etrough\u003c/sub\u003e among the three hypoalbuminaemia groups\u003c/h2\u003e \u003cp\u003eOf the 58 patients administered standard loading dose regimen, there were 15, 27, and 16 patients with eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, 30\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, and eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, respectively; there were 10, 35, and 13 patients in groups 1, 2, and 3, respectively. Among the 39 patients administered the high loading dose regimen, 15, 18, and 6 patients had eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, 30\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, and eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, respectively, while groups 1, 2, and 3 had 12, 19, and 8 patients, respectively.\u003c/p\u003e \u003cp\u003eIn the standard loading dose regimen, the median TEIC D\u003csub\u003eloading\u003c/sub\u003e was 39.5 [33.8\u0026thinsp;\u0026minus;\u0026thinsp;45.2], 39.8 [35.4\u0026thinsp;\u0026minus;\u0026thinsp;45.8], and 41.1 [35.7\u0026thinsp;\u0026minus;\u0026thinsp;46.2] mg/kg/3 days for groups 1, 2, and 3, respectively, with no significant difference (p\u0026thinsp;=\u0026thinsp;0.82); however, the median total TEIC C\u003csub\u003etrough\u003c/sub\u003e was significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) among the three groups at 9.9 [9.2\u0026thinsp;\u0026minus;\u0026thinsp;12.1], 15.2 [12.5\u0026thinsp;\u0026minus;\u0026thinsp;17.8], and 17.1 [14.6\u0026thinsp;\u0026minus;\u0026thinsp;20.8] \u0026micro;g/mL, respectively. Similarly, in the high loading dose regimen, although the median total TEIC D\u003csub\u003eloading\u003c/sub\u003e was not significantly different among the three groups\u0026mdash;44.0 [40.9\u0026thinsp;\u0026minus;\u0026thinsp;51.6], 44.8 [43.5\u0026thinsp;\u0026minus;\u0026thinsp;49.5], and 48.6 [45.7\u0026thinsp;\u0026minus;\u0026thinsp;52.5] mg/kg/3 days for groups 1, 2, and 3, respectively (p\u0026thinsp;=\u0026thinsp;0.38), the median total TEIC C\u003csub\u003etrough\u003c/sub\u003e was 13.5 [9.6\u0026thinsp;\u0026minus;\u0026thinsp;16.4], 17.6 [16.7\u0026thinsp;\u0026minus;\u0026thinsp;19.8], and 20.7 [17.4\u0026thinsp;\u0026minus;\u0026thinsp;23.6] \u0026micro;g/mL, respectively, showing significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As the severity of hypoalbuminaemia increased, the total TEIC C\u003csub\u003etrough\u003c/sub\u003e decreased. Meanwhile, the median predicted free TEIC C\u003csub\u003etrough\u003c/sub\u003e for groups 1, 2, and 3 was 2.7 [2.4\u0026thinsp;\u0026minus;\u0026thinsp;3.0], 2.9 [2.5\u0026thinsp;\u0026minus;\u0026thinsp;3.2], and 2.7 [2.3\u0026thinsp;\u0026minus;\u0026thinsp;3.2] \u0026micro;g/mL, respectively, in the standard loading dose regimen, showing no significant differences (p\u0026thinsp;=\u0026thinsp;0.71), and 3.2 [2.5\u0026thinsp;\u0026minus;\u0026thinsp;4.1], 3.3 [2.9\u0026thinsp;\u0026minus;\u0026thinsp;4.0], and 3.1 [2.7\u0026thinsp;\u0026minus;\u0026thinsp;3.6] \u0026micro;g/mL, respectively, in the high loading dose region, again exhibiting no significant difference (p\u0026thinsp;=\u0026thinsp;0.64) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData are presented as the median, IQR, and range. Each albumin groups were compared using the Kruskal\u0026thinsp;\u0026minus;\u0026thinsp;Wallis test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of pharmacokinetic parameters among hypoalbuminaemia severity groups\u003c/h2\u003e \u003cp\u003eThe individual PK parameters of each Alb group are listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. CL (L/h) was 0.39 [0.31\u0026thinsp;\u0026minus;\u0026thinsp;0.60], 0.44 [0.35\u0026thinsp;\u0026minus;\u0026thinsp;0.59], and 0.45 [0.36\u0026thinsp;\u0026minus;\u0026thinsp;0.52] for groups 1, 2 and 3, with no significant difference (p\u0026thinsp;=\u0026thinsp;0.67). Meanwhile, the steady state volume of distribution (Vss) (L) was 113.3 [89.9\u0026thinsp;\u0026minus;\u0026thinsp;144.1], 93.1 [79.4\u0026thinsp;\u0026minus;\u0026thinsp;111.2], and 84.3 [68.4\u0026thinsp;\u0026minus;\u0026thinsp;101.1], for groups 1, 2 and 3, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that Vss increased with decreasing serum Alb. Similarly, the volume of distribution of central compartment (V\u003csub\u003e1\u003c/sub\u003e) and volume of distribution of peripheral compartment (V\u003csub\u003e2\u003c/sub\u003e) tended to increase with decreasing serum Alb (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, V\u003csub\u003e1\u003c/sub\u003e and V\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePharmacokinetic parameters of total TEIC for each albumin group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003cp\u003eAlb\u0026thinsp;\u0026lt;\u0026thinsp;2.0 g/dL\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003cp\u003e2.0\u0026thinsp;\u0026le;\u0026thinsp;Alb\u0026thinsp;\u0026lt;\u0026thinsp;3.0 g/dL\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003cp\u003e3.0 g/dL\u0026thinsp;\u0026le;\u0026thinsp;Alb\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;32)\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\u003eCL, L/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0.39 [0.31\u0026thinsp;\u0026minus;\u0026thinsp;0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e0.44 [0.35\u0026thinsp;\u0026minus;\u0026thinsp;0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e0.45 [0.36\u0026thinsp;\u0026minus;\u0026thinsp;0.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVss, L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e113.3 [89.9\u0026thinsp;\u0026minus;\u0026thinsp;144.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e93.1 [79.4\u0026thinsp;\u0026minus;\u0026thinsp;111.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e84.3 [68.4\u0026thinsp;\u0026minus;\u0026thinsp;101.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003csub\u003e1\u003c/sub\u003e, L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e11.5 [10.3\u0026thinsp;\u0026minus;\u0026thinsp;12.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e10.5 [9.6\u0026thinsp;\u0026minus;\u0026thinsp;11.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e9.9 [8.6\u0026thinsp;\u0026minus;\u0026thinsp;10.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003csub\u003e2\u003c/sub\u003e, L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e101.8 [79.7\u0026thinsp;\u0026minus;\u0026thinsp;131.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e82.6 [69.9\u0026thinsp;\u0026minus;\u0026thinsp;99.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e74.4 [59.8\u0026thinsp;\u0026minus;\u0026thinsp;90.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData were expressed as medians (interquartile ranges).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCL, clearance of teicoplanin; Vss, steady state volume of distribution; V\u003csub\u003e1\u003c/sub\u003e, volume of distribution of central compartment; V\u003csub\u003e2\u003c/sub\u003e, volume of distribution of peripheral compartment.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eEach albumin groups were compared using the Kruskal\u0026ndash;Wallis test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study evaluated the impact of hypoalbuminaemia severity on the total TEIC concentration after the loading dose. We found that hypoalbuminaemia was a significant factor that decreased the total TEIC concentrations after the loading dose, with more severe hypoalbuminaemia leading to lower total TEIC concentrations; however, hypoalbuminaemia did not affect the predicted free TEIC concentrations. Furthermore, the reason for hypoalbuminaemia lowering the total TEIC concentrations was attributed to increasing Vd rather than increasing CL. Clinicians should consider serum Alb levels when performing TDM for total TEIC concentrations and lower the target total TEIC concentrations in cases of severe hypoalbuminaemia.\u003c/p\u003e \u003cp\u003eSignificant factors that decreased the total TEIC C\u003csub\u003etrough\u003c/sub\u003e were older age, hypoalbuminaemia, and TEIC D\u003csub\u003eloading\u003c/sub\u003e. Similar to previous reports [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], hypoalbuminaemia was a significant factor in our study. Considering the standardised partial regression coefficient of 0.46 for D\u003csub\u003eloading\u003c/sub\u003e and 0.28 for serum Alb, it was suggested that serum Alb relatively influenced the toral TEIC C\u003csub\u003etrough\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In general, older patients tend to have lower serum Alb levels [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and the patients included in our study were also mostly older adults with lower serum Alb levels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although multiple linear regression analysis showed that the VIF for age and serum Alb were less than 10, with no multicollinearity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), they might have been potentially related. In our study, univariate linear regression analysis revealed that serum creatinine and eGFR were not potentially independent variables influencing the total TEIC C\u003csub\u003etrough\u003c/sub\u003e after 3 days loading dose. Because TEIC is excreted mainly by the kidneys, renal function significantly affects the CL of TEIC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Additionally, because TEIC has a long half-life of 83\u0026ndash;163 h [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], a steady state was not achieved at 84\u0026ndash;96 h after the initial TEIC administration. Total TEIC concentrations after the loading dose might have been substantially influenced by TEIC D\u003csub\u003eloading\u003c/sub\u003e and Vd but not by renal function.\u003c/p\u003e \u003cp\u003eTwo previous studies on standard loading dose regimen have reported that the percentage of patients achieving TEIC C\u003csub\u003etrough\u003c/sub\u003e \u0026gt; 15 \u0026micro;g/mL at day 4 were 76.1% (137/180 patients) and 68.3% (41/60 patients) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, none of these reports described serum Alb levels. In our study, none of patients achieved a total TEIC C\u003csub\u003etrough\u003c/sub\u003e \u0026gt;15 \u0026micro;g/mL even though TEIC was administered according to the standard loading dose regimen in group 1 (serum Alb\u0026thinsp;\u0026lt;\u0026thinsp;2.0 g/dL). Regarding the high loading dose regimen, Ueda et al. showed that the severity of hypoalbuminaemia reduced TEIC trough concentrations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], consistent with our results. Previous reports have shown that hypoalbuminaemia decreases total TEIC concentrations but not free TEIC concentrations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our study as well, the total TEIC C\u003csub\u003etrough\u003c/sub\u003e decreased with the severity of hypoalbuminaemia, whereas the predicted TEIC C\u003csub\u003etrough\u003c/sub\u003e showed no significant difference among the Alb groups in either of the regimens. Prediction equations for free TEIC concentrations have been reported by Yano et al. and Byrne et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Yano et al. developed the prediction equations in Japan using only total TEIC concentrations and serum Alb levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while the prediction equations proposed by Byrne et al. were developed in Ireland and are more complicated to calculate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Since both prediction equations had comparable accuracy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], we adopted the Yano et al. equation because the patients in our study were similar to theirs. Our findings suggest that when serum Alb\u0026thinsp;\u0026lt;\u0026thinsp;2.0 g/dL, target total TEIC concentrations should be reduced to 10 and 13 \u0026micro;g/mL in the standard and high dose regimen, respectively. However, whether lowering the target total TEIC trough concentrations in accordance with the serum Alb level would have an equivalent clinical effect remains unclear and requires further investigation.\u003c/p\u003e \u003cp\u003eAlthough the CL of TEIC has been found to be very low at 11 mL/h/kg in healthy volunteers [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], hypoalbuminaemia reduces total TEIC trough concentrations after 84\u0026thinsp;\u0026minus;\u0026thinsp;96 h, which is not a steady state. This suggests that the Vd variation was more involved in reducing total TEIC concentrations than the CL variation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). TEIC had a large Vd of more than 50 L [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In general, the Vd of a total drug with Vd\u0026thinsp;\u0026gt;\u0026thinsp;50 L is proportional to fraction unbound in blood (fuB). Yano et al. showed that TEIC fuB was approximately 0.15, 0.18, and 0.23 for serum Alb 3.0, 2.5, and 2.0 g/dL, respectively [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In our study, Vss increased proportionally with fuB. In contrast, Vd of the free drug is generally unaffected by fuB. We calculated the PK parameters using the Bayesian method from the PPK model developed by Nakayama et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This PPK model included creatinine clearance and body weight as covariates of CL [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As there were no significant differences in body weight and SCr in groups 1, 2, and 3 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we cannot rule out the possibility that no significant differences were found in CL among these three groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Further studies on variations in PK parameters in hypoalbuminaemia are required.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, this was a single-centre retrospective observational study, and potential bias could not be avoided. Second, we could not assess the severity of sepsis based on the Sequential Organ Failure Assessment score, as data on the level of consciousness could not be collected. To assess severity, our study used intensive care unit stay as a surrogate index, but no significant difference was detected among the three groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, a higher incidence of bacteraemia was reported in groups 1 and 2 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, sepsis could have been more frequent and severe in groups 1 and 2 than in group 3. Sepsis may have affected C\u003csub\u003etrough\u003c/sub\u003e because it increases Vd [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Third, we recruited patients who were administered standard and high loading dose regimens to compare the three serum Alb groups, but the sample size might have been insufficient for each group. Fourth, because the free TEIC concentrations were not measured, we used the values predicted by the prediction equations. Finally, as previously mentioned, we calculated the PK parameters using the Bayesian method according to the PPK model equation of Nakayama et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, we cannot dismiss the possibility that this was inadequate.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHypoalbuminaemia is a significant factor that decreases total TEIC concentrations after the loading dose. The greater the severity of hypoalbuminaemia, the lower the total TEIC concentration after the loading dose, which is possibly caused by the elevated Vd for total TEIC. However, hypoalbuminaemia did not affect the predicted free TEIC concentrations. Clinicians should consider serum Alb levels when performing TDM for total TEIC and aim to reduce the target total TEIC concentration in cases of severe hypoalbuminaemia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e The authors did not receive support from any organisation for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement:\u003c/strong\u003e This observational study adhered to the principles of the Declaration of Helsinki and its later amendments. The study protocol was approved by the Institutional Review Board of Tokyo Dental College, Ichikawa General Hospital (approval number: I2502–2504), and of the requirement for informed consent was waived because of the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Data are provided in the manuscript. Data supporting the results of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e We would like to thank Editage (www.editage.com) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Yuki Shimizu, Kazuhiko Hanada, Takeaki Watanabe, and Keiko Kadota conceptualised the analysis. Yuki Shimizu was the chief investigator and performed the data analysis. Yuki Shimizu, Mari Araki, Keisuke Aoyama, and Tomoya Sakai collected the data from medical records. Yuki Shimizu, Kazuhiko Hanada, Takeaki Watanabe and Keiko Kadota interpreted the results. The first draft of the manuscript was written by Yuki Shimizu, and all authors commented on previous versions of the manuscript. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShea KW, Cunha BA (1995) Teicoplanin. 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Crit Care Med 37:840\u0026ndash;851 quiz 859. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/CCM.0b013e3181961bff\u003c/span\u003e\u003cspan address=\"10.1097/CCM.0b013e3181961bff\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Teicoplanin, Serum albumin, Hypoalbuminaemia, Total concentrations, Loading dose","lastPublishedDoi":"10.21203/rs.3.rs-6607696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6607696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eHypoalbuminaemia is known to decrease total teicoplanin (TEIC) concentration; however, whether total TEIC concentration varies depending on hypoalbuminaemia severity remains unclear. This study investigated the correlation between the severity of hypoalbuminaemia and total TEIC concentrations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective observational study was conducted on patients (\u0026ge;\u0026thinsp;18 years) administered TEIC between January 2017 and December 2024. Factors influencing total TEIC trough concentrations after 3 days loading dose (C\u003csub\u003etrough\u003c/sub\u003e) were determined using multiple linear regression. Serum albumin levels were stratified into three groups. The total and predicted free TEIC C\u003csub\u003etrough\u003c/sub\u003e of patients administered standard and high loading dose regimens were compared among the groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe total TEIC C\u003csub\u003etrough\u003c/sub\u003e was negatively correlated with age (p\u0026thinsp;=\u0026thinsp;0.01) and positively correlated with serum albumin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and TEIC loading dose for within 3 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The median total TEIC C\u003csub\u003etrough\u003c/sub\u003e for the three groups were significantly different in both dosing regimens: standard, 9.9 [interquartile range, 9.2\u0026thinsp;\u0026minus;\u0026thinsp;12.1], 15.2 [12.5\u0026thinsp;\u0026minus;\u0026thinsp;17.8], and 17.1 [14.6\u0026thinsp;\u0026minus;\u0026thinsp;20.8] \u0026micro;g/mL; high, 13.5 [9.6\u0026thinsp;\u0026minus;\u0026thinsp;16.4], 17.6 [16.7\u0026thinsp;\u0026minus;\u0026thinsp;19.8], and 20.7 [17.4\u0026thinsp;\u0026minus;\u0026thinsp;23.6] \u0026micro;g/mL for albumin\u0026thinsp;\u0026lt;\u0026thinsp;2.0 g/dL, 2.0\u0026thinsp;\u0026le;\u0026thinsp;albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.0 g/dL, and albumin\u0026thinsp;\u0026ge;\u0026thinsp;3.0 g/dL, respectively. However, the predicted free TEIC C\u003csub\u003etrough\u003c/sub\u003e was not significantly different among the albumin groups for either regimen.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe more severe the hypoalbuminaemia, the lower the total TEIC C\u003csub\u003etrough\u003c/sub\u003e, but predicted free TEIC C\u003csub\u003etrough\u003c/sub\u003e was not affected. Clinicians should consider lowering the target total TEIC concentrations in severe hypoalbuminaemia cases.\u003c/p\u003e","manuscriptTitle":"The impact of hypoalbuminaemia severity on total teicoplanin concentrations after 3 days loading dose: a retrospective observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 02:23:24","doi":"10.21203/rs.3.rs-6607696/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":"801d3ad0-7eab-4c5b-a64a-0a0b9f5d07b8","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T19:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 02:23:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6607696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6607696","identity":"rs-6607696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}