Development and validation of the lung donor (LUNDON) acceptability score for pulmonary transplantation.

There is a chronic shortage of donor lungs for pulmonary transplantation due, in part, to low lung utilization rates in the United States. We performed a retrospective cohort study using data from the Scientific Registry of Transplant Recipients database (2006-2019) and developed the lung donor (LUNDON) acceptability score. A total of 83 219 brain-dead donors were included and were randomly divided into derivation (n = 58 314, 70%) and validation (n = 24 905, 30%) cohorts. The overall lung acceptance was 27.3% (n = 22 767). Donor factors associated with the lung acceptance were age, maximum creatinine, ratio of arterial partial pressure of oxygen to fraction of inspired oxygen, mechanism of death by asphyxiation or drowning, history of cigarette use (≥20 pack-years), history of myocardial infarction, chest x-ray appearance, bloodstream infection, and the occurrence of cardiac arrest after brain death. The prediction model had high discriminatory power (C statistic, 0.891; 95% confidence interval, 0.886-0.895) in the validation cohort. We developed a web-based, user-friendly tool (available at https://sites.wustl.edu/lundon) that provides the predicted probability of donor lung acceptance. LUNDON score was also associated with recipient survival in patients with high lung allocation scores. In conclusion, the multivariable LUNDON score uses readily available donor characteristics to reliably predict lung acceptability. Widespread adoption of this model may standardize lung donor evaluation and improve lung utilization rates.

Commentary: 20 years online with "Your Disease Risk".

The Your Disease Risk tool ( yourdiseaserisk.wustl.edu ) went live to the public in January 2000 and was one of the first personalized health risk assessment sites on the Internet. Its launch marked the culmination of years of work by a large, multi-disciplinary university team whose primary goal was to translate the science on cancer prevention into accurate, engaging, and useful messages for the public. Today, 20 years on, Your Disease Risk has expanded from its initial four cancers to include 18 different tools designed for today's users. This commentary reviews important moments and lessons learned in the first two decades of Your Disease Risk.

Development of a scalable weight loss intervention for low-income workers through adaptation of interactive obesity treatment approach (iOTA).

BACKGROUND: Describing how and why an evidence-based intervention is adapted for a new population and setting using a formal evaluation and an adaptation framework can inform others seeking to modify evidence-based weight management interventions for different populations or settings. The Working for You intervention was adapted, to fit a workplace environment, from Be Fit Be Well, an evidence-based intervention that targets weight-control and hypertension in patients at an outpatient clinic. Workplace-based efforts that promote diet and activity behavior change among low-income employees have potential to address the obesity epidemic. This paper aims to explicitly describe how Be Fit Be Well was adapted for this new setting and population. METHODS: To describe and understand the worksite culture, environment, and policies that support or constrain healthy eating and activity in the target population, we used qualitative and quantitative methods including key informant interviews, focus groups, and a worker survey; these data informed intervention adaptation. We organized the adaptations made to Be Fit Be Well using an adaptation framework from implementation science. RESULTS: The adapted intervention, Working for You, maintains the theoretical premise and evidence-base underpinning Be Fit Be Well. However, it was modified in terms of the means of delivery (i.e., rather than using interactive voice response, Working for You employs automated SMS text messaging), defined as a modification to context by the adaptation framework. The adaptation framework also includes modifications to content; in this case the behavioral goals were modified for the target population based on updated science related to weight loss and to target a workplace population (e.g., a goal to avoiding free food at work). CONCLUSIONS: If effective, this scalable and relatively inexpensive intervention can be translated to other work settings to reduce obesity and diabetes risk among low-SES workers, a group with a higher prevalence of these conditions. Using a formal evaluation and framework to guide and organize how and why an evidence-based intervention is adapted for a new population and setting can push the field of intervention research forward. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02934113 ; Received: October 12, 2016; Updated: November 7, 2017.

Validation of a risk prediction tool for coronary heart disease in middle-aged women.

BACKGROUND: Health risk appraisal tools may be useful for identifying individuals who would benefit from lifestyle changes and increased surveillance. We evaluated the validity of the Your Disease Risk tool (YDR) for estimating relative risk of coronary heart disease (CHD) among middle-aged women. METHODS: We included 55,802 women in the Nurses' Health Study who completed a mailed questionnaire about risk factors in 1994 and had no history of heart disease at that time. Participants were followed through 2004 for the occurrence of CHD. We estimated each woman's 10-year relative risk of CHD using YDR, and we compared the estimated YDR relative risk category (ranging from "very much below average" to "very much above average") to the observed relative risk for each category using logistic regression. We also examined the discriminatory accuracy of YDR using concordance statistics (c-statistics). RESULTS: There were 1165 CHD events during the 10-year follow-up period. Compared to the "about average" category, the observed age-adjusted relative risk was 0.43 (95 % confidence interval: 0.33, 0.56) for the "very much below average" category and 2.48 (95 % confidence interval: 1.68, 3.67) for the "very much above average" category. The age-adjusted c-statistic for the model including the YDR relative risk category was 0.71 (95 % confidence interval: 0.69, 0.72). The model performed better in younger than older women. CONCLUSION: The YDR tool appears to have moderate validity for estimating 10-year relative risk of CHD in this population of middle-aged women. Further research should aim to improve the tool's performance and to examine its validity in other populations.

Eight ways to stay healthy after cancer: an evidence-based message.

PURPOSE: Since 1999, in conjunction with the internationally known and award-winning Your Disease Risk ( yourdiseaserisk.org ) risk assessment tool, the "Eight Ways to Stay Healthy and Prevent Cancer" message campaign has provided an evidence-based, but user-friendly, approach to cancer prevention. The scientific evidence behind the campaign is robust and while not a complete list, provides a great deal of benefit in the reduction of cancer risk. With 12 million cancer survivors in the United States, there is a need for a parallel set of recommendations that oncologists and primary care providers may routinely use for individuals following a cancer diagnosis focused on improving the quantity and quality of life after diagnosis. With increasing survival rates and many cancer survivors dying from noncancer causes, survivorship care necessarily focuses on more than just risk of cancer recurrence and cancer-related mortality. METHODS: To provide a foundation for living a healthy life after a cancer diagnosis, we developed a set of evidence-based health messages for cancer survivors. "Cancer Survivors' Eight Ways to Stay Healthy After Cancer," published by the Siteman Cancer Center at Washington University School of Medicine and Barnes Jewish Hospital, documents both the evidence supporting the recommendations as well as tips for implementing them. RESULTS: The one-line summary messages are: (1) don't smoke, (2) avoid secondhand smoke, (3) exercise regularly, (4) avoid weight gain, (5) eat a healthy diet, (6) drink alcohol in moderation, if at all, (7) stay connected with friends, family, and other survivors, (8) get screening tests and go to your regular checkups. CONCLUSIONS: The cancer survivors' eight ways are the foundation for an evidence-based health promotion program for survivors.

Use of a web-based risk appraisal tool for assessing family history and lifestyle factors in primary care.

BACKGROUND: Primary care clinicians can play an important role in identifying individuals at increased risk of cancer, but often do not obtain detailed information on family history or lifestyle factors from their patients. OBJECTIVE: We evaluated the feasibility and effectiveness of using a web-based risk appraisal tool in the primary care setting. DESIGN: Five primary care practices within an academic care network were assigned to the intervention or control group. PARTICIPANTS: We included 15,495 patients who had a new patient visit or annual exam during an 8-month period in 2010-2011. INTERVENTION: Intervention patients were asked to complete a web-based risk appraisal tool on a laptop computer immediately before their visit. Information on family history of cancer was sent to their electronic health record (EHR) for clinicians to view; if accepted, it populated coded fields and could trigger clinician reminders about colon and breast cancer screening. MAIN MEASURES: The main outcome measure was new documentation of a positive family history of cancer in coded EHR fields. Secondary outcomes included clinician reminders about screening and discussion of family history, lifestyle factors, and screening. KEY RESULTS: Among eligible intervention patients, 2.0% had new information on family history of cancer entered in the EHR within 30 days after the visit, compared to 0.6% of eligible control patients (adjusted odds ratio = 4.3, p = 0.03). There were no significant differences in the percent of patients who received moderate or high risk reminders for colon or breast cancer screening. CONCLUSIONS: Use of this tool was associated with increased documentation of family history of cancer in the EHR, although the percentage of patients with new family history information was low in both groups. Further research is needed to determine how risk appraisal tools can be integrated with workflow and how they affect screening and health behaviors.

Commentary: eight ways to prevent cancer: a framework for effective prevention messages for the public.

Research over the past 40 years has convincingly shown that lifestyle factors play a huge role in cancer incidence and mortality. The public, though, can often discount the preventability of cancer. That health information on the Internet is a vast and often scientifically suspect commodity makes promoting important and sound cancer prevention messages to the public even more difficult. To help address these issues and improve the public's knowledge of, and attitudes toward, cancer prevention, there need to be concerted efforts to create evidence-based, user-friendly information about behaviors that could greatly reduce overall cancer risk. Toward this end, we condensed the current scientific evidence on the topic into eight key behaviors. While not an end in themselves, "Eight Ways to Stay Healthy and Prevent Cancer" forms an evidence-based and targeted framework that supports broader cancer prevention efforts.

Mental imagery-based self-regulation: Effects on physical activity behaviour and its cognitive and affective precursors over time.

OBJECTIVES: (1) Test whether a mental imagery-based self-regulation intervention increases physical activity behaviour over 90 days; (2) Examine cognitive and affective precursors of change in physical activity behaviour. DESIGN: A randomized control trial with participants (N = 500) randomized to one of six intervention conditions in a 3 (risk communication format: bulleted list, table, risk ladder) x 2 (mental imagery behaviour: physical activity, active control [sleep hygiene]) factorial design. METHODS: After receiving personalized risk estimates via a website on a smartphone, participants listened to an audiorecording that guided them through a mental imagery activity related to improving physical activity (intervention group) or sleep hygiene behaviour (active control). Participants received text message reminders to complete the imagery for 3 weeks post-intervention, 4 weekly text surveys to assess behaviour and its cognitive and affective precursors, and a mailed survey 90 days post-baseline. RESULTS: Physical activity increased over 90 days by 19.5 more minutes per week (95%CI: 2.0, 37.1) in the physical activity than the active control condition. This effect was driven by participants in the risk ladder condition, who exercised 54.8 more minutes (95%CI 15.6, 94.0) in the physical activity condition than participants in the active control sleep hygiene group. Goal planning positively predicted physical activity behaviour (b = 12.2 minutes per week, p = 0.002), but self-efficacy, image clarity, and affective attitudes towards behaviours did not (p > 0.05). CONCLUSIONS: Mental imagery-based self-regulation interventions can increase physical activity behaviour, particularly when supported by personalized disease risk information presented in an easy-to-understand format.

Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases.

BACKGROUND: Personalized medicine may increase the amount of probabilistic information patients encounter. Little guidance exists about communicating risk for multiple diseases simultaneously or about communicating how changes in risk factors affect risk (hereafter "risk reduction"). PURPOSE: To determine how to communicate personalized risk and risk reduction information for up to 5 diseases associated with insufficient physical activity in a way laypeople can understand and that increases intentions. METHODS: We recruited 500 participants with <150 min weekly of physical activity from community settings. Participants completed risk assessments for diabetes, heart disease, stroke, colon cancer, and breast cancer (women only) on a smartphone. Then, they were randomly assigned to view personalized risk and risk reduction information organized as a bulleted list, a simplified table, or a specialized vertical bar graph ("risk ladder"). Last, they completed a questionnaire assessing outcomes. Personalized risk and risk reduction information was presented as categories (e.g., "very low"). Our analytic sample (N = 372) included 41.3% individuals from underrepresented racial/ethnic backgrounds, 15.9% with vocational-technical training or less, 84.7% women, 43.8% aged 50 to 64 y, and 71.8% who were overweight/obese. RESULTS: Analyses of covariance with post hoc comparisons showed that the risk ladder elicited higher gist comprehension than the bulleted list (P = 0.01). There were no significant main effects on verbatim comprehension or physical activity intentions and no moderation by sex, race/ethnicity, education, numeracy, or graph literacy (P > 0.05). Sequential mediation analyses revealed a small beneficial indirect effect of risk ladder versus list on intentions through gist comprehension and then through perceived risk (bIndirectEffect = 0.02, 95% confidence interval: 0.00, 0.04). CONCLUSION: Risk ladders can communicate the gist meaning of multiple pieces of risk information to individuals from many sociodemographic backgrounds and with varying levels of facility with numbers and graphs.

Pilot test of an interactive obesity treatment approach among employed adults in a university medical billing office.

BACKGROUND: There is a need for workplace programs promoting healthy eating and activity that reach low-wage employees and are scalable beyond the study site. Interventions designed with dissemination in mind aim to utilize minimal resources and to fit within existing systems. Technology-based interventions have the potential to promote healthy behaviors and to be sustainable as well as scalable. We developed an interactive obesity treatment approach (iOTA), to be delivered by SMS text messaging, and therefore accessible to a broad population. The aim of this pilot study was to evaluate participant engagement with, and acceptability of, this iOTA to promote healthy eating and activity behaviors among low-wage workers with obesity. METHODS: Twenty participants (self-reporting body mass index ≥ 30 kg/m2) of a single workgroup employed by a university medical practice billing office had access to the full intervention and study measures and provided feedback on the experience. Height and weight were measured by trained research staff at baseline. Each participant was offered a quarterly session with a health coach. Measured weight and a self-administered survey, including dietary and activity behaviors, were also collected at baseline, 3, 6, 12, 18, and 24 months. Participant engagement was assessed through responsiveness to iOTA SMS text messages throughout the 24-month pilot. A survey measure was used to assess satisfaction with iOTA at 3 months. Due to the small sample size and pilot nature of the current study, we conducted descriptive analyses. Engagement, weight change, and duration remaining in coaching are presented individually for each study participant. RESULTS: The pilot was originally intended to last 3 months, but nearly all participants requested to continue; we thus continued for 24 months. Most (14/20) participants remained in coaching for 24 months. At the 3-month follow-up, eight (47%) of the remaining 17 participants had lost weight; by 24 months, five (36%) of the remaining 14 participants had lost weight (one had bariatric surgery). Participants reported very high satisfaction. CONCLUSIONS: This pilot provides important preliminary results on acceptability and participant engagement with iOTA, which has significant potential for dissemination and sustainability.

Comparison of Performance Between a Short Categorized Lifestyle Exposure-based Colon Cancer Risk Prediction Tool and a Model Using Continuous Measures.

Risk prediction models that estimate an individual's risk of developing colon cancer could be used for a variety of clinical and public health interventions, including offering high-risk individuals enhanced screening or lifestyle interventions. However, if risk prediction models are to be translated into actual clinical and public health practice, they must not only be valid and reliable, but also be easy to use. One way of accomplishing this might be to simplify the information that users of risk prediction tools have to enter, but it is critical to ensure no resulting detrimental effects on model performance. We compared the performance of a simplified, largely categorized exposure-based colon cancer risk model against a more complex, largely continuous exposure-based risk model using two prospective cohorts. Using data from the Nurses' Health Study and the Health Professionals Follow-up Study we included 816 incident colon cancer cases in women and 412 in men. The discrimination of models was not significantly different comparing a categorized risk prediction model with a continuous prediction model in women (c-statistic 0.600 vs. 0.609, P diff = 0.07) and men (c-statistic 0.622 vs. 0.618, P diff = 0.60). Both models had good calibration in men [observed case count/expected case count (O/E) = 1.05, P > 0.05] but not in women (O/E = 1.19, P < 0.01). Risk reclassification was slightly improved using categorized predictors in men [net reclassification index (NRI) = 0.041] and slightly worsened in women (NRI = -0.065). Categorical assessment of predictor variables may facilitate use of risk assessment tools in the general population without significant loss of performance.