ICU Admission Tool for Congenital Heart Catheterization (iCATCH): A Predictive Model for High Level Post-Catheterization Care and Patient Management.

Brian P Quinn, Lauren C Shirley, Mary J Yeh, Kimberlee Gauvreau, Juan C Ibla, Sarah G Kotin, Diego Porras, Lisa J Bergersen
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引用次数: 1

Abstract

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.

先天性心脏导管置入术的ICU入院工具(iCATCH):高水平置管后护理和患者管理的预测模型。
目的:目前,尚无预测工具可用于识别先天性心脏病心导管置入术后需要高复杂性护理的患者。我们试图开发一种方法来预测患者在择期心导管置入术后需要重症监护水平资源的可能性。设计:前瞻性单中心研究,捕捉重要的患者和程序特征,以预测ICU出院。衍生数据集(2017年7月1日至2019年12月31日)在0.10水平上显著的特征被考虑纳入最终的多变量逻辑回归模型。在测试数据集中(2020年1月1日至2020年12月31日)对模型进行了验证。新的术前心脏状态(PCS)特征于2019年1月开始收集,并在使用2019年至2020年数据集的最终模型中单独评估。地点:第三儿科心脏中心。患者:所有2017年7月至2020年12月期间在家中或非icu接受心导管插入术的患者。干预措施:没有。测量和主要结果:衍生数据集中共记录了2192例病例,其中11% (n = 245)的患者入住ICU, 64% (n = 1413)的患者入住医疗单位,24% (n = 534)的患者出院回家。在多变量分析中,确定了以下预测因素:1)体重小于5 kg和5-9.9 kg, 2)存在全身性疾病,3)最近心脏干预少于90天,4)ICU入院工具先天性心导管病例类型风险类别(1-5),推导和检验队列的C -统计值分别为0.79和0.76。在最终模型中加入PCS特征后,C统计量为0.79。结论:在大容量、单中心、学术机构中建立一个有效的先天性心导管术后ICU入院术前风险预测模型,将改善这一复杂患者群体的资源分配和能力需求预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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