Examinando por Autor "Borges-Sa, Marcio"

Mostrando 1 - 9 de 9
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    Activity and Outcomes of a Multidisciplinary Sepsis Unit: Fifty Thousand Consultations over Thirteen Years
    (MDPI, 0030-07-26) Borges-Sa, Marcio; Martin-Martinez, Elena; Aranda, Maria; Socias, Antonia; Tejada, Sofia; del Castillo, Alberto; Garau, Margarita; Pérez de Amezaga, Luis; Mena, Joana; Giglio, Andres
    Background. Sepsis is a leading, time-dependent cause of in-hospital death, and guidelines now prioritize organized care such as code-sepsis protocols. Yet little is known about the long-term activity, organization and outcomes of permanent, hospital-scale programs. We characterized the activity of a hospital-wide multidisciplinary sepsis unit (MSU) over thirteen years and evaluated mortality trends. Methods. Retrospective cohort study using the registry of a hospital-wide MSU (2011–2023). The analysis unit was the sepsis event. Activity, referral pathways, interventions, follow-up and mortality were analyzed; temporal trends used multivariable logistic regression and standardized mortality ratios (SMR). Results. The unit attended 10,874 patients across 15,723 events and 50,925 consultations (median age 67; 59.3% men), referred mainly through the sepsis code (45.0%) and, increasingly, proactive early-warning detection (15.2%). An antibiotic change was recommended in 57.2% of events, and 89.8% of all suggested changes were implemented. Follow-up was sustained (median 3 visits; 61.8%). In-hospital mortality was 7.6%, and 11.8% in protocol-confirmed sepsis. Crude mortality did not fall, but severity rose (organ dysfunctions 1.67 to 2.72; septic shock 8 to 13%; both p < 0.001) with stable age; after adjustment the calendar-year effect disappeared (adjusted OR 1.00/year, p = 0.70) and the SMR stayed near 1.0, including pandemic years. Age, ICU admission (OR 2.65) and organ-dysfunction count (OR 1.37) independently predicted death. Conclusions. Over thirteen years, this hospital-wide MSU delivered large-scale, multi-channel, longitudinal care to an increasingly severe population while maintaining stable risk-adjusted mortality, supporting the long-term feasibility of a model integrating early detection, multidisciplinary decision-making, antimicrobial stewardship and follow-up.
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    Activity and Outcomes of a Multidisciplinary Sepsis Unit: Fifty Thousand Consultations over Thirteen Years
    (MDPI, 2026-07-30) Borges-Sa, Marcio; Martin-Martinez, Elena; Aranda, Maria; Socias, Antonia; Tejada, Sofia; Garau, Margarita; Pérez de Amezaga, Luis; Mena, Joana; Giglio, Andres
    Resumen: Background. Sepsis is a leading, time-dependent cause of in-hospital death, and guidelines now prioritize organized care such as code-sepsis protocols. Yet little is known about the long-term activity, organization and outcomes of permanent, hospital-scale programs. We characterized the activity of a hospital-wide multidisciplinary sepsis unit (MSU) over thirteen years and evaluated mortality trends. Methods. Retrospective cohort study using the registry of a hospital-wide MSU (2011-2023). The analysis unit was the sepsis event. Activity, referral pathways, interventions, follow-up and mortality were analyzed; temporal trends used multivariable logistic regression and standardized mortality ratios (SMR). Results. The unit attended 10,874 patients across 15,723 events and 50,925 consultations (median age 67; 59.3% men), referred mainly through the sepsis code (45.0%) and, increasingly, proactive early-warning detection (15.2%). An antibiotic change was recommended in 57.2% of events, and 89.8% of all suggested changes were implemented. Follow-up was sustained (median 3 visits; 61.8%). In-hospital mortality was 7.6%, and 11.8% in protocol-confirmed sepsis. Crude mortality did not fall, but severity rose (organ dysfunctions 1.67 to 2.72; septic shock 8 to 13%; both p < 0.001) with stable age; after adjustment the calendar-year effect disappeared (adjusted OR 1.00/year p = 0.70) and the SMR stayed near 1.0, including pandemic years. Age, ICU admission (OR 2.65) and organ-dysfunction count (OR 1.37) independently predicted death. Conclusions. Over thirteen years, this hospital-wide MSU delivered large-scale, multi-channel, longitudinal care to an increasingly severe population while maintaining stable risk-adjusted mortality, supporting the long-term feasibility of a model integrating early detection, multidisciplinary decision-making, antimicrobial stewardship and follow-up.
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    Decoding sepsis: a 16-year retrospective analysis of activation patterns, mortality predictors, and outcomes from a hospital-wide sepsis protocol
    (MDPI, 2025-08-14) Borges-Sa, Marcio
    Background: Sepsis remains a leading cause of mortality in hospitalized patients. We evaluated characteristics and outcomes of patients identified through a comprehensive hospital-wide sepsis protocol over a 16-year period. Methods: This retrospective cohort study analyzed hospital-wide sepsis protocol activations at a tertiary care hospital in Spain from 2006 to 2022. The protocol required at least two SIRS criteria plus evidence of organ dysfunction in patients over 14 years old. We analyzed demographics, activation criteria, hospital location, mortality predictors using univariate and multivariate analyses, including propensity score modeling, and resource utilization trends. Results: A total of 10,919 patients with 14,546 protocol activations were identified. The median age was 69 years (IQR: 56–78), with 60.9% male patients. Protocol activations occurred in the emergency department (54%), ICU (34.2%), and inpatient wards (11.8%). The most common SIRS criteria were tachycardia (75.6%), tachypnea (50.4%), and fever (48.5%). Prevalent organ dysfunctions included hypotension (53%), hypoxemia (50.1%), oliguria (28.9%), and altered mental status (22%). Overall in-hospital mortality showed a significant linear downward trend from 26.5% in the first year to 13.6% in later years (p < 0.01). Propensity score analysis confirmed independent mortality predictors included hyperlactatemia (aOR 2.21), altered consciousness (aOR 2.09), hypotension (aOR 1.87), and leukopenia (aOR 1.79). ICU admission rate decreased from 58% to 24% over the study period. Conclusions: This 16-year analysis shows that comprehensive hospital-wide sepsis protocols achieve sustained mortality reduction with improved resource utilization efficiency. These findings support implementing comprehensive sepsis protocols as an effective strategy for improving sepsis outcomes.
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    From development to clinical practice: deployment of an interoperable and secure ML-based CDSS to aid in the early detection of sepsis
    (Elsevier, 2026-06-17) Serrano García, Ana; López, David; Macias-Fassio, Eric; Salas-Sosa, Santiago; Pascual, Iván; Pruenza, Cristina; Borges-Sa, Marcio; Giglio, Andrés; Cruz-Rojo, Jaime; Pacheco-Puig, Rodrigo
    Recent research has increasingly focused on machine learning (ML) models for early disease prediction, yet practical frameworks for integrating these models into clinical workflows remain limited. BIAlert is a microservices-based framework designed to operate as a real-time early-warning system for ML-driven disease prediction in hospitalised patients. It can be deployed remotely on physical or virtual servers and is composed of coupled microservices that communicate through Apache Kafka queues, using HL7 FHIR resources as the message format. The system comprises four core components: (1) the Connector, which ingests raw hospital data and converts it into standardised healthcare formats; (2) the Writer, which stores FHIR-formatted data in an internal database and triggers the prediction pipeline; (3) the Predictor, which hosts ML models and generates patient-specific alerts; and (4) the Model Evaluator, which supports prospective monitoring of model performance. Alerts are displayed through the BIAlert user interface and can also be integrated directly into the electronic health record (EHR). BIAlert is currently deployed and operating in real-time clinical settings in two hospitals, demonstrating its feasibility as a scalable and interoperable solution for ML-based clinical decision support.
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    Hospital-wide sepsis detection: A machine learning model based on prospectively expert-validated cohort
    (MDPI, 2026-01-21) Borges-Sa, Marcio
    Background/Objectives: Sepsis detection remains challenging due to clinical heterogeneity and limitations of traditional scoring systems. This study developed and validated a hospital-wide machine learning model for sepsis detection using retrospectively developed data from prospectively expert-validated cases, aiming to improve diagnostic accuracy beyond conventional approaches. Methods: This retrospective cohort study analysed 218,715 hospital episodes (2014–2018) at a tertiary care centre. Sepsis cases (n = 11,864, 5.42%) were prospectively validated in real-time by a Multidisciplinary Sepsis Unit using modified Sepsis-2 criteria with organ dysfunction. The model integrated structured data (26.95%) and unstructured clinical notes (73.04%) extracted via natural language processing from 2829 variables, selecting 230 relevant predictors. Thirty models including random forests, support vector machines, neural networks, and gradient boosting were developed and evaluated. The dataset was randomly split (5/7 training, 2/7 testing) with preserved patient-level independence. Results: The BiAlert Sepsis model (random forest + Sepsis-2 ensemble) achieved an AUC-ROC of 0.95, sensitivity of 0.93, and specificity of 0.84, significantly outperforming traditional approaches. Compared to the best rule-based method (Sepsis-2 + qSOFA, AUC-ROC 0.90), BiAlert reduced false positives by 39.6% (13.10% vs. 21.70%, p < 0.01). Novel predictors included eosinopenia and hypoalbuminemia, while traditional variables (MAP, GCS, platelets) showed minimal univariate association. The model received European Medicines Agency approval as a medical device in June 2024. Conclusions: This hospital-wide machine learning model, trained on prospectively expert-validated cases and integrating extensive NLP-derived features, demonstrates superior sepsis detection performance compared to conventional scoring systems. External validation and prospective clinical impact studies are needed before widespread implementation.
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    International multidisciplinary consensus statement on sepsis code guidelines: A Delphi approach
    (John Wiley & Sons Ltd, 0004-08-26) Borges-Sa, Marcio; Giglio, Andrés; Martin-Loeches, Ignacio; Nates, Joseph; González del Castillo, Josemaría; Cárdenas, Yenny; Mergulhao, P.; Candel, F. J.; Zaragoza, R.; Larrosa-Escartin, M. N.; Vidal-Cortés, P.; Maseda, E.; Reina, R.; Paiva, J. A.; del Pozo, J. L.; Villegas, M. V.; Salavert, M.; Soriano, C.; Hidalgo, J.; Machuca, I.; Gonçalves-Pereira, J.; Manuel-Vázquez, A.; Lisboa, T.; Pichardo-Viñas, M.; Barberán, J.; Esparza, G.; Castillo-Abrego, G.; Estella, A.; Ortega, K.; Matos, A.; Raimondi, N.; Ferrer, R.; Sanchez, J. R.; Huelmo, I.; Gorordo-Delsol, L. A.; Campozano, V.; Soriano, A.; Perez, J.; Rodriguez, A.
    Background. Sepsis remains a major global health challenge. International guidelines exist, but their implementation is inconsistent, and supporting evidence largely comes from high-income settings. The objective of this study was to generate international, multidisciplinary expert consensus on controversial aspects of sepsis management within the framework of sepsis code programs. Methods. A multinational modified Delphi study was conducted with 164 experts from 22 countries, 12 specialties, and 105 scientific societies. Seven domains were evaluated: early diagnosis, biomarkers, diagnostic microbiology, hemodynamic monitoring, source control, antimicrobial therapy, and hemodynamic management. Consensus was defined as ≥70% agreement across three iterative rounds using Likert scales (Rounds 1–2) and binary format (Round 3). Results. Consensus was achieved for 40 statements. Strong endorsement (82%–95%) was reached for structured hospital sepsis programs, NEWS-2 as the preferred early recognition tool, biomarker use (notably procalcitonin) to complement clinical assessment, urgent source control within 6 h independent of hemodynamic status, rapid molecular diagnostics integrated with antimicrobial stewardship, and norepinephrine as first-line vasopressor therapy. Experts also supported pharmacokinetic- and pharmacodynamic-guided antibiotic dosing, prolonged infusion of time-dependent agents, and dynamic rather than fixed fluid strategies. No consensus was reached on routine reliance on Sepsis-2, Sepsis-3, or qSOFA; high mean arterial pressure targets; or universal combination antimicrobial therapy. Conclusions. These results provide multidisciplinary guidance for sepsis management, with emphasis on rapid recognition, targeted antimicrobial therapy, timely source control, and hemodynamic management guided by patient physiology. The recommendations are applicable to high- and middle-income healthcare systems.
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    Mapping intensive care across Ibero-America: The FEPIMCTI multinational survey of bed capacity, workforce, and pandemic response
    (Elsevier, 0022-07-26) Matos, Alfredo; Giglio, Andrés; Pérez-Fernandez, Javier; Nates, Joseph; Cárdenas, Yenny; Raimondi, Nestor; Sánchez, Jorge; Hidalgo, Jorge; Rezende, Ederlon; Borges-Sa, Marcio
    Background: Intensive care resources are unequally distributed across Ibero-America, and reliable comparable data are scarce, hindering cross-national comparison and health-system planning. Methods: We conducted a cross-sectional survey of the 25 national critical care societies affiliated with FEPIMCTI. Presidents or designated representatives reported country-level data on ICU beds, intensivist and nursing workforce, organizational models, training pathways, and surge capacity before and during the COVID-19 pandemic. Resource density was summarized as the country-level median with IQR and as the population-weighted regional rate. Results: Twenty-one countries, including Spain and Portugal, participated, representing 693.6 million inhabitants. A total of 78,723 ICU beds were reported (country-level median 6.1 per 100,000, IQR 3.3–12.4; population-weighted rate 11.3), with public ICUs accounting for 51.6%. A total of 23,803 intensivists were identified (median 2.6 per 100,000, IQR 1.0–5.0), with five countries below 1 per 100,000. Nurse-to-patient ratios ranged from 1:1 to 1:7, and universal 24/7 intensivist coverage was reported in only a subset of countries. During the pandemic, ICU bed capacity rose 69%; among the 20 countries with data for both periods, physician involvement rose 32%, largely through redeployment of non-intensivists rather than growth of the certified workforce. Conclusions: Critical care capacity, workforce, and organization vary widely across Ibero-America, with many countries below high-income benchmarks and pandemic surges met largely through temporary redeployment. These findings provide the first coordinated regional benchmark to guide workforce development, standardized training, and organizational strengthening.
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    Prospective economic evaluation of a predictive artificial intelligence model for sepsis: Effects on hospital costs and return on investment
    (PLOS, 2026-08-31) Giglio, Andres; Macias-Fassio, Eric; Salas-Sosa, Santiago; Lopez, David; Pruenza, Cristina; Morales, Aythami; Borges-Sa, Marcio
    Resumen: Early sepsis detection is essential for improving outcomes and reducing costs, but traditional rule-based systems have limited accuracy and real-world evidence for machine-learning alternatives remains scarce. In this context, BIAlert-Sepsis predicts sepsis risk within 24 hours using historical hospital data, and evaluating its implementation in a tertiary hospital setting provides an opportunity to quantify its clinical benefits and economic value. We conducted a retrospective quasi-experimental before–after study including all septic patients admitted from January 2011 to June 2024. The baseline period (Jan 2011 – Mar 2019) was compared with the AI implementation period (Apr 2019 – June 2024), excluding the COVID-19 interval. Outcomes were assessed using adjusted generalized linear models and interrupted time series regression. A hospital-perspective economic evaluation incorporated implementation and maintenance costs, and a 5-year model estimated net benefit and return on investment (ROI), supported by deterministic and probabilistic sensitivity analyses. A total of 8,039 patients were included (6,168 baseline period; 1,871 AI period). Demographic and clinical characteristics were comparable across periods. During the AI period, ICU admissions decreased from 34.4% to 30.4% (adjusted p = 0.001), accompanied by significant reductions of 0.35 ICU days and 0.59 ward days per patient. Mean admission costs declined from 26,517€ to 24,630€ (adjusted p = 0.005). After covariate adjustment, AI implementation was associated with a 26.1–31.1% reduction in mean admission costs across GLM models. Interrupted time series analysis identified a modest immediate cost level change after AI implementation and a larger sustained decline during the post-COVID period. The 5-year economic model projected a cumulative discounted net benefit of 3.55M€ and a 528% ROI. BIAlert-Sepsis was associated with favourable clinical outcomes and lower costs, with economic modelling suggesting early breakeven and positive financial returns
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    Service-Specific Heterogeneity in Sepsis Variable Significance and Machine Learning Model Performance: A Stratified Analysis of the BIAlert Cohort
    (MDPI, 2026-06-24) Borges-Sa, Marcio; Macias-Fassio, Eric; Delgado, Alejandro; Santiago Salas Sosa; Aranda, María; Socias, Antonia; del Castillo, Alberto; Giglio, Andrés
    Background/Objectives: Sepsis detection relies on clinical variables and scoring systems assumed to perform uniformly across hospital settings. However, sepsis phenotype distributions shift between clinical environments, suggesting that variable importance may be setting dependent. This study aimed to quantify service-specific variability in the discriminatory capacity of clinical variables for sepsis detection and to evaluate whether this heterogeneity translates into differential performance of machine learning models compared to traditional clinical scoring systems. Methods: This stratified sub-analysis of the BIAlert Sepsis cohort (203,755 patients; 11,864 sepsis episodes, 2014–2018) evaluated 61 structured quantitative variables across nine hospital services (≥90 sepsis episodes each). Within each service, the Mann–Whitney–Wilcoxon test (p < 0.01, Holm-corrected) assessed differences between septic and non-septic episodes. Five machine learning models (Random Forest/BIAlert, XGBoost, CatBoost, SVM, Neural Network) and three clinical rules (NEWS, SIRS, qSOFA) were evaluated globally and stratified across four clinical environments. Results: The proportion of significant variables ranged from 95.1% in the Emergency Department (58/61) to 37.7% in the Intensive Care Unit (23/61). Lactate was the only universally significant variable (9/9 services). Clinical scoring systems collapsed in Critical Care (qSOFA and NEWS AUC 0.459). BIAlert maintained the highest AUC across all environments (0.975–0.857). The Friedman test confirmed significant differences (χ2 = 28.00, p < 0.001), with BIAlert achieving a mean rank of 1.0. Conclusions: The discriminatory capacity of clinical variables for sepsis detection is not uniform across hospital services. ML models, particularly BIAlert, maintained robust performance where fixed-rule scoring systems failed.