Stage-based Model Research Articles

Co-design personal sleep health technology for and with university students.

University students often experience sleep disturbances and disorders. Personal digital technologies present a great opportunity for sleep health promotion targeting this population. However, studies that engage university students in designing and implementing digital sleep health technologies are scarce. This study sought to understand how we could build digital sleep health technologies that meet the needs of university students through a co-design process. We conducted three co-design workshops with 51 university students to identify design opportunities and to generate features for sleep health apps through workshop activities. The generated ideas were organized using the stage-based model of self-tracking so that our findings could be well-situated within the context of personal health informatics. Our findings contribute new design opportunities for sleep health technologies targeting university students along the dimensions of sleep environment optimization, online community, gamification, generative AI, materializing sleep with learning, and personalization.

A stage-based life cycle implementation for individual-based population viability analyses of grey wolves (Canis lupus) in Europe

Population viability analyses (PVA) are important tools for decision-making and planning of adaptive wildlife management actions. While earlier approaches on individual based PVAs have often been age-based, analyses of species with strong social structure might benefit from a stage-based model approach. In this study, we designed an individual-based and stage-based PVA within the software Vortex. As a case study, we applied our model to the German part of the European wolf population, making use of comprehensive data sets originating from the German monitoring regime including individual genotypes. Genetic diversity and inbreeding were important considerations in our analysis, as they could greatly impact population dynamics. We aimed to assess the population's trajectory, extinction risk, and genetic integrity under different scenarios while analyzing factors that could affect its survival. We found that mortality rates at different life cycle stages had varying effects on population growth. Higher mortality rates among pups and dispersers led to negative growth and increased the risk of extinction. In comparison, higher mortality rates among yearlings and subdominant wolves still resulted in positive growth but at a slower rate. Mortality among the breeding individuals within the pack (territorials) had the highest impact on population trajectory. Although the German wolves represent a rather recently founded population, our simulations predicted generally good genetic diversity as long as the population was not held at artificially low numbers.Ultimately, we present a generic, easy-to-use-and-adapt model built within the Vortex environment, that after appropriate modification, calibration and testing could be used within conservation practice and management in collaboration with scientific research. Our study highlights the importance of stage-based modeling for understanding the demographic traits of social species like wolves.

The Stage-Based Model of Addiction-Using Drosophila to Investigate Alcohol and Psychostimulant Responses.

Addiction is a progressive and complex disease that encompasses a wide range of disorders and symptoms, including substance use disorder (SUD), for which there are few therapeutic treatments. SUD is the uncontrolled and chronic use of substances despite the negative consequences resulting from this use. The progressive nature of addiction is organized into a testable framework, the neurobiological stage-based model, that includes three behavioral stages: (1) binge/intoxication, (2) withdrawal/negative affect, and (3) preoccupation/anticipation. Human studies offer limited opportunities for mechanistic insights into these; therefore, model organisms, like Drosophila melanogaster, are necessary for understanding SUD. Drosophila is a powerful model organism that displays a variety of SUD-like behaviors consistent with human and mammalian substance use, making flies a great candidate to study mechanisms of behavior. Additionally, there are an abundance of genetic tools like the GAL4/UAS and CRISPR/Cas9 systems that can be used to gain insight into the molecular mechanisms underlying the endophenotypes of the three-stage model. This review uses the three-stage framework and discusses how easily testable endophenotypes have been examined with experiments using Drosophila, and it outlines their potential for investigating other endophenotypes.

Effects of redesigning the communication of low-income residential energy efficiency programs in the U.S.

Researchers have speculated that limited participation in low-income energy efficiency programs is due, in part, to poor communication of program information. In response, we modified Bamberg’s stage-based model of behavior change (SSBC) to understand decision-making amongst low-income residents regarding residential energy efficiency programs. We then used the model to develop a framework for creating more informative communications. As a test of the framework, we redesigned web pages describing low-income energy efficiency programs in accordance with the framework and examined the effect of the change by randomly assigning low-income survey participants (n=348) to an original or redesigned version of the web page for three electric distribution companies. We found that redesigning these web pages directly, or indirectly, via mediation from likeability of a web page, improved low-income respondents’ assessment of program eligibility; increased their ratings of the importance and value of program outcomes; and increased their intentions to apply for and complete the program. The results signify the legitimacy of the framework as a method for reducing barriers to participation in energy efficiency programs, thereby having practical implications for the organizations and policymakers seeking to increase involvement in these programs. We discuss predictors of application intentions and implications for program communications.

Stage-based model of population dynamics and harvest of Broad-snouted caiman (<i>Caiman latirostris<i>) under different management scenarios

We created a matrix model structured by stages (divided into 5 stages) to evaluate the population dynamics of Caiman latirostris and the population behavior at different management intensities through ranching and hunting of adult individuals. We generated 5000 matrices by sampling the mean and variance values of survivals (pi) and hatching for each stage. For each matrix, we obtained the growth rate λ and performed elasticity analyses. Modifying the mean matrix obtained from the previous analysis, we evaluated different scenarios of ranching, reintroduction, and hunting of adult females of the last two stages (E) raised here (class III animals, > 60 cm snout-vent length, divided into two: E4 and E5). We obtained a mean λ of 1.035 (range 0.88 − 1.12), and 11.9% of the simulations had λ < 1. The vital rate with the highest elasticity and variance was that of the adult females of the last stage. Natural populations can tolerate a maximum of 5% adult female hunting, and ranching can extract 55% of nests from the wild without reintroduction or 80% of nest harvest, returning to the wild at least 3% of hatched animals in the ranching programs. Our model showed that hunting and ranching with reintroduction are feasible strategies to be applied without threatening natural populations. Increasing reintroduction makes it possible to extract more adult individuals and maintain the species’ populations at sustainable levels.

Evolutionary models of academic spin-offs establishment - comparison of Catalonia and South-Moravian region

ABSTRACT The transfer of knowledge from universities has received tremendous attention in the scientific literature. This paper contributes to the discussion about the differences in spin-off process in universities, taking different perspective for comparative case study. First, we put a focus on differences in stage-based model of spin-off creation in conditions of university allocated in developed region and dynamically developing region of transitional (post-socialistic) economy. Then we observe different approaches towards spin-off emergence support in case of universities with long- and short-knowledge transfer tradition and different volumes and quality of research output. The results allow us to explain different definitions of spin-off and support mechanisms in spin-off process, while we primarily observe supportive tools of university TLOs in various stages of development of spin-offs. The study delivers practical implications for universities and TLOs that are just setting-up or rethinking the own models of spin-off establishment support.

Centering the Strengths of American Indian Culture, Families and Communities to Overcome Type 2 Diabetes.

Type 2 diabetes (T2D) is a critical Indigenous health inequity rooted in experiences of colonization and marginalization including disproportionate exposure to stressors, disruption of traditional family and food systems, and attacks on cultural practices that have led to more sedentary lifestyles. Thus, an important step in redressing inequities is building awareness of and interventions attuned to unique Indigenous contexts influencing T2D and Indigenous culture as a pathway to community wellbeing. Using a dynamic, stage-based model of intervention development and evaluation, we detail the creation and evolution of a family-based, culturally centered T2D preventive intervention: Together on Diabetes (later Together Overcoming Diabetes) (TOD). The TOD program was built by and for Indigenous communities via community-based participatory research and has been implemented across diverse cultural contexts. The TOD curriculum approaches health through a holistic lens of spiritual, mental, physical and emotional wellness. Preliminary evidence suggests TOD is effective in reducing diabetes risk factors including lowering BMI and depressive symptoms, and the program is viewed favorably by participants and community members. We discuss lessons learned regarding collaborative intervention development and adaptation across Indigenous cultures, as well as future directions for TOD.

Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma

BackgroundAmong various clinicopathologic factors used to identify low-risk upper tract urothelial carcinoma (UTUC), tumor grade and stage are of utmost importance. The clinical value added by inclusion of other risk factors remains unproven. ObjectiveTo assess the performance of a tumor grade- and stage-based (GS) model to identify patients with UTUC for whom kidney-sparing surgery (KSS) could be attempted. Design, setting, and participantsIn this international study, we reviewed the medical records of 1240 patients with UTUC who underwent radical nephroureterectomy. Complete data needed for risk stratification according to the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) guidelines were available for 560 patients. Outcome measurements and statistical analysisUnivariable and multivariable logistic regression analyses were performed to determine if risk factors were associated with the presence of localized UTUC. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the GS, EAU, and NCCN models in predicting pathologic stage were calculated. Results and limitationsOverall, 198 patients (35%) had clinically low-grade, noninvasive tumors, and 283 (51%) had ≤pT1disease. On multivariable analyses, none of the EAU and NCCN risk factors were associated with the presence of non–muscle-invasive UTUC among patients with low-grade and low-stage UTUC. The GS model exhibited the highest accuracy, sensitivity, and negative predictive value among all three models. According to the GS, EAU, and NCCN models, the proportion of patients eligible for KSS was 35%, 6%, and 4%, respectively. Decision curve analysis revealed that the net benefit of the three models was similar within the clinically reasonable range of probability thresholds. ConclusionsThe GS model showed favorable predictive accuracy and identified a greater number of KSS-eligible patients than the EAU and NCCN models. A decision-making algorithm that weighs the benefits of avoiding unnecessary kidney loss against the risk of undertreatment in case of advanced carcinoma is necessary for individualized treatment for UTUC patients. Patient summaryWe assessed the ability of three models to predict low-grade, low-stage disease in patients with cancer of the upper urinary tract. No risk factors other than grade assessed on biopsy and stage assessed from scans were associated with better prediction of localized cancer. A model based on grade and stage may help to identify patients who could benefit from kidney-sparing treatment of their cancer.

A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases.

Background The optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial. Methods We selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model. Results A total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively. Conclusions Our nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases.

Ready to roll: Strategies and actions to enhance organizational readiness for implementation in community mental health.

Organizational readiness for implementation (ORI) is a barrier to disseminating recovery-oriented evidence-based practices for people with severe mental illnesses. However, there is a knowledge gap regarding implementation strategies and specific actions related to those strategies that may enhance ORI across organizations. The Transtheoretical Model (TTM) is a well-established stage-based model used to support organizational change. In TTM, the first three stages-Pre-contemplation, Contemplation, and Preparation-represent the pre-implementation phase, during which ORI should be developed. This study explored ORI enhancement strategies reported as useful by different stakeholders in the field of community mental health (CMH) and tested the applicability of the TTM readiness stages as an organizing framework to direct ORI development. The study included the following two phases: (1) a qualitative exploration of ORI enhancement actions reported by various stakeholders in the CMH field (N = 16) based on their experience in successful implementation projects and (2) a consolidation process performed by the research team to create coherent groups of actions and their overarching implementation strategies per readiness stage. A comprehensive list of strategies and related actions was created. The strategies and actions correspond with each of the first three stages of the TTM. Stakeholders differed in their methods of facilitating engagement across the organization and in their level of involvement in developing ORI. This study is a first step in conceptualizing a systematic process for developing ORI using the TTM as a theoretical framework. Future studies should test the transferability and effectiveness of this repository to other implementation efforts, expend the lists per stage, and further explore differences between stakeholders' role in ORI development. Interventions that were proven effective in supporting the recovery of people with mental illnesses are not sufficiently available in the field of community mental health. One barrier to getting those interventions implemented is the lack of willingness and preparedness of organizations to change their practice, also referred to as organizational readiness for implementation (ORI). Little is known about how to enhance ORI to increase the availability of interventions. This study explored actions used to enhance ORI by people in different roles who successfully implemented new recovery-oriented interventions in community mental health settings. The actions were organized into three stages of readiness development based on the Transtheoretical Model (TTM) of behavioral change. Differences were found between administrators, consultants, supervisors, and providers in the type and number of strategies they described. The results show the applicability of the TTM as an organizing framework for ORI development and provide sets of strategies and specific actions to support different readiness needs across the organization.

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification.

The tumor stroma plays an important role in tumor progression and chemotherapeutic resistance; however, its role in colon cancer (CC) survival prognosis remains to be investigated. Here, we identified tumor stroma biomarkers and evaluated their role in CC prognosis stratification. Four independent datasets containing a total of 1,313 patients were included in this study and were divided into training and testing sets. Stromal scores calculated using the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithm were used to assess the tumor stroma level. Kaplan-Meier curves and the log-rank test were used to identify relationships between stromal score and prognosis. Tumor stroma biomarkers were identified by cross-validation of multiple datasets and bioinformatics methods. Cox proportional hazards regression models were constructed using four prognosis factors (age, tumor stage, the ESTIMATE stromal score, and the biomarker stromal score) in different combinations for prognosis prediction and compared. Patients with high stromal scores had a lower overall survival rate (p = 0.00016), higher risk of recurrence (p < 0.0001), and higher probability of chemotherapeutic resistance (p < 0.0001) than those with low scores. We identified 16 tumor stroma biomarkers and generated a new prognosis indicator termed the biomarker stromal score (ranging from 0 to 16) based on their expression levels. Its addition to an age/tumor stage-based model significantly improved prognosis prediction accuracy. In conclusion, the tumor stromal score is significantly negatively associated with CC survival prognosis, and the new tumor stroma indicator can improve CC prognosis stratification.

Designing collaborative problem solving assessment tasks in engineering: an evaluative judgement perspective

This paper argues that development of students’ evaluative judgement capability, which encapsulates a range of cognitive and social skills, should be considered as a primary objective of the collaborative problem-solving assessment practices in engineering. This is a response to call for preparing job-ready students. To meet the multiple demands of complex, agile and innovative projects in the world of work, engineering graduates need to draw on their own decision-making ability and make informed judgements in various collaborative problem-solving situations. However, a review of literature shows that there is a lack of a robust model on which assessment designers can rely as a guidance to develop collaborative problem-solving assessment activities that might progressively contribute to the development of this much-needed capability. By conceptualising the concept of evaluative judgement and identifying the elements of collaborative problem-solving that can foster it, the paper concludes by proposing a stage-based model for designing and implementing assessment activities at the individual course level.

'Yes, I comply!'

As science becomes increasingly data-intensive, the requirements for comprehensive Research Data Management (RDM) grow. This often overwhelms scientists, requiring more workload and training. The failure to conduct effective RDM leads to producing research artefacts that cannot be reproduced or reused. Past research placed high value on supporting data science workers, but focused mainly on data production, collection, processing, and sensemaking. In order to understand practices and needs of data science workers in relation to documentation, preservation, sharing, and reuse, we conducted a cross-domain study with 15 scientists and data managers from diverse scientific domains. We identified five core concepts which describe requirements, drivers, and boundaries in the development of commitment for RDM, essential for generating reproducible research artefacts: Practice, Adoption, Barriers, Education, and Impact. Based on those concepts, we introduce a stage-based model of personal RDM commitment evolution. The model can be used to drive the design of future systems that support a transition to open science. We discuss infrastructure, policies, and motivations involved at the stages and transitions in the model. Our work supports designers in understanding the constraints and challenges involved in designing for reproducibility in an age of data-driven science.

Adapting the stage-based model of personal informatics for low-resource communities in the context of type 2 diabetes.

Growing availability of self-monitoring technologies creates new opportunities for collection of personal health data and their use in personalized health informatics interventions. However, much of the previous empirical research and existing theories of individuals' engagement with personal data focused on early adopters and data enthusiasts. Less is understood regarding ways individuals from medically underserved low-income communities who live with chronic diseases engage with self-monitoring in health. In this research, we adapted a widely used theoretical framework, the stage-based model of personal informatics, to the unique attitudes, needs, and constraints of low-income communities. We conducted a qualitative study of attitudes and perceptions regarding tracking and planning in health and other contexts (e.g., finances) among low-income adults living with type 2 diabetes. This study showed distinct differences in participants' attitudes and behaviors around tracking and planning, as well as wide variability in their sense of being in charge of different areas of one's life. Ultimately, we found a strong connection between these two: perceptions of being in charge seems to be strongly connected to an individual's proactive or reactive tracking and planning in that area. Whereas individuals with a greater sense of being in charge of their health were more proactive, meaning they were likely to engage with all the stages of personal informatics model on their own, those with less of a sense of being in charge were more likely to be reactive-relying on their healthcare providers for several critical stages of self-monitoring (deciding what data to collect, integrating data from multiple sources, reflecting over patterns in collected data, and arriving at conclusions and implications for action). Perhaps as a result, these individuals were less likely to experience increases in self-awareness and self-knowledge, common motivating factors to engaging in self-monitoring in the future. We argue that adapting this framework in a way that highlights gaps in individuals' engagement has a number of important implications for future research in biomedical informatics and for the design of new interventions that promote engagement with self-monitoring, and that are robust in light of fragmented engagement.

Travelling to a Sport Event: Profiling Sport Fans Against the Transtheoretical Model of change

This article profiles the travel behavior of sport fans against the transtheoretical model of change (TTM) and its application to sport events. Using the four constructs of the TTM, we distributed a self-reporting survey to sport fans prior to home fixtures. There is some synergy with the theorized stages of change and processes of change in this context. Notwithstanding, the results showed a high level of commitment to others in the early stages of change—a movement away from the prescribed theory. Results from decisional balance and self-efficacy items reaffirmed the congruence with theory and the application of the TTM to sport fans and their travel behavior. This study assessed the application of a stage-based model of change within a sport event context; it provides an exploration of the antecedents of behavior change indicators relevant to sport fans, thus enabling policy makers to make informed decisions about future travel behavior change.

Transtheoretical model as a framework for promoting cardiovascular health through behaviour change: a systematic review

Introduction: Cardiovascular disease (CVD) remains the single leading cause of death around the globe. The Transtheoretical model (TTM) is a successful framework for guiding behaviour change programmes for several health behaviours, yet there are limited reviews on how TTM has been used to facilitate behaviour change associated with reduced CVD. Therefore, the aims of this systematic review are to determine (i) how TTM has been used in promoting physical activity, fruit and vegetable consumption, and smoking cessation and (ii) gaps in knowledge in the use of stage-based model to improve physical activity, intake of fruit and vegetables, and smoking cessation. Materials and Methods: The approach to this systematic review was informed by current recommendations known as Preferred Reporting Items for Systematic Reviews and Meta-analysis Approach (PRISMA). Results: Constructs of TTM were used in the design of studies, facilitation of health behaviour change, monitoring of change in the movement of the participants across the change continuum, and evaluation of the effectiveness of health behaviour programmes. TTM was incorporated with other models, including Health Belief Model (HBM), social-ecological model, and social cognitive theory to facilitate behaviour change. There were limited studies that used all the four constructs of TTM to promote health behaviour change. Conclusion: TTM is a promising tool for designing, facilitating, monitoring, and evaluating behaviour change associated with reduced cardiovascular disease (CVD). TTM may be incorporated with other models, including HBM, social-ecological model, and social cognitive theory to facilitate behaviour change.

Dynamic capabilities triggered by cloud sourcing: a stage-based model of business model innovation

Current research offers very limited insights on the process of how the adoption and continued use of cloud sourcing might trigger and push the development of business model innovation and affect the competitive advantage of a firm. Applying an abductive approach, with two longitudinal case studies of cloud sourcing firms, and a theoretical framework based on stage-based models of business model innovation and the dynamic capability view of the firm, we develop a model of stage-based business model innovation related to the adoption and continued use of cloud sourcing. The model identifies three business model innovation stages characterized by specific types of capabilities. In between the three stages, we identify three dynamic junctures that the firm and its managers have to overcome to progress from one stage to another. In the dynamic junctures three types of dynamic capabilities were key; sensing, seizing and transformation capabilities, to pass to the next stage. The model contributes to a better understanding of the evolution of dynamic capabilities as well as the evolution of the cloud sourcing firm and cloud-based business model innovation.

The Prognostic Value Of Lymph Node Ratio In Patients With N2 Stage Lung Squamous Cell Carcinoma: A Nomogram And Heat Map Approach.

BackgroundLymph node ratio (LNR), defined as the ratio of the number of positive lymph nodes to the total of all resected nodes, has been reported to be a predictor of survival of patients with several types of cancer. However, the prognostic value of LNR and other factors in patients with resected N2 stage lung squamous cell carcinoma has never been considered.MethodsData from 1778 patients with resected N2 stage lung squamous cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The optimal cutoff value of LNR was identified by X-tile. A multivariable Cox model and corresponding nomogram were constructed to predict overall survival (OS) and cancer-specific survival (CSS). Both the cutoff value of LNR and the model were further validated in 146 similar patients treated in Zhongshan Hospital. Heat maps were created to visualize the distribution of LNR and the number of positive lymph nodes with the predicted survival probabilities.ResultsThe optimal cutoff value for LNR was identified as 0.42. Multivariable analysis showed that age, sex, tumor laterality, type of surgery, T stage, chemotherapy and LNR were independently correlated with OS. Harrell’s C-index of the nomogram (0.64) was significantly higher than the index of the T stage-based model (0.54). Calibration curves showed good agreement between predicted and observed survival probabilities. The robustness of the model was also demonstrated by external validation.ConclusionLNR less than 0.42 was associated with improved OS and CSS for patients with resected N2 stage lung squamous cell carcinoma.

Impacts of congestion pricing and reward strategies on automobile travelers’ morning commute mode shift decisions

This paper studies the impacts of congestion pricing and reward strategies on automobile travelers’ morning commute mode shift decisions using stated preference travel mode choice data of over 1000 automobile travelers collected in Beijing inner districts. To address the complex impacts of these strategies on automobile travelers, a stage-based model framework is developed to analyze their mode shift decision-making process (whether they will shift from using automobile to public transit, biking or walking or continue using automobile) under these strategies. Four multilevel structural equation models are created for participants using automobile (personal vehicle and/or taxi) as the most common mode of transportation (hereafter referred to as “more habitual automobile travelers”) and those using automobile as second most common mode (hereafter referred to as “less habitual automobile travelers”) under each strategy. Model estimation results show that the impacts of latent psychological factors on mode shift decisions under congestion pricing and reward strategies are significantly different between more and less habitual automobile travelers. The results also show that congestion pricing strategies are more effective than reward strategies in promoting mode shifts among more habitual automobile travelers, while the opposite is observed for less habitual automobile travelers. This study provides insights for designing congestion pricing and reward strategies and illustrates the importance of developing complementary modules that target numerous factors in different stages of the mode shift decision-making process to effectively promote mode shifts from automobile to sustainable travel modes in China.

Development and validation of a prognostic model to predict the prognosis of patients who underwent chemotherapy and resection of pancreatic adenocarcinoma: a large international population-based cohort study

BackgroundPancreatic cancer (PaC) remains extremely lethal worldwide even after resection. PaC resection rates are low, making prognostic studies in resected PaC difficult. This large international population-based study aimed at exploring factors associated with survival in patients with resected TNM stage I–II PaC receiving chemotherapy and at developing and internationally validating a survival-predicting model.MethodsData of stage I–II PaC patients resected and receiving chemotherapy in 2003–2014 were obtained from the national cancer registries of Belgium, the Netherlands, Slovenia, and Norway, and the US Surveillance, Epidemiology, and End Results (SEER)-18 Program. Multivariable Cox proportional hazards models were constructed to investigate the associations of patient and tumor characteristics with overall survival, and analysis was performed in each country respectively without pooling. Prognostic factors remaining after backward selection in SEER-18 were used to build a nomogram, which was subjected to bootstrap internal validation and external validation using the European datasets.ResultsA total of 11,837 resected PaC patients were analyzed, with median survival time of 18–23 months and 3-year survival rates of 21–31%. In the main analysis, patient age, tumor T stage, N stage, and differentiation were associated with survival across most countries, with country-specific association patterns and strengths. However, tumor location was mostly not significantly associated with survival. Resection margin, hospital type, tumor size, positive and harvested lymph node number, lymph node ratio, and comorbidity number were associated with survival in certain countries where the information was available. A median survival time- and 1-, 2-, 3-, and 5-year survival probability-predictive nomogram incorporating the backward-selected variables in the main analysis was established. It fits each European national cohort similarly well. Calibration curves showed very good agreement between nomogram-prediction and actual observation. The concordance index of the nomogram (0.60) was significantly higher than that of the T and N stage-based model (0.56) for predicting survival.ConclusionsIn these large international population-based cohorts, patients with resected PaC receiving chemotherapy have distinct characteristics independently associated with survival, with country-specific patterns and strengths. A robust benchmark population-based survival-predicting model is established and internationally validated. Like previous models predicting survival in resected PaC, our nomogram performs modestly.