Decrease in suicide rates in Brazil during the COVID-19 pandemic

Natural disasters such as public health epidemics may potentially affect suicide rates. The global COVID-19 pandemic poses an unprecedented challenge for healthcare systems and general populations worldwide. In this retrospective ecological study, we aimed to examine any changes in the suicide rates during the COVID-19 pandemic and to assess the relationship between COVID-19 death rates and deaths by suicide in Brazil. Data on suicide and COVID-19 case numbers were extracted from the Ministry of Health agencies and grouped weekly. We performed a time series analysis of suicide rates, a comparison of mean suicide rates between the pre-COVID-19 period and the COVID-19 period, and conducted a Poisson regression to examine the relationship between deaths due to COVID-19 and suicide rates. Our results showed decreased suicide rates during the COVID-19 pandemic. We also found that deaths owing to COVID-19 impact those owing to suicide after 10 weeks in the upward direction; however, we did not observe for enough time to see a change in the suicide rate curve. These findings are fundamental to understand suicidal behaviors in epidemic situations. However, the field needs more studies evaluating the impact of significant public health events on suicidality, incorporating extended follow-up periods.

Coronavirus disease 2019 related parosmia: an exploratory survey of demographics and treatment strategies

OBJECTIVE

To investigate the clinical features, therapeutic efficacy and symptom time course of post-coronavirus disease 2019 parosmia.

METHODS

A 22-item online questionnaire was distributed to AbScent research group and Facebook coronavirus disease 2019 anosmia group adult members to assess clinical features, interventions and their subjective efficacy for parosmia.

RESULTS

A total of 209 participants (86 per cent females) reported: smell loss on average 3 days after coronavirus symptoms, recovery 4 weeks later, and first parosmia symptoms 12 weeks post infection. Respondents reported 10 per cent body weight loss, and listed onion and garlic as significant parosmia triggers. Regarding quality of life, depression was the most cited item (54 per cent). Smell training was trialled by 74 per cent of participants, followed by nasal corticosteroid spray (49 per cent). Stellate ganglion block, trialled by 16 per cent of respondents, had the highest reported improvement (45 per cent), with 21 per cent reporting a sustained benefit - the highest rate amongst registered treatment options.

CONCLUSION

Post-coronavirus parosmia has a significant impact and remains challenging to treat. Stellate ganglion block appears to be successful relative to other reported treatments. Further research into the pathophysiology, efficacy and mechanism of stellate ganglion block effect is warranted.

An interpretable machine learning approach to estimate the influence of inflammation biomarkers on cardiovascular risk assessment

BACKGROUND AND OBJECTIVE

Cardiovascular disease has a huge impact on health care services, originating unsustainable costs at clinical, social, and economic levels. In this context, patients' risk stratification tools are central to support clinical decisions contributing to the implementation of effective preventive health care. Although useful, these tools present some limitations, in particular, some lack of performance as well as the impossibility to consider new risk factors potentially important in the prognosis of severe cardiac events. Moreover, the actual use of these tools in the daily practice requires the physicians' trust. The main goal of this work addresses these two issues: (i) evaluate the importance of inflammation biomarkers when combined with a risk assessment tool; (ii) incorporation of personalization and interpretability as key elements of that assessment.

METHODS

Firstly, machine learning based models were created to assess the potential of the inflammation biomarkers applied in secondary prevention, namely in the prediction of the six month risk of death/myocardial infarction. Then, an approach based on three main phases was created: (i) set of interpretable rules supported by clinical evidence; (ii) selection based on a machine learning classifier able to identify for a given patient the most suitable subset of rules; (iii) an ensemble scheme combining the previous subset of rules in the estimation of the patient cardiovascular risk. All the results were statistically validated (t-test, Wilcoxon-signed rank test) according to a previous verification of data normality (Shapiro-Wilk).

RESULTS

The proposed methodology was applied to a real acute coronary syndrome patients dataset (N = 1544) from the Cardiology Unit of Coimbra Hospital and Universitary centre. The first assessment was based on the GRACE tool and a Random Forest classifier, the incorporation of inflammation biomarkers achieved SE=0.83; SP=0.84 whereas the original GRACE risk factors reached SE=0.75; SP=0.85. In the second phase, the proposed approach with inflammation biomarkers achieved SE=0.763 and SP=0.778.

CONCLUSIONS

This approach confirms the potential of combining inflammation markers with the GRACE score, increasing SE and SP, when compared with the original GRACE. Additionally, it assures interpretability and personalization, which are critical issues to allow its application in the daily clinical practice.

Assessing cadmium-based quantum dots effect on the gonads of the marine mussel Mytilus galloprovincialis

This study assesses the sex-specific effects induced by CdTe QDs, on the marine mussel Mytilus galloprovincialis in comparison to its dissolved counterpart. A 14 days exposure to CdTe QDs and dissolved Cd was conducted (10 μg Cd L^-1), analysing Cd accumulation, oxidative stress, biotransformation, metallothionein and oxidative damage in the gonads. Both Cd forms caused significant antioxidant alterations, whereby QDs were more pro-oxidant, leading to oxidative damage, being females more affected. Overall, biochemical impairments on gonads of M. galloprovincialis demonstrate that the reproductive toxicity induced by CdTe QDs in mussels are sex-dependent and mediated by oxidative stress and lipid peroxidation. It is crucial to acknowledge how gametes are affected by metal-based nanoparticles, such as Cd-based QDs. As well as understanding the potential changes they may undergo at the cellular level during gametogenesis, embryogenesis and larval development potentially leading to serious impacts on population sustainability and ecosystem health.

Latent states extraction through Kalman Filter for the prediction of heart failure decompensation events

Cardiac function deterioration of heart failure patients is frequently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions.In this paper we introduce a methodology with the goal of finding onsets of worsening progressions from multiple physiological parameters which may have predictive value in decompensation events. The best performance was obtained for the model composed by only two features using a telemonitoring dataset (myHeart) with 41 patients. Results were achieved by applying leave-one-subject-out validation and correspond to a geometric mean of 83.67%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.

Progression of micturition dysfunction associated with the development of heart failure in rats: Model of overactive bladder

Heart failure (HF) has a strong association with the development of lower urinary tract symptoms, especially overactive bladder (OAB); although this condition remains poorly investigated. In this study, we assess the aortocaval fistula (ACF) model as a novel experimental model of micturition dysfunction, associated with HF, focused on the molecular and functional studies to evaluate the autonomic nervous system and urinary bladder remodeling. Male rats were submitted to ACF for HF induction. Echocardiography, cystometric, histomorphometry and molecular analysis, as well as concentration-response curves to carbachol and ATP and frequency-response curves to electrical field stimulation (EFS) were evaluated in Sham and HF (4- and 12-weeksendpoint) groups. Compared to SHAM, HF groups exhibited progressive increases in the left ventricle (LV) mass and fractional shortening which indicates cardiac dysfunction, although HF was characterized only after 12 weeks by the reduced ejection fraction. For micturition function, HF groups presented increased non-voiding contractions (NVC) and decreased bladder capacity; however, when comparing HF groups, these urinary parameters were significantly impaired over the weeks (12-weeks). The contractile responses induced by CCh, ATP and EFS were greater in detrusor muscle (DSM) from HF rats. mRNA expression for muscarinic receptors (M2 and M3) was higher in DSM only after 12 weeks of ACF, in addition to MMP9 and TGF-beta. Histomorphometric revealed increased urothelium thickness in both HF groups, whereas DSM thickness occurred only after 12 weeks. Thus, the ACF model induced cardiac dyfunction with progressive micturition dysfunction over the weeks, characterized by increased DSM contractile mechanisms as well as extracellular matrix remodeling in the urinary bladder, representing a useful tool to evaluate the OAB associated with HF.

Risk Prediction of Heart Failure Decompensation Events in Multiparametric Feature Spaces

Cardiac function deterioration of heart failure patients is frequently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions. In this paper we introduce a methodology with the goal of finding relevant feature spaces from multiple physiological parameters which may have predictive value in decompensation events. The best performance was obtained for the feature space comprising the following features: mean weight, standard deviation of the blood pressure and mean of extra-thoracic impedance in a time window of 20 days. Results were achieved by applying leave-one-out validation and correspond to a geometric mean of 88.32%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.

Measuring improvement in knowledge of drug policy reforms following a police education program in Tijuana, Mexico

Background

Mexico’s 2009 “narcomenudeo reform” decriminalized small amounts of drugs, shifting some drug law enforcement to the states and mandating drug treatment diversion instead of incarceration. Data from Tijuana suggested limited implementation of this harm reduction-oriented policy. We studied whether a police education program (PEP) improved officers’ drug and syringe policy knowledge, and aimed to identify participant characteristics associated with improvement of drug policy knowledge.

Methods

Pre- and post-training surveys were self-administered by municipal police officers to measure legal knowledge. Training impact was assessed through matched paired nominal data using McNemar’s tests. Multivariable logistic regression was used to identify predictors of improved legal knowledge, as measured by officers’ ability to identify conceptual legal provisions related to syringe possession and thresholds of drugs covered under the reform.

Results

Of 1750 respondents comparing pre- versus post training, officers reported significant improvement (p < 0.001) in their technical understanding of syringe possession (56 to 91%) and drug amounts decriminalized, including marijuana (9 to 52%), heroin (8 to 71%), and methamphetamine (7 to 70%). The training was associated with even greater success in improving conceptual legal knowledge for syringe possession (67 to 96%) (p < 0.001), marijuana (16 to 91%), heroin (11 to 91%), and methamphetamine (11 to 89%). In multivariable modeling, those with at least a high school education were more likely to exhibit improvement of conceptual legal knowledge of syringe possession (adjusted odds ratio [aOR] 2.6, 95% CI 1.4–3.2) and decriminalization for heroin (aOR 2.7, 95% CI 1.3–4.3), methamphetamine (aOR 2.2, 95% CI 1.4–3.2), and marijuana (aOR 2.5, 95% CI 1.6–4).

Conclusions

Drug policy reform is often necessary, but not sufficient to achieve public health goals because of gaps in translating formal laws to policing practice. To close such gaps, PEP initiatives bundling occupational safety information with relevant legal content demonstrate clear promise. Our findings underscore additional efforts needed to raise technical knowledge of the law among personnel tasked with its enforcement. Police professionalization, including minimum educational standards, appear critical for aligning policing with harm reduction goals.

An interpretable data-driven approach for rules construction: Application to cardiovascular risk assessment

The development of models able to produce an understandable decision by the clinicians is of great importance to support their decision. Therefore, the research of methodologies able to extract useful knowledge from existing datasets, as well as to integrate this knowledge into the current clinical evidence, is a key aspect in the enhancement of the clinical decision. This work focuses on the development of interpretable models to assess the patient's condition based on supervised clustering theories, enabling the discovery of a set of features that best represents that condition. At the same time, the technique is supported on a structure that enables the formulation of simple and interpretable rules. Despite its general applicability, the proposed methodology is applied to coronary artery disease (CAD), particularly, in the risk of death assessment (30 days after the admission) of patients that have been admitted to the emergency unit. The validation is performed using a real dataset with Acute Coronary Syndromes, provided by the Portuguese Society of Cardiology. While the methodology produces simple and interpretable rules, the performance achieves an improvement of 7% in relation to geometric mean, when compared with GRACE model (commonly used in Portugal).

A non-exercise based V02max prediction using FRIEND dataset with a neural network

The main goal of this work is the development of models, based on computational intelligence techniques, in particular neural networks, to predict the maximum oxygen consumption value. While the maximum oxygen consumption is a direct mark of the cardiorespiratory fitness, several studies have also confirmed it also as a powerful predictor of risk for adverse outcomes, such as hypertension, obesity, and diabetes. Therefore, the existence of simpler and accurate models, establishing an alternative to standard cardiopulmonary exercise tests, with the potential to be employed in the stratification of the general population in daily clinical practice, would be of major importance. In the current study, different models were implemented and compared: 1) the traditional Wasserman/Hansen equation; 2) linear regression and; 3) non-linear neural networks. Their performance was evaluated based on the "FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base" [1] being, in the present study, a subset of 12262 individuals employed. The accuracy of the models was performed through the computation of sensitivity and specificity values. The results show the superiority of neural networks in the prediction of maximum oxygen consumption.

Ecotoxicological assessment of the anticancer drug cisplatin in the polychaete Nereis diversicolor

Anticancer drugs are designed to inhibit tumor cell proliferation by interacting with DNA and altering cellular growth factors. When released into the waterbodies of municipal and hospital effluents these pharmaceutical compounds may pose a risk to non-target aquatic organisms, due to their mode of action (cytotoxic, genotoxic, mutagenic and teratogenic). The present study aimed to assess the ecotoxicological potential of the alkylating agent cisplatin (CisPt) to the polychaete Nereis diversicolor, at a range of relevant environmental concentrations (i.e. 0.1, 10 and 100ngPtL^-1). Behavioural impairment (burrowing kinetic impairment), ion pump effects (SR Ca²⁺-ATPase), neurotoxicity (AChE activity), oxidative stress (SOD, CAT and GPXs activities), metal exposure (metallothionein-like proteins - MTLP), biotransformation (GST), oxidative damage (LPO) and genotoxicity (DNA damage), were selected as endpoints to evaluate the sublethal responses of the ragworms after 14-days of exposure in a water-sediment system. Significant burrowing impairment occurred in worms exposed to the highest CisPt concentration (100ngPtL^-1) along with neurotoxic effects. The activity of antioxidant enzymes (SOD, CAT) and second phase biotransformation enzyme (GST) was inhibited but such effects were compensated by MTLP induction. Furthermore, LPO levels also increased. Results showed that the mode of action of cisplatin may pose a risk to this aquatic species even at the range of ngL^-1.

Contagious equine metritis in Portugal: A retrospective report of the first outbreak in the country and recent contagious equine metritis test results

Contagious equine metritis (CEM), a highly contagious bacterial venereal infection of equids, caused by Taylorella equigenitalis, is of major international concern, causing short-term infertility in mares. Portugal has a long tradition of horse breeding and exportation and until recently was considered CEM-free. However, in 2008, T. equigenitalis was isolated at our laboratory from a recently imported stallion and 2 mares from the same stud. Following this first reported outbreak, the Portuguese Veterinary Authority (DGVA) performed mandatory testing on all remaining equines at the stud (n=30), resulting in a further 4 positive animals. All positive animals were treated and subsequently tested negative for T. equigenitalis. Since this outbreak, over 2000 genital swabs from Portuguese horses have been tested at our laboratory, with no further positive animals identified. The available data suggests that this CEM outbreak was an isolated event and we have no further evidence of CEM cases in Portugal, however, an extended and wider epidemiological study would be needed to better evaluate the incidence of the disease in Portuguese horses.

Improving clinical models based on knowledge extracted from current datasets: a new approach

The Cardiovascular Diseases (CVD) are the leading cause of death in the world, being prevention recognized to be a key intervention able to contradict this reality. In this context, although there are several models and scores currently used in clinical practice to assess the risk of a new cardiovascular event, they present some limitations. The goal of this paper is to improve the CVD risk prediction taking into account the current models as well as information extracted from real and recent datasets. This approach is based on a decision tree scheme in order to assure the clinical interpretability of the model. An innovative optimization strategy is developed in order to adjust the decision tree thresholds (rule structure is fixed) based on recent clinical datasets. A real dataset collected in the ambit of the National Registry on Acute Coronary Syndromes, Portuguese Society of Cardiology is applied to validate this work. In order to assess the performance of the new approach, the metrics sensitivity, specificity and accuracy are used. This new approach achieves sensitivity, a specificity and an accuracy values of, 80.52%, 74.19% and 77.27% respectively, which represents an improvement of about 26% in relation to the accuracy of the original score.

Expert knowledge integration in the data mining process with application to cardiovascular risk assessment

The data mining process, when applied to clinical databases, suffers from critical data problems, from noisy acquisitions to missing or incomplete data points. Expert knowledge, in the form of practitioners' experience and clinical guidelines, is already used to manually correct some of these problems, while enhancing expert's confidence in such systems. In this work, we propose the Knowledge-Biased Tree (KB3), a knowledge biased decision tree inducer that is able to exploit IF THEN rules to guide the tree inducing process. The KB3 approach was tested against its unbiased counterpart, the C5.0 algorithm in the cardiovascular risk assessment task. Using a clinical dataset provided by the hospital of Sta Cruz (Lisbon, Portugal) the performance of the proposed algorithm is compared against the unbiased C5.0 and the state of the art risk score used in clinical practice (GRACE risk score).

Assessment of cardiovascular risk based on a data-driven knowledge discovery approach

The cardioRisk project addresses the development of personalized risk assessment tools for patients who have been admitted to the hospital with acute myocardial infarction. Although there are models available that assess the short-term risk of death/new events for such patients, these models were established in circumstances that do not take into account the present clinical interventions and, in some cases, the risk factors used by such models are not easily available in clinical practice. The integration of the existing risk tools (applied in the clinician's daily practice) with data-driven knowledge discovery mechanisms based on data routinely collected during hospitalizations, will be a breakthrough in overcoming some of these difficulties. In this context, the development of simple and interpretable models (based on recent datasets), unquestionably will facilitate and will introduce confidence in this integration process. In this work, a simple and interpretable model based on a real dataset is proposed. It consists of a decision tree model structure that uses a reduced set of six binary risk factors. The validation is performed using a recent dataset provided by the Portuguese Society of Cardiology (11113 patients), which originally comprised 77 risk factors. A sensitivity, specificity and accuracy of, respectively, 80.42%, 77.25% and 78.80% were achieved showing the effectiveness of the approach.

Integration of Different Risk Assessment Tools to Improve Stratification of Patients with Coronary Artery Disease

Cardiovascular disease (CVD) causes unaffordable social and health costs that tend to increase as the European population ages. In this context, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Some useful tools have been developed to predict the risk of occurrence of a cardiovascular disease event (e.g. hospitalization or death). However, these tools present some drawbacks. These problems are addressed through two methodologies: (i) combination of risk assessment tools: fusion of naïve Bayes classifiers complemented with a genetic optimization algorithm and (ii) personalization of risk assessment: subtractive clustering applied to a reduced-dimensional space to create groups of patients. Validation was performed based on two ACS-NSTEMI patient data sets. This work improved the performance in relation to current risk assessment tools, achieving maximum values of sensitivity, specificity, and geometric mean of, respectively, 79.8, 83.8, and 80.9 %. Additionally, it assured clinical interpretability, ability to incorporate of new risk factors, higher capability to deal with missing risk factors and avoiding the selection of a standard CVD risk assessment tool to be applied in the clinical practice.

Personalization algorithms applied to cardiovascular disease risk assessment

Cardiovascular disease (CVD) is the major cause of death in the world. Clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient as the correct stratification of patients may significantly contribute to the optimization of the health care strategies. This work further explores the personalization of CVD risk assessment, supported on the evidence that a specific CVD risk assessment tool may have good performance within a given group of patients and might perform poorly within other groups. Two main personalization methods based on the proper creation of groups of patients are presented: i) clustering patients approach; ii) similarity measures approach. These two methodologies were validated in a Portuguese population (460 Acute Coronary Syndrome with non-ST segment elevation (ACS-NSTEMI) patients). The similarity measures approach had the best performance, achieving maximum values of sensitivity, specificity and geometric mean of, respectively, 77.7%, 63.2%, 69.7%. These values represent an enhancement in relation to the best performance obtained with current CVD risk assessment tools applied in clinical practice (78.5%, 53.2%, 64.4%).

Prediction of Heart Failure Decompensation Events by Trend Analysis of Telemonitoring Data

This paper aims to assess the predictive value of physiological data daily collected in a telemonitoring study in the early detection of heart failure (HF) decompensation events. The main hypothesis is that physiological time series with similar progression (trends) may have prognostic value in future clinical states (decompensation or normal condition). The strategy is composed of two main steps: a trend similarity analysis and a predictive procedure. The similarity scheme combines the Haar wavelet decomposition, in which signals are represented as linear combinations of a set of orthogonal bases, with the Karhunen-Loève transform, that allows the selection of the reduced set of bases that capture the fundamental behavior of the time series. The prediction process assumes that future evolution of current condition can be inferred from the progression of past physiological time series. Therefore, founded on the trend similarity measure, a set of time series presenting a progression similar to the current condition is identified in the historical dataset, which is then employed, through a nearest neighbor approach, in the current prediction. The strategy is evaluated using physiological data resulting from the myHeart telemonitoring study, namely blood pressure, respiration rate, heart rate, and body weight collected from 41 patients (15 decompensation events and 26 normal conditions). The obtained results suggest, in general, that the physiological data have predictive value, and in particular, that the proposed scheme is particularly appropriate to address the early detection of HF decompensation.

An effective wavelet strategy for the trend prediction of physiological time series with application to pHealth systems

This work proposes a wavelet decomposition based scheme to estimate the evolution trend of physiological time series. The scheme does not involve the explicit development of a model and is essentially supported on the hypothesis that future evolution of a biosignal can be estimated from similar historic patterns. The strategy considers an a-trous wavelet decomposition, where the most representative trends are extracted from the historic similar patterns. Then, a set of distance-based measures able to assess the prediction likelihood of each representative trend, is introduced. From these measures and through an optimization process, a subset of these trends is selected and aggregated to derive the required time series evolution trend. The effectiveness of the methodology is validated in the prediction of blood pressure signals collected in two telemonitoring studies: TEN-HMS and MyHeart. Additionally, Friedman and Nemenyi statistics tests are implemented to rank several methods, confirming the value of the proposed strategy.

Toxic effects of glibenclamide in fetuses of normoglycemic rats: an alternative therapy for gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is defined as glucose intolerance first diagnosed during the second or third trimester of pregnancy. The treatment aims at glycemic control through changes in the patient's diet with or without exercise, but some patients need insulin therapy. An alternative would be to use oral hypoglycemic agents such as glibenclamide (GLIB). The present study aims to analyze the toxic effects of GLIB in fetuses of pregnant rats which received 5 or 20mg/kg doses of GLIB. Glycemic dosage reveals no significant difference between control (deionized water) and treated groups, showing that these concentrations of GLIB were not effective to cause hypoglycemia in rats. The vitality of the fetuses in all groups was 100%. GLIB administration promoted increase in weight and significant changes in measures of external morphological parameters of treated fetuses. Histological analysis revealed that liver lobes, lobules and central lobular veins were well defined for all treatments. However, GLIB animals presented a light brownish precipitate into the center-lobular veins and in the liver parenchyma among the hepatocytes. These results indicated a possible passage of the drug through the blood-placental membrane, without serious changes that impair the development of neither bone tissue, nor the liver of these animals.

Cardiovascular disease risk assessment innovative approaches developed in HeartCycle project

Two innovative CVD event risk assessment strategies were developed in the scope of HeartCycle project: i) combination of individual risk assessment tools; ii) personalization of risk assessment based on grouping of patients. These approaches aimed to defeat some of the major limitations of the tools currently applied in the daily clinical practice, namely to: i) improve the risk prediction performance when comparing it to the one achieved by the individual current risk assessment tools; ii) consider the available knowledge provided by other risk assessment tools; iii) cope with missing risk factors; iv) incorporate additional clinical knowledge. Two different real patients' datasets were applied to validate the developed strategies: i) Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI1 patients; ii) Leiria Pombal Hospital Centre, Portugal, N=99 ACS-NSTEMI. Based on the gathered results, we propose a new strategy in order to improve patients' stratification.

Telehealth streams reduction based on pattern recognition techniques for events detection and efficient storage in EHR

This work proposes a framework for telehealth streams analysis, founded on a pattern recognition technique that evaluates the similarity between multi-sensorial biosignals. The strategy combines the Haar wavelet with the Karhunen-Loève transforms to describe biosignals by means of a reduced set of parameters. These, that reflect the dynamic behavior of the biosignals, can support the detection of relevant clinical conditions. Moreover, the simplicity and fast execution of the proposed approach allow its application in real-time operation, as well as provide a practical way to manage historical electronic health records: i) common and uncommon behaviors can be distinguished; ii) the creation of different models, tailored to specific conditions can be efficiently stored. The efficiency of the methodology is assessed through its performance analysis, namely by computing the required number of operations and the compression rate. Its effectiveness is evaluated in the prediction of decompensation episodes using biosignals daily collected in the myHeart study (blood pressure, weight, respiration and heart rates).

A multibiomarker approach in the clam Ruditapes decussatus to assess the impact of pollution in the Ria Formosa lagoon, South Coast of Portugal

The Ria Formosa lagoon is an ecosystem whose water quality reflects the anthropogenic influence upon the surrounding areas. In this lagoon, the clam Ruditapes decussatus has a great economical importance and has been widely used as a biomonitor. A multibiomarker approach (δ-aminolevulinic acid dehydratase, metallothionein, lipid peroxidation, acetylcholinesterase, alkali-labile phosphates, DNA damage) was applied to assess the environmental quality of this ecosystem and the accumulation of contaminants and their potential adverse effects on clams. Clams were sampled in different shellfish beds in the period between July 2007 and December 2008 and abiotic parameters (temperature, salinity, pH and dissolved oxygen of seawater and organic matter in the sediment), condition index, metals (Cd, Cu, Zn, Ni, Pb), TBTs and PAHs concentrations were measured in clam tissues. Data was integrated using Principal Component Analyses and biomarker indices: IBR (Integrated Biomarker Response) and HSI (Health Status Index). This multibiomarker approach enabled discrimination of a time and space trend between sites with different degrees of anthropogenic contamination, identifying one of them (site 2) as the most stressful and summer months as the most critical period for clams due to an increase of environmental stress (anthropogenic pressure along with extreme environmental conditions, e.g. temperature, dissolved oxygen, organic matter in the sediments, etc). The selected biomarkers provided an integrated response to assess the environmental quality of the system, proving to be a useful approach when complex mixtures of contaminants occur.

Improvement of CVD risk assessment tools' performance through innovative patients' grouping strategies

There are available in the clinical community several practical risk tools to assess the risk of occurrence of a cardiovascular event. Although valuable, these tools typically present some lack of performance (low sensitivity/low specificity) when applied to a general (average) patient. This flaw is addressed in this work through an innovative personalization strategy that is supported on the evidence that current risk assessment tools perform differently among different populations/groups of patients. The proposed methodology is based on two main hypotheses: i) patients are grouped through a proper dimension reduction technique complemented with an unsupervised learning algorithm, ii) for each group the most suitable risk assessment tool can be selected improving the risk prediction performance. As a result, risk personalization is simply achieved by the identification of the group that patients belong to. The validation of the strategy is carried out through the combination of three current risk assessment tools (GRACE, TIMI, PURSUIT) developed to predict the risk of an event in coronary artery disease patients. The combination of these tools is validated with a real patient testing dataset: Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI patients. Considering the obtained results with the available dataset it is possible to state that the main objective of this work was achieved.

An efficient strategy for evaluating similarity between time series based on Wavelet / Karhunen-Loève transforms

The present work aims to present an innovative measure able to efficiently evaluate the similarity between two physiological time series. The proposed methodology combines the Haar wavelet decomposition, in which signals are represented as linear combinations of a set of orthogonal basis, with the Karhunen-Loève transform, that allows for the optimal reduction of that set of basis. The similarity measure is based on the Euclidean distance, but indirectly calculated through the linear combination coefficients of both time series. Moreover, an iterative scheme for computing the referred coefficients significantly decreases the computational complexity of the method that, due to its simplicity and fast execution, can be easily applicable in clinical applications, namely in computational demanding contexts such as telemonitoring environments. This strategy has been successfully implemented and validated inside HeartCycle project, applied to blood pressure signals collected by a telemonitoring platform (TEN-HMS) in the recognition of hypertension episodes.

Wavelet based time series forecast with application to acute hypotensive episodes prediction

This paper presents a generic methodology for time series prediction, based on a wavelet decomposition/ reconstruction technique, together with a feedforward neural networks structure. The proposed methodology combines the flexibility and learning abilities of neural networks with a compact description of the signals, inherent to wavelets. In a first phase a wavelet decomposition of the signal is performed, providing a small number of coefficients that summarizes signal time evolution dynamics. The prediction problem is then effectively addressed by means of a neural networks model, previously trained using coefficients of the training dataset. The particular problem of forecasting acute hypotensive episodes (AHE) occurring in intensive care units was used to prove the effectiveness of the proposed strategy. The dataset, extracted from MIMIC-II, was made available in the context of the PhysioNet-Computers in Cardiology Challenge 2009. Results attained in this work were similar to the best ones achieved under that challenge.

Long term cardiovascular risk models' combination

The correct diagnosis of cardiovascular disease is a key factor to reduce social and economic costs. In this context, cardiovascular disease risk assessment tools are of fundamental importance. This work addresses two major drawbacks of the current cardiovascular risk score systems: reduced number of risk factors considered by each individual tool and the inability of these tools to deal with incomplete information. To achieve these goals a two phase strategy was followed. In the first phase, a common representation procedure, based on a Naïve-Bayes classifier methodology, was applied to a set of current risk assessment tools. Classifiers' individual parameters and conditional probabilities were initially evaluated through a frequency estimation method. In a second phase, a combination scheme was proposed exploiting the particular features of Bayes probabilistic reasoning, followed by conditional probabilities optimization based on a genetic algorithm approach. This strategy was applied to describe and combine ASSIGN and Framingham models. Validation results were obtained based on individual models, assuming their statistical correctness. The achieved results are very promising, showing the potential of the strategy to accomplish the desired goals.

Fusion of risk assessment models with application to coronary artery disease patients

Several risk score models are available in literature to predict death/myocardial infarction event for coronary artery disease (CAD) patients, within a short period of time. However, the choice of the most adequate model is not straightforward since there might not be a consensus about the best model to use in clinical practice Moreover, individually, these models present some weaknesses, such as the inability to deal with missing information. This work addresses these problems, proposing a Bayesian classifier strategy enabling the simultaneous use of several models (models' fusion). Thus, a higher number of risk factors can be used in the common model, while it can deal with missing information. The validation of the strategy is carried out through the combination of three current risk score models (GRACE, TIMI, PURSUIT). Results were obtained based on a dataset that comprises 460 consecutive patients admitted to the Cardiology Department of Santa Cruz Hospital, Lisbon, from 1999 to 2001. A comparison with the voting scheme, which considers exclusively the outputs of models to combine (models output combination) is also carried out. The proposed Bayesian approach had very satisfactory results, confirming the potential of its application to the clinical practice.

A wavelet-based approach for time series pattern detection and events prediction applied to telemonitoring data

This work aims the development of a predictive strategy able to estimate future events with relevant impact in the cardiovascular status. Based on wavelet transform, a new time series similarity metric is introduced, which is capable to detect a pre-defined pattern in time series data. In addition, a methodology combining a wavelet scheme with state space multi-models is proposed to achieve the prediction of future signal values. Blood pressure signals, collected by a telemonitoring platform (TEN-HMS), are used to detect the occurrence of future hypertension events.

Long term cardiovascular risk models' combination - a new approach

This work addresses two major drawbacks of the current cardiovascular risk score systems: reduced number of risk factors considered by each individual tool and the inability of these tools to deal with incomplete information. To achieve this goal a two phase strategy was followed. In the first phase, a common representation procedure was considered, based on a Naïve-Bayes classifier methodology. Conditional probabilities parameters were initially evaluated through a frequency estimation method and after that optimized using a Genetic Algorithm approach. In a second phase, a combination scheme was proposed exploiting the particular features of Bayes probabilistic reasoning. This strategy was applied to describe and combine SCORE, ASSIGN and Framingham models. Validation results were obtained based on individual models, assuming their statistical correctness. The achieved results are very promising, showing the potential of the strategy to accomplish the desired goals.

Cardiovascular risk and status assessment

This work focuses on the development of models to support the assessment of a patient's global cardiovascular condition. Three types of models, based on different types of information, have been developed: long term cardiovascular risk models, that evaluate the risk of occurring of cardiovascular event within a long period of time (years); short term cardiovascular risk models, to assess the risk of death within a short period of time (months); cardiovascular status assessment models, to estimate the current cardiovascular condition of a patient. Three major drawbacks of current cardiovascular tools are addressed: reduced number of risk factors considered by each individual tool, inappropriateness of these tools to incorporate empirical clinical expertise and incapacity of these tools to deal with incomplete information. Methodologies and preliminary results, obtained under FP7 HeartCycle project, as well as future directions of research are also presented in this paper.

Ischemia detection in the context of a cardiovascular status assessment tool

In this work a new strategy for ischemic episodes automatic detection is proposed, considering ST segment deviation and T wave and QRS morphology characteristics. A new measure of ST deviation based on time-frequency analysis, and the use of the expansion in Hermite functions technique for T wave and QRS complex morphology characterization, are the key points of the proposed methodology. HeartCycle is a European project that aims to improve life quality of coronary artery disease (CAD) and heart failure (HF) patients. Within this project, the Medical Risk Assessment module is responsible for develop models to assess cardiovascular (CV) risk and status of referred patients. The present work was performed under the context of CV status models, where myocardial ischemia plays a central role. For algorithms validation purposes, the European Society of Cardiology (ESC) ST-T database was used. A sensitivity of 96.7% and a positive predictivity of 96.2% reveal the capacity of the proposed strategy to perform ischemic episodes identification.