Centers for Disease Control and Prevention criteria for chronic fatigue syndrome, and each had at least six months of unexplained fatigue accompanied by the required additional symptoms. Recruitment took place between September 18, 2025, and April 2026 at a Traditional Chinese Medicine internal medicine outpatient clinic. Of the 225 questionnaires distributed, 214 were valid, producing a response rate of 95.11%.

The sample was predominantly female, with 142 women, or 66.36%, and 72 men. The largest age group comprised 93 patients aged 31 to 40 years, or 43.46%. College or undergraduate education was reported by 146 participants, and 37 had a master’s degree or higher.

Symptom burden was extensive. Fatigue was reported by every participant, difficulty concentrating by 194 patients, or 90.7%, and feeling drowsy by 186, or 86.9%. Nineteen symptoms occurred in more than half of the sample.

Shortness of breath had the highest mean severity score in the study’s main table, at 2.31, followed by feeling nervous at 2.28; fatigue had a mean severity score of 1.94. Researchers recorded symptoms as present or absent and, when present, assessed their frequency, severity and distress over the preceding seven days. They used a 35-item adapted Memorial Symptom Assessment Scale, adding bradyphrenia, chills, noise sensitivity and palpitations while removing an item about problems with sexual interest or activity because prospective participants frequently declined to answer it.

For cluster identification, the researchers first performed exploratory factor analysis with principal-axis factoring and Promax rotation, using a permutation-based parallel analysis to determine the number of factors. They then applied principal component analysis with varimax rotation to symptoms present in at least 35% of participants. Components were retained when they had an eigenvalue of at least one, contained at least two symptoms and produced loadings of at least 0.40.

Dry mouth was excluded after its loading fell below that threshold in the relevant analysis. The final model identified six components with eigenvalues greater than one, together explaining 63.114% of the variance. The Kaiser–Meyer–Olkin measure was 0.787, while Bartlett’s test of sphericity gave χ² = 1,625.864 with P < 0.001, indicating that the data were suitable for factor analysis.

The resulting clusters were emotional-sleep, fatigue, physical, self-image, gastrointestinal and neurological symptoms. The fatigue cluster contained difficulty concentrating, bradyphrenia, fatigue, feeling drowsy and pain, with loadings ranging from 0.620 to 0.810. The emotional-sleep cluster comprised feeling nervous, irritability, sadness, difficulty sleeping and worrying.

Physical symptoms included shortness of breath, chills, palpitations and sweating. The self-image cluster contained skin changes, the sensation of not looking like oneself and hair loss; gastrointestinal symptoms comprised constipation, bloating and lack of appetite; and the neurological cluster consisted of dizziness and noise sensitivity. The analysis therefore grouped the three most prevalent complaints—fatigue, concentration difficulty and drowsiness—within one cluster, while showing that the wider burden extends into several partly distinct domains.

The team next constructed a 22-node Gaussian graphical model using the EBICglasso method in R. Nodes represented symptoms, while edges represented regularized partial correlations after accounting for the other symptoms. Thicker edges indicated stronger conditional associations.

The strongest estimated connections were between constipation and lack of appetite, between not looking like oneself and hair loss, and between shortness of breath and palpitations. Expected influence summarized a symptom’s net connectivity with the network, while bridge expected influence measured its net connections across the six PCA-derived communities. Fatigue ranked first in expected influence at 1.541, ahead of worrying at 1.169 and lack of appetite at 1.072.

Fatigue was also the leading bridge symptom, with a bridge expected influence of 0.430, followed by pain at 0.363 and difficulty sleeping at 0.309. These centrality estimates were supported by several robustness checks. One thousand nonparametric bootstrap resamples were used to estimate 95% confidence intervals for edge weights, and case-dropping bootstrapping produced correlation stability coefficients of 0.593 for strength and expected influence, indicating good stability.

Bridge strength and bridge expected influence had coefficients of 0.285 and 0.360, respectively, which the investigators categorized as acceptable. Networks based on automatic correlations, unadjusted Pearson correlations and Pearson residuals after adjustment for age and sex were highly similar, with edge-weight correlations from 0.978 to 0.993. Expected-influence rankings remained strongly correlated across models, with Spearman correlations of 0.905 to 0.965, as did one-step bridge rankings, at 0.820 to 0.913.

Fatigue and pain retained the two highest bridge expected influence positions in all three networks, although some weak cross-community edges disappeared after adjustment. Node-redundancy screening raised concerns about eight of 231 symptom pairs, but the researchers retained all 22 symptoms after reviewing their content. In 13 models that separately removed a flagged node, expected-influence rankings correlated with the primary model at 0.914 to 0.997, and bridge expected influence rankings at 0.934 to 0.997.

Fatigue remained first in expected influence in every model. It ranked first in bridge expected influence in 12 models and third when difficulty sleeping was removed; removal of feeling drowsy did not alter fatigue’s first-place ranking on either measure. These analyses indicate that the broad results were not driven by a single pair of highly similar items, though they do not remove the uncertainty inherent in an exploratory network.

The findings offer a structured way to assess the coexistence of symptoms rather than treating fatigue, mood, sleep and pain as isolated problems. In the studied population, fatigue combined high prevalence with the strongest measured connections to other symptoms, while worrying, lack of appetite, pain and difficulty sleeping occupied other prominent network positions. The authors say the results may support simultaneous screening of these domains and individualized planning, but they do not show that targeting a statistically central symptom improves outcomes.

They specifically caution that network centrality can identify candidates for clinical assessment and prioritization but cannot replace etiological evaluation, differential diagnosis or individualized decisions. Longitudinal and interventional studies would be needed to determine whether symptom networks change over time and whether management based on these clusters improves patient outcomes. Several limitations restrict how broadly the results can be interpreted.

The participants were recruited by convenience sampling from one tertiary hospital’s Traditional Chinese Medicine outpatient clinic, so people with different access to care, functional status or treatment preferences may be underrepresented. The sample was also relatively concentrated in middle adulthood and highly educated. Diagnosis relied on the 1994 Fukuda criteria, which do not require post-exertional malaise, and the symptom assessment did not specifically characterize post-exertional malaise or orthostatic intolerance.

As a result, the findings may not generalize to populations diagnosed under the 2015 Institute of Medicine criteria. The adapted Memorial Symptom Assessment Scale had high internal consistency in this sample, with a Cronbach’s alpha of 0.949, but it has not been formally validated in people with chronic fatigue syndrome. Self-report bias, cross-sectional design and adjustment only for age and sex further constrain causal or treatment interpretations.

The study therefore provides an exploratory map of symptom relationships in a defined clinic population, not a validated model of chronic fatigue syndrome or evidence of a treatment effect.

Subject of Research: Symptom clusters and network connections in adults with chronic fatigue syndrome

Article Title: Symptom clusters and symptom network characteristics in chronic fatigue syndrome patients: a cross-sectional study

Article References: Zi, A., Zhang, Y., Sun, M., Liu, T., Xu, Y., & Xu, M. (2026). Symptom clusters and symptom network characteristics in chronic fatigue syndrome patients: a cross-sectional study. Frontiers in Medicine, 13, Article 1955021. https://doi.org/10.3389/fmed.2026.1955021

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.1955021

Keywords: chronic fatigue syndrome, ME/CFS, symptom clusters, symptom networks, fatigue, cross-sectional study, pain, sleep disturbance, Symptom, clusters, network, characteristics

At the end of the first month of anti-tuberculosis treatment, researchers assessed 17 symptoms using the Pulmonary Tuberculosis Patient Symptom Cluster Assessment Scale. Latent class analysis identified a low symptom burden subtype in 128 patients, or 42.7%; a high psychogastrointestinal symptoms subtype in 92 patients, or 30.7%; and a high respiratory symptoms-dominant subtype in 80 patients, or 26.6%. The psychogastrointestinal subtype was characterized by the co-occurrence and high burden of depression, anxiety, nausea and vomiting, while the respiratory subtype was characterized by a high burden of respiratory symptoms.

Medication adherence was assessed in the third month of treatment among 285 patients who completed follow-up, using the researchers’ self-developed and validated Treatment Adherence Scale for Tuberculosis. A score above 36 points was classified as good adherence. The good-adherence rate was 45.7% in the high psychogastrointestinal symptoms group, 60.0% in the high respiratory symptoms-dominant group and 77.3% in the low symptom burden group; the difference among the three groups was significant at P<0.001. Univariate and multivariable logistic regression analyses examined associations between symptom subtype and good adherence.

After adjustment for confounding factors, the high psychogastrointestinal symptoms subtype had the strongest reported association with poor adherence. Its adjusted odds ratio for good adherence was 0.24, with a 95% confidence interval of 0.13–0.45 and P<0.001, indicating lower odds of good adherence than in the low symptom burden group. Coexisting diabetes mellitus was also independently associated with lower odds of good adherence, with an adjusted odds ratio of 0.52, a 95% confidence interval of 0.28–0.97 and P=0.037. These results show associations, not causal effects. The study was limited to newly diagnosed older inpatients with pulmonary tuberculosis in four wards at two hospitals, and adherence was assessed once during the first three months of treatment. Fifteen enrolled patients did not complete the adherence follow-up, leaving 285 participants in that analysis. The reported findings do not establish that symptom patterns cause nonadherence or that the associations will generalize to other populations and settings.

Subject of Research: Symptom subtypes and medication adherence among older patients with pulmonary tuberculosis

Article Title: Classification of older pulmonary tuberculosis patients based on symptom clusters and differences in medication adherence: a latent class analysis

Article References: Cai, Y., Cheng, X., & Rui, Y. (2026). Classification of older pulmonary tuberculosis patients based on symptom clusters and differences in medication adherence: a latent class analysis. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14486-x

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14486-x

Keywords: pulmonary tuberculosis, older patients, medication adherence, symptom clusters, latent class analysis, psychogastrointestinal symptoms, respiratory symptoms, observational cohort, multicenter study, Classification, older, pulmonary

Autologous haematopoietic stem cell transplantation, in which a patient’s own stem cells are harvested and reinfused after high-dose chemotherapy, is a mainstay of treatment for multiple myeloma, relapsed lymphoma, and certain other haematological malignancies. The procedure works because the chemotherapy ablates the bone marrow and the reinfused cells rebuild it, but the interval between ablation and recovery leaves patients profoundly neutropenic, with absolute neutrophil counts low enough that even ordinary bacterial flora can become life-threatening. During this window, typically one to two weeks post-infusion, patients are nursed in protective isolation, monitored for fever, and managed with broad supportive care. Clinical guidelines define the danger signs clearly, yet the patients living through the window experience it from the inside, and the study set out to understand how they interpret what is happening to them.

The researchers employed a qualitative descriptive design, purposively sampling adults aged eighteen years or older who had undergone autologous transplantation and were currently neutropenic as inpatients in the transplantation unit. The sampling strategy deliberately sought variation across age, diagnosis, and treatment experience so that the findings would not reflect a narrow slice of the transplant population. Data collection consisted of semi-structured interviews, audio-recorded with consent and transcribed verbatim, conducted with twenty-two participants between post-transplant day plus five and day plus eighteen, precisely the period when neutrophil counts bottom out and begin to recover. The transcripts were then analysed using reflexive thematic analysis, an approach in which the researchers explicitly acknowledge and interrogate their own role in shaping the themes that emerge from the data rather than treating themes as if they were simply discovered.

The first major finding concerns how patients conceptualised their symptoms. Rather than reporting discrete problems such as nausea, fatigue, mucositis, or fever as separate items on a checklist, participants described them as interlocking constellations, clusters that rose and fell together and that could only be understood in relation to one another. A bout of diarrhoea paired with a new wave of fatigue carried a different meaning than either symptom alone, and the combination of oral pain with difficulty swallowing was appraised differently from oral pain in isolation. This cluster-based perception aligns with a growing body of symptom science showing that patients experience toxicity burdens holistically, but the study adds a temporal dimension that is often missing from cross-sectional symptom assessments.

That temporal dimension proved to be the hinge of the entire study. Participants did not merely catalogue what they felt; they tracked trajectories, comparing today’s symptoms with yesterday’s and using the direction of change as their primary signal. Worsening intensity, the appearance of new symptom combinations, or symptoms that simply felt different from the previous day prompted a re-appraisal of the situation from expected treatment toxicity to possible deterioration. In contrast, symptoms that followed a familiar course and matched what patients had been told to expect were often interpreted as the ordinary price of the transplant and tolerated without alarm. Fever-related patterns stood apart from this calculus: fever, or the constellation of sensations surrounding it, usually triggered immediate escalation and prompt reporting to staff, reflecting the consistent messaging patients receive about infection risk during neutropenia.

The second major finding concerns the behavioural consequences of these appraisals. Help-seeking during the neutropenic window was not uniform. The researchers identified a spectrum ranging from prompt reporting, in which patients escalated concerns to nursing staff as soon as they noticed a troubling trajectory, to watchful waiting, in which patients monitored symptoms over time before deciding whether to speak up. Some participants practised self-management first, attempting to control a symptom on their own and only reporting if their measures failed, while others withheld information altogether, choosing not to mention certain symptoms unless directly asked. These strategies were not random; they reflected deliberate calculations made under conditions of considerable uncertainty.

Three forces shaped those calculations. The first was uncertainty itself: patients often could not tell whether a symptom belonged to the expected toxicity profile or signalled something dangerous, and that ambiguity made them hesitant to act. The second was perceived legitimacy, the sense that a concern was serious enough to warrant staff attention; patients who judged their symptoms as minor or as things they should be able to endure felt reluctant to raise them. The third was a distinctly relational barrier, a reluctance to bother staff during protective isolation, where nursing contact is already frequent and patients are acutely aware of the workload their care demands. Together, these forces explain why a patient with a genuinely concerning trajectory might wait hours or a full day before mentioning it, a delay that matters enormously when the underlying risk is overwhelming sepsis in a person with essentially no neutrophil reserve.

The clinical implications follow directly from the mechanism the study describes. If patients appraise urgency by tracking symptom clusters and trajectories, then assessment tools that capture symptoms only as static lists will miss the signal patients are actually monitoring. The authors argue for cluster-informed assessment, in which nursing evaluations explicitly ask about combinations of symptoms and how they are changing from day to day, mirroring the cognitive framework patients already use. They also propose escalation coaching, structured conversations in which patients are taught which trajectories warrant immediate reporting, which symptoms can be self-managed with a defined threshold for escalation, and crucially, that reporting a concern is never a burden on staff. Such coaching could convert the watchful waiting and withholding behaviours documented in the study into earlier, safer escalation.

The study’s qualitative design means its findings describe the experiences of twenty-two patients in a single Chinese transplantation unit rather than establishing prevalence across transplant populations, and the authors are careful about the scope of their claims. Nevertheless, the mechanistic insight, that urgency appraisal is built from symptom constellations and their trajectories, is likely to generalise, because it reflects how patients anywhere must reason about their bodies when they cannot rely on laboratory values they cannot see. The work also fills a gap in the supportive care literature, which has extensively quantified symptom burden after transplantation but far less often asked patients how they decide what those symptoms mean.

For oncology nursing, the study arrives at a moment when transplantation volumes are rising globally and supportive care is increasingly delivered through protocols that assume patients will report problems promptly. The Wuxi findings suggest that assumption is fragile, and that the fix is not simply to tell patients to report everything, which ignores the legitimacy calculus and the reluctance to bother staff that the interviews revealed. Instead, the researchers point toward a partnership model in which nurses teach patients the trajectory language of danger, validate the reporting of ambiguous symptoms, and design assessment rounds that ask about clusters and change rather than isolated complaints. In a setting where a single day of delayed recognition can separate a manageable infection from a catastrophic one, teaching patients to read their own symptom constellations may prove to be one of the most cost-effective safety interventions in transplant care.

Subject of Research: Patient symptom appraisal and help-seeking behaviour during neutropenia after autologous haematopoietic stem cell transplantation

Article Title: Symptom clusters and help-seeking behaviours during neutropenia after autologous haematopoietic stem cell transplantation: A qualitative study to inform oncology nursing support

Article References: Jiang, L., Hua, J., Teng, F., Xu, T., Cao, H., & Zhou, J. (2026). Symptom clusters and help-seeking behaviours during neutropenia after autologous haematopoietic stem cell transplantation: A qualitative study to inform oncology nursing support. PLOS One, 21(10), e0361023. https://doi.org/10.1371/journal.pone.0361023

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0361023

Keywords: neutropenia, autologous stem cell transplantation, symptom clusters, help-seeking behaviour, oncology nursing, qualitative research, protective isolation, fever, patient safety, supportive care, thematic analysis, haematological malignancy

The research team, led by investigators at Soochow University in Suzhou, China, together with collaborators at NYU Rory Meyers College of Nursing and the University of Nevada, Reno, followed 380 patients undergoing adjuvant chemotherapy after colorectal cancer surgery. Rather than simply counting symptoms at each visit, the researchers modeled the entire symptom landscape as a network—a web of interacting nodes in which each symptom influences the others. They then estimated a quantity called network temperature, a measure of how freely the system fluctuates among different possible configurations of symptom states.

The concept comes from the Ising model, a mathematical framework originally developed to describe magnetism in physics, in which units flip between two states—in this case, the presence or absence of a symptom—under the influence of their neighbors. In a hot network, symptoms behave erratically: a patient’s overall symptom configuration can shift dramatically from one moment to the next, with different combinations of problems appearing and disappearing. In a cold network, the system is locked into a small number of stable configurations, meaning the symptom pattern a patient experiences tends to persist. Temperature, in this sense, is a direct readout of the stability of a person’s symptom experience.

To carry out the analysis, the team assessed patients at three stages of chemotherapy and included only symptoms with a prevalence of at least 20 percent at every assessment, ensuring the network contained problems that were genuinely common across the treatment course. They fitted multigroup Ising network models to the data and used the Spinglass community detection algorithm—another import from statistical physics—to identify clusters of symptoms that hang together more strongly with each other than with the rest of the network. Confidence intervals for the temperature estimates were generated through 1,000 patient-level nonparametric bootstrap resamples, a resampling technique that quantifies how much uncertainty surrounds each measurement.

Three symptom clusters emerged from the analysis: an emotional distress cluster, a somatic–gastrointestinal cluster, and a fatigue–neuropathy cluster. This grouping mirrors what clinicians observe at the bedside, where physical complaints such as nausea, appetite loss, and bowel symptoms tend to travel together, while fatigue and chemotherapy-induced peripheral neuropathy form their own distinct axis of toxicity.

The headline finding was that the overall network temperature of the symptom system decreased across the three chemotherapy stages. In plain terms, as treatment went on, patients’ symptom configurations became more constrained—less prone to wholesale rearrangement from one assessment to the next. The symptom experience, chaotic at the outset of therapy, gradually settled into more predictable patterns. This is the first time network temperature has been applied to symptom science in oncology, adapting a metric previously used to study the stability of depression symptoms across adolescence.

But the cluster-level results told two very different stories. The fatigue–neuropathy cluster showed a marked and steady cooling: its relative temperature fell from a normalized reference value of 1.00 at the first assessment to 0.60 (95 percent confidence interval 0.49–0.74) at the second stage and 0.45 (0.36–0.55) at the third. That means the way fatigue and neuropathy co-occurred in individual patients became progressively more locked-in over the course of chemotherapy. The authors suggest this reflects the biology of these toxicities—both are driven by cumulative, treatment-related mechanisms such as the metabolic disruptions underlying cancer-related fatigue and the nerve damage produced by oxaliplatin, a standard adjuvant drug for stage III colon cancer whose neurotoxicity is well documented in recent trials.

The emotional distress cluster moved in the opposite direction. Its temperature rose over time, from the reference value of 1.00 to 1.06 (0.96–1.19) at stage two and 1.12 (1.01–1.25) at stage three. Although the confidence intervals overlap the reference value at the second stage, the upper trajectory indicates that anxiety and depression maintained—or even increased—their configurational variability throughout treatment. Unlike the physical toxicities, which become entrenched, the emotional dimension of the patient experience remained fluid, with different patterns of distress surfacing at different points. This is consistent with a broader literature showing that fear of cancer recurrence, coping strategies, and psychological adjustment continue to evolve long after the acute physical side effects have stabilized, and that anxiety and depression among colorectal cancer survivors are common and closely tied to quality of life.

The clinical implications of this asymmetry are significant. If physical symptom clusters become stable and predictable as chemotherapy proceeds, clinicians can, in principle, anticipate them: a patient showing fatigue and neuropathy early in treatment is likely to retain that configuration, making it a sensible target for early, sustained interventions such as dose modification, exercise programs, or neuropathy management protocols. Emotional distress, by contrast, demands repeated, longitudinal screening, because its presentation is not fixed—what a patient reports at week six may bear little resemblance to what they report at week eighteen. The authors argue that their findings highlight the importance of longitudinal, cluster-informed symptom assessment during chemotherapy, rather than one-off symptom surveys that may miss the dynamic structure of the burden.

The study also demonstrates the practical value of importing tools from network science into supportive cancer care. Network analysis of multivariate data has matured rapidly in psychological science over the past decade, and metrics such as network temperature offer a way to quantify not just which symptoms a patient has, but how stable and interdependent their symptom system is—a dimension that conventional severity scores cannot capture. For colorectal cancer, a disease whose global incidence continues to climb according to the latest GLOBOCAN estimates, and whose survivors face months of adjuvant therapy, understanding the architecture and dynamics of symptom networks may prove as important as understanding the drugs that cause them. The work was supported by the Major Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province, and the datasets are available from the corresponding author on reasonable request.

Subject of Research: Temporal stability of symptom clusters measured by network temperature in colorectal cancer patients receiving adjuvant chemotherapy

Article Title: Network temperature and stability of symptom clusters in colorectal cancer patients undergoing adjuvant chemotherapy: a prospective longitudinal study

Article References: Cao, C., Liu, S., Shan, S., Qi, X., Mao, W., Yang, Z., & Tian, L. (2026). Network temperature and stability of symptom clusters in colorectal cancer patients undergoing adjuvant chemotherapy: a prospective longitudinal study. Supportive Care in Cancer, 34(10), Article 951. https://doi.org/10.1007/s00520-026-11188-7

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11188-7

Keywords: colorectal cancer, adjuvant chemotherapy, symptom clusters, network analysis, network temperature, Ising model, fatigue, peripheral neuropathy, emotional distress, supportive care, longitudinal study, patient-reported outcomes

The research team enrolled 378 consecutive patients who underwent curative-intent esophagectomy for esophageal cancer between April 2023 and March 2024. After accounting for twelve patients lost to follow-up and five who died within the first postoperative month, the final analytic cohort comprised 361 participants. One month after surgery, each patient completed two widely validated quality-of-life instruments: the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30, known as QLQ-C30, and the esophageal cancer-specific module QLQ-OES18. Together, these questionnaires captured eighteen distinct symptoms, ranging from dysphagia and reflux to cough, insomnia, pain, and constipation. The timing was deliberate. The first postoperative month is a critical transition period during which patients progress from enteral to oral nutrition, and more than half experience complications at this stage, including pulmonary infections and anastomotic leaks.

To identify symptom clusters, the researchers turned to exploratory factor analysis, a classical statistical technique that detects latent constructs explaining the covariance among observed variables. Symptoms with factor loadings of at least 0.40 were retained, and factor retention was guided by Kaiser’s criterion, parallel analysis, and scree plot evaluation. The analysis proved feasible, with a Kaiser-Meyer-Olkin measure of 0.70 and a highly significant Bartlett’s test of sphericity. Five factors emerged, accounting for 52.2 percent of cumulative variance. The team cross-validated this solution using principal component analysis and hierarchical cluster analysis, but both alternatives produced either overly broad groupings or clusters with considerable symptom overlap and poorly defined boundaries. The five-factor structure from exploratory factor analysis exhibited the clearest distinctions and closest consistency with clinical observations, so it was retained.

The five clusters were labeled eating-related, breathing-related, pain-related, fatigue-speech-related, and excretion-related. The eating-related cluster, encompassing eating difficulty, reflux, choking when swallowing, appetite loss, nausea and vomiting, and dysphagia, was present in 49.0 percent of patients. The breathing-related cluster, combining cough, dyspnea, and insomnia, affected 47.0 percent. The pain-related cluster, comprising esophageal pain and generalized pain, was present in 48.0 percent. The fatigue-speech-related cluster, uniting fatigue, dry mouth, and trouble talking, showed the highest prevalence at 51.0 percent. The excretion-related cluster, consisting of constipation and diarrhea, was present in 50.0 percent. Notably, approximately 67.0 percent of patients exhibited two or more concurrent clusters, indicating a substantial comorbid symptom burden.

The clustering of fatigue alongside dry mouth and speech difficulties is clinically justified, the authors argue, because dry mouth and dysarthria frequently co-occur during and following chemoradiotherapy, attributable to shared pathophysiological mechanisms such as salivary gland impairment and cranial nerve damage. Given that radiotherapy directly exacerbates fatigue, grouping these symptoms makes sense. The cluster’s status as the most prevalent in the cohort suggests it warrants dedicated management protocols. The researchers also identified two high-risk subgroups for postoperative fatigue: patients aged 60 to 74 years had adjusted odds of 1.84, and those receiving neoadjuvant therapy had adjusted odds of 1.86, compared with their respective reference groups.

To determine core symptoms, the team applied network analysis, an emerging computational modeling framework originally developed in psychopathology to investigate how symptoms interact. The researchers constructed a mixed graphical model with sixteen symptom nodes specified as continuous variables on a 0 to 100 severity scale, conditioned on three covariates: surgical method, postoperative hospitalization duration, and neoadjuvant therapy. Regularization was applied via graphical LASSO with the penalty selected by ten-fold cross-validation, and edges were retained only if selected by the LASSO procedure. The strongest conditional dependencies were observed between esophageal pain and generalized pain, between eating difficulty and choking when swallowing, and between nausea and vomiting and reflux. Fatigue showed high predictability, with 36.9 percent of its variance explained by neighboring symptoms.

Centrality analysis identified fatigue as the symptom with the highest strength centrality, with a relative strength of 2.83, followed by pain at 1.69 and insomnia at 1.58. Constipation exhibited the lowest strength centrality at 0.42. Critically, fatigue also demonstrated the highest bridge strength, at 1.09, confirming its dual role as both a core postoperative symptom and a pivotal bridge node interconnecting symptom clusters. Network stability was assessed through 1000 bootstrap samples, yielding a correlation stability coefficient of 0.52 for strength and expected influence centrality, which indicates good stability. Bootstrap analysis confirmed high edge weight accuracy, with narrow 95 percent confidence intervals closely aligning with the estimated weights.

The researchers then examined whether these symptom profiles predicted survival. Using a one-month landmark design to avoid immortal time bias, follow-up was counted from postoperative day 31, and the primary endpoint was one-year all-cause mortality. Of the 361 patients, 293 with complete one-year follow-up data were included in the primary analysis. During a median follow-up of 335 days, sixteen deaths occurred. Kaplan-Meier analysis showed no statistically significant differences in one-year survival between patients with or without any symptom cluster or core symptom. In univariable and multivariable Cox regression models, none of the symptom clusters or core symptoms were significantly associated with increased one-year mortality risk. The authors attribute this null finding partly to insufficient statistical power, given the limited number of mortality events and wide confidence intervals, and partly to contemporary improvements in esophageal cancer outcomes. A recent Swedish population-based study including 2291 patients who underwent esophagectomy between 2000 and 2020 demonstrated substantial survival improvements over time, with the adjusted hazard ratio for five-year all-cause mortality falling to 0.57 when comparing the most recent surgery period with the earliest.

The study has limitations that warrant cautious interpretation. It was conducted at a single tertiary hospital in China, which may limit generalizability to other populations or healthcare settings. The cohort was predominantly male, at 96.7 percent, reflecting the higher incidence of esophageal cancer in men but limiting applicability to female patients. Symptom presence was defined using a cutoff of at least 3 on the four-point Likert scale, which may underestimate subclinical burden in patients scoring 2. The cross-sectional nature of the network analysis cannot establish causal relationships between symptoms. The authors recommend longitudinal studies with larger, more diverse, and sex-balanced cohorts to validate the findings and delineate the temporal evolution of postoperative symptoms.

Nevertheless, the clinical implications are significant. Dysphagia showed the highest frequency at 93.1 percent, and fatigue the highest severity, with a mean score of 33.33 on the standardized scale. Evidence indicates that 30 to 50 percent of patients experience acute or chronic post-esophagectomy pain, and persistent insomnia may exceed twelve months after surgery. Yet 66 percent of patients do not report fatigue symptoms to clinicians, and 43 percent lack awareness of management strategies. The authors suggest that symptom network centrality metrics may serve as useful research endpoints in oncology practice, with routine clinical data applied to quantify symptom severity, construct symptom networks, and calculate centrality indices to guide evidence-based symptom management. Future studies should develop time-resolved dynamic symptom networks to track centrality trajectories and clarify causal interrelationships between symptoms. In an era of improving survival, precision management targeting fatigue represents a critical strategy for optimizing quality of life, distinct from survival-focused interventions, and reframes clinical priorities from solely extending survival to improving how patients actually live after esophageal cancer surgery.

Subject of Research: Health

Article Title: Symptom clusters and core symptoms in early postoperative esophageal cancer patients

Article References: Zhong, M., Wang, C., Liu, X., Wang, Y., Qi, X., & Xie, S. (2026). Symptom clusters and core symptoms in early postoperative esophageal cancer patients. Frontiers in Gastroenterology, 5, Article 1766306. https://doi.org/10.3389/fgstr.2026.1766306

Image Credits: AI Generated

DOI: 10.3389/fgstr.2026.1766306

Keywords: esophageal cancer, esophagectomy, symptom clusters, network analysis, fatigue, postoperative care, survival analysis, quality of life, Symptom, clusters, core, symptoms

The study, led by Edith Visser of the Department of Epidemiology at Frisius MC in Leeuwarden, together with colleagues from University Medical Centre Groningen, Radboud University Medical Centre and other Dutch institutions, enrolled adults aged 18 and over who had been referred to post-COVID outpatient clinics at three hospitals in the Netherlands. All participants experienced persistent symptoms following a SARS-CoV-2 infection. In total, 1,155 patients formed the analytic cohort, a population that was 67 percent female with a median age of 50 years. At their initial clinic visit, the median time since infection stood at 24.1 weeks, placing most patients well into the chronic phase of their illness when they were first characterised.

The methodological heart of the study lies in its use of K-means clustering, a workhorse algorithm of unsupervised machine learning. Rather than asking clinicians to sort patients into categories by intuition, the researchers fed the algorithm the pattern of reported post-COVID symptoms recorded at each patient’s first appointment. K-means works by iteratively assigning each data point to the nearest of a predefined number of cluster centres, then recalculating those centres as the mean of the points assigned to them, until the grouping stabilises. The result is a partition of the patient population into groups whose members resemble one another more closely than they resemble anyone else, without any prior assumption about what those groups should look like. The researchers complemented this with multiple correspondence analysis, a related dimensionality-reduction technique suited to categorical data, to visualise and validate the symptom structure underlying the clusters.

Four clusters emerged from the analysis, and their relative sizes were strikingly uneven. The largest group by far, accounting for 47.7 percent of the cohort, was labelled the Respiratory-dominant and Dyspnoea cluster, characterised chiefly by breathing difficulties. The second-largest, at 27.6 percent, was the Fatigue-dominant and Neurocognitive cluster, in which profound tiredness coexisted with cognitive complaints. A third group, the Fatigue-dominant and Multisystem cluster, made up 12.7 percent of patients and carried the broadest symptom burden, spanning multiple organ systems. The smallest, at 11.9 percent, was the Respiratory-dominant and Airway-Systemic cluster. Notably, the authors highlight, only around 12 percent of patients belonged to the group with predominantly systemic symptoms, a finding that challenges assumptions that post-COVID is primarily a multisystem disorder in clinic-referred populations.

Clustering patients is only scientifically useful if the groups mean something clinically, and this is where the study’s second phase becomes important. The researchers examined how patients in each cluster moved through the healthcare system during their initial care. Patients in the two fatigue-dominated clusters were referred significantly more often to occupational therapy, speech and language therapy, mental health services, or a rehabilitation programme than those in the respiratory-dominant clusters, with all comparisons reaching statistical significance at p below 0.01. Patients in the respiratory-dominant groups, by contrast, more frequently received pharmacological treatment, reflecting the different therapeutic logic of managing airway and breathing symptoms compared with the functional and cognitive impairments that dominate fatigue-led illness.

The long-term outlook was assessed after a mean follow-up of 2.5 years, when all surviving participants were invited to complete a digital questionnaire covering their current health status and quality of life. The analysis leaned heavily on patient-reported outcome measures, or PROMs, instruments that capture how patients themselves rate their fatigue, functional capacity, quality of life and psychological state. Among the tools referenced in the study are the Checklist Individual Strength for fatigue, the Hospital Anxiety and Depression Scale, known as HADS, the EuroQol 5-Dimensions questionnaire for health-related quality of life, and the Clinical COPD Questionnaire for respiratory-specific health status. These instruments translate subjective experience into quantifiable scores that can be compared across groups and over time.

The follow-up data delivered the study’s most consequential finding: the fatigue-dominated clusters continued to show a higher disease burden at two and a half years, reflected in worse PROM outcomes compared with the respiratory-dominated clusters. In other words, the symptom pattern identified at the very first clinic visit predicted who would still be struggling years later. Patients whose initial illness was dominated by breathlessness and respiratory complaints fared comparatively better on these self-reported measures, while those in the fatigue and neurocognitive or multisystem groups remained more impaired. This prognostic separation, visible from day one, suggests that a simple symptom-based classification performed at intake could help clinics identify early which patients will need the most intensive and sustained support.

Perhaps the most troubling trend in the data concerns mental health. Anxiety and depression scores, measured with the HADS instrument, increased over time in all four clusters, not merely in the groups with the heaviest physical symptom burden. This universal deterioration cuts against any hopeful narrative of spontaneous recovery across the board and underscores the psychological toll of living with a chronic, poorly understood condition. It also carries practical implications for care planning: mental health support cannot be reserved for the fatigue clusters alone, because rising psychological distress appears to be a shared feature of the post-COVID experience regardless of the dominant physical symptoms.

The study was funded by the Netherlands Organisation for Health Research and Development, ZonMw, under grant 10430302250002, with the funders having no role in study design, data collection, analysis, interpretation, or the decision to publish. Ethical oversight followed Dutch legislation, with the retrospective use of pre-existing clinical data deemed exempt from full review and informed consent waived for that component, while written informed consent was obtained from all respondents to the follow-up questionnaire. The authors declared various competing interests, including trial-related roles for two investigators with pharmaceutical companies outside the submitted work, while the remaining authors reported none.

For clinicians and health system planners, the message of this research is that the post-COVID population, despite its notorious heterogeneity, is not an undifferentiated mass. Distinct symptom-based groups can be identified with a standard clustering algorithm at the point of first assessment, and those groups differ meaningfully in the care they consume and the outcomes they experience. The authors argue that these findings can support more tailored management strategies based on patients’ symptom profiles, enabling effective care planning and earlier access to appropriate interventions. A patient assigned to a fatigue-dominant cluster at intake might be fast-tracked toward rehabilitation and multidisciplinary support, while a respiratory-dominant patient might be directed toward pharmacological management of airway symptoms. As health systems worldwide continue to absorb the long tail of the pandemic, evidence that a data-driven snapshot taken in the first months of illness can forecast the years that follow offers a practical tool for allocating scarce specialist resources where they are needed most.

Subject of Research: Symptom-based clustering of post-COVID patients and its association with healthcare utilisation and long-term health outcomes

Article Title: Symptom-based clustering of post-COVID patients and its association with healthcare utilisation and long-term health outcomes: a multicentre cohort study

Article References: Visser, E., Fokkens, A. S., Koning, K. J., van Geffen, W. H., Visser, A., & Kuijvenhoven, J. C. (2026). Symptom-based clustering of post-COVID patients and its association with healthcare utilisation and long-term health outcomes: a multicentre cohort study. BMC Medicine. https://doi.org/10.1186/s12916-026-05307-8

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05307-8

Keywords: post-COVID, long COVID, K-means clustering, symptom clusters, fatigue, dyspnoea, healthcare utilisation, patient-reported outcome measures, anxiety, depression, cohort study, BMC Medicine

To understand the argument, it helps to understand what latent profile analysis actually does. The method belongs to a family of person-centered statistical approaches, meaning that instead of examining how variables relate to one another across a sample, it looks for subgroups of individuals who share similar response patterns. Technically, the procedure is a finite mixture model: it assumes that the observed population is a mixture of a small number of unobserved, or latent, subpopulations, each with its own characteristic distribution on a set of measured indicators. The analyst specifies a candidate number of classes, the model estimates the probability that each participant belongs to each class, and a series of fit statistics and theoretical judgments determine how many classes best describe the data. When done well, the result is an empirically derived typology, a set of patient profiles that might otherwise remain invisible in averages and correlation matrices.

The appeal for supportive care researchers is obvious. Cancer care is increasingly organized around the recognition that patients are heterogeneous, and that a one-size-fits-all approach to symptom management, psychological support, or rehabilitation leaves many people underserved. Latent profile analysis promises to turn that heterogeneity into something concrete: named subgroups, each with a prevalence estimate and a set of associated risk factors, that can in principle guide screening and targeted intervention. Recent studies cited in the commentary illustrate the breadth of this ambition. Researchers have used the method to identify profiles of reproductive concerns in young women with cervical cancer, to classify the severity of financial toxicity in young and middle-aged patients, to characterize multidimensional dyspnea in advanced lung cancer, and to map patterns of information needs among patients with hematological malignancies in China.

But the Smiths’ central point is that not all of these applications are doing the same kind of work, and the difference matters. They distinguish between mono-construct applications, in which the indicators fed into the model are all facets of a single underlying construct, and multi-construct applications, in which the indicators come from several conceptually distinct domains. In a mono-construct analysis, the latent profiles represent different levels or configurations of one thing. A study of dyspnea, for example, might profile patients according to multiple dimensions of the same breathlessness experience, such as its intensity, its emotional impact, and its functional consequences. The resulting classes are, in effect, a severity typology, and interpreting them as such is straightforward.

Multi-construct applications are a different animal entirely. When a researcher combines indicators of, say, sleep quality, fatigue, and emotional distress into a single latent profile model, the resulting classes are not levels of any one construct. They are instead complex syndromes, constellations of co-occurring problems that may or may not share a common cause. A class characterized by poor sleep and high fatigue and elevated distress is a clinically meaningful pattern, but it is not a point on a single dimension, and the language used to describe it should reflect that. The commentary suggests that much of the confusion in the literature stems from applying mono-construct interpretive habits, such as ordering classes from low to high, to multi-construct solutions where no such ordering exists.

The stakes of this distinction become clearer when one considers how latent profile results are typically translated into clinical recommendations. A mono-construct severity typology invites a fairly direct response: patients in the highest-severity class need the most intensive intervention, and screening tools can be calibrated to detect them. A multi-construct syndrome typology demands more nuanced thinking, because membership in a class does not by itself indicate which problem should be treated first or whether the co-occurrence of symptoms reflects a shared mechanism, a cascade of consequences, or mere statistical coincidence. Treating a syndrome class as if it were a severity group risks designing interventions that target the wrong thing, or that assume a causal structure the analysis never tested.

Several of the recent supportive care studies highlighted in the commentary show how varied the practice has become. One team examined the co-occurrence of severe pain and sleep disturbance in oncology outpatients receiving chemotherapy, a question that sits naturally at the boundary between mono- and multi-construct thinking, since pain and sleep are distinct constructs whose interaction is itself the object of study. Another group profiled sleep hygiene behaviors and dysfunctional sleep beliefs in lung cancer patients undergoing chemotherapy, and examined their impact on cancer-related fatigue. A third applied latent profile analysis to meaning in life among young and middle-aged cancer patients, while a fourth combined latent profiles of emotion regulation strategies with mediation analysis to explore psychological flexibility and subjective well-being in breast cancer patients. Each of these choices about which indicators to combine carries interpretive consequences.

The commentary also touches on the temporal dimension of person-centered modeling. Latent transition analysis, a longitudinal extension of the same modeling framework, was used in one cited study to track how symptom patterns changed among patients undergoing surgery for esophageal cancer. Extending profile analysis over time adds another layer of interpretive complexity, because researchers must now decide whether the classes themselves are stable entities that patients move between, or whether the underlying structure changes as recovery progresses. The Smiths’ framework, by distinguishing what the indicators represent before any modeling begins, offers a way to keep these questions grounded: the meaning of a transition between classes depends on whether those classes describe levels of one construct or configurations of several.

What practical guidance emerges from all of this? The commentary’s implicit message is that rigor in latent profile analysis begins before the software is opened, with a clear articulation of the construct or constructs the indicators are meant to capture. In mono-construct applications, researchers should be able to defend the claim that their indicators are facets of a single dimension, and should interpret class differences along that dimension. In multi-construct applications, they should resist the temptation to rank classes or to describe them as mild and severe, and should instead characterize them as distinct patterns of co-occurrence whose clinical significance requires separate justification. Fit indices and statistical tests, while necessary, cannot settle these questions, because they are questions of meaning rather than of model fit.

For a field that increasingly relies on person-centered methods to justify personalized care, the message is timely. Supportive care in oncology is built on the premise that understanding individual differences improves outcomes, and latent profile analysis is a powerful tool for that purpose when it is matched to the right kind of question. The Smiths’ commentary, published as Volume 34, article 1019 of Supportive Care in Cancer, does not call for the method to be abandoned; it calls for it to be used judiciously, with researchers stating plainly whether their profiles describe degrees of one thing or combinations of many. As the volume of latent profile studies in cancer supportive care continues to grow, that simple act of clarification may prove to be one of the most important methodological habits the field can adopt.

Subject of Research: Methodological guidance on latent profile analysis in supportive cancer care research

Article Title: Judicious use of latent profile analysis in supportive care: the distinction between mono- and multi-construct applications

Article References: Smith, D. K., & Smith, L. E. (2026). Judicious use of latent profile analysis in supportive care: the distinction between mono- and multi-construct applications. Supportive Care in Cancer, 34(10), Article 1019. https://doi.org/10.1007/s00520-026-11226-4

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11226-4

Keywords: latent profile analysis, supportive care, cancer, statistics, person-centered methods, methodology, symptom clusters, oncology, psychosocial research, multivariate analysis, patient subgroups, research methods