On estimation and cross-validation of dynamic treatment regimes with competing risks

The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomized controlled trials have tried to answer this question, but these contrast only a limited number of treatment initiation strategies. In view of this, we use routinely collected observational data from the Ghent University Hospital intensive care units (ICUs) to investigate different prespecified timing strategies for renal replacement therapy initiation based on time-updated levels of serum potassium, pH, and fluid balance in critically ill patients with AKI with the aim to minimize 30-day ICU mortality. For this purpose, we apply statistical techniques for evaluating the impact of specific dynamic treatment regimes in the presence of ICU discharge as a competing event. We discuss two approaches, a nonparametric one - using an inverse probability weighted Aalen-Johansen estimator - and a semiparametric one - using dynamic-regime marginal structural models. Furthermore, we suggest an easy to implement cross-validation technique to assess the out-of-sample performance of the optimal dynamic treatment regime. Our work illustrates the potential of data-driven medical decision support based on routinely collected observational data.

Automated summary

The optimal timing for initiating renal replacement therapy in patients with acute kidney injury remains challenging. This study explores different timing strategies based on serum potassium, pH, and fluid balance, using routinely collected observational data. Statistical techniques are employed to evaluate the impact of dynamic treatment regimes, considering ICU discharge as a competing event. Two approaches - nonparametric and semiparametric - are discussed, along with a cross-validation technique to assess out-of-sample performance.