Discussion
toxicity, critically ill
CRITICALLY ILL
In this study, we presented three models to predict daily CrCl in critically ill adults, based on information derived from routinely collected clinical data. The predictive performance remained similar when adding high-resolution data. The developed models were externally validated on previously unseen patients with good performance. Finally, the models demonstrated smaller prediction errors than using the CrCl of the day of prediction (reflecting the current clinical practice. This is mainly explained by the better performance of the prediction model as compared to the reference of the previous day on the days with a bigger change in the CrCl (i.e., ‘unstable days’), whereas the model performed similarly as the reference of the previous days on the days that the CrCl remained stable (‘stable days’). To the best of our knowledge, this study presents the first machine-learning algorithm for daily CrCl prediction in the ICU.There are many reasons why there is a need for such daily prediction of CrCl during the entire ICU stay. First, measured urinary CrCl is currently considered the most suitable method to estimate the GFR in clinical practice [Having a reference to compare against helps to understand whether the models could have clinical usefulness. Compared to the current clinical practice of assuming the same CrCl as the day of prediction, our developed models reduced the RMSE from 40.1 to 28.9 ml/min. Importantly, in the subgroup of patient-days with stable kidney function, the developed models demonstrated a clinically insignificant larger RMSE, around 4 ml/min on average, than the reference. This difference, however, is too small to be of clinical relevance and unlikely to cause treatment failure or drug toxicity due to altered renal clearance. Noticeably, in the subgroup of patient-days with unstable kidney function (comprising 30–40% of all patient-days), the developed models had clinically relevant smaller RMSEs, around 23 ml/min on average. This subgroup analysis of days with high CrCl instability clearly exhibited our models’ capability of better capturing the dynamics of kidney function. Nevertheless, despite the large reduction in prediction errors during unstable days, whether or not the models help improve drug dosing and patient outcomes still needs to be investigated prospectively.Our study has many strengths. First, the use of a general ICU population instead of a specific subset of patients made it more generally applicable, and the daily prediction truthfully reflected the fluctuating kidney function on each patient day, allowing for risk stratification and drug dose adjustment. Second, the reporting of this study was performed following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines [There are several limitations in our study. First, the development cohort was based on a RCT database in Belgium dating back to 2010, which might limit its generalizability in other settings. However, model performance remained unchanged when externally validated on a very large database with patient data collected up to 2018. Second, the use of high-resolution data might be difficult to implement in hospitals with limited resources, and some settings might even struggle to have the necessary data for the lower-resolution Core model. Third, there might be a selection bias resulting from the exclusion from the analyses of patient-days with KRT on the day of prediction and in previous week, or of patient-days when less than 2 consecutive CrCls were available, or patient-days after the first 90 days in ICU. These exclusion criteria were necessary to ensure reliable CrCl prediction models could be developed. Fourth, this study was based on retrospective data, and the developed models still need prospective validation in independent cohorts. Fifth, the model performance was not compared against novel biomarkers such as cystatin C and proenkephalin that may be less biased, and urea clearance that may add valuable information especially in those with low CrCl. However, measured CrCl is a fast and cheap test, which are important characteristics as the measurements were taken on a daily basis. Sixth, the measurement of creatinine changed from the Jaffe method in the development cohort to the enzymatic method in the validation cohort, and it was found that the Jaffe method yielded higher creatinine values [