Variáveis predictivas de readmissão hospitalar nas enfermarias de medicina

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014

Precision medicine in patients with allergic diseases: Airway diseases and atopic dermatitis-PRACTALL document of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology

In this consensus document we summarize the current knowledge on major asthma, rhinitis, and atopic dermatitis endotypes under the auspices of the PRACTALL collaboration platform. PRACTALL is an initiative of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology aiming to harmonize the European and American approaches to best allergy practice and science. Precision medicine is of broad relevance for the management of asthma, rhinitis, and atopic dermatitis in the context of a better selection of treatment responders, risk prediction, and design of disease-modifying strategies. Progress has been made in profiling the type 2 immune response-driven asthma. The endotype driven approach for non-type 2 immune response asthma, rhinitis, and atopic dermatitis is lagging behind. Validation and qualification of biomarkers are needed to facilitate their translation into pathway-specific diagnostic tests. Wide consensus between academia, governmental regulators, and industry for further development and application of precision medicine in management of allergic diseases is of utmost importance. Improved knowledge of disease pathogenesis together with defining validated and qualified biomarkers are key approaches to precision medicine.

NT-proBNP in patients with out-of-hospital cardiac arrest: Results from the FINNRESUSCI Study

AIM To assess whether the established cardiovascular biomarker N-terminal pro-B-type natriuretic peptide (NT-proBNP) provides prognostic information in patients with out-of-hospital cardiac arrest due to ventricular tachycardia or fibrillation (OHCA-VT/VF). METHODS We measured NT-proBNP levels in 155 patients with OHCA-VT/VF enrolled into a prospective multicenter observational study in 21 ICUs in Finland. Blood samples were drawn <6h of OHCA-VT/VF and later after 24h, 48h, and 96h. The end-points were mortality and neurological outcome classified according to Cerebral Performance Category (CPC) after one year. NT-proBNP levels were compared to high-sensitivity troponin T (hs-TnT) levels and established risk scores. RESULTS NT-proBNP levels were higher in non-survivors compared to survivors on study inclusion (median 1003 [quartile (Q) 1-3 502-2457] vs. 527 [179-1284]ng/L, p=0.001) and after 24h (1913 [1012-4573] vs. 1080 [519-2210]ng/L, p<0.001). NT-proBNP levels increased from baseline to 96h after ICU admission (p<0.001). NT-proBNP levels were significantly correlated to hs-TnT levels after 24h (rho=0.27, p=0.001), but not to hs-TnT levels on study inclusion (rho=0.05, p=0.67). NT-proBNP levels at all time points were associated with clinical outcome, but only NT-proBNP levels after 24h predicted mortality and poor neurological outcome, defined as CPC 3-5, in models that adjusted for SAPS II and SOFA scores. hs-TnT levels did not add prognostic information to NT-proBNP measurements alone. CONCLUSION NT-proBNP levels at 24h improved risk assessment for poor outcome after one year on top of established risk indices, while hs-TnT measurements did not further add to risk prediction.

Prognostic Value of Secretoneurin in Critically Ill Patients With Infections.

OBJECTIVES Secretoneurin is produced in neuroendocrine cells, and the myocardium and circulating secretoneurin levels provide incremental prognostic information to established risk indices in cardiovascular disease. As myocardial dysfunction contributes to poor outcome in critically ill patients, we wanted to assess the prognostic value of secretoneurin in two cohorts of critically ill patients with infections. DESIGN Two prospective, observational studies. SETTING Twenty-four and twenty-five ICUs in Finland. PATIENTS A total of 232 patients with severe sepsis (cohort #1) and 94 patients with infections and respiratory failure (cohort #2). INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We measured secretoneurin levels by radioimmunoassay in samples obtained early after ICU admission and compared secretoneurin with other risk indices. In patients with severe sepsis, admission secretoneurin levels (logarithmically transformed) were associated with hospital mortality (odds ratio, 3.17 [95% CI, 1.12-9.00]; p = 0.030) and shock during the hospitalization (odds ratio, 2.17 [1.06-4.46]; p = 0.034) in analyses that adjusted for other risk factors available on ICU admission. Adding secretoneurin levels to age, which was also associated with hospital mortality in the multivariate model, improved the risk prediction as assessed by the category-free net reclassification index: 0.35 (95% CI, 0.06-0.64) (p = 0.02). In contrast, N-terminal pro-B-type natriuretic peptide levels were not associated with mortality in the multivariate model that included secretoneurin measurements, and N-terminal pro-B-type natriuretic peptide did not improve patient classification on top of age. Secretoneurin levels were also associated with hospital mortality after adjusting for other risk factors and improved patient classification in cohort #2. In both cohorts, the optimal cutoff for secretoneurin levels at ICU admission to predict hospital mortality was ≈ 175 pmol/L, and higher levels were associated with mortality also when adjusting for Simplified Acute Physiology Score II and Sequential Organ Failure Assessment scores. CONCLUSIONS Secretoneurin levels provide incremental information to established risk indices for the prediction of mortality and shock in critically ill patients with severe infections.

Criminal offending in schizophrenia over a 25-year period marked by deinstitutionalization and increasing prevalence of comorbid substance use disorders

Objective: This study examined the pattern of criminal convictions in persons with schizophrenia over a 25-year period marked by both radical deinstitutionalization and increasing rates of substance abuse problems among persons with schizophrenia in the community. Method: The criminal records of 2,861 patients (1,689 of whom were male) who had a first admission for schizophrenia in the Australian state of Victoria in 1975, 1980, 1985, 1990, and 1995 were compared for the period from 1975 to 2000 with those of an equal number of community comparison subjects matched for age, gender, and neighborhood of residence. Results: Relative to the comparison subjects, the patients with schizophrenia accumulated a greater total number of criminal convictions (8,791 versus 1,119) and were significantly more likely to have been convicted of a criminal offense (21.6% versus 7.8%) and of an offense involving violence (8.2% versus 1.8%). The proportion of patients who had a conviction increased from 14.8% of the 1975 cohort to 25.0% of the 1995 cohort, but a proportionately similar increase from 5.1% to 9.6% occurred among the comparison subjects. Rates of known substance abuse problems among the schizophrenia patients increased from 8.3% in 1975 to 26.1% in 1995. Significantly higher rates of criminal conviction were found for patients with substances abuse problems than for those without substance abuse problems (68.1% versus 11.7%). Conclusions: A significant association was demonstrated between having schizophrenia and a higher rate of criminal convictions, particularly for violent offenses. However, the rate of increase in the frequency of convictions over the 25-year study period was similar among schizophrenia patients and comparison subjects, despite a change from predominantly institutional to community care and a dramatic escalation in the frequency of substance abuse problems among persons with schizophrenia. The results do not support theories that attempt to explain the mediation of offending behaviors in schizophrenia by single factors, such as substance abuse, active symptoms, or characteristics of systems of care, but suggest that offending reflects a range of factors that are operative before, during, and after periods of active illness.

Looking to the future : Predicting renal replacement outcomes in a large community cohort with chronic kidney disease

Peer reviewed

Looking to the future : Predicting renal replacement outcomes in a large community cohort with chronic kidney disease

Peer reviewed

Looking to the future : Predicting renal replacement outcomes in a large community cohort with chronic kidney disease

Peer reviewed

Modeling Hospital Readmissions in Dalarna County, Sweden

Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.

Neural representations of social and non-social uncertainty in human decision making

The social landscape is filled with an intricate web of species-specific desired objects and course of actions. Humans are highly social animals and, as they navigate this landscape, they need to produce adapted decision-making behaviour. Traditionally social and non-social neural mechanisms affecting choice have been investigated using different approaches. Recently, in an effort to unite these findings, two main theories have been proposed to explain how the brain might encode social and non-social motivational decision-making: the extended common currency and the social valuation specific schema (Ruff & Fehr 2014). One way to test these theories is to directly compare neural activity related to social and non-social decision outcomes within the same experimental setting. Here we address this issue by focusing on the neural substrates of social and non-social forms of uncertainty. Using functional magnetic resonance imaging (fMRI) we directly compared the neural representations of reward and risk prediction and errors (RePE and RiPE) in social and non- social situations using gambling games. We used a trust betting game to vary uncertainty along a social dimension (trustworthiness), and a card game (Preuschoff et al. 2006) to vary uncertainty along a non-social dimension (pure risk). The trust game was designed to maintain the same structure of the card game. In a first study, we exposed a divide between subcortical and cortical regions when comparing the way these regions process social and non-social forms of uncertainty during outcome anticipation. Activity in subcortical regions reflected social and non-social RePE, while activity in cortical regions correlated with social RePE and non-social RiPE. The second study focused on outcome delivery and integrated the concept of RiPE in non-social settings with that of fairness and monetary utility maximisation in social settings. In particular these results corroborate recent models of anterior insula function (Singer et al. 2009; Seth 2013), and expose a possible neural mechanism that weights fairness and uncertainty but not monetary utility. The third study focused on functionally defined regions of the early visual cortex (V1) showing how activity in these areas, traditionally considered only visual, might reflect motivational prediction errors in addition to known perceptual prediction mechanisms (den Ouden et al 2012). On the whole, while our results do not support unilaterally one or the other theory modeling the underlying neural dynamics of social and non-social forms of decision making, they provide a working framework where both general mechanisms might coexist.

DeepSoft: a vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.

Asociación entre la escala de riesgo Framingham y el puntaje de calcio coronario

Introducción: Entre las diferentes herramientas clínicas para evaluar la presencia de enfermedad coronaria mediante puntajes, la más usada es la Escala de Riesgo cardiovascular de Framingham. Desde hace unos años, se creó el puntaje de calcio coronario el cual mide el riesgo cardiovascular según la presencia de placas ateromatosas vistas por tomografía computarizada. Se evaluó la asociación entre la escala de Framigham y el puntaje de calcio coronario en una población de sujetos sanos asintomáticos. Metodología: Se realizó un estudio transversal para evaluar la asociación entre el puntaje de calcio coronario y la escala de Framingham en sujetos asintomáticos que se practicaron exámen médico preventivo en la Fundación Cardioinfantil- Instituto de Cardiología (FCI-IC) en el periodo comprendido entre 1 de Julio 2011 hasta el 31 de octubre de 2015. Resultados: Se evaluaron 262 pacientes en total. La prevalencia de riesgo cardiovascular fue bajo en un 77.86% de la población, medio en 18.70% y alto en 3.44%, según la escala de Framingham. El riesgo cardiovascular según el puntaje de Calcio coronario fue nulo 70.99%, bajo en 21.75%, medio en 4.19%, severo en 3.05%. Se encontró una asociación entre ambos puntajes para riesgo estadísticamente significativa (p0,00001) Discusión: El riesgo cardiovascular establecido por escala de Framingham se relaciona de forma significativa con la presencia de placas aterioscleróticas. El estudio demostró que en una muestra de sujetos asintomáticos, hay una alteración estructural coronaria temprana.

Factores asociados a la mortalidad en el síndrome de Sjögren

Antecedentes: El síndrome de Sjögren (SS) es una patología crónica, autoinmune, de características multifactoriales en su etiología. También es conocida como una epitelitis autoinmune, caracterizada por síntomas secos como xeroftalmia y xerostomía, pero que también puede tener compromiso sistémico, dado por manifestaciones extra-glandulares. En la actualidad es poco reconocida como tal, y por lo tanto, la tasa de sobrevida en estos pacientes se encuentra disminuida pero poco tenida en cuenta a la hora de la valoración de ellos. Este trabajo describe la evidencia encontrada acerca de las causas de mortalidad y sus factores asociados luego de realizar una revisión sistemática de la literatura. Objetivos: El objetivo de este estudio fue reunir de forma exhaustiva y sistemática toda la evidencia empírica, publicada o no, que cumpla los criterios de búsqueda y elegibilidad sobre factores asociados al incremento de la mortalidad o disminución en la sobrevida de los pacientes con diagnóstico de SS. Métodos: Se realizó una revisión sistemática de la literatura mediante una búsqueda exhaustiva de todos los estudios publicados en las bases de datos electrónicas preestablecidas, hasta abril de 2015, con el fin de determinar las causas más frecuentes de mortalidad en pacientes con SS y los factores asociados a ella. Resultados: Se encontraron 4,654 resultados que coincidían con los criterios de búsqueda establecidos; de estos, 33 cumplieron con los criterios de inclusión y se distribuyeron de la siguiente forma: el 66.6% (22/33) correspondieron a estudios de corte cohorte, 30.3% (10/33) a estudios de corte transversal y el 3.03% (1/33) a estudios casos y controles. Se obtuvieron resultados en cuanto a frecuencias de mortalidad, razón estandarizada de mortalidad, tasas de supervivencia, causas más frecuentes de mortalidad y sus factores asociados. Conclusiones: La mortalidad reportada en los diferentes estudios fue entre el 1.2% hasta el 30%. Aquellos estudios que reportaron una tasa de mortalidad inferior al 5%, tuvieron un tiempo de seguimiento menor 8 años [1,7,33,60,64,86]. La mayoría de los casos sigue un curso relativamente estable, pero hay un porcentaje importante que presenta otras manifestaciones sistémicas con mayor frecuencia de complicaciones durante la evolución del SS. Por tanto, son los que requieren un seguimiento más estrecho, debido a una mayor necesidad de tratamiento sistémico y al mayor riesgo de ingreso hospitalario y de mortalidad, especialmente por el desarrollo de procesos linfoproliferativos B. La presencia de factores pronósticos en el paciente con SS obligará a realizar un seguimiento clínico e inmunológico mucho más estrecho, lo cual permitirá identificar lo antes posible las complicaciones que puedan aparecer e instaurar las correspondientes medidas terapéuticas, para aumentar las tasas de supervivencia.

Evaluación del desempeño pronóstico de dos puntajes de predicción de mortalidad a siete días en pacientes adultos oncológicos críticamente enfermos admitidos a una unidad de cuidados intensivos en Bogotá

Introducción Los sistemas de puntuación para predicción se han desarrollado para medir la severidad de la enfermedad y el pronóstico de los pacientes en la unidad de cuidados intensivos. Estas medidas son útiles para la toma de decisiones clínicas, la estandarización de la investigación, y la comparación de la calidad de la atención al paciente crítico. Materiales y métodos Estudio de tipo observacional analítico de cohorte en el que reviso las historias clínicas de 283 pacientes oncológicos admitidos a la unidad de cuidados intensivos (UCI) durante enero de 2014 a enero de 2016 y a quienes se les estimo la probabilidad de mortalidad con los puntajes pronósticos APACHE IV y MPM II, se realizó regresión logística con las variables predictoras con las que se derivaron cada uno de los modelos es sus estudios originales y se determinó la calibración, la discriminación y se calcularon los criterios de información Akaike AIC y Bayesiano BIC. Resultados En la evaluación de desempeño de los puntajes pronósticos APACHE IV mostro mayor capacidad de predicción (AUC = 0,95) en comparación con MPM II (AUC = 0,78), los dos modelos mostraron calibración adecuada con estadístico de Hosmer y Lemeshow para APACHE IV (p = 0,39) y para MPM II (p = 0,99). El ∆ BIC es de 2,9 que muestra evidencia positiva en contra de APACHE IV. Se reporta el estadístico AIC siendo menor para APACHE IV lo que indica que es el modelo con mejor ajuste a los datos. Conclusiones APACHE IV tiene un buen desempeño en la predicción de mortalidad de pacientes críticamente enfermos, incluyendo pacientes oncológicos. Por lo tanto se trata de una herramienta útil para el clínico en su labor diaria, al permitirle distinguir los pacientes con alta probabilidad de mortalidad.