Distinct Symptom Profiles Research Articles

Home Healthcare Patients With Distinct Psychological, Cognitive, and Behavioral Symptom Profiles and At-Risk Subgroup for Hospitalization and Emergency Department Visits Using Latent Class Analysis.

One-third of home healthcare patients are hospitalized or visit emergency departments during a 60-day episode of care. Among all risk factors, psychological, cognitive, and behavioral symptoms often remain underdiagnosed or undertreated in older adults. Little is known on subgroups of older adults receiving home healthcare services with similar psychological, cognitive, and behavioral symptom profiles and an at-risk subgroup for future hospitalization and emergency department visits. Our cross-sectional study used data from a large, urban home healthcare organization (n = 87,943). Latent class analysis was conducted to identify meaningful subgroups of older adults based on their distinct psychological, cognitive, and behavioral symptom profiles. Adjusted multiple logistic regression was used to understand the association between the latent subgroup and future hospitalization and emergency department visits. Descriptive and inferential statistics were conducted to describe the individual characteristics and to test for significant differences. The three-class model consisted of Class 1: "Moderate psychological symptoms without behavioral issues," Class 2: "Severe psychological symptoms with behavioral issues," and Class 3: "Mild psychological symptoms without behavioral issues." Compared to Class 3, Class 1 patients had 1.14 higher odds and Class 2 patients had 1.26 higher odds of being hospitalized or visiting emergency departments. Significant differences were found in individual characteristics such as age, gender, race/ethnicity, and insurance. Home healthcare clinicians should consider the different latent subgroups of older adults based on their psychological, cognitive, and behavioral symptoms. In addition, they should provide timely assessment and intervention especially to those at-risk for hospitalization and emergency department visits.

Relationship between Symptom Profiles and Endotypes among Patients with Obstructive Sleep Apnea: A Latent Class Analysis.

Rationale: Obstructive sleep apnea (OSA) is a heterogeneous syndrome with various endotypic traits and symptoms. A link among symptoms, endotypes, and disease prognosis has been proposed but remains unsupported by empirical data. Objectives: To link symptom profiles and endotypes by clustering endotypic traits estimated using polysomnographic signals. Methods: We recruited 509 patients with moderate to severe OSA from a single sleep center. Polysomnographic data were collected between May 2020 and January 2022. Endotypic traits, namely arousal threshold, upper airway collapsibility, loop gain, and upper airway muscle compensation, were retrieved using polysomnographic signals during non-rapid eye movement periods. We used latent class analysis to group participants into endotype clusters. Demographic and polysomnographic parameter differences were compared between clusters, and associations between endotype clusters and symptom profiles were examined using logistic regression analyses. Results: Three endotype clusters were identified, characterized by high collapsibility/loop gain, low arousal threshold, and low compensation, respectively. Patients in each cluster exhibited similar demographic characteristics, but those in the high collapsibility/loop gain cluster had the highest proportion of obesity and severe oxygen desaturation observed in polysomnographic studies. The low compensation cluster was characterized by fewer sleepy symptoms and exhibited a lower rate of diabetes mellitus. Compared with the excessively sleepy group, disturbed sleep symptoms were associated with the low arousal threshold cluster (odds ratio, 1.89; 95% confidence interval, 1.16-3.10). Excessively sleepy symptoms were associated with the high collapsibility/loop gain cluster (odds ratio, 2.16; 95% confidence interval, 1.39-3.37) compared with the minimally symptomatic group. Conclusions: Three pathological endotype clusters were identified among patients with moderate to severe OSA, each exhibiting distinct polysomnographic characteristics and clinical symptom profiles.

P12 Unsupervised Clustering and Binary Classification Analysis for Predicting COVID Syndrome (LONG COVID) in US Adults

This study investigated the risk factors of developing COVID Syndrome and identified potential disease profiles that may exist among those who have contracted COVID-19. Data on 13,953 adults who had experienced COVID-19 at any time were analyzed from the 2022 US National Health and Wellness Survey. XGBoost binary classification with 10-fold cross-validation was used to predict long COVID among those who reported experiencing COVID-19 and to extract feature importance. Synthetic minority oversampling technique (SMOTE) was used to address class imbalance in the outcome variable. Variable selection was conducted based on SHAP values. Fifty variables including demographic characteristics, COVID-19 symptoms, comorbidities, and health characteristics were used in the final model. Parameters were tuned using AUC. Among the 2,665 respondents who were diagnosed with long COVID, k-medoids clustering with t-SNE dimensionality reduction was implemented to determine whether distinct symptom profiles exist. Average silhouette score was used to determine the optimal number of clusters. The XGBoost binary classification for predicting long COVID among those with COVID-19 had an AUC of 0.9145, accuracy of 0.9072, sensitivity of 0.9630, specificity of 0.8328, and Brier score of 0.0928. The most important features in predicting long COVID were age, smoking habits, COVID-19 vaccination status, certain COVID-19 symptoms experienced, and certain comorbidities. Among those diagnosed with long COVID, the clustering analysis found nine unique clusters of symptoms. The cluster that experienced the most severe symptoms was older, female, lower income, lower vaccination rate, and had more comorbidities like asthma, chronic bronchitis, and allergies. In a broadly representative US adult population, XGBoost model identified a selection of risk factors for developing long COVID. K-medoids clustering identified clusters of patients that were at risk for developing severe symptoms.

Profiling post-COVID-19 condition across different variants of SARS-CoV-2: a prospective longitudinal study in unvaccinated wild-type, unvaccinated alpha-variant, and vaccinated delta-variant populations

Self-reported symptom studies rapidly increased understanding of SARS-CoV-2 during the COVID-19 pandemic and enabled monitoring of long-term effects of COVID-19 outside hospital settings. Post-COVID-19 condition presents as heterogeneous profiles, which need characterisation to enable personalised patient care. We aimed to describe post-COVID-19 condition profiles by viral variant and vaccination status. In this prospective longitudinal cohort study, we analysed data from UK-based adults (aged 18-100 years) who regularly provided health reports via the Covid Symptom Study smartphone app between March 24, 2020, and Dec 8, 2021. We included participants who reported feeling physically normal for at least 30 days before testing positive for SARS-CoV-2 who subsequently developed long COVID (ie, symptoms lasting longer than 28 days from the date of the initial positive test). We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B.1.1.7), or delta (B.1.617.2 and AY.x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people. We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We identified distinct profiles of symptoms for post-COVID-19 condition within and across variants: four endotypes were identified for infections due to the wild-type variant (in unvaccinated people), seven for the alpha variant (in unvaccinated people), and five for the delta variant (in vaccinated people). Across all variants, we identified a cardiorespiratory cluster of symptoms, a central neurological cluster, and a multi-organ systemic inflammatory cluster. These three main clusers were confirmed in a testing sample. Gastrointestinal symptoms clustered in no more than two specific phenotypes per viral variant. Our unsupervised analysis identified different profiles of post-COVID-19 condition, characterised by differing symptom combinations, durations, and functional outcomes. Our classification could be useful for understanding the distinct mechanisms of post-COVID-19 condition, as well as for identification of subgroups of individuals who might be at risk of prolonged debilitation. UK Government Department of Health and Social Care, Chronic Disease Research Foundation, The Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare, UK National Institute for Health Research, UK Medical Research Council, British Heart Foundation, UK Alzheimer's Society, and ZOE.

Distinct latent symptom profiles in late-onset depressive symptoms in community-dwelling older adults

Objectives To examine the symptom profiles of late-onset depressive symptoms in a sample of older adults. Method The sample included 1,192 participants from the National Alzheimer’s Coordinating Center Data Set. Participants were ≥65 years old, community-dwelling, and without cognitive impairment or a prior history of depression. Depressive symptoms were assessed using the Geriatric Depression Scale, 15-item (GDS-15). Latent class analysis (LCA) was used to identify and group participants based on profiles of depressive symptoms. Results LCA revealed three distinct symptom profiles: (1) an Anhedonia/Amotivation profile with a higher probability of endorsing a combination of low positive emotion and amotivation (6%), (2) an Amotivation/Withdrawal profile with a high probability of endorsing only amotivational depressive symptoms (35%), and (3) an asymptomatic profile with no probability of endorsing any depressive symptoms (59%). Amotivational depressive symptoms were observed across both symptomatic profiles, while depressed mood (e.g. sadness) did not predominantly characterize any profile in this sample. There were also significant differences among symptom profiles in terms of demographic and clinical characteristics. Conclusions Findings highlight the importance of understanding depression at the symptom pattern level. A profile-based diagnostic approach may help improve the recognition of depressive symptoms in older adults.

Sex differences in the temporal dynamics of autistic children’s natural conversations

BackgroundAutistic girls are underdiagnosed compared to autistic boys, even when they experience similar clinical impact. Research suggests that girls present with distinct symptom profiles across a variety of domains, such as language, which may contribute to their underdiagnosis. In this study, we examine sex differences in the temporal dynamics of natural conversations between naïve adult confederates and school-aged children with or without autism, with the goal of improving our understanding of conversational behavior in autistic girls and ultimately improving identification.MethodsForty-five school-aged children with autism (29 boys and 16 girls) and 47 non-autistic/neurotypical (NT) children (23 boys and 24 girls) engaged in a 5-min “get-to-know-you” conversation with a young adult confederate that was unaware of children’s diagnostic status. Groups were matched on IQ estimates. Recordings were time-aligned and orthographically transcribed by trained annotators. Several speech and pause measures were calculated. Groups were compared using analysis of covariance models, controlling for age.ResultsAutistic girls used significantly more words than autistic boys, and produced longer speech segments than all other groups. Autistic boys spoke more slowly than NT children, whereas autistic girls did not differ from NT children in total word counts or speaking rate. Autistic boys interrupted confederates’ speech less often and produced longer between-turn pauses (i.e., responded more slowly when it was their turn) compared to other children. Within-turn pause duration did not differ by group.LimitationsOur sample included verbally fluent children and adolescents aged 6–15 years, so our study results may not replicate in samples of younger children, adults, and individuals who are not verbally fluent. The results of this relatively small study, while compelling, should be interpreted with caution and replicated in a larger sample.ConclusionThis study investigated the temporal dynamics of everyday conversations and demonstrated that autistic girls and boys have distinct natural language profiles. Specifying differences in verbal communication lays the groundwork for the development of sensitive screening and diagnostic tools to more accurately identify autistic girls, and could inform future personalized interventions that improve short- and long-term social communication outcomes for all autistic children.

Symptom Experiences Among Individuals With Prostate Cancer and Their Partners: Influence of Sociodemographic and Cancer Characteristics.

To determine if subgroups of individuals with prostate cancer and their partners could be identified based on their distinct symptom profiles and to identify and characterize subgroups based on sociodemographic and cancer characteristics. 263 individuals with prostate cancer and 263 partners recruited from three academic cancer centers in the Midwest. Latent class analysis was applied to divide individuals into subgroups based on symptom prevalence. Multinomial logistic regression models were used to estimate the prevalence of each symptom, predict subgroup membership, and adjust for direct or indirect effects of covariates on the symptoms. Three distinct subgroups (low, moderate, and high symptoms) were identified among individuals with prostate cancer and partners, respectively. Education and household income of individuals with prostate cancer were associated with different symptom burdens. Partners' household income differentiated among the subgroups. Understanding the influence of sociodemographic and cancer characteristics can inform risk stratification and tailored symptom management interventions.

Three distinct symptom profiles among older adults with severe dementia: A latent class analysis.

Older adults with severe dementia experience multiple symptoms at the end of life. This study aimed to delineate distinct symptom profiles of older adults with severe dementia and to assess their association with older adults' and caregiver characteristics and 1-year mortality among older adults. We used baseline data from a cohort of 215 primary informal caregivers of older adults with severe dementia in Singapore. We identified 10 indicators representing physical, emotional, and functional symptoms, and responsive behaviors, and conducted latent class analysis. We assessed the association between delineated older adults' symptom profiles and their use of potentially burdensome health-care interventions in the past 4months; older adults' 1-year mortality; and caregiver outcomes. We delineated 3 profiles of older adults - primarily responsive behaviors (Class 1; 33%); physical and emotional symptoms with responsive behaviors (Class 2; 20%); and high functional deficits with loss of speech and eye contact (Class 3; 47%). Classes 2 and 3 older adults were more likely to have received a potentially burdensome intervention for symptoms in the past 4 months and have a greater hazard for 1-year mortality. Compared to Class 1, caregivers of Class 2 older adults were more likely to experience adverse caregiver outcomes, that is, higher distress, impact on schedule and health, anticipatory grief, and coping and lower satisfaction with care received (p<0.01 for all). The 3 delineated profiles of older adults can be used to plan or optimize care plans to effectively manage symptoms of older adults and improve their caregivers' outcomes.

Management of Intractable Painful Diabetic Peripheral Neuropathy: The Perspective of a Pain Specialist

Peripheral neuropathy is one of the most common complications of both type 1 and type 2 diabetes. The most common diabetic neuropathy (DN) is distal symmetric polyneuropathy, with characteristic glove- and stocking-like presentation of distal sensory or motor function loss. Because painful DN is associated with increased mortality and morbidity, early recognition and preventive measures are essential. Nevertheless, diagnosing DN or painful DN is challenging, particularly in patients with early and mild neuropathy, and there is no established gold standard. Furthermore, there is no established DN treatment other than improved glycemic control, and only symptomatic management is available for painful DN. However, thanks to health-conscious living, almost one-third of patients with painful DN derive sufficient pain relief with existing pharmacotherapies. These include antidepressants (tricyclic acid, serotonin-norepinephrine reuptake inhibitor), anticonvulsants (calcium-channel blocker, sodium channel blocker), and others (sarpogrelate). A more precise and distinct symptom profile from patients with painful DN may help identify patients more responsive to one treatment versus another. In addition to pharmacological, physical, cognitive, or educational management for painful DN, large randomized clinical trials are needed to identify the most effective minimally invasive interventions. Transcutaneous electrical nerve stimulation, pain scrambler therapy, sympathetic ganglion block, and botulinum toxin injections have been investigated as alternative therapeutic outcomes.

Cortical and subcortical morphological alterations in motor subtypes of Parkinson’s disease

Parkinson’s disease (PD) can be classified into an akinetic-rigid (AR) and a tremor-dominant (TD) subtype based on predominant motor symptoms. Patients with different motor subtypes often show divergent clinical manifestations; however, the underlying neural mechanisms remain unclear. This study aimed to characterize the cortical and subcortical morphological alterations in motor subtypes of PD. T1-weighted MRI images were obtained for 90 patients with PD (64 with the AR subtype and 26 with the TD subtype) and 56 healthy controls (HCs). Cortical surface area, sulcal depth (measured by Freesurfer’s Sulc index), and subcortical volume were computed to identify the cortical and subcortical morphological alterations in the two motor subtypes. Compared with HCs, we found widespread surface area reductions in the AR subtype yet sparse surface area reductions in the TD subtype. We found no significant Sulc change in the AR subtype yet increased Sulc in the right supramarginal gyrus in the TD subtype. The hippocampal volumes in both subtypes were lower than those of HCs. In PD patients, the surface area of left posterior cingulate cortex was positively correlated with Mini-Mental State Examination (MMSE) score, while the Sulc value of right middle frontal gyrus was positively correlated with severity of motor impairments. Additionally, the hippocampal volumes were positively correlated with MMSE and Montreal Cognitive Assessment scores and negatively correlated with severity of motor impairments and Hoehn & Yahr scores. Taken together, these findings may contribute to a better understanding of the neural substrates underlying the distinct symptom profiles in the two PD subtypes.

Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach.

Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles. We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP. Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences. These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.

Subgroups of internet gaming disorder based on addiction-related resting-state functional connectivity.

To identify subgroups of people with internet gaming disorder (IGD) based on addiction-related resting-state functional connectivity and how these subgroups show different clinical correlates and responses to treatment. Secondary analysis of two functional magnetic resonance imaging (fMRI) data sets. Zhejiang province and Beijing, China. One hundred and sixty-nine IGD and 147 control subjects. k-Means algorithmic and support-vector machine-learning approaches were used to identify subgroups of IGD subjects. These groups were examined with respect to assessments of craving, behavioral activation and inhibition, emotional regulation, cue-reactivity and guessing-related measures. Two groups of subjects with IGD were identified and defined by distinct patterns of connectivity in brain networks previously implicated in addictions: subgroup 1 ('craving-related subgroup') and subgroup 2 ('mixed psychological subgroup'). Clustering IGD on this basis enabled the development of diagnostic classifiers with high sensitivity and specificity for IGD subgroups in 10-fold validation (n = 218) and out-of-sample replication (n = 98) data sets. Subgroup 1 is characterized by high craving scores, cue-reactivity during fMRI and responsiveness to a craving behavioral intervention therapy. Subgroup 2 is characterized by high craving, behavioral inhibition and activations scores, non-adaptive emotion-regulation strategies and guessing-task fMRI measures. Subgroups 1 and 2 showed largely opposite functional-connectivity patterns in overlapping networks. There appear to be two subgroups of people with internet gaming disorder, each associated with differing patterns of brain functional connectivity and distinct clinical symptom profiles and gender compositions.

One Size Does Not Fit All: Variability in Urinary Symptoms and Microbial Communities

The discovery of the urinary microbiome prompted researchers to begin characterizing microbiota associated with various health and disease states; however, the etiology of bladder infections, the most common urinary tract infection (UTI), is still simplistically attributed to the invasion of a uropathogen, mainly Escherichia coli, without regard for the resident microbial community. In addition, the clinical variability of UTI symptoms remains poorly understood. Very little research has been done to investigate the role of baseline microbial communities in development and resolution of UTI symptoms. The goal of this study was to identify associations between urinary microbiota and lower urinary tract symptoms profiles in adult women with UTI symptoms. This is a secondary analysis of a previously published IRB-approved study that included 225 women who reported having acute UTI symptoms, submitted catheterized urine specimens for analysis by an enhanced urine culture method and were assessed for symptom resolution 7-10 days after receiving culture-directed antibiotic treatment. In this UTI population, we identified six distinct symptom profiles, termed symptotypes, that were characterized by varying severity and degree of bother of certain lower urinary tract symptoms. These symptotypes were not associated with urotype or the presence of specific microbes. In participants with pain on presentation, the presence of non-E. coli and non-uropathogens was associated with persistence of symptoms at follow up; however, this was not true for those with E. coli urotype. These data suggest that the presence of E. coli may not account for the underlying cause of typical UTI symptoms; instead, co-existence of a uropathogen in the context of the existing urinary microbiota and the host may be responsible for these symptom profiles.

Identification of Distinct Symptom Profiles in Cancer Patients Using a Pre-Specified Symptom Cluster.

Pain, fatigue, sleep disturbance, and depression often co-occur in oncology patients and negatively impact quality of life (QOL). Study purposes were to identify subgroups of patients with distinct symptom profiles based on their experiences with a pre-specified symptom cluster (i.e., pain, fatigue, depression, sleep disturbance) and to identify demographic, clinical, and symptom characteristics and QOL outcomes associated with each distinct profile. Patients with breast, lung, gastrointestinal, and gynecologic cancers (n=1340) were recruited from outpatient clinics during their first or second cycle of chemotherapy. They completed valid and reliable measures of pain, fatigue, sleep disturbance. depression, and QOL prior to their next dose of chemotherapy. Latent class profile analysis was used to identify the patient subgroups. Differences among the profiles were evaluated using parametric and non-parametric tests. Three distinct profiles were identified (i.e., Low (44.0%), Moderate (45.1%), High (10.8%). Compared to Low class, Moderate and High classes were younger and more likely to be female. Compared to the other two classes, High class was less likely to be married/partnered and employed, more likely to have a lower income and childcare responsibilities, had lower functional status, a higher body mass index, and exercised less. For both QOL scales, differences in subscale and total scores followed the same pattern (Low>Moderate>High). Over 55% of patients undergoing chemotherapy had a moderate to high symptom burden associated with these four common co-occurring symptoms. Multimodal interventions are needed to decrease symptom burden and improve QOL outcomes in these patients.

Higher stress and symptom severity are associated with worse depressive symptom profiles in patients receiving chemotherapy.

In a sample of oncology patients, identify subgroups of patients with distinct depressive symptom profiles and evaluate for differences in demographic and clinical characteristics, levels of stress and resilience, and the severity of common co-occurring symptoms. Patients (n=1327) had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; and were scheduled to receive at least two additional cycles of chemotherapy. Demographic and clinical characteristics, stress, resilience, and co-occurring symptoms were evaluated at enrollment. Depressive symptoms were evaluated using the Center for Epidemiological Studies-Depression (CES-D) scale a total of six times over two cycles of chemotherapy. Latent profile analysis (LPA) was used to identify subgroups of patients (i.e., latent classes) with distinct depressive symptom profiles using the six CES-D scores. Based on the findings from the LPA, 47.3% of the patients were classified as "None"; 33.6% as "Subsyndromal"; 13.8% as "Moderate"; and 5.3% as "High". Compared to None class, patients in the Subsyndromal, Moderate, and High classes had a lower functional status, a higher comorbidity burden, and a self-reported diagnosis of depression or back pain. Those patients with higher levels of depressive symptoms reported higher levels of stress, lower levels of resilience, and increased severity of co-occurring symptoms. Inter-individual variability in depressive symptoms was associated with demographic and clinical characteristics, multiple types of stress and levels of resilience, as well as with the increased severity of multiple co-occurring symptoms. The risk factors associated with worse depressive symptom profiles can assist clinicians to identify high risk patients and initiate more timely supportive care interventions.

Symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil

BackgroundCOVID-19 has shown a broad clinical spectrum, ranging from asymptomatic to mild, moderate, and severe infections. Many symptoms have already been identified as typical of COVID-19, but few studies show how they can be useful in identifying clusters of patients with different severity of illness. This interpretation may help to recognize the different profiles of symptoms of COVID-19 expressed in a population at certain time. The aim of this study was to identify symptom-based clusters of hospitalized patients with severe acute respiratory illness by SARS-CoV-2 in Brazil. The clusters were evaluated based on sociodemographic characteristics, admission to the Intensive Care Unit (ICU), use of respiratory support, and outcome. MethodsThe Multiple Correspondence Analysis (MCA)-based cluster analysis was applied to symptoms presented before admission. Pearson's chi-square test was used to compare the proportions of symptoms between the clusters and to examine differences in the calculated rates for the following variables: sex, age group, race, Brazilian region, use of respiratory support, admission to the ICU and outcome. ResultsThree COVID-19 clusters with distinct symptom profiles were identified by MCA-based cluster analysis. Cluster 1 had the mildest severity profile, with the lowest frequencies for most symptoms investigated. Cluster 2 had a severe respiratory profile, with the highest frequencies of patients with dyspnea, respiratory discomfort and O2 saturation< 95%. Cluster 2 was also the most prevalent in all Brazilian regions and had the highest percentages of patients who used invasive respiratory support (27.4%) (p-value<0.001), were admitted to the ICU (42.6%) (p -value<0.001) and died (39.0%) (p-value<0.001). Cluster 3 had a prominent profile of gastrointestinal symptoms. ConclusionsThe study identified three distinct COVID-19 clusters based on the symptoms presented by patients with severe acute respiratory illness by SARS-CoV-2, but without distinction in their prevalence in the Brazilian regions.

Differences in Circulating Extracellular Vesicle and Soluble Cytokines in Older Versus Younger Breast Cancer Patients With Distinct Symptom Profiles.

Because extracellular vesicle (EV)-associated cytokines, both encapsulated and surface bound, have been associated with symptom severity, and may vary over the lifespan, they may be potential biomarkers to uncover underlying mechanisms of various conditions. This study evaluated the associations of soluble and EV-associated cytokine concentrations with distinct symptom profiles reported by 290 women with breast cancer prior to surgery. Patients were classified into older (≥60 years, n = 93) and younger (< 60 years, n = 197) cohorts within two previously identified distinct symptom severity profiles, that included pain, depressive symptoms, sleep disturbance, and fatigue (i.e., High Fatigue Low Pain and All Low). EVs were extracted using ExoQuick. Cytokine concentrations were determined using Luminex multiplex assay. Mann Whitney U test evaluated the differences in EV and soluble cytokine levels between symptom classes and between and within the older and younger cohorts adjusting for Karnofsky Performance Status (KPS) score, body mass index (BMI), and stage of disease. Partial correlation analyses were run between symptom severity scores and cytokine concentrations. Results of this study suggest that levels of cytokine concentrations differ between EV and soluble fractions. Several EV and soluble pro-inflammatory cytokines had positive associations with depressive symptoms and fatigue within both age cohorts and symptom profiles. In addition, in the older cohort with High Fatigue Low Pain symptom profile, EV GM-CSF concentrations were higher compared to the All Low symptom profile (p < 0.05). Albeit limited by a small sample size, these exploratory analyses provide new information on the association between cytokines and symptom profiles of older and younger cohorts. Of note, unique EV-associated cytokines were found in older patients and in specific symptom classes. These results suggest that EVs may be potential biomarker discovery tools. Understanding the mechanisms that underlie distinct symptom class profiles categorized by age may inform intervention trials and offer precision medicine approaches.

Profile Comparison of Patient-Reported and Proxy-Reported Symptoms in Pediatric Patients With Cancer Receiving Chemotherapy

The variability in individual symptom and adverse event reporting between pediatric patient-reports and proxy-reports is widely reported. However, the question of whether symptom profiles based on reports from children with cancer and their caregivers are similar or disparate have not yet been studied. To compare proxy symptom reports with patient self-reports to assess alignment. A multicenter cohort study was conducted from October 2016 to December 2018 from data collected at 9 pediatric cancer centers. Participants were a convenience sample of family caregivers or proxies of children aged 7 to 18 years who had received disease-directed oncology treatment in the form of chemotherapy for at least 1 month. Data were analyzed identifying clusters of individuals (ie, latent profiles) based on various responses (ie, indicators) in August 2021. The children of proxy participants received upfront chemotherapy. Children and proxies completed Patient-Reported Outcomes Measurement Information System (PROMIS) surveys at 2 time points: within 72 hours preceding treatment initiation and following the course of chemotherapy. The latent profile analysis methods were applied to caregiver-proxy reports of PROMIS Pediatric symptom and function measures (anxiety, depressive symptoms, pain interference, fatigue, psychological stress, and physical function-mobility). The instrument categorized respondents as high symptom suffering, medium symptom suffering, and low symptom suffering (hereafter, high, medium, and low symptom groups, respectively). Of 580 approached proxies, 431 (368 [85.00%] were female) identified as legal guardians of children aged 7 to 18 years with a first cancer diagnosis (mean [SD] age, 13.03 [3.40] years; 235 [54.65%] were male). Proxy reports of children's experiences based on the 5 proxy PROMIS measures comprised 3 distinct symptom profiles. The most common proxy assessments of children's experiences were the moderate symptom groups (45.7% [197 of 431]) and the low symptom groups profiles (40.1% [173 of 431]). A high symptom groups profile emerged which represented 14.2% (61 of 431) of proxy assessments. The number of profiles and observed distribution of profile membership was similar between child and proxy reports. Proxy reports of individual symptoms generally recorded higher scores than child reports; however, no significant difference was observed between proxies and child profile model results for the PROMIS measures. Results of this cohort study suggest that, at the level of symptom severity profile, proxy caregiver reports may approximate the children's reports and may serve as a guide to care when the child is not able to self-report.

Profiles of post-migration stressors and mental health in refugees: A latent class analysis

Studies have documented the mental health effects of pre-migration trauma on resettled refugees and identified distinct psychological symptom profiles using person centred statistical techniques (e.g., latent class analysis; LCA). These techniques have advanced our understanding of the complex presentation of trauma and psychopathology in refugees. The current study employs LCA to examine patterns of exposure to stressors including post-migration stressors, allowing us to identify patterns of post-migration stress exposure and their association with mental health outcomes. Participants were 1085 Arabic, Farsi, Tamil, or English speaking adult refugees. Pre-migration trauma, post-migration stressors, PTSD, depression, anger, and functional impairment was measured. LCA was conducted to identify distinct classes of post-migration stressors and associations with mental health and adjustment in the resettlement environment. Latent class analysis revealed 5 classes of participants: high difficulties class (7.2%), immigration fear class (14.4%), social disconnection class (17.3%), moderate difficulties class (28.9%) and a low difficulties class (32.3%). Each of the five classes shared commonalities in addition to key differences associated with specific demographic characteristics and psychopathology. Post-migration stressors appear to map onto distinct profiles, which uniquely contribute to functional impairment and mental health outcomes in refugees. These findings have substantial implications for public health and social services working with resettled refugee communities.

A latent class analysis of intermittent explosive disorder symptoms

Intermittent explosive disorder (IED), the sole diagnosis in the DSM-5 for which the cardinal symptom is recurrent affective aggressive outbursts, is a common and substantially impairing disorder. IED is also associated with several cognitive and affective impairments. However, little is known about the heterogeneity of the disorder and how this may correspond to aggression severity and related adverse outcomes. The current study employed a latent class analysis (LCA) among individuals diagnosed with lifetime DSM-5 IED to derive distinct subgroups that differed in the quality and/or frequency of cognitive-affective symptoms. These subgroups were then externally validated on a number of adverse outcomes. Statistical and clinical indicators supported a four-class model. Classes were distinguished mainly by the level of emotion dysregulation participants endorsed, with two moderate emotion dysregulation classes differing on their emotional information processing and impulsive tendencies. The external validation analysis revealed that classes differed in terms of various adverse outcomes (e.g., interpersonal problems, life satisfaction, suicide risk). Overall, the present study suggests distinct cognitiveaffective symptom profiles among those with IED that differ meaningfully with regard to risk for adverse outcomes. These findings provide evidence of the heterogeneity within IED and may suggest a more personalized therapeutic approach to patients with IED.