A Tool to Assess Patient and Surrogate Knowledge About the POLST (Physician Orders for Life-Sustaining Treatment) Program

ContextIt is especially important that patients are well informed when making high-stakes, preference-sensitive decisions like those on the Physician Orders for Life-Sustaining Treatment (POLST) form. However, there is currently no way to easily evaluate whether patients understand key concepts when making these important decisions.ObjectivesTo develop a POLST knowledge survey.MethodsExpert (n = 62) ratings of key POLST facts were used to select items for a POLST knowledge survey. The survey was administered to nursing facility residents (n = 97) and surrogate decision-makers (n = 112). A subset (n = 135) were re-administered the survey after a standardized advance care planning discussion to assess the scale's responsiveness to change.ResultsThe 19-item survey demonstrated adequate reliability (α = 0.72.). Residents' scores (x = 11.4, standard deviation 3.3) were significantly lower than surrogate scores (x = 14.7, standard deviation 2.5) (P < 0.001). Scores for both groups increased significantly after administration of a standardized advance care planning discussion (P < 0.001). Although being a surrogate, age, race, education, cognitive functioning, and health literacy were significantly associated with higher POLST Knowledge Survey scores in univariate analyses, only being a surrogate (P < 0.001) and being white (P = 0.028) remained significantly associated with higher scores in multivariate analyses.ConclusionThe 19-item POLST Knowledge Survey demonstrated adequate reliability and responsiveness to change. Findings suggest the survey could be used to identify knowledge deficits and provide targeted education to ensure adequate understanding of key clinical decisions when completing POLST.     

Patient-Reported Quality of Life Before and After Chemoradiation for Intact Pancreas Cancer: A Prospective Registry Study

PurposeOur purpose was to determine the effect of chemoradiotherapy (CRT) on patient-reported quality of life (QOL) for patients with intact pancreas cancer.Methods and MaterialsWe reviewed a prospective QOL registry for patients with intact, clinically localized pancreatic ductal adenocarcinoma treated with CRT between June 2015 and November 2018. QOL was assessed pre-CRT (immediately before CRT, after neoadjuvant chemotherapy) and at the completion of CRT with the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) and its component parts: FACT-General (FACT-G) and hepatobiliary cancer subscore (HCS). A minimally important difference from pre-CRT was defined as ≥ 6, 5, and 8 points for FACT-G, HCS, and FACT-Hep, respectively.ResultsOf 157 patients who underwent CRT, 100 completed both pre- and post-CRT surveys and were included in the primary analysis. Median age at diagnosis was 65 years (range, 23-90). National Comprehensive Cancer Network resectability status was resectable (3%), borderline resectable (40%), or locally advanced (57%). Folinic acid, 5-fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX) (75%) or gemcitabine and nab-paclitaxel (42%) were given for a median of 6 cycles (range, 0-42) before CRT. Radiation therapy techniques included 3-dimensional conformal (22%), intensity modulated photon (55%), and intensity modulated proton (23%) radiation therapy to a median dose of 50 Gy (range, 36-62.5). Concurrent chemotherapy was most commonly capecitabine (82%). Sixty-three patients (63%) had surgery after CRT. The mean decline in FACT-G, HCS subscale, and FACT-Hep from pre- to post-CRT was 3.5 (standard deviation [SD], 13.7), 1.7 (SD 7.8), and 5.2 (SD 19.4), respectively. Each of these changes were statistically significant, but did not meet the minimally important difference threshold. Pancreatic head tumor location was associated with decline in FACT-Hep. Nausea was the toxicity with the greatest increase from pre- to post-CRT by both physician-assessment and patient-reported QOL.ConclusionsFor patients with intact pancreatic adenocarcinoma, modern CRT is well tolerated with minimal decline in QOL during treatment.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

Impact of overweight and obesity on US renal cell carcinoma incidence trends (1995-2018)

In the United States, renal cell carcinoma (RCC) incidence and the prevalence of obesity, an established risk factor for RCC, have been increasing for several decades. RCC is more common among older individuals. We sought to quantify the contribution of excess adiposity to the rising incidence of RCC among individuals 60 years or older. National Institutes of Health-American Association of Retired Persons Diet and Health Study data (n = 453 859 participants, enrolled in 1995-1996, age at enrollment 50-71 years) were used to estimate multivariable-adjusted hazard ratios (HRs) for RCC across body mass index categories and HRs associated with smoking. Population attributable fractions (PAFs) were calculated using estimated HRs and annual overweight/obesity prevalence from the National Health Interview Survey (1985-2008). PAF estimates were combined with RCC incidence from Surveillance, Epidemiology and End Results-13 to calculate annual percent changes in RCC incidence attributable (and unrelated) to overweight/obesity. We found that between 1995 and 2018, among individuals aged 60 years and older, PAF for overweight/obesity increased from 18% to 29% for all RCCs. In comparison, the PAF for smoking declined from 12% to 9%. RCC incidence increased 1.8% per year (95% confidence interval [CI] 1.5%-2.1%) overall, while RCC incidence attributable to overweight/obesity increased 3.8% per year (95%CI 3.5%-4.2%) and RCC incidence unrelated to overweight/obesity increased 1.2% per year (95% CI 0.9%-1.4%). In conclusion, overweight/obesity appears to have contributed importantly to the rising incidence of RCC in the United States since the mid-1990s. Public health interventions focused on reducing overweight and obesity could help substantially in curbing this trend.     

Detrended fluctuation analysis detects altered coordination of running gait in athletes following a heavy period of training

Objectives

To investigate whether functional overreaching affects locomotor system behaviour when running at fixed relative intensities and if any effects were associated with changes in running performance.

For better or worse? Investigating the validity of best–worst discrete choice experiments in health

Discrete choice experiments (DCEs) are frequently used in health economics to measure preferences for nonmarket goods. Best–worst discrete choice experiment (BWDCE) has been proposed as a variant of the traditional “pick the best” approach. BWDCE, where participants choose the best and worst options, is argued to generate more precise preference estimates because of the additional information collected. However, the validity of the approach relies on two necessary conditions: (a) best and worst decisions provide similar information about preferences and (b) asking individuals to answer more than one choice question per task does not reduce data quality. Whether these conditions hold in empirical applications remains under researched. This is the first study to compare participants' choices across three experimental conditions: (a) BEST choices only, (b) WORST choices only, and (c) BEST and WORST choices (BWDCE). We find responses to worst choices are noisier. Implied preferences from the best only and worst only choices are qualitatively different, leading to different WTP values. Responses to BWDCE tasks have lower consistency, and respondents are more likely to use simplifying decision heuristics. We urge caution in using BWDCE as an alternative to the traditional “pick the best” DCE.     

Validation of PATHFx 2.0: An open-source tool for estimating survival in patients undergoing pathologic fracture fixation

Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost-effective solution. Using open-source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open-source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1-month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open-source software and externally validated it in two unique patient populations, which can be used as a cost-effective option to guide surgical decisions in patients with metastatic bone disease.