Juan Carlos Ruiz-Rodríguez

VHIR Vall d’Hebron Research Institute, Barcino, Catalonia, Spain

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Publications (10)20.14 Total impact

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    Vicent J. Ribas Ripoll, Alfredo Vellido, Enrique Romero, Juan Carlos Ruiz-Rodríguez
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    ABSTRACT: Objective This paper presents an algorithm to assess the risk of death in patients with sepsis. Sepsis is a common clinical syndrome in the intensive care unit (ICU) that can lead to severe sepsis, a severe state of septic shock or multi-organ failure. The proposed algorithm may be implemented as part of a clinical decision support system that can be used in combination with the scores deployed in the ICU to improve the accuracy, sensitivity and specificity of mortality prediction for patients with sepsis. Methodology: In this paper, we used the Simplified Acute Physiology Score (SAPS) for ICU patients and the Sequential Organ Failure Assessment (SOFA) to build our kernels and algorithms. In the proposed method, we embed the available data in a suitable feature space and use algorithms based on linear algebra, geometry and statistics for inference. We present a simplified version of the Fisher kernel (practical Fisher kernel for multinomial distributions), as well as a novel kernel that we named the Quotient Basis Kernel (QBK). These kernels are used as the basis for mortality prediction using soft-margin support vector machines. The two new kernels presented are compared against other generative kernels based on the Jensen-Shannon metric (centred, exponential and inverse) and other widely used kernels (linear, polynomial and Gaussian). Clinical relevance is also evaluated by comparing these results with logistic regression and the standard clinical prediction method based on the initial SAPS score. Results As described in this paper, we tested the new methods via cross-validation with a cohort of 400 test patients. The results obtained using our methods compare favourably with those obtained using alternative kernels (80.18% accuracy for the QBK) and the standard clinical prediction method, which are based on the basal SAPS score or logistic regression (71.32% and 71.55%, respectively). The QBK presented a sensitivity and specificity of 79.34% and 83.24%, which outperformed the other kernels analysed, logistic regression and the standard clinical prediction method based on the basal SAPS score. Conclusion Several scoring systems for patients with sepsis have been introduced and developed over the last 30 years. They allow for the assessment of the severity of disease and provide an estimate of in-hospital mortality. Physiology-based scoring systems are applied to critically ill patients and have a number of advantages over diagnosis-based systems. Severity score systems are often used to stratify critically ill patients for possible inclusion in clinical trials. In this paper, we present an effective algorithm that combines both scoring methodologies for the assessment of death in patients with sepsis that can be used to improve the sensitivity and specificity of the currently available methods.
    Artificial intelligence in medicine 01/2014; · 1.65 Impact Factor
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    Juan Carlos Ruiz-Rodríguez, Jordi Rello
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    ABSTRACT: Procalcitonin has been proposed as a specific biomarker of bacterial infections and has been related to the severity of sepsis. The prognostic ability of the initial concentrations of procalcitonin in sepsis is controversial. Some studies find higher initial concentrations in non-survivors but others find no differences. Prognostic assessment based on follow-up of procalcitonin levels may be better than evaluation of the initial levels of procalcitonin. The persistence of elevated procalcitonin levels is indicative of poor prognosis and is associated with mortality. Procalcitonin kinetics could be a tool for assessing the evolution of severe sepsis and sepsis shock. Procalcitonin should find its place as a biomarker for predicting treatment failure of severe sepsis and septic shock.
    Critical care (London, England) 09/2013; 17(5):180. · 4.72 Impact Factor
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    ABSTRACT: PURPOSE: To develop and validate a continuous non-invasive blood pressure (BP) monitoring system using photoplethysmography (PPG) technology through pulse oximetry (PO). METHODS: This prospective study was conducted at a critical care department and post-anesthesia care unit of a university teaching hospital. Inclusion criteria were critically ill adult patients undergoing invasive BP measurement with an arterial catheter and PO monitoring. Exclusion criteria were arrhythmia, imminent death condition, and disturbances in the arterial or the PPG curve morphology. Arterial BP and finger PO waves were recorded simultaneously for 30 min. Systolic arterial pressure (SAP), mean arterial pressure (MAP), and diastolic arterial pressure (DAP) were extracted from computer-assisted arterial pulse wave analysis. Inherent traits of both waves were used to construct a regression model with a Deep Belief Network-Restricted Boltzmann Machine (DBN-RBM) from a training cohort of patients and in order to infer BP values from the PO wave. Bland-Altman analysis was performed. RESULTS: A total of 707 patients were enrolled, of whom 135 were excluded. Of the 572 studied, 525 were assigned to the training cohort (TC) and 47 to the validation cohort (VC). After data processing, 53,708 frames were obtained from the TC and 7,715 frames from the VC. The mean prediction biases were -2.98 ± 19.35, -3.38 ± 10.35, and -3.65 ± 8.69 mmHg for SAP, MAP, and DAP respectively. CONCLUSIONS: BP can be inferred from PPG using DBN-RBM modeling techniques. The results obtained with this technology are promising, but its intrinsic variability and its wide limits of agreement do not allow clinical application at this time.
    European Journal of Intensive Care Medicine 06/2013; · 5.17 Impact Factor
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    Vicent J. Ribas, Alfredo Vellido, Juan Carlos Ruiz-Rodríguez, Jordi Rello
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    ABSTRACT: Fund raising appeals often announce that some funds have already been raised in order to reach a certain threshold. This article reports results from a field experiment examining the role of seed money (i.e., no, 50%, and 67%) in combination with threshold ...
    Expert Systems with Applications 12/2012; 39(18):13552. · 1.85 Impact Factor
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    ABSTRACT: Objective: To evaluate procalcitonin clearance as a prognostic biomarker in septic shock. Design: Prospective, observational pilot study. Setting: Intensive care unit. Patients: Patients admitted to the ICU due to septic shock and multiorgan dysfunction. Interventions: Serum concentrations of procalcitonin were determined within 12h of onset of septic shock and multiorgan dysfunction (coinciding with admission to the ICU), and the following extractions were obtained after 24, 48 and 72h in patients who survived. Data collected: Demographic data, Acute Physiology and Chronic Health Evaluation II score, and Sequential Organ Failure Assessment score, data on the primary focus of infection, and patient outcome (ICU mortality). Results: Procalcitonin clearance was higher in survivors than in non-survivors, with significant differences at 24h (73.9 [56.4-83.8]% vs 22.7 [-331-58.4], p<0.05) and 48h (81.6 [71.6-91.3]% vs -7.29 [-108.2-82.3], p<0.05). The area under the ROC curve was 0.74 (95%CI, 0.54-0.95, p<0.05) for procalcitonin clearance at 24h, and 0.86 (95%CI, 0.69-1.0, p<0.05) at 48h. Conclusions: ICU mortality was associated to sustained high procalcitonin levels, suggesting that procalcitonin clearance at 48h may be a valuable prognostic biomarker.
    Medicina Intensiva 10/2012; 36(7):475-480. · 1.32 Impact Factor
  • N Duran, J Riera, X Nuvials, J C Ruiz-Rodriguez, J Serra, J Rello
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    ABSTRACT: To obtain an accurate audit during in-hospital cardiac arrest, following recommendations of the Utstein style and measuring time intervals between the different interventions, is difficult. To assess whether the use of an audio recording system during in-hospital cardiac arrest resuscitation allows the register of more items during cardiopulmonary resuscitation. Prospective observational study between January 2008 and December 2009. The population that were included, were hospitalized patients and non-hospitalized patients assisted by a cardiac arrest team, except for critical areas. An audio recording system with a timer was turned on when cardiac arrest team was alerted. Recordings were reviewed to fill in the items recommended by the Utstein style. Time intervals were calculated. Mean number of completed items per patient were compared between recorded and non-recorded cardiac arrest. 119 CA team alerts took place. 64 (53.7%) cases were real CA and 37 (57.8%) of them were properly recorded. A mean number of items per patient in recorded cardiac arrest cases were 18.18 (±3.2) vs. 15.96 (±4.1) in non-recorded cardiac arrest cases (p<0.05). In the recorded cases, mean times were: alert - arrival: 1.23 (±0.95)min; arrival - cardiopulmonary resuscitation initiation: 0.63 (±0.38)min; arrival - first defibrillation: 2.06 (±1.33)min; arrival - intubation: 8.42 (±4.64)min; arrival - first adrenaline: 3.30 (±1.98)min. The audio recording system permits the register of a larger number of items per patient during in-hospital cardiac arrest and allows measurement of time intervals between the different interventions during cardiopulmonary resuscitation.
    Resuscitation 07/2012; 83(10):1219-22. · 4.10 Impact Factor
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    ABSTRACT: To evaluate procalcitonin clearance as a prognostic biomarker in septic shock. Prospective, observational pilot study. Intensive care unit. Patients admitted to the ICU due to septic shock and multiorgan dysfunction. Serum concentrations of procalcitonin were determined within 12h of onset of septic shock and multiorgan dysfunction (coinciding with admission to the ICU), and the following extractions were obtained after 24, 48 and 72h in patients who survived. Demographic data, Acute Physiology and Chronic Health Evaluation II score, and Sequential Organ Failure Assessment score, data on the primary focus of infection, and patient outcome (ICU mortality). Procalcitonin clearance was higher in survivors than in non-survivors, with significant differences at 24h (73.9 [56.4-83.8]% vs 22.7 [-331-58.4], p<0.05) and 48h (81.6 [71.6-91.3]% vs -7.29 [-108.2-82.3], p<0.05). The area under the ROC curve was 0.74 (95%CI, 0.54-0.95, p<0.05) for procalcitonin clearance at 24h, and 0.86 (95%CI, 0.69-1.0, p<0.05) at 48h. ICU mortality was associated to sustained high procalcitonin levels, suggesting that procalcitonin clearance at 48h may be a valuable prognostic biomarker.
    Medicina Intensiva 01/2012; 36(7):475-80. · 1.32 Impact Factor
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    Vicent J. Ribas, Alfredo Vellido, Juan Carlos Ruiz-Rodriguez, Jordi Rello
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    ABSTRACT: Sepsis is one of the main causes of death for non-coronary ICU (Intensive Care Unit) patients and has become the 10th most common cause of death in western societies. This is a transversal condition affecting immunocompromised patients, critically ill patients, post-surgery patients, patients with AIDS, and the elderly. In western countries, septic patients account for as much as 25% of ICU bed utilization and the pathology affects 1–2% of all hospitalizations. Its mortality rates range from 12.8% for sepsis to 45.7% for septic shock.The prediction of mortality caused by sepsis is, therefore, a relevant research challenge from a medical viewpoint. The clinical indicators currently in use for this type of prediction have been criticized for their poor prognostic significance. In this study, we redescribe sepsis indicators through latent model-based feature extraction, using factor analysis. These extracted indicators are then applied to the prediction of mortality caused by sepsis. The reported results show that the proposed method improves on the results obtained with the current standard mortality predictor, which is based on the APACHE II score.
    Expert Syst. Appl. 01/2012; 39:1937-1943.
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    ABSTRACT: Sepsis is a transversal pathology and one of the main causes of death at the Intensive Care Unit (ICU). It has in fact become the tenth most common cause of death in western societies. Its mortality rates can reach up to 45.7% for septic shock, its most acute manifestation. For these reasons, the prediction of the mortality caused by sepsis is an open and relevant medical research challenge. This problem requires prediction methods that are robust and accurate, but also readily interpretable. This is paramount if they are to be used in the demanding context of real-time decision making at the ICU. In this brief paper, such a method is presented. It is based on a variant of the well-known support vector machine (SVM) model and provides an automated ranking of relevance of the mortality predictors. The reported results show that it outperforms in terms of accuracy alternative techniques currently in use, while simultaneously assessing the relative impact of individual pathology indicators.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:100-3.
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    ABSTRACT: Sepsis is one of the main causes of death for noncoronary ICU (Intensive Care Unit) patients and has become the tenth most common cause of death in western societies. This is a transversal condition affecting immunocompromised patients, critically ill patients, post-surgery patients, patients with AIDS, and the elderly. In western countries, septic patients account for as much as 25% of ICU bed utilization and the pathology affects 1% - 2% of all hospitalizations. Its mortality rates range from 12.8% for sepsis to 45.7% for septic shock. Early administration of antibiotics is known to be crucial for ICU outcomes. In this regard, statins, a class of drug, have been shown to present good anti-inflammatory properties beyond their regulation of the biosynthesis of cholesterol. In this brief paper, we hypothesize that preadmission use of statins improves ICU outcomes. We test this hypothesis in a prospective study in patients admitted with severe sepsis and multiorgan failure at the ICU of Vall d' Hebron University Hospital (Barcelona, Spain), using statistic algebraic models and regression trees.
    Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, part of the IEEE Symposium Series on Computational Intelligence 2011, April 11-15, 2011, Paris, France; 01/2011