An Innovative Model to Predict Pediatric Emergency Department Return Visits

Development and Internal Validation of a Multivariable Prediction Model to Predict Repeat Attendances in the Pediatric Emergency Department: A Retrospective Cohort Study

OBJECTIVE

Unplanned reattendances to the pediatric emergency department (PED) occur commonly in clinical practice. Multiple factors influence the decision to return to care, and understanding risk factors may allow for better design of clinical services. We developed a clinical prediction model to predict return to the PED within 72 hours from the index visit.

METHODS

We retrospectively reviewed all attendances to the PED of Royal Manchester Children's Hospital between 2009 and 2019. Attendances were excluded if they were admitted to hospital, aged older than 16 years or died in the PED. Variables were collected from Electronic Health Records reflecting triage codes. Data were split temporally into a training (80%) set for model development and a test (20%) set for internal validation. We developed the prediction model using LASSO penalized logistic regression.

RESULTS

A total of 308,573 attendances were included in the study. There were 14,276 (4.63%) returns within 72 hours of index visit. The final model had an area under the receiver operating characteristic curve of 0.64 (95% confidence interval, 0.63-0.65) on temporal validation. The calibration of the model was good, although with some evidence of miscalibration at the high extremes of the risk distribution. After-visit diagnoses codes reflecting a nonspecific problem ("unwell child") were more common in children who went on to reattend.

CONCLUSIONS

We developed and internally validated a clinical prediction model for unplanned reattendance to the PED using routinely collected clinical data, including markers of socioeconomic deprivation. This model allows for easy identification of children at the greatest risk of return to PED.

Non-urgent presentations to the paediatric emergency department: a literature review

It is estimated that between 58% and 82% of children and young people who present to paediatric emergency department (PEDs) have a non-urgent condition. This systematic review of the literature explores why parents of children with non-urgent conditions present to the PED rather than to community healthcare services. Five databases were searched for studies on children and young people's presentations to the PED for the treatment of a non-urgent condition, as identified by a low priority triage code. This article describes and discusses the findings of the 18 included studies.

Evaluation of the Pattern of Use of a Pediatric Emergency Department in Italy

OBJECTIVES

The aim of this study was to evaluate access to a pediatric emergency department (PED) in a large hospital, in particular to estimate the prevalence of potentially avoidable accesses and the characteristics of return visits.

METHODS

Clinical health records from the PED of San Gerardo Hospital, Monza, Italy, were retrospectively reviewed. The study population was composed of subjects younger than 18 years who attended the PED during the period from October 1, 2017, to November 30, 2017.Accesses were defined nonurgent if characterized by white or green triage codes and patient's discharge as the outcome and were defined potentially avoidable if nonurgent and with no diagnostic/therapeutic procedures performed except a visit by the ED pediatrician.Return visits were defined as accesses that occurred within 72 hours of the first index visit.

RESULTS

A total of 2064 children and adolescents younger than 18 years had at least 1 ED attendance between October and November 2017, for a total of 2364 accesses.The most frequent diagnoses were upper respiratory tract infections (29.5% of accesses), followed by gastroenteritis (7.0%) and abdominal pain (7.0%). In all, 1810 accesses (88%) were classified as "nonurgent," and 1228 (60%) potentially avoidable, 373 of which were probably avoidable because they occurred when the primary care physician was available.The number of return visits was 98 (5% of the accesses): 74 were nonurgent, 31 of which potentially avoidable. On 17 occasions, both index and return visits were potentially avoidable.

CONCLUSIONS

We confirm that most of the accesses to a PED are nonurgent and potentially avoidable. Interventions are needed to improve the appropriateness of use of emergency services.

Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

Background

Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding.

Objective

The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding.

Methods

We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies.

Results

Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments.

Conclusions

There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

Association of limited English proficiency and increased pediatric emergency department revisits

OBJECTIVE

Limited English proficiency (LEP) is a risk factor for health care inequity and an important focus for improving communication and care quality. This study examines the association between LEP and pediatric emergency department (ED) revisits.

METHODS

This was a retrospective, cross-sectional study of patients 0 to 21 years old discharged home after an initial visit from an academic, tertiary care pediatric ED from January 1, 2017, to June 30, 2018. We calculated rates of ED revisits within 72 h resulting in discharge or hospitalization and assessed rate differences between LEP and English-proficient (EP) patients. Multivariable logistic regression models examined the association between revisits and LEP status controlling for age, race, ethnicity, triage acuity, clinical complexity, and ED arrival time. Sensitivity models including insurance were also conducted.

RESULTS

There were 63,601 index visits in the study period; 12,986 (20%) were by patients with LEP. There were 2,387 (3.8%) revisits within 72 h of initial ED visit. Among LEP and EP patient visits, there were 4.53 and 3.55 revisits/100 initial ED visits, respectively (rate difference = 0.97, 95% confidence interval [CI] = 0.58 to 1.37). In the multivariable analyses, LEP was associated with increased odds of revisits resulting in discharge (odds ratio [OR] = 1.15, 95% CI = 1.01 to 1.30) and in hospitalization (OR = 1.28, 95% CI = 1.03 to 1.58). Sensitivity analyses additionally adjusting for insurance status attenuated these results.

CONCLUSIONS

These results suggest that LEP was associated with increased pediatric ED revisits. Improved understanding of language barrier effects on clinical care is important for decreasing health care disparities in the ED.

A clinical prediction model to identify children at risk for revisits with serious illness to the emergency department: A prospective multicentre observational study

BACKGROUND

To develop a clinical prediction model to identify children at risk for revisits with serious illness to the emergency department.

METHODS AND FINDINGS

A secondary analysis of a prospective multicentre observational study in five European EDs (the TRIAGE study), including consecutive children aged <16 years who were discharged following their initial ED visit ('index' visit), in 2012-2015. Standardised data on patient characteristics, Manchester Triage System urgency classification, vital signs, clinical interventions and procedures were collected. The outcome measure was serious illness defined as hospital admission or PICU admission or death in ED after an unplanned revisit within 7 days of the index visit. Prediction models were developed using multivariable logistic regression using characteristics of the index visit to predict the likelihood of a revisit with a serious illness. The clinical model included day and time of presentation, season, age, gender, presenting problem, triage urgency, and vital signs. An extended model added laboratory investigations, imaging, and intravenous medications. Cross validation between the five sites was performed, and discrimination and calibration were assessed using random effects models. A digital calculator was constructed for clinical implementation. 7,891 children out of 98,561 children had a revisit to the ED (8.0%), of whom 1,026 children (1.0%) returned to the ED with a serious illness. Rates of revisits with serious illness varied between the hospitals (range 0.7-2.2%). The clinical model had a summary Area under the operating curve (AUC) of 0.70 (95% CI 0.65-0.74) and summary calibration slope of 0.83 (95% CI 0.67-0.99). 4,433 children (5%) had a risk of > = 3%, which was useful for ruling in a revisit with serious illness, with positive likelihood ratio 4.41 (95% CI 3.87-5.01) and specificity 0.96 (95% CI 0.95-0.96). 37,546 (39%) had a risk <0.5%, which was useful for ruling out a revisit with serious illness (negative likelihood ratio 0.30 (95% CI 0.25-0.35), sensitivity 0.88 (95% CI 0.86-0.90)). The extended model had an improved summary AUC of 0.71 (95% CI 0.68-0.75) and summary calibration slope of 0.84 (95% CI 0.71-0.97). As study limitations, variables on ethnicity and social deprivation could not be included, and only return visits to the original hospital and not to those of surrounding hospitals were recorded.

CONCLUSION

We developed a prediction model and a digital calculator which can aid physicians identifying those children at highest and lowest risks for developing a serious illness after initial discharge from the ED, allowing for more targeted safety netting advice and follow-up.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

BACKGROUND

Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.

OBJECTIVE

To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.

MATERIALS

An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost.

METHODS

Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.

RESULTS

Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.

CONCLUSIONS

We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

Emergency department use by paediatric patients in Lombardy Region, Italy: a population study

OBJECTIVES

To estimate the rate of paediatric attendance at emergency departments (EDs) in the Lombardy Region, Italy, and to determine the factors contributing to different patterns of use.

METHODS

By analysing healthcare administrative databases, ED attendance by 1.6 million youths <18 years old during 2012 in the Lombardy Region was assessed. The pattern of use was categorised based on the number of ED visits and level of emergency, defined by triage code and outcome of the visit. Logistic regression analyses were performed to identify the characteristics of access for non-urgent reasons and those of patients with frequent non-urgent access (≥4 accesses for non-urgent reasons only). A case-control study was carried out to compare healthcare resource use by children 1-5 years old who were 'frequent non-urgent users' with that of randomly selected controls, matched by age, gender, nationality and primary care physician.

RESULTS

During 2012, 440 284 (27%) of children and adolescents had at least one ED attendance, with trauma (26%) and respiratory tract infections (22%) as the most frequent diagnoses. In all, 533 037 (79%) accesses were for non-urgent reasons, and 12 533 (3% of the ED users) were frequent non-urgent users. Male gender (OR 1.12; 95% CI 1.08 to 1.17), preschool age (OR 3.14; 95% CI 2.98 to 3.31) and place of residence (OR 1.74; 95% CI 1.70 to 1.99) were associated with a higher risk of being a frequent non-urgent user. Moreover, a greater healthcare consumption was observed in this group.

CONCLUSIONS

One out of four children and adolescents attended the ED at least once per year, 3% of whom were frequent non-urgent users, with a high overall healthcare resource consumption.