20 research outputs found

    Health Professional Training and Capacity Strengthening Through International Academic Partnerships: The First Five Years of the Human Resources for Health Program in Rwanda

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    Abstract Background: The Rwanda Human Resources for Health Program (HRH Program) is a 7-year (2012-2019) health professional training initiative led by the Government of Rwanda with the goals of training a large, diverse, and competent health workforce and strengthening the capacity of academic institutions in Rwanda. Methods: The data for this organizational case study was collected through official reports from the Rwanda Ministry of Health (MoH) and 22 participating US academic institutions, databases from the MoH and the College of Medicine and Health Sciences (CMHS) in Rwanda, and surveys completed by the co-authors. Results: In the first 5 years of the HRH Program, a consortium of US academic institutions has deployed an average of 99 visiting faculty per year to support 22 training programs, which are on track to graduate almost 4600 students by 2019. The HRH Program has also built capacity within the CMHS by promoting the recruitment of Rwandan faculty and the establishment of additional partnerships and collaborations with the US academic institutions. Conclusion: The milestones achieved by the HRH Program have been substantial although some challenges persist. These challenges include adequately supporting the visiting faculty; pairing them with Rwandan faculty (twinning); ensuring strong communication and coordination among stakeholders; addressing mismatches in priorities between donors and implementers; the execution of a sustainability strategy; and the decision by one of the donors not to renew funding beyond March 2017. Over the next 2 academic years, it is critical for the sustainability of the 22 training programs supported by the HRH Program that the health-related Schools at the CMHS significantly scale up recruitment of new Rwandan faculty. The HRH Program can serve as a model for other training initiatives implemented in countries affected by a severe shortage of health professionals

    Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study.

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    Charted vital signs and laboratory results represent intermittent samples of a patient's dynamic physiologic state and have been used to calculate early warning scores to identify patients at risk of clinical deterioration. We hypothesized that the addition of cardiorespiratory dynamics measured from continuous electrocardiography (ECG) monitoring to intermittently sampled data improves the predictive validity of models trained to detect clinical deterioration prior to intensive care unit (ICU) transfer or unanticipated death.We analyzed 63 patient-years of ECG data from 8,105 acute care patient admissions at a tertiary care academic medical center. We developed models to predict deterioration resulting in ICU transfer or unanticipated death within the next 24 hours using either vital signs, laboratory results, or cardiorespiratory dynamics from continuous ECG monitoring and also evaluated models using all available data sources. We calculated the predictive validity (C-statistic), the net reclassification improvement, and the probability of achieving the difference in likelihood ratio χ2 for the additional degrees of freedom. The primary outcome occurred 755 times in 586 admissions (7%). We analyzed 395 clinical deteriorations with continuous ECG data in the 24 hours prior to an event. Using only continuous ECG measures resulted in a C-statistic of 0.65, similar to models using only laboratory results and vital signs (0.63 and 0.69 respectively). Addition of continuous ECG measures to models using conventional measurements improved the C-statistic by 0.01 and 0.07; a model integrating all data sources had a C-statistic of 0.73 with categorical net reclassification improvement of 0.09 for a change of 1 decile in risk. The difference in likelihood ratio χ2 between integrated models with and without cardiorespiratory dynamics was 2158 (p value: <0.001).Cardiorespiratory dynamics from continuous ECG monitoring detect clinical deterioration in acute care patients and improve performance of conventional models that use only laboratory results and vital signs

    Relative statistical significance of component predictors included.

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    <p>Heatmap depiction of statistical significance of predictors (rows) in each model (columns) with corresponding C-statistics. Saturation or transparency depicts the statistical significance as calculated by the square root of (χ<sup>2</sup> minus degrees of freedom (df)) and can be likened to the absolute value of a Z score adjusted for the degrees of freedom associated with a predictor due to higher order terms. Hue or color represents the data source: individual data sources (green), pairwise combinations (blue), fully integrated model using all available data (red). Predictors that failed to achieve statistical significance in any of the models are not displayed. VS-HR: charted heart rate, VS-RR: charted respiratory rate, O2.Flow: charted oxygen flow rate, SpO2: charted oxygen saturation, GCS: Glasgow Coma Scale, SBP: systolic blood pressure, WBC: white blood cell count, BUN: blood urea nitrogen, AST: aspartate aminotransferase, Plts: platelets, CO2: carbon dioxide, Glu: glucose, pCO2: partial pressure of carbon dioxide, K: potassium, Na: sodium, sCr: creatinine, ECG-SNR: Signal-to-Noise metric from ECG-derived respirations, ECG-HR: heart rate from continuous ECG, HR-D: trend of HR over prior 24 hours, ECG-RR: ECG-derived respiratory rate, RR-D: trend of RR over prior 24 hours, AF: atrial fibrillation, EMR: electronic medical record.</p

    Distributions of measured heart rate and respiratory rate.

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    <p>Heart rate (left) and respiratory rate (right) measurement distributions according to source, charted vital signs (VS; blue) vs electrocardiography (ECG; red) where each source had equal numbers of measurements.</p

    Schematic of sequential integration of additional data sources.

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    <p>Each circle represents an individual model with the C-statistic reported below each model abbreviation. Area of each circle is proportionate to the total likelihood ratio test χ<sup>2</sup> minus two times the degrees of freedom (df) of all non-intercept terms.</p
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