Appropriate drug dosing is a critical issue in hospital settings, particularly for renally eliminated and nephrotoxic therapies. Vancomycin is one of the most widely used antibiotics in intensive care units (ICU), with a narrow therapeutic window [1,2]. Considering the rise in resistant gram-positive infections, the use of vancomycin continues to rise [3]. Insufficient drug dosing leads to subtherapeutic serum concentrations, which increases the risk of clinical and microbiologic failure and the development of antibiotic resistance [4]. On the other hand, supratherapeutic concentrations can lead to dose-dependent drug-associated toxicity [[5], [6], [7]]. Indeed, nephrotoxin-associated acute kidney injury (AKI) occurs in 5–20% of vancomycin-treated individuals [[8], [9], [10], [11], [12], [13], [14], [15]]. In turn, AKI is associated with a longer ICU length of stay, higher mortality rate, increased need for mechanical ventilation, and many other adverse outcomes among ICU patients [16,17]. Therefore, identifying and mitigating factors associated with vancomycin nephrotoxicity may decrease the incidence, severity, and complications [18]. Selecting a proper individualized dosing regimen for vancomycin to maintain the drug concentrations within the target trough level range is essential for optimizing its safe and effective use [[19], [20], [21], [22]].

With the growing utilization of electronic health records (EHR), there is an incredible opportunity to employ data science methodologies in online monitoring and predictive and prescriptive analytics to deliver precise drug dosing in clinical settings, such as ICU [23]. Recently, Sutherland et al. reviewed the existing approaches in predicting nephrotoxin-associated AKI and recommended applying big data analytics to develop more complex data-driven models [24]. Also, Dorajoo et al. suggested considering the essential clinical needs in practice when developing data-driven prediction models [25]. Therefore, successful development and implementation of an AI-based dosing decision support models focusing on clinical workflow could help identify those patients with a higher risk for sub- and supra-therapeutic range and allow the providers to take individualized, timely preventive actions at the right time, such as replacing or dose-adjusting the drug [26,27].

This study aimed to predict the steady-state vancomycin trough concentration and identify the main contributory patient factors to facilitate dosing decisions. To begin with, we 1) developed and validated ensemble machine learning models to predict the drug level threshold, namely, sub-therapeutic (<10 mg/L), therapeutic (10–20 mg/L), and supra-therapeutic (>20 mg/L) for ICU adult patients and estimated the risk of inappropriate vancomycin trough levels; 2) we identified the predictive factors for the vancomycin steady-state trough level and ascertain their relative contribution; and 3) predicted the vancomycin steady-state trough level for each ICU patient.