Machine learning in critical care: state-of-the-art and a sepsis case study

Background: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. Results: The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. Conclusion: We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.Peer ReviewedPostprint (published version

PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS

Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease

Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room

INTRODUCTION: Neural networks are new methodological tools based on nonlinear models. They appear to be better at prediction and classification in biological systems than do traditional strategies such as logistic regression. This paper provides a practical example that contrasts both approaches within the setting of suspected sepsis in the emergency room. METHODS: The study population comprised patients with suspected bacterial infection as their main diagnosis for admission to the emergency room at two University-based hospitals. Mortality within the first 28 days from admission was predicted using logistic regression with the following variables: age, immunosuppressive systemic disease, general systemic disease, Shock Index, temperature, respiratory rate, Glasgow Coma Scale score, leucocyte counts, platelet counts and creatinine. Also, with the same input and output variables, a probabilistic neural network was trained with an adaptive genetic algorithm. The network had three neurone layers: 10 neurones in the input layer, 368 in the hidden layer and two in the output layer. Calibration was measured using the Hosmer-Lemeshow goodness-of-fit test and discrimination was determined using receiver operating characteristic curves. RESULTS: A total of 533 patients were recruited and overall 28-day mortality was 19%. The factors chosen by logistic regression (with their score in parentheses) were as follows: immunosuppressive systemic disease or general systemic disease (2), respiratory rate 24–33 breaths/min (1), respiratory rate ≥ 34 breaths/min (3), Glasgow Come Scale score ≤12 (3), Shock Index ≥ 1.5 (2) and temperature <38°C (2). The network included all variables and there were no significant differences in predictive ability between the approaches. The areas under the receiver operating characteristic curves were 0.7517 and 0.8782 for the logistic model and the neural network, respectively (P = 0.037). CONCLUSION: A predictive model would be an extremely useful tool in the setting of suspected sepsis in the emergency room. It could serve both as a guideline in medical decision-making and as a simple way to select or stratify patients in clinical research. Our proposed model and the specific development method – either logistic regression or neural networks – must be evaluated and validated in an independent population

Detecting Sepsis Using Sepsis-Related Organ Failure Assessment (SOFA) and an Electronic Sepsis Prompt in Intensive Care Unit Adult Patients

Sepsis is an elusive and costly syndrome that is one of the leading causes of death globally. Annually, there are approximately 19 million cases of sepsis that result in more than 5 million deaths. The Agency for Healthcare Research and Quality (AHRQ) ranked sepsis as the most expensive condition ($23.7 billion) for patients treated in hospitals in the United States (U.S.). Nurses are critical in the early identification of sepsis and implementation of therapeutic interventions known as the “sepsis bundle”. Previously, sepsis was described as a systemic, pro-inflammatory response to an infection. Sepsis was defined as two or more systemic inflammatory response syndrome (SIRS) criteria with a suspected infection, severe sepsis was defined as sepsis with organ failure and septic shock was defined as severe sepsis with shock. For several decades SIRS criteria with organ failure criteria have been used to develop measurement systems for detection of sepsis. A recent study comparing SIRS criteria to the sepsis-related organ failure assessment (SOFA) score demonstrated that SOFA had greater prognostic accuracy of mortality in patients with an infection than SIRS. This led to sepsis definition changes in 2016. The term “severe sepsis” was dropped and sepsis was defined as a life-threatening organ dysfunction caused by a dysregulated host response to an infection leading to tissue injury and organ failure. Many clinicians were concerned that this new definition might lead to late detection of sepsis. What was unknown was how well SIRS with organ failure criteria compared with SOFA in detection of sepsis. Many clinicians in the U.S. working in a TeleICU had been using SIRS with organ failure criteria to support early identification of sepsis. Using human factors science concepts, their practice was studied and an electronic sepsis alert (sepsis prompt) was developed. Thus, the overall objective of this dissertation was to conduct a retrospective study using a large U.S. data repository to determine if an electronic prompt, that uses SIRS and organ failure (OF) criteria, can detect sepsis. Another objective of this study was to determine the prognostic accuracy of the SOFA score and the sepsis prompt in discriminating in-hospital mortality among patients with sepsis in the intensive care unit. Among 2,020,489 patients admitted to ICUs associated with a TeleICU from January 1, 2010, to December 31, 2015, at 459 hospitals throughout the U.S., we identified 912,509 (45%) eligible patients at 183 hospitals. We compared the performance of the SOFA score and sepsis prompt criteria in detecting sepsis. Of those in the primary cohort, a secondary cohort was derived based on presence of sepsis resulting 186,870 (20.5%) patients. To assess performances of the SOFA score and the sepsis prompt (a Fuzzy Logic SIRS and OF algorithm) to detect sepsis, we calculated diagnostic performance of an increase in the SOFA score of 2 or more and criteria met for the Fuzzy Logic SIRS and OF algorithm. For predictive validity, training of baseline risk models was performed on training sets with prediction and performance analytics completed on test sets for each cohort for the outcomes of mortality and sepsis. Results were expressed as the fold change in outcome over deciles of baseline risk of death or risk of sepsis, area under the receiver operating characteristic curve (AUROC), and sensitivity, specificity, and negative and positive predictive values. In the primary cohort (912,509) there were 86,219 (9.4%) who did not survive their hospital stay and 186,870 (20.5%) with suspected sepsis of whom 34,617 (18.5%) did not survive hospitalization. The Fuzzy Logic SIRS/OF (crude AUROC 0.67, 99% CI: 0.66-0.67 and adjusted AUROC 0.77, 99% CI: 0.77-0.77) outperformed SOFA (crude AUROC 0.61, 99% CI: 0.61-0.61 and adjusted AUROC 0.74, 99% CI: 0.74-0.74) in discrimination of sepsis in both crude and adjusted AUROC (in-between differences AUROC 0.06; z-value 49.06 and AUROC 0.03; z-value 36.22, respectively). In the primary cohort, Fuzzy Logic SIRS/OF (crude AUROC 0.67, 99% CI: 0.67-0.68 and adjusted AUROC 0.78, 99% CI: 0.77-0.78) outperformed SOFA (crude AUROC 0.64, 99% CI: 0.64-0.64 and adjusted AUROC 0.76, 99% CI: 0.76-0.76) in prognostic accuracy of mortality in both crude and adjusted AUROC (in-between differences AUROC 0.03; z-value 24.68 and AUROC 0.02; z-value 14.74, respectively). In the secondary cohort, Fuzzy Logic SIRS/OF (crude AUROC 0.57, 99% CI: 0.57-0.58 and adjusted AUROC 0.69, 99% CI: 0.68-0.70) outperformed SOFA (crude AUROC 0.56, 99% CI: 0.56-0.56 and adjusted AUROC 0.68, 99% CI: 0.67-0.68) in prognostic accuracy of mortality in both crude and adjusted AUROC (in-between differences AUROC 0.01; z-value 6.86 and AUROC 0.01; z-value 7.53, respectively). The results of this study demonstrated that among adult ICU patients, the predictive validity for sepsis and in-hospital mortality of a complex algorithm based on Fuzzy Logic applied to expanded SIRS criteria with organ failure criteria was better than SOFA for detection of sepsis and for prognostic accuracy of mortality. The findings of this study support the use of a computer-enhanced algorithm that includes a combination of expanded SIRS with organ failure criteria as a tool to assist nurses and healthcare providers in early identification of sepsis

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

Integral estimation of systemic inflammatory response under sepsis

Currently, the most significant mediators of the systemic inflammatory response (SIR), specific to the development of critical states in sepsis, have the chaotic changes of concentrations in the blood. The solution to the problem is using integral indicators. A scoring scale of the SIR (0–16 points) is proposed based on the determination in the blood plasma of CRP, TNF-α, IL-6, IL-8 and IL-10. The scale was used in the survey of 167 patients with a diagnosis of sepsis (43 patients with sepsis according to definitions of “Sepsis-1 or 2” and 124 patients with sepsis according to the criteria of “Sepsis-3”); septic shock was verified in 31 cases and in 48 cases lethal outcomes were recorded. The association of SIR with critical complications of sepsis was revealed, especially under acute septic shock and in cases of a “second wave” (days 5–7) of critical complications. In contrast, prolonged/ subacute sepsis (more than 14 days) under tertiary peritonitis is characterised by a lesser dependence of the criticality of the state on the severity of SIR. The proposed scale is an open system and allows you to modify the range of used particular indicators that are compatible by pathogenetic and diagnostic significance. © 2020, Slovak Academy of Sciences. All rights reserved.The work was carried out within the framework of the IIP UrB RAS theme No АААА-А18-118020590108-7

On the intelligent management of sepsis in the intensive care unit

The management of the Intensive Care Unit (ICU) in a hospital has its own, very specific requirements that involve, amongst others, issues of risk-adjusted mortality and average length of stay; nurse turnover and communication with physicians; technical quality of care; the ability to meet patient's family needs; and avoid medical error due rapidly changing circumstances and work overload. In the end, good ICU management should lead to an improvement in patient outcomes. Decision making at the ICU environment is a real-time challenge that works according to very tight guidelines, which relate to often complex and sensitive research ethics issues. Clinicians in this context must act upon as much available information as possible, and could therefore, in general, benefit from at least partially automated computer-based decision support based on qualitative and quantitative information. Those taking executive decisions at ICUs will require methods that are not only reliable, but also, and this is a key issue, readily interpretable. Otherwise, any decision tool, regardless its sophistication and accuracy, risks being rendered useless. This thesis addresses this through the design and development of computer based decision making tools to assist clinicians at the ICU. It focuses on one of the main problems that they must face: the management of the Sepsis pathology. Sepsis is one of the main causes of death for non-coronary ICU patients. Its mortality rate can reach almost up to one out of two patients for septic shock, its most acute manifestation. It is a transversal condition affecting people of all ages. Surprisingly, its definition has only been standardized two decades ago as a systemic inflammatory response syndrome with confirmed infection. The research reported in this document deals with the problem of Sepsis data analysis in general and, more specifically, with the problem of survival prediction for patients affected with Severe Sepsis. The tools at the core of the investigated data analysis procedures stem from the fields of multivariate and algebraic statistics, algebraic geometry, machine learning and computational intelligence. Beyond data analysis itself, the current thesis makes contributions from a clinical point of view, as it provides substantial evidence to the debate about the impact of the preadmission use of statin drugs in the ICU outcome. It also sheds light into the dependence between Septic Shock and Multi Organic Dysfunction Syndrome. Moreover, it defines a latent set of Sepsis descriptors to be used as prognostic factors for the prediction of mortality and achieves an improvement on predictive capability over indicators currently in use.La gestió d'una Unitat de Cures Intensives (UCI) hospitalària presenta uns requisits força específics incloent, entre altres, la disminució de la taxa de mortalitat, la durada de l'ingrès, la rotació d'infermeres i la comunicació entre metges amb al finalitad de donar una atenció de qualitat atenent als requisits tant dels malalts com dels familiars. També és força important controlar i minimitzar els error mèdics deguts a canvis sobtats i a la presa ràpida de deicisions assistencials. Al cap i a la fi, la bona gestió de la UCI hauria de resultar en una reducció de la mortalitat i durada d'estada. La presa de decisions en un entorn de crítics suposa un repte de presa de decisions en temps real d'acord a unes guies clíniques molt restrictives i que, pel que fa a la recerca, poden resultar en problemes ètics força sensibles i complexos. Per tant, el personal sanitari que ha de prendre decisions sobre la gestió de malalts crítics no només requereix eines de suport a la decisió que siguin fiables sinó que, a més a més, han de ser interpretables. Altrament qualsevol eina de decisió que no presenti aquests trets no és considerarà d'utilitat clínica. Aquesta tesi doctoral adreça aquests requisits mitjançant el desenvolupament d'eines de suport a la decisió per als intensivistes i es focalitza en un dels principals problemes als que s'han denfrontar: el maneig del malalt sèptic. La Sèpsia és una de les principals causes de mortalitats a les UCIS no-coronàries i la seva taxa de mortalitat pot arribar fins a la meitat dels malalts amb xoc sèptic, la seva manifestació més severa. La Sèpsia és un síndrome transversal, que afecta a persones de totes les edats. Sorprenentment, la seva definició ha estat estandaritzada, fa només vint anys, com a la resposta inflamatòria sistèmica a una infecció corfimada. La recerca presentada en aquest document fa referència a l'anàlisi de dades de la Sèpsia en general i, de forma més específica, al problema de la predicció de la supervivència de malalts afectats amb Sèpsia Greu. Les eines i mètodes que formen la clau de bòveda d'aquest treball provenen de diversos camps com l'estadística multivariant i algebràica, geometria algebraica, aprenentatge automàtic i inteligència computacional. Més enllà de l'anàlisi per-se, aquesta tesi també presenta una contribució des de el punt de vista clínic atès que presenta evidència substancial en el debat sobre l'impacte de l'administració d'estatines previ a l'ingrès a la UCI en els malalts sèptics. També s'aclareix la forta dependència entre el xoc sèptic i el Síndrome de Disfunció Multiorgànica. Finalment, també es defineix un conjunt de descriptors latents de la Sèpsia com a factors de pronòstic per a la predicció de la mortalitat, que millora sobre els mètodes actualment més utilitzats en la UCI