Masahumi Okada

Hyperkalemia is associated with increased risks of mortality and adverse clinical outcomes. The treatment of hyperkalemia often leads to the discontinuation or restriction of beneficial but potassium-increasing therapy such as renin-angiotensin-aldosterone inhibitors (RAASi) and high-potassium diet including fruits and vegetables. To date, limited evidence is available for personalized risk evaluation in this heterogeneous and multifactorial pathophysiological condition. We developed risk prediction models using extreme gradient boosting (XGB), multiple logistic regression (LR), and deep neural network. Models were derived from a retrospective cohort of hyperkalemic patients with either heart failure or chronic kidney disease stage ≥3a from a Japanese nationwide database (1 April 2008–30 September 2018). Studied outcomes included all-cause death, renal replacement therapy introduction (RRT), hospitalization for heart failure (HHF), and cardiovascular events within three years after hyperkalemic episodes. The best performing model was further validated using an external cohort. A total of 24,949 adult hyperkalemic patients were selected for model derivation and internal validation. A total of 1452 deaths (16.6%), 887 RRT (10.1%), 1,345 HHF (15.4%), and 621 cardiovascular events (7.1%) were observed. XGB outperformed other models. The area under receiver operator characteristic curves (AUROCs) of XGB vs. LR (95% CIs) for death, RRT, HHF, and cardiovascular events were 0.823 (0.805–0.841) vs. 0.809 (0.791–0.828), 0.957 (0.947–0.967) vs. 0.947 (0.936–0.959), 0.863 (0.846–0.880) vs. 0.838 (0.820–0.856), and 0.809 (0.784–0.834) vs. 0.798 (0.772–0.823), respectively. In the external dataset including 86,279 patients, AUROCs (95% CIs) for XGB were: death, 0.747 (0.742–0.753); RRT, 0.888 (0.882–0.894); HHF, 0.673 (0.666–0.679); and cardiovascular events, 0.585 (0.578–0.591). Kaplan–Meier curves of the high-risk predicted group showed a statistically significant difference from that of the low-risk predicted groups for all outcomes (p < 0.005; log-rank test). These findings suggest possible use of machine learning models for real-world risk assessment as a guide for observation and/or treatment decision making that may potentially lead to improved outcomes in hyperkalemic patients while retaining the benefit of life-saving therapies.

The human papillomavirus (HPV) vaccination coverage rate has fallen sharply in Japan since 2013, when newspapers began covering negative campaigns against the vaccination. We examined and compared contents from newspaper articles before and after the start of this HPV vaccination crisis. We collected articles published between January 2005 and September 2017 in the four daily national Japanese newspapers with the highest domestic circulation. We then conducted text mining analysis to chronologically examine content distribution. From among the 1178 articles analyzed, 12 types of contents were identified. Contents related to cervical cancer prevention, such as on the risk of developing cervical cancer, causes of cervical cancer, and the effects of vaccination, were frequently conveyed until 2012. However, after March 2013, they were replaced with anti-vaccination contents, such as on adverse effects to vaccines, alleged victims, and related lawsuits. Meanwhile pro-vaccination contents, such as safety statements from the World Health Organization, scarcely received coverage. Newspaper contents changed profoundly before and after the start of the vaccination crisis. Those newspaper reports potentially had impact on readers’ beliefs and actions. Journalists should strive for impartial coverage so readers can make more-informed decisions. Health professionals should be expected to work with journalists to help improve impartiality in newspaper coverage. The Ministry of Health, Labour and Welfare should discus benefits and risks of the HPV vaccination based on the scientific evidences, and consider to resume the proactive recommendation of HPV vaccination. Well-organized advocacy among medical societies, scientists and health professionals will also be needed to influence the government.

Health materials to promote health behaviors should be readable and generate favorable evaluations of the message. Processing fluency (the subjective experience of ease with which people process information) has been increasingly studied over the past decade. In this review, we explore effects and instantiations of processing fluency and discuss the implications for designing effective health materials. We searched seven online databases using “processing fluency” as the key word. In addition, we gathered relevant publications using reference snowballing. We included published records that were written in English and applicable to the design of health materials. We found 40 articles that were appropriate for inclusion. Various instantiations of fluency have a uniform effect on human judgment: fluently processed stimuli generate positive judgments (e.g., liking, confidence). Processing fluency is used to predict the effort needed for a given task; accordingly, it has an impact on willingness to undertake the task. Physical perceptual, lexical, syntactic, phonological, retrieval, and imagery fluency were found to be particularly relevant to the design of health materials. Health-care professionals should consider the use of a perceptually fluent design, plain language, numeracy with an appropriate degree of precision, a limited number of key points, and concrete descriptions that make recipients imagine healthy behavior. Such fluently processed materials that are easy to read and understand have enhanced perspicuity and persuasiveness.