A Computational Pipeline for Sepsis Patients’ Stratification and Diagnosis

Sepsis is still a little acknowledged public health issue, despite its increasing incidence and the growing mortality rate. In addition, a clear diagnosis can be lengthy and complicated, due to highly variable symptoms and non-specific criteria, causing the disease to be diagnosed and treated too late. This paper presents the HemoSpec platform, a decision support system which, by collecting and automatically processing data from several acquisition devices, can help in the early diagnosis of sepsis

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

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome.

In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS

AFICILL: a single-cohort, retrospective study on Atrial Fibrillation In Critically ILL patients admitted to a medical sub-intensive care unit: implications for clinical management, outcomes and elaboration of new data-driven models

Introduction: atrial fibrillation (AF) is common among critically-ill patients, who are considered at increased cardioembolic and haemorragic risk. Consequently, anticoagulant therapy might be ineffective or harmful for an excess of haemorragic events which could not be counterbalanced by an adequate reduction of cardioembolic occurrences. Aims: main outcome (MO) was the composite of death or intensive care unit (ICU) transfer in a population of critically-ill subjects admitted to a medical subintensive care unit (sICU); we assessed (i) thromboembolic events (TEE) and major haemorrhages (MH); (ii) current guidelines (GL) adherence and related outcomes; (iii) performance of validated risk scores for TEE and MH; we engineered (iv) new scores adopting machine learning (ML) predicting MO, TEE, MH. Patients and Methods: single-center, retrospective study enrolling all the consecutive AF-affected patients admitted to a sICU for critical illness. Demographic, clinical, therapeutic and laboratoristic data were collected. Performance of CHA2DS2-VASc and HAS-BLED scores was evaluated. GL-adherence and its relationship with outcomes was studied. ML was used to engineer new predictive models. Results: we enrolled 1430 subjects; CHA2DS2-VASc (AUC:0.516;95%CI:0.472-0.560) and HAS-BLED (AUC:0.493;95%CI:0.443-0.543) did not predict TEE or MH; in-hospital warfarin use was associated to increased MO risk (OR:1.73;95%CI:1.06-2.83; p<0.05); low-molecular-weight-heparin use was not associated to an increased MO risk; antiplatelet drugs use was associated to MO risk reduction (OR:0.51;95%CI:0.34-0.78;p<0.002). GL-adherent treatment was associated to TEE risk reduction and MH and MO risk increase; ML identified specific features for MO, TEE, MH: ML-based classifiers outperformed CHA2DS2-VASc (AUC: from 0.516 to 0.90, p<0.0001) and HAS-BLED (AUC: from 0.493 to 0.82, p<0.0001). Discussion: AF-related outcomes cannot be predicted in critically-ill patients with currently validated methods. GL-adherence is associated to a significant TEE reduction, but also to MH and MO increase. ML algorithms can identify the most important features and shape specific scores able to outperform the classical models

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Peptidome profiling for the immunological stratification in sepsis: a proof of concept study

Sepsis has been called the graveyard of pharmaceutical companies due to the numerous failed clinical trials. The lack of tools to monitor the immunological status in sepsis constrains the development of therapies. Here, we evaluated a test based on whole plasma peptidome acquired by MALDI-TOF-mass spectrometer and machine-learning algorithms to discriminate two lipopolysaccharide-(LPS) induced murine models emulating the pro- and anti-inflammatory/immunosuppression environments that can be found during sepsis. The LPS group was inoculated with a single high dose of LPS and the IS group was subjected to increasing doses of LPS, to induce proinflammatory and anti-inflammatory/immunosuppression profiles respectively. The LPS group showed leukopenia and higher levels of cytokines and tissue damage markers, and the IS group showed neutrophilia, lymphopenia and decreased humoral response. Principal component analysis of the plasma peptidomes formed discrete clusters that mostly coincided with the experimental groups. In addition, machine-learning algorithms discriminated the different experimental groups with a sensitivity of 95.7% and specificity of 90.9%. Data reveal the potential of plasma fingerprints analysis by MALDI-TOF-mass spectrometry as a simple, speedy and readily transferrable method for sepsis patient stratification that would contribute to therapeutic decision-making based on their immunological status.Fil: Ledesma, Martin Manuel. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Todero, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Maceira, Lautaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Prieto, Monica. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas; Argentina. Administración Nacional de Laboratorio e Institutos de Salud "Dr. Carlos G. Malbrán". Instituto Nacional de Epidemiologia. Departamento de Investigación; ArgentinaFil: Vay, Carlos. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; ArgentinaFil: Galas, Marcelo Fabián. Organización Panamericana de la Salud; Estados UnidosFil: López, Beatriz. Administración Nacional de Laboratorio e Institutos de Salud "Dr. Carlos G. Malbrán". Instituto Nacional de Epidemiologia. Departamento de Investigación; ArgentinaFil: Yokobori, Noemí. Administración Nacional de Laboratorio e Institutos de Salud "Dr. Carlos G. Malbrán". Instituto Nacional de Epidemiologia. Departamento de Investigación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rearte, María Bárbara. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; Argentin