1162. A data-driven approach to predict plasma leakage using explainable machine learning

Background Dengue could cause complications with an estimated 10,000 deaths per annum. It mostly affects low- and middle-income countries such as Sri Lanka with limited healthcare resources to handle seasonal outbreaks. A third of dengue patients usually have a critical phase characterized by plasma leakage with increased risk of life-threatening complications. A data-driven approach was required to find early predictors of plasma leakage that are usually available in routine care from a resource limited setting, as means of triaging patients for hospital admission. Methods We utilized a prospective cohort (The Colombo dengue study in Sri Lanka) that recruits patients meeting the clinical case definition of dengue fever. The cohort includes 4,781 instances of clinical signs, symptoms, and in-hospital laboratory tests from N=877 patients (60.3% patients infected by Dengue) recorded in first four days of fever. By excluding incomplete patient instances, the data was randomly split to a development set (N=378) and a test set (N=144). From the development set, five most informative features were identified using the minimum description length (MDL) algorithm. Logistic regression was used to create a prediction model using the development set. Shapley analysis was used to explain the model on the test set extracting the extent by which each predictor contributed to the predictions. Results The MDL algorithm revealed that hemoglobin (HGB), hematocrit (HCT), aspartate aminotransferase (AST), age, and gender to be the most informative predictors of plasma leakage. The logistic regression model predicted plasma leakage with (AUC = 0.76) on the test set (Fig 1). The HGB appeared to contribute the most to the predictions with the higher values associated with higher predicted risk of plasma leakage and vice versa (Fig 2). Figure 2 SHAP decision plot for the logistic regression model on the test set. The features are sorted from top to bottom by their mean absolute SHAP values (higher interpreted as more contributing). Feature values are normalised to [0 1] by the min-max normalisation method and colour-coded (grey points are missing values), outliers were squished to the range using Hampel filter. For gender, male is indicated by red and female by blue. The colour of each line is the same as the value of the feature connected to in downwards direction. Conclusion Our results give support to the predictability of plasma leakage in patients suspected of Dengue fever in their first four days of fever onset. The study also underlines the relevance of the machine learning approach to identify the predictors and the practicality of the prediction model as reflected by the prediction performance to triage patients for hospital admission in resource limited settings. Disclosures All Authors: No reported disclosures

A scoping review of registry captured indicators for evaluating quality of critical care in ICU

Background: Excess morbidity and mortality following critical illness is increasingly attributed to potentially avoidable complications occurring as a result of complex ICU management (Berenholtz et al., J Crit Care 17:1-2, 2002; De Vos et al., J Crit Care 22:267-74, 2007; Zimmerman J Crit Care 1:12-5, 2002). Routine measurement of quality indicators (QIs) through an Electronic Health Record (EHR) or registries are increasingly used to benchmark care and evaluate improvement interventions. However, existing indicators of quality for intensive care are derived almost exclusively from relatively narrow subsets of ICU patients from high-income healthcare systems. The aim of this scoping review is to systematically review the literature on QIs for evaluating critical care, identify QIs, map their definitions, evidence base, and describe the variances in measurement, and both the reported advantages and challenges of implementation. Method: We searched MEDLINE, EMBASE, CINAHL, and the Cochrane libraries from the earliest available date through to January 2019. To increase the sensitivity of the search, grey literature and reference lists were reviewed. Minimum inclusion criteria were a description of one or more QIs designed to evaluate care for patients in ICU captured through a registry platform or EHR adapted for quality of care surveillance. Results: The search identified 4780 citations. Review of abstracts led to retrieval of 276 full-text articles, of which 123 articles were accepted. Fifty-one unique QIs in ICU were classified using the three components of health care quality proposed by the High Quality Health Systems (HQSS) framework. Adverse events including hospital acquired infections (13.7%), hospital processes (54.9%), and outcomes (31.4%) were the most common QIs identified. Patient reported outcome QIs accounted for less than 6%. Barriers to the implementation of QIs were described in 35.7% of articles and divided into operational barriers (51%) and acceptability barriers (49%). Conclusions: Despite the complexity and risk associated with ICU care, there are only a small number of operational indicators used. Future selection of QIs would benefit from a stakeholder-driven approach, whereby the values of patients and communities and the priorities for actionable improvement as perceived by healthcare providers are prioritized and include greater focus on measuring discriminable processes of care

Physical inactivity among physiotherapy undergraduates: Exploring the knowledge-practice gap

Background\ud \ud Physical inactivity is a common risk factor for several non-communicable diseases (NCDs). Increasing physical activity could reduce the burden of disease due to major NCDs and increase life expectancy. Undergraduate physiotherapy students represent a group of young-adults expected to have a good knowledge of physical activity. We evaluated physical activity levels of undergraduate physiotherapy students of University of Colombo, Sri Lanka and determined their motives and barriers for participation in physical activity.\ud \ud Methods\ud \ud All physiotherapy undergraduates studying at the University of Colombo, Sri Lanka in 2013 were invited for the study. Phase one was a quantitative study to evaluate the physical activity levels and phase two was a qualitative study to identify motives and barriers for physical activity and sports in the same cohort. Physical activity levels (phase 1) were assessed using the interviewer administered International Physical Activity Questionnaire (long-version). The qualitative study (phase 2) was conducted in the same population using Focus Group Discussions (n = 3) and individual In-depth Interviews (n = 5).\ud \ud Results\ud \ud Sample size in phase 1 and phase 2 were 113 (response rate = 98%; [N-115]) and 87 (response rat = 97%; [N-90]) respectively. Mean age (±SD) of participants was 23.4 ± 1 years. The mean weekly total MET minutes (±SD) of the study population was 1791.25 ± 3097. According to the IPAQ categorical score a higher percentage of participants were ‘inactive’ (48.7%), while only 15.9% were in the ‘Highly active’ group. Lack of support and encouragement received during childhood to engage in sports activity seem to have played an important role in continuing their exercise behavior through to the adult life. Academic activities were given priority by both parents and teachers. The environment and support from teachers, family and friends were important to initiate and adhere to sports and physical activity.\ud \ud Conclusions\ud \ud A higher percentage of participants were ‘inactive’, in spite of belonging to a group which is presumed to be knowledgeable regarding the benefits of physical activity. A significant negative attitude towards physical activity was observed in this cohort of young-adults. This seems to stem from earlier in life, due to lack of support and motivation for physical exercise and sports, received during primary and secondary schooling. This negative attitude has become a significant ‘internal’ barrier, which has not been changed in spite of their education

Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients.

BackgroundAt least a third of dengue patients develop plasma leakage with increased risk of life-threatening complications. Predicting plasma leakage using laboratory parameters obtained in early infection as means of triaging patients for hospital admission is important for resource-limited settings.MethodsA Sri Lankan cohort including 4,768 instances of clinical data from N = 877 patients (60.3% patients with confirmed dengue infection) recorded in the first 96 hours of fever was considered. After excluding incomplete instances, the dataset was randomly split into a development and a test set with 374 (70%) and 172 (30%) patients, respectively. From the development set, five most informative features were selected using the minimum description length (MDL) algorithm. Random forest and light gradient boosting machine (LightGBM) were used to develop a classification model using the development set based on nested cross validation. An ensemble of the learners via average stacking was used as the final model to predict plasma leakage.ResultsLymphocyte count, haemoglobin, haematocrit, age, and aspartate aminotransferase were the most informative features to predict plasma leakage. The final model achieved the area under the receiver operating characteristics curve, AUC = 0.80 with positive predictive value, PPV = 76.9%, negative predictive value, NPV = 72.5%, specificity = 87.9%, and sensitivity = 54.8% on the test set.ConclusionThe early predictors of plasma leakage identified in this study are similar to those identified in several prior studies that used non-machine learning based methods. However, our observations strengthen the evidence base for these predictors by showing their relevance even when individual data points, missing data and non-linear associations were considered. Testing the model on different populations using these low-cost observations would identify further strengths and limitations of the presented model

Additional file 1: of Physical inactivity among physiotherapy undergraduates: exploring the knowledge-practice gap

Interviewer Guide (contains the set of open-ended semi-structured questions used during the focus group discussions by the interviewers to guide the participants and to keep uniformity between the different focus groups). (DOC 31 kb

Traumatic brain injury (TBI) outcomes in an LMIC tertiary care centre and performance of trauma scores

Abstract Background This study evaluates post-ICU outcomes of patients admitted with moderate and severe Traumatic Brain Injury (TBI) in a tertiary neurocritical care unit in an low middle income country and the performance of trauma scores: A Severity Characterization of Trauma, Trauma and Injury Severity Score, Injury Severity Score and Revised Trauma Score in this setting. Methods Adult patients directly admitted to the neurosurgical intensive care units of the National Hospital of Sri Lanka between 21st July 2014 and 1st October 2014 with moderate or severe TBI were recruited. A telephone administered questionnaire based on the Glasgow Outcome Scale Extended (GOSE) was used to assess functional outcome of patients at 3 and 6 months after injury. The economic impact of the injury was assessed before injury, and at 3 and 6 months after injury. Results One hundred and one patients were included in the study. Survival at ICU discharge, 3 and 6 months after injury was 68.3%, 49.5% and 45.5% respectively. Of the survivors at 3 months after injury, 43 (86%) were living at home. Only 19 (38%) patients had a good recovery (as defined by GOSE 7 and 8). Three months and six months after injury, respectively 25 (50%) and 14 (30.4%) patients had become “economically dependent”. Selected trauma scores had poor discriminatory ability in predicting mortality. Conclusions This observational study of patients sustaining moderate or severe TBI in Sri Lanka (a LMIC) reveals only 46% of patients were alive at 6 months after ICU discharge and only 20% overall attained a good (GOSE 7 or 8) recovery. The social and economic consequences of TBI were long lasting in this setting. Injury Severity Score, Revised Trauma Score, A Severity Characterization of Trauma and Trauma and Injury Severity Score, all performed poorly in predicting mortality in this setting and illustrate the need for setting adapted tools

To: The Epimed Monitor ICU Database®: A cloud-based national registry for adult intensive care unit patients in Brazil

We congratulate the Epimed collaborators(1) on their impressive results from a privately owned registry in Brazil, an upper-middle income country. In addition to the examples from high income countries cited by the authors, Sri Lanka - a lower-middle-income country in South Asia - has implemented a national cloud-based intensive care unit (ICU) registry,(2) directly overseen by the Ministry of Health and Information and Communications Technology Agency (ICTA), in partnership with other national and overseas collaborators, including the Dutch National Intensive Care Evaluation (NICE) foundation. Founded in 2012, the cloud-based critical care unit registry, as part of a codesigned agile mobile data platform, the so called Network for Improving Critical Care Systems and Training (NICST; www.nicst.com), encompasses almost the entire network of state ICUs island-wide and includes pediatric, neonatal and specialized units

Experiences of ICU survivors in a low middle income country- a multicenter study

Abstract Background Stressful patient experiences during the intensive care unit (ICU) stay is associated with reduced satisfaction in High Income Countries (HICs) but has not been explored in Lower and Middle Income Countries (LMICs). This study describes the recalled experiences, stress and satisfaction as perceived by survivors of ICUs in a LMIC. Methods This follow-up study was carried out in 32 state ICUs in Sri Lanka between July and December 2015.ICU survivors’ experiences, stress factors encountered and level of satisfaction were collected 30 days after ICU discharge by a telephone questionnaire adapted from Granja and Wright. Results Of 1665 eligible ICU survivors, 23.3% died after ICU discharge, 49.1% were uncontactable and 438 (26.3%) patients were included in the study. Whilst 78.1% (n = 349) of patients remembered their admission to the hospital, only 42.3% (n = 189) could recall their admission to the ICU. The most frequently reported stressful experiences were: being bedridden (34.2%), pain (34.0%), general discomfort (31.7%), daily needle punctures (32.9%), family worries (33.6%), fear of dying and uncertainty in the future (25.8%). The majority of patients (376, 84.12%) found the atmosphere of the ICU to be friendly and calm. Overall, the patients found the level of health care received in the ICU to be “very satisfactory” (93.8%, n = 411) with none of the survivors stating they were either “dissatisfied” or “very dissatisfied”. Conclusion In common with HIC, survivors were very satisfied with their ICU care. In contrast to HIC settings, specific ICU experiences were frequently not recalled, but those remembered were reported as relatively stress-free. Stressful experiences, in common with HIC, were most frequently related to uncertainty about the future, dependency, family, and economic concerns