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Background Clinical research and medical practice can be advanced through the prediction of an individual’s health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that are static, rather than dynamic. To benefit from the increasing emergence of large, heterogeneous data sets, such as electronic health records (EHRs), novel tools to support improved clinical decision making through methods for individual-level risk prediction that can handle multiple variables, their interactions, and time-varying values are necessary. Methods We introduce a novel dynamic approach to clinical risk prediction for survival, longitudinal, and multivariate (SLAM) outcomes, called random forest for SLAM data analysis (RF-SLAM). RF-SLAM is a continuous-time, random forest method for survival analysis that combines the strengths of existing statistical and machine learning methods to produce individualized Bayes estimates of piecewise-constant hazard rates. We also present a method-agnostic approach for time-varying evaluation of model performance. Results We derive and illustrate the method by predicting sudden cardiac arrest (SCA) in the Left Ventricular Structural (LV) Predictors of Sudden Cardiac Death (SCD) Registry. We demonstrate superior performance relative to standard random forest methods for survival data. We illustrate the importance of the number of preceding heart failure hospitalizations as a time-dependent predictor in SCA risk assessment. Conclusions RF-SLAM is a novel statistical and machine learning method that improves risk prediction by incorporating time-varying information and accommodating a large number of predictors, their interactions, and missing values. RF-SLAM is designed to easily extend to simultaneous predictions of multiple, possibly competing, events and/or repeated measurements of discrete or continuous variables over time.Trial registration: LV Structural Predictors of SCD Registry (clinicaltrials.gov, NCT01076660), retrospectively registered 25 February 2010

Estimating a patient’s disease trajectory as defined by clinical measures is an essential task in medicine. Given multiple biomarkers, there is a practical choice of whether to estimate the joint distribution of all biomarkers in a single model or to model the univariate marginal distribution of each marker separately ignoring the covariance structure among measures. To fully utilize all trajectory-relevant information in multiple longitudinal markers, a joint model is required, but its complexity and computational burden may only be warranted when joint estimates of trajectories are substantially more efficient than separate estimates. This paper derives general expressions for the inefficiency of univariate or “separated\" estimates of population-average trajectories and individual’s random effects as compared to the fully efficient multivariate or “combined\" estimates. Then, in two settings: (1) a general bivariate case; and (2) our motivating clinical case study with 5 measures, we find that separated estimates of fixed effects are nearly fully efficient. However, joint estimates of random effects can be meaningfully more efficient for measures with substantial missing data when other strongly correlated measures are observed more frequently. This increased efficiency of the joint model derives more from joint shrinkage of random effects in multivariate space than from improved estimates of the subject-specific trajectories obtained when accounting for correlations in measurements. These findings have application to a diverse array of chronic diseases where biomarkers’ trajectories guide clinical decisions.

Background: Population-based studies have estimated health risks of short-term exposure to fine particles using mass of${\\rm PM}_{2.5}$(particulate matter ≤ 2.5 μm in aerodynamic diameter) as the indicator. Evidence regarding the toxicity of the chemical components of the${\\rm PM}_{2.5}$mixture is limited. Objective: In this study we investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of${\\rm PM}_{2.5}$in the United States. Methods: We used a national database comprising daily data for 2000-2006 on emergency hospital admissions for cardiovascular and respiratory outcomes, ambient levels of major${\\rm PM}_{2.5}$chemical components [sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), and sodium and ammonium ions], and weather. Using Bayesian hierarchical statistical models, we estimated the associations between daily levels of${\\rm PM}_{2.5}$components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees (≥ 65 years of age). Results: In multiple-pollutant models that adjust for the levels of other pollutants, an interquartile range (IQR) increase in EC was associated with a 0.80% [95% posterior interval (PI), 0.34-1.27%] increase in risk of same-day cardiovascular admissions, and an IQR increase in OCM was associated with a 1.01% (95% PI, 0.04-1.98%) increase in risk of respiratory admissions on the same day. Other components were not associated with cardiovascular or respiratory hospital admissions in multiple-pollutant models. Conclusions: Ambient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of${\\rm PM}_{2.5}$.

Background: Prospective cohort studies constitute the major source of evidence about the mortality effects of chronic exposure to particulate air pollution. Additional studies are needed to provide evidence on the health effects of chronic exposure to particulate matter ≤ 2.5 μm in aerodynamic diameter $({\\rm PM}_{2.5})$ because few studies have been carried out and the cohorts have not been representative. Objectives: This study was designed to estimate the relative risk of death associated with long-term exposure to ${\\rm PM}_{2.5}$ by region and age groups in a U.S. population of elderly, for the period 2000-2005. Methods: By linking ${\\rm PM}_{2.5}$ monitoring data to the Medicare billing claims by ZIP code of residence of the enrollees, we have developed a new retrospective cohort study, the Medicare Cohort Air Pollution Study. The study population comprises 13.2 million participants living in 4,568 ZIP codes having centroids within 6 miles of a ${\\rm PM}_{2.5}$ monitor. We estimated relative risks adjusted by socioeconomic status and smoking by fitting log-linear regression models. Results: In the eastern and central regions, a 10-μg/m³ increase in 6-year average of ${\\rm PM}_{2.5}$ is associated with 6.8% [95% confidence interval (CI), 4.9-8.7%] and 13.2% (95% CI, 9.5-16.9) increases in mortality, respectively. We found no evidence of an association in the western region or for persons ≥ 85 years of age. Conclusions: We established a cohort of Medicare participants for investigating air pollution and mortality on longer-term time frames. Chronic exposure to ${\\rm PM}_{2.5}$ was associated with mortality in the eastern and central regions, but not in the western United States.

Background As the global burden of malaria decreases, routine health information systems (RHIS) have become invaluable for monitoring progress towards elimination. The District Health Information System, version 2 (DHIS2) has been widely adopted across countries and is expected to increase the quality of reporting of RHIS. In this study, we evaluated the quality of reporting of key indicators of childhood malaria from January 2014 through December 2017, the first 4 years of DHIS2 implementation in Senegal. Methods Monthly data on the number of confirmed and suspected malaria cases as well as tests done were extracted from the Senegal DHIS2. Reporting completeness was measured as the number of monthly reports received divided by the expected number of reports in a given year. Completeness of indicator data was measured as the percentage of non-missing indicator values. We used a quasi-Poisson model with natural cubic spline terms of month of reporting to impute values missing at the facility level. We used the imputed values to take into account the percentage of malaria cases that were missed due to lack of reporting. Consistency was measured as the absence of moderate and extreme outliers, internal consistency between related indicators, and consistency of indicators over time. Results In contrast to public facilities of which 92.7% reported data in the DHIS2 system during the study period, only 15.3% of the private facilities used the reporting system. At the national level, completeness of facility reporting increased from 84.5% in 2014 to 97.5% in 2017. The percentage of expected malaria cases reported increased from 76.5% in 2014 to 94.7% in 2017. Over the study period, the percentage of malaria cases reported across all districts was on average 7.5% higher ( P  < 0.01) during the rainy season relative to the dry season. Reporting completeness rates were lower among hospitals compared to health centers and health posts. The incidence of moderate and extreme outlier values was 5.2 and 2.3%, respectively. The number of confirmed malaria cases increased by 15% whereas the numbers of suspected cases and tests conducted more than doubled from 2014 to 2017 likely due to a policy shift towards universal testing of pediatric febrile cases. Conclusions The quality of reporting for malaria indicators in the Senegal DHIS2 has improved over time and the data are suitable for use to monitor progress in malaria programs, with an understanding of their limitations. Senegalese health authorities should maintain the focus on broader adoption of DHIS2 reporting by private facilities, the sustainability of district-level data quality reviews, facility-level supervision and feedback mechanisms at all levels of the health system.

IntroductionThis study evaluates the association of multidimensional social determinants of health (SDoH) with non-adherence to diabetic retinopathy examinations.Research design and methodsThis was a post-hoc subgroup analysis of adults with diabetes in a prospective cohort study of enrollees in the Washington, DC Medicaid program. At study enrollment, participants were given a comprehensive SDoH survey based on the WHO SDoH model. Adherence to recommended dilated diabetic retinopathy examinations, as determined by qualifying Current Procedural Terminology codes in the insurance claims, was defined as having at least one eye examination in the 2-year period following study enrollment.ResultsOf the 8943 participants enrolled in the prospective study, 1492 (64% female, 91% non-Hispanic Black) were included in this post-hoc subgroup analysis. 47.7% (n=712) were adherent to the recommended biennial diabetic eye examinations. Not having a regular provider (eg, a primary care physician) and having poor housing conditions (eg, overcrowded, inadequate heating) were associated with decreased odds of adherence to diabetic eye examinations (0.45 (95% CI 0.31 to 0.64) and 0.70 (95% CI 0.53 to 0.94), respectively) in the multivariate logistic regression analysis controlling for age, sex, race/ethnicity, overall health status using the Chronic Disability Payment System, diabetes severity using the Diabetes Complications Severity Index, history of eye disease, and history of diabetic eye disease treatment.ConclusionsA multidimensional evaluation of SDoH revealed barriers that impact adherence to diabetic retinopathy examinations. Having poor housing conditions and not having a regular provider were associated with poor adherence. A brief SDoH assessment could be incorporated into routine clinical care to identify social risks and connect patients with the necessary resources to improve adherence to diabetic retinopathy examinations.

Studies showing that current levels of air pollution in the cities of many developed and developing countries are associated with increased rates of mortality and morbidity have heightened concern that air pollution continues to pose a threat to public health. 1 – 3 The evidence suggests that small airborne particles are a toxic component of urban air pollution. Using this interpretation of the evidence as a rationale, the Environmental Protection Agency implemented a new standard for fine particulate matter. 4 The existing standard, promulgated in 1987, specified the maximal levels allowable in a 24-hour period and on an annual basis for particulate matter . . .

Background Scleroderma is a serious chronic autoimmune disease in which a patient’s disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-threatening key clinical events including interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH). The statistical challenge is to accurately and precisely predict these events by using all of the clinical history for the patient at hand and for a reference population of patients. Methods We use a Bayesian mixed model approach to simultaneously characterize each individual’s future trajectories for several biomarkers. We estimate this model using a large population of patients from the Johns Hopkins Scleroderma Center Research Registry. The joint probabilities of critical lung and heart events are then calculated as a byproduct of the mixed model. Results The performance of this approach is substantially better than standard, more common alternatives. In order to predict an individual’s risks in a clinical setting, we also develop a cross-validated, sequential prediction (CVSP) algorithm. As additional data are observed during a patient’s visit, the algorithm sequentially produces updated predictions for the future longitudinal trajectories and for ILD, cardiomyopathy, and PH. The updated prediction distributions with little additional computing, for example within an electronic health record (EHR). Conclusions This method that generates real-time personalized risk estimates has been implemented within the electronic health record system for clinical testing. To our knowledge, this work represents the first approach to compute personalized risk estimates for multiple scleroderma complications.

Objective The goal of the paper is to determine inter-rater reliability of trained examiners performing standardized strength assessments using manual muscle testing (MMT). Design, subjects, and setting The authors report on 19 trainees undergoing quality assurance within a multi-site prospective cohort study. Intervention Inter-rater reliability for specially trained evaluators (“trainees”) and a reference rater, performing MMT using both simulated and actual patients recovering from critical illness was evaluated. Measurements and results Across 26 muscle groups tested by 19 trainee-reference rater pairs, the median (interquartile range) percent agreement and intraclass correlation coefficient (ICC; 95% CI) were: 96% (91, 98%) and 0.98 (0.95, 1.00), respectively. Across all 19 pairs, the ICC (95% CI) for the overall composite MMT score was 0.99 (0.98–1.00). When limited to actual patients, the ICC was 1.00 (95% CI 0.99–1.00). The agreement (kappa; 95% CI) in detecting clinically significant weakness was 0.88 (0.44–1.00). Conclusions MMT has excellent inter-rater reliability in trained examiners and is a reliable method of comprehensively assessing muscle strength.

ObjectivesIn low-income countries, birth weights for home deliveries are often measured at the nadir when babies may lose up of 10% of their birth weight, biasing estimates of small-for-gestational age (SGA) and low birth weight (LBW). We aimed to develop an imputation model that predicts the ‘true’ birth weight at time of delivery.DesignWe developed and applied a model that recalibrates weights measured in the early neonatal period to time=0 at delivery and uses those recalibrated birth weights to impute missing birth weights.SettingThis is a secondary analysis of pregnancy cohort data from two studies in Sarlahi district, Nepal.ParticipantsThe participants are 457 babies with daily weights measured in the first 10 days of life from a subsample of a larger clinical trial on chlorhexidine (CHX) neonatal skin cleansing and 31 116 babies followed through the neonatal period to test the impact of neonatal massage oil type (Nepal Oil Massage Study (NOMS)).Outcome measuresWe developed an empirical Bayes model of early neonatal weight change using CHX trial longitudinal data and applied it to the NOMS dataset to recalibrate and then impute birth weight at delivery. The outcomes are size-for-gestational age and LBW.ResultsWhen using the imputed birth weights, the proportion of SGA is reduced from 49% (95% CI: 48% to 49%) to 44% (95% CI: 43% to 44%). Low birth weight is reduced from 30% (95% CI: 30% to 31%) to 27% (95% CI: 26% to 27%). The proportion of babies born large-for-gestational age increased from 4% (95% CI: 4% to 4%) to 5% (95% CI: 5% to 5%).ConclusionsUsing weights measured around the nadir overestimates the prevalence of SGA and LBW. Studies in low-income settings with high levels of home births should consider a similar recalibration and imputation model to generate more accurate population estimates of small and vulnerable newborns.