Clustering analysis reveals different profiles associating long-term post-COVID symptoms, COVID-19 symptoms at hospital admission and previous medical co-morbidities in previously hospitalized COVID-19 survivors.

PURPOSE: To identify subgroups of COVID-19 survivors exhibiting long-term post-COVID symptoms according to clinical/hospitalization data by using cluster analysis in order to foresee the illness progress and facilitate subsequent prognosis. METHODS: Age, gender, height, weight, pre-existing medical comorbidities, Internal Care Unit (ICU) admission, days at hospital, and presence of COVID-19 symptoms at hospital admission were collected from hospital records in a sample of patients recovered from COVID-19 at five hospitals in Madrid (Spain). A predefined list of post-COVID symptoms was systematically assessed a mean of 8.4 months (SD 15.5) after hospital discharge. Anxiety/depressive levels and sleep quality were assessed with the Hospital Anxiety and Depression Scale and Pittsburgh Sleep Quality Index, respectively. Cluster analysis was used to identify groupings of COVID-19 patients without introducing any previous assumptions, yielding three different clusters associating post-COVID symptoms with acute COVID-19 symptoms at hospital admission. RESULTS: Cluster 2 grouped subjects with lower prevalence of medical co-morbidities, lower number of COVID-19 symptoms at hospital admission, lower number of post-COVID symptoms, and almost no limitations with daily living activities when compared to the others. In contrast, individuals in cluster 0 and 1 exhibited higher number of pre-existing medical co-morbidities, higher number of COVID-19 symptoms at hospital admission, higher number of long-term post-COVID symptoms (particularly fatigue, dyspnea and pain), more limitations on daily living activities, higher anxiety and depressive levels, and worse sleep quality than those in cluster 2. CONCLUSIONS: The identified subgrouping may reflect different mechanisms which should be considered in therapeutic interventions.

Clustering analysis identifies two subgroups of women with fibromyalgia with different psychological, cognitive, health-related, and physical features but similar widespread pressure pain sensitivity.

OBJECTIVE: Given that identification of groups of patients can help to better understand risk factors related to each group and to improve personalized therapeutic strategies, this study aimed to identify subgroups (clusters) of women with fibromyalgia syndrome (FMS) according to pain, pain-related disability, neurophysiological, cognitive, health, psychological, or physical features. METHODS: Demographic, pain, sensory, pain-related disability, psychological, health, cognitive, and physical variables were collected in 113 women with FMS. Widespread pressure pain thresholds were also assessed. K-means clustering was used to identify groups of women without any previous assumption. RESULTS: Two clusters exhibiting similar widespread sensitivity to pressure pain (pressure pain thresholds) but differing in the remaining variables were identified. Overall, women in one cluster exhibited higher pain intensity and pain-related disability; more sensitization-associated and neuropathic pain symptoms; higher kinesiophobia, hypervigilance, and catastrophism levels; worse sleep quality; higher anxiety/depressive levels; lower health-related function; and worse physical function than women in the other cluster. CONCLUSIONS: Cluster analysis identified one group of women with FMS exhibiting worse sensory, psychological, cognitive, and health-related features. Widespread sensitivity to pressure pain seems to be a common feature of FMS. The present results suggest that this group of women with FMS might need to be treated differently.

Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer.

The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2 Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.

Headache as a COVID-19 onset symptom and post-COVID-19 symptom in hospitalized COVID-19 survivors infected with the Wuhan, Alpha, or Delta SARS-CoV-2 variants.

OBJECTIVE: This study looked at differences in the presence of headache as an onset symptom of coronavirus disease 2019 (COVID-19) and as a post-COVID-19 symptom in individuals previously hospitalized owing to infection with the Wuhan, Alpha, or Delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). BACKGROUND: Headache can be present in up to 50% of individuals during the acute phase of SARS-CoV-2 infection and in 10% of subjects during the post-COVID-19 phase. There are no data on differences in the occurrence of headache in the acute- and post-COVID-19 phase according to the SARS-CoV-2 variants. METHODS: A cross-sectional cohort study was conducted. Unvaccinated subjects previously hospitalized for COVID-19 caused by the Wuhan (n = 201), Alpha (n = 211), or Delta (n = 202) SARS-CoV-2 variants were scheduled for a telephone interview 6 months after hospital discharge. Hospitalization data were collected from hospital medical records. RESULTS: The presence of headache as a COVID-19 onset symptom at hospitalization was higher in subjects with the Delta variant (66/202, 32.7%) than in those infected with the Wuhan (42/201, 20.9%; odds ratio [OR] 1.83, 95% confidence interval [CI] 1.17-2.88) or Alpha (25/211, 11.8%; OR 3.61, 95% CI, 2.16-6.01) variants. The prevalence of post-COVID-19 headache 6 months after hospital discharge was higher in individuals infected with the Delta variant (26/202, 12.9%) than in those infected with the Wuhan (11/201, 5.5%; OR 2.52, 95% CI 1.22-5.31) or Alpha (eight of 211, 3.8%; OR 3.74, 95% CI 1.65-8.49) variants. The presence of headache as a COVID-19 onset symptom was associated with post-COVID-19 headache in subjects infected with the Wuhan (OR 7.75, 95% CI 2.15-27.93) and Delta variants (OR 2.78, 95% CI 1.20-6.42) but not with the Alpha variant (OR 2.60, 95% CI 0.49-13.69). CONCLUSION: Headache was a common symptom in both the acute- and post-COVID-19 phase in subjects infected with the Wuhan, Alpha, and Delta variants but mostly in those infected with the Delta variant.

Retrieving Quantum Information with Active Learning.

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies.

Investigating the fluctuating nature of post-COVID pain symptoms in previously hospitalized COVID-19 survivors: the LONG-COVID-EXP multicenter study.

Objective: This cohort study used Sankey plots and exponential bar plots for visualizing the fluctuating nature and trajectory of post-COVID pain in previously hospitalized COVID-19 survivors. Methods: A cohort of 1266 subjects hospitalised because of COVID-19 during the first wave of the pandemic were scheduled for a telephone interview at 8.4 (T1), 13.2 (T2), and 18.3 (T3) months in average after hospitalization for collecting data about post-COVID pain. Patients were asked for about pain symptomatology that was attributed to the infection. Hospitalization and clinical data were collected from medical records. Results: The prevalence of myalgia as COVID-19-associated symptom was 29.82% (n = 389) at hospitalization (T0). The prevalence of post-COVID pain was 41.07% (n = 520) at T1, 34.29% (n = 434) at T2, and 28.47% (n = 360) at T3. The recovery exponential curve revealed a decrease trend visualizing that post-COVID pain improved over the time span investigated. Pain in the lower extremity and widespread pain were the most prevalent locations. Female sex (OR 1.507, 95% CI 1.047-2.169), pre-existing pain symptoms (OR 1.724, 95% CI 1.237-2.403), headache as onset-symptom (OR 2.374, 95% CI 1.550-3.639), days at hospital (OR 1.012, 95% CI 1.000-1.025), and presence of post-COVID pain at T1 (OR 13.243, 95% CI 9.428-18.601) were associated with post-COVID pain at T2. Only the presence of post-COVID pain at T1 (OR 5.383, 95% CI 3.896-7.439) was associated with post-COVID pain at T3. Conclusion: Current results show a fluctuating evolution with a decreasing tendency of post-COVID pain during the first years after hospitalization. The development of post-COVID pain soon after SARS-CoV-2 infection predispose for long-lasting chronic pain.

Trajectory of anxiety/depressive symptoms and sleep quality in individuals who had been hospitalized by COVID-19: The LONG-COVID-EXP multicenter study.

OBJECTIVE: To apply Sankey plots and exponential bar plots for visualizing the evolution of anxiety/depressive symptoms and poor sleep in previously hospitalized COVID-19 survivors. METHODS: A sample of 1266 subjects who were hospitalized due to a SARS-CoV-2 from March-May 2020 were assessed at 8.4 (T1), 13.2 (T2) and 18.3 (T3) months after hospitalization. The Hospital Anxiety and Depression Scale was used to determine anxiety (HADS-A) and depressive (HADS-D) symptoms. The Pittsburgh Sleep Quality Index (PSQI) evaluated sleep quality. Clinical features, onset symptoms and hospital data were collected from medical records. RESULTS: Sankey plots revealed that the prevalence of anxiety symptomatology (HADS-A ≥ 8 points) was 22.5% (n = 285) at T1, 17.6% (n = 223) at T2, and 7.9% (n = 100) at T3, whereas the prevalence of depressive symptoms (HADS-D ≥ 8 points) was 14.6% (n = 185) at T1, 10.9% (n = 138) at T2, and 6.1% (n = 78) at T3. Finally, the prevalence of poor sleep (PSQI≥8 points) decreased from 32.8% (n = 415) at T1, to 28.8% (n = 365) at T2, and to 24.8% (n = 314) at T3. The recovery curves show a decrease trend visualizing that these symptoms recovered the following years after discharge. The regression models did not reveal medical records associated with anxiety/depressive symptoms or poor sleep. CONCLUSION: The use of Sankey plots shows a fluctuating evolution of anxiety/depressive symptoms and poor sleep during the first years after the infection. In addition, exponential bar plots revealed a decrease prevalence of these symptoms during the first years after hospital discharge. No risk factors were identified in this cohort.

From electronic health records to clinical management systems: how the digital transformation can support healthcare services.

Healthcare systems worldwide are currently undergoing significant transformations in response to increasing costs, a shortage of healthcare professionals and the growing complexity of medical needs among the population. Value-based healthcare reimbursement systems are emerging as an attempt to incentivize patient-centricity and cost containment. From a technological perspective, the transition to digitalized services is intended to support these transformations. A Health Information System (HIS) is a technological solution designed to govern the data flow generated and consumed by healthcare professionals and administrative staff during the delivery of healthcare services. However, the exponential growth of digital capabilities and applied advanced analytics has expanded their traditional functionalities and brought the promise of automating administrative procedures and simple repetitive tasks, while enhancing the efficiency and outcomes of healthcare services by incorporating decision support tools for clinical management. The future of HIS is headed towards modular architectures that can facilitate implementation and adaptation to different environments and systems, as well as the integration of various tools, such as artificial intelligence (AI) models, in a seamless way. As an example, we present the experience and future developments of the European Clinical Database (EuCliD®). EuCliD is a multilingual HIS used by 20 000 nurses and physicians on a daily basis to manage 105 000 patients treated in 1100 clinics in 43 different countries. EuCliD encompasses patients' follow-up, automatic reporting and mobile applications while enabling efficient management of clinical processes. It is also designed to incorporate multiagent systems to automate repetitive tasks, AI modules and advanced dynamic dashboards.

Trajectory of Gastrointestinal Symptoms in Previously Hospitalized COVID-19 Survivors: The Long COVID Experience Multicenter Study.

This multicenter cohort study used Sankey plots and exponential bar plots to visualize the fluctuating evolution and the trajectory of gastrointestinal symptoms in previously hospitalized COVID-19 survivors during the first 18 months after acute SARS-CoV-2 infection. A total of 1266 previously hospitalized COVID-19 survivors were assessed at four points: hospital admission (T0), at 8.4 months (T1), at 13.2 months (T2), and at 18.3 months (T3) after hospitalization. Participants were asked about their overall gastrointestinal symptoms and particularly diarrhea. Clinical and hospitalization data were collected from hospital medical records. The prevalence of overall gastrointestinal post-COVID symptomatology was 6.3% (n = 80) at T1, 3.99% (n = 50) at T2 and 2.39% (n = 32) at T3. The prevalence of diarrhea decreased from 10.69% (n = 135) at hospital admission (T0), to 2.55% (n = 32) at T1, to 1.04% (n = 14) at T2, and to 0.64% (n = 8) at T3. The Sankey plots revealed that just 20 (1.59%) and 4 (0.32%) patients exhibited overall gastrointestinal post-COVID symptoms or diarrhea, respectively, throughout the whole follow-up period. The recovery fitted exponential curves revealed a decreasing prevalence trend, showing that diarrhea and gastrointestinal symptoms recover during the first two or three years after COVID-19 in previously hospitalized COVID-19 survivors. The regression models did not reveal any symptoms to be associated with the presence of gastrointestinal post-COVID symptomatology or post-COVID diarrhea at hospital admission or at T1. The use of Sankey plots revealed the fluctuating evolution of gastrointestinal post-COVID symptoms during the first two years after infection. In addition, exponential bar plots revealed the decreased prevalence of gastrointestinal post-COVID symptomatology during the first three years after infection.

Trajectory of Post-COVID Self-Reported Fatigue and Dyspnoea in Individuals Who Had Been Hospitalized by COVID-19: The LONG-COVID-EXP Multicenter Study.

Fatigue and dyspnoea are common post-COVID symptoms. The aim of this study was to apply Sankey plots and exponential bar plots for visualizing the evolution and trajectory of post-COVID fatigue and dyspnoea symptoms in a cohort of previously hospitalized COVID-19 survivors. A total of 1266 previously hospitalized patients due to COVID-19 participated in this multicentre study. They were assessed at hospital admission (T0), 8.4 months (T1), 13.2 months (T2) and 18.3 months (T3) after hospital discharge and were asked about the presence of self-reported fatigue or dyspnoea symptoms. Fatigue was defined as a self-perceived feeling of constant tiredness and/or weakness whereas dyspnoea was defined as a self-perceived feeling of shortness of breath at rest. We specifically asked for fatigue and dyspnoea that participants attributed to the infection. Clinical/hospitalization data were collected from hospital medical records. The prevalence of post-COVID fatigue was 56.94% (n = 721) at T1, 52.31% (n = 662) at T2 and 42.66% (n = 540) at T3. The prevalence of dyspnoea at rest decreased from 28.71% (n = 363) at hospital admission (T0), to 21.29% (n = 270) at T1, to 13.96% (n = 177) at T2 and 12.04% (n = 153) at T3. The Sankey plots revealed that 469 (37.08%) and 153 (12.04%) patients exhibited fatigue and dyspnoea at all follow-up periods. The recovery exponential curves show a decreased prevalence trend, showing that fatigue and dyspnoea recover the following three years after hospitalization. The regression models revealed that the female sex and experiencing the symptoms (e.g., fatigue, dyspnoea) at T1 were factors associated with the presence of post-COVID fatigue or dyspnoea at T2 and T3. The use of Sankey plots shows a fluctuating evolution of post-COVID fatigue and dyspnoea during the first two years after infection. In addition, exponential bar plots revealed a decreased prevalence of these symptoms during the first years after. The female sex is a risk factor for the development of post-COVID fatigue and dyspnoea.

Prevalence of Self-Reported Anosmia and Ageusia in Elderly Patients Who Had Been Previously Hospitalized by SARS-CoV-2: The LONG-COVID-EXP Multicenter Study.

We explored two different graph methods for visualizing the prevalence of self-reported post-COVID anosmia and ageusia in a large sample of individuals who had been previously hospitalized in five different hospitals. A cohort of 1266 previously hospitalized COVID-19 survivors participated. Participants were assessed at hospitalization (T0) and at three different follow-up periods: 8.4 (T1), 13.2 (T2), and 18.3 (T3) months after hospital discharge. They were asked about the presence of self-reported anosmia and ageusia that they attributed to infection. Anosmia was defined as a self-perceived feeling of complete loss of smell. Ageusia was defined as a self-perceived feeling of complete loss of taste. Data about hospitalization were recorded from medical records. The results revealed that the prevalence of anosmia decreased from 8.29% (n = 105) at hospitalization (T0), to 4.47% (n = 56) at T1, to 3.27% (n = 41) at T2, and 3.35% (n = 42) at T3. Similarly, the prevalence of ageusia was 7.10% (n = 89) at the onset of SARS-CoV-2 infection (T0), but decreased to 3.03% (n = 38) at T1, to 1.99% (n = 25) at T2, and 1.36% (n = 17) at T3. The Sankey plots showed that only 10 (0.8%) and 11 (0.88%) patients exhibited anosmia and ageusia throughout all the follow-ups. The exponential curves revealed a progressive decrease in prevalence, demonstrating that self-reported anosmia and ageusia improved in the years following hospitalization. The female sex (OR4.254, 95% CI 1.184-15.294) and sufferers of asthma (OR7.086, 95% CI 1.359-36.936) were factors associated with the development of anosmia at T2, whereas internal care unit admission was a protective factor (OR0.891, 95% CI 0.819-0.970) for developing anosmia at T2. The use of a graphical method, such as a Sankey plot, shows that post-COVID self-reported anosmia and ageusia exhibit fluctuations during the first years after SARS-CoV-2 infection. Additionally, self-reported anosmia and ageusia also show a decrease in prevalence during the first years after infection, as expressed by exponential bar plots. The female sex was associated with the development of post-COVID anosmia, but not ageusia, in our cohort of elderly patients previously hospitalized due to COVID-19.

Exploring the trajectory curve of long-term musculoskeletal post-COVID pain symptoms in hospitalized COVID-19 survivors: a multicenter study.

ABSTRACT: This multicenter cohort study investigated the prevalence of musculoskeletal post-COVID pain during the first year after the infection with mosaic plots and an exponential bar plot model and its associated risk factors. Patients hospitalized because of COVID-19 in 5 hospitals of Madrid (Spain) were scheduled for a telephone interview at 2 follow-up periods after hospitalization for collecting data about musculoskeletal post-COVID pain. Hospitalization and clinical data were collected from hospital medical records. From 2000 patients initially recruited, 1593 (44.6% women, age: 61 ± 15 years) were assessed at T0 (hospital admission), T1 (mean: 8.0 ± 1.5 months after discharge), and T2 (mean: 13.2 ± 1.5 months after discharge). The prevalence of musculoskeletal pain (myalgia) was 30.3% (n = 483) at T0, increased to 43.4% (n = 692) at T1, and decreased to 37.8% (n = 603) at T2. The trajectory curve revealed a decreasing prevalence trend of musculoskeletal post-COVID pain the following years after hospitalization. According to the presence of pre-existing pain symptoms, the prevalence of new-onset post-COVID pain was 75.9%. Female sex (odds ratio [OR] 1.593, 95% confidence interval [CI] 1.148-2.211), history of musculoskeletal pain (OR 1.591, 95% CI 1.211-2.07), the presence of myalgia (OR 1.371, 95% CI 1.032-1.821) or headache (OR 2.278, 95% CI 1.622-3.199) at hospitalization, the days of hospitalization (OR 1.013, 95% CI 1.000-1.025), and the presence of post-COVID pain at T1 (OR 11.02, 95% CI 8.493-14.305) were factors associated with musculoskeletal post-COVID pain 1 year after hospitalization. In conclusion, musculoskeletal post-COVID pain remains highly prevalent 1 year after hospitalization. Female sex, previous history of pain symptoms, pain symptoms at onset, and days at hospital were factors associated with musculoskeletal post-COVID pain 1 year after hospitalization.

Trajectory of post-COVID brain fog, memory loss, and concentration loss in previously hospitalized COVID-19 survivors: the LONG-COVID-EXP multicenter study.

Objective: This study aimed to apply Sankey plots and exponential bar plots for visualizing the trajectory of post-COVID brain fog, memory loss, and concentration loss in a cohort of previously hospitalized COVID-19 survivors. Methods: A sample of 1,266 previously hospitalized patients due to COVID-19 during the first wave of the pandemic were assessed at 8.4 (T1), 13.2 (T2), and 18.3 (T3) months after hospital discharge. They were asked about the presence of the following self-reported cognitive symptoms: brain fog (defined as self-perception of sluggish or fuzzy thinking), memory loss (defined as self-perception of unusual forgetfulness), and concentration loss (defined as self-perception of not being able to maintain attention). We asked about symptoms that individuals had not experienced previously, and they attributed them to the acute infection. Clinical and hospitalization data were collected from hospital medical records. Results: The Sankey plots revealed that the prevalence of post-COVID brain fog was 8.37% (n = 106) at T1, 4.7% (n = 60) at T2, and 5.1% (n = 65) at T3, whereas the prevalence of post-COVID memory loss was 14.9% (n = 189) at T1, 11.4% (n = 145) at T2, and 12.12% (n = 154) at T3. Finally, the prevalence of post-COVID concentration loss decreased from 6.86% (n = 87) at T1, to 4.78% (n = 60) at T2, and to 2.63% (n = 33) at T3. The recovery exponential curves show a decreasing trend, indicating that these post-COVID cognitive symptoms recovered in the following years after discharge. The regression models did not reveal any medical record data associated with post-COVID brain fog, memory loss, or concentration loss in the long term. Conclusion: The use of Sankey plots shows a fluctuating evolution of post-COVID brain fog, memory loss, or concentration loss during the first years after the infection. In addition, exponential bar plots revealed a decrease in the prevalence of these symptoms during the first years after hospital discharge. No risk factors were identified in this cohort.

Exploring the trajectory recovery curve of the number of post-COVID Symptoms: The LONG-COVID-EXP-CM Multicenter Study.

OBJECTIVES: This multicenter study investigated the recovery curve of the number of post-COVID-19 symptoms in previously hospitalized patients using an exponential decay model and mosaic plots. METHODS: Patients hospitalized during the first wave of the pandemic (from March 10, 2010-May 31, 2020) due to COVID-19 from 5 hospitals in Madrid, Spain were scheduled for 2 telephone interviews at 2 follow-ups with a 5-month period in between and were asked about the presence of post-COVID-19 symptoms. The total number of post-COVID-19 symptoms was monitored. Clinical features, symptoms at hospital admission, and hospitalization data were collected from medical records. RESULTS: A total of 1593 patients who had COVID-19 were assessed 8.4 (T1) and 13.2 (T2) months after hospitalization. The mean number of post-COVID-19 symptoms was 2.6 (SD 2.0) at T1 and 1.5 (SD 1.4) at T2. The trajectory curve showed a decrease in prevalence trend. The analysis also revealed that 985 (61.8%) subjects reported more (T1>T2), 549 (34.5%) equal (T1 = T2), and 59 (3.7%) fewer (T1

Differences in Long-COVID Symptoms between Vaccinated and Non-Vaccinated (BNT162b2 Vaccine) Hospitalized COVID-19 Survivors Infected with the Delta Variant.

This study compared differences in the presence of post-COVID symptoms among vaccinated and non-vaccinated COVID-19 survivors requiring hospitalization due to the Delta (B.1.617.2) variant. This cohort study included hospitalized subjects who had survived SARS-CoV-2 infection (Delta variant) from July to August 2021 in an urban hospital in Madrid, Spain. Individuals were classified as vaccinated if they received full administration (i.e., two doses) of BNT162b2 ("Pfizer-BioNTech") vaccines. Other vaccines were excluded. Those with just one dose of the BNT162b2 vaccine were considered as non-vaccinated. Patients were scheduled for a telephone interview at a follow-up around six months after infection for assessing the presence of post-COVID symptoms with particular attention to those symptoms starting after acute infection and hospitalization. Anxiety/depressive levels and sleep quality were likely assessed. Hospitalization and clinical data were collected from medical records. A total comprising 109 vaccinated and 92 non-vaccinated COVID-19 survivors was included. Vaccinated patients were older and presented a higher number of medical comorbidities, particular cardiorespiratory conditions, than non-vaccinated patients. No differences in COVID-19 onset symptoms at hospitalization and post-COVID symptoms six months after hospital discharge were found between vaccinated and non-vaccinated groups. No specific risk factor for any post-COVID symptom was identified in either group. This study observed that COVID-19 onset-associated symptoms and post-COVID symptoms six-months after hospitalization were similar between previously hospitalized COVID-19 survivors vaccinated and those non-vaccinated. Current data can be applied to the Delta variant and those vaccinated with BNT162b2 (Pfizer-BioNTech) vaccine.

Bayesian Linear Regressions Applied to Fibromyalgia Syndrome for Understanding the Complexity of This Disorder.

A better understanding of the connection between factors associated with pain sensitivity and related disability in people with fibromyalgia syndrome may assist therapists in optimizing therapeutic programs. The current study applied mathematical modeling to analyze relationships between pain-related, psychological, psychophysical, health-related, and cognitive variables with sensitization symptom and related disability by using Bayesian Linear Regressions (BLR) in women with fibromyalgia syndrome (FMS). The novelty of the present work was to transfer a mathematical background to a complex pain condition with widespread symptoms. Demographic, clinical, psychological, psychophysical, health-related, cognitive, sensory-related, and related-disability variables were collected in 126 women with FMS. The first BLR model revealed that age, pain intensity at rest (mean-worst pain), years with pain (history of pain), and anxiety levels have significant correlations with the presence of sensitization-associated symptoms. The second BLR showed that lower health-related quality of life and higher pain intensity at rest (mean-worst pain) and pain intensity with daily activities were significantly correlated with related disability. These results support an application of mathematical modeling for identifying different interactions between a sensory (i.e., Central Sensitization Score) and a functional (i.e., Fibromyalgia Impact Questionnaire) aspect in women with FMS.

Associated-Onset Symptoms and Post-COVID-19 Symptoms in Hospitalized COVID-19 Survivors Infected with Wuhan, Alpha or Delta SARS-CoV-2 Variant.

This study compared associated-symptoms at the acute phase of infection and post-COVID-19 symptoms between individuals hospitalized with the Wuhan, Alpha or Delta SARS-CoV-2 variant. Non-vaccinated individuals hospitalized because of SARS-CoV-2 infection in one hospital during three different waves of the pandemic (Wuhan, Alpha or Delta) were scheduled for a telephone interview. The presence of post-COVID-19 symptoms was systematically assessed. Hospitalization and clinical data were collected from medical records. A total of 201 patients infected with the Wuhan variant, 211 with the Alpha variant and 202 with Delta variant were assessed six months after hospitalization. Patients infected with the Wuhan variant had a greater number of symptoms at hospital admission (higher prevalence of fever, dyspnea or gastrointestinal problems) than those infected with Alpha or Delta variant (p < 0.01). A greater proportion of patients infected with the Delta variant reported headache, anosmia or ageusia as onset symptoms (p < 0.01). The mean number of post-COVID-19 symptoms was higher (p < 0.001) in individuals infected with the Wuhan variant (mean: 2.7 ± 1.3) than in those infected with the Alpha (mean: 1.8 ± 1.1) or Delta (mean: 2.1 ± 1.5) variant. Post-COVID-19 dyspnea was more prevalent (p < 0.001) in people infected with the Wuhan variant, whereas hair loss was higher in those infected with the Delta variant (p = 0.002). No differences in post-COVID-19 fatigue by SARS-CoV-2 variant were found (p = 0.594). Differences in COVID-19 associated onset symptoms and post-COVID-19 dyspnea were observed depending on the SARS-CoV-2 variant. The presence of fatigue was a common post-COVID-19 symptom to all SARS-CoV-2 variants.