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Development and Internal Validation of a Multivariable Prediction Model to Predict Repeat Attendances in the
Tim Seers1, Charles Reynard, Glen P Martin
1From the Emergency Department, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, United Kingdom.
Insights
A new clinical prediction model identifies children at high risk of unplanned return visits to the pediatric emergency department (PED) within 72 hours. This tool uses routine clinical data to improve patient care and resource allocation.
Area of Science:
- Pediatric Emergency Medicine
- Clinical Prediction Modeling
- Health Services Research
Background:
- Unplanned reattendances to the pediatric emergency department (PED) are frequent.
- Understanding risk factors for return visits is crucial for optimizing clinical services.
- Previous models have not fully utilized routinely collected data.
Purpose of the Study:
- To develop and internally validate a clinical prediction model for unplanned reattendance to the PED within 72 hours.
- To identify key predictors of pediatric emergency department return visits.
- To facilitate better risk stratification for children attending the PED.
Main Methods:
- Retrospective review of 308,573 pediatric emergency department attendances (2009-2019).
- Exclusion of patients admitted, over 16 years old, or deceased in the PED.
- Development of a prediction model using LASSO penalized logistic regression on Electronic Health Record data.
Main Results:
- 4.63% of children (14,276/308,573) reattended the PED within 72 hours.
- The final model achieved an area under the receiver operating characteristic curve of 0.64 (95% CI, 0.63-0.65) on temporal validation.
- Nonspecific "unwell child" diagnosis codes were associated with higher reattendance rates.
Conclusions:
- A validated clinical prediction model for unplanned PED reattendance was developed using routine data.
- The model incorporates socioeconomic deprivation markers to identify high-risk children.
- This tool aids in the early identification of children requiring closer follow-up or intervention.
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.
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