BackgroundTuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control.ObjectiveTo identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure.MethodsOn a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone.ResultsThe complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30–51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance.ConclusionMachine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.

In recent years, there has been a growing focus on the unreliability of published biomedical and clinical research. To introduce effective new scientific contributors to the culture of health care, we propose a “datathon” or “hackathon” model in which participants with disparate, but potentially synergistic and complementary, knowledge and skills effectively combine to address questions faced by clinicians. The continuous peer review intrinsically provided by follow-up datathons, which take up prior uncompleted projects, might produce more reliable research, either by providing a different perspective on the study design and methodology or by replication of prior analyses.

RationaleFactors associated with one-year mortality after recovery from critical illness are not well understood. Clinicians generally lack information regarding post-hospital discharge outcomes of patients from the intensive care unit, which may be important when counseling patients and families.ObjectiveWe sought to determine which factors among patients who survived for at least 30 days post-ICU admission are associated with one-year mortality.MethodsSingle-center, longitudinal retrospective cohort study of all ICU patients admitted to a tertiary-care academic medical center from 2001–2012 who survived ≥30 days from ICU admission. Cox’s proportional hazards model was used to identify the variables that are associated with one-year mortality. The primary outcome was one-year mortality.Results32,420 patients met the inclusion criteria and were included in the study. Among patients who survived to ≥30 days, 28,583 (88.2%) survived for greater than one year, whereas 3,837 (11.8%) did not. Variables associated with decreased one-year survival include: increased age, malignancy, number of hospital admissions within the prior year, duration of mechanical ventilation and vasoactive agent use, sepsis, history of congestive heart failure, end-stage renal disease, cirrhosis, chronic obstructive pulmonary disease, and the need for renal replacement therapy. Numerous effect modifications between these factors were found.ConclusionAmong survivors of critical illness, a significant number survive less than one year. More research is needed to help clinicians accurately identify those patients who, despite surviving their acute illness, are likely to suffer one-year mortality, and thereby to improve the quality of the decisions and care that impact this outcome.

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ObjectiveTo explore the issue of counterintuitive data via analysis of a representative case in which the data obtained was unexpected and inconsistent with current knowledge. We then discuss the issue of counterintuitive data while developing a framework for approaching such findings.DesignThe case study is a retrospective analysis of a cohort of coronary artery bypass graft (CABG) patients. Regression was used to examine the association between perceived pain in the intensive care unit (ICU) and selected outcomes.SettingMedical Information Mart for Intensive Care-III, a publicly available, de-identified critical care patient database.Participants844 adult patients from the database who underwent CABG surgery and were extubated within 24 hours after ICU admission.Outcomes30 day mortality, 1 year mortality and hospital length of stay (LOS).ResultsIncreased pain levels were found to be significantly associated with reduced mortality at 30 days and 1 year, and shorter hospital LOS. A one-point increase in mean pain level was found to be associated with a reduction in the odds of 30 day and 1 year mortality by a factor of 0.457 (95% CI 0.304 to 0.687, p<0.01) and 0.710 (95% CI 0.571 to 0.881, p<0.01) respectively, and a 0.916 (95% CI −1.159 to –0.673, p<0.01) day decrease in hospital LOS.ConclusionThe finding of an association between increased pain and improved outcomes was unexpected and clinically counterintuitive. In an increasingly digitised age of medical big data, such results are likely to become more common. The reliability of such counterintuitive results must be carefully examined. We suggest several issues to consider in this analytic process. If the data is determined to be valid, consideration must then be made towards alternative explanations for the counterintuitive results observed. Such results may in fact indicate that current clinical knowledge is incomplete or not have been firmly based on empirical evidence and function to inspire further research into the factors involved.

Background Noncommunicable diseases (NCDs) account for an increasing proportion of global morbidity and mortality and unsparingly affecting war-torn populations. Diabetes and hypertension, in particular, were implicated in 80% of deaths in pre-conflict Syria (ca. 2010) [Sethi], and are as persistent as ever throughout the ongoing Syrian civil war. Over the past several years, evidence has been accruing to suggest that mobile health ("mHealth") interventions are efficacious in improving health outcomes all over the world. Sana, an interdisciplinary organization comprising many healthcare-sector stakeholders, has devised a patientcontrolled health records (PCHR) app that will allow physicians to monitor and impact their patients' long-term health outcomes. Objective We intend to implement this technology solution in close collaboration with front-line healthcare workers, patients, local governments, and humanitarian organizations, so as to better understand the on-the-ground populations we are seeking to serve. Methods The first phase of product development and testing is slated to occur within 21 months of the project's commencement. During months 0-6, the Sana.PCHR application will be iterated and optimized using available guidelines and inputs from country-based healthcare providers. At the same time, data on existing NCD treatment will be collected at local healthcare facilities for comparison purposes. During months 7-8, frontline healthcare workers will be trained to use the app, which will be subsequently deployed in selected health care facilities. Finally, during months 9-21, use of the application will be monitored and supported by MIT Sana and JHU, and modifications will be