The limited value of triage vital signs in predicting influenza infection in children aged 5 years and under in the emergency department : A single-center retrospective cross-sectional study
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SUBMITTER: Lam R
PROVIDER: S-EPMC8568403 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Vital signs are critical markers of illness severity in the emergency department (ED). Providers need to understand the abnormal vital signs in older adults that are problematic. We hypothesized that in patients age > 65 years discharged from the ED, there are abnormal vital signs that are associated with an admission to an inpatient bed within 7 days of discharge.We conducted a retrospective cohort study using data from a regional integrated health system of members age > 65 years during the years 2009 to 2010. We used univariate contingency tables to assess the relationship between hospital admission within 7 days of discharge and vital sign (including systolic blood pressure [sBP], heart rate [HR], body temperature, and pulse oximetry [SpO2 ] values measured closest to discharge) using standard thresholds for abnormal and thresholds derived from the study data.Of 104,025 ED discharges, 4,638 (4.5%) were followed by inpatient admission within 7 days. Vital signs had a greater odds of admission beyond a single cutoff. The vital signs with at least twice the odds of admission were sBP < 97 mm Hg (odds ratio [OR] = 2.02, 95% CI = 1.57-2.60), HR > 101 beats/min (OR = 2.00 95% CI = 1.75-2.29), body temperature > 37.3°C (OR = 2.14, 95% CI = 1.90-2.41), and pulse oximetry < 92 SpO2 (OR = 2.04, 95% CI = 1.55-2.68). Patients with two vital sign abnormalities per the analysis had the highest odds of admission. A majority of patients discharged with abnormal vital signs per the analysis were not admitted within 7 days of ED discharge.While we found a majority of patients discharged with abnormal vital signs as defined by the analysis, not to be admitted after discharge, we identified vital signs associated with at least twice the odds of admission.
| S-EPMC5935002 | biostudies-other
Project description:BackgroundVital signs are used in emergency care settings in the first assessment of children to identify those that need immediate attention. We aimed to develop and validate vital sign based Manchester Triage System (MTS) discriminators to improve triage of children at the emergency department.Methods and findingsThe TrIAGE project is a prospective observational study based on electronic health record data from five European EDs (Netherlands (n = 2), United Kingdom, Austria, and Portugal). In the current study, we included 117,438 consecutive children <16 years presenting to the ED during the study period (2012-2015). We derived new discriminators based on heart rate, respiratory rate, and/or capillary refill time for specific subgroups of MTS flowcharts. Moreover, we determined the optimal cut-off value for each vital sign. The main outcome measure was a previously developed 3-category reference standard (high, intermediate, low urgency) for the required urgency of care, based on mortality at the ED, immediate lifesaving interventions, disposition and resource use. We determined six new discriminators for children <1 year and ?1 year: "Very abnormal respiratory rate", "Abnormal heart rate", and "Abnormal respiratory rate", with optimal cut-offs, and specific subgroups of flowcharts. Application of the modified MTS reclassified 744 patients (2.5%). Sensitivity increased from 0.66 (95%CI 0.60-0.72) to 0.71 (0.66-0.75) for high urgency patients and from 0.67 (0.54-0.76) to 0.70 (0.58-0.80) for high and intermediate urgency patients. Specificity decreased from 0.90 (0.86-0.93) to 0.89 (0.85-0.92) for high and 0.66 (0.52-0.78) to 0.63 (0.50-0.75) for high and intermediate urgency patients. These differences were statistically significant. Overall performance improved (R2 0.199 versus 0.204).ConclusionsSix new discriminators based on vital signs lead to a small but relevant increase in performance and should be implemented in the MTS.
| S-EPMC7872278 | biostudies-literature
Project description:IntroductionChildren often present to the emergency department (ED) with minor conditions such as fever and have persistently abnormal vital signs. We hypothesized that a significant portion of children discharged from the ED would have abnormal vital signs and that those discharged with abnormal vital signs would experience very few adverse events.MethodsWe performed a retrospective chart review encompassing a 44-month period of all pediatric patients (aged two months to 17 years) who were discharged from the ED with an abnormal pulse rate, respiratory rate, temperature, or oxygen saturation. We used a local quality assurance database to identify pre-defined adverse events after discharge in this population. Our primary aim was to determine the proportion of children discharged with abnormal vital signs and the frequency and nature of adverse events. Additionally, we performed a sub-analysis comparing the rate of adverse events in children discharged with normal vs. abnormal vital signs, as well as a standardized review of the nature of each adverse event.ResultsOf 33,185 children discharged during the study period, 5,540 (17%) of these patients had at least one abnormal vital sign. There were 24/5,540 (0.43%) adverse events in the children with at least one abnormal vital sign vs. 47/27,645 (0.17%) adverse events in the children with normal vital signs [relative risk = 2.5 (95% confidence interval, 1.6 to 2.4)].However, upon review of each adverse event we found only one case that was related to the index visit, was potentially preventable by a 23-hour hospital observation, and caused permanent disability.ConclusionIn our study population, 17% of the children were discharged with at least one abnormal vital sign, and there were very few adverse (0.43%) events associated with this practice. Heart rate was the most common abnormal vital sign leading to an adverse event. Severe adverse events that were potentially related to the abnormal vital sign(s) were exceedingly rare. Additional research is needed in broader populations to better determine the rate of adverse events and possible methods of avoiding them.
| S-EPMC5576624 | biostudies-literature
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.
| S-EPMC6054406 | biostudies-literature
Project description:Background/objectivesCognitive impairment is a frequent problem among older patients attending the Emergency Department (ED) and can be the result of pre-existing cognitive impairment, delirium, or neurologic disorders. Another cause can also be acute disturbance of brain perfusion and oxygenation, which may be reversed by optimal resuscitation. This study aimed to assess the relationship between vital signs, as a measure of acute hemodynamic changes, and cognitive impairment in older ED patients.DesignProspective cohort study.SettingED's of two tertiary care and two secondary care hospitals in the Netherlands.Participants2629 patients aged 70-years and older.MeasurementsVital signs were measured at the moment of ED arrival as part of routine clinical care. Cognition was measured using the Six-Item Cognitive Impairment Test (6-CIT).ResultsThe median age of patients was 78 years (IQR 74-84). Cognitive impairment was present in 738 patients (28.1%). When comparing lowest with highest quartiles, a systolic blood pressure of <129 mmHg (OR 1.30, 95% confidence interval (95%CI) 0.98-1.73)was associated with increased risk of cognitive impairment. A higher respiratory rate (>21/min) was associated with increased risk of impaired cognition (OR 2.16, 95% CI 1.58-2.95) as well as oxygen saturation of <95% (OR 1.64, 95%CI 1.24-2.19).ConclusionAbnormal vital signs associated with decreased brain perfusion and oxygenation are also associated with cognitive impairment in older ED patients. This may partially be explained by the association between disease severity and delirium, but also by acute disturbance of brain perfusion and oxygenation. Future studies should establish whether normalization of vital signs will also acutely improve cognition.
| S-EPMC6586336 | biostudies-literature
Project description:IntroductionRapid-response teams (RRTs) are interdisciplinary groups created to rapidly assess and treat patients with unexpected clinical deterioration marked by decline in vital signs. Traditionally emergency department (ED) disposition is partially based on the patients' vital signs (VS) at the time of hospital admission. We aimed to identify which patients will have RRT activation within 12 hours of admission based on their ED VS, and if their outcomes differed.MethodsWe conducted a case-control study of patients presenting from January 2009 to December 2012 to a tertiary ED who subsequently had RRT activations within 12 hours of admission (early RRT activations). The medical records of patients 18 years and older admitted to a non-intensive care unit (ICU) setting were reviewed to obtain VS at the time of ED arrival and departure, age, gender and diagnoses. Controls were matched 1:1 on age, gender, and diagnosis. We evaluated VS using cut points (lowest 10%, middle 80% and highest 10%) based on the distribution of VS for all patients. Our study adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational studies.ResultsA total of 948 patients were included (474 cases and 474 controls). Patients who had RRT activations were more likely to be tachycardic (odds ratio [OR] 2.02, 95% CI [1.25-3.27]), tachypneic (OR 2.92, 95% CI [1.73-4.92]), and had lower oxygen saturations (OR 2.25, 95% CI [1.42-3.56]) upon arrival to the ED. Patients who had RRT activations were more likely to be tachycardic at the time of disposition from the ED (OR 2.76, 95% CI [1.65-4.60]), more likely to have extremes of systolic blood pressure (BP) (OR 1.72, 95% CI [1.08-2.72] for low BP and OR 1.82, 95% CI [1.19-2.80] for high BP), higher respiratory rate (OR 4.15, 95% CI [2.44-7.07]) and lower oxygen saturation (OR 2.29, 95% CI [1.43-3.67]). Early RRT activation was associated with increased healthcare utilization and worse outcomes including increased rates of ICU admission within 72 hours (OR 38.49, 95%CI [19.03-77.87]), invasive interventions (OR 5.49, 95%CI [3.82-7.89]), mortality at 72 hours (OR 4.24, 95%CI [1.60-11.24]), and mortality at one month (OR 4.02, 95%CI [2.44-6.62]).ConclusionAfter matching for age, gender and ED diagnosis, we found that patients with an abnormal heart rate, respiratory rate or oxygen saturation at the time of ED arrival or departure are more likely to trigger RRT activation within 12 hours of admission. Early RRT activation was associated with higher mortality at 72 hours and one month, increased rates of invasive intervention and ICU admission. Determining risk factors of early RRT activation is of clinical, operational, and financial importance, as improved medical decision-making regarding disposition would maximize allocation of resources while potentially limiting morbidity and mortality.
| S-EPMC4899064 | biostudies-literature
Project description:Study objectiveTriage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep-learning approaches could identify critically ill patients only using data immediately available at triage.MethodsWe conducted a retrospective, cross-sectional study at an urban tertiary care center, from January 1, 2012-January 1, 2020. De-identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep-learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver-operator curve (AUC).ResultsA total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519-0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671-0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802-0.804), 2-layer neural network with structured data with AUC 0.811 (95% CI = 0.807-0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818-0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849-0.852). All successive increases in AUC were statistically significant.ConclusionDeep-learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.
| S-EPMC7593422 | biostudies-literature
Project description:BackgroundEmergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications.ObjectiveEvaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED.DesignAn institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality.Key resultsOverall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality.ConclusionThe gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.
| S-EPMC6957629 | biostudies-literature
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.
| S-EPMC10570890 | biostudies-literature
Project description:Febrile infants younger than 3 months old present a diagnostic dilemma to the emergency physician. We aim to describe a large population of febrile infants less than 3 months old presenting to a pediatric emergency department (ED) and to assess the performance of current heart rate guidelines in the prediction of serious infections (SI).We performed a retrospective review of febrile infants younger than 3 months old, between March 2015 and Feb 2016, in a large tertiary pediatric ED. We documented the primary outcome of SI for each infant, as well as the clinical findings, vital signs, and Severity Index Score (SIS). We assessed the performance of the Paediatric Canadian Triage and Acuity Scale (PaedCTAS), Advanced Pediatric Life Support (APLS) guidelines and Fleming normal reference values, using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under receiver operating characteristics curve (AUC).1057 infants were analyzed, with 326 (30.6%) infants diagnosed with SI. High temperature, tachycardia, and low SIS score were significantly associated with SI. Item analysis showed that the SIS performance was driven by the presence of mottling (p = 0.003) and high temperature (p<0.001). The APLS guideline had the highest sensitivity (66.0%, 95% CI 60.5-71.1%), NPV (73.3%, 95% CI 69.7-76.5%) and AUC (0.538), while the PaedCTAS (2 standard deviation from normal) had the highest specificity (98.5%, 95% CI 97.3-99.3%) and PPV (55.2%, 95% CI 32.7-71.0%).Current guidelines on infantile heart rates have a variable performance. In our study, the APLS heart rate guidelines performed with the highest sensitivity, but no individual guideline predicted for SIs satisfactorily.
| S-EPMC5755800 | biostudies-literature