Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics

Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers

BACKGROUND

Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation.

METHODS

In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software.

RESULTS

Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset.

DISCUSSION

Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Clinical Radiobiology for Radiation Oncology

This chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.

Prevalence of Osteoporosis Treatment and Its Effect on Post-Operative Complications, Revision Surgery and Costs After Multi-Level Spinal Fusion

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

To study the prevalence of pre-operative osteoporosis treatment, and its effect on risk of ORC, revision surgery and costs in osteoporotic patients undergoing ≥3-level spinal fusion for degenerative pathology.

METHODS

Patients and procedures of interest were included using International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) coding. Our outcome measures were ORC at 1-year post-operatively and included instrumentation complications, pathological fracture, and revision surgery. Kaplan-Meier survival curves and Cox proportional hazards analysis was done to study the effect of osteoporosis treatment on risk of ORC.

RESULTS

We included a total of 849 patients with documented osteoporosis undergoing ≥3-level spinal fusion. White (85.6%), female (82.7%), and 60-79 years of age (79.9%) was the most common demographic. Of entire cohort, 121(14.3%) were on osteoporosis treatment prior to spinal fusion. Of treated patients, 52/121 (43.0%) had continued prescriptions at 1 year post-operatively. Treated patients and not-treated patients had 1-year ORC incidence of 9.1% and 15.0%, respectively. The average 1-year reimbursement/patient for managing ORC was $3,053 (treated) and $21,147 (not-treated). On adjusted cox analysis, pre-operative osteoporosis treatment was associated with a lower risk of ORC (HR: 0.53, 95% CI: 0.28-0.99, p = 0.04).

CONCLUSIONS

Pre-operative osteoporosis treatment is associated with lower risk of ORC and revision surgery at 1-year after ≥3-level spinal fusion. There is a low incidence of osteoporosis treatment prior to spinal fusion, and subsequently a low rate of treatment continuation after surgery. These findings highlight the need for heightened awareness, patient education and management of osteoporosis before elective multi-level spinal fusion.

The use and meaning of the parachute metaphor in biomedicine: a citation analysis of a systematic review and a randomized trial of the parachute for freefall

Background: Numerous authors have used the 'parachute' analogy to comment on the importance of and need for randomized, controlled trials (RCTs) in the hierarchy of medical evidence. Methods: The authors completed a retrospective literature analysis examining publications citing the 2003 parachute paper by Smith and Pell and a 2018 RCT of a parachute by Yeh et al. For all of the articles that directly analogized a medical intervention to a parachute, the authors identified the desired outcome of the practice and searched PubMed for relevant RCTs. Results: Authors citing the parachute analogy are often critical of RCTs and often draw comparisons to interventions that are not parachutes.

Data Quality and Cost-effectiveness Analyses of Electronic and Paper-Based Interviewer-Administered Public Health Surveys: Systematic Review

Background

A population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys. However, there is a lack of systematic evidence to show the potential impact of electronic data collection tools on data quality and cost reduction in interviewer-administered surveys compared with the standard paper-based data collection system.

Objective

This systematic review aims to evaluate the impact of the interviewer-administered electronic data collection methods on data quality and cost reduction in PLS compared with traditional methods.

Methods

We conducted a systematic search of MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit, Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and nonrandomized studies that examined data quality and cost reduction outcomes, as well as usability, user experience, and usage parameters. In total, 2 independent authors screened the title and abstract, and extracted data from selected papers. A third author mediated any disagreements. The review authors used EndNote for deduplication and Rayyan for screening.

Results

Our search produced 3817 papers. After deduplication, we screened 2533 papers, and 14 fulfilled the inclusion criteria. None of the studies were randomized controlled trials; most had a quasi-experimental design, for example, comparative experimental evaluation studies nested on other ongoing cross-sectional surveys. A total of 4 comparative evaluations, 2 pre-post intervention comparative evaluations, 2 retrospective comparative evaluations, and 4 one-arm noncomparative studies were included. Meta-analysis was not possible because of the heterogeneity in study designs, types, study settings, and level of outcome measurements. Individual paper synthesis showed that electronic data collection systems provided good quality data and delivered faster compared with paper-based data collection systems. Only 2 studies linked cost and data quality outcomes to describe the cost-effectiveness of electronic data collection systems. Field data collectors reported that an electronic data collection system was a feasible, acceptable, and preferable tool for their work. Onsite data error prevention, fast data submission, and easy-to-handle devices were the comparative advantages offered by electronic data collection systems. Challenges during implementation included technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems.

Conclusions

Although evidence exists of the comparative advantages of electronic data collection compared with paper-based methods, the included studies were not methodologically rigorous enough to combine. More rigorous studies are needed to compare paper and electronic data collection systems in public health surveys considering data quality, work efficiency, and cost reduction.

International Registered Report Identifier (IRRID)

RR2-10.2196/10678

Clinical Trials Developed in Brazil on Covid-19: What Is Being Researched?

Background: The coronavirus disease 2019 pandemic (COVID-19) has brought great public health challenges into our lives. To date, there has been no specific therapeutic protocol for this disease, which requires a study with high-quality evidence. Objectives: To analyze clinical trials on COVID-19 in Brazil. Methods: Documentary research was conducted on the clinical trial registration platform. For the search strategy, the “COVID-19” keyword was established in the “condition or disease” section and “Brazil” in the “country” section. No limit on the search period was considered. Data were analyzed and presented using descriptive statistics. Results: Of the 81 registered clinical trials, 48 met the eligibility criteria. The sample size ranged from 10 to 3,000 individuals. Most studies recruited individuals aged 18 - 64 years (48.5%) and > 65 years (48.5%). Regarding the study design, randomized (91.6%), parallel (89.5%), quadruple-blind (39.6%), and therapeutic (97.9%) types were more frequent. Most studies used standard two-arm trial (70.8%), used drugs (79.2%), placebo (58.3%), and were sponsored by pharmaceutical/biotechnology companies and universities with 33.3% and 29.2%, respectively. Conclusions: Clinical trials under development in Brazil on COVID-19 are mostly carried out with adult and elderly participants, and regarding the study design, have a predominance of randomized allocation, parallel model, quadruple-blind masking with a therapeutic purpose. Most studies use antithrombotic agents or combinations of antithrombotic agents.

Comparing the Performance of Charlson and Elixhauser Comorbidity Indices to Predict In-Hospital Mortality Among a Chinese Population

Objective

Earlier comorbidity measures have been developed or validated using the North American population. This study aims to compare five Charlson or Elixhauser comorbidity indices to predict in-hospital mortality using a large electronic medical record database from Shanxi, China.

Methods

Using the primary diagnosis code and surgery procedure codes, we identified four hospitalized patient cohorts, hospitalized between 2013 and 2017, in Shanxi, China, as follows: congestive heart failure (CHF, n=41,577), chronic renal failure (CRF, n=40,419), diabetes (n=171,355), and percutaneous coronary intervention (PCI, n=39,097). We used logistic regression models and c-statistics to evaluate the in-hospital mortality predictive performance of two multiple comorbidity indicator variables developed by Charlson in 1987 and Elixhauser in 1998 and three single numeric scores by Quan in 2011, van Walraven in 2009, and Moore 2017.

Results

Elixhauser comorbidity indicator variables had consistently higher c-statistics (0.824, 0.843, 0.904, 0.853) than all other four comorbidity measures, across all four disease cohorts. Moore’s comorbidity score outperformed the other two score systems in CHF, CRF, and diabetes cohorts (c-statistics: 0.776, 0.832, 0.869), while van Walraven’s score outperformed all others among PCI patients (c-statistics: 0.827).

Conclusion

Elixhauser comorbidity indicator variables are recommended, when applied to large Chinese electronic medical record databases, while Moore’s score system is appropriate for relatively small databases.

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a systematic literature review to identify relevant randomised controlled trials conducted between 2013 and 2018. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, Cochrane Library, ASSIA, Emerald, Health Business Fulltext Elite, ProQuest Public Health, Social Care Online, and grey literature sources. Search terms were conceptualised into three groups: artificial intelligence-related terms, computerised decision support -related terms, and terms relating to health and social care. Terms within groups were combined using the Boolean operator OR, and groups were combined using the Boolean operator AND. Two reviewers independently screened studies against the eligibility criteria and two independent reviewers extracted data on eligible studies onto a customised sheet. We assessed the quality of studies through the Critical Appraisal Skills Programme checklist for randomised controlled trials. We then conducted a narrative synthesis. We identified 68 hits of which five studies satisfied the inclusion criteria. These studies varied substantially in relation to quality, settings, outcomes, and technologies. None of the studies was conducted in social care settings, and three randomised controlled trials showed no difference in patient outcomes. Of these, one investigated the use of Bayesian triage algorithms on forced expiratory volume in 1 second (FEV1) and health-related quality of life in lung transplant patients. Another investigated the effect of image pattern recognition on neonatal development outcomes in pregnant women, and another investigated the effect of the Kalman filter technique for warfarin dosing suggestions on time in therapeutic range. The remaining two randomised controlled trials, investigating computer vision and neural networks on medication adherence and the impact of learning algorithms on assessment time of patients with gestational diabetes, showed statistically significant and clinically important differences to the control groups receiving standard care. However, these studies tended to be of low quality lacking detailed descriptions of methods and only one study used a double-blind design. Although the evidence of effectiveness of data-driven artificial intelligence to support decision-making in health and social care settings is limited, this work provides important insights on how a meaningful evidence base in this emerging field needs to be developed going forward. It is unlikely that any single overall message surrounding effectiveness will emerge - rather effectiveness of interventions is likely to be context-specific and calls for inclusion of a range of study designs to investigate mechanisms of action.

Using Cluster Analysis to Identify Patient Factors Linked to Differential Functional Gains After Total Knee Arthroplasty

BACKGROUND

The basis of poor outcomes following total knee arthroplasty (TKA) is multifactorial. Previous research aimed at predicting outcome following TKA focuses largely on outcomes measured between two specific time points (pre-to post-TKA). Analysis of outcomes measured over multiple time points (trajectory) may expose relationships between patients' characteristics and longitudinal outcome patterns that may otherwise remain obscured.

METHODS

The current study analyzed Short Form 36 Physical Component Score (PCS) trajectories of 656 patients composed of 3 time points over a 1-year period. Clusters were constructed utilizing MultiExperiment Viewer hierarchical clustering algorithm. Statistical significance of these clusters was assessed using MeV's built-in bootstrapping method. Patient characteristics of the resulting statistically conserved clusters were summarized and compared using Wilcoxon rank-sum test or chi-squared test as appropriate.

RESULTS

Two distinct clusters of outcome trajectory were identified. Cluster 1 included 550 patients (84%) who demonstrated persistent PCS improvement at 6 and 12 months. Cluster 2 included 106 patients (16%) who demonstrated decline in PCS at 6 months followed by improvement at 12 months. Cluster 1 achieved earlier success, greater absolute mental and physical health scores as compared to Cluster 2 (P < .05), and demonstrated higher baseline mental health scores, lower baseline PCS, and a significantly higher proportion of non-Hispanic Whites (P ≤ .05).

CONCLUSION

Cluster analysis identified distinct functional outcome trajectories following TKA. Specific differentiating patient factors were associated with differing trajectories. Future studies should focus on this method's ability to inform predictive models regarding patient outcomes.

Extreme Consumers of Health Care: Patterns of Care Utilization in Patients with Multiple Chronic Conditions Admitted to a Novel Integrated Clinic

PURPOSE

Patients with multiple chronic conditions (MCC) of diabetes, cardiovascular and kidney diseases; hereafter referred to as HND (heart/cardiac-, nephrology-, diabetes mellitus-) patients, are high utilizers of health care. However, the care received is often insufficiently coordinated between different specialties and health-care providers. This study aims to describe the characteristics of HND patients and to explore the initial effects of a multidisciplinary and person-centered care on total care utilization.

PATIENTS AND METHODS

We conducted a sub-study of HND patients recruited in an ongoing randomized trial CareHND (NCT03362983). Descriptive statistics of patient characteristics, including diagnostic data and Charlson Comorbidity Index scores, informed a comparison of care utilization patterns between HND patient care and traditional care. Diagnostic and care utilization data were collected from a regional database. Wilcoxon signed ranked sum tests were performed to compare care utilization frequencies between the two groups.

RESULTS

Patients included in the study were care-intensive with several diagnoses and experienced a high level of variation in care utilization and diagnoses profiles. HND patients were sicker than their counterparts in the control group. Utilization indicators were similar between the two arms. There was some indication that the HND center is beginning to perform as expected, but no results were statistically significant.

CONCLUSION

This study sits among many studies reporting difficulties obtaining statistically significant findings for MCC patients. However, previous research has shown that the key components of this intervention, such as integrated, multidisciplinary, inter-professional collaboration within patient-centered care have had a positive effect on health-care outcomes. More innovative methods beyond the RCT, such as machine learning should be explored to evaluate the impact of integrated care interventions on care utilization.

Observational designs in clinical multiple sclerosis research: Particulars, practices and potentialities

Observational studies investigate a wide range of topics in multiple sclerosis research. This paper presents an overview of the various observational designs and their applications in clinical studies. Observational studies are well suited for making discoveries and assessing new explanations of phenomena, but less so for establishing causal relationships, due to confounding by indication (selection bias), co-morbidity, socio-economic or other factors. Whether observational findings are demonstrative, indicative or only suggestive, depends on the research question, whether and how the design fits this question, analytical techniques, and the quality of data. Observational studies may be cross-sectional vs. longitudinal, and prospective vs. retrospective. The term 'retrograde' is proposed to explicate that cross-sectional studies may obtain data that cover (long) preceding periods. Case reports and case series are usually based on accidental observations or routinely collected data. Cross-sectional studies, by simultaneously assessing clinical phenomena and external factors, enable the discovery and quantification of associations. In ecological studies the unit of analysis is population or group, and relationships on patient level cannot be established. A cohort study is a longitudinal study that investigates patients with a defining characteristic, e.g. diagnosis or specific treatment, by analyzing data acquired at various intervals. Prospective cohort studies use (some) data that are not yet available at the time the research is conceived, whereas in retrospective studies the data already exist. In a case-control study a representative group of patients with a specific clinical feature is compared with controls, and the frequencies at which an external factor, e.g. infection, has occurred in each group is compared; in a nested case-control study controls are drawn from a fully known cohort. Randomized controlled trial (RCT)-extension studies are informative because, due to RCT randomization, they are free from confounding by indication. Patient or disease registries are organised systems for the long-term collection of uniform data on a population that is defined by a particular disease, condition or exposure, with the purpose to study changes over time. In pharmacotherapeutic research, accidental observations of unexpected beneficial effects may lead to further research into a drug's efficacy in other conditions. Uncontrolled phase 1 studies investigate safety and dosing aspects. Observational studies are alternatives to RCTs when these are not feasible for ethical or practical reasons. Phase 4 observational studies play a crucial role in the evaluation of the effectiveness of treatments in daily practice, the validation of RCT-based side effect profiles, and the discovery of late occurring or rare, potentially life-threatening side effects. Combinations of multidisciplinary longitudinal data bases into large data sets enable the development of algorithms for personalized treatments. To improve the reporting of observational findings on treatment effectiveness, it is proposed that abstracts define the research question(s) the study was meant to answer, study design and analytical methods, and identify and quantify the patient population, treatment of interest, relevant outcomes and the study's strengths and limitations. The development of guidelines for Strengthening the Reporting of Observational Studies in Effectiveness Research (STROBER), as an extension of the guidelines used in epidemiology, is wanted.

A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine

Congenital heart disease (CHD) is one of the most common birth anomalies, and the care of children with CHD has improved over the past 4 decades. However, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. The proliferation of electronic health record systems and sophisticated patient monitors affords the opportunity to capture and analyze large amounts of CHD patient data, and the application of novel, effective analytics methods to these data can enable clinicians to enhance their care of pediatric CHD patients. This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

Inferring Smoking Status from User Generated Content in an Online Cessation Community

Introduction

User generated content (UGC) is a valuable but underutilized source of information about individuals who participate in online cessation interventions. This study represents a first effort to passively detect smoking status among members of an online cessation program using UGC.

Methods

Secondary data analysis was performed on data from 826 participants in a web-based smoking cessation randomized trial that included an online community. Domain experts from the online community reviewed each post and comment written by participants and attempted to infer the author's smoking status at the time it was written. Inferences from UGC were validated by comparison with self-reported 30-day point prevalence abstinence (PPA). Following validation, the impact of this method was evaluated across all individuals and time points in the study period.

Results

Of the 826 participants in the analytic sample, 719 had written at least one post from which content inference was possible. Among participants for whom unambiguous smoking status was inferred during the 30 days preceding their 3-month follow-up survey, concordance with self-report was almost perfect (kappa = 0.94). Posts indicating abstinence tended to be written shortly after enrollment (median = 14 days).

Conclusions

Passive inference of smoking status from UGC in online cessation communities is possible and highly reliable for smokers who actively produce content. These results lay the groundwork for further development of observational research tools and intervention innovations.

Implications

A proof-of-concept methodology for inferring smoking status from user generated content in online cessation communities is presented and validated. Content inference of smoking status makes a key cessation variable available for use in observational designs. This method provides a powerful tool for researchers interested in online cessation interventions and establishes a foundation for larger scale application via machine learning.

Abiraterone in chemotherapy-naive patients with metastatic castration-resistant prostate cancer: a systematic review of 'real-life' studies

Background

To assess the efficacy and safety of treatment with abiraterone acetate (AA) in chemotherapy-naïve men with metastatic castration-resistant prostate cancer (mCRPC) in the 'real-life' setting.

Methods

Data acquisition on the outcomes of the use of AA in chemotherapy-naive patients with mCRPC was performed by a MEDLINE comprehensive systematic literature search using combinations of the following key words: 'prostate cancer', 'metastatic', 'castration resistant', 'abiraterone', 'real life', and excluding controlled clinical trials (phase II and III studies). Identification and selection of the studies was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) criteria. Outcomes of interest were overall survival (OS), progression-free survival (PFS), 12-week 50% reduction in prostate-specific antigen (PSA), and grade 3 and higher adverse events. Data were narratively synthesized in light of methodological and clinical heterogeneity.

Results

Within the eight identified studies that fulfilled the criteria, a total of 801 patients were included in the meta-analysis. Baseline PSA ranged between 9.5 and 212.0 ng/ml. Most of the patients had bone metastases. Duration of treatment with AA was longer in the studies with lower baseline PSA levels. The median OS ranged between 14 and 36.4 months. The PFS, assessed according to different definitions, ranged from 3.9 to 18.5 months. A 50% PSA reduction at 12 weeks was reached by a variable percentage of patients ranging from 36.0% to 62.1%. Finally, the rate of grade 3 and higher adverse events was reported in three studies and ranged from 4.4% to 15.5%.

Conclusions

Despite the high grade of heterogeneity among studies, treatment with AA seems to ensure good survival outcomes in the 'real-life' setting. However, prospective studies based on patients' characteristics being more similar to 'real-life' patients are necessary.

Three controversies in health data science

The routine operation of modern healthcare systems produces a wealth of data in electronic health records, administrative databases, clinical registries, and other clinical systems. It is widely acknowledged that there is great potential for utilising these routine data for health research to derive new knowledge about health, disease, and treatments. However, the reuse of routine healthcare data for research is not beyond debate. In this paper, we discuss three issues that have stirred considerable controversy among health data scientists. First, we discuss van der Lei's 1st Law of Medical Informatics, which states that data shall be used only for the purpose for which they were collected. Then, we discuss to which extent routine data sources and innovations in analytical methods alleviate the need to conduct randomised clinical trials. Finally, we address questions of governance, privacy, and trust when routine health data are made available for research. While we don't think that there is a definite "right answer" for any of these issues, we argue that data scientists should be aware of the arguments for different viewpoints, respect their validity, and contribute constructively to the debate. The three controversies discussed in this paper relate to core challenges for research with health data and define an essential research agenda for the health data science community.

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

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A LASSO based forecast model for endemic infectious diseases is proposed.

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Predictions at 4 weeks achieve desirable accuracy.

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Models predict outbreaks but may struggle to predict outbreak size.

Introduction

Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response.

Methods

We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions.

Results

There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importance in prediction accuracy.

Conclusions

For LASSO models used for prediction, including different sets of predictors has varying effect in different situations. Short term predictions generally perform better than longer term predictions, suggesting public health agencies may need the capacity to respond at short-notice to early warnings.

Connected Health Devices for Health Care in French General Medicine Practice: Cross-Sectional Study

Background

The integration of Connected Health Devices (CHDs) is growing within mobile health (mHealth) and telemedicine, encouraged by institutions and industries. The idea is to improve lifestyle habits and health behaviors as a preventive goal in an aging population with fewer physicians available. However, their ill-defined place in health care does not promote their use in current medical practice.

Objective

The primary objective of this study was to quantify CHDs’ use rate by general practitioners (GPs). A secondary objective was to evaluate their benefits and limitations in usual care.

Methods

A cross-sectional study through an Internet-based survey was addressed to French GPs via regional medical unions and continuous education agencies, supplemented with an informative website, from March 2015 to July 2015. Surveys where either the form was insufficiently filled or the main question was left unanswered were excluded from the study.

Results

A total of 1084 answers were analyzed, of which 19.46% (211/1084, 95% CI 17.1-21.8) GPs used CHDs, and 10.15% (110/1084, 95% CI 8.5-12.1) prescribed a CHD. CHD users statistically prescribed more CHDs (7.38% [80/1084] in the user group vs 2.86% [31/1084] in nonusers; P<.001) and were more likely to use them in the future. Major interests in their utilization were in patient monitoring for 84.96% (921/1084) and patient education for 75.83% (822/1084), especially for diabetes (89.67%, 972/1084) and hypertension (84.13%, 912/1084). Generated data had to be managed securely by the patient primarily for 85.79% (930/1084) of the GPs. CHDs had to not constrain GPs outside clinical consultation, nor restrain their time for 75.83% (822/1084). Additional actors in patient care were not desired for 79.98% (867/1084) of the GPs. Questions about data management issues and technical difficulties were raised.

Conclusions

CHDs are little used by French GPs and even less prescribed to their patients, as only a few GPs use these tools. Their benefits as tools of patient empowerment, although expected, remain to be demonstrated in real-life setups.

Promoting Secondary Analysis of Electronic Medical Records in China: Summary of the PLAGH-MIT Critical Data Conference and Health Datathon

Electronic health records (EHRs) have been widely adopted among modern hospitals to collect and track clinical data. Secondary analysis of EHRs could complement the traditional randomized control trial (RCT) research model. However, most researchers in China lack either the technical expertise or the resources needed to utilize EHRs as a resource. In addition, a climate of cross-disciplinary collaboration to gain insights from EHRs, a crucial component of a learning healthcare system, is not prevalent. To address these issues, members from the Massachusetts Institute of Technology (MIT) and the People's Liberation Army General Hospital (PLAGH) organized the first clinical data conference and health datathon in China, which provided a platform for clinicians, statisticians, and data scientists to team up and address information gaps in the intensive care unit (ICU).

Web-Based Tools for Text-Based Patient-Provider Communication in Chronic Conditions: Scoping Review

Background

Patients with chronic conditions require ongoing care which not only necessitates support from health care providers outside appointments but also self-management. Web-based tools for text-based patient-provider communication, such as secure messaging, allow for sharing of contextual information and personal narrative in a simple accessible medium, empowering patients and enabling their providers to address emerging care needs.

Objective

The objectives of this study were to (1) conduct a systematic search of the published literature and the Internet for Web-based tools for text-based communication between patients and providers; (2) map tool characteristics, their intended use, contexts in which they were used, and by whom; (3) describe the nature of their evaluation; and (4) understand the terminology used to describe the tools.

Methods

We conducted a scoping review using the MEDLINE (Medical Literature Analysis and Retrieval System Online) and EMBASE (Excerpta Medica Database) databases. We summarized information on the characteristics of the tools (structure, functions, and communication paradigm), intended use, context and users, evaluation (study design and outcomes), and terminology. We performed a parallel search of the Internet to compare with tools identified in the published literature.

Results

We identified 54 papers describing 47 unique tools from 13 countries studied in the context of 68 chronic health conditions. The majority of tools (77%, 36/47) had functions in addition to communication (eg, viewable care plan, symptom diary, or tracker). Eight tools (17%, 8/47) were described as allowing patients to communicate with the team or multiple health care providers. Most of the tools were intended to support communication regarding symptom reporting (49%, 23/47), and lifestyle or behavior modification (36%, 17/47). The type of health care providers who used tools to communicate with patients were predominantly allied health professionals of various disciplines (30%, 14/47), nurses (23%, 11/47), and physicians (19%, 9/47), among others. Over half (52%, 25/48) of the tools were evaluated in randomized controlled trials, and 23 tools (48%, 23/48) were evaluated in nonrandomized studies. Terminology of tools varied by intervention type and functionality and did not consistently reflect a theme of communication. The majority of tools found in the Internet search were patient portals from 6 developers; none were found among published articles.

Conclusions

Web-based tools for text-based patient-provider communication were identified from a wide variety of clinical contexts and with varied functionality. Tools were most prevalent in contexts where intended use was self-management. Few tools for team-based communication were found, but this may become increasingly important as chronic disease care becomes more interdisciplinary.

Personalized treatment of patients with very early hepatocellular carcinoma

Hepatocellular carcinoma (HCC), in its very early stage, is heterogeneous both in terms of liver function (i.e., presence or absence of portal hypertension, model for end-stage liver disease score, Child-Pugh score 5 or 6, bilirubin level) and tumor characteristics (i.e., location, alpha-fetoprotein values, pathological features such as microvascular invasion, tumor grade and satellitosis). Existing evidence in comparing different curative options for patients with very early HCC is poor due to small sample sizes and lack of solid subgroup analyses. Large observational studies are available, with the potential to identify effective interventions in different subgroup of patients and to discover which treatments work "in a real world setting". These studies suggest some important treatment selection strategies in very early HCC patients. According to extent of liver resection, and liver function, percutaneous ablation or liver resection are the recommended first line therapies in these patients. Laparoscopic surgery (resection or ablation) is the preferable strategy when the tumor is in the surface of the liver or close to extra-hepatic organs. Due to scarce donor resources and competition with patients at high transplant benefit (HCC patients unsuitable for non-transplant radical therapies and non-HCC patients with decompensated cirrhosis), transplantation is recommended only as second line therapy in patients with very early stage HCC in case of tumor recurrence or liver failure after ablation or liver resection.

To establish a body of evidence on safety for postmarketing Chinese medicine: A new research paradigm

The issue of safety evaluation on postmarketing Chinese medicines has become a hot topic in mainland China recently. Researchers and decision-makers can obtain a variety of evidence resources about safety in order to evaluate the safety profile for postmarketing Chinese medicines. A registry study on ten Chinese medicine injections for postmarketing surveillance has come to the end. From such a study observing more than 300,000 patients for more than 4 years, a theoretical research question emerges, that is, how to identify and evaluate safety evidence systematically. We put forward a brand new research paradigm on the theory level, which is to establish a body of evidence on safety evaluation for postmarketing Chinese medicine. Therefore, multiple information sources were explored and extracted from preclinical experiments for toxicity, postmarketing clinical trials for effificacy and safety evaluation, registry study for surveillance, retrospective data analysis from hospital information system and spontaneous response system, and case reports and systematic review from literature. Greater efforts for this idea and cooperation with experts in this fifield both in China and abroad are urgently needed.