Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice.

PURPOSE: This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice. METHODS: We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions. RESULTS: Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population. CONCLUSION: Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.

Characteristics of good home-based end-of-life care: analysis of 5-year data from a nationwide mortality follow-back survey in England.

BACKGROUND: Recently, there has been an emphasis on providing good-quality end-of-life care; however, little is known about it and its determinants for patients living at home. AIM: To determine what characterises good-quality end-of-life care for patients living at home. DESIGN AND SETTING: An observational study using 5-year data from the National Survey of Bereaved People (Views of Informal Carers - Evaluation of Services [VOICES]) in England. METHOD: Analysis was based on data for 63 598 decedents, who were cared for at home in the last 3 months of life. Data were drawn from 110 311 completed mortality follow-back surveys of a stratified sample of 246 763 deaths registered in England between 2011 and 2015. Logistic regression analyses were used to identify independent variables associated with overall quality of end-of-life care and other indicators of end-of-life care quality. RESULTS: Patients who received good continuity of primary care (adjusted odds ratio [AOR] 2.03; 95% confidence interval [CI] = 2.01 to 2.06) and palliative care support (AOR 1.86; 95% CI = 1.84 to 1.89) experienced better overall quality of end-of-life care than those who did not, as perceived by relatives. Decedents who died from cancer (AOR 1.05; 95% CI = 1.03 to 1.06) or outside of hospital were more likely to receive good end-of-life care, as perceived by relatives. Being older, female (AOR 1.16; 95% CI = 1.15 to 1.17), from areas with least socioeconomic deprivation, and White (AOR 1.09; 95% CI = 1.06 to 1.12) were associated with better overall end-of-life care, as perceived by relatives. CONCLUSION: Better quality of end-of-life care was associated with good continuity of primary care, specialist palliative care support, and death outside of hospital. Disparities still exist for those from minority ethnic groups and those living in areas of socioeconomic deprivation. Future commissioning and initiatives must consider these variables to provide a more-equitable service.

Associations with Non-Persistence with Intra-Vitreal Therapy for Neovascular Age-Related Macular Degeneration at 24 Months.

AIMS: The aim of the study was to investigate non-persistence with treatment for neovascular age-related macular degeneration (NvAMD) before day 720 (24 months) after initiation, explore associations with baseline characteristics and variation between sites. METHODS: Anonymised demographic and clinical data were extracted from electronic medical records at treating National Health Service (NHS) Trusts for NvAMD eyes starting intra-vitreal therapy from 2017 to 2018. Time to non-persistence with treatment, defined as no recorded attendance for either monitoring or treatment for a period ≥6 months, was visualised with a Kaplan-Meier survival plot. Associations with treatment non-persistence were investigated using a Cox proportional hazards model. RESULTS: Analysis included 7,970 eyes of 7,112 patients treated at 13 NHS trusts. Censoring deaths and those eyes in which treatment was stopped permanently, the Kaplan-Meier analyses demonstrated survival figures of 77.7% for persistence with treatment to day 360 and 71.8% to day 720. Hazard ratios for non-persistence with treatment were reduced at 10 sites, relative to the reference, with first-treated eye status and with baseline acuity worse than or equal to LogMAR 1.0. Hazard ratios increased with younger age, in the presence of other ocular co-morbidities and with baseline acuity better than or equal to LogMAR 0.5. After an episode of non-persistence, visual acuity decreased by at least 0.1 and 0.3 LogMAR in 39% and 18% of eyes, respectively. CONCLUSIONS: Non-persistence with treatment was common, especially in the first year of treatment, and was often associated with a decrease in visual acuity. Treatment site, baseline visual acuity, and age were the strongest predictors of treatment non-persistence before day 720. Understanding and addressing reasons for non-persistence are important to ensure that effective but expensive treatments are used cost-effectively and to maintain acuity. Variation in non-persistence between sites, even after adjustment for other variables, suggests that local factors in treatment provision may be particularly important.

Bedside breath tests in children with abdominal pain: a prospective pilot feasibility study.

BACKGROUND: There is no definitive method of accurately diagnosing appendicitis before surgery. We evaluated the feasibility of collecting breath samples in children with abdominal pain and gathered preliminary data on the accuracy of breath tests. METHODS: We conducted a prospective pilot study at a large tertiary referral paediatric hospital in the UK. We recruited 50 participants with suspected appendicitis, aged between 5 and 15 years. Five had primary diagnosis of appendicitis. The primary outcome was the number of breath samples collected. We also measured the number of samples processed within 2 h and had CO2 ≥ 3.5%. Usability was assessed by patient-reported pain pre- and post-sampling and user-reported sampling difficulty. Logistic regression analysis was used to predict appendicitis and evaluated using the area under the receiver operator characteristic curve (AUROC). RESULTS: Samples were collected from all participants. Of the 45 samples, 36 were processed within 2 h. Of the 49 samples, 19 had %CO2 ≥ 3.5%. No difference in patient-reported pain was observed (p = 0.24). Sampling difficulty was associated with patient age (p = 0.004). The logistic regression model had AUROC = 0.86. CONCLUSIONS: Breath tests are feasible and acceptable to patients presenting with abdominal pain in clinical settings. We demonstrated adequate data collection with no evidence of harm to patients. The AUROC was better than a random classifier; more specific sensors are likely to improve diagnostic performance. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03248102. Registered 14 Aug 2017.