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Aims/hypothesis: Tables reporting life expectancies by common risk factors are available for individuals with type 2 diabetes; however, there is currently no published equivalent for individuals with type 1 diabetes. We aimed to develop a life expectancy table using a recently published simulation model for individuals with type 1 diabetes., Methods: The simulation model was developed using data from a real-world population of patients with type 1 diabetes selected from the Swedish National Diabetes Register. The following six important risk factors were included in the life table: sex; age; current smoking status; BMI; eGFR; and HbA 1c . For each of 1024 cells in the life expectancy table, a synthetic cohort containing 1000 individuals was created, with other risk factors assigned values representative of the real-world population. The simulations were executed for all synthetic cohorts and life expectancy for each cell was calculated as mean survival time of the individuals in the respective cohort., Results: There was a substantial variation in life expectancy across patients with different risk factor levels. Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0-53.9 years). The variation in life expectancy was a function of the combination of risk factor values, with HbA 1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. Individuals with the lowest level (20 kg/m 2 ) and highest level of BMI (35 kg/m 2 ) had a lower life expectancy compared with those with a BMI of 25 kg/m 2 . Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors., Conclusions/interpretation: The life expectancy table generated in this study shows a substantial variation in life expectancy across individuals with different modifiable risk factors. The table allows for rapid communications of risk in an easily understood format between healthcare professionals, health economists, researchers, policy makers and patients. Particularly, it supports clinicians in their discussion with patients about the benefits of improving risk factors., (© 2021. The Author(s).)

Introduction: Interstitial lung diseases are characterised by scarring of lung tissue that leads to reduced transfer of oxygen into the blood, decreased exercise capacity and premature death. Ambulatory oxygen therapy may be used to treat exertional oxyhaemoglobin desaturation, but there is little evidence to support its efficacy and there is wide variation in clinical practice. This study aims to compare the clinical efficacy and cost-effectiveness of ambulatory oxygen versus ambulatory air in people with fibrotic interstitial lung disease and exertional desaturation., Methods and Analysis: A randomised, controlled trial with blinding of participants, clinicians and researchers will be conducted at trial sites in Australia and Sweden. Eligible participants will be randomised 1:1 into two groups. Intervention participants will receive ambulatory oxygen therapy using a portable oxygen concentrator (POC) during daily activities and control participants will use an identical POC modified to deliver air. Outcomes will be assessed at baseline, 3 months and 6 months. The primary outcome is change in physical activity measured by number of steps per day using a physical activity monitor (StepWatch). Secondary outcomes are functional capacity (6-minute walk distance), health-related quality of life (St George Respiratory Questionnaire, EQ-5D-5L and King's Brief Interstitial Lung Disease Questionnaire), breathlessness (Dyspnoea-12), fatigue (Fatigue Severity Scale), anxiety and depression (Hospital Anxiety and Depression Scale), physical activity level (GENEActive), oxygen saturation in daily life, POC usage, and plasma markers of skeletal muscle metabolism, systematic inflammation and oxidative stress. A cost-effectiveness evaluation will also be undertaken., Ethics and Dissemination: Ethical approval has been granted in Australia by Alfred Hospital Human Research Ethics Committee (HREC/18/Alfred/42) with governance approval at all Australian sites, and in Sweden (Lund Dnr: 2019-02963). The results will be published in peer-reviewed scientific journals, presented at conferences and disseminated to consumers in publications for lay audiences., Trial Registration Number: ClinicalTrials.gov Registry (NCT03737409)., Competing Interests: Competing interests: All authors report non-financial support from BOC Australia in the delivery of the trial devices. AEH, YHK, LT, NSLG and CFM report non-financial support from Air Liquide Healthcare, outside the submitted work. YHK reports grants and personal fees from Boehringer Ingelheim, and personal fees from Roche, outside the submitted work. MS received research grants from Boehringer Ingelheim and Roche, outside the submitted work., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To develop a patient-level simulation model for predicting lifetime health outcomes of patients with type 1 diabetes and as a tool for economic evaluation of type 1 diabetes treatment based on data from a large, longitudinal cohort., Research Design and Methods: Data for model development were obtained from the Swedish National Diabetes Register. We derived parametric proportional hazards models predicting the absolute risk of diabetes complications and death based on a wide range of clinical variables and history of complications. We used linear regression models to predict risk factor progression. Internal validation was performed, estimates of life expectancies for different age-sex strata were computed, and the impact of key risk factors on life expectancy was assessed., Results: The study population consisted of 27,841 patients with type 1 diabetes with a mean duration of follow-up of 7 years. Internal validation showed good agreement between the predicted and observed cumulative incidence of death and 10 complications. Simulated life expectancy was ∼13 years lower than that of the sex- and age-matched general population, and patients with type 1 diabetes could expect to live with one or more complications for ∼40% of their remaining life. Sensitivity analysis showed the importance of preventing renal dysfunction, hypoglycemia, and hyperglycemia as well as lowering HbA 1c in reducing the risk of complications and death., Conclusions: Our model was able to simulate risk factor progression and event histories that closely match the observed outcomes and to project events occurring over patients' lifetimes. The model can serve as a tool to estimate the impact of changing clinical risk factors on health outcomes to inform economic evaluations of interventions in type 1 diabetes., (© 2020 by the American Diabetes Association.)