Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis.

This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.

Appropriateness of prescribing profiles and intake adherence to non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation: analysis of a retrospective longitudinal study using real-world data from Northern Portugal (AF-React Study).

OBJECTIVES: This study aimed to assess the appropriateness of prescribing profiles and intake adherence to non-vitamin K antagonist oral anticoagulants (NOACs) in patients with atrial fibrillation (AF). DESIGN: Retrospective longitudinal study. SETTING: The study was conducted in the Regional Health Administration of Northern Portugal. PARTICIPANTS: The authors selected a database of 21 854 patients with prescriptions for NOACs between January 2016 and December 2018 and were classified with AF until December 2018. OUTCOME MEASURES: The appropriate dosage of NOAC for patients with AF divided into three categories: contraindicated, inconsistent and consistent, based on the 2020 European Society of Cardiology guidelines for AF. RESULTS: Dabigatran had a lower percentage of guideline-consistent doses (n=1657, 50.1%) than other drugs such as rivaroxaban (n=4737, 81.6%), apixaban (n=3830, 78.7%) and edoxaban (n=436, 82.1%). Most patients with an inconsistent dose were prescribed a lower dose than recommended based on their glomerular filtration rate (GFR). Among patients younger than 75 years with GFR >60 mL/min, 59.8% (n=10 028) had an adequate GFR range, while 27.8% (n=7166) of GFR measurements from patients older than 75 years old and 29.4% (n=913) of GFR measurements from patients younger than 75 years with GFR <60 mL/min were within an adequate time range. Adherence to NOACs varied across different drugs, with 59.1% (n=540) adhering to edoxaban, 56.3% (n=5443) to rivaroxaban, 55.3% (n=3143) to dabigatran and 53.3% (n=4211) to apixaban. CONCLUSIONS: Dabigatran had the lowest percentage of guideline-consistent doses. Patients younger than 75 years with GFR >60 mL/min had the highest percentage with an adequate GFR range, while other groups who require closer GFR monitoring had lower percentages within an adequate GFR range. Adherence to NOACs differed among different drugs, with greater adherence to treatment with edoxaban and less adherence to apixaban.

Using Markov chains and temporal alignment to identify clinical patterns in Dementia.

In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features and clinical pathways of patients with multimorbidity suffering from Dementia. This disease can result from many heterogeneous factors, potentially becoming more prevalent as the population ages. We present a set of methods that allow us to identify medical appointment patterns within a cohort of 1924 patients followed from January 2007 to August 2021 in Hospital da Luz (Lisbon), and to stratify patients into subgroups that exhibit similar patterns of interaction. With Markov Chains, we are able to identify the most prevailing medical appointments attended by Dementia patients, as well as recurring transitions between these. To perform patient stratification, we applied AliClu, a temporal sequence alignment algorithm for clustering longitudinal clinical data, which allowed us to successfully identify patient subgroups with similar medical appointment activity. A feature analysis per cluster obtained allows the identification of distinct patterns and characteristics. This pipeline provides a tool to identify prevailing clinical pathways of medical appointments within the dataset, as well as the most common transitions between medical specialities within Dementia patients. This methodology, alongside demographic and clinical data, has the potential to provide early signalling of the most likely clinical pathways and serve as a support tool for health providers in deciding the best course of treatment, considering a patient as a whole.

Safety Climate Evaluation in Primary Health Care: A Cross-Sectional Study.

The perception of the existence of deficits in patient safety, the associated costs and the limitation of resources have made it essential to define improvement strategies. Important concepts have emerged, such as safety climate, which evaluates the perceptions of safety status held by professionals in relation to their organization. The aim of this study is to characterize the safety climate in primary health care (PHC) using the Safety Attitudes Questionnaire (SAQ)-Short Form 2006 PT and to assess associations between SAQ-Short Form 2006 PT scores and demographic and professional characteristics. A cross-sectional study was conducted in all public PHC units in the northern region of Portugal. Data were collected through an online questionnaire shared via institutional emails and by means of a snowball approach. Descriptive and inferential statistical analysis were performed. Statistical significance set at p ≤ 0.05. A total of 649/7427 (8.7%) responses were included in the analyses. The mean and median total SAQ-Short Form 2006 PT scores were 69.23 (SD = 15.73, range 22.22-100.00) and 71.53 [59.03; 79.86], respectively. This is the first study to assess the safety climate in PHC in Portugal. The median obtained total SAQ-Short Form 2006 PT score was 71.53 [59.03; 79.86], which is below the threshold of ≥75, indicating safety deficits.

Non-selective distribution of infectious disease prevention may outperform risk-based targeting.

Epidemic control often requires optimal distribution of available vaccines and prophylactic tools, to protect from infection those susceptible. Well-established theory recommends prioritizing those at the highest risk of exposure. But the risk is hard to estimate, especially for diseases involving stigma and marginalization. We address this conundrum by proving that one should target those at high risk only if the infection-averting efficacy of prevention is above a critical value, which we derive analytically. We apply this to the distribution of pre-exposure prophylaxis (PrEP) of the Human Immunodeficiency Virus (HIV) among men-having-sex-with-men (MSM), a population particularly vulnerable to HIV. PrEP is effective in averting infections, but its global scale-up has been slow, showing the need to revisit distribution strategies, currently risk-based. Using data from MSM communities in 58 countries, we find that non-selective PrEP distribution often outperforms risk-based, showing that a logistically simpler strategy is also more effective. Our theory may help design more feasible and successful prevention.

Revealing semantic and emotional structure of suicide notes with cognitive network science.

Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is "love", which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of "love" combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.

Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series.

Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter timescales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Unwrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

Large-Scale Simulations of Bacterial Populations Over Complex Networks.

The understanding of bacterial population genetics and evolution is crucial in epidemic outbreak studies and pathogen surveillance. However, all epidemiological studies are limited to their sampling capacities which, by being usually biased or limited due to economic constraints, can hamper the real knowledge of the bacterial population structure of a given species. To this end, mathematical models and large-scale simulations can provide a quantitative analytical framework that can be used to assess how or if limited sampling can infer the true population structure. In this article, we address the large-scale simulation of genetic evolution of bacterial populations, using Wright-Fisher model, in the presence of complex host contact networks. We present an efficient approach for large-scale simulations over complex host contact networks, using MapReduce on top of Apache Spark and GraphX API. We evaluate the relation between cluster computing power and simulations speedup and include insights on how bacterial population diversity can be affected by mutation and recombination rates, and network topology.

Diagnostic and laboratory test ordering in Northern Portuguese Primary Health Care: a cross-sectional study.

OBJECTIVES: To characterise the test ordering pattern in Northern Portugal and to investigate the influence of context-related factors, analysing the test ordered at the level of geographical groups of family physicians and at the level of different healthcare organisations. DESIGN: Cross-sectional study. SETTING: Northern Primary Health Care, Portugal. PARTICIPANTS: Records about diagnostic and laboratory tests ordered from 2035 family physicians working at the Northern Regional Health Administration, who served approximately 3.5 million Portuguese patients, in 2014. OUTCOMES: To determine the 20 most ordered diagnostic and laboratory tests in the Northern Regional Health Administration; to identify the presence and extent of variations in the 20 most ordered diagnostic and laboratory tests between the Groups of Primary Care Centres and between health units; and to study factors that may explain these variations. RESULTS: The 20 most ordered diagnostic and laboratory tests almost entirely comprise laboratory tests and account for 70.9% of the total tests requested. We can trace a major pattern of test ordering for haemogram, glucose, lipid profile, creatinine and urinalysis. There was a significant difference (P<0.001) in test orders for all tests between Groups of Primary Care Centres and for all tests, except glycated haemoglobin (P=0.06), between health units. Generally, the Personalised Healthcare Units ordered more than Family Health Units. CONCLUSIONS: The results from this study show that the most commonly ordered tests in Portugal are laboratory tests, that there is a tendency for overtesting and that there is a large variability in diagnostic and laboratory test ordering in different geographical and organisational Portuguese primary care practices, suggesting that there may be considerable potential for the rationalisation of test ordering. The existence of Family Health Units seems to be a strong determinant in decreasing test ordering by Portuguese family physicians. Approaches to ensuring more rational testing are needed.

The effect of a test ordering software intervention on the prescription of unnecessary laboratory tests - a randomized controlled trial.

BACKGROUND: The way software for electronic health records and laboratory tests ordering systems are designed may influence physicians' prescription. A randomised controlled trial was performed to measure the impact of a diagnostic and laboratory tests ordering system software modification. METHODS: Participants were family physicians working and prescribing diagnostic and laboratory tests. The intervention group had a modified software with a basic shortcut menu changes, where some tests were withdrawn or added, and with the implementation of an evidence-based decision support based on United States Preventive Services Task Force (USPSTF) recommendations. This intervention group was compared with usual software (control group). The outcomes were the number of tests prescribed from those: withdrawn from the basic menu; added to the basic menu; marked with green dots (USPSTF's grade A and B); and marked with red dots (USPSTF's grade D). RESULTS: Comparing the monthly average number of tests prescribed before and after the software modification, from those tests that were withdrawn from the basic menu, the control group prescribed 33.8 tests per 100 consultations before and 30.8 after (p = 0075); the intervention group prescribed 31.3 before and 13.9 after (p < 0001). Comparing the tests prescribed between both groups during the intervention, from those tests that were withdrawn from the basic menu, the intervention group prescribed a monthly average of 14.0 vs. 29.3 tests per 100 consultations in the control group (p < 0.001). From those tests that are USPSTF's grade A and B, intervention group prescribed 66.8 vs. 74.1 tests per 100 consultations in the control group (p = 0.070). From those tests categorised as USPSTF grade D, the intervention group prescribed an average of 9.8 vs. 11.8 tests per 100 consultations in the control group (p = 0.003). CONCLUSIONS: Removing unnecessary tests from a quick shortcut menu of the diagnosis and laboratory tests ordering system had a significant impact and reduced unnecessary prescription of tests. The fact that it was not possible to perform the randomization at the family physicians' level, but only of the computer servers is a limitation of our study. Future research should assess the impact of different tests ordering systems during longer periods. TRIAL REGISTRATION: ISRCTN45427977 , May 1st 2014 (retrospectively registered).

Not seeing the forest for the trees: size of the minimum spanning trees (MSTs) forest and branch significance in MST-based phylogenetic analysis.

Trees, including minimum spanning trees (MSTs), are commonly used in phylogenetic studies. But, for the research community, it may be unclear that the presented tree is just a hypothesis, chosen from among many possible alternatives. In this scenario, it is important to quantify our confidence in both the trees and the branches/edges included in such trees. In this paper, we address this problem for MSTs by introducing a new edge betweenness metric for undirected and weighted graphs. This spanning edge betweenness metric is defined as the fraction of equivalent MSTs where a given edge is present. The metric provides a per edge statistic that is similar to that of the bootstrap approach frequently used in phylogenetics to support the grouping of taxa. We provide methods for the exact computation of this metric based on the well known Kirchhoff's matrix tree theorem. Moreover, we implement and make available a module for the PHYLOViZ software and evaluate the proposed metric concerning both effectiveness and computational performance. Analysis of trees generated using multilocus sequence typing data (MLST) and the goeBURST algorithm revealed that the space of possible MSTs in real data sets is extremely large. Selection of the edge to be represented using bootstrap could lead to unreliable results since alternative edges are present in the same fraction of equivalent MSTs. The choice of the MST to be presented, results from criteria implemented in the algorithm that must be based in biologically plausible models.