Dataset Information

Performance characteristics of a local triage tool and internationally validated tools among under-fives presenting to an urban emergency department in Tanzania.

ABSTRACT: BACKGROUND:A number of region-specific validated triage systems exist; however very little is known about their performance in resource limited settings. We compare the local triage tool and internationally validated tools among under-fives presenting to an urban emergency department in Tanzania. METHODOLOGY:Prospective descriptive study of consecutive under-fives seen at Muhimbili National Hospital (MNH), ED between November 2017 to April 2018. Patients were triaged according to Local Triage System (LTS), and the information collected were used to assign acuities in the other triage scales: Canadian Triage and Acuity Scale (CTAS), Australasian Triage Scale (ATS), Manchester Triage Scale (MTS) and South African Triage Scale (SATS). Patients were then followed up to determine disposition and 24?h outcome. Sensitivity, specificity, positive and negative predictive values for admission and mortality were then calculated. RESULTS:A total of 384 paediatric patients were enrolled, their median age was 17?months (IQR 7-36?months). Using LTS, 67(17.4%) patients were triaged in level one, 291(75.8%) level 2 and 26 (6.8%) in level 3 categories. Overall admission rate was 59.6% and at 24?h there were five deaths (1.3%). Using Level 1 in LTS, and Levels 1 and 2 in other systems, sensitivity and specificity for admission for all triage scales ranged between 27.1-28.4% and 95.4-98% respectively, (PPV 90.3-95.3%, NPV 47.1-47.4%). Sensitivity for mortality was 80% for LTS, and 100% for the other scales, while specificity was low, yielding a PPV for all scales between 6.9 and 8%. CONCLUSION:All triage scales showed poor ability to predict need for admission, however all triage scales except LTS predicted mortality. The test characteristics for the other scales were similar. Future studies should focus on determining the reliability and validity of each of these triage tools in our setting.

SUBMITTER: Marombwa NR

PROVIDER: S-EPMC6357459 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Performance characteristics of a local triage tool and internationally validated tools among under-fives presenting to an urban emergency department in Tanzania.

Marombwa Nafsa R NR Sawe Hendry R HR George Upendo U Kilindimo Said S SS Lucumay Nanyori J NJ Mjema Kilalo M KM Mfinanga Juma A JA Weber Ellen J EJ

BMC pediatrics 20190201 1

<h4>Background</h4>A number of region-specific validated triage systems exist; however very little is known about their performance in resource limited settings. We compare the local triage tool and internationally validated tools among under-fives presenting to an urban emergency department in Tanzania.<h4>Methodology</h4>Prospective descriptive study of consecutive under-fives seen at Muhimbili National Hospital (MNH), ED between November 2017 to April 2018. Patients were triaged according to ...[more]

PMID: 30709389

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Project description:OBJECTIVE:To investigate the appropriateness of cases presenting to the emergency department (ED) following ambulance-based secondary telephone triage. DESIGN:A pragmatic retrospective cohort analysis of all the planned and unplanned ED presentations within 48 hours of a secondary telephone triage. SETTING:The secondary telephone triage service, called the Referral Service, and the hospitals were located in metropolitan Melbourne, Australia and operated 24 hours a day, servicing 4.25 million people. The Referral Service provides an in-depth secondary triage of cases classified as low acuity when calling the Australian emergency telephone number. POPULATION:Cases triaged by the Referral Service between September 2009 and June 2012 were linked to ED and hospital admission records (N=44,523). Planned ED presentations were cases referred to the ED following the secondary triage, unplanned ED presentations were cases that presented despite being referred to alternative care pathways. MAIN OUTCOME MEASURES:Appropriateness was measured using an ED suitability definition and hospital admission rates. These were compared with mean population data which consisted of all of the ED presentations for the state (termed the 'average Victorian ED presentation'). RESULTS:Planned ED presentations were more likely to be ED suitable than unplanned ED presentations (OR 1.62; 95% CI 1.5 to 1.7; p<0.001) and the average Victorian ED presentation (OR 1.85; 95% CI 1.01 to 3.4; p=0.046). They were also more likely to be admitted to the hospital than the unplanned ED presentation (OR 1.5; 95% CI 1.4 to 1.6; p<0.001) and the average Victorian ED presentation (OR 2.3, 95% CI 2.24 to 2.33; p<0.001). Just under 15% of cases diverted away from the emergency care pathways presented in the ED (unplanned ED attendances), and 9.5% of all the alternative care pathway cases were classified as ED suitable and 6.5% were admitted to hospital. CONCLUSIONS:Secondary telephone triage was able to appropriately identify many ED suitable cases, and while most cases referred to alternative care pathways did not present in the ED. Further research is required to establish that these were not inappropriately triaged away from the emergency care pathways.

Project description:BackgroundThe emergency department (ED) triage system to classify and prioritize patients from high risk to less urgent continues to be a challenge.ObjectiveThis study, comprising 80,433 patients, aims to develop a machine learning algorithm prediction model of critical care outcomes for adult patients using information collected during ED triage and compare the performance with that of the baseline model using the Korean Triage and Acuity Scale (KTAS).MethodsTo predict the need for critical care, we used 13 predictors from triage information: age, gender, mode of ED arrival, the time interval between onset and ED arrival, reason of ED visit, chief complaints, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, oxygen saturation, and level of consciousness. The baseline model with KTAS was developed using logistic regression, and the machine learning model with 13 variables was generated using extreme gradient boosting (XGB) and deep neural network (DNN) algorithms. The discrimination was measured by the area under the receiver operating characteristic (AUROC) curve. The ability of calibration with Hosmer-Lemeshow test and reclassification with net reclassification index were evaluated. The calibration plot and partial dependence plot were used in the analysis.ResultsThe AUROC of the model with the full set of variables (0.833-0.861) was better than that of the baseline model (0.796). The XGB model of AUROC 0.861 (95% CI 0.848-0.874) showed a higher discriminative performance than the DNN model of 0.833 (95% CI 0.819-0.848). The XGB and DNN models proved better reclassification than the baseline model with a positive net reclassification index. The XGB models were well-calibrated (Hosmer-Lemeshow test; P>.05); however, the DNN showed poor calibration power (Hosmer-Lemeshow test; P<.001). We further interpreted the nonlinear association between variables and critical care prediction.ConclusionsOur study demonstrated that the performance of the XGB model using initial information at ED triage for predicting patients in need of critical care outperformed the conventional model with KTAS.

Project description: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.

Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Project description:ImportanceEmergency department (ED) triage substantially affects how long patients wait for care but triage scoring relies on few objective criteria. Prior studies suggest that Black and Hispanic patients receive unequal triage scores, paralleled by disparities in the depth of physician evaluations.ObjectivesTo examine whether racial disparities in triage scores and physician evaluations are present across a multicenter network of academic and community hospitals and evaluate whether patients who do not speak English face similar disparities.Design, setting, and participantsThis was a cross-sectional, multicenter study examining adults presenting between February 28, 2019, and January 1, 2023, across the Mass General Brigham Integrated Health Care System, encompassing 7 EDs: 2 urban academic hospitals and 5 community hospitals. Analysis included all patients presenting with 1 of 5 common chief symptoms.ExposuresEmergency department nurse-led triage and physician evaluation.Main outcomes and measuresAverage Triage Emergency Severity Index [ESI] score and average visit work relative value units [wRVUs] were compared across symptoms and between individual minority racial and ethnic groups and White patients.ResultsThere were 249 829 visits (149 861 female [60%], American Indian or Alaska Native 0.2%, Asian 3.3%, Black 11.8%, Hispanic 18.8%, Native Hawaiian or Other Pacific Islander <0.1%, White 60.8%, and patients identifying as Other race or ethnicity 5.1%). Median age was 48 (IQR, 29-66) years. White patients had more acute ESI scores than Hispanic or Other patients across all symptoms (eg, chest pain: Hispanic, 2.68 [95% CI, 2.67-2.69]; White, 2.55 [95% CI, 2.55-2.56]; Other, 2.66 [95% CI, 2.64-2.68]; P < .001) and Black patients across most symptoms (nausea/vomiting: Black, 2.97 [95% CI, 2.96-2.99]; White: 2.90 [95% CI, 2.89-2.91]; P < .001). These differences were reversed for wRVUs (chest pain: Black, 4.32 [95% CI, 4.25-4.39]; Hispanic, 4.13 [95% CI, 4.08-4.18]; White 3.55 [95% CI, 3.52-3.58]; Other 3.96 [95% CI, 3.84-4.08]; P < .001). Similar patterns were seen for patients whose primary language was not English.Conclusions and relevanceIn this cross-sectional study, patients who identified as Black, Hispanic, and Other race and ethnicity were assigned less acute ESI scores than their White peers despite having received more involved physician workups, suggesting some degree of mistriage. Clinical decision support systems might reduce these disparities but would require careful calibration to avoid replicating bias.

Dataset Information

Performance characteristics of a local triage tool and internationally validated tools among under-fives presenting to an urban emergency department in Tanzania.

Publications

Performance characteristics of a local triage tool and internationally validated tools among under-fives presenting to an urban emergency department in Tanzania.

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