Clinical phenotypes of cardiogenic shock survivors: insights into late host responses and long-term outcomes.

AIMS: An elevated risk of adverse events persists for years in cardiogenic shock (CS) survivors with high mortality rate and physical/mental disability. This study aims to link clinical CS-survivor phenotypes with distinct late host-response patterns at intensive care unit (ICU) discharge and long-term outcomes using model-based clustering. METHODS AND RESULTS: In the original prospective, observational, international French and European Outcome Registry in Intensive Care Units (FROG-ICU) study, ICU patients with CS on admission were identified (N = 228). Among them, 173 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct CS-survivor phenotypes at ICU discharge using 15 readily available clinical and laboratory variables. The primary endpoint was 1 year of mortality after ICU discharge. Secondary endpoints were readmission and physical/mental disability [short form-36 questionnaire (SF-36) score] within 1 year after ICU discharge. Two distinct phenotypes at ICU discharge were identified (A and B). Patients in Phenotype B (38%) were more anaemic and had higher circulating levels of lactate, sustained kidney injury, and persistent elevation in plasma markers of inflammation, myocardial fibrosis, and endothelial dysfunction compared with Phenotype A. They had also a higher rate of non-ischaemic origin of CS and right ventricular dysfunction on admission. CS survivors in Phenotype B had higher 1 year of mortality compared with Phenotype A (P = 0.045, Kaplan-Meier analysis). When adjusted for traditional risk factors (i.e. age, severity of illness, and duration of ICU stay), Phenotype B was independently associated with 1 year of mortality [adjusted hazard ratio = 2.83 (95% confidence interval 1.21-6.60); P = 0.016]. There was a significantly lower physical quality of life in Phenotype B patients at 3 months (i.e. SF-36 physical component score). CONCLUSIONS: A phenotype with sustained inflammation, myocardial fibrosis, and endothelial dysfunction at ICU discharge was identified from readily available data and was independently associated with poor long-term outcomes in CS survivors.

Identification of obstructive sleep apnea in children with obesity: A cluster analysis approach.

BACKGROUND: Obstructive sleep apnea (OSA) is a heterogeneous disorder with a prevalence of 25%-60% in children with obesity. There is a lack of diagnostic tools to identify those at high risk for OSA. METHOD: Children with obesity, aged 8-19 years old, were enrolled into an ongoing multicenter, prospective cohort study related to OSA. We performed k-means cluster analysis to identify clinical variables which could help identify obesity related OSA. RESULTS: In this study, 118 participants were included in the analysis; 40.7% were diagnosed with OSA, 46.6% were female and the mean (SD) body mass index (BMI) and age were 39.7 (9.6) Kg/m², and 14.4 (2.6) years, respectively. The mean (SD) obstructive apnea-hypopnea index (OAHI) was 11.0 (21.1) events/h. We identified two distinct clusters based on three clustering variables (age, BMI z-score, and neck-height ratio [NHR]). The prevalence of OSA in clusters 1 and 2, were 22.4% and 58.3% (p = 0.001), respectively. Children in cluster 2, in comparison to cluster 1, had higher BMI z-score (4.7 (1.1) versus 3.2 (0.7), p < 0.001), higher NHR (0.3 (0.02) versus 0.2 (0.01), p < 0.001) and were older (15.0 (2.2) versus 13.7 (2.9) years, p = 0.09), respectively. However, there were no significant differences in sex and OSA symptoms between the clusters. The results from hierarchical clustering were similar to k-means analysis suggesting that the resulting OSA clusters were stable to different analysis approaches. INTERPRETATION: BMI, NHR, and age are easily obtained in a clinical setting and can be utilized to identify children at high risk for OSA.

Joint clustering multiple longitudinal features: A comparison of methods and software packages with practical guidance.

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co-existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model-based (including frequentist and Bayesian) approaches and algorithm-based approaches. To evaluate their performance, we compare these approaches using real-life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed.

Review of Randomized Controlled Trials Using e-Health Interventions for Patients With Eating Disorders.

BACKGROUND: In a world of technological advancements, electronic devices and services seem to be a promising way to increase patients' engagement in treatment and to help manage their symptoms. Here, we identified and analyzed the current evidence of RCTs to evaluate the effectiveness and acceptability of e-health interventions in the eating disorder (ED) field. METHODS: We screened an initial cluster of 581 papers. In the end, 12 RCTs in clinical ED cohorts were included. RESULTS: Some studies were conceived as stand-alone interventions, while others were presented as add-ons to ED-specific treatments. Studies varied in the type of EDs under investigation and in the e-health intervention applied (with vs. without therapist support vs. blended interventions; smartphone- vs. web-based). Only four studies reported explicit acceptability measures. Out of those, two reported high acceptability, one reported low acceptability, and one reported no significant difference in acceptability between groups. Four studies reported higher effectiveness of the e-health intervention compared to the control condition, e.g., reduction in maladaptive eating behaviors. Regarding control groups, three used a wait list design and nine had another kind of intervention (e.g., face-to-face CBT, or treatment as usual) as control. DISCUSSION: So far, the evidence for acceptability and effectiveness of e-health interventions in EDs is very limited. There is also a lack of studies in older patients, adolescents, men, sexual and ethnic minorities. Shame/stigma is discussed in the context of e-health interventions for EDs. It remains unclear how severity of EDs affects the effectiveness of e-health interventions, how patients can channel the knowledge they acquire from e-health interventions into their actual behaviors, and how such interventions can better fit the needs of the individual patient to increase acceptability and effectiveness.