Exploring the Clinical Characteristics of COVID-19 Clusters Identified Using Factor Analysis of Mixed Data-Based Cluster Analysis

Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes

BACKGROUND

AKI is a heterogeneous syndrome. Current subphenotyping approaches have only used limited laboratory data to understand a much more complex condition.

METHODS

We focused on patients with AKI from the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI (ASSESS-AKI). We used hierarchical clustering with Ward linkage on biomarkers of inflammation, injury, and repair/health. We then evaluated clinical differences between subphenotypes and examined their associations with cardiorenal events and death using Cox proportional hazard models.

RESULTS

We included 748 patients with AKI: 543 (73%) of them had AKI stage 1, 112 (15%) had AKI stage 2, and 93 (12%) had AKI stage 3. The mean age (±SD) was 64 (13) years; 508 (68%) were men; and the median follow-up was 4.7 (Q1: 2.9, Q3: 5.7) years. Patients with AKI subphenotype 1 ( N =181) had the highest kidney injury molecule (KIM-1) and troponin T levels. Subphenotype 2 ( N =250) had the highest levels of uromodulin. AKI subphenotype 3 ( N =159) comprised patients with markedly high pro-brain natriuretic peptide and plasma tumor necrosis factor receptor-1 and -2 and low concentrations of KIM-1 and neutrophil gelatinase-associated lipocalin. Finally, patients with subphenotype 4 ( N =158) predominantly had sepsis-AKI and the highest levels of vascular/kidney inflammation (YKL-40, MCP-1) and injury (neutrophil gelatinase-associated lipocalin, KIM-1). AKI subphenotypes 3 and 4 were independently associated with a higher risk of death compared with subphenotype 2 and had adjusted hazard ratios of 2.9 (95% confidence interval, 1.8 to 4.6) and 1.6 (95% confidence interval, 1.01 to 2.6, P = 0.04), respectively. Subphenotype 3 was also independently associated with a three-fold risk of CKD and cardiovascular events.

CONCLUSIONS

We discovered four AKI subphenotypes with differing clinical features and biomarker profiles that are associated with longitudinal clinical outcomes.

Temporomandibular disorders and neck pain in primary headache patients: a retrospective machine learning study

OBJECTIVES

To evaluate the linkage underpinning different clinical conditions as painful TMD and neck pain in patients affected by primary headaches.

MATERIALS AND METHODS

In this machine learning study, data from medical records of patients with headaches as migraine, tension-type headache (TTH) and other primary ones, referring to a University Hospital over a 10-year period were analysed. VAS was used to evaluate the intensity of the TMD and neck pain. Moreover, the magnetic resonance imaging was used to supplement the clinical data.

RESULTS

A total of 300 patients (72 male, 228 female), mean aged 37.78 ± 5.11 years, were included. Higher TMD and neck pain VAS in migraine patients were reported. The machine learning analysis focussed on type of primary headache demonstrated that a higher TMD VAS was correlated to migraine, whereas a higher neck pain VAS was correlated to TTH or migraine. Concerning the TMD type, arthrogenous and mixed TMD were correlated to mild-moderate TMD pain (depending on neck pain intensity), whereas myogenic TMD was correlated to moderate-severe TMD pain.

CONCLUSIONS

Machine-learning approach highlighted the complexity of diagnosis process and demonstrated that neck pain might be an influential variable on the belonging to different group of headaches in TMD patients.

Longitudinal Cluster Analysis of Hemodialysis Patients with COVID-19 in the Pre-Vaccination Era

Coronavirus disease 2019 (COVID-19) in hemodialysis patients (HD) is characterized by heterogeneity of clinical presentation and outcomes. To stratify patients, we collected clinical and laboratory data in two cohorts of HD patients at COVID-19 diagnosis and during the following 4 weeks. Baseline and longitudinal values were used to build a linear mixed effect model (LME) and define different clusters. The development of the LME model in the derivation cohort of 17 HD patients (66.7 ± 12.3 years, eight males) allowed the characterization of two clusters (cl1 and cl2). Patients in cl1 presented a prevalence of females, higher lymphocyte count, and lower levels of lactate dehydrogenase, C-reactive protein, and CD8 + T memory stem cells as a possible result of a milder inflammation. Then, this model was tested in an independent validation cohort of 30 HD patients (73.3 ± 16.3 years, 16 males) assigned to cl1 or cl2 (16 and 14 patients, respectively). The cluster comparison confirmed that cl1 presented a milder form of COVID-19 associated with reduced disease activity, hospitalization, mortality rate, and oxygen requirement. Clustering analysis on longitudinal data allowed patient stratification and identification of the patients at high risk of complications. This strategy could be suitable in different clinical settings.

Clinical-epidemiologic variation in patients treated in the first and second wave of COVID-19 in Lambayeque, Peru: A cluster analysis

OBJECTIVES

To identify differences in the clinical and epidemiologic characteristics of patients during the first and second waves of the COVID-19 pandemic at the EsSalud Lambayeque health care network, Peru.

METHODS

An analytical cross-sectional study of 53,912 patients enrolled during the first and second waves of COVID-19 was conducted. Cluster analysis based on clustering large applications (CLARA) was applied to clinical-epidemiologic data presented at the time of care. The two pandemic waves were compared using clinical-epidemiologic data from epidemiologic surveillance.

RESULTS

Cluster analysis identified four COVID-19 groups with a characteristic pattern. Cluster 1 included the largest number of participants in both waves, and the participants were predominantly female. Cluster 2 included patients with gastrointestinal, respiratory, and systemic symptoms. Cluster 3 was the "severe" cluster, characterized by older adults and patients with dyspnea or comorbidities (cardiovascular, diabetes, obesity). Cluster 4 included asymptomatic, pregnant, and less severe patients. We found differences in all clinical-epidemiologic characteristics according to the cluster to which they belonged.

CONCLUSION

Using cluster analysis, we identified characteristic patterns in each group. Respiratory, gastrointestinal, dyspnea, anosmia, and ageusia symptoms were higher in the second COVID-19 wave than the first COVID-19 wave.

Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study

BACKGROUND

Post-acute sequelae of COVID-19 (PASC) includes a heterogeneous group of patients with variable symptomatology, who may respond to different therapeutic interventions. Identifying phenotypes of PASC and therapeutic strategies for different subgroups would be a major step forward in management.

METHODS

In a prospective cohort study of patients hospitalized with COVID-19, 12-month symptoms and quantitative outcome metrics were collected. Unsupervised hierarchical cluster analyses were performed to identify patients with: (1) similar symptoms lasting ≥4 weeks after acute SARS-CoV-2 infection, and (2) similar therapeutic interventions. Logistic regression analyses were used to evaluate the association of these symptom and therapy clusters with quantitative 12-month outcome metrics (modified Rankin Scale, Barthel Index, NIH NeuroQoL).

RESULTS

Among 242 patients, 122 (50%) reported ≥1 PASC symptom (median 3, IQR 1-5) lasting a median of 12-months (range 1-15) post-COVID diagnosis. Cluster analysis generated three symptom groups: Cluster1 had few symptoms (most commonly headache); Cluster2 had many symptoms including high levels of anxiety and depression; and Cluster3 primarily included shortness of breath, headache and cognitive symptoms. Cluster1 received few therapeutic interventions (OR 2.6, 95% CI 1.1-5.9), Cluster2 received several interventions, including antidepressants, anti-anxiety medications and psychological therapy (OR 15.7, 95% CI 4.1-59.7) and Cluster3 primarily received physical and occupational therapy (OR 3.1, 95%CI 1.3-7.1). The most severely affected patients (Symptom Cluster 2) had higher rates of disability (worse modified Rankin scores), worse NeuroQoL measures of anxiety, depression, fatigue and sleep disorder, and a higher number of stressors (all P<0.05). 100% of those who received a treatment strategy that included psychiatric therapies reported symptom improvement, compared to 97% who received primarily physical/occupational therapy, and 83% who received few interventions (P = 0.042).

CONCLUSIONS

We identified three clinically relevant PASC symptom-based phenotypes, which received different therapeutic interventions with varying response rates. These data may be helpful in tailoring individual treatment programs.

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

BACKGROUND

COVID-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.

METHODS

The 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.

RESULTS

Three 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.

CONCLUSIONS

The 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.

Anti-Ma2 Antibody-Associated Paraneoplastic Neurological Syndromes: A Pilot Study

Paraneoplastic neurologic syndromes (PNSs) are a heterogeneous group of disorders caused by the remote effects of cancer with immune-mediated pathogenesis. Anti-Ma2 antibody was defined as one of the well-characterized onconeural antibodies that could help establish a definite PNS diagnosis. We aimed to report and explore patients with anti-Ma2 antibody-associated paraneoplastic neurologic syndrome (Ma2-PNS) who frequently exhibit sensorimotor neuropathy (SMN) using a new method of factor analysis of mixed data (FAMD). Clinical data from a case series of eight patients with definite diagnoses were retrospectively reviewed. FAMD conducted further analyses with a comprehensive visualization in R software. Our cohort, with a predominance of females (5/8), presented more frequently with SMN (4/8), followed by limbic encephalitis (LE) (3/8). Two patients with LE were found to have a testicular germ-cell tumor and a thymoma, respectively. In addition, a patient who developed chronic SMN was diagnosed with multiple myeloma (MM) involving multiple organs. FAMD exhibited the overall features into a two-dimensional coordinate and located each individual into their corresponding position with high relevance. It provided a clue for determining their potential relationships and predictors. Our findings indicated that Ma2-PNS could frequently involve the peripheral nervous system, MM might be one of its associated cancers with a presentation of chronic SMN, and FAMD might be a clinically valuable tool.