Factors Associated with Intensive Care Unit Admission Following Elective Cardiac Catheterization in Children without a Recent History of Cardiac Surgery or Major Noncardiac Comorbidities

Abstract

Background:

As pediatric cardiac catheterization procedures become more complex, critically ill children increasingly require postprocedure intensive care unit (ICU) admissions.

Aims: Primary:

The primary aim was to evaluate the association between patient demographics and ICU admission after elective cardiac catheterization in children without recent surgery or pre-existing systemic illness (acute renal failure, hepatic failure, active infection).

Secondary:

The secondary aim was to analyze associations between ICU admission and American Society of Anesthesiologists (ASA) physical grade, and Catheterization Risk Score for Pediatrics (CRISP) online model-based precatheterization diagnosis, physiological category, and procedure risk category.

Settings and Design:

A prospective cohort study conducted at a single tertiary cardiac care center.

Materials and Methods:

The study included 193 pediatric patients undergoing elective cardiac catheterization from June 2023 to May 2024 under general anesthesia. Postprocedure, patients were admitted to ICU or transferred to wards. Demographic data, ASA physical status, and CRISP-based categories were analyzed for ICU admission predictors using univariate and multivariate analyses.

Results:

Of 193 participants, 169 met eligibility criteria, and 27 required ICU admission. Univariate analysis showed that age (P < 0.001), weight (P < 0.01), ASA Grade 4 physical status (P = 0.001), CRISP-based category 2 and 3 of precatheterization diagnosis, and physiology were factors associated with children admitted to the ICU. Multivariate analysis showed weight [odds ratio (OR): 0.816, 95% CI: 0.679–0.980, P = 0.029], ASA physical grade 4 [OR: 2.966, 95% CI: 1.155 – 7.612, P = 0.024], CRISP-based pre-catheterization diagnosis category 3 [OR: 28.304, 95% CI: 1.025 – 781.835, P = 0.048], and physiologic risk category 3 [OR: 6.816, 95% CI: 1.275 – 36.439, P = 0.025] were independently associated with ICU admissions after elective cardiac catheterization.

Conclusions:

The results showed that the child’s weight, ASA physical grade, and the diagnosis and physiologic categories by the CRISP model may predict the possibility of a pediatric patient requiring an ICU admission after cardiac catheterization procedures.

INTRODUCTION

Pediatric cardiac catheterization and interventional procedures are increasingly being performed for diagnostic, curative, and palliative purposes. Intra- and postprocedure management of these cases is often challenging and may warrant admission to the intensive care unit (ICU). The risk of adverse events occurring during congenital cardiac catheterization has been studied, resulting in the development of many registries and risk-scoring systems. The Catheterization Risk Score for Pediatrics (CRISP) is one such scoring system that takes into consideration the age, weight, need for inotropic support, uncontrolled or multiorgan failure, physiologic category, precatheterization diagnosis, procedural category, and procedure to predict the likelihood of serious adverse events in individual patients.[1] The Procedural Risk in Congenital Cardiac Catheterization (PREDIC3T) tool for risk assessment was another tool developed for congenital cardiac catheterization based on data from the Congenital Cardiac Catheterization Project on Outcomes multicenter registry, and this system uses six procedural risk categories to identify a patient’s risk of experiencing a clinically significant adverse event or a high-severity adverse event.[2] Congenital Heart Disease Adjustment for Risk Method II (CHARM II), developed from a large multicenter dataset, is another recent scoring system that applies modern risk assessment and case-type categories to enable comparison of adverse events in congenital cardiac catheterization across institutions and operators.[3] Although these tools predict the risk of adverse events, there are no preprocedural anesthetic evaluation criteria related to patients or procedures that can predict the need for an ICU bed before elective pediatric cardiac catheterization.[4] Unplanned ICU admissions following diagnostic or therapeutic cardiac catheterizations typically result from procedure-related complications and, by definition, cannot be anticipated.[4] Conversely, ICU backup requests for elective catheterization procedures involve a distinct decision-making process, often influenced by institutional protocols and individual clinical judgment, leading to inconsistencies. This ambiguity often results in unnecessary preprocedure ICU bed reservations. Due to the limited availability of pediatric ICU beds, which are usually shared by interventional, surgical, and ICU teams, inappropriate requests for ICU backup may negatively affect the flow of pediatric cardiac patients, potentially causing cancellations of surgical and cardiological procedures. To address this issue, this study aimed to identify preoperative criteria in children undergoing elective cardiac catheterization that may help predict postprocedure ICU admissions. An “‘Expert Consensus Statement on Cardiac Catheterization for Pediatric Patients and Adults with Congenital Heart Disease” suggested that risk assessment may be performed using preprocedural risk calculators such as CRISP, to help plan periprocedural resources, including postprocedural recovery.[5] As the CRISP score has also been shown to be a reliable preprocedural risk model, albeit with modifications, we considered applying individual parameters of the scoring system to be evaluated in our study.[6] The novelty of this observational study was to explore the possibility of finding an association between admission of children to the ICU with each patient’s characteristics, and with three established parameters of the CRISP score, i.e., the precatheterization diagnosis, procedural risk, and physiological categories. We considered age and weight, which are used combinedly for CRISP score calculation, along with the American Society of Anesthesiologists (ASA) physical grade as independent patient characteristics. The CRISP precatheterization diagnosis category component helped stratify patients based on their underlying cardiac conditions. The CRISP procedure risk category assessed the complexity of catheterization and potential complications, while the physiological category considered hemodynamic status of the child. We examined our institution’s existing ICU admission practices and sought this association. We assumed that this study’s findings would aid in the preprocedural decision to request ICU backup rather than relying on the existing practice of individual perceptions. This study hypothesized that there may not be identifiable preprocedural criteria in patients with congenital heart disease undergoing elective cardiac catheterization that may help predict postprocedure ICU admission. The primary objective was to evaluate whether patient demographic data may be associated with ICU admission following elective pediatric diagnostic and/or interventional cardiac catheterization procedures. The secondary objectives were to evaluate any association between the ICU admission and criteria like the ASA physical grade, and the CRISP model-based precatheterization diagnosis category, physiologic category, and procedure risk category [Supplementary Table 1].

Supplementary Table 1.

Catheterization Risk Score for Pediatrics (CRISP) online model-based categories*

Precatheterization Diagnosis

Category 1;

Aortopulmonary window, atrial septal defects, atrioventricular septal defects, cor triatriatum, coronary artery anomalies from aorta or pulmonary artery, double chamber right ventricles, transposition of great arteries, mitral regurgitation, partial and total anomalous pulmonary venous connections, patent ductus arteriosus, patent foramen ovale, pulmonary insufficiency, pulmonary hypertension due to left to right shunt, pulmonary stenosis, tricuspid regurgitation and tricuspid stenosis, vascular ring, ventricular septal defects, etc.

Category 2;

Aortic arch hypoplasia, aortic insufficiency, aortic stenosis, atrial isomerism, coarctation of aorta, conduit failure, congenitally corrected transposition of great arteries, Coronary artery anomaly Fistula, double outlet left ventricle, tetralogy of Fallot with double outlet right ventricle, Ebstein’s anomaly, hypoplastic left and right heart syndrome, interrupted aortic arch, major aortopulmonary collaterals, left ventricle to aorta tunnel, mitral regurgitation and mitral stenosis, hemitruncus, pulmonary artery stenosis (hypoplasia), pulmonary artery stenosis (branch, central), pulmonary atresia (intact ventricular septum, ventricular septal defect, tetralogy of Fallot), pseudo truncus, pulmonary arteriovenous fistula, pulmonary insufficiency and pulmonary stenosis, single ventricle anomalies, complex transposition of great arteries, tetralogy of Fallot, infracardiac and mixed total anomalous pulmonary venous connection, tricuspid stenosis, truncus arteriosus, scimitar

Category 3:

Aortic insufficiency and aortic stenosis, pulmonary vascular disease, and pulmonary embolism, Eisenmenger’s, tetralogy of Fallot with absent pulmonary valve

Procedure Risk Category:

Category 1:

Atrial septostomy, balloon, balloon angioplasty of right ventricular outflow tract, main, and branch pulmonary arteries, coil occlusion/device/systemic arterial collaterals, coil occlusion of left superior vena cava, patent ductus arteriosus, and veno–veno collaterals, device closure of atrial septal defect, patent ductus arteriosus, patent foramen ovale, hemodynamic catheterization, oxygen-nitric trial for pulmonary vascular resistance reversal, transesophageal echicardiography, stent placement in systemic vein, aorta, stent redilations, pulmonary valvuloplasty ≥1 month age.

Category 2:

Balloon angioplasties of lobar segment left pulmonary artery (LPA) or right pulmonary artery (RPA)/dilation <8 atmospheres and < than 4 vessels, proximal left pulmonary artery (LPA) or right pulmonary artery (RPA)/dilation ≥l–8 atmospheres, coil occlusion of coronary fistula, systemic shunts of the aorta, stent placements, valvuloplasty of the aorta (≥ l to 1 month of age), valvuloplasty of the pulmonary valve (<1 month of age).

Category 3:

Atrial septostomy dilation and stent, balloon angioplasty/lobar segment left pulmonary artery or right pulmonary artery/dilation greater ≥8 atmospheres or compression banding and fewer than four vessels, balloon angioplasty/lobar segment left pulmonary artery or right pulmonary artery and ≥4 vessels, balloon angioplasty or stent/pulmonary vein, device closure of perivalvar leak or ventricular septal defect, atretic valve perforation, stent placement/intracardiac/ventricular, stent placement/systemic shunt or ductus arteriosus, stent redilation/intracardiac/ventricular/ductus arteriosus, valvuloplasty aorta less than 1 month of age, valvuloplasty mitral less than 1 year of age, valvuloplasty mitral ≥ to 1 year of age.

Physiologic Category

Category 1:

• Saturation: >80%

• Pulmonary Vascular Resistance Index (PVRI): Normal

• Pulmonary Venous Obstruction (PVO): None

• Right Ventricular Systolic Pressure (RVSP): Less than half of the systemic pressure (in biventricular hearts)

Category 2:

• Saturation: 65–79%

• Pulmonary Vascular Resistance Index (PVRI): 4–8 Wood Units per square meter

• Right Ventricular Systolic Pressure (RVSP): Half to systemic pressure (in biventricular hearts)

• Anemia: Needing elective transfusion

• Dynamic Subpulmonary Obstruction: Present (no spells)

Category 3:

• Saturation: Less than 65%

• Pulmonary Vascular Resistance Index (PVRI): Greater than 8 Wood Units per square meter or fixed pulmonary venous obstruction

• Right Ventricular Systolic Pressure (RVSP): Suprasystemic

• Anemia: Needing urgent transfusion

• Hypercyanotic Spells: Present

• Severe Systemic Atrioventricular Valve Regurgitation: Present

Procedure Type

• Diagnostic, Interventional, Hybrid

*Nykanen DG, Forbes TJ, Du W, Divekar AA, Reeves JH, Hagler DJ, Fagan TE, Pedra CA, Fleming GA, Khan DM, Javois AJ, Gruenstein DH, Qureshi SA, Moore PM, Wax DH; Congenital Cardiac Interventional Study Consortium (CCISC). CRISP: Catheterization RISk score for Pediatrics: A Report from the Congenital Cardiac Interventional Study Consortium (CCISC). Catheter Cardiovasc Interv. 2016;8:302–309. doi: 10.1002/ccd. 26300

MATERIALS AND METHODS

In this prospective observational study conducted at a single tertiary cardiac care center, 193 pediatric patients undergoing an elective cardiac catheterization procedure were enrolled between June 1, 2023 and May 31, 2024. Institutional ethical committee approval [MOH/CSR/23/27662] and informed consent from the parents/guardians were obtained. Patients <13 years old undergoing an elective cardiac catheterization with or without an interventional procedure were included. Exclusion criteria included those undergoing hybrid procedures, emergency cardiac catheterization procedures, children with syndromes, facial abnormalities, major noncardiac comorbidities (such as seizure disorders on medication, hepatic and renal failure, chronic lung diseases, ongoing sepsis, and obstructive sleep apnea). Children with a history of difficult airway access, unstable patients on precatheterization hemodynamic and/or ventilatory support, those on oxygen supplementation or noninvasive ventilatory support, patients receiving nasogastric alimentation, hypotonia, and those who have had cardiological or cardiac surgical intervention within 90 days were also excluded. During the preanesthetic assessment, the demographic data [age, weight, and gender] and the ASA physical grade were recorded for all patients. With the help of the online portal of CRISP, the precatheterization diagnosis category, procedure risk category, and physiologic category were the factors recorded for each child [Supplementary Table 1].

All patients received general anesthesia with endotracheal intubation under standard ASA monitoring guidelines. Intraprocedural adverse events like atrial and ventricular arrhythmia producing hemodynamic instability and needing inotropic administration, bronchospasm, need for blood transfusion, and cardiac arrest were documented. The responsibility of deciding the appropriate postprocedure destination for the children rested with the attending anesthesiologist and the pediatric cardiologist. The children were grouped into those admitted to the ICU and those admitted to high dependency or general wards. Patients’ demographic data and data related to categories based on the CRISP model were compared between the ICU group and the high dependency/general ward group.

The incidence of adverse events recorded for each child in this study included the need for blood transfusion, inotrope usage, arrhythmias, bronchospasm, and cardiac arrest. We used the components of diagnosis, procedure, and physiologic categories of CRISPR to identify the association of ICU admissions in children undergoing elective cardiac catheterization procedures.

Statistical analysis

The sample size was calculated based on the goal of detecting whether our model had a discriminative ability, measured by the receiver operating characteristic (ROC) area under the curve (AUC). We calculated the minimal sample size as 20 intensive care admissions using an expected AUC of 0.75, a significance level (alpha) of 5%, and a power of 90%. The AUC of 0.75 was selected as it is a widely accepted threshold and is considered a reasonable benchmark for a model with good performance. We used a null hypothesis of an AUC of 0.50, which would suggest no ability to differentiate between the groups. The calculated sample size ensured that we had enough statistical power to confidently detect any real differences. The data were analyzed using IBM SPSS Statistics 29.0 (IBM Corp. Released 2023. IBM SPSS Statistics for Windows, Version 29.0.2.0 Armonk, NY: IBM Corp). The ROC analysis was done using MedCalc 22.0 (MedCalc software limited). Non-normal continuous variables are presented as median with quartiles and categorical variables as frequencies and percentages for descriptive statistics. The Chi-square test was used for categorical variables, and the Mann–Whitney “U” test was used for continuous variables to compare groups. Univariate analysis assessed the association between patients’ demographic and clinical variables with ICU admission. ROC curve analysis was performed to evaluate the predictive accuracy of continuous variables, including age and weight, for ICU admission. Multivariate logistic regression was conducted to assess the independent association of significant variables, adjusting for potential confounders. Significant and close-to-significant variables in univariate analysis were included in the multivariate model. A P-value of < 0.05 was considered statistically significant in all analyses.

RESULTS

Of the enrolled 193 children, 169 completed the study [Figure 1]. Twenty-seven children [16%] were admitted to the ICU after the procedure. Diagnosis and procedure characterization are mentioned in Table 1. The distribution of the children based on ASA physical status and the physiologic, diagnostic, and procedure risk categories of CRISP is shown in Figure 2. Univariate analysis showed a significant association between demographics, ASA physiology grade, CRISP-based diagnostic and physiologic risk categories, and ICU admissions [Table 2]. Patients admitted to the ICU were younger (median age (interquartile range [IQR]): 4 months (0.3–11.0)) and had lower weight (median (IQR): 4.5 Kg (3.0, 7.0)), with gender having no association. A significantly higher proportion of children admitted to the ICU belonged to ASA physical status grade 4 (17/27, 63%) compared to those admitted to the high dependency unit (HDU)/ward (30/142, 27%) (P < 0.001). The CRISP diagnosis Categories 2 and 3 (P < 0.001) and Categories 2 and 3 for physiological status were higher (48.1% and 22.2%, respectively) compared to the high dependency/ward group (28.2% and 3.5%) (P < 0.001). Regarding the CRISP procedure risk category, the distribution across categories was similar between the groups (P = 0.052).

Figure 1.

Patient Flow chart. *PPVI: Percutaneous pulmonary valve implantation. +TEE: Transoesophageal echocardiography

Table 1.

Diagnosis and catheterization procedures performed in children admitted to the intensive care unit.

Diagnosis	n=27
Severe aortic stenosis	4
Branch or main pulmonary artery stenosis/atresia with ventricular septal defect	7
Branched or main pulmonary artery stenosis/atresia with intact ventricular septum	8
Coarctation with or without left ventricular outflow tract obstruction	1
Major aortopulmonary collateral arteries	1
Isolated ventricular septal defect	1
Tetralogy of Fallot with severe right (1) and left (1) ventricular outflow tract obstruction	2
Post-Rastelli procedure with severe right ventricular outflow tract obstruction	1
Pulmonary vein stenosis	1
Partial anomalous pulmonary venous return with hypoplastic left side, post-Glenn procedure	1
Procedural characteristics	n=23
Balloon valvuloplasty of the pulmonary valve±branch pulmonary arteries	8
Pulmonary valvuloplasty with stenting	2
Right ventricular outflow tract stenting	2
Patent ductus arteriosus stenting	5
Balloon aortic valvuloplasty	4
Aortopulmonary collateral coil embolization	2
Diagnostic catheterization [Pulmonary atresia—1, major aortopulmonary collaterals—1]	2

Figure 2.

Distribution of patients based on ASA physical status and Catheterization Risk Score for Pediatrics (CRISP) model-based categories[1]. ICU = Intensive care unit

Table 2.

Univariate analysis identifying the association between demographic and clinical variables with intensive care unit admission

Variable	Total number of patients (n=169)		P
	High dependency/ward (n=142)	Intensive care unit (n=27)
Age in months—Median (IQR)	38.0 (12.0, 75.0)	4.0 (0.3, 11.0)	<0.001
Weight—Median (IQR)	12.9 (8.7, 17.6)	4.5 (3.0, 7.0)	<0.001
Gender
• Male	76 (53.5%)	15 (55.6%)	0.840
• Female	66 (46.5%)	12 (44.4%)
ASA PHYSICAL STATUS GRADE*
• 3	81 (73%)	10 (37%)	0.001
• 4	30 (27%)	17 (63%)
DIAGNOSIS CATEGORY**
• Category 1	64 (45.1%)	3 (11.1)	<0.001
• Category 2	77 (54.2%)	21 (78.8%)
• Category 3	1 (0.7%)	3 (11.1%)
PROCEDURE RISK**
• Category 1	88 (61.9%)	10 (37%)	0.052
• Category 2	38 (26.8%)	11 (40.7%)
• Category 3	16 (11.3%)	6 (22.2%)
PHYSIOLOGIC RISK**
• Category 1	97 (68.3%)	8 (29.6%)	<0.001
• Category 2	40 (28.2%)	13 (48.1%)
• Category 3	5 (3.5%)	6 (22.2%)

IQR=Interquartile range, *American Society of Anesthesiologists grade, **Catheterization Risk Score for Pediatrics (CRISP) model-based categories[1]

The ROC curves of age and weight demonstrated the model’s ability to distinguish between ICU and high dependency/ward admission across various decision thresholds, revealing a significant association between a child’s age and weight with ICU admissions [age: Youden index J − 0.6140; weight: Youden index J − 0.6369] [Figures 3 and 4]. The age achieved an AUC of 0.825 (95% CI: 0.759–0.879, sensitivity: 74.7% and specificity: 87.32%, P < 0.001), and the weight achieved an AUC of 0.844 (95% CI: 0.780–0.895, sensitivity: 77.8%, specificity: 85.9%, P < 0.001).

Figure 3.

Receiver operating characteristic (ROC) area under the curve (AUC) results for age in months

Figure 4.

Receiver operating characteristic (ROC) area under the curve (AUC) results for weight in kilograms

A multivariate logistic regression analysis identified factors independently associated with ICU admission [Table 3]. The age of the child in months was positively associated with ICU admission; however, this was not statistically significant (P = 0.120). Weight reduction of 1 kg, increased ICU admission (odds ratio (OR) =0.816; 95% CI: 0.679–0.980, P = 0.029), suggesting that lower weight increases the likelihood of ICU admission. ASA physical grade 4 was independently associated with ICU admission (OR = 2.966; 95% CI: 1.155–7.612, P = 0.024). CRISP-based precatheterization diagnosis category 3 (OR = 28.304; 95% CI: 1.025–781.835, P = 0.048) and physiologic category 3 (OR = 6.816; 95% CI: 1.275 – 36.439, P = 0.025) also showed significantly higher odds of ICU admission. In the multivariate analysis logistic regression model, the 2 Log likelihood was 98.584, and the model exhibited a moderate amount of variance, as indicated by a Cox and Snell R-Square of 0.255 and a Nagelkerke R-Square of 0.436.

Table 3.

Multivariate logistic regression analysis identifying independent factors associated with intensive care unit admission after routine, elective pediatric cardiac catheterization

Variables	Beta	P	Odds Ratio	95% CI for Odds
				Lower	Upper
Age in months	0.023	0.096	1.023	0.996	1.051
Weight in kilograms	− 0.204	0.029	0.816	0.679	0.980
ASA Physical Grade 4*	1.087	0.024	2.966	1.155	7.612
Precatheterization Diagnosis**
• Category 1	1.245	0.096	3.474	0.803	15.022
• Category 2	3.343	0.048	28.304	1.025	781.835
• Category 3
Procedure Risk Category**
• Category 1	0.194	0.757	1.214	0.355	4.153
• Category 2	0.598	0.408	1.818	0.441	7.493
• Category 3
Physiologic Category**
• Category 1	0.589	0.315	1.802	0.571	5.684
• Category 2	1.919	0.025	6.816	1.275	36.439
• Category 3

ASA=American Society of Anesthesiologists, *American Society of Anesthesiologists physical status grade, **Catheterization Risk Score for Pediatrics (CRISP) model-based categories[1] [Supplementary Table 1]

DISCUSSION

This study was conducted to identify any patient and procedural characteristics associated with children admitted to the ICU following elective pediatric cardiac catheterization procedures. The study showed that the “null” hypothesis—that there may not be any independent criteria indicating ICU admission—was rejected. This study suggests that the child’s weight, ASA physical status, CRISP-based precatheterization diagnosis [category 3], and physiologic status [category 3] may be independent predictors of ICU admission following elective pediatric cardiac catheterization procedures.

Maisat et al.[7] observed children weighing less than 5 kg and male children with pulmonary vein stenosis had a higher likelihood of requiring ICU admission and mechanical ventilation. Quinn et al.[8] reported that weights less than 5 kg and 5–9.9 kg were independent predictors of ICU admission. Our study also had similar results and identified that a weight reduction of 1 kg increased the ICU admission probability by 18.4%, which was statistically significant [P = 0.029; OR: 0.816]. Our study showed no association between gender and ICU admissions.

The “ICU Admission Tool for Congenital Heart Catheterization (iCATCH)” included the presence of systemic illness and recent cardiac interventions (within 90 days), and the case type risk categories would increase the likelihood of ICU admission.[8] The current study included only children for elective cardiac catheterization. It excluded children with systemic diseases like acute renal failure, coagulopathy, active infection, and liver failure, as well as those who had a cardiological or surgical intervention in the previous 90 days, who invariably are admitted to the ICU postprocedure. This adds to the uniqueness of this study in the sense that we tried to identify predictors of intensive care admission following only elective procedures.

Intraoperative hypotension requiring inotropes and red blood cell transfusion was suggested as an indicator for postprocedure intensive care admission.[7] In eight patients admitted to the ICU, adverse events during the procedure included the use of inotropes (4), blood transfusions (3), hemodynamically unstable arrhythmia (3), bronchospasms (3), and cardiac arrests (2). These belonged to the CRISP-based diagnosis Category 3 and physiologic Category 3. Seven children who were not admitted to the ICU experienced adverse events: inotrope use (1), blood transfusion (1), hemodynamically unstable arrhythmias (3), and bronchospasm (3). No one in this group experienced a cardiac arrest that required resuscitation. It should be noted that in both groups, a few patients had more than one adverse event.

Quinn and colleagues also used case-type risk categories to predict ICU admission.[8] Our study did not find a correlation with ICU admission based on the CRISP procedure risk category.

The PREDIC3T Tool, developed from the largest registry-based dataset encompassing 13 institutions in the U.S. and 23,119 cases from 2014 to 2017, categorized risk solely based on procedures to identify the risk of adverse events.[2] Procedures like angioplasty, stent implantation, valve implantation, closure of atrial septal or ventricular septal defect, vascular or valvar perforation procedure, transseptal puncture, and hybrid procedures benefit from ICU care.[5] Our study did not include elective hybrid procedures, as they have ICU backup routinely. The CRISP-based procedure risk category in elective catheterization procedures was not an independent risk factor for ICU admission.

The ASA physical status classification is helpful in evaluating surgical risk, but it has shown poor interrater reliability when applied to pediatric patients, primarily due to its subjective and adult-focused definitions.[9] Although ASA physical status grade 4 was independently linked to ICU admission (OR: 2.966; P = 0.024), some children with ASA grade 4 were not admitted to the ICU. These patients lacked additional high-risk features, such as precatheterization CRISP diagnosis Category 3 or physiologic Category 3. Hence, not all ASA physical status grade 4 patients may have the same risk profile for ICU admission. While intraoperative adverse events, like inotrope use for hypotension and blood transfusions, were common among those shifted to the ICU, similar events also occurred in some patients who were not admitted to the ICU. These two observations highlight the inherent subjectivity in postprocedural disposition decisions made by the anesthesiologist and pediatric cardiologist, which is a recognized limitation of this study. This study aimed to identify objective preprocedural parameters that could more reliably predict ICU admission decisions, thereby resolving any ambiguities.

Existing risk stratification tools, such as PREDIC3T and iCATCH, provide structured methodologies for assessing postprocedural risk; however, their applicability to elective cardiac catheterizations in pediatrics remains inconclusive.[2,8] Our study findings emphasize the need for standardized guidelines to improve consistency in ICU admissions following elective cardiac catheterization procedures in children. Combining predictive models with clinical judgment can enhance decision-making and ensure appropriate postprocedural care.

Limitations

1. ICU admissions were decided by attending anesthesiologists and cardiologists, which introduced subjective bias.

2. The study was conducted at a single tertiary cardiac care center, limiting the external validity and generalizability of the findings due to variations in practices and patient populations across different institutions.

3. The relatively small sample size, especially for patients admitted to the ICU (27 out of 169), may increase the margin of error when detecting significant associations.

4. The study excluded higher-risk patients and hybrid procedures, which may restrict the applicability of the study.

CONCLUSION

In conclusion, this study highlights preoperative factors like younger age and lower weight, poor ASA physical status grade, and specific CRISP-based clinical criteria, which emerged as significant predictors associated with ICU admission following elective pediatric cardiac catheterization. These findings underscore the potential to enhance preanesthetic assessments, allowing clinicians to make more informed decisions regarding ICU bed reservations, optimize resource utilization, and reduce unnecessary requests.

Ethical approval and consent to participate

Study approved by the Institutional Scientific Research Committee [MOH/CSR/23/27662].

Informed written consent from patients.

Author’s individual contribution

Madan Mohan Maddali: This author contributed toward the conception the work, acquisition of data: interpretation of data, drafting of the manuscript, intellectual content, final agreement of manuscript, and agreement to be accountable for all aspects of the manuscript.

Malay Hemantlal Patel: This author contributed toward the acquisition of data: interpretation of data, final agreement of manuscript, and agreement to be accountable for all aspects of the manuscript.

Is’haq Al Aamri: This author contributed toward the conception of the work, final agreement of manuscript, and agreement to be accountable for all aspects of the manuscript.

Panchatcharam Murthi Sathiya: This author contributed toward statistical input and data analysis, the final agreement of the manuscript, and the agreement to be accountable for all aspects of the manuscript.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.