Imputing pre-diagnosis health behaviour in cancer registry data and investigating its relationship with oesophageal cancer survival time

Background As oesophageal cancer has short survival, it is likely pre-diagnosis health behaviours will have carry-over effects on post-diagnosis survival times. Cancer registry data sets do not usually contain pre-diagnosis health behaviours and so need to be augmented with data from external health surveys. A new algorithm is introduced and tested to augment cancer registries with external data when one-to-one data linkage is not available. Methods The algorithm is to use external health survey data to impute pre-diagnosis health behaviour for cancer patients, estimate misclassification errors in these imputed values and then fit misclassification corrected Cox regression to quantify the association between pre-diagnosis health behaviour and post-diagnosis survival. Data from US cancer registries and a US national health survey are used in testing the algorithm. Results It is demonstrated that the algorithm works effectively on simulated smoking data when there is no age confounding. But age confounding does exist (risk of death increases with age and most health behaviours change with age) and interferes with the performance of the algorithm. The estimate of the hazard ratio (HR) of pre-diagnosis smoking was HR = 1.32 (95% CI 0.82,2.68) with HR = 1.93 (95% CI 1.08,7.07) in the squamous cell sub-group and pre-diagnosis physical activity was protective of survival with HR = 0.25 (95% CI 0.03, 0.81). But the method failed for less common behaviours (such as heavy drinking). Conclusions Further improvements in the I2C2 algorithm will permit enrichment of cancer registry data through imputation of new variables with negligible risk to patient confidentiality, opening new research opportunities in cancer epidemiology.

Comparison of two different frailty scales in the longitudinal Swedish Adoption/Twin Study of Aging (SATSA)

Aims: Although up to 25% of older adults are frail, assessing frailty can be difficult, especially in registry data. This study evaluated the utility of a code-based frailty score in registry data by comparing it to a gold-standard frailty score to understand how frailty can be quantified in population data and perhaps better addressed in healthcare. Methods: We compared the Hospital Frailty Risk Score (HFRS), a frailty measure based on 109 ICD codes, to a modified version of the Frailty Index (FI) Frailty Index (FI), a self-report frailty measure, and their associations with all-cause mortality both cross-sectionally and longitudinally (follow-up = 36 years) in a Swedish cohort study ( n = 1368). Results: The FI and HFRS were weakly correlated (rho = 0.11, p < 0.001). Twenty-two percent ( n = 297) of participants were considered frail based on published cut-offs of either measure. Only 3% ( n = 35) of participants were classified as frail by both measures; 4% ( n = 60) of participants were considered frail by only the HFRS; and 15% ( n = 202) of participants were considered frail based only on the FI. Frailty as measured by the HFRS showed greater variance and no clear increase or decrease with age, while frailty as measured by the FI increased steadily with age. In adjusted Cox proportional hazard models, baseline HFRS frailty (HR = 1.17, 95% CI 0.92, 1.49) was not statistically significantly associated with mortality, while FI frailty was (HR = 2.89, 95% CI 1.61, 2.23). These associations were modified by age and sex. Conclusions: The HFRS may not capture the full spectrum of frailty among community-dwelling individuals, particularly at younger ages, in Swedish registry data.

A nonrandomized phase 2 trial of oral thymic peptides in hospitalized patients with Covid-19

BackgroundCoronavirus disease 2019 (Covid-19) active cases continue to demand the development of safe and effective treatments. This is the first clinical trial to evaluate the safety and efficacy of oral thymic peptides.MethodsWe conducted a nonrandomized phase 2 trial with a historic control group to evaluate the safety and efficacy of a daily 250-mg oral dose of thymic peptides in the treatment of hospitalized Covid-19 patients. Comparison based on standard care from registry data was performed after propensity score matching. The primary outcomes were survival, time to recovery and the number of participants with treatment-related adverse events or side effects by day 20.ResultsA total of 44 patients were analyzed in this study, 22 in the thymic peptides group and 22 in the standard care group. There were no deaths in the intervention group, compared to 24% mortality in standard care by day 20 (log-rank P=0.02). The Kaplan-Meier analysis showed a significantly shorter time to recovery by day 20 in the thymic peptides group as compared with standard care (median, 6 days vs. 12 days; hazard ratio for recovery, 2.75 [95% confidence interval, 1.34 to 5.62]; log-rank P=0.002). No side effects or adverse events were reported.ConclusionIn patients hospitalized with Covid-19, the use of thymic peptides reported no side effects, adverse events, or deaths by day 20. When compared with registry data, a significantly shorter time to recovery and mortality reduction was measured. The Catholic University of Honduras Medical Research Group (GIMUNICAH) is working on a more extensive phase 3 trial.Trial registrationClinicalTrials.govNCT04771013. February 25, 2021.

Dotting the “i” of Interoperability in FAIR Cancer-Registry Data Sets

To conform to FAIR principles, data should be findable, accessible, interoperable, and reusable. Whereas tools exist for making data findable and accessible, interoperability is not straightforward and can limit data reusability. Most interoperability-based solutions address semantic description and metadata linkage, but these alone are not sufficient for the requirements of inter-comparison of population-based cancer data, where strict adherence to data-rules is of paramount importance. Ontologies, and more importantly their formalism in description logics, can play a key role in the automation of data-harmonization processes predominantly via the formalization of the data validation rules within the data-domain model. This in turn leads to a potential quality metric allowing users or agents to determine the limitations in the interpretation and comparability of the data. An approach is described for cancer-registry data with practical examples of how the validation rules can be modeled with description logic. Conformance of data to the rules can be quantified to provide metrics for several quality dimensions. Integrating these with metrics derived for other quality dimensions using tools such as data-shape languages and data-completion tests builds up a data-quality context to serve as an additional component in the FAIR digital object to support interoperability in the wider sense.

Frailty prevalence and association with mortality across birth cohorts in Swedish registry data

Frailty is associated with poor health outcomes, reduced quality of life, and mortality. To understand how prevalence of frailty may have changed across birth cohorts, we investigated frailty prevalence at ages 75, 85, and 95 in people born in 1910, 1920, and 1930 in Swedish national registry data. Frailty was assessed with the Hospital Frailty Risk Score, a weighted sum of 109 ICD codes, which we calculated for each year leading up to the specified ages. We additionally investigated the association between frailty and mortality in these birth cohorts. We observed, at 75, a decrease in prevalence of frailty across birth cohorts (16.9%, 10.8%, and 8.8%, respectively). Interestingly, at 85, we found a U-shaped pattern, where those born in 1920 (14.1%) had lower prevalence of frailty than those born in either 1910 (27.7%) or 1930 (25.1%). At age 95, we saw a low prevalence of frailty in the 1910 (7.3%) and 1920 (3.8%) birth cohorts –potentially because of selective survival. There were not substantial differences in prevalence of frailty by sex or birth country. In Cox proportional hazard models adjusted for sex, frailty was consistently associated with mortality. We observed the greatest hazard ratios in the 1930 birth cohort at 75 (HR=2.79, 95% CI 2.62, 2.97) and 85 (HR=2.26, 95% CI 2.01, 2.53) and the 1920 birth cohort at 75 (HR=2.19, 95% CI 2.09, 2.29), where risk was double that of those who were not frail. Understanding changes in prevalence of frailty will help inform public health and intervention measures.