22 research outputs found

    Assessment of Surgical Care Provided in National Health Services Hospitals in Mozambique: The Importance of Subnational Metrics in Global Surgery

    No full text
    IntroductionSurgery plays a critical role in sustainable healthcare systems. Validated metrics exist to guide implementation of surgical services, but low-income countries (LIC) struggle to report recommended metrics and this poses a critical barrier to addressing unmet need. We present a comprehensive national sample of surgical encounters from a LIC by assessing the National Health Services of Mozambique.Material and methodsA prospective cohort of all surgical encounters from Mozambique's National Health Service was gathered for all provinces between July and December 2015. Primary outcomes were timely access, provider densities for surgery, anesthesiology, and obstetrics (SAO) per 100,000 population, annualized surgical procedure volume per 100,000, and postoperative mortality (POMR). Secondary outcomes include operating room density and efficiency.ResultsFifty-four hospitals had surgical capacity in 11 provinces with 47,189 surgeries. 44.9% of Mozambique's population lives in Districts without access to surgical services. National SAO density was 1.2/100,000, ranging from 0.4/100,000 in Manica Province to 9.8/100,000 in Maputo City. Annualized national surgical case volume was 367 procedures/100,000 population, ranging from 180/100,000 in Zambezia Province to 1,897/100,000 in Maputo City. National POMR was 0.74% and ranged from 0.23% in Maputo Province to 1.78% in Niassa Province.DiscussionSurgical delivery in Mozambique falls short of international targets. Subnational deficiencies and variations between provinces pose targets for quality improvement in advancing national surgical plans. This serves as a template for LICs to follow in gathering surgical metrics for the WHO and the World Bank and offers short- and long-term targets for surgery as a component of health systems strengthening

    Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study

    No full text
    IntroductionPatient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients.MethodsA retrospective analysis of a trauma registry was used to identify patients admitted to a level 1 trauma center for >24 hours from October 2019 to July 2020. We evaluated the performance of the EDI, which is constructed from 125 objective patient measures within the EHR, in predicting mortality and unplanned ICU admissions. We performed a 5 to 1 match on age because it is a major component of EDI, then examined the area under the receiver operating characteristic curve (AUROC), and benchmarked it against Injury Severity Score (ISS) and new injury severity score (NISS).ResultsThe study cohort consisted of 1,325 patients admitted with a mean age of 52.5 years and 91% following blunt injury. The in-hospital mortality rate was 2%, and unplanned ICU admission rate was 2.6%. In predicting mortality, the maximum EDI within 24 hours of admission had an AUROC of 0.98 compared with 0.89 of ISS and 0.91 of NISS. For unplanned ICU admission, the EDI slope within 24 hours of ICU admission had a modest performance with an AUROC of 0.66.ConclusionEpic Deterioration Index appears to perform strongly in predicting in-patient mortality similarly to ISS and NISS. In addition, it can be used to predict unplanned ICU admissions. This study helps validate the use of this real-time EHR ML-based tool, suggesting that EDI should be incorporated into the daily care of trauma patients.Level of evidencePrognostic, level III
    corecore