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Defining Survivorship Trajectories Across Patients With Solid Tumors: An Evidence-Based Approach.

ABSTRACT: Importance:Survivorship involves a multidisciplinary approach to surveillance and management of comorbidities and secondary cancers, overseen by oncologists, surgeons, and primary care physicians. Optimal timing and coordination of care, however, is unclear and often based on arbitrary 5-year cutoffs. Objective:To determine high- and low-risk periods for all tumor types that could define when survivorship care might best be overseen by oncologists and when to transition to primary care physicians. Design, Setting, and Participants:In this pan-cancer, longitudinal, observational study, excess mortality hazard, calculated as an annualized mortality risk above a baseline population, was plotted over time. The time this hazard took to stabilize defined a high-risk period. The percent morality elevation above age- and sex-matched controls in the latter low-risk period was reported as a mortality gap. The US population-based Surveillance, Epidemiology, and End Results database defined the cancer population, and the US Census life tables defined controls. Incident cases of patients with cancer were separated into tumor types based on International Classification of Diseases for Oncology definitions. Exposures:Population-level data on incident cancer cases was compared with the general US population. Main Outcomes and Measures:Overall mortality and cause of death were reported on observed cancer cases. Results:A total of 2 317 185 patients (median age, 63 years; 49.8% female) with 66 primary tumor types were evaluated. High-risk surveillance period durations ranged from less than 1 year (breast, prostate, lip, ocular, and parathyroid cancers) up to 19 years (unspecified gastrointestinal cancers). The annualized mortality gap, representing the excess mortality in the stable period, ranged from a median 0.26% to 9.33% excess annual mortality (thyroid and hypopharyngeal cancer populations, respectively). Cluster analysis produced 6 risk cluster groups: group 1, with median survival of 16.2 (5th to 95th percentile range [PR], 10.7-40.2) years and median high-risk period of 2.5 (PR, 0-5.0) years; group 2, 8.3 (PR, 5.1-23.3) and 2.5 (PR, 4.0-8.0) years; group 3, 2.8 (PR, 1.4-3.7) and 7.0 (PR, 6.0-11.1) years; group 4, 1.6 (PR, 1.5-1.8) and 6.0 (PR, 5.1-11.4) years; group 5, 0.8 (PR, 0.5-1.2) and 0.8 (PR, 0.5-1.2) years; and group 6, 0.5 (PR, 0.4-0.8) and 12.0 (PR, 9.3-12.9) years, respectively. Subanalyses of selected tumor types in these groups revealed that stratifying on stage and histologic type can change the risk cluster and guidance for care. Conclusions and Relevance:These findings indicate that a standardized 5-year surveillance period is inadequate for some cancers while excessive for others. High-risk cancers require the most resources with the longest high-risk period, highest persistent baseline mortality risk, and longest period of primary cancer mortality, all arguing for longer follow-up with an oncologist in these cancers.

SUBMITTER: Dood RL

PROVIDER: S-EPMC6248088 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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Defining Survivorship Trajectories Across Patients With Solid Tumors: An Evidence-Based Approach.

Dood Robert L RL Zhao Yang Y Armbruster Shannon D SD Coleman Robert L RL Tworoger Shelley S Sood Anil K AK Baggerly Keith A KA

JAMA oncology 20181101 11

<h4>Importance</h4>Survivorship involves a multidisciplinary approach to surveillance and management of comorbidities and secondary cancers, overseen by oncologists, surgeons, and primary care physicians. Optimal timing and coordination of care, however, is unclear and often based on arbitrary 5-year cutoffs.<h4>Objective</h4>To determine high- and low-risk periods for all tumor types that could define when survivorship care might best be overseen by oncologists and when to transition to primary ...[more]

PMID: 29860375

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Project description:BACKGROUND:Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients. METHODS:This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critical illness was defined at the time when acute physiological characteristics were no longer more predictive of in-hospital mortality (i.e., vital status at hospital discharge) than antecedent characteristics. Latent growth mixture modeling was used to identify distinct trajectory classes by using Sequential Organ Failure Assessment score measured during intensive care unit stay as the outcome, and persistent critical illness transition time was explored in each latent class. RESULTS:The mortality was 16.7% (3828/22,868) in the study cohort. Acute physiological model was no longer more predictive of in-hospital mortality than antecedent characteristics at 15?days after intensive care unit admission in the overall population. Only a minority of the study subjects (n?=?643, 2.8%) developed persistent critical illness, but they accounted for 19% (15,834/83,125) and 10% (19,975/198,833) of the total intensive care unit and hospital bed-days, respectively. Five latent classes were identified. Classes 1 and 2 showed increasing Sequential Organ Failure Assessment score over time and transition to persistent critical illness occurred at 16 and 27?days, respectively. The remaining classes showed a steady decline in Sequential Organ Failure Assessment scores and the transition to persistent critical illness occurred between 6 and 8?days. Elevated urea-to-creatinine ratio was a good biochemical signature of persistent critical illness. CONCLUSIONS:While persistent critical illness occurred in a minority of patients with sepsis, it consumed vast medical resources. The transition time differs substantially across latent classes, indicating that the allocation of medical resources should be tailored to different classes of patients.

Project description:BackgroundImmune checkpoint inhibitors (ICIs) have particular, immune-related adverse events (irAEs), as a consequence of interfering with self-tolerance mechanisms. The incidence of irAEs varies depending on ICI class, administered dose and treatment schedule. The aim of this study was to define a baseline (T0) immune profile (IP) predictive of irAE development.MethodsA prospective, multicenter study evaluating the immune profile (IP) of 79 patients with advanced cancer and treated with anti-programmed cell death protein 1 (anti-PD-1) drugs as a first- or second-line setting was performed. The results were then correlated with irAEs onset. The IP was studied by means of multiplex assay, evaluating circulating concentration of 12 cytokines, 5 chemokines, 13 soluble immune checkpoints and 3 adhesion molecules. Indoleamine 2, 3-dioxygenase (IDO) activity was measured through a modified liquid chromatography-tandem mass spectrometry using the high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) method. A connectivity heatmap was obtained by calculating Spearman correlation coefficients. Two different networks of connectivity were constructed, based on the toxicity profile.ResultsToxicity was predominantly of low/moderate grade. High-grade irAEs were relatively rare, while cumulative toxicity was high (35%). Positive and statistically significant correlations between the cumulative toxicity and IP10 and IL8, sLAG3, sPD-L2, sHVEM, sCD137, sCD27 and sICAM-1 serum concentration were found. Moreover, patients who experienced irAEs had a markedly different connectivity pattern, characterized by disruption of most of the paired connections between cytokines, chemokines and connections of sCD137, sCD27 and sCD28, while sPDL-2 pair-wise connectivity values seemed to be intensified. Network connectivity analysis identified a total of 187 statistically significant interactions in patients without toxicity and a total of 126 statistically significant interactions in patients with toxicity. Ninety-eight interactions were common to both networks, while 29 were specifically observed in patients who experienced toxicity.ConclusionsA particular, common pattern of immune dysregulation was defined in patients developing irAEs. This immune serological profile, if confirmed in a larger patient population, could lead to the design of a personalized therapeutic strategy in order to prevent, monitor and treat irAEs at an early stage.

Dataset Information

Defining Survivorship Trajectories Across Patients With Solid Tumors: An Evidence-Based Approach.

Publications

Defining Survivorship Trajectories Across Patients With Solid Tumors: An Evidence-Based Approach.

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