Classification of positive blood cultures:computer algorithms versus physicians' assessment - development of tools for surveillance of bloodstream infection prognosis using population-based laboratory databases

BACKGROUND: Information from blood cultures is utilized for infection control, public health surveillance, and clinical outcome research. This information can be enriched by physicians’ assessments of positive blood cultures, which are, however, often available from selected patient groups or pathogens only. The aim of this work was to determine whether patients with positive blood cultures can be classified effectively for outcome research in epidemiological studies by the use of administrative data and computer algorithms, taking physicians’ assessments as reference. METHODS: Physicians’ assessments of positive blood cultures were routinely recorded at two Danish hospitals from 2006 through 2008. The physicians’ assessments classified positive blood cultures as: a) contamination or bloodstream infection; b) bloodstream infection as mono- or polymicrobial; c) bloodstream infection as community- or hospital-onset; d) community-onset bloodstream infection as healthcare-associated or not. We applied the computer algorithms to data from laboratory databases and the Danish National Patient Registry to classify the same groups and compared these with the physicians’ assessments as reference episodes. For each classification, we tabulated episodes derived by the physicians’ assessment and the computer algorithm and compared 30-day mortality between concordant and discrepant groups with adjustment for age, gender, and comorbidity. RESULTS: Physicians derived 9,482 reference episodes from 21,705 positive blood cultures. The agreement between computer algorithms and physicians’ assessments was high for contamination vs. bloodstream infection (8,966/9,482 reference episodes [96.6%], Kappa = 0.83) and mono- vs. polymicrobial bloodstream infection (6,932/7,288 reference episodes [95.2%], Kappa = 0.76), but lower for community- vs. hospital-onset bloodstream infection (6,056/7,288 reference episodes [83.1%], Kappa = 0.57) and healthcare-association (3,032/4,740 reference episodes [64.0%], Kappa = 0.15). The 30-day mortality in the discrepant groups differed from the concordant groups as regards community- vs. hospital-onset, whereas there were no material differences within the other comparison groups. CONCLUSIONS: Using data from health administrative registries, we found high agreement between the computer algorithms and the physicians’ assessments as regards contamination vs. bloodstream infection and monomicrobial vs. polymicrobial bloodstream infection, whereas there was only moderate agreement between the computer algorithms and the physicians’ assessments concerning the place of onset. These results provide new information on the utility of computer algorithms derived from health administrative registries

Serum cytokine levels as predictive biomarkers of benefit from ipilimumab in small cell lung cancer

Background. Immunotherapy has shown efficacy in small cell lung cancer (SCLC), but only a subset of patients benefits. Surrogate biomarkers are urgently needed. Our aim was to evaluate serum Th1, Th2, and proinflammatory cytokines in two cohorts of SCLC patients before and during treatment with chemotherapy with or without ipilimumab and to correlate them with survival. Patients and methods. Two cohorts of SCLC patients were studied: patients treated with chemotherapy (n = 47), and patients treated with chemotherapy plus ipilimumab (n = 37). Baseline, on-treatment and after-treatment serum samples were evaluated for the presence of IL-1beta, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IFN-gamma, TNF-alpha, GM-CSF, and Mip-1alpha using a Luminex assay. Differential changes in cytokines between cohorts were analyzed. Associations between cytokine levels and their changes with overall survival were evaluated. Results. Patients treated with ipilimumab showed a global increase of all cytokines after treatment initiation. A high level of IL-8 at baseline was associated with worse prognosis regardless of treatment. Baseline increased IL-2 levels predicted sensitivity to ipilimumab, while high IL-6 and TNF-alpha predicted resistance. An on-treatment increase in IL-4 levels in patients treated with immune-chemotherapy was associated with a better overall survival. Conclusions. The addition of ipilimumab to standard chemotherapy in SCLC modulates the serum levels of cytokines. Baseline levels and their change over time relate to overall survival. Blood-based biomarkers are convenient for patients, and our results support prospective validation of cytokines as predictive biomarkers for ipilimumab in SCLC

Assessment of neuronal autoantibodies in patients with small cell lung cancer treated with chemotherapy with or without ipilimumab

Small-cell lung cancer (SCLC) is often associated with paraneoplastic syndromes. To assess the role of anti-neuronal autoantibodies (NAAs) as biomarkers of treatment outcome, we assessed NAAs in serial samples from SCLC patients treated with chemoimmunotherapy compared to chemotherapy alone. We evaluated 2 cohorts: in cohort 1 (C1), 47 patients received standard platinum/etoposide, and in cohort 2 (C2), 38 patients received ipilimumab, carboplatin and etoposide. Serum samples at baseline and subsequent time points were analyzed for the presence of NAAs. NAAs were detected at baseline in 25 patients (53.2%) in C1 and in 20 patients (52.6%) in C2 (most frequently anti-Sox1). NAA at baseline was associated with limited disease (75% vs 50%; p: 0.096) and better overall survival (15.1 m vs 11.7 m; p: 0.032) in C1. Thirteen patients (28.9%) showed 2 or more reactivities before treatment; this was associated with worse PFS (5.5 m vs 7.3 m; p: 0.005) in patients treated with chemoimmunotherapy. NAA titers decreased after therapy in 68.9% patients, with no differential patterns of change between cohorts. Patients whose NAA titer decreased after treatment, showed longer OS [18.5 m (95% CI: 15.8 – 21.2)] compared with those whose NAA increased [12.3 m (95% CI: 8.1 – 16.5; p 0.049)], suggesting that antibody levels correlate to tumor load. Our findings reinforce the role of NAAs as prognostic markers and tumor activity/burden in SCLC, warrant further investigation in their predictive role for immunotherapy and raise concern over the use of immunotherapy in patients with more than one anti-NAA reactivity

Staphylococcus aureus bacteriuria as a prognosticator for outcome of Staphylococcus aureus bacteremia: a case-control study

<p>Abstract</p> <p>Background</p> <p>When <it>Staphylococcus aureus </it>is isolated in urine, it is thought to usually represent hematogenous spread. Because such spread might have special clinical significance, we evaluated predictors and outcomes of <it>S. aureus </it>bacteriuria among patients with <it>S. aureus </it>bacteremia.</p> <p>Methods</p> <p>A case-control study was performed at John H. Stroger Jr. Hospital of Cook County among adult inpatients during January 2002-December 2006. Cases and controls had positive and negative urine cultures, respectively, for <it>S. aureus</it>, within 72 hours of positive blood culture for <it>S. aureus</it>. Controls were sampled randomly in a 1:4 ratio. Univariate and multivariable logistic regression analyses were done.</p> <p>Results</p> <p>Overall, 59% of patients were African-American, 12% died, 56% of infections had community-onset infections, and 58% were infected with methicillin-susceptible <it>S. aureus </it>(MSSA). Among 61 cases and 247 controls, predictors of <it>S. aureus </it>bacteriuria on multivariate analysis were urological surgery (OR = 3.4, p = 0.06) and genitourinary infection (OR = 9.2, p = 0.002). Among patients who died, there were significantly more patients with bacteriuria than among patients who survived (39% vs. 17%; p = 0.002). In multiple Cox regression analysis, death risks in bacteremic patients were bacteriuria (hazard ratio 2.9, CI 1.4-5.9, p = 0.004), bladder catheter use (2.0, 1.0-4.0, p = 0.06), and Charlson score (1.1, 1.1-1.3, p = 0.02). Neither length of stay nor methicillin-resistant <it>Staphylococcus aureus </it>(MRSA) infection was a predictor of <it>S. aureus </it>bacteriuria or death.</p> <p>Conclusions</p> <p>Among patients with <it>S. aureus </it>bacteremia, those with <it>S. aureus </it>bacteriuria had 3-fold higher mortality than those without bacteriuria, even after adjustment for comorbidities. Bacteriuria may identify patients with more severe bacteremia, who are at risk of worse outcomes.</p

Sistema simulador de propuestas de inversi?n aplicando RNA para una empresa de asesoramiento financiero

El proyecto a presentar tiene como alcance el desarrollo de un sistema integral que asesore al cliente en la inversi?n de activos financieros a trav?s de la predicci?n de los precios de los activos que determinan cierta cartera de inversi?n. A la vez, se le otorgar? mayor facilidad al asesor financiero sobre el asesoramiento de carteras de activos al inversionista a trav?s de herramientas donde pueda interactuar y simular un proceso de inversi?n. Para tal fin, se utilizar? data de los precios actuales de los activos financieros aplicando metodolog?as de inteligencia artificial para las predicciones, espec?ficamente el de las redes neuronales artificiales. Para plantear la soluci?n se describe primero la Fundamentaci?n Te?rica que sustentan los conocimientos te?ricos del tema y sus tendencias actuales. Luego se describe el Objeto de Estudio; la organizaci?n y sus procesos principales. A continuaci?n se explica el detalle del Campo de Acci?n a tomar en cuenta dentro del objeto de estudio donde se desarrolla el proyecto, la descripci?n de sus procesos, identificaci?n de sistemas vinculados, las reglas del negocio, identificaci?n y evaluaci?n de la situaci?n problem?tica y los problemas que se resolver?n. En seguida se describen los objetivos del proyecto, fundamentando su elecci?n y cu?les ser?n los indicadores de logro de estos objetivos. Asimismo se describe el modelado del negocio que es la parte m?s importante de este entregable y los documentos de administraci?n del proyecto. Finalmente, se describe tambi?n los requerimientos de sistema tanto funcionales como no funcionales, la seguridad del sistema, el modelo de caso de uso como tambi?n el modelo conceptual del sistema. Cabe mencionar que el modelado del negocio y de sistema ha sido desarrollado siguiendo la metodolog?a RUP (Rational Unified Process) y utilizando el Lenguaje Unificado de Modelamiento (UML).Tesi

Manejo del quemado en el hospital Vicente Corral Moscoso

Doctor en Medicina y CirugiaCuenc

Classification of positive blood cultures: computer algorithms versus physicians' assessment - development of tools for surveillance of bloodstream infection prognosis using population-based laboratory databases

Abstract Background Information from blood cultures is utilized for infection control, public health surveillance, and clinical outcome research. This information can be enriched by physicians’ assessments of positive blood cultures, which are, however, often available from selected patient groups or pathogens only. The aim of this work was to determine whether patients with positive blood cultures can be classified effectively for outcome research in epidemiological studies by the use of administrative data and computer algorithms, taking physicians’ assessments as reference. Methods Physicians’ assessments of positive blood cultures were routinely recorded at two Danish hospitals from 2006 through 2008. The physicians’ assessments classified positive blood cultures as: a) contamination or bloodstream infection; b) bloodstream infection as mono- or polymicrobial; c) bloodstream infection as community- or hospital-onset; d) community-onset bloodstream infection as healthcare-associated or not. We applied the computer algorithms to data from laboratory databases and the Danish National Patient Registry to classify the same groups and compared these with the physicians’ assessments as reference episodes. For each classification, we tabulated episodes derived by the physicians’ assessment and the computer algorithm and compared 30-day mortality between concordant and discrepant groups with adjustment for age, gender, and comorbidity. Results Physicians derived 9,482 reference episodes from 21,705 positive blood cultures. The agreement between computer algorithms and physicians’ assessments was high for contamination vs. bloodstream infection (8,966/9,482 reference episodes [96.6%], Kappa = 0.83) and mono- vs. polymicrobial bloodstream infection (6,932/7,288 reference episodes [95.2%], Kappa = 0.76), but lower for community- vs. hospital-onset bloodstream infection (6,056/7,288 reference episodes [83.1%], Kappa = 0.57) and healthcare-association (3,032/4,740 reference episodes [64.0%], Kappa = 0.15). The 30-day mortality in the discrepant groups differed from the concordant groups as regards community- vs. hospital-onset, whereas there were no material differences within the other comparison groups. Conclusions Using data from health administrative registries, we found high agreement between the computer algorithms and the physicians’ assessments as regards contamination vs. bloodstream infection and monomicrobial vs. polymicrobial bloodstream infection, whereas there was only moderate agreement between the computer algorithms and the physicians’ assessments concerning the place of onset. These results provide new information on the utility of computer algorithms derived from health administrative registries.</p