Dataset Information

Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers.

ABSTRACT:

Background

COVID-19 symptom reports describe varying levels of disease severity with differing periods of recovery and symptom trajectories. Thus, there are a multitude of disease and symptom characteristics clinicians must navigate and interpret to guide care.

Objective

To find natural groups of patients with similar constellations of post-acute sequelae of COVID-19 (PASC) symptoms.

Design

Cohort SETTING: Outpatient COVID-19 recovery clinic with patient referrals from 160 primary care clinics serving 36 counties in Texas.

Patients

Adult patients seeking COVID-19 recovery clinic care between November 15, 2020, and July 31, 2021, with laboratory-confirmed mild (not hospitalized), moderate (hospitalized), or severe (hospitalized with critical care) COVID-19.

Main measures

Demographics, COVID illness onset, and duration of persistent PASC symptoms via semi-structured medical assessments.

Key results

Four hundred forty-one patients (mean age 51.5 years; 295 [66.9%] women; 99 [22%] Hispanic, and 170 [38.5%] non-White, racial minority) met inclusion criteria. Using a k-medoids algorithm, we found that PASC symptoms cluster into two distinct groups: neuropsychiatric (N = 186) (e.g., subjective cognitive dysfunction) and pulmonary (N = 255) (e.g., dyspnea, cough). The neuropsychiatric cluster had significantly higher incidences of otolaryngologic (X² = 14.3, p < 0.001), gastrointestinal (X² = 6.90, p = 0.009), neurologic (X² = 441, p < 0.001), and psychiatric sequelae (X² = 40.6, p < 0.001) with more female (X² = 5.44, p = 0.020) and younger age (t = 2.39, p = 0.017) patients experiencing longer durations of PASC symptoms before seeking care (t = 2.44, p = 0.015). Patients in the pulmonary cluster were more often hospitalized for COVID-19 (X² = 3.98, p = 0.046) and had significantly higher comorbidity burden (U = 20800, p = 0.019) and pulmonary sequelae (X² = 13.2, p < 0.001).

Conclusions

Health services clinic data from a large integrated health system offers insights into the post-COVID symptoms associated with care seeking for sequelae that are not adequately managed by usual care pathways (self-management and primary care clinic visits). These findings can inform machine learning algorithms, primary care management, and selection of patients for earlier COVID-19 recovery referral.

Trial registration

N/A.

SUBMITTER: Danesh V

PROVIDER: S-EPMC9663188 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers.

Danesh Valerie V Arroliga Alejandro C AC Bourgeois James A JA Boehm Leanne M LM McNeal Michael J MJ Widmer Andrew J AJ McNeal Tresa M TM Kesler Shelli R SR

Journal of general internal medicine 20221114 2

<h4>Background</h4>COVID-19 symptom reports describe varying levels of disease severity with differing periods of recovery and symptom trajectories. Thus, there are a multitude of disease and symptom characteristics clinicians must navigate and interpret to guide care.<h4>Objective</h4>To find natural groups of patients with similar constellations of post-acute sequelae of COVID-19 (PASC) symptoms.<h4>Design</h4>Cohort SETTING: Outpatient COVID-19 recovery clinic with patient referrals from 160 ...[more]

PMID: 36376627

Similar Datasets

Project description:Background and objectiveCOVID-19 manifests with a broad range of symptoms. This study investigates whether clusters of respiratory, gastrointestinal, or neurological symptoms can be used to diagnose COVID-19.MethodsWe surveyed symptoms of 483 subjects who had completed COVID-19 laboratory tests in the last 30 days. The survey collected data on demographic characteristics, self-reported symptoms for different types of infections within 14 days of onset of illness, and self-reported COVID-19 test results. Robust LASSO regression was used to create 3 nested models. In all 3 models, the response variable was the COVID-19 test result. In the first model, referred to as the "main effect model," the independent variables were demographic characteristics, history of chronic symptoms, and current symptoms. The second model, referred to as the "hierarchical clustering model," added clusters of variables to the list of independent variables. These clusters were established through hierarchical clustering. The third model, referred to as the "interaction-terms model," also added clusters of variables to the list of independent variables; this time clusters were established through pairwise and triple-way interaction terms. Models were constructed on a randomly selected 80% of the data and accuracy was cross-validated on the remaining 20% of the data. The process was bootstrapped 30 times. Accuracy of the 3 models was measured using the average of the cross-validated area under the receiver operating characteristic curves (AUROCs).ResultsIn 30 bootstrap samples, the main effect model had an AUROC of 0.78. The hierarchical clustering model had an AUROC of 0.80. The interaction-terms model had an AUROC of 0.81. Both the hierarchical cluster model and the interaction model were significantly different from the main effect model (α = .04). Patients with different races/ethnicities, genders, and ages presented with different symptom clusters.ConclusionsUsing clusters of symptoms, it is possible to more accurately diagnose COVID-19 among symptomatic patients.

Project description:BackgroundAround 10% of people infected by SARS-COV-2 report symptoms that persist longer than 3 months. Little has been reported about sex differences in symptoms and clustering over time of non-hospitalised patients in primary care settings.MethodsThis is a descriptive study of a cohort of mainly non-hospitalized patients with a persistence of symptoms longer than 3 months from the clinical onset in co-creation with the Long Covid Catalan affected group using an online survey. Recruitment was from March 2020 to June 2021. Exclusion criteria were being admitted to an ICU, < 18 years of age and not living in Catalonia. We focused on 117 symptoms gathered in 18 groups and performed cluster analysis over the first 21 days of infection, at 22-60 days, and ≥ 3 months.ResultsWe analysed responses of 905 participants (80.3% women). Median time between symptom onset and the questionnaire response date was 8.7 months. General symptoms (as fatigue) were the most prevalent with no differences by sex, age, or wave although its frequency decreased over time (from 91.8 to 78.3%). Dermatological (52.1% in women, 28.5% in men), olfactory (34.9% women, 20.9% men) and neurocognitive symptoms (70.1% women, 55.8% men) showed the greatest differences by sex. Cluster analysis showed five clusters with a predominance of Taste & smell (24.9%) and Multisystemic clusters (26.5%) at baseline and _Multisystemic (34.59%) and Heterogeneous (24.0%) at ≥3 months. The Multisystemic cluster was more prevalent in men. The Menstrual cluster was the most stable over time, while most transitions occurred from the Heterogeneous cluster to the Multisystemic cluster and from Taste & smell to Heterogeneous.ConclusionsGeneral symptoms were the most prevalent in both sexes at three-time cut-off points. Major sex differences were observed in dermatological, olfactory and neurocognitive symptoms. The increase of the Heterogeneous cluster might suggest an adaptation to symptoms or a non-specific evolution of the condition which can hinder its detection at medical appointments. A carefully symptom collection and patients' participation in research may generate useful knowledge about Long Covid presentation in primary care settings.

Project description:BackgroundIn the Netherlands, the prevalence of post COVID-19 condition is estimated at 12.7% at 90-150 days after SARS-CoV-2 infection. This study aimed to determine the occurrence of fatigue and other symptoms, to assess how many patients meet the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) criteria, to identify symptom-based clusters within the P4O2 COVID-19 cohort and to compare these clusters with clusters in a ME/CFS cohort.MethodsIn this multicentre, prospective, observational cohort in the Netherlands, 95 post COVID-19 patients aged 40-65 years were included. Data collection at 3-6 months after infection included demographics, medical history, questionnaires, and a medical examination. Follow-up assessments occurred 9-12 months later, where the same data were collected. Fatigue was determined with the Fatigue Severity Scale (FSS), a score of ≥ 4 means moderate to high fatigue. The frequency and severity of other symptoms and the percentage of patients that meet the ME/CFS criteria were assessed using the DePaul Symptom Questionnaire-2 (DSQ-2). A self-organizing map was used to visualize the clustering of patients based on severity and frequency of 79 symptoms. In a previous study, 337 Dutch ME/CFS patients were clustered based on their symptom scores. The symptom scores of post COVID-19 patients were applied to these clusters to examine whether the same or different clusters were found.ResultsAccording to the FSS, fatigue was reported by 75.9% of the patients at 3-6 months after infection and by 57.1% of the patients 9-12 months later. Post-exertional malaise, sleep disturbances, pain, and neurocognitive symptoms were also frequently reported, according to the DSQ-2. Over half of the patients (52.7%) met the Fukuda criteria for ME/CFS, while fewer patients met other ME/CFS definitions. Clustering revealed specific symptom patterns and showed that post COVID-19 patients occurred in 11 of the clusters that have been observed in the ME/CFS cohort, where 2 clusters had > 10 patients.ConclusionsThis study shows persistent fatigue and diverse symptomatology in post COVID-19 patients, up to 12-18 months after SARS-CoV-2 infection. Clustering showed that post COVID-19 patients occurred in 11 of the clusters that have been observed in the ME/CFS cohort.

Dataset Information

Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers.

Background

Objective

Design

Patients

Main measures

Key results

Conclusions

Trial registration

Publications

Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets