Machine learning approaches to optimise the management of patients with sepsis

The goal of this PhD was to generate novel tools to improve the management of patients with sepsis, by applying machine learning techniques on routinely collected electronic health records. Machine learning is an application of artificial intelligence (AI), where a machine analyses data and becomes able to execute complex tasks without being explicitly programmed. Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients. This represents a key clinical challenge and a top research priority. The main contribution of the research has been the development of a reinforcement learning framework and algorithms, in order to tackle this sequential decision-making problem. The model was built and then validated on three large non-overlapping intensive care databases, containing data collected from adult patients in the U.S.A and the U.K. Our agent extracted implicit knowledge from an amount of patient data that exceeds many-fold the life-time experience of human clinicians and learned optimal treatment by having analysed myriads of (mostly sub-optimal) treatment decisions. We used state-of-the-art evaluation techniques (called high confidence off-policy evaluation) and demonstrated that the value of the treatment strategy of the AI agent was on average reliably higher than the human clinicians. In two large validation cohorts independent from the training data, mortality was the lowest in patients where clinicians’ actual doses matched the AI policy. We also gained insight into the model representations and confirmed that the AI agent relied on clinically and biologically meaningful parameters when making its suggestions. We conducted extensive testing and exploration of the behaviour of the AI agent down to the level of individual patient trajectories, identified potential sources of inappropriate behaviour and offered suggestions for future model refinements. If validated, our model could provide individualized and clinically interpretable treatment decisions for sepsis that may improve patient outcomes.Open Acces

Employing machine learning to assess the accuracy of near-infrared spectroscopy of spent dialysate fluid in monitoring the blood concentrations of uremic toxins

Hemodialysis (HD) removes nitrogenous waste products from patients’ blood through a semipermeable mem- brane along a concentration gradient. Near-infrared spectroscopy (NIRS) is an underexplored method of monitoring the concentrations of several molecules that reflect the efficacy of the HD process in dialysate samples. In this study, we aimed to evaluate NIRS as a technique for the non-invasive detection of uremic solutes by assessing the correlations between the spectrum of the spent dialysate and the serum levels of urea, creatinine, and uric acid. Blood and dialysate samples were taken from 35 patients on maintenance HD. The absorption spectrum of each dialysate sample was measured three times in the wavelength range of 700-1700 nm, resulting in a dataset with 315 spectra. The artificial neural network (ANN) learn- ing technique was used to assess the correlations between the recorded NIR-absorbance spectra of the spent dialysate and serum levels of selected uremic toxins. Very good correlations between the NIR-absorbance spectra of the spent dialysate fluid with serum urea (R=0.91) and uric acid (R=0.91) and an excellent correlation with serum creatinine (R=0.97) were obtained. These results support the application of NIRS as a non-invasive, safe, accurate, and repetitive technique for online monitoring of uremic toxins to assist clinicians in assessing HD efficiency and individualization of HD treatments

Deep-learning based real-time prediction of acute kidney injury after cardiac surgery

The increasing digitisation of medical data and advances in artificial intelligence have enabled us to use the tremendous amount of data that is recorded during a hospital stay in a much more sophisticated way than is currently the case. In the study undertaken and published in the context of this doctoral project, this approach was taken for predicting postoperative acute kidney injury (AKI) – one of the most common and severe complications after cardiothoracic interventions. Using 96 parameters, standardly recorded during a hospital stay, a recurrent neural network (RNN) was developed that predicted AKI within the first seven postoperative days. The training of the model was based on n = 2224 admissions gathered from n = 15,564 admissions at a tertiary care hospital for cardiothoracic surgery. The performance of the model was assessed using an independent test set of n = 350 clinical cases and an area under the curve (AUC) (95% confidence interval) of 0.893 (0.862 - 0.924) was obtained. Additionally, a head-to-head comparison of the RNN against experienced physicians was conducted. The RNN exceeded the physicians in terms of all determined statistical measures (e.g., AUC = 0.901 vs 0.745, p < 0.001). In contrast to the predictions of physicians, who generally underrated the risk of developing AKI, the RNN showed good calibration. The integration of such a model into existing digital medical record systems could allow preventive steps to be taken in time to prevent complications by predicting AKI well before its onset. It could be used as a real-time surveillance system and support physicians' decision-making process. However, when using such a technique, there are several ethical aspects to be considered concerning data protection, model development, and clinical deployment, which are also discussed in this work.Die zunehmende Digitalisierung medizinischer Daten und die Fortschritte im Bereich der künstlichen Intelligenz ermöglichen es, die enorme Menge an Daten, die während eines Krankenhausaufenthalts gesammelt wird, auf viel komplexere Weise zu nutzen, als es bislang der Fall war. In der im Rahmen der Promotion durchgeführten Studie wurde dieser Ansatz für die Echtzeit-Vorhersage von postoperativem akutem Nierenversagen (ANV) verfolgt – eine der häufigsten Komplikationen nach kardiothorakalen Eingriffen. Anhand von 96 Parametern, die standardmäßig während eines Krankenhausaufenthalts aufgezeichnet werden, wurde ein rekurrentes neuronales Netz (RNN) entwickelt, das ANV innerhalb der ersten sieben postoperativen Tage vorhersagen kann. Das Modell wurde mit Daten aus n = 2224 Aufnahmen trainiert, welche aus n = 15.564 klinischen Fällen in einem Krankenhaus der tertiären Versorgung für kardiothorakale Chirurgie zusammengestellt wurden. Die Leistung des RNN wurde anhand eines unabhängigen Testsets aus n = 350 klinischen Fällen bewertet, und es wurde eine area under the curve (AUC) (95 % Konfidenzintervall) von 0,893 (0,862 - 0,924) ermittelt. Zusätzlich wurde ein direkter Vergleich der Vorhersagegüte zwischen dem RNN und erfahrenen ÄrztInnen durchgeführt. Das RNN übertraf die ÄrztInnen in Bezug auf alle ermittelten statistischen Messwerte (z.B. AUC = 0,901 vs. 0,745, p < 0,001). Im Gegensatz zu den Vorhersagen der ÄrztInnen, die das Risiko der Entwicklung eines ANV generell unterschätzten, zeigte das RNN eine gute Kalibrierung. Die Integration eines solchen Modells in bestehende elektronische Patientendatensysteme könnte durch frühzeitige Vorhersage von ANV ermöglichen, präventive Maßnahmen rechtzeitig zu ergreifen, um Komplikationen zu verhindern. Es könnte als Echtzeit-Überwachungssystem eingesetzt werden und die Entscheidungsprozesse der ÄrztInnen unterstützen. Bei der Verwendung eines solchen Systems sind neben seiner Vorhersagegüte aber auch ethische und rechtliche Aspekte zu berücksichtigen, die den Datenschutz, die Modellentwicklung und den klinischen Einsatz betreffen, und die in dieser Arbeit ebenfalls erörtert werden

Acute kidney injury in the critically ill: an updated review on pathophysiology and management.

Acute kidney injury (AKI) is now recognized as a heterogeneous syndrome that not only affects acute morbidity and mortality, but also a patient's long-term prognosis. In this narrative review, an update on various aspects of AKI in critically ill patients will be provided. Focus will be on prediction and early detection of AKI (e.g., the role of biomarkers to identify high-risk patients and the use of machine learning to predict AKI), aspects of pathophysiology and progress in the recognition of different phenotypes of AKI, as well as an update on nephrotoxicity and organ cross-talk. In addition, prevention of AKI (focusing on fluid management, kidney perfusion pressure, and the choice of vasopressor) and supportive treatment of AKI is discussed. Finally, post-AKI risk of long-term sequelae including incident or progression of chronic kidney disease, cardiovascular events and mortality, will be addressed

Developing Predictive Models for Risk of Postoperative Complications and Hemodynamic Instability in Patients Undergoing Surgery

Patients undergoing high-risk surgeries are often at higher risk of developing hemodynamic instability during surgery resulting in poor postoperative outcomes. This is usually associated with significantly increased postoperative morbidity and mortality, which therefore makes the early identification of these critical events and those patients at risk of postoperative complications crucial. With these motivations in mind, we first created a large deidentified research dataset of surgical case medical records from University of California, Irvine Medical Center (UCIMC) matched with physiological waveforms as well as intermittent vital sign values, lab values, and ventilator settings. To our knowledge, such a dataset does not currently exist for the intraoperative environment. We hope that creating a such a dataset will allow for advances in machine learning for intraoperative care. Using medical data from UCLA, we have developed deep neural network models to classify the risks of postoperative mortality, acute kidney injury, and reintubation utilizing readily available intraoperative information. Our risk scores were compared to currently commonly used risk indices ASA and Surgical Apgar as well as logistic regression. While the deep neural network models performed better than the risk scores and logistic regression, clinicians require additional information to assess what led to increased risk of complications. To address this, we also assessed the use of generalized additive neural networks (GANNs) to create a graphical look at how different features contributed to the risk of in hospital mortality. Finally, we were also interested in predicting critical intraoperative events to allow for time for the clinician to avoid such events. We focused on intraoperative hypotension as it is easier to define and has been shown to lead to increased risk of acute kidney injury, stroke, and myocardial injury. For the hypotension prediction models, we looked at the arterial pressure waveform and EMR data as inputs. Overall, these aims address a gap in current clinical decision guidance and support to reduce adverse events during surgery as well complications after

Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost

ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient’s medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.ResultsThe final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).ConclusionAn ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent