Dataset Information

A clustering approach to identify severe bronchiolitis profiles in children.

ABSTRACT:

Objective

Although bronchiolitis is generally considered a single disease, recent studies suggest heterogeneity. We aimed to identify severe bronchiolitis profiles using a clustering approach.

Methods

We analysed data from two prospective, multicentre cohorts of children younger than 2 years hospitalised with bronchiolitis, one in the USA (2007-2010 winter seasons, n=2207) and one in Finland (2008-2010 winter seasons, n=408). Severe bronchiolitis profiles were determined by latent class analysis, classifying children based on clinical factors and viral aetiology.

Results

In the US study, four profiles were identified. Profile A (12%) was characterised by history of wheezing and eczema, wheezing at the emergency department (ED) presentation and rhinovirus infection. Profile B (36%) included children with wheezing at the ED presentation, but, in contrast to profile A, most did not have history of wheezing or eczema; this profile had the largest probability of respiratory syncytial virus infection. Profile C (34%) was the most severely ill group, with longer hospital stay and moderate-to-severe retractions. Profile D (17%) had the least severe illness, including non-wheezing children with shorter length of stay. Two of these profiles (A and D) were replicated in the Finnish cohort; a third group ('BC') included Finnish children with characteristics of profiles B and/or C in the US population.

Conclusions

Several distinct clinical profiles (phenotypes) were identified by a clustering approach in two multicentre studies of children hospitalised for bronchiolitis. The observed heterogeneity has important implications for future research on the aetiology, management and long-term outcomes of bronchiolitis, such as future risk of childhood asthma.

SUBMITTER: Dumas O

PROVIDER: S-EPMC5036390 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A clustering approach to identify severe bronchiolitis profiles in children.

Dumas Orianne O Mansbach Jonathan M JM Jartti Tuomas T Hasegawa Kohei K Sullivan Ashley F AF Piedra Pedro A PA Camargo Carlos A CA

Thorax 20160623 8

<h4>Objective</h4>Although bronchiolitis is generally considered a single disease, recent studies suggest heterogeneity. We aimed to identify severe bronchiolitis profiles using a clustering approach.<h4>Methods</h4>We analysed data from two prospective, multicentre cohorts of children younger than 2 years hospitalised with bronchiolitis, one in the USA (2007-2010 winter seasons, n=2207) and one in Finland (2008-2010 winter seasons, n=408). Severe bronchiolitis profiles were determined by latent ...[more]

PMID: 27339060

Similar Datasets

Project description:ObjectivesBronchiolitis is a common indication for mechanical ventilation in the PICU. Both bronchiolitis and invasive mechanical ventilation may cause adverse long-term pulmonary outcomes. This study investigates children with a history of invasive mechanical ventilation for bronchiolitis, addressing: 1) the extent, 2) potential explanatory factors, and 3) possible impact on daily life activities of adverse long-term pulmonary outcomes.DesignSingle-center cohort study.SettingOutpatient PICU follow-up clinic.PatientsChildren 6-12 years old with a history of invasive mechanical ventilation for bronchiolitis (age < 2 yr).InterventionsNone.Measurements and main resultsLong-term pulmonary outcomes were assessed by a standardized questionnaire and by spirometry. Nineteen out of 74 included children (26%) had adverse long-term pulmonary outcomes, of whom the majority had asthma (14/74, 19%). By logistic regression analysis, we assessed whether background characteristics and PICU-related variables were associated with long-term pulmonary outcomes. In general, we failed to identify any explanatory factors associated with adverse long-term pulmonary outcomes. Nonetheless, atopic disease in family and longer duration of invasive mechanical ventilation (days) were associated with greater odds of having asthma at follow-up (odds ratio, 6.4 [95% CI, 1.2-36.0] and 1.3 [95% CI, 1.0-1.7], respectively). Adverse pulmonary outcome at follow-up was associated with more frequent use of pulmonary medication after PICU discharge. In comparison with those without adverse pulmonary outcomes, we did not identify any difference in frequency of sports performance or school absenteeism.ConclusionsIn this single-center cohort, one-quarter of the children attending follow-up with a history of invasive mechanical ventilation for bronchiolitis had adverse, mostly previously undetected, long-term pulmonary outcomes at 6-12 years. Atopic disease in family and longer duration of invasive mechanical ventilation were associated with presence of asthma. The presence of adverse pulmonary outcomes was associated with more frequent use of pulmonary medication after PICU discharge.

Project description:BackgroundBronchiolitis is the primary infection of the lower respiratory tract in children under 2 years of age. Although it is generally considered a single nosological entity, recent studies suggested remarkable clinical heterogeneity. To date, no studies have identified classes of children with bronchiolitis within the Italian population. This study aimed to identify discrete profiles of Italian children hospitalized with bronchiolitis using a clustering approach and to compare findings with those obtained in international cohorts.MethodsThis was a retrospective single-centre study conducted on children aged ≤2 years hospitalised with bronchiolitis (n = 401) at the Department of Infectious Diseases and the University Department of General Pediatrics in "Giovanni Di Cristina" Pediatric Hospital of Palermo, Italy, between November 2012 and May 2019. Bronchiolitis profiles were determined by latent class analysis, classifying children based on clinical characteristics at admission and viral aetiology.ResultsThree profiles were identified. Class 1 (49%) was composed of 45% male children; all children were aged ≤6 months at hospitalization; 77% were infected with RSV; 100% had respiratory distress, 11% had apnea and none had cough. Class 2 (77%) was mainly composed of male subjects (51%); 19% were aged > 6 months at admission; 37% were infected with RSV; 12% had respiratory distress, 5% had apnea and 90% had cough. Class 3 (19%) included the largest proportion of male subjects (94%) and was mostly composed of children aged > 6 months at the time of admission (68%); 70% had cough, 12% showed respiratory distress and none presented with apnoea. Children in Class 1 were more frequently born near the epidemic season (p = 0.028); breastfeeding duration was significantly longer for children in Class 3 (p = 0.004).ConclusionsThe study identified distinct clinical profiles of bronchiolitis by a clustering approach in a single-centre study of children hospitalised for bronchiolitis in Italy. The three bronchiolitis profiles share some similarities with those identified in international studies using the same statistical approach. These findings may help to increase the understanding of the phenotypic variability that typically characterizes bronchiolitis, with relevant implications for future research.

Project description:BackgroundDNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic.MethodA Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters.ResultThe proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.

Project description:ImportanceMedically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date.ObjectiveTo define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters.Design, setting, and participantsThis cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles.Main outcomes and measuresComplex patient profiles, 1-year health care utilization, and mortality outcomes by profile.ResultsThe analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management.Conclusions and relevanceThe findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Project description:BackgroundBronchiolitis is the leading cause of infants hospitalization in the U.S. and Europe. Additionally, bronchiolitis is a major risk factor for the development of childhood asthma. Growing evidence suggests heterogeneity within bronchiolitis. We sought to identify distinct, reproducible bronchiolitis subgroups (profiles) and to develop a decision rule accurately predicting the profile at the highest risk for developing asthma.MethodsIn three multicenter prospective cohorts of infants (age < 12 months) hospitalized for bronchiolitis in the U.S. and Finland (combined n = 3081) in 2007-2014, we identified clinically distinct bronchiolitis profiles by using latent class analysis. We examined the association of the profiles with the risk for developing asthma by age 6-7 years. By performing recursive partitioning analyses, we developed a decision rule predicting the profile at highest risk for asthma, and measured its predictive performance in two separate cohorts.FindingsWe identified four bronchiolitis profiles (profiles A-D). Profile A (n = 388; 13%) was characterized by a history of breathing problems/eczema and non-respiratory syncytial virus (non-RSV) infection. In contrast, profile B (n = 1064; 34%) resembled classic RSV-induced bronchiolitis. Profile C (n = 993; 32%) was comprised of the most severely ill group. Profile D (n = 636; 21%) was the least-ill group. Profile A infants had a significantly higher risk for asthma, compared to profile B infants (38% vs. 23%, adjusted odds ratio [adjOR] 2⋅57, 95%confidence interval [CI] 1⋅63-4⋅06). The derived 4-predictor (RSV infection, history of breathing problems, history of eczema, and parental history of asthma) decision rule strongly predicted profile A-e.g., area under the curve [AUC] of 0⋅98 (95%CI 0⋅97-0⋅99), sensitivity of 1⋅00 (95%CI 0⋅96-1⋅00), and specificity of 0⋅90 (95%CI 0⋅89-0⋅93) in a validation cohort.InterpretationIn three prospective cohorts of infants with bronchiolitis, we identified clinically distinct profiles and their longitudinal relationship with asthma risk. We also derived and validated an accurate prediction rule to determine the profile at highest risk. The current results should advance research into the development of profile-specific preventive strategies for asthma.

Dataset Information

A clustering approach to identify severe bronchiolitis profiles in children.

Objective

Methods

Results

Conclusions

Publications

A clustering approach to identify severe bronchiolitis profiles in children.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets