The data driven intensive care unit: Artificial Intelligence in Sepsis and COVID-19

The work in this thesis is the result of answers that we aimed to find for clinical questions. We initially focused on critically-ill sepsis patients, but when faced with the COVID-19 pandemic in the intensive care unit in 2020, we broadened our scope to also include COVID-19 patients. In Part I of this thesis, we developed a scale to evaluate machine learning (ML) readiness. Although AI is poised to reshape medicine, routine clinical care in the ICU remains devoid of AI solutions. In Chapter 2 we hypothesized that machine learning models follow a trajectory towards clinical practice similar to aerospace technology. We found that 93% of all critical care AI models were in the prototyping and model development stage. Only two articles shadow tested their models, which means integrating a model into the electronic health system without presenting the results to clinicians, and only two publications evaluated models against clinically relevant outcomes. Secondly, we evaluated the diagnostic test accuracy of all identifiable machine learning models to predict the onset of sepsis. We found 28 papers that contained 130 models eligible for inclusion. Overall, machine learning models were able to accurately predict sepsis onset on retrospective data. However, only 6.2% of these models were implemented prospectively and only one study was implemented in clinical practice. Part II of this thesis describes the unification of clinical data from ICU patients with COVID-19. Both Chapter 4 and 5 describe the legal and technical details of the Dutch Data Warehouse (DDW), which contains 3464 patients from 25 hospitals, both from wave 1 (before the summer of 2020) and wave 2 in the Netherlands. In parallel with data collection for the DDW, we addressed several questions that came up from clinical care for COVID-19 patients. In Part III, Chapter 6, we identified the risk factors for extubation failure in COVID-19 patients. The most important risk factors for extubation failure were ventilatory characteristics, indicators of inflammation, the Glasgow Coma Scale, and body mass index. They are recognized clinically for their importance in predicting sepsis onset. The second application in Chapter 7 describes the risk factors for mortality, ICU-free days and ventilator free days in COVID-19 patients. We found that the strongest predictor of mortality in critically-ill COVID-19 patients was age. Similar to Chapter 6, we identified clinical factors that require monitoring and capturing in the EHR as they were strongly associated to patient outcomes, relative to the other parameters in the model. These factors include clinical parameters not typically present in registry datasets, such as pH, P/F ratio and driving pressure. Overall, the clinical parameters most associated with adverse outcomes may easily be recorded in EHRs and are familiar to ICU clinicians. In Part IV, we outline our implementation of clinical decision support software in clinical practice. Clinical implementation is one of the central hurdles in critical care machine learning. Chapter 8 describes the use case for clinical decision support to guide antibiotic dosing in critically-ill patients. We performed a cross-sectional survey study to assess pharmacokinetic knowledge among intensive care clinicians. Pass rates of the test were below 50% for intensivists, intensive care fellows, residents and ICU nurses. These results demonstrate the need for aiding clinicians in pharmacokinetic guided antibiotic dosing. In Chapter 9 and 10 we describe the clinical implementation of AutoKinetics, a computerized dosing advice software for four antibiotics. The software provides continuous and real-time dosing advice to clinicians at the intensive care bedside. We show that with AutoKinetics, real-time, continuous clinical decision support is feasible at the ICU bedside and can be integrated into the clinical workflow.