Cluster trajectory of SOFA score in predicting mortality in sepsis

Objective: Sepsis is a life-threatening condition. Sequential Organ Failure Assessment (SOFA) score is commonly used to assess organ dysfunction and predict ICU mortality, but it is taken as a static measurement and fails to capture dynamic changes. This study aims to investigate the relationship between dynamic changes in SOFA scores over the first 72 hours of ICU admission and patient outcomes. Design, setting, and participants: 3,253 patients in the Medical Information Mart for Intensive Care IV database who met the sepsis-3 criteria and were admitted from the emergency department with at least 72 hours of ICU admission and full-active resuscitation status were analysed. Group-based trajectory modelling with dynamic time warping and k-means clustering identified distinct trajectory patterns in dynamic SOFA scores. They were subsequently compared using Python. Main outcome measures: Outcomes including hospital and ICU mortality, length of stay in hospital and ICU, and readmission during hospital stay, were collected. Discharge time from ICU to wards and cut-offs at 7-day and 14-day were taken. Results: Four clusters were identified: A (consistently low SOFA scores), B (rapid increase followed by a decline in SOFA scores), C (higher baseline scores with gradual improvement), and D (persistently elevated scores). Cluster D had the longest ICU and hospital stays, highest ICU and hospital mortality. Discharge rates from ICU were similar for Clusters A and B, while Cluster C had initially comparable rates but a slower transition to ward. Conclusion: Monitoring dynamic changes in SOFA score is valuable for assessing sepsis severity and treatment responsiveness.Comment: 26 pages, 4 figures, 2 table

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

Subphenotypes in acute kidney injury : a narrative review

Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.Peer reviewe

Subphenotypes in acute kidney injury : a narrative review

Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.Peer reviewe

Precision medicine in sepsis and septic shock: From omics to clinical tools

Endotype; Organ dysfunction; SepsisEndotipo; Disfunción de órganos; SepsisEndotip; Disfunció d'òrgans; SèpsiaSepsis is a heterogeneous disease with variable clinical course and several clinical phenotypes. As it is associated with an increased risk of death, patients with this condition are candidates for receipt of a very well-structured and protocolized treatment. All patients should receive the fundamental pillars of sepsis management, which are infection control, initial resuscitation, and multiorgan support. However, specific subgroups of patients may benefit from a personalized approach with interventions targeted towards specific pathophysiological mechanisms. Herein, we will review the framework for identifying subpopulations of patients with sepsis, septic shock, and multiorgan dysfunction who may benefit from specific therapies. Some of these approaches are still in the early stages of research, while others are already in routine use in clinical practice, but together will help in the effective generation and safe implementation of precision medicine in sepsis

Aspects of risk factors, pathophysiology and outcomes in trauma

Trauma is a global health concern. Many trauma patients succumb on the scene or in the immediate phase after trauma. Patients surviving the initial phase may die at a later stage or suffer debilitating consequences in the post-resuscitation phase of trauma care in intensive care units. This thesis is focused on factors associated with outcomes and complications after trauma, as well as early recognition of these complications. Trauma patients using β-adrenergic receptor antagonists (β-blockers) at the time of injury had more comorbidities and an increased mortality compared to non-users. However, when adjusting for relevant confounders no association between pre-traumatic β-blockade and mortality survival was seen. Previous research suggesting a protective effect of β-blockers in trauma could therefore not be supported. We investigated thioredoxin (TRX), a potent endogenous antioxidant, and its associations with post-injury sepsis. TRX was elevated after an inflicted femur fracture and subsequent hemorrhage in an animal trauma model. Plasma-levels of thioredoxin was also evaluated in 83 severely injured trauma patients and were significantly higher when compared to healthy controls. This biomarker was associated with injury severity, shock on arrival and massive transfusion. Further, an association between TRX and post-injury sepsis was shown after adjustments for confounders. The new sepsis definition, sepsis-3, was evaluated and compared with the previous definition, sepsis-2, in 722 severely injured trauma patients. Fewer patients were diagnosed with sepsis when using the new sepsis-3 definition as compared with the old sepsis-2 definition. No association was seen between sepsis, regardless of definition used and overall mortality. However, after censoring patients dying on the first day, before being at risk for sepsis, sepsis-3 was associated with 30-day mortality, whereas sepsis-2 was not. The new definition was feasible and had a stronger association with mortality. Risk factors for post-injury sepsis as defined by the new sepsis-3 criteria included: age, spineand chest-injuries, shock on arrival and blood transfusion. Moreover, there was an association between blood alcohol at admission and later development of sepsis previously not described. Patients who developed post-injury sepsis had a complicated clinical course with an increased need for vasopressor treatment, mechanical ventilation and had more days with organ dysfunction. A significant association between post-injury sepsis and mortality was shown, but only after early censoring for trauma-related deaths. Using a technique for longitudinal clustering, we identified five distinct trajectories of organ dysfunction after trauma. Each one with different baseline characteristics, evolution of organ dysfunction and outcomes. These trajectories had unequal times until stabilization, indicating that some trajectories are easier to identify in an early stage. The study underlines the heterogenous course after trauma and suggests that there exist subsets of traumatically injured patients that might benefit from targeted measures

Soft Phenotyping for Sepsis via EHR Time-aware Soft Clustering

Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories identifying sepsis sub-phenotypes is crucial to advance our understanding of sepsis characterization, identifying targeted treatments and optimal timing of interventions, and improving prognostication. Prior studies have described different sub-phenotypes of sepsis with organ-specific characteristics. These studies applied clustering algorithms to electronic health records (EHRs) to identify disease sub-phenotypes. However, prior approaches did not capture temporal information and made uncertain assumptions about the relationships between the sub-phenotypes for clustering procedures. We develop a time-aware soft clustering algorithm guided by clinical context to identify sepsis sub-phenotypes using data from the EHR. We identified six novel sepsis hybrid sub-phenotypes and evaluated them for medical plausibility. In addition, we built an early-warning sepsis prediction model using logistic regression. Our results suggest that these novel sepsis hybrid sub-phenotypes are promising to provide more precise information on the recovery trajectory which can be important to inform management decisions and sepsis prognosis