Development of Predictive Models for COVID-19 Prognosis based on Patients’ Demographic and Clinical Data

Thesis to obtain the Master’s Degree in Biomedical EngineeringBackground – Cases of infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were first reported in late December 2019. Due to the large spectrum of clinical presentations and outcomes, the disease was named Coronavirus Disease 2019 (COVID-19) and characterized as a pandemic due to the elevated number of cases worldwide, the high transmission rate and the lack of action measures. Since then, a lot of progress has been made, but the study of demographic and clinical information and the determination of possible laboratory biomarkers for COVID-19 prognosis is crucial. Purpose – Determine predictive biomarkers for COVID-19’s outcome (death or survival), in critically ill patients, using clinical, demographic and laboratory data from the intensive care unit (ICU). Methods – Demographic, clinical and laboratory data from 337 COVID-19 patients admitted to the ICU of Centro Hospitalar Universitário Lisboa Central, Portugal, between March 2020 and March 2021, was extracted from the hospital’s electronic medical record system, pre-processed, and analyzed. Comparisons were made regarding death, the need of invasive mechanic ventilation (IMV), the first three COVID-19 waves and age groups. Longitudinal data was gathered over the course of the patients stay in the ICU. To infer about the evolution of the patients' condition in the first week of ICU admission, a comparative analysis was carried out between the data from the 2nd (335 patients) and 7th days (216 patients). Comparisons of laboratory parameters between discharged and deceased patients, at these time points were performed. The associations between the several biomarkers and death were tested by means of Univariate Generalized Estimating Equations (GEEs) models. Additionally, to analyze the impact of some biomarkers in mortality, crude odds ratios were estimated and interpreted, with the corresponding 95% confidence intervals (CIs). Death event-free survival rates were obtained by the Kaplan-Meier estimator. All P values were considered statistically significant at P<0.05. Results – Deceased patients were considerably older, had more comorbidities, required more IMV, and spent less time in the hospital than discharged patients. Death rates did not differ significantly between COVID-19 waves. Patients from the 1st wave were significantly older and relied more on IMV and extracorporeal membrane oxygenation (ECMO). Most of the detected differences regarding laboratory biomarkers were found between discharged and deceased patients from the 2nd and 3rd waves, being that the deceased ones had almost always worse results. In general, worse results were obtained in the 1st wave and in the 7th day of ICU admission. In 2nd day of ICU admission, 2nd wave, higher mortality rates were observed for patients with lymphocyte (LYM) levels under normality ranges. In the 3rd wave, mortality rates were higher for patients with high sensitivity troponin I (hs-cTn I) levels above normality ranges in the 2nd day of ICU admission, with LYM levels under normality ranges in the 7th day of ICU admission, and with platelet (PLT) levels below normality ranges, either in the 2nd or 7th days of ICU admission. Through the univariate logistic regression’s results in 2nd day of ICU admission, 2nd wave, hs-cTn I, red blood cell (RBC) counts, platelet-lymphocyte ratio (PLR) and neutrophil-lymphocyte ratio (NLR) showed significant association with the risk of death. In 7th day of ICU admission, C-reactive protein (CRP), RBC counts, hematocrit (HCT), hemoglobin (HGB), white blood cell (WBC) and neutrophil (NEU) counts, eosinophil (EO) counts and NLR, revealed significant association with the risk of death. In the 2nd day of ICU admission, 3rd wave, hs-cTn I, PLT counts, lactate dehydrogenase (LDH) and CRP showed significant association with the risk of death. For the 7th day, PCT, CRP, WBC and NEU counts, LYM counts, NLR and PLT counts results were also associated with higher risks of death. Univariate GEEs models results demonstrated that, in the 1st wave, hs-cTn I, myoglobin, EO counts, results were associated with higher risks of death. In the 2nd wave, the risk of death was significantly associated with hs-cTn I, myoglobin levels, EO counts, WBC and NEU counts, LYM counts, and INR. Finally, in the 3rd wave, hs-cTn I, CK, EO counts, WBC and NEU counts, LYM counts, NLR and PLT counts, were also associated with the risk of death. Conclusion - This study provides useful information for prognostic evaluation that can be used to guide treatment and monitoring. Most importantly, it consists of valuable data that can be employed as the foundation of a variety of future research. Aside from the positive results, more research is needed to develop reliable and robust biomarkers for COVID-19’s outcomes.Introdução – Casos de infeção pelo vírus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) foram relatados pela primeira vez no final de dezembro de 2019. Devido ao grande espetro de apresentações e outcomes clínicos, a doença foi denominada Coronavirus Disease 2019 (COVID-19) e considerada uma pandemia devido ao elevado número de casos em todo o mundo, à alta taxa de transmissão e à falta de medidas de ação. Apesar desta patologia estar a ser aprofundadamente investigada, o estudo de informação demográfica e clínica e a determinação de possíveis biomarcadores laboratoriais para o prognóstico da COVID-19 continua a ser crucial. Objetivos – Determinar biomarcadores preditivos para o outcome da COVID-19 (morte ou vida), em pacientes críticos, usando dados clínicos, demográficos e laboratoriais da unidade de cuidados intensivos (UCI). Métodos – Dados demográficos, clínicos e laboratoriais de 337 pacientes com COVID-19 internados na UCI do Centro Hospitalar Universitário Lisboa Central, em Portugal, entre março de 2020 e março de 2021, foram extraídos das bases de dados eletrónicas do hospital, pré-processados e analisados. Foram feitas comparações em relação ao óbito na UCI, necessidade de ventilação mecânica invasiva (VMI), três vagas de COVID-19 e faixas etárias. Dados longitudinais foram obtidos ao longo da permanência dos pacientes na UCI. Para inferir sobre a evolução do quadro dos pacientes na primeira semana de internamento na UCI, foi realizada uma análise comparativa entre os dados do 2º (335 pacientes) e 7º dias (216 pacientes). Foram realizadas comparações de parâmetros laboratoriais entre pacientes que receberam alta e pacientes falecidos, nestes momentos. As associações entre os diversos biomarcadores e a morte foram testadas por meio de modelos, do inglês, Generalized Estimating Equation (GEEs) univariados. Adicionalmente, para analisar o impacto de alguns biomarcadores na mortalidade, foram estimados e interpretados os odds ratios, com os correspondentes intervalos de confiança de 95%. As taxas de sobrevivência, em relação a cada biomarcador, foram obtidas pelo estimador Kaplan-Meier. Todos os valores de P foram considerados estatisticamente significantes para P<0,05. Resultados – Os pacientes que faleceram eram consideravelmente mais velhos, tinham mais comorbidades, necessitavam mais de VMI e passavam menos tempo no hospital do que os pacientes que receberam alta. As taxas de mortalidade não diferiram significativamente entre as vagas de COVID-19. Os pacientes da 1ª vaga eram significativamente mais velhos e dependiam mais da VMI e da ECMO. A maioria das diferenças detetadas quanto aos biomarcadores laboratoriais foi entre pacientes que receberam alta e os que faleceram na 2ª e 3ª ondas, sendo que os falecidos demonstraram quase sempre piores resultados. A nível de biomarcadores, os piores resultados foram obtidos na 1ª vaga e no 7º dia de internamento UCI. Na 2ª vaga, as maiores taxas de mortalidade foram observadas para pacientes com níveis de linfócitos abaixo da normalidade no 2º dia de internamento na UCI. Na 3ª vaga, as taxas de mortalidade foram maiores para pacientes com níveis de troponina de alta sensibilidade acima da normalidade no 2º dia de internamento na UCI, com níveis de linfócitos abaixo da normalidade no 7º dia de internamento na UCI e com níveis de plaquetas abaixo da normalidade, no 2º e 7º dias de internamento na UCI. Por meio de regressão logística univariada, determinou-se que, para a 2ª vaga, os resultados das troponinas de alta sensibilidade, eritrócitos, rácios entre plaquetas e linfócitos e dos rácios entre neutrófilos e linfócitos poderiam prever o risco de morte no 2º dia de internamento na UCI. O mesmo foi observado para a proteína C-reativa, hemácias, hematócrito, hemoglobina, leucócitos, neutrófilos, eosinófilos e rácio entre neutrófilos e linfócitos, no 7º dia de internamento. Na 3ª vaga, os resultados das troponinas de alta sensibilidade, plaquetas, lactato desidrogenase e proteína C-reativa também demonstraram capacidade para prever o risco de morte no 2º dia de internamento na UCI. Para o 7º dia, os resultados da procalcitonina, proteína C-reativa, leucócitos, linfócitos, neutrófilos e dos rácios entre neutrófilos e linfócitos e plaquetas e linfócitos também demonstraram capacidade preditiva para riscos de morte superiores. Através dos modelos Generalized Estimating Equation (GEEs) univariados, na 1ª vaga os resultados das troponinas de elevada sensibilidade, mioglobina e eosinófilos foram associados a maiores riscos de morte. Na 2ª vaga, o mesmo foi novamente verificado para as troponinas de elevada sensibilidade, a mioglobina e os eosinófilos, e também para os leucócitos, neutrófilos, linfócitos e INR. Por fim, na 3ª vaga, as troponinas de elevada sensibilidade, a creatinina cinase, eosinófilos, leucócitos, neutrófilos, linfócitos, rácio entre neutrófilos e linfócitos e as plaquetas também foram associados ao risco de morte. Conclusão - Este estudo fornece informações úteis para uma avaliação prognóstica e que podem ser usadas para orientar o tratamento e a monitorização de pacientes com COVID-19. É ainda composto por dados que podem vir a ser empregados numa grande variedade de estudos futuros. Além dos resultados positivos, é necessária mais investigação nesta área de maneira a desenvolver biomarcadores confiáveis e robustos para os outcomes da COVID-19.info:eu-repo/semantics/publishedVersio

Development of predictive models for COVID-19 prognosis based on patients’ demographic and clinical data

This work was supported by Instituto Politécnico de Lisboa, grant IDI&CA/IPL/2020/NephoMD/ISEL, and the FCT grant DSAIPA/DS/0117/2020 - PREMO - Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine. This work was conducted in the Engineering & Health Laboratory, established through a collaboration between Universidade Católica Portuguesa and Instituto Politécnico de Lisboa.Mestrado em Engenharia BiomédicaABSTRACT - Background – Cases of infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were first reported in late December 2019. Due to the large spectrum of clinical presentations and outcomes, the disease was named Coronavirus Disease 2019 (COVID-19) and characterized as a pandemic due to the elevated number of cases worldwide, the high transmission rate, and the lack of action measures. Since then, a lot of progress has been made, but the study of demographic and clinical information and the determination of possible laboratory biomarkers for COVID-19 prognosis is crucial. Purpose – Determine predictive biomarkers for COVID-19’s outcome (death or survival), in critically ill patients, using clinical, demographic, and laboratory data from the intensive care unit (ICU). Methods – Demographic, clinical, and laboratory data from 337 COVID-19 patients admitted to the ICU of Centro Hospitalar Universitário Lisboa Central, Portugal, between March 2020 and March 2021, was extracted from the hospital’s electronic medical record system, pre-processed, and analyzed. Comparisons were made regarding death, the need for invasive mechanical ventilation (IMV), the first three COVID-19 waves, and age groups. Longitudinal data was gathered throughout the patient's stay in the ICU. To infer the evolution of the patient's condition in the first week of ICU admission, a comparative analysis was carried out between the data from the 2nd (335 patients) and 7th days (216 patients). Comparisons of laboratory parameters between discharged and deceased patients, at these time points were performed. The associations between the several biomarkers and death were tested by means of Univariate Generalized Estimating Equations (GEEs) models. Additionally, to analyze the impact of some biomarkers on mortality, crude odds ratios were estimated and interpreted, with the corresponding 95% confidence intervals (CIs). Death event-free survival rates were obtained by the Kaplan-Meier estimator. All P values were considered statistically significant at P<0.05. Results – Deceased patients were considerably older, had more comorbidities, required more IMV, and spent less time in the hospital than discharged patients. Death rates did not differ significantly between COVID-19 waves. Patients from the 1st wave were significantly older and relied more on IMV and extracorporeal membrane oxygenation (ECMO). Most of the detected differences regarding laboratory biomarkers were found between discharged and deceased patients from the 2nd and 3rd waves, being that the deceased ones had almost always worse results. In general, worse results were obtained in the 1st wave and the 7th day of ICU admission. On 2nd day of ICU admission, 2nd wave, higher mortality rates were observed for patients with lymphocyte (LYM) levels under normality ranges. In the 3rd wave, mortality rates were higher for patients with high sensitivity troponin I (hs-cTn I) levels above normality ranges on the 2nd day of ICU admission, with LYM levels under normality ranges on the 7th day of ICU admission, and with platelet (PLT) levels below normality ranges, either in the 2nd or 7th days of ICU admission. Through the univariate logistic regression’s results on 2nd day of ICU admission, 2nd wave, hs-cTn I, red blood cell (RBC) counts, platelet-lymphocyte ratio (PLR) and neutrophil-lymphocyte ratio (NLR) showed significant association with the risk of death. On the 7th day of ICU admission, C-reactive protein (CRP), RBC counts, hematocrit (HCT), hemoglobin (HGB), white blood cell (WBC), and neutrophil (NEU) counts, eosinophil (EO) counts and NLR, revealed significant association with the risk of death. On the 2nd day of ICU admission, the 3rd wave, hs-cTn I, PLT counts, lactate dehydrogenase (LDH), and CRP showed significant association with the risk of death. For the 7th day, PCT, CRP, WBC, and NEU counts, LYM counts, NLR, and PLT count results were also associated with higher risks of death. Univariate GEEs model results demonstrated that, in the 1st wave, hs-cTn I, myoglobin, and EO counts, results were associated with higher risks of death. In the 2nd wave, the risk of death was significantly associated with hs-cTn I, myoglobin levels, EO counts, WBC and NEU counts, LYM counts, and INR. Finally, in the 3rd wave, hs-cTn I, CK, EO counts, WBC and NEU counts, LYM counts, NLR, and PLT counts, were also associated with the risk of death. Conclusion - This study provides useful information for prognostic evaluation that can be used to guide treatment and monitoring. Most importantly, it consists of valuable data that can be employed as the foundation of a variety of future research. Aside from the positive results, more research is needed to develop reliable and robust biomarkers for COVID-19’s outcomes.RESUMO - Introdução – Casos de infeção pelo vírus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) foram relatados pela primeira vez no final de dezembro de 2019. Devido ao grande espetro de apresentações e outcomes clínicos, a doença foi denominada Coronavirus Disease 2019 (COVID-19) e considerada uma pandemia devido ao elevado número de casos em todo o mundo, à alta taxa de transmissão e à falta de medidas de ação. Apesar desta patologia estar a ser aprofundadamente investigada, o estudo de informação demográfica e clínica e a determinação de possíveis biomarcadores laboratoriais para o prognóstico da COVID-19 continua a ser crucial. Objetivos – Determinar biomarcadores preditivos para o outcome da COVID-19 (morte ou vida), em pacientes críticos, usando dados clínicos, demográficos e laboratoriais da unidade de cuidados intensivos (UCI). Métodos – Dados demográficos, clínicos e laboratoriais de 337 pacientes com COVID-19 internados na UCI do Centro Hospitalar Universitário Lisboa Central, em Portugal, entre março de 2020 e março de 2021, foram extraídos das bases de dados eletrónicas do hospital, pré-processados e analisados. Foram feitas comparações em relação ao óbito na UCI, necessidade de ventilação mecânica invasiva (VMI), três vagas de COVID-19 e faixas etárias. Dados longitudinais foram obtidos ao longo da permanência dos pacientes na UCI. Para inferir sobre a evolução do quadro dos pacientes na primeira semana de internamento na UCI, foi realizada uma análise comparativa entre os dados do 2º (335 pacientes) e 7º dias (216 pacientes). Foram realizadas comparações de parâmetros laboratoriais entre pacientes que receberam alta e pacientes falecidos, nestes momentos. As associações entre os diversos biomarcadores e a morte foram testadas por meio de modelos, do inglês, Generalized Estimating Equation (GEEs) univariados. Adicionalmente, para analisar o impacto de alguns biomarcadores na mortalidade, foram estimados e interpretados os odds ratios, com os correspondentes intervalos de confiança de 95%. As taxas de sobrevivência, em relação a cada biomarcador, foram obtidas pelo estimador Kaplan-Meier. Todos os valores de P foram considerados estatisticamente significantes para P<0,05. Resultados – Os pacientes que faleceram eram consideravelmente mais velhos, tinham mais comorbidades, necessitavam mais de VMI e passavam menos tempo no hospital do que os pacientes que receberam alta. As taxas de mortalidade não diferiram significativamente entre as vagas de COVID-19. Os pacientes da 1ª vaga eram significativamente mais velhos e dependiam mais da VMI e da ECMO. A maioria das diferenças detetadas quanto aos biomarcadores laboratoriais foi entre pacientes que receberam alta e os que faleceram na 2ª e 3ª ondas, sendo que os falecidos demonstraram quase sempre piores resultados. A nível de biomarcadores, os piores resultados foram obtidos na 1ª vaga e no 7º dia de internamento UCI. Na 2ª vaga, as maiores taxas de mortalidade foram observadas para pacientes com níveis de linfócitos abaixo da normalidade no 2º dia de internamento na UCI. Na 3ª vaga, as taxas de mortalidade foram maiores para pacientes com níveis de troponina de alta sensibilidade acima da normalidade no 2º dia de internamento na UCI, com níveis de linfócitos abaixo da normalidade no 7º dia de internamento na UCI e com níveis de plaquetas abaixo da normalidade, no 2º e 7º dias de internamento na UCI. Por meio de regressão logística univariada, determinou-se que, para a 2ª vaga, os resultados das troponinas de alta sensibilidade, eritrócitos, rácios entre plaquetas e linfócitos e dos rácios entre neutrófilos e linfócitos poderiam prever o risco de morte no 2º dia de internamento na UCI. O mesmo foi observado para a proteína C-reativa, hemácias, hematócrito, hemoglobina, leucócitos, neutrófilos, eosinófilos e rácio entre neutrófilos e linfócitos, no 7º dia de internamento. Na 3ª vaga, os resultados das troponinas de alta sensibilidade, plaquetas, lactato desidrogenase e proteína C-reativa também demonstraram capacidade para prever o risco de morte no 2º dia de internamento na UCI. Para o 7º dia, os resultados da procalcitonina, proteína C-reativa, leucócitos, linfócitos, neutrófilos e dos rácios entre neutrófilos e linfócitos e plaquetas e linfócitos também demonstraram capacidade preditiva para riscos de morte superiores. Através dos modelos Generalized Estimating Equation (GEEs) univariados, na 1ª vaga os resultados das troponinas de elevada sensibilidade, mioglobina e eosinófilos foram associados a maiores riscos de morte. Na 2ª vaga, o mesmo foi novamente verificado para as troponinas de elevada sensibilidade, a mioglobina e os eosinófilos, e também para os leucócitos, neutrófilos, linfócitos e INR. Por fim, na 3ª vaga, as troponinas de elevada sensibilidade, a creatinina cinase, eosinófilos, leucócitos, neutrófilos, linfócitos, rácio entre neutrófilos e linfócitos e as plaquetas também foram associados ao risco de morte. Conclusão - Este estudo fornece informações úteis para uma avaliação prognóstica e que podem ser usadas para orientar o tratamento e a monitorização de pacientes com COVID-19. É ainda composto por dados que podem vir a ser empregados numa grande variedade de estudos futuros. Além dos resultados positivos, é necessária mais investigação nesta área de maneira a desenvolver biomarcadores confiáveis e robustos para os outcomes da COVID-19.N/

Laboratory biomarkers associated to death in the first three COVID-19 waves in Portugal

Funding Information: This study is inserted in the project Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO), supported by Fundação para Publisher Copyright: © 2023 IEEE.Besides the pandemic being over, new SARS-CoV-2 lineages, and sub-lineages, still pose risks to global health. Thus, in this preliminary study, to better understand the characteristics of COVID-19 patients and the effect of certain hematologic biomarkers on their outcome, we analyzed data from 337 patients admitted to the ICU of a single-center hospital in Lisbon, Portugal, in the first three waves of the pandemic. Most patients belonged to the second (40.4%) and third (41.2%) waves. The ones from the first wave were significantly older and relied more on respiratory techniques like invasive mechanic ventilation and extracorporeal membrane oxygenation. There were no significant differences between waves regarding mortality in the ICU. In general, non-survivors had worse laboratory results. Biomarkers significantly associated with death changed depending on the waves. Increased high-sensitivity cardiac troponin I results, and lower eosinophil counts were associated to death in all waves. In the second and third waves, the international normalized ratio, lymphocyte counts, and neutrophil counts were also associated to mortality. A higher risk of death was linked to increased myoglobin results in the first two waves, as well as increased creatine kinase results, and lower platelet counts in the third wave.publishersversionpublishe

An interactive dashboard for statistical analysis of intensive care unit COVID-19 data

COVID-19 caused a pandemic, due to its ease of transmission and high number of infections. The evolution of the pandemic and its consequences for the mortality and morbidity of populations, especially the elderly, generated several scientific studies and many research projects. Among them, we have the Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO) research project. For such a project with many data records, it is necessary to provide a smooth graphical analysis to extract value from it. Methods: In this paper, we present the development of a full-stack Web application for the PREMO project, consisting of a dashboard providing statistical analysis, data visualization, data import, and data export. The main aspects of the application are described, as well as the diverse types of graphical representations and the possibility to use filters to extract relevant information for clinical practice. Results: The application, accessible through a browser, provides an interactive visualization of data from patients admitted to the intensive care unit (ICU), throughout the six waves of COVID-19 in two hospitals in Lisbon, Portugal. The analysis can be isolated per wave or can be seen in an aggregated view, allowing clinicians to create many views of the data and to study the behavior and consequences of different waves. For instance, the experimental results show clearly the effect of vaccination as well as the changes on the most relevant clinical parameters on each wave. Conclusions: The dashboard allows clinicians to analyze many variables of each of the six waves as well as aggregated data for all the waves. The application allows the user to extract information and scientific knowledge about COVID-19’s evolution, yielding insights for this pandemic and for future pandemics.info:eu-repo/semantics/publishedVersio

Simplifying data analysis in biomedical research: an automated, user-friendly tool

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.info:eu-repo/semantics/publishedVersio

Discovery of delirium biomarkers through minimally invasive serum molecular fingerprinting

This research was funded by project grant DSAIPA/DS/0117/2020, supported by Fundação para a Ciência e a Tecnologia, Portugal. C. P. Von Rekowski and R. Araújo also acknowledge the PhD grants from FCT, numbers 2023.01951.BD and 2021.05553.BD, respectively.Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.info:eu-repo/semantics/publishedVersio

Characteristics and laboratory findings of SARS-CoV-2 infected patients during the first three COVID-19 waves in Portugal – a retrospective single-center study

Background and Objectives: Given the wide spectrum of clinical and laboratory manifestations of the coronavirus disease 2019 (COVID-19), it is imperative to identify potential contributing factors to patients’ outcomes. However, a limited number of studies have assessed how the different waves affected the progression of the disease, more so in Portugal. Therefore, our main purpose was to study the clinical and laboratory patterns of COVID-19 in an unvaccinated population admitted to the intensive care unit, identifying characteristics associated with death, in each of the first three waves of the pandemic. Materials and Methods: This study included 337 COVID-19 patients admitted to the intensive care unit of a single-center hospital in Lisbon, Portugal, between March 2020 and March 2021. Comparisons were made between three COVID-19 waves, in the second (n = 325) and seventh (n = 216) days after admission, and between discharged and deceased patients. Results: Deceased patients were considerably older (p = 0.021) and needed greater ventilatory assistance (p = 0.023), especially in the first wave. Differences between discharged and deceased patients’ biomarkers were minimal in the first wave, on both analyzed days. In the second wave significant differences emerged in troponins, lactate dehydrogenase, procalcitonin, C-reactive protein, and white blood cell subpopulations, as well as platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios (all p < 0.05). Furthermore, in the third wave, platelets and D-dimers were also significantly different between patients’ groups (all p < 0.05). From the second to the seventh days, troponins and lactate dehydrogenase showed significant decreases, mainly for discharged patients, while platelet counts increased (all p < 0.01). Lymphocytes significantly increased in discharged patients (all p < 0.05), while white blood cells rose in the second (all p < 0.001) and third (all p < 0.05) waves among deceased patients. Conclusions: This study yields insights into COVID-19 patients’ characteristics and mortality-associated biomarkers during Portugal’s first three COVID-19 waves, highlightinginfo:eu-repo/semantics/publishedVersio

Comparison of Analytical Methods Of Serum Untargeted Metabolomics

Funding Information: IV. ACKNOWLEDGEMENTS This research was funded by Fundação para a Ciência e a Tecnologia (FCT), grant DSAIPA/DS/0117/2020 and RNEM-LISBOA-01-0145-FEDER-022125 (Portuguese Mass Spectrometry Network). Centro de Química Estrutural is a Research Unit funded by FCT through projects UIDB/00100/2020 and UIDP/00100/2020. Institute of Molecular Sciences is an Associate Laboratory funded by FCT through project LA/P/0056/2020. Publisher Copyright: © 2023 IEEE.Metabolomics has emerged as a powerful tool in the discovery of new biomarkers for medical diagnosis and prognosis. However, there are numerous challenges, such as the methods used to characterize the system metabolome. In the present work, the comparison of two analytical platforms to acquire the serum metabolome of critically ill patients was conducted. The untargeted serum metabolome analysis by ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) enabled to identify a set of metabolites statistically different between deceased and discharged patients. This set of metabolites also enabled to develop a very good predictive model, based on linear discriminant analysis (LDA) with a sensitivity and specificity of 80% and 100%, respectively. Fourier Transform Infrared (FTIR) spectroscopy was also applied in a high-throughput, simple and rapid mode to analyze the serum metabolome. Despite this technique not enabling the identification of metabolites, it allowed to identify molecular fingerprints associated to each patient group, while leading to a good predictive model, based on principal component analysis-LDA, with a sensitivity and specificity of 100% and 90%, respectively. Therefore, both analytical techniques presented complementary characteristics, that should be further explored for metabolome characterization and application as for biomarkers discovery for medical diagnosis and prognosis.publishersversionpublishe

Infection Biomarkers Based on Metabolomics

Funding Information: Funding: This work was supported by the project grant DSAIPA/DS/0117/2020 supported by Fundação para a Ciência e a Tecnologia, Portugal; and by the project grant NeproMD/ISEL/2020 financed by Instituto Politécnico de Lisboa.Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.publishersversionpublishe

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy

Funding Information: This research was funded by Fundação para a Ciência e a Tecnologia (FCT), Grants DSAIPA/DS/0117/2020 and RNEM-LISBOA-01-0145-FEDER-022125 (Portuguese Mass Spectrometry Network). The Centro de Química Estrutural is a Research Unit funded by FCT through projects UIDB/00100/2020 and UIDP/00100/2020. The Institute of Molecular Sciences is an Associate Laboratory funded by FCT through project LA/P/0056/2020. Publisher Copyright: © 2023 The Authors. Published by American Chemical Society.Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient’s survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.publishersversionpublishe