OBJECTIVE: Efficient, effective health care requires rapid availability of patient information. We designed, implemented, and assessed the impact of a primary care electronic medical record (EMR) in three rural Kenyan health centers. METHOD: Local clinicians identified data required for primary care and public health reporting. We designed paper encounter forms to capture these data in adult medicine, pediatric, and antenatal clinics. Encounter form data were hand-entered into a new primary care module in an existing EMR serving onsite clinics serving patients infected with the human immunodeficiency virus (HIV). Before subsequent visits, Summary Reports were printed containing selected patient data with reminders for needed HIV care. We assessed effects on patient flow and provider work with time-motion studies before implementation and two years later, and we surveyed providers' satisfaction with the EMR. RESULTS: Between September 2008 and December 2011, 72 635 primary care patients were registered and 114 480 encounter forms were completed. During 2011, 32 193 unique patients visited primary care clinics, and encounter forms were completed for all visits. Of 1031 (3.2%) who were HIV-infected, 85% received HIV care. Patient clinic time increased from 37 to 81 min/visit after EMR implementation in one health center and 56 to 106 min/visit in the other. However, outpatient visits to both health centers increased by 85%. Three-quarters of increased time was spent waiting. Despite nearly doubling visits, there was no change in clinical officers' work patterns, but the nurses' and the clerks' patient care time decreased after EMR implementation. Providers were generally satisfied with the EMR but desired additional training. CONCLUSIONS: We successfully implemented a primary care EMR in three rural Kenyan health centers. Patient waiting time was dramatically lengthened while the nurses' and the clerks' patient care time decreased. Long-term use of EMRs in such settings will require changes in culture and workflow.

BACKGROUND: Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment. RESULTS: The proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Compared to the existing methods, the proposed approach improved peak alignment in terms of various performance measures. Also, post-hoc approach was verified to improve peak alignment by manual inspection. CONCLUSIONS: The proposed approach, which combines the information of metabolite identification and alignment, clearly improves the accuracy of peak alignment in terms of several performance measures. R package and examples using a dataset are available at http://mrr.sourceforge.net/download.html.

BACKGROUND: Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS) has been used for metabolite profiling in metabolomics. However, there is still much experimental variation to be controlled including both within-experiment and between-experiment variation. For efficient analysis, an ideal peak alignment method to deal with such variations is in great need. RESULTS: Using experimental data of a mixture of metabolite standards, we demonstrated that our method has better performance than other existing method which is not model-based. We then applied our method to the data generated from the plasma of a rat, which also demonstrates good performance of our model. CONCLUSIONS: We developed a model-based peak alignment method to process both homogeneous and heterogeneous experimental data. The unique feature of our method is the only model-based peak alignment method coupled with metabolite identification in an unified framework. Through the comparison with other existing method, we demonstrated that our method has better performance. Data are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development/mspa. The R source codes are available at http://www.biostat.iupui.edu/~ChangyuShen/CodesPeakAlignment.zip. TRIAL REGISTRATION: 2136949528613691.

BACKGROUND: Mass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolite's similarity score with other spectra in the library searched against. The competition score allows the model to properly assess the evidence on the presence/absence status of a metabolite based on whether or not the metabolite is matched to some sample spectrum. RESULTS: With a mixture of metabolite standards, we demonstrated that our method has better identification accuracy than other four existing methods. Moreover, our method has reliable false discovery rate estimate. We also applied our method to the data collected from the plasma of a rat and identified some metabolites from the plasma under the control of false discovery rate. CONCLUSIONS: We developed an empirical Bayes model for metabolite identification and validated the method through a mixture of metabolite standards and rat plasma. The results show that our hierarchical model improves identification accuracy as compared with methods that do not structurally model the involved variables. The improvement in identification accuracy is likely to facilitate downstream analysis such as peak alignment and biomarker identification. Raw data and result matrices can be found at http://www.biostat.iupui.edu/~ChangyuShen/index.htm. TRIAL REGISTRATION: 2123938128573429.

OBJECTIVE: Little evidence exists on effective interventions to integrate HIV-care guidelines into practices within developing countries. This study tested the hypothesis that clinical summaries with computer-generated reminders could improve clinicians' compliance with CD4 testing guidelines in the resource-limited setting of sub-Saharan Africa. DESIGN: A prospective comparative study of two randomly selected outpatient adult HIV clinics in western Kenya. Printed summaries with reminders for overdue CD4 tests were made available to clinicians in the intervention clinic but not in the control clinic. MEASUREMENTS: Changes in order rates for overdue CD4 tests were compared between and within the two clinics. RESULTS: The computerized reminder system identified 717 encounters (21%) with overdue CD4 tests. Analysis by study assignment (regardless of summaries being printed or not) revealed that with computer-generated reminders, CD4 order rates were significantly higher in the intervention clinic compared to the control clinic (53% vs 38%, OR = 1.80, CI 1.34 to 2.42, p < 0.0001). When comparison was restricted to encounters where summaries with reminders were printed, order rates in intervention clinic were even higher (63%). The intervention clinic increased CD4 ordering from 42% before reminders to 63% with reminders (50% increase, OR = 2.32, CI 1.67 to 3.22, p < 0.0001), compared to control clinic with only 8% increase from prestudy baseline (CI 0.83 to 1.46, p = 0.51). Limitations Evaluation was conducted at two clinics in a single institution. CONCLUSIONS: Clinical summaries with computer-generated reminders significantly improved clinician compliance with CD4 testing guidelines in the resource-limited setting of sub-Saharan Africa. This technology can have broad applicability to improve quality of HIV care in these settings.

Current models for implementing electronic health records (EHRs) in resource-limited settings may not be scalable because they fail to address human-resource and cost constraints. This paper describes an implementation model which relies on shared responsibility between local sites and an external three-pronged support infrastructure consisting of: (1) a national technical expertise center, (2) an implementer's community, and (3) a developer's community. This model was used to implement an open-source EHR in three Ugandan HIV-clinics. Pre-post time-motion study at one site revealed that Primary Care Providers spent a third less time in direct and indirect care of patients (p<0.001) and 40% more time on personal activities (p=0.09) after EHRs implementation. Time spent by previously enrolled patients with non-clinician staff fell by half (p=0.004) and with pharmacy by 63% (p<0.001). Surveyed providers were highly satisfied with the EHRs and its support infrastructure. This model offers a viable approach for broadly implementing EHRs in resource-limited settings.

The objective of this study is to evaluate the ability of dynamic contrast enhanced computed tomography (DCE-CT) to assess intratumor physiological heterogeneity in tumors with different angiogenic phenotypes. DCE-CT imaging was performed on athymic nude mice bearing xenograft wild type (MCF-7(neo)) and VEGF-transfected (MCF-7(VEGF)) tumors by using a clinical multislice CT, and compared to skeletal muscle. Parametrical maps of tumor physiology--perfusion (F), permeability-surface area (PS), fractional intravascular plasma (f(p)), and interstitial space (f(is))--were obtained by fitting the time-dependent contrast-enhanced curves to a two-compartmental kinetic model for each voxel (0.3 x 0.3 x 0.75 mm(3)). Mean physiological measurements were compared with (positron emission tomography (PET) imaging, and the spatial distribution of tumor vasculature was compared with histology. No statistically significant difference was found in mean physiological values of F, PS, and f(p) in MCF-7(neo) and muscle, while f(is) of MCF-7(neo) was a factor of two higher ( p < 0.04). MCF-7(neo) tumors also showed a radial heterogeneity with significant higher physiological values in periphery than those in middle and core regions ( p < 0.01 for all physiological parameters). MCF-7(VEGF) tumors demonstrated significant increases in all physiological parameters compared with MCF-7(neo) tumors, and a distinct saccular heterogeneous pattern compared with MCF-7(neo) and muscle. Both PET imaging and histological results showed good correlation with the above results for this same mouse model. No statistically significant difference was found in the mean perfusion and intravascular volume measured by PET imaging and DCE-CT. Increases in cross-sectional area of blood vessels ( p < 0.002) were observed in MCF-7(VEGF) tumors than MCF-7(neo), and their spatial distribution correlated well with the spatial distribution of f(p) obtained by DCE-CT. The results of this study demonstrated the feasibility of DCE-CT in quantification of spatial heterogeneity in tumor physiology in small animal models. Monitoring variations in the tumor environment using DCE-CT offers an in vivo tool for the evaluation and optimization of new therapeutic strategies.

MOTIVATION: Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing. RESULTS: We developed a hierarchical statistical model (HSM) that jointly models the uncertainty of the identified peptides and proteins and can be applied to any scoring system. With data sets of a standard mixture and the yeast proteome, we demonstrate that the HSM offers a reliable or at least conservative false discovery rate (FDR) estimate for peptide and protein identifications. The probability measure of HSM also offers a powerful discriminating score for peptide identification. AVAILABILITY: The algorithm is available upon request from the authors.