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ICU Admission Tool for Congenital Heart Catheterization (iCATCH): A Predictive Model for High Level
Brian P Quinn1, Lauren C Shirley1, Mary J Yeh1
1Department of Cardiology, Boston Children's Hospital, Boston, MA.
Insights
A new tool predicts intensive care unit (ICU) admission after congenital heart catheterization. This helps allocate resources for pediatric cardiac patients needing complex care.
Area of Science:
- Pediatric Cardiology
- Medical Device Development
- Healthcare Resource Management
Background:
- No current tools exist to predict high-complexity care needs post-congenital heart catheterization.
- Accurate prediction is crucial for resource allocation in pediatric cardiac intensive care.
Purpose of the Study:
- To develop and validate a predictive model for intensive care unit (ICU) admission following elective cardiac catheterization in congenital heart disease patients.
- To identify key patient and procedural characteristics associated with increased ICU resource utilization.
Main Methods:
- Prospective single-center study involving 2,192 pediatric cardiac catheterization cases (July 2017-December 2020).
- Multivariable logistic regression analysis identified predictors of ICU admission.
- Model performance was assessed using C-statistics in derivation and testing cohorts.
- A novel pre-procedure cardiac status (PCS) feature was evaluated.
Main Results:
- 11% of patients required ICU admission.
- Significant predictors included low weight, systemic illness, recent cardiac intervention, and risk categories from the ICU Admission Tool.
- The model achieved C-statistics of 0.79 (derivation) and 0.76 (testing).
- Inclusion of the PCS feature maintained a C-statistic of 0.79.
Conclusions:
- A validated pre-procedural risk prediction model for ICU admission after congenital cardiac catheterization has been developed.
- This tool can improve resource allocation and capacity planning for complex pediatric cardiac patients.
- The model aids in anticipating the need for intensive care level resources.
Objectives:
Currently, there are no prediction tools available to identify patients at risk of needing high-complexity care following cardiac catheterization for congenital heart disease. We sought to develop a method to predict the likelihood a patient will require intensive care level resources following elective cardiac catheterization.
Design:
Prospective single-center study capturing important patient and procedural characteristics for predicting discharge to the ICU. Characteristics significant at the 0.10 level in the derivation dataset (July 1, 2017 to December 31, 2019) were considered for inclusion in the final multivariable logistic regression model. The model was validated in the testing dataset (January 1, 2020 to December 31, 2020). The novel pre-procedure cardiac status (PCS) feature, collection started in January 2019, was assessed separately in the final model using the 2019 through 2020 dataset.
Setting:
Tertiary pediatric heart center.
Patients:
All elective cases coming from home or non-ICU who underwent a cardiac catheterization from July 2017 to December 2020.
Interventions:
None.
Measurements And Main Results:
A total of 2,192 cases were recorded in the derivation dataset, of which 11% of patients ( n = 245) were admitted to the ICU, while 64% ( n = 1,413) were admitted to a medical unit and 24% ( n = 534) were discharged home. In multivariable analysis, the following predictors were identified: 1) weight less than 5 kg and 5-9.9 kg, 2) presence of systemic illness, 3) recent cardiac intervention less than 90 days, and 4) ICU Admission Tool for Congenital Heart Catheterization case type risk categories (1-5), with C -statistics of 0.79 and 0.76 in the derivation and testing cohorts, respectively. The addition of the PCS feature fit into the final model resulted in a C -statistic of 0.79.
Conclusions:
The creation of a validated pre-procedural risk prediction model for ICU admission following congenital cardiac catheterization using a large volume, single-center, academic institution will improve resource allocation and prediction of capacity needs for this complex patient population.
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