Dataset Information

Relative mortality in soft tissue sarcoma patients: a Danish population-based cohort study.

ABSTRACT:

Background

Cancer-specific survival estimates rely on precise and correct data on the cause of death; however, these data can be difficult to acquire, particularly in elderly patients where comorbidity is common. Furthermore, while some deaths are directly related to cancer, others are more complex, with cancer merely contributing. Another, more precise, method is to assess the relative mortality, i.e., mortality in patients compared to the general population. The aims of this study were to describe the relative mortality in soft tissue sarcoma, and to compare the relative mortality with the cancer-specific mortality.

Methods

We included 1246 patients treated for soft tissue sarcoma and 6230 individually age- and sex-matched individuals from the general population. The relative mortality was estimated as rates, and rate ratios adjusted for comorbidity. Mortality rate ratios were computed using the Cox proportional hazard model for 0-5 years and 5-10 years, according to age, sex and level of comorbidity. The cancer-specific mortality was compared to the corresponding relative mortality.

Results

The overall 5- and 10-year relative mortality was 32.8% and 36.0%. Patients with low-grade soft tissue sarcoma did not have increased mortality compared with the general population. Soft tissue sarcoma patients had a 4.4 times higher risk of dying within the first five years after diagnosis and a 1.6 times higher risk between five and ten years compared with the general comparison cohort. The relative mortality varied according to age, grade, stage at diagnosis, and level of comorbidity, being highest in younger patients and in patients without comorbidity. The overall 5- and 10-year cancer-specific mortality was underestimated by 1.5 and overestimated by 0.7 percentage points compared to the relative mortality, respectively. No statistical significant difference between the relative and the cancer-specific mortality was found.

Conclusion

The relative mortality provides an unbiased and accurate method to differentiate between cancer-specific and non-cancer-specific deaths. However, when data on the cause of death is of a sufficient quality, there is no difference between relative mortality and disease-specific mortality based on death certificates.

SUBMITTER: Maretty-Nielsen K

PROVIDER: S-EPMC4177240 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Publications

Relative mortality in soft tissue sarcoma patients: a Danish population-based cohort study.

Maretty-Nielsen Katja K Aggerholm-Pedersen Ninna N Keller Johnny J Safwat Akmal A Baerentzen Steen S Pedersen Alma B AB

BMC cancer 20140919

<h4>Background</h4>Cancer-specific survival estimates rely on precise and correct data on the cause of death; however, these data can be difficult to acquire, particularly in elderly patients where comorbidity is common. Furthermore, while some deaths are directly related to cancer, others are more complex, with cancer merely contributing. Another, more precise, method is to assess the relative mortality, i.e., mortality in patients compared to the general population. The aims of this study were ...[more]

PMID: 25239186

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Project description:IntroductionSarcoma is a rare type of cancer. The incidence increases with age and elderly patients may have comorbidity that affects the prognosis. The aim of this study was to describe the type and prevalence of comorbidity in a nationwide population-based study in Denmark from 2000-2013 and to analyse the impact of the different comorbidities on mortality.Material and methodsThe Danish Sarcoma Registry is a national clinical database containing all patients with sarcoma in the extremities or trunk wall from 2000 and onwards. By linking data to other registries, we were able to get patient information on an individual level including date and cause of death as well as the comorbidity type up to 10 years prior to the sarcoma diagnosis. Based on diseases in the Charlson Comorbidity Index, we pooled the patients into six categories: no comorbidity, cardiopulmonary disease, gastrointestinal disease, neurovascular disease, malignant neoplasms, and miscellaneous (diabetes, renal and connective tissue diseases). 2167 patients were included.ResultsThe prevalence of comorbidity was 20%. For patients with localized disease, comorbidity increased the disease-specific mortality significantly (HR 1.70 (95% CI 1.36-2.13)). For patients with metastatic disease at the time of diagnosis, comorbidity did not affect the disease-specific mortality (HR 1.05 (95% CI 0.78-1.42)). The presence of another cancer diagnosis within 10 years prior to the sarcoma diagnosis was the only significant independent prognostic factor of disease-specific mortality with an increase of 66% in mortality rate compared to patients with no comorbidity (HR 1,66 (95% CI 1.22-2.25)).ConclusionComorbidity is a strong independent prognostic factor of mortality in patients with localized disease. This study emphasizes the need for optimizing the general health of comorbid patients in order to achieve a survival benefit from treatment of patients with localized disease, as this is potentially modifiable.

Project description:Objective: Trabectedin is an anti-cancer drug commonly used for the treatment of patients with metastatic soft tissue sarcoma (mSTS). Despite its recognized efficacy, significant variability in pharmacological response has been observed among mSTS patients. To address this issue, this pharmacometabolomics study aimed to identify pre-dose plasma metabolomics signatures that can explain individual variations in trabectedin pharmacokinetics and overall clinical response to treatment. Methods: In this study, 40 mSTS patients treated with trabectedin administered by 24 h-intravenous infusion at a dose of 1.5 mg/m2 were enrolled. The patients' baseline plasma metabolomics profiles, which included derivatives of amino acids and bile acids, were analyzed using multiple reaction monitoring LC-MS/MS together with their pharmacokinetics profile of trabectedin. Multivariate Partial least squares regression and univariate statistical analyses were utilized to identify correlations between baseline metabolite concentrations and trabectedin pharmacokinetics, while Partial Least Squares-Discriminant Analysis was employed to evaluate associations with clinical response. Results: The multiple regression model, derived from the correlation between the AUC of trabectedin and pre-dose metabolomics, exhibited the best performance by incorporating cystathionine, hemoglobin, taurocholic acid, citrulline, and the phenylalanine/tyrosine ratio. This model demonstrated a bias of 4.6% and a precision of 17.4% in predicting drug AUC, effectively accounting for up to 70% of the inter-individual pharmacokinetic variability. Through the use of Partial least squares-Discriminant Analysis, cystathionine and hemoglobin were identified as specific metabolic signatures that effectively distinguish patients with stable disease from those with progressive disease. Conclusions: The findings from this study provide compelling evidence to support the utilization of pre-dose metabolomics in uncovering the underlying causes of pharmacokinetic variability of trabectedin, as well as facilitating the identification of patients who are most likely to benefit from this treatment. Graphical Abstract

Dataset Information

Relative mortality in soft tissue sarcoma patients: a Danish population-based cohort study.

Background

Methods

Results

Conclusion

Publications

Relative mortality in soft tissue sarcoma patients: a Danish population-based cohort study.

Similar Datasets

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