{"doi":"10.1093/eurjcn/zvae064","title":"Which of these symptom trajectories is not like the other?","abstract":"This invited commentary refers to ‘Multi-trajectories of symptoms and their associations with unplanned 30-day hospital readmission among patients with heart failure: a longitudinal study’, by Q. Lv et al., https://doi.org/10.1093/eurjcn/zvae038. Whether you have an acute illness or a chronic condition, symptoms are perceived indicators that there is a change in the functioning of the body.1 However, symptoms are incredibly complex and nuanced, and they are influenced by previous experience with symptoms, the degree to which symptoms are a ‘bother’, and the multi-layered context in which one experiences symptoms. Symptoms can be viewed from the limited perspective of a single, isolated symptom or from the more informative perspective of multiple symptoms that cluster together, as described in the theory of unpleasant symptoms.1 Even better, multiple symptoms can be studied longitudinally to understand baseline symptomatology and variability in symptoms over time. Tracking symptoms is particularly important in chronic conditions that are often punctuated by events (e.g. hospitalization) as symptoms can be both affected by the event itself and harbingers of future events. For patients with heart failure (HF), identifying and tracking multiple symptoms are critical components of managing their chronic condition.2 Symptoms in HF, however, cover a wide range from classic dyspnoea symptoms to non-specific fatigue symptoms. Furthermore, symptoms are not always physical in nature; they can present independent of or concurrent with psychological symptoms such as depression and anxiety. To make it more complex, even the seemingly straightforward symptoms are nuanced, such as with dyspnoea.3 Thus, considering heterogeneity in symptoms and moving away from the ‘one-size-fits-all’ approach is absolutely necessary. In parallel with our emerging understanding of patient-reported outcomes has been the evolution of statistical techniques that are designed to handle heterogeneity, yielding latent classes or profiles. For example, latent mixture modelling4,5 and related approaches have revealed distinct classes based on HF symptom type (e.g. dyspnoea3 and overall physical symptoms6), symptom severity (e.g. mild to severe symptoms7), and symptom congruence (e.g. relationship between physical and affective symptoms8). Moving forward, it is necessary to continue to leverage sophisticated statistical approaches to evolve our understanding of symptoms in HF. In this issue of the European Journal of Cardiovascular Nursing, Lv et al.9 analysed longitudinal data from 248 patients hospitalized with HF and who completed the 30-day study, to identify latent trajectories of change in symptoms from the time of HF hospitalization to 30 days post-discharge. Using group-based multi-trajectory modelling,10 they found that a three-class solution provided the best model fit based on the symptoms reported with the Symptom Status Questionnaire-Heart Failure. In other words, patients were grouped into one of three distinct symptom trajectories: mild, moderate, and severe symptom status. Moreover, these groups had clinical significance: patients in the severe symptom group (almost 20% of the sample) had significantly higher odds of an unplanned 30-day hospital readmission than those in the mild symptom group. Patients in all three groups appeared to report similar levels of dyspnoea at discharge (although there was no direct comparison of the intercept); however, there was a marked differentiation in how these symptoms resolved (or did not resolve) after hospitalization. In contrast, there appeared to be notable differences in psychological symptoms at discharge: those patients in the severe symptom group started with markedly higher levels of depressive and anxiety symptoms, and they remained high post-discharge, compared with the mild and moderate groups. Another differentiating symptom was sleep disturbance, which was high at discharge and remained high among those","journal":"European Journal of Cardiovascular Nursing","year":2024,"id":497589,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9564,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":536362,"name":"Mary C. Davis","orcid":"0000-0002-3226-4516","position":1,"is_corresponding":false},{"id":510205,"name":"Quin E. Denfeld","orcid":"0000-0001-7568-9568","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:09:34.764412Z","pmid":"38748905","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}