Precision Medicine and its Role in the Treatment of Sepsis: A Personalised View

Lazăr, Alexandra; Georgescu, Anca Meda; Vitin, Alexander A.; Azamfirei, Leonard

doi:10.2478/jccm-2019-0017

Cited by 20 publications

(14 citation statements)

References 37 publications

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“…In the U.S., sepsis is responsible for 6% of all hospitalizations and 35% of all in-hospital deaths [1,2], and an economic burden of more than $20B per year [3]. The treatment of sepsis is extremely challenging, due to the high variability among patients, with respect to both the progression of the disease, the host response to infection, and the response to medical interventions, suggesting the need for a dynamic and personalized approach to treatment [4][5][6]. Presently, the search for treatment strategies to optimize sepsis patient outcomes remains an open challenge in critical care medicine, despite decades of research.…”

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confidence: 99%

Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment

et al. 2022

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Sepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient’s state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there’s still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty-aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies, that are consistent with clinical knowledge. Further our method consistently identifies high-risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research.

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confidence: 99%

Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment

et al. 2022

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“…Consistent with previous work [13], [14], [15], we focus on vasopressor and IV fluid treatments, which whilst regarded important, studies have shown significant variation among patient responses to them [3], [4] [16]. Further there is little agreement among medical researchers on best practices that guide fluid or vasopressor administration beyond initial resuscitation.…”

Section: Introductionmentioning

confidence: 69%

Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment

Nanayakkara,

Clermont,

Langmead

et al. 2021

Preprint

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Sepsis is the leading cause of mortality in the ICU, responsible for 6% of all hospitalizations and 35% of all in-hospital deaths in USA [1]. However, there is no universally agreed upon strategy for vasopressor and fluid administration [2]. It has also been observed that different patients respond differently to treatment [3], [4], highlighting the need for individualized treatment. Vasopressors and fluids are administrated with specific effects to cardiovascular physiology in mind and medical research has suggested that physiologic, hemodynamically guided, approaches to treatment is prudent [5]. Thus we propose a novel approach, exploiting and unifying complementary strengths of Mathematical Modelling, Deep Learning, Reinforcement Learning and Uncertainty Quantification, to learn individualized, safe, and uncertainty aware treatment strategies. We first infer patient-specific, dynamic cardiovascular states using a novel physiology-driven recurrent neural network trained in an unsupervised manner. This information, along with a learned lowdimensional representation of the patient's lab history and observable data, is then used to derive value distributions using Batch Distributional Reinforcement Learning. Moreover in a safety critical domain it is essential to know what our agent does and does not know, for this we also quantify the model uncertainty associated with each patient state and action, and propose a general framework for uncertainty aware, interpretable treatment policies. This framework can be tweaked easily, to reflect a clinician's own confidence of the framework, and can be easily modified to factor in human expert opinion, whenever it's accessible. Using representative patients and a validation cohort, we show that our method has learned physiologically interpretable generalizable policies. We posit that this work takes an important step towards a safe, physiology and uncertainty aware, decision support system for septic patients. It also provides a general framework for using Reinforcement Learning to support healthcare applications involving sequential decisions.

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“…Development of new biomarkers including gene expressions have been ongoing in this decade to facilitate effective diagnosis and prognosis and help to better understand the underlying pathophysiology of sepsis. Personalized precision medicine has been gaining traction where the sepsis treatment could be tailored for an individual patient based on their unique digital map created with their own data [14].…”

Section: Discussionmentioning

confidence: 99%

Practical Machine Learning-Based Sepsis Prediction

Pettinati¹,

Chen²,

Rajput³

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Sepsis is a life-threatening clinical syndrome and one of the most expensive conditions treated in hospitals. It is challenging to detect due to the nonspecific clinical signs and the absence of gold standard diagnostics. However, early recognition of sepsis and optimal treatments for sepsis are of paramount importance to improve the condition's management and patient outcomes. This paper aims to delineate key aspects of current sepsis detection systems, including their dependency on clinical expert and laboratory biometric features requiring ongoing critical care intervention, the efficacy of vital sign measures, and the effect of the study population with respect to the precision of sepsis prediction. The AUROC performances of XGBoost models trained on a heterogenous ICU patient group (n=3932) showed significant degradations (p<0.05) as the expert and laboratory biomarker features are removed systematically and vital sign features taken in ICU settings are left. The performance of XGBoost models trained only with vital sign features on a more homogeneous group of ICU patients (n=1927) had a significantly (P<0.05) improved AUPRC to moderate level. The presented results highlight the importance of making a practical machine learning system for sepsis prediction by considering the availability of dominant features as well as personalizing sepsis prediction by configuring it to the specific demographics of a targeted population.

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Precision Medicine and its Role in the Treatment of Sepsis: A Personalised View

Cited by 20 publications

References 37 publications

Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment

Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment

Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment

Practical Machine Learning-Based Sepsis Prediction

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