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Modified systemic inflammatory response syndrome and provider gestalt predicting adverse outcomes in children under 5 years presenting to an urban emergency department of a tertiary hospital in Tanzania.

ABSTRACT: Background:Modified systemic inflammatory response syndrome (mSIRS) criteria for the pediatric population together with the provider gestalt have the potential to predict clinical outcomes. However, this has not been studied in low-income countries. We investigated the ability of mSIRS and provider gestalt to predict mortality and morbidity among children presenting to the ED of a tertiary level hospital in Tanzania. Methods:This prospective observational study enrolled a convenience sample of children under 5?years old, presenting to the Emergency Medicine Department of Muhimbili National Hospital from September 2015 to April 2016. Trained researchers used a structured case report form to record patient demographics, clinical presentation, initial provider gestalt of severity of illness, and the mSIRS criteria. Primary outcomes were 24-h mortality and overall in-hospital mortality. Data was analyzed using simple descriptive statistics, Kruskal-Wallis, Mann-Whitney U, and chi-squared tests. Results:We enrolled 1350 patients, median age 17?months (interquartile range 8-32?months), and 58% were male. Provider gestalt estimates of illness severity were recorded for all patients and 1030 (76.3%) had complete data for mSIRS categorization. Provider gestalt classified 97 (7.2%) patients as healthy, 546 (40.4%) as mildly ill, 457 (33.9%) as moderately ill, and 250 (18.5%) as severely ill. Of the patients, classifiable by mSIRS, 411/1030 (39.9%) had ??2 mSIRS criteria. In predicting 24-h mortality, the ??2 mSIRS and gestalt "severely ill" had sensitivities of 82% and 81%, respectively, and specificity of 61% and 84%, respectively. In predicting overall in-hospital mortality, the ??2 mSIRS and gestalt "severely ill" had sensitivities of 66% and 70% with a specificity of 62% and 86% respectively. Conclusion:Both the mSIRS and provider gestalt were highly specific for predicting 24-h and overall in-hospital mortality in our patient population. The clinical utility of these assessment methods is limited by the low positive predictive value.

SUBMITTER: Nariadhara MR

PROVIDER: S-EPMC6359824 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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Modified systemic inflammatory response syndrome and provider gestalt predicting adverse outcomes in children under 5 years presenting to an urban emergency department of a tertiary hospital in Tanzania.

Nariadhara Meera R MR Sawe Hendry R HR Runyon Michael S MS Mwafongo Victor V Murray Brittany L BL

Tropical medicine and health 20190201

<h4>Background</h4>Modified systemic inflammatory response syndrome (mSIRS) criteria for the pediatric population together with the provider gestalt have the potential to predict clinical outcomes. However, this has not been studied in low-income countries. We investigated the ability of mSIRS and provider gestalt to predict mortality and morbidity among children presenting to the ED of a tertiary level hospital in Tanzania.<h4>Methods</h4>This prospective observational study enrolled a convenie ...[more]

PMID: 30766443

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Project description:Background:Elevated serum lactate levels have been shown in numerous studies to be associated with serious adverse events, including mortality. Point of care lactate level is increasingly available in resource-limited emergency department (ED) settings. However, little is known about the predictive ability of for serious adverse events. Aim of the study:We aimed to describe the utility of serum lactate level as a predictor of 24-h serious adverse events among adult patients presenting at the Emergency Medicine Department (EMD) of Muhimbili National Hospital (MNH) and MUHAS Academic Medical Center (MAMC). Methods:This was a prospective observational study involving adult patients presenting to the EMD-MNH and MAMC from 1 September 2018 and 31 October 2018. Eligible patients with at least one lactate level test drawn while in the ED were examined in terms of their demographics, relevant clinical characteristics, and any serious adverse event (SAE) within 24?h of arrival. The sensitivity and specificity of lactate level to predict outcomes of interest were determined using the best cut-off point constructed from AUROC to see how well lactate level could discriminate which patients would have adverse events in the next 24?h. Categorical and continuous variables were compared with the chi-square test and two-sample t test, respectively. Results:We screened 2057 (20.9%) out of 9828 patients who presented during study period, and enrolled 387 (18.8%). The overall median age was 54?years (interquartile range 40-68?years) and 206 (53.2%) were male. Using local triaging system, a total of 322 (83.2%) was triaged as an emergency category. The mean lactate level was 3.2?±?3.6?mmol/L, 65 (16.8%) patients developed at least one SAE, with 42 (11%) who required ICU/HDU, 37 (10%) needed ventilator support, 10 (3%) required inotropes, and 9 (2%) developed cardiac arrest. The overall 24-h mortality was 28 (7%). The AUC of serum lactate level for overall 24-h mortality was 0.801 (95%CI, 0.7-0.9, P???0.001). At the optimal cutoff value (3.8?mmol/L), lactate level had a sensitivity and specificity for 24-h mortality of 64% and 85%, respectively. Mortality of the high-lactate level group (33.8?mmol/L) was significantly higher than that of the low-lactate level group (<?3.8?mmol/L), 23.8% vs. 2.9%, respectively (95%CI 3.8-17.2, p?<?0.001), with the relative risk of mortality in the high-lactate level group being 8.1 times higher compared to the low-lactate level group. Conclusion:The utility of lactate level in predicting mortality was similar to that seen in high-resource settings. A serum lactate level of 33.8?mmol/L predicted 24-h serious adverse events in unselected patients seen in the high-acuity area of our ED. Incorporating serum lactate level in ED in lower- and middle-income countries (LMICs) can help identify patients at risk of developing serious adverse events.

Project description:BackgroundSepsis is a life-threatening condition, causing almost one fifth of all deaths worldwide. The aim of the current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the presentation of septic patients arriving to the emergency department (ED) using machine learning.MethodsRetrospective cross-sectional design, including all patients arriving to the ED at Södersjukhuset in Sweden during 2013 and discharged with an International Classification of Diseases (ICD)-10 code corresponding to sepsis. All predictions were made using a Balanced Random Forest Classifier and 91 variables reflecting ED presentation. An exhaustive search was used to remove unnecessary variables in the final model. A 10-fold cross validation was performed and the accuracy was described using the mean value of the following: AUC, sensitivity, specificity, PPV, NPV, positive LR and negative LR.ResultsThe study population included 445 septic patients, randomised to a training (n = 356, 80%) and a validation set (n = 89, 20%). The six most important variables for predicting 7-day mortality were: "fever", "abnormal verbal response", "low saturation", "arrival by emergency medical services (EMS)", "abnormal behaviour or level of consciousness" and "chills". The model including these variables had an AUC of 0.83 (95% CI: 0.80-0.86). The final model predicting 30-day mortality used similar six variables, however, including "breathing difficulties" instead of "abnormal behaviour or level of consciousness". This model achieved an AUC = 0.80 (CI 95%, 0.78-0.82).ConclusionsThe results suggest that six specific variables were predictive of 7- and 30-day mortality with good accuracy which suggests that these symptoms, observations and mode of arrival may be important components to include along with vital signs in a future prediction tool of mortality among septic patients presenting to the ED. In addition, the Random Forests appears to be a suitable machine learning method on which to build future studies.

Dataset Information

Modified systemic inflammatory response syndrome and provider gestalt predicting adverse outcomes in children under 5 years presenting to an urban emergency department of a tertiary hospital in Tanzania.

Publications

Modified systemic inflammatory response syndrome and provider gestalt predicting adverse outcomes in children under 5 years presenting to an urban emergency department of a tertiary hospital in Tanzania.

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