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Distinct Subgroups of Patients With Lung Cancer Receiving Chemotherapy: A Latent Transition Analysis.

ABSTRACT: Objectives:To identify subgroups of patients with lung cancer receiving chemotherapy based on the severity dimension of symptom experience, and to examine changes in membership between these subgroups over time. Methods:Patients who were scheduled to receive chemotherapy completed the Chinese version of the MD Anderson Symptom Inventory and the revised lung cancer module with a total of 19 symptom items. Data were collected at three time points: two weeks before chemotherapy (T1), after chemotherapy cycle 1 (T2), and after chemotherapy cycle 3 or above (T3). The latent profile analysis and latent transition analysis were used to identify underlying subgroups and describe changes in subgroup membership over time. Results:From the total sample (N = 195), 160 patients completed the symptom assessment at T1, T2, and T3. Two distinct latent symptom profiles of patients could be identified at T1, T2, and T3, which were classified as "Mild" and "Moderate-Severe" profiles. From T1 to T2 and T3, members in the Mild profile were more likely to move to the Moderate-Severe profile. Chemotherapy protocols, prior surgery treatment, and level of education can predict the transitions. Conclusion:Results provide a better understanding of the patient's different symptom experiences and characteristics. These could help clinicians to anticipate symptom patterns and develop interventions in lung cancer patients who were scheduled to receive chemotherapy for the first time.

SUBMITTER: Li N

PROVIDER: S-EPMC7591394 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Distinct Subgroups of Patients With Lung Cancer Receiving Chemotherapy: A Latent Transition Analysis.

Li Nannan N Hou Lili L Li Shu S

Frontiers in oncology 20201014

<h4>Objectives</h4>To identify subgroups of patients with lung cancer receiving chemotherapy based on the severity dimension of symptom experience, and to examine changes in membership between these subgroups over time.<h4>Methods</h4>Patients who were scheduled to receive chemotherapy completed the Chinese version of the MD Anderson Symptom Inventory and the revised lung cancer module with a total of 19 symptom items. Data were collected at three time points: two weeks before chemotherapy (T1), ...[more]

PMID: 33163391

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Project description:BACKGROUND:Symptom clusters reflect the person's experience of multiple cooccurring symptoms. Although a variety of statistical methods are available to address the clustering of symptoms, latent transition analysis (LTA) characterizes patient membership in classes defined by the symptom experience and captures changes in class membership over time. OBJECTIVES:The purposes of this article are to demonstrate the application of LTA to cancer symptom data and to discuss the advantages and disadvantages of LTA relative to other methods of managing and interpreting data on multiple symptoms. METHODS:Data from a total of 495 adult cancer patients who participated in randomized clinical trials of two symptom management interventions were analyzed. Eight cancer- and treatment-related symptoms reflected the symptom experience. Latent transition analysis was employed to identify symptom classes and evaluate changes in symptom class membership from baseline to the end of the interventions. RESULTS:Three classes, "A (mild symptoms)," "B (physical symptoms)," and "C (physical and emotional symptoms)," were identified. Class A patients had less comorbidity, better physical and emotional role effect, and better physical function than the other classes did. The number of symptoms, general health perceptions, and social functioning were significantly different across the three classes and were poorest in Class C. Emotional role functioning was poorest in Class C. Older adults were more likely to be in Class B than younger adults were. Younger adults were more likely to be in Class C (p < .01). Among patients in Class C at baseline, 41.8% and 29.0%, respectively, transitioned to Classes A and B at the end of the interventions. DISCUSSION:These results demonstrate that symptom class membership characterizes differences in the patient symptom experience, function, and quality of life. Changes in class membership represent longitudinal changes in the course of symptom management. Latent class analysis overcomes the problem of multiple statistical testing that separately addresses each symptom.

Project description:Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the 'comparator variables').This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above.The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and from 6.8%-20.3% for disability, and highest at the 2 weeks follow-up.Latent Class-derived subgroups provided additional prognostic information when compared to a range of variables, but the improvements were not substantial enough to warrant further development into a new prognostic tool. Further research could investigate if these novel subgrouping approaches may help to improve existing tools that subgroup low back pain patients.

Project description:Interindividual variation in genetic background may influence the response to chemotherapy and overall survival for patients with advanced-stage non-small cell lung cancer (NSCLC).To identify genetic variants associated with poor overall survival in these patients, we conducted a genome-wide scan of 307,260 single-nucleotide polymorphisms (SNPs) in 327 advanced-stage NSCLC patients who received platinum-based chemotherapy with or without radiation at the University of Texas MD Anderson Cancer Center (the discovery population). A fast-track replication was performed for 315 patients from the Mayo Clinic followed by a second validation at the University of Pittsburgh in 420 patients enrolled in the Spanish Lung Cancer Group PLATAX clinical trial. A pooled analysis combining the Mayo Clinic and PLATAX populations or all three populations was also used to validate the results. We assessed the association of each SNP with overall survival by multivariable Cox proportional hazard regression analysis. All statistical tests were two-sided.SNP rs1878022 in the chemokine-like receptor 1 (CMKLR1) was statistically significantly associated with poor overall survival in the MD Anderson discovery population (hazard ratio [HR] of death = 1.59, 95% confidence interval [CI] = 1.32 to 1.92, P = 1.42 × 10(-6)), in the PLATAX clinical trial (HR of death = 1.23, 95% CI = 1.00 to 1.51, P = .05), in the pooled Mayo Clinic and PLATAX validation (HR of death = 1.22, 95% CI = 1.06 to 1.40, P = .005), and in pooled analysis of all three populations (HR of death = 1.33, 95% CI = 1.19 to 1.48, P = 5.13 × 10(-7)). Carrying a variant genotype of rs10937823 was associated with decreased overall survival (HR of death = 1.82, 95% CI = 1.42 to 2.33, P = 1.73 × 10(-6)) in the pooled MD Anderson and Mayo Clinic populations but not in the PLATAX trial patient population (HR of death = 0.96, 95% CI = 0.69 to 1.35).These results have the potential to contribute to the future development of personalized chemotherapy treatments for individual NSCLC patients.

Dataset Information

Distinct Subgroups of Patients With Lung Cancer Receiving Chemotherapy: A Latent Transition Analysis.

Publications

Distinct Subgroups of Patients With Lung Cancer Receiving Chemotherapy: A Latent Transition Analysis.

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