A Risk Prediction Flowchart of Vancomycin-Induced Acute Kidney Injury to Use When Starting Vancomycin Administration: A Multicenter Retrospective Study

Development and validation of a nomogram to predict the risk of vancomycin-related acute kidney injury in critical care patients

Background

Vancomycin-associated acute kidney injury (AKI) leads to underestimated morbidity in the intensive care unit (ICU). It is significantly important to predict its occurrence in advance. However, risk factors and nomograms to predict this AKI are limited.

Methods

This was a retrospective analysis of two databases. A total of 1,959 patients diagnosed with AKI and treated with vancomycin were enrolled from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. According to the 7:3 ratio, the training set (n = 1,372) and the internal validation set (n = 587) were randomly allocated. The external validation set included 211 patients from the eICU Collaborative Research Database (eICU). Next, to screen potential variables, the least absolute shrinkage and selection operator (LASSO) regression was utilized. Subsequently, the nomogram was developed by the variables of the selected results in the multivariable logistic regression. Finally, discrimination, calibration, and clinical utility were evaluated to validate the nomogram.

Results

The constructed nomogram showed fine discrimination in the training set (area under the receiver operator characteristic curve [AUC] = 0.791; 95% confidence interval [CI]: 0.758-0.823), internal validation set (AUC = 0.793; 95% CI: 0.742-0.844), and external validation set (AUC = 0.755; 95% CI: 0.663-0.847). Moreover, it also well demonstrated calibration and clinical utility. The significant improvement (P < 0.001) in net reclassification improvement (NRI) and integrated differentiation improvement (IDI) confirmed that the predictive model outperformed others.

Conclusion

This established nomogram indicated promising performance in determining individual AKI risk of vancomycin-treated critical care patients, which will be beneficial in making clinical decisions.

Flowchart for predicting achieving the target area under the concentration-time curve of vancomycin in critically ill Japanese patients: A multicenter retrospective study

INTRODUCTION

In therapeutic drug monitoring (TDM) of vancomycin (VCM), the area under the concentration-time curve (AUC) is related to the clinical efficacy and toxicity. Therefore, herein, we examined the factors associated with achieving the target AUC at follow-up and developed a decision flowchart for achieving the target AUC in critically ill patients.

METHODS

This multicenter retrospective observational study was conducted at eight hospitals. We retrospectively analyzed data from patients who had received VCM in the intensive care unit from January 2020 to December 2022. Decision-tree (DT) analysis was performed using factors with p < 0.1 in univariate analysis as the independent variables. Case data were split up to two times, and four subgroups were included. The primary endpoint was achieving the target AUC at the follow-up TDM (AUCfollow-up) and target AUCfollow-up achievement was defined as an AUC of 400-600 μg‧h/mL. The initial AUC values were calculated with the 2-point concentrations (peak and trough) using the Bayesian estimation software Practical AUC-guided TDM (PAT).

RESULTS

Among 70 patients (median age [interquartile range], 66 [56, 79] years; 50 % women), the AUCfollow-up was achieved in 70 % (49/70). Three factors were selected for the decision flow chart: predicted AUCfollow-up of 400-600 μg‧h/mL, dosing at 12-h intervals, and CCr of 130 mL/min/1.73 m2 or higher; the accuracy was adequate (92 %, R2 0.52).

CONCLUSION

We successfully identified the factors associated with achieving the target AUC of VCM at follow-up TDM and developed a simple-to-use DT model. However, the validity of the findings needs to be evaluated.

Prevention and management of antibiotic associated acute kidney injury in critically ill patients: new insights

PURPOSE OF REVIEW

Drug associated kidney injury (D-AKI) occurs in 19-26% of hospitalized patients and ranks as the third to fifth leading cause of acute kidney injury (AKI) in the intensive care unit (ICU). Given the high use of antimicrobials in the ICU and the emergence of new resistant organisms, the implementation of preventive measures to reduce the incidence of D-AKI has become increasingly important.

RECENT FINDINGS

Artificial intelligence is showcasing its capabilities in early recognition of at-risk patients for acquiring AKI. Furthermore, novel synthetic medications and formulations have demonstrated reduced nephrotoxicity compared to their traditional counterparts in animal models and/or limited clinical evaluations, offering promise in the prevention of D-AKI. Nephroprotective antioxidant agents have had limited translation from animal studies to clinical practice. The control of modifiable risk factors remains pivotal in avoiding D-AKI.

SUMMARY

The use of both old and new antimicrobials is increasingly important in combating the rise of resistant organisms. Advances in technology, such as artificial intelligence, and alternative formulations of traditional antimicrobials offer promise in reducing the incidence of D-AKI, while antioxidant medications may aid in minimizing nephrotoxicity. However, maintaining haemodynamic stability using isotonic fluids, drug monitoring, and reducing nephrotoxic burden combined with vigilant antimicrobial stewardship remain the core preventive measures for mitigating D-AKI while optimizing effective antimicrobial therapy.

Assessing Clinical Outcomes of Vancomycin Treatment in Adult Patients with Vancomycin-Susceptible Enterococcus faecium Bacteremia

PURPOSE

Enterococcal bacteremia is associated with high mortality and long-term hospitalization. Here, we aimed to investigate the clinical outcomes and evaluate the risk factors for mortality in adult patients treated with vancomycin (VCM) for vancomycin-susceptible Enterococcus faecium (E. faecium) bacteremia.

METHODS

This is a retrospective, record-based study. The data were collected from inpatients at a single university hospital between January 2009 and December 2020. The area under the curve (AUC) of VCM was calculated using the Bayesian approach. The primary outcome was a 30-day in-hospital mortality.

RESULTS

A univariate analysis showed significant differences in the concomitant use of vasopressors, history of the use of no clinically relevant activity antimicrobial agents against E. faecium, VCM plasma trough concentration, and renal dysfunction during VCM administration between the 30-day in-hospital mortality and survival groups. However, the groups' AUC/minimum inhibitory concentration (MIC) were not significantly different. A multivariate analysis suggested that concomitant vasopressors may be an independent risk factor for 30-day in-hospital mortality (odds ratio, 7.81; 95% confidence interval, 1.16-52.9; p = 0.035). The VCM plasma trough concentrations and the AUC/MIC in the mortality group were higher than those in the surviving group. No association between the AUC/MIC and the treatment effect in E. faecium bacteremia was assumed, because the known, target AUC/MIC were sufficiently achieved in the mortality group.

CONCLUSIONS

There may be no association between the AUC/MIC and the treatment effect in E. faecium bacteremia. When an immunocompromised host develops E. faecium bacteremia with septic shock, especially when a vasopressor is used in a patient with unstable hemodynamics, it may be difficult to treat it, despite efforts to ensure the appropriate AUC/MIC and therapeutic vancomycin concentration levels.

Machine Learning-Based Prediction of Digoxin Toxicity in Heart Failure: A Multicenter Retrospective Study

Digoxin toxicity (plasma digoxin concentration ≥0.9 ng/mL) is associated with worsening heart failure (HF). Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of adverse drug reactions. The present study aimed to construct a flowchart using DT analysis that can be used by medical staff to predict digoxin toxicity. We conducted a multicenter retrospective study involving 333 adult patients with HF who received oral digoxin treatment. In this study, we employed a chi-squared automatic interaction detection algorithm to construct DT models. The dependent variable was set as the plasma digoxin concentration (≥ 0.9 ng/mL) in the trough during the steady state, and factors with p < 0.2 in the univariate analysis were set as the explanatory variables. Multivariate logistic regression analysis was conducted to validate the DT model. The accuracy and misclassification rates of the model were evaluated. In the DT analysis, patients with creatinine clearance <32 mL/min, daily digoxin dose ≥1.6 µg/kg, and left ventricular ejection fraction ≥50% showed a high incidence of digoxin toxicity (91.8%; 45/49). Multivariate logistic regression analysis revealed that creatinine clearance <32 mL/min and daily digoxin dose ≥1.6 µg/kg were independent risk factors. The accuracy and misclassification rates of the DT model were 88.2 and 46.2 ± 2.7%, respectively. Although the flowchart created in this study needs further validation, it is straightforward and potentially useful for medical staff in determining the initial dose of digoxin in patients with HF.

Risk evaluation of carbapenem-induced liver injury based on machine learning analysis

INTRODUCTION

Information regarding carbapenem-induced liver injury is limited, and the rate of liver injury caused by meropenem (MEPM) and doripenem (DRPM) remains unknown. Decision tree (DT) analysis, a machine learning method, has a flowchart-like model where users can easily predict the risk of liver injury. Thus, we aimed to compare the rate of liver injury between MEPM and DRPM and construct a flowchart that can be used to predict carbapenem-induced liver injury.

METHODS

We investigated patients treated with MEPM (n = 310) or DRPM (n = 320) and confirmed liver injury as the primary outcome. We used a chi-square automatic interaction detection algorithm to construct DT models. The dependent variable was set as liver injury from a carbapenem (MEPM or DRPM), and factors including alanine aminotransferase (ALT), albumin-bilirubin (ALBI) score, and concomitant use of acetaminophen were used as explanatory variables.

RESULTS

The rates of liver injury were 22.9% (71/310) and 17.5% (56/320) in the MEPM and DRPM groups, respectively; no significant differences in the rate were observed (95% confidence interval: 0.710-1.017). Although the DT model of MEPM could not be constructed, DT analysis showed that the incidence of introducing DRPM in patients with ALT >22 IU/L and ALBI scores > -1.87 might be high-risk.

CONCLUSIONS

The risk of developing liver injury did not differ significantly between the MEPM and DRPM groups. Since ALT and ALBI score are evaluated in clinical settings, this DT model is convenient and potentially useful for medical staff in assessing liver injury before DRPM administration.

Use of Japanese big data from electronic medical records to investigate risk factors and identify their high-risk combinations for linezolid-induced thrombocytopenia

PURPOSE

Thrombocytopenia is a major event associated with linezolid (LZD) therapy. Factors affecting LZD-induced thrombocytopenia (LIT) have been reported in previous studies. However, several issues pertaining to LIT have not yet been clarified. In the present study, we used Japanese big data to investigate associated factors and their high-risk combinations that influence LIT.

METHODS

Patients administered LZD between May 2006 and October 2020 were included in this study. LIT was defined as either a 30% or more reduction from the baseline platelets or platelet values of < 100,000/µL. We evaluated factors affecting LIT and combinations of factors that alter LIT risk according to a decision tree (DT) analysis, a typical machine learning method.

RESULTS

We successfully enrolled 1399 patients and LIT occurred in 44.7% of the patients (n = 626). We classified the laboratory data on renal function, LZD duration, age, and body weight (BW) into smaller categories. The results of multivariate analysis showed that prolonged LZD therapy, BW < 45 kg, estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m², and dialysis were risk factors for LIT. The DT analysis revealed that the highest risk was a combination of LZD duration ≥ 14 days and eGFR < 30 mL/min/1.73 m².

CONCLUSIONS

The present study extracted four risk factors and identified high-risk combinations for LIT. Patients with these risk factors should be closely monitored.

The influence of vancomycin on renal functions, the predictors and associated factors for nephrotoxicity

BACKGROUND

Vancomycin has been widely used in the last six decades to treat methicillin-resistant S. aureus (MRSA) and other resistant gram-positive infections. The risk of vancomycin toxicity increases with the utilization of higher doses while treating the resistant form of bacterial infections. Nephrotoxicity is one of the major complications reported to be a hinderance in the prognosis of vancomycin therapy.

OBJECTIVES

This hospital-based study aimed to highlight the influence of vancomycin on renal function with special emphasis on identifying the predictors and augmenting factors for nephrotoxicity.

METHODOLOGY

A cross-sectional, unicentric, hospital-based study was conducted at King Fahad Specialist Hospital (KFSH) in Qassim region in Saudi Arabia (KSA). It included 319 hospitalized patients who received vancomycin at intermittent doses (15 to 30 mg/kg IV per day) based on the diseased state. Data regarding vancomycin dose, frequency, duration and data of renal function tests and type of admission were analysed to evaluate their influence on the renal function using parameters such as blood urea, serum creatinine levels and creatinine clearance. One-way ANOVA and Spearman correlation test were used in the analysis of data.

RESULTS

Both male and female patients treated with vancomycin had significantly (p<0.05) elevated blood urea and serum creatinine levels compared to baseline levels while creatinine clearance was non-significantly varied. Increasing age, increasing body weight, higher vancomycin dose and trough levels, increased vancomycin frequency and duration, critically ill patients and site of infection were factors associated with significant (p<0.05) increases in blood urea and serum creatinine levels with reduction in creatinine clearance.

CONCLUSION

Data suggested that vancomycin treatment reduced the renal function in patients and indicated its association with several predictors and confounding factors. The findings of the study might assist in identifying the patients under risk from the vancomycin-induced nephrotoxicity and in designing the preventive strategies to reduce such complications.

Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock—a two-centre randomised clinical trial

Background

Adequate antibiotic dosing may improve outcomes in critically ill patients but is challenging due to altered and variable pharmacokinetics. To address this challenge, AutoKinetics was developed, a decision support system for bedside, real-time, data-driven and personalised antibiotic dosing. This study evaluates the feasibility, safety and efficacy of its clinical implementation.

Methods

In this two-centre randomised clinical trial, critically ill patients with sepsis or septic shock were randomised to AutoKinetics dosing or standard dosing for four antibiotics: vancomycin, ciprofloxacin, meropenem, and ceftriaxone. Adult patients with a confirmed or suspected infection and either lactate > 2 mmol/L or vasopressor requirement were eligible for inclusion. The primary outcome was pharmacokinetic target attainment in the first 24 h after randomisation. Clinical endpoints included mortality, ICU length of stay and incidence of acute kidney injury.

Results

After inclusion of 252 patients, the study was stopped early due to the COVID-19 pandemic. In the ciprofloxacin intervention group, the primary outcome was obtained in 69% compared to 3% in the control group (OR 62.5, CI 11.4–1173.78, p < 0.001). Furthermore, target attainment was faster (26 h, CI 18–42 h, p < 0.001) and better (65% increase, CI 49–84%, p < 0.001). For the other antibiotics, AutoKinetics dosing did not improve target attainment. Clinical endpoints were not significantly different. Importantly, higher dosing did not lead to increased mortality or renal failure.

Conclusions

In critically ill patients, personalised dosing was feasible, safe and significantly improved target attainment for ciprofloxacin.

Trial registration: The trial was prospectively registered at Netherlands Trial Register (NTR), NL6501/NTR6689 on 25 August 2017 and at the European Clinical Trials Database (EudraCT), 2017-002478-37 on 6 November 2017.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04098-7.

Using Japanese big data to investigate novel factors and their high-risk combinations that affect vancomycin-induced nephrotoxicity

AIMS

Several factors related to vancomycin-induced nephrotoxicity (VIN) have not yet been clarified. In the present study, we used Japanese big data to investigate novel factors and their high-risk combinations that influence VIN.

METHODS

We employed a large Japanese electronic medical record database and included patients who had been administered intravenous vancomycin between June 2000 and December 2020. VIN was defined as an increase in serum creatinine ≥0.5 mg/dL or 1.5-fold higher than the baseline. The outcomes were: (1) factors affecting VIN that were identified using multiple logistic regression analysis, and (2) combinations of factors that affect the risk of VIN according to a decision tree analysis, which is a typical machine learning method.

RESULTS

Of the 7,306 patients that were enrolled, VIN occurred in 14.2% of them (1,035). A multivariate analysis extracted 22 variables as independent factors. Concomitant ramelteon use (odds ratio; 0.701, 95% confidence interval; 0.512-0.959), ward pharmacy service (0.741, 0.638-0.861), duration of VCM <7 days (0.748, 0.623-0.899) and trough concentrations 10-15 mg/L (0.668, 0.556-0.802) reduce the risk of VIN. Meanwhile, concomitant piperacillin-tazobactam use (2.056, 1.754-2.409) and piperacillin use (2.868, 1.298-6.338) increase the risk. The decision tree analysis showed that a combination of vancomycin trough concentrations ≥20 mg/L and concomitant piperacillin-tazobactam use was associated with the highest risk.

CONCLUSIONS

We revealed that the concomitant ramelteon use and ward pharmacy service may decrease the risk of VIN, while the concomitant use of not only piperacillin-tazobactam but also piperacillin may increase the risk.

Identification of Risk Factors for Daptomycin-Associated Creatine Phosphokinase Elevation and Development of a Risk Prediction Model for Incidence Probability

BACKGROUND

In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation.

METHODS

Patients who received daptomycin at our hospital were classified into the non-elevated or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis.

RESULTS

The non-elevated and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR], 4.45 [95% confidence interval {CI}, 1.40-14.47]; risk score 4), concomitant antihistamine use (OR, 5.66 [95% CI, 1.58-20.75]; risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR, 14.48 [95% CI, 2.90-87.13]; risk score 5) and ≥30.0 µg/mL (OR, 24.64 [95% CI, 3.21-204.53]; risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%-<25% (moderate risk), and ≥25% (high risk) with total risk scores of ≤4, 5-6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiver operating characteristic curve, 0.85 [95% CI, .74-.95]).

CONCLUSIONS

These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

Safety and efficacy of intravenously administered cidofovir in adult haematopoietic cell transplant recipients: a retrospective multicentre cohort study

OBJECTIVES

To evaluate the safety and efficacy of cidofovir for the treatment of double-stranded DNA (dsDNA) viral infections following allogeneic haematopoietic cell transplant (HCT).

METHODS

This was a retrospective multicentre cohort study including adult HCT recipients who received ≥1 dose of IV-administered cidofovir for any dsDNA viral infection from 2006 to 2019. The objectives were to describe the rate of and risk factors for nephrotoxicity and virological response by the end of treatment (EOT).

RESULTS

We included 165 patients from nine centres. Cidofovir was administered at 5 mg/kg/week (N = 115; 69.7%), 1 mg/kg/week (18; 10.9%), 3 mg/kg/week (12; 7.3%) or 1 mg/kg three times/week (11; 6.7%). Cidofovir was administered for adenovirus, cytomegalovirus (CMV) and BK virus infection in 75 (45.5%), 64 (38.8%) and 51 (30.9%) patients, respectively. Among 158 patients with renal function data at baseline and EOT, 40 (25.3%) developed nephrotoxicity. In multivariable analyses, age (OR 1.04; P = 0.05), weight (OR 1.05; P = 0.01), CMV infection (OR 3.6; P = 0.02), liposomal amphotericin B (OR 8.06; P = 0.05) and IV voriconazole/posaconazole (OR 13.0; P = 0.003) were predictors of nephrotoxicity. Creatinine concentration was significantly higher at EOT (1.16 ± 0.95 mg/dL) compared with baseline (0.91 ± 0.39 mg/dL; P < 0.001), but improved by 2 weeks (0.91 ± 0.84 mg/dL; P = 0.007) and 4 weeks (0.96 ± 0.89 mg/dL; P = 0.03) post-EOT. Median viral load significantly declined for patients with adenovirus DNAaemia by EOT (P < 0.0001) and for patients with CMV DNAaemia by EOT + 4 weeks (P = 0.003), but not for patients with BK virus DNAaemia.

CONCLUSIONS

One in four HCT recipients treated with IV cidofovir developed largely reversible nephrotoxicity. Careful selection of patients and close follow-up of renal function may minimize toxicity.