Predicting Delayed Diagnosis in Critical Congenital Heart Disease: Risk Score Development and Economic Analysis

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Murni, Muhammad T. Wirawan, Imtiyaz H. Zahra, Esta R. Sativa, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7463764/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Delayed diagnosis of critical congenital heart disease (CCHD) is associated with increased morbidity, mortality, and healthcare costs, particularly in low- and middle-income countries. Reliable predictive tools for the early identification of infants at risk for delayed diagnosis are unavailable. This study aimed to develop and validate a clinical risk score for predicting delayed CCHD diagnosis, assess its relationship with mortality, and evaluate the cost effectiveness of timely compared with delayed diagnosis. Methods A retrospective cohort of 871 children with echocardiography-confirmed CCHD (2019–2024) was analyzed. Predictors of delayed diagnosis were identified through multivariable logistic regression and converted into an additive point-based risk score. Model discrimination was evaluated via the area under the receiver operating characteristic curve (AUC), with Youden’s index used to identify the optimal threshold. Mortality associations were analyzed via adjusted logistic regression. Economic evaluation compared timely and delayed diagnoses on the basis of direct medical costs and disability-adjusted life years (DALYs) from the healthcare provider perspective, with one-way sensitivity analysis performed. Results Delayed diagnosis occurred in 72.44% of the patients. Syndromic features, low birth weight, rural residence, and low socioeconomic status independently predict delay. The risk score achieved moderate discriminatory performance (AUC 0.66; 95% CI 0.62–0.70), and a cutoff of ≥ 5 points identified 42% of infants as high risk (sensitivity 59% (95% CI 0.55–0.63), specificity 64% (95% CI 0.58–0.70)). High-risk classification was not associated with mortality (aOR 1.01; 95% CI 0.52–1.98), whereas delayed diagnosis was associated with lower mortality (aOR 0.36; 95% CI 0.26–0.49). Timely diagnosis resulted in lower overall expected costs despite similar DALYs, producing an incremental cost-effectiveness ratio of -IDR388,897 per DALY averted. Conclusions A simple, clinically applicable risk score can identify infants at risk of delayed CCHD diagnosis. Although delayed diagnosis does not predict mortality, timely diagnosis reduces overall healthcare costs, reinforcing the value of early detection and equitable access to pediatric cardiac care in resource-limited settings. Critical congenital heart disease delayed diagnosis risk score mortality cost-effectiveness Figures Figure 1 Figure 2 Figure 3 Background Congenital heart disease (CHD) is the most common congenital anomaly worldwide, occurring in approximately 6–10 per 1000 live births[ 1 – 4 ]. A significant subset, critical congenital heart defects (CCHDs)[ 2 , 5 ], accounts for 25–33% of CHDs and requires surgical or catheter-based intervention within the first year of life[ 2 , 3 , 6 ]. Without timely treatment, up to 30% of newborns with CCHD may die within the first month of life[ 6 ]. A delayed diagnosis of CCHD is consistently associated with increased morbidity, mortality, and economic burden[ 2 , 7 – 9 ]. Compared with those diagnosed early (16%), infants diagnosed after hospital discharge have a higher first-year mortality rate (27%) [ 2 ]. Delayed detection also increases hospitalizations by 52%, hospital days by 18%, and infancy hospitalization costs by 35%[ 5 , 10 , 11 ]. Several factors contribute to these delays, including difficulty in detecting certain defects prenatally, nonspecific neonatal signs missed during routine examinations, and limitations of pulse oximetry screening, which may fail to detect lesions such as coarctation of the aorta, tetralogy of Fallot, or total anomalous pulmonary venous return[ 7 ]. Health system delays are also common, involving late consultations (37.2–40.3%), diagnostic errors or missed diagnoses (22.5–57.5%)[ 5 , 12 , 13 ], and referral delays (13.3–19.9%)[ 9 , 12 ]. Socioeconomic and demographic barriers, such as financial constraints, maternal illiteracy, and deliveries outside tertiary hospitals, further exacerbate the problem [ 12 , 14 ]. In low- and middle-income countries (LMICs), where diagnostic resources and access to specialized care are limited, delayed diagnosis is particularly prevalent[ 4 ]. In Indonesia, six out of ten children with CHD are diagnosed late, increasing to 86.2% for those with cyanotic CHDs[ 8 , 9 ]. Many infants are only diagnosed after developing severe complications, including heart failure (49.4%) and irreversible pulmonary hypertension (15.8%). The contributing factors included delayed diagnosis by doctors (57.5%), midwifery-related delays (14.4%), financial barriers (9.7%), and delayed referrals or follow-up (9.2%). Independent risk factors include cyanotic CCHD, rural residence, no syndromic conditions, low family income, normal labor, and term gestation[ 9 ]. Despite these challenges, no validated clinical risk scoring system exists to predict delayed diagnosis, highlighting the urgent need for tools to support early detection, guide referrals, and improve healthcare resource allocation. This study aimed to (1) develop a clinical risk scoring system to predict delayed diagnosis of CCHD, (2) evaluate whether delayed diagnosis or high-risk scores are independently associated with mortality, and (3) examine the cost-effectiveness of timely versus delayed diagnosis in terms of direct medical costs and disability-adjusted life years (DALYs). Methods Study Design and Population A retrospective cohort study was conducted at Dr. Sardjito Hospital, Yogyakarta, Indonesia, including all children younger than 18 years with echocardiography-confirmed critical congenital heart disease (CCHD) diagnosed between January 2019 and December 2024. Delayed diagnosis was defined as failure to diagnose CCHD during birth hospitalization, whereas infants discharged home without a diagnosis were classified as delayed[ 15 , 16 ]. Patients who underwent corrective procedures prior to diagnostic confirmation were excluded. Predictor Variables The variables were categorized as binary (yes/no) to facilitate statistical analysis and were selected on the basis of their clinical relevance and evidence from previous literature. Syndromic features refer to the presence of a genetic syndrome, such as Down syndrome. Prematurity was defined as a gestational age of less than 37 weeks, whereas low birth weight corresponded to a birth weight less than 2,500 grams. Primiparity indicated the mother’s first live birth. Low maternal education applied to mothers who had not completed at least nine years of formal education, and young maternal age was defined as less than 25 years at the time of diagnosis. Rural residence refers to individuals with a registered home address in a rural or remote administrative area. Low socioeconomic status was determined by meeting at least one of the following criteria: enrollment in PBI-JKN (government-subsidized insurance), a monthly household income below IDR2,000,000, or documentation in hospital records as having “low” socioeconomic status. Model development and score derivation A multivariable logistic regression model was constructed with delayed diagnosis as the dependent variable. Variables demonstrating statistical significance (p < 0.05) or clinical importance were retained in the final model. Regression coefficients were transformed into an additive risk score by scaling relative to the smallest nonzero coefficient, multiplying by a constant for interpretability, and rounding to the nearest integer[ 17 ]. The resulting score was calculated for each individual, with higher values reflecting an increased risk of delayed diagnosis. Score Performance and Cutoff Selection The discriminatory ability of the developed score was evaluated via a receiver operating characteristic (ROC) curve, expressed as the area under the curve (AUC) with 95% confidence intervals. The AUC value is a summary metric of the ROC curve that reflects a test's ability to distinguish between diseased and nondiseased individuals, with values ranging from 0.5 (chance) to 1.0 (perfect discrimination)[ 18 ]. Values above 0.80 are generally considered clinically useful. The optimal cutoff for classifying high-risk individuals was determined via Youden's index [ 18 , 19 ]. Association with Mortality The prognostic relevance of delayed diagnosis and high-risk scores was assessed in separate multivariable logistic regression models using mortality as the outcome. Confounding variables were adjusted for on the basis of clinical judgment and prior evidence. Adjusted odds ratios (aORs) with 95% confidence intervals are reported. Statistical significance was set at p < 0.05 [ 20 ]. All analyses were performed via Stata 17 (StataCorp, College Station, TX, USA). Economic analysis Analytical Framework A simplified decision-analytic approach was employed to compare the economic consequences of timely versus delayed diagnosis of critical congenital heart disease (CCHD). Patients were first categorized by diagnostic timing (timely or delayed). For each diagnostic group, the mean diagnostic cost was calculated. Each group was then divided into two clinical outcomes (survival and death). Within each outcome branch, three cost components were included: intervention (surgical or catheter-based), ward care, and medication use. Each outcome branch had an associated probability, enabling calculation of both probability-weighted costs (p.cost) and probability-weighted disability-adjusted life years (p.DALYs). Data Sources and Perspective The analysis used patient-level data from 838 children diagnosed with CCHD, of whom 615 (73.39%) experienced delayed diagnosis and 223 (26.61%) received timely diagnosis. These cases were selected from an initial cohort of 871 patients; 33 patients were excluded because their economic data were incomplete. The analysis was conducted from the healthcare provider's perspective and focused on short-term diagnostic and immediate treatment episodes without projecting lifetime costs or long-term outcomes. Given this short-term horizon, no discounting was applied to costs or health outcomes[ 21 ]. Cost Estimation The costs were derived from hospital billing systems and the Indonesian case-based groups (INA-CBG) tariff schedule. The included cost categories were diagnostic investigations, interventional procedures, ward care, and medications. All costs were expressed in Indonesian rupiah (IDR) and adjusted to the base year of analysis. Effectiveness Measurement Effectiveness was measured in disability-adjusted life years (DALYs), combining years of life lost (YLL) due to premature mortality and years lived with disability (YLD). Disability weights were sourced from the Global Burden of Disease (GBD) study[ 21 , 22 ]. The total burden of CCHD in Indonesia was estimated via a DALY calculator without discounting. Both timely and delayed diagnostic strategies were modeled to yield the same fixed overall health burden (72.23 DALYs), allowing for an analysis focused on differences in costs for identical health outcomes. Cost-Effectiveness Analysis The expected costs and DALYs for each diagnostic pathway were calculated by multiplying each branch’s probability by its associated cost and DALY value and then summing across branches. The incremental cost-effectiveness ratio (ICER) was calculated via the following standard formula[ 21 , 23 ]: A negative ICER indicates that timely diagnosis is cost-saving[ 23 ]. The willingness-to-pay (WTP) threshold was set at an IDRof 74.3 million per DALY averted, equivalent to one times Indonesia’s gross domestic product (GDP) per capita[ 24 – 26 ]. Sensitivity analysis A one-way sensitivity analysis was performed to assess parameter uncertainty. The key cost inputs and DALY estimates were varied by ± 20%, and the resulting ICER values were presented in a tornado diagram to identify the parameters with the greatest influence on the cost-effectiveness results[ 27 , 28 ]. Ethical considerations Ethical approval for the study was granted by the Medical and Health Research Ethics Committee, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada (No. KE/FK/1783/EC/2024). Given that the study utilized retrospective anonymized data, the need to obtain individual informed consent was waived. All procedures adhered to the ethical principles outlined in the Declaration of Helsinki. Results A total of 871 patients with critical congenital heart disease (CCHD) were included in the primary analysis. The mean age at diagnosis was 2.20 ± 4.04 months. Slightly more than half of the patients were male (55.91%), and the mean birth weight was 2,871 ± 555 g. Most infants (72.44%) experienced delayed diagnosis. Rural residents accounted for 59.7% of the patients, and 45.92% were from low socioeconomic households. Cyanotic CCHDs, including Tetralogy of Fallot (24.34%) and pulmonary atresia (17.11%), were common. More than half of the patients (55.22%) survived during the study period (Table 1 ). Table 1 Baseline characteristics of the study population Patient Characteristics n = 871 Nondelayed Delayed p value Age at diagnosis (months), mean (± SD) 2.20 (4.04) 0.19 (1.19) 2.96 (4.46) 0.000 Sex, n (%) Male 487 (55.91) 134 (27.52) 353 (72.48) 0.977 Female 384 (44.09) 106 (27.60) 278 (72.40) Weight at diagnosis (kg), mean (± SD) 8.37 (9.61) 3.44 (4.21) 10.22 (10.34) 0.000 Nutritional status, n (%) Good nutrition 303 (34.79) 82 (27.06) 221 (72.94) 0.011 Undernutrition 313 (35.94) 103 (32.91) 210 (67.09) Severe malnutrition 255 (29.28) 55 (21.57) 200 (78.43) Syndromic features, n (%) Down syndrome 41 (4.71) 23 (56.10) 18 (43.90) 0.000 Others 29 (3.33) 9 (31.03) 20 (68.97) History of gestational age, n (%) 40 weeks 101 (11.6) 73 (72.28) 28 (27.72) Birth weight (gr), mean (± SD) 2871 (555) 2752(653) 2916(505) 0.000 Birth weight history, n (%) < 2500 grams 175 (20.09) 72 (41.14) 103 (58.86) 0.000 2500–4000 grams 685 (78.65) 164 (23.94) 521 (76.06) ≥ 4000 grams 11 (1.26) 4 (36.36) 7 (63.64) Parity, n(%) Primipara 348 (39.95) 108 (31.03) 240 (68.97) 0.061 Multipara 523 (60.05) 132 (25.24) 391 (74.76) Delayed diagnosis, n (%) 631 (72.44) Patient residence, n (%) Rural 520 (59.7) 109 (20.96) 411 (79.04) 0.000 Urban 351 (40.3) 131 (37.32) 220 (62.68) Family socioeconomic status, n (%) Low 400 (45.92) 92 (23.00) 308 (77.00) 0.017 Middle 464 (53.27) 145 (31.25) 319 (68.75) High 7 (0.8) 3 (42.86) 4 (57.14) Maternal age at diagnosis (years), mean (± SD) 31.85 (6.89) 30.78 (6.35) 32.27 (7.04) 0.004 Maternal age at diagnosis, n (%) < 25 years 130 (14.93) 43 (33.08) 87 (66.92) 0.013 ≥ 25 years 741 (85.07) 197 (26.59) 544 (73.41) Maternal education, n (%) < 9 years 114 (13.09) 23 (20.18) 91 (79.82) 0.059 ≥ 9 years 757 (86.91) 217 (28.67) 540 (71.33) Type of critical CHD, n (%) Tetralogy of Fallot 212 (24.34) 26 (12.26) 186 (87.74) 0.000 Transposition of the great arteries 107 (12.28) 43 (40.19) 64 (59.81) Persistent truncus arteriosus 44 (5.05) 12 (27.27) 32 (72.73) Hypoplastic left ventricle 10 (1.15) 3 (30.00) 7 (70.00) Pulmonary atresia 149 (17.11) 65 (43.62) 84 (56.38) Tricuspid atresia 46 (5.28) 15 (32.61) 31 (67.39) Total anomalous pulmonary venous drainage 25 (2.87) 7 (28.00) 18 (72.00) Others 278 (31.92) 69 (24.82) 209 (75.18) Intervention, n (%) Catheterization intervention 528 (60.62) 118 (22.35) 410 (77.65) 0.000 Surgical intervention 162 (18.6) 29 (17.90) 133 (82.10) 0.002 Outcome, n (%) Survival 481 (55.22) 122 (25.36) 359 (74.64) 0.108 Death 390 (44.78) 118 (30.26) 272 (69.74) Age at death (months), mean (± SD) 20.31 (41.24) 4.08 (9.13) 31.61 (50.19) 0.000 SD = standard deviation Risk Score Development and Performance Four independent predictors of delayed diagnosis were identified: syndromic features, low birth weight, rural residence, and low socioeconomic status. The regression coefficients were converted to integer weights to produce a practical risk score (Table 2 ). Table 2 Diagnostic Value of Predictor Variables for Delayed Diagnosis Predictor Coefficient Score (Rounded) Std. error p value 95% CI Syndromic features -0.708 -7 0.264 0.007 -1.226 – -0.190 Low birth weight -0.593 -6 0.187 0.002 -0.959 – -0.227 Rural residence 0.751 8 0.159 < 0.001 0.439–1.062 Low socioeconomic status 0.370 4 0.161 0.022 0.344–0.883 For clinical use, each variable was assigned points on the basis of whether the feature was present or absent, as shown in Table 3 . Table 3 Scoring Model for Delayed Diagnosis Risk Predictor Category Score Syndromic features Yes No -7 0 Low birth weight Yes No -6 0 Rural residence Yes No 8 0 Low socioeconomic status Yes No 4 0 The score demonstrated moderate discrimination, with an AUC of 0.66 (95% CI 0.62–0.70). The optimal cutoff, determined by Youden’s index, was 4.5 (rounded to ≥ 5), yielding 59% (95% CI 0.55–0.63) sensitivity and 64% (95% CI 0.58–0.70) specificity, classifying 42% of patients as high risk (Fig. 1 ). Association with Mortality Univariable analysis revealed that delayed diagnosis was associated with lower mortality (38.0 percent compared with 63.8 percent for timely diagnosis; χ² = 46.4; p < 0.001). After adjustment for syndromic features, low birth weight, rural residence, and socioeconomic status, delayed diagnosis remained associated with reduced odds of mortality (adjusted odds ratio [aOR] 0.36; 95% CI 0.26 to 0.49; p < 0.001). High-risk score classification (≥ 5 points) was not associated with mortality (aOR 1.01; 95% CI 0.52 to 1.98; p = 0.976). Syndromic features (aOR 2.48; 95% CI 1.42–4.34; p = 0.001) and low socioeconomic status (aOR 1.46; 95% CI 1.08–1.98; p = 0.015) were independently associated with increased mortality (Table 4 ). Table 4 Associations of Delayed Diagnosis, High-Risk Scores, and Covariates with Mortality Predictor Odds ratio Std. Err. z p value 95% CI High risk 1.009 0.358 0.02 0.980 0.504–2.020 Delayed diagnosis 0.348 0.058 -6.33 < 0.001 0.251–0.483 Syndromic features 2.195 0.644 2.68 0.007 1.234–3.901 Prematurity 1.576 0.395 2.06 0.039 0.965–2.572 Low birth weight 1.063 0.239 0.27 0.785 0.685–1.649 Primiparity 0.845 0.135 -1.20 0.289 0.619–1.154 Low maternal education 0.832 0.187 -0.82 0.413 0.536–1.291 Young maternal age 0.932 0.204 -0.32 0.747 0.607–1.430 Rural residence 0.887 0.298 -0.36 0.721 0.459–1.714 Low socioeconomic status 1.602 0.258 2.93 0.003 1.169–2.194 Economic analysis A total of 838 children with critical congenital heart disease (CCHD) were included in the cost analysis, of whom 615 (73.4%) experienced delayed diagnosis and 223 (26.6%) received timely diagnosis. The total diagnostic cost was similar between the groups (IDR5,148,856 for delayed diagnosis vs. IDR5,155,260 for timely diagnosis). Among children with delayed diagnosis, 229 (37.2%) died and 386 (62.8%) survived, incurring intervention costs of IDR12,898,976 and IDR18,032,266; ward costs of IDR6,023,911 and IDR6,138,257; and medication costs of IDR9,158,718 and IDR12,248,620, respectively. In the timely diagnosis group, 139 (62.3%) died and 84 (37.7%) survived, with deaths incurring intervention, ward, and medication costs of IDR16,341,128, IDR9,324,253, and IDR11,412,176, respectively, whereas survivors incurred costs of IDR20,845,275, IDR10,999,340, and IDR13,951,847. The decision-analytic framework for these cost and outcome pathways is shown in Fig. 2 . The probability-adjusted costs (p.costs) and disability-adjusted life years (p.DALYs) showed that, despite a higher proportion of deaths in the timely diagnosis group, the overall expected costs were lower than those in the delayed diagnosis group. Both strategies resulted in the same overall health burden of 72.23 DALYs, reflecting fixed health outcomes in the model. With equal health outcomes and lower overall costs, timely diagnosis was cost-saving, producing a base-case incremental cost-effectiveness ratio (ICER) of -IDR388,897 per DALY averted. Subanalysis focusing on early diagnostic screening alone produced an ICER of -IDR2,912 per DALY averted, whereas treatment-related components (intervention, medication, and ward care) produced an ICER of -IDR1,337,685 per DALY averted. One-way sensitivity analysis revealed that treatment costs, particularly intervention costs, had the greatest effect on ICER values, whereas DALY variations had a smaller impact. In all the tested scenarios, the ICER remained negative, confirming that timely diagnosis consistently reduced overall costs (Fig. 3 ). Discussion This study successfully developed and evaluated a practical point-based risk score to predict delayed diagnosis of critical congenital heart disease (CCHD). Using a multivariable logistic regression model, four independent predictors were identified: syndromic features, low birth weight, rural residence, and low socioeconomic status. Regression coefficients were transformed into integer weights to enhance the score’s clinical applicability. The score demonstrated moderate discriminatory ability, with an area under the receiver operating characteristic curve of 0.66 (95% CI 0.62–0.70), indicating its ability to differentiate between infants likely and unlikely to experience delayed diagnosis. A high-risk classification (≥ 5 points) was not associated with mortality (adjusted odds ratio [aOR] 1.01; 95% CI 0.52–1.98; p = 0.976), suggesting that the score predicts the likelihood of diagnostic delay rather than intrinsic disease severity or survival risk. Unexpectedly, delayed diagnosis was associated with lower mortality (38.0% compared with 63.8% for timely diagnosis), and this association persisted after adjustment for syndromic features, low birth weight, rural residence, and socioeconomic status (aOR 0.36; 95% CI 0.26–0.49; p < 0.001). From an economic perspective, timely diagnosis of CCHD was associated with lower overall expected costs than delayed diagnosis was, despite a greater proportion of deaths in the timely diagnosis group. Both diagnostic strategies resulted in an identical overall health burden of 72.23 disability-adjusted life years (DALYs). With equivalent health outcomes and lower costs, timely diagnosis emerged as a cost-saving strategy, producing a base-case incremental cost-effectiveness ratio (ICER) of -IDR388,897 per DALY averted. Subanalyses confirmed that early diagnostic screening and treatment-related components were also cost-saving. In summary, the risk score effectively stratified infants on the basis of diagnostic delay risk but did not predict mortality. Syndromic features and low socioeconomic status independently predict increased mortality, and the observed lower mortality with delayed diagnosis is likely explained by case severity bias. Economically, timely diagnosis consistently achieves comparable health outcomes at a lower cost. The inverse association between delayed diagnosis and mortality is best explained by case severity bias. Infants with severe or complex cardiac lesions typically present with prominent clinical signs, prompting early detection, yet face a higher risk of early mortality despite prompt treatment. In contrast, infants with milder or less apparent lesions are more likely to experience diagnostic delay but have inherently better short-term survival. The absence of an association between high-risk scores and mortality reinforces that the score predicts diagnostic delay rather than intrinsic disease severity or survival probability. Economically, the finding that timely diagnosis reduces overall costs, even with higher individual treatment costs for survivors, is consistent with international evidence. Early detection of CCHD has been shown to reduce the need for emergency interventions, prevent severe complications, and optimize resource utilization, ultimately resulting in more efficient and less costly care pathways. The risk factors for delayed CCHD diagnosis identified in this study are consistent with barriers reported internationally. Studies from the United States and Pakistan have highlighted nontertiary hospital delivery, isolated CCHD, low socioeconomic status, and rural residence as contributors to delayed diagnosis[ 10 , 29 , 30 ]. Maternal illiteracy and low socioeconomic status have also been implicated in delayed detection in Ethiopia and Pakistan, whereas a study from Massachusetts reported no significant socioeconomic disparities, likely reflecting the strengths of its health system[ 7 , 12 – 14 , 29 ]. Delayed detection also varies by type of CCHD[ 4 , 6 – 8 , 14 , 29 ]. Coarctation of the aorta is frequently diagnosed late[ 4 , 7 , 14 , 29 ], and in our Indonesian cohort, cyanotic CCHD was an independent factor for delayed diagnosis[ 9 ], whereas acyanotic CCHD was associated with longer delays in Pakistan[ 12 , 13 ]. Ethiopian studies have noted maternal illiteracy as a predictor[ 5 ], and US studies have shown that infants born in lower-level nurseries (Levels I and II) are more prone to late CCHD detection than are those born in Level III facilities[ 10 , 14 ]. Several factors unique to Indonesia contribute to diagnostic delays. Many deliveries are performed by midwives, who often lack adequate training in CCHD recognition, and neonatal CCHD screening is not yet a routine practice[ 4 , 9 ]. There is a shortage of pediatric cardiac programs and specialists, and general pediatricians frequently lack training in pediatric cardiology, similar to findings in Pakistan and Ethiopia. The referral system remains suboptimal, delaying access to tertiary care, whereas social and cultural barriers, such as health illiteracy, stigma, cultural beliefs, and reliance on traditional remedies, further postpone medical attention[ 5 , 9 , 12 , 13 ]. The economic analysis revealed that the timely diagnosis of CCHD is a cost-saving strategy that achieves equal health outcomes at a lower cost. This result aligns with findings from other CCHD screening studies. Pulse oximetry screening in the United States reported ICERs of approximately $ 40,385 per life-year gained and $ 24,677 per timely detected case[ 11 ]. Studies in the United Kingdom reported ICERs ranging from £1,489 to £24,000 per infant, all of which are considered cost-effective[ 31 , 32 ]. In China, clinical assessment alone is highly cost-effective (Int $ 7,528 per DALY averted)[ 33 ], whereas a combined approach of pulse oximetry plus clinical evaluation, although initially more expensive, offers better health outcomes and potential dominance with improved access[ 31 , 33 ]. Colombia reported that pulse oximetry was cost-effective at $ 100 per infant but not at a higher survival threshold (US $ 26,292 per DALY)[ 31 ], whereas Canadian studies reported a 92.3% probability of cost-effectiveness at basic thresholds[ 31 ]. Debate exists regarding appropriate cost-effectiveness thresholds in low- and middle-income countries, often cited as one to three times GDP per capita[ 34 ], with some recommending lower thresholds. However, our finding that timely diagnosis is cost-saving bypasses this debate, as it provides clear economic benefits regardless of threshold selection[ 22 , 34 ]. This study is the first to develop and validate a clinical risk score specifically for delayed CCHD diagnosis in Indonesia, addressing a critical gap in regions with unique healthcare challenges. It also provides context-specific cost-effectiveness data showing timely diagnosis as a cost-saving strategy in a resource-limited setting. Although the single-center retrospective design limits direct numerical generalizability[ 7 , 9 , 12 , 35 ], the identified risk factors, including socioeconomic status, rural residence, and low birth weight, are common in many low- and middle-income countries and suggest that this framework has broader relevance[ 5 , 9 , 10 , 12 , 13 , 29 ]. The risk score enables early identification of infants likely to experience diagnostic delays, allowing targeted referral and screening interventions. However, diagnostic delay itself was not linked to increased mortality, likely reflecting case severity bias, where more severe lesions are detected early yet carry higher intrinsic mortality risk. Timely diagnosis has proven cost-saving even with higher treatment costs, supporting investments in early detection strategies, the integration of risk-based and physiological screening, and health system interventions to reduce socioeconomic and geographic disparities. These findings have several clinical implications. The risk score provides a practical tool for identifying infants at risk of delayed CCHD diagnosis during birth hospitalization or early postnatal examinations, supporting timely referral and diagnostic workup[ 1 , 2 , 7 , 10 ]. An emphasis on risk factors such as rural residence and low socioeconomic status can help target interventions to reduce disparities and promote equity in pediatric cardiac care[ 9 , 13 ]. The score can also complement physiological screening methods such as pulse oximetry, improving the detection of asymptomatic newborns and those with CCHDs that may not cause immediate hypoxemia[ 1 , 3 ]. From a policy perspective, timely diagnosis as a cost-saving strategy supports the prioritization of early CCHD detection within Indonesia’s national insurance program (JKN). This would increase equitable access to cardiac care and reduce out-of-pocket costs[ 9 , 12 , 33 , 36 ]. Implementation requires investment in healthcare professional training, especially for midwives and general practitioners; expanded diagnostic capacity, including echocardiography; and enhanced telemedicine support for remote areas[ 1 , 7 , 35 ]. Policies explicitly addressing disparities associated with rural residence and low socioeconomic status are also crucial for reducing inequities in pediatric cardiac care[ 5 , 9 , 13 ]. This study has several limitations. The lower mortality observed among infants with delayed diagnoses likely reflects case severity bias, as more severe cases are detected earlier but experience poorer outcomes despite treatment[ 4 , 7 , 29 , 32 ]. The retrospective design limited variable availability, such as detailed prenatal history, and introduced the risk of misclassification regarding the timing of diagnosis[ 7 , 10 , 14 ]. Furthermore, the analysis focused primarily on early mortality and DALYs, without including long-term morbidity or survival outcomes[ 29 ]. Future research should focus on external validation of the risk score in diverse clinical settings to establish its generalizability and robustness. Studies exploring the integrated use of this risk score with physiological screening methods, including pulse oximetry and prenatal screening, could enhance early detection strategies. Additionally, future economic evaluations should incorporate quality of life measures, such as quality-adjusted life years (QALYs) and lifetime cost data, to provide a more comprehensive assessment of the benefits of early CCHD detection. Conclusion This study developed and validated a practical risk score to identify infants at risk for delayed diagnosis of critical congenital heart disease on the basis of easily obtainable demographic and clinical variables. Although the score effectively stratified diagnostic delay risk, it did not predict mortality, highlighting that delayed diagnosis is influenced by healthcare access factors rather than intrinsic disease severity. Syndromic features and low socioeconomic status were independently associated with increased mortality risk. Economic evaluation demonstrated that timely diagnosis is a cost-saving strategy, even when early treatment incurs higher individual costs. These findings support early detection initiatives, the integration of risk-based screening with physiological methods, and targeted health policies to reduce diagnostic inequities and improve resource allocation for pediatric cardiac care in resource-limited settings. Abbreviations CCHD Critical congenital heart disease CHD Congenital heart disease DALY Disability adjusted life year ICER Incremental cost effectiveness ratio AUC Area under the curve JKN Jaminan Kesehatan Nasional PBI-JKN Penerima Bantuan Iuran – Jaminan Kesehatan Nasional INA-CBG Indonesian Case-Based Groups. Declarations Acknowledgements The authors would like to acknowledge the Islamic Development Bank for its support to Sardjito Hospital through Project IDN 1031. Authors’ contributions IKM conceived and designed the study. MTW, ERS, IRM, and IHZ collected and analyzed the data. IKM, MTW, and IHZ contributed to data interpretation. IKM, NA, SN, and N supervised the study. IHZ drafted the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the Islamic Development Bank (Project IDN 1031). Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Ethical approval for the study was granted by the Medical and Health Research Ethics Committee, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada (No. KE/FK/1783/EC/2024). As the study utilized retrospective anonymized data, individual informed consent was waived. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable References Singh Y. Diagnosis and management of critical congenital heart defects in infants. Paediatr Child Health . 2022;32:332–8. https://doi.org/10.1016/j.paed.2022.07.003 Willim HA, Cristianto, Supit AI. Critical congenital heart disease in newborns: early detection, diagnosis, and management. 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19:32:04","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172285,"visible":true,"origin":"","legend":"","description":"","filename":"bc614653b95b4825b030bebb8bf8e2e11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7463764/v1/df16db18bd389d95c9d396c6.xml"},{"id":94138210,"identity":"1c7b0ec8-a46d-45a6-9aa1-8d9b13ca9c49","added_by":"auto","created_at":"2025-10-22 19:24:04","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181726,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7463764/v1/168253f1bb450dcce9e93eba.html"},{"id":94138187,"identity":"9bd5d830-190a-4686-a29b-574cbb36eb23","added_by":"auto","created_at":"2025-10-22 19:24:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198225,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve for the risk score\u003c/p\u003e","description":"","filename":"Figure1.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7463764/v1/0e6a349995a543d8a13bfe8e.jpg"},{"id":94139247,"identity":"480e16c2-84a2-4066-9cff-74d7e1f8eefa","added_by":"auto","created_at":"2025-10-22 19:32:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":529373,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the Cost and Outcome Pathways\u003c/p\u003e","description":"","filename":"Figure2.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7463764/v1/ae8864d3fdb43cc09bbc77e9.jpg"},{"id":94138192,"identity":"7af5fabd-6d18-4191-b0a6-6baf9ac493d1","added_by":"auto","created_at":"2025-10-22 19:24:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1012101,"visible":true,"origin":"","legend":"\u003cp\u003eTornado diagram of one-way sensitivity analysis\u003c/p\u003e","description":"","filename":"Figure3.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7463764/v1/857acdc1b2d612c850bbf547.jpg"},{"id":94140318,"identity":"783fc49b-7158-4169-867d-d3bb85b49900","added_by":"auto","created_at":"2025-10-22 19:40:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3075780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7463764/v1/f7e0ad4f-e87d-4820-a911-bafe477b3e7b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Delayed Diagnosis in Critical Congenital Heart Disease: Risk Score Development and Economic Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eCongenital heart disease (CHD) is the most common congenital anomaly worldwide, occurring in approximately 6\u0026ndash;10 per 1000 live births[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A significant subset, critical congenital heart defects (CCHDs)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], accounts for 25\u0026ndash;33% of CHDs and requires surgical or catheter-based intervention within the first year of life[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Without timely treatment, up to 30% of newborns with CCHD may die within the first month of life[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA delayed diagnosis of CCHD is consistently associated with increased morbidity, mortality, and economic burden[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Compared with those diagnosed early (16%), infants diagnosed after hospital discharge have a higher first-year mortality rate (27%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Delayed detection also increases hospitalizations by 52%, hospital days by 18%, and infancy hospitalization costs by 35%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Several factors contribute to these delays, including difficulty in detecting certain defects prenatally, nonspecific neonatal signs missed during routine examinations, and limitations of pulse oximetry screening, which may fail to detect lesions such as coarctation of the aorta, tetralogy of Fallot, or total anomalous pulmonary venous return[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Health system delays are also common, involving late consultations (37.2\u0026ndash;40.3%), diagnostic errors or missed diagnoses (22.5\u0026ndash;57.5%)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and referral delays (13.3\u0026ndash;19.9%)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Socioeconomic and demographic barriers, such as financial constraints, maternal illiteracy, and deliveries outside tertiary hospitals, further exacerbate the problem [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn low- and middle-income countries (LMICs), where diagnostic resources and access to specialized care are limited, delayed diagnosis is particularly prevalent[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Indonesia, six out of ten children with CHD are diagnosed late, increasing to 86.2% for those with cyanotic CHDs[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Many infants are only diagnosed after developing severe complications, including heart failure (49.4%) and irreversible pulmonary hypertension (15.8%). The contributing factors included delayed diagnosis by doctors (57.5%), midwifery-related delays (14.4%), financial barriers (9.7%), and delayed referrals or follow-up (9.2%). Independent risk factors include cyanotic CCHD, rural residence, no syndromic conditions, low family income, normal labor, and term gestation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these challenges, no validated clinical risk scoring system exists to predict delayed diagnosis, highlighting the urgent need for tools to support early detection, guide referrals, and improve healthcare resource allocation.\u003c/p\u003e\u003cp\u003eThis study aimed to (1) develop a clinical risk scoring system to predict delayed diagnosis of CCHD, (2) evaluate whether delayed diagnosis or high-risk scores are independently associated with mortality, and (3) examine the cost-effectiveness of timely versus delayed diagnosis in terms of direct medical costs and disability-adjusted life years (DALYs).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was conducted at Dr. Sardjito Hospital, Yogyakarta, Indonesia, including all children younger than 18 years with echocardiography-confirmed critical congenital heart disease (CCHD) diagnosed between January 2019 and December 2024. Delayed diagnosis was defined as failure to diagnose CCHD during birth hospitalization, whereas infants discharged home without a diagnosis were classified as delayed[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Patients who underwent corrective procedures prior to diagnostic confirmation were excluded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePredictor Variables\u003c/h3\u003e\n\u003cp\u003eThe variables were categorized as binary (yes/no) to facilitate statistical analysis and were selected on the basis of their clinical relevance and evidence from previous literature. Syndromic features refer to the presence of a genetic syndrome, such as Down syndrome. Prematurity was defined as a gestational age of less than 37 weeks, whereas low birth weight corresponded to a birth weight less than 2,500 grams. Primiparity indicated the mother\u0026rsquo;s first live birth. Low maternal education applied to mothers who had not completed at least nine years of formal education, and young maternal age was defined as less than 25 years at the time of diagnosis. Rural residence refers to individuals with a registered home address in a rural or remote administrative area. Low socioeconomic status was determined by meeting at least one of the following criteria: enrollment in PBI-JKN (government-subsidized insurance), a monthly household income below IDR2,000,000, or documentation in hospital records as having \u0026ldquo;low\u0026rdquo; socioeconomic status.\u003c/p\u003e\n\u003ch3\u003eModel development and score derivation\u003c/h3\u003e\n\u003cp\u003eA multivariable logistic regression model was constructed with delayed diagnosis as the dependent variable. Variables demonstrating statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) or clinical importance were retained in the final model. Regression coefficients were transformed into an additive risk score by scaling relative to the smallest nonzero coefficient, multiplying by a constant for interpretability, and rounding to the nearest integer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The resulting score was calculated for each individual, with higher values reflecting an increased risk of delayed diagnosis.\u003c/p\u003e\n\u003ch3\u003eScore Performance and Cutoff Selection\u003c/h3\u003e\n\u003cp\u003eThe discriminatory ability of the developed score was evaluated via a receiver operating characteristic (ROC) curve, expressed as the area under the curve (AUC) with 95% confidence intervals. The AUC value is a summary metric of the ROC curve that reflects a test's ability to distinguish between diseased and nondiseased individuals, with values ranging from 0.5 (chance) to 1.0 (perfect discrimination)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Values above 0.80 are generally considered clinically useful. The optimal cutoff for classifying high-risk individuals was determined via Youden's index [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAssociation with Mortality\u003c/h3\u003e\n\u003cp\u003eThe prognostic relevance of delayed diagnosis and high-risk scores was assessed in separate multivariable logistic regression models using mortality as the outcome. Confounding variables were adjusted for on the basis of clinical judgment and prior evidence. Adjusted odds ratios (aORs) with 95% confidence intervals are reported. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. All analyses were performed via Stata 17 (StataCorp, College Station, TX, USA).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEconomic analysis\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eAnalytical Framework\u003c/h2\u003e\u003cp\u003eA simplified decision-analytic approach was employed to compare the economic consequences of timely versus delayed diagnosis of critical congenital heart disease (CCHD). Patients were first categorized by diagnostic timing (timely or delayed). For each diagnostic group, the mean diagnostic cost was calculated. Each group was then divided into two clinical outcomes (survival and death). Within each outcome branch, three cost components were included: intervention (surgical or catheter-based), ward care, and medication use. Each outcome branch had an associated probability, enabling calculation of both probability-weighted costs (p.cost) and probability-weighted disability-adjusted life years (p.DALYs).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eData Sources and Perspective\u003c/h3\u003e\n\u003cp\u003eThe analysis used patient-level data from 838 children diagnosed with CCHD, of whom 615 (73.39%) experienced delayed diagnosis and 223 (26.61%) received timely diagnosis. These cases were selected from an initial cohort of 871 patients; 33 patients were excluded because their economic data were incomplete. The analysis was conducted from the healthcare provider's perspective and focused on short-term diagnostic and immediate treatment episodes without projecting lifetime costs or long-term outcomes. Given this short-term horizon, no discounting was applied to costs or health outcomes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCost Estimation\u003c/h2\u003e\u003cp\u003eThe costs were derived from hospital billing systems and the Indonesian case-based groups (INA-CBG) tariff schedule. The included cost categories were diagnostic investigations, interventional procedures, ward care, and medications. All costs were expressed in Indonesian rupiah (IDR) and adjusted to the base year of analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEffectiveness Measurement\u003c/h2\u003e\u003cp\u003eEffectiveness was measured in disability-adjusted life years (DALYs), combining years of life lost (YLL) due to premature mortality and years lived with disability (YLD). Disability weights were sourced from the Global Burden of Disease (GBD) study[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The total burden of CCHD in Indonesia was estimated via a DALY calculator without discounting. Both timely and delayed diagnostic strategies were modeled to yield the same fixed overall health burden (72.23 DALYs), allowing for an analysis focused on differences in costs for identical health outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCost-Effectiveness Analysis\u003c/h2\u003e\u003cp\u003eThe expected costs and DALYs for each diagnostic pathway were calculated by multiplying each branch\u0026rsquo;s probability by its associated cost and DALY value and then summing across branches. The incremental cost-effectiveness ratio (ICER) was calculated via the following standard formula[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"481\" height=\"73\"\u003e\u003c/p\u003e\u003cp\u003eA negative ICER indicates that timely diagnosis is cost-saving[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The willingness-to-pay (WTP) threshold was set at an IDRof 74.3\u0026nbsp;million per DALY averted, equivalent to one times Indonesia\u0026rsquo;s gross domestic product (GDP) per capita[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\u003cp\u003eA one-way sensitivity analysis was performed to assess parameter uncertainty. The key cost inputs and DALY estimates were varied by \u0026plusmn;\u0026thinsp;20%, and the resulting ICER values were presented in a tornado diagram to identify the parameters with the greatest influence on the cost-effectiveness results[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEthical considerations\u003c/h2\u003e\u003cp\u003eEthical approval for the study was granted by the Medical and Health Research Ethics Committee, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada (No. KE/FK/1783/EC/2024). Given that the study utilized retrospective anonymized data, the need to obtain individual informed consent was waived. All procedures adhered to the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 871 patients with critical congenital heart disease (CCHD) were included in the primary analysis. The mean age at diagnosis was 2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04 months. Slightly more than half of the patients were male (55.91%), and the mean birth weight was 2,871\u0026thinsp;\u0026plusmn;\u0026thinsp;555 g. Most infants (72.44%) experienced delayed diagnosis. Rural residents accounted for 59.7% of the patients, and 45.92% were from low socioeconomic households. Cyanotic CCHDs, including Tetralogy of Fallot (24.34%) and pulmonary atresia (17.11%), were common. More than half of the patients (55.22%) survived during the study period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient Characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;871\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNondelayed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDelayed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at diagnosis (months), mean (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.20 (4.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19 (1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.96 (4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e487 (55.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134 (27.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e353 (72.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e384 (44.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (27.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278 (72.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight at diagnosis (kg), mean (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.37 (9.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.44 (4.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.22 (10.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNutritional status, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood nutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e303 (34.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (27.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221 (72.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUndernutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e313 (35.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (32.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e210 (67.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere malnutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (29.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (21.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200 (78.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSyndromic features, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDown syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (4.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (56.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (43.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (3.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (31.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (68.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory of gestational age, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 37 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (35.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (64.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e37\u0026ndash;40 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e662 (76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174 (26.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e488 (73.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;40 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (72.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (27.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth weight (gr), mean (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2871 (555)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2752(653)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2916(505)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBirth weight history, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 2500 grams\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (20.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (41.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (58.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2500\u0026ndash;4000 grams\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e685 (78.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164 (23.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e521 (76.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; 4000 grams\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (36.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (63.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParity, n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimipara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e348 (39.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (31.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240 (68.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultipara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e523 (60.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132 (25.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e391 (74.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDelayed diagnosis, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e631 (72.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient residence, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e520 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (20.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e411 (79.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e351 (40.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (37.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220 (62.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily socioeconomic status, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400 (45.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (23.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e308 (77.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e464 (53.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145 (31.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319 (68.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (42.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (57.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal age at diagnosis (years), mean (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.85 (6.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.78 (6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.27 (7.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal age at diagnosis, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (14.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (33.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (66.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; 25 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e741 (85.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e197 (26.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e544 (73.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal education, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (13.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (20.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (79.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge; 9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e757 (86.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217 (28.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e540 (71.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eType of critical CHD, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTetralogy of Fallot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212 (24.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (12.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186 (87.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransposition of the great arteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 (12.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (40.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (59.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersistent truncus arteriosus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (5.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (27.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (72.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoplastic left ventricle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (30.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (70.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary atresia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149 (17.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (43.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84 (56.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTricuspid atresia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (5.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (32.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (67.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal anomalous pulmonary venous drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (28.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (72.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e278 (31.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (24.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e209 (75.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntervention, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheterization intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e528 (60.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (22.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e410 (77.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (17.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133 (82.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvival\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e481 (55.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (25.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e359 (74.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e390 (44.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (30.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e272 (69.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at death (months), mean (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.31 (41.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.08 (9.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.61 (50.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSD\u0026thinsp;=\u0026thinsp;standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eRisk Score Development and Performance\u003c/h2\u003e\u003cp\u003eFour independent predictors of delayed diagnosis were identified: syndromic features, low birth weight, rural residence, and low socioeconomic status. The regression coefficients were converted to integer weights to produce a practical risk score (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Value of Predictor Variables for Delayed Diagnosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScore (Rounded)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyndromic features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.226 \u0026ndash; -0.190\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birth weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.959 \u0026ndash; -0.227\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.439\u0026ndash;1.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow socioeconomic status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.344\u0026ndash;0.883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor clinical use, each variable was assigned points on the basis of whether the feature was present or absent, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eScoring Model for Delayed Diagnosis Risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyndromic features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birth weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow socioeconomic status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe score demonstrated moderate discrimination, with an AUC of 0.66 (95% CI 0.62\u0026ndash;0.70). The optimal cutoff, determined by Youden\u0026rsquo;s index, was 4.5 (rounded to \u0026ge;\u0026thinsp;5), yielding 59% (95% CI 0.55\u0026ndash;0.63) sensitivity and 64% (95% CI 0.58\u0026ndash;0.70) specificity, classifying 42% of patients as high risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eAssociation with Mortality\u003c/h2\u003e\u003cp\u003eUnivariable analysis revealed that delayed diagnosis was associated with lower mortality (38.0 percent compared with 63.8 percent for timely diagnosis; χ\u0026sup2; = 46.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjustment for syndromic features, low birth weight, rural residence, and socioeconomic status, delayed diagnosis remained associated with reduced odds of mortality (adjusted odds ratio [aOR] 0.36; 95% CI 0.26 to 0.49; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High-risk score classification (\u0026ge;\u0026thinsp;5 points) was not associated with mortality (aOR 1.01; 95% CI 0.52 to 1.98; p\u0026thinsp;=\u0026thinsp;0.976). Syndromic features (aOR 2.48; 95% CI 1.42\u0026ndash;4.34; p\u0026thinsp;=\u0026thinsp;0.001) and low socioeconomic status (aOR 1.46; 95% CI 1.08\u0026ndash;1.98; p\u0026thinsp;=\u0026thinsp;0.015) were independently associated with increased mortality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of Delayed Diagnosis, High-Risk Scores, and Covariates with Mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.504\u0026ndash;2.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelayed diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.251\u0026ndash;0.483\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyndromic features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.234\u0026ndash;3.901\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrematurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.965\u0026ndash;2.572\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birth weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.685\u0026ndash;1.649\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimiparity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.619\u0026ndash;1.154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow maternal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.536\u0026ndash;1.291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYoung maternal age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.607\u0026ndash;1.430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.459\u0026ndash;1.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow socioeconomic status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.169\u0026ndash;2.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEconomic analysis\u003c/h2\u003e\u003cp\u003eA total of 838 children with critical congenital heart disease (CCHD) were included in the cost analysis, of whom 615 (73.4%) experienced delayed diagnosis and 223 (26.6%) received timely diagnosis. The total diagnostic cost was similar between the groups (IDR5,148,856 for delayed diagnosis vs. IDR5,155,260 for timely diagnosis).\u003c/p\u003e\u003cp\u003eAmong children with delayed diagnosis, 229 (37.2%) died and 386 (62.8%) survived, incurring intervention costs of IDR12,898,976 and IDR18,032,266; ward costs of IDR6,023,911 and IDR6,138,257; and medication costs of IDR9,158,718 and IDR12,248,620, respectively. In the timely diagnosis group, 139 (62.3%) died and 84 (37.7%) survived, with deaths incurring intervention, ward, and medication costs of IDR16,341,128, IDR9,324,253, and IDR11,412,176, respectively, whereas survivors incurred costs of IDR20,845,275, IDR10,999,340, and IDR13,951,847. The decision-analytic framework for these cost and outcome pathways is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe probability-adjusted costs (p.costs) and disability-adjusted life years (p.DALYs) showed that, despite a higher proportion of deaths in the timely diagnosis group, the overall expected costs were lower than those in the delayed diagnosis group. Both strategies resulted in the same overall health burden of 72.23 DALYs, reflecting fixed health outcomes in the model. With equal health outcomes and lower overall costs, timely diagnosis was cost-saving, producing a base-case incremental cost-effectiveness ratio (ICER) of -IDR388,897 per DALY averted. Subanalysis focusing on early diagnostic screening alone produced an ICER of -IDR2,912 per DALY averted, whereas treatment-related components (intervention, medication, and ward care) produced an ICER of -IDR1,337,685 per DALY averted. One-way sensitivity analysis revealed that treatment costs, particularly intervention costs, had the greatest effect on ICER values, whereas DALY variations had a smaller impact. In all the tested scenarios, the ICER remained negative, confirming that timely diagnosis consistently reduced overall costs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study successfully developed and evaluated a practical point-based risk score to predict delayed diagnosis of critical congenital heart disease (CCHD). Using a multivariable logistic regression model, four independent predictors were identified: syndromic features, low birth weight, rural residence, and low socioeconomic status. Regression coefficients were transformed into integer weights to enhance the score\u0026rsquo;s clinical applicability. The score demonstrated moderate discriminatory ability, with an area under the receiver operating characteristic curve of 0.66 (95% CI 0.62\u0026ndash;0.70), indicating its ability to differentiate between infants likely and unlikely to experience delayed diagnosis.\u003c/p\u003e\u003cp\u003eA high-risk classification (\u0026ge;\u0026thinsp;5 points) was not associated with mortality (adjusted odds ratio [aOR] 1.01; 95% CI 0.52\u0026ndash;1.98; p\u0026thinsp;=\u0026thinsp;0.976), suggesting that the score predicts the likelihood of diagnostic delay rather than intrinsic disease severity or survival risk. Unexpectedly, delayed diagnosis was associated with lower mortality (38.0% compared with 63.8% for timely diagnosis), and this association persisted after adjustment for syndromic features, low birth weight, rural residence, and socioeconomic status (aOR 0.36; 95% CI 0.26\u0026ndash;0.49; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFrom an economic perspective, timely diagnosis of CCHD was associated with lower overall expected costs than delayed diagnosis was, despite a greater proportion of deaths in the timely diagnosis group. Both diagnostic strategies resulted in an identical overall health burden of 72.23 disability-adjusted life years (DALYs). With equivalent health outcomes and lower costs, timely diagnosis emerged as a cost-saving strategy, producing a base-case incremental cost-effectiveness ratio (ICER) of -IDR388,897 per DALY averted. Subanalyses confirmed that early diagnostic screening and treatment-related components were also cost-saving. In summary, the risk score effectively stratified infants on the basis of diagnostic delay risk but did not predict mortality. Syndromic features and low socioeconomic status independently predict increased mortality, and the observed lower mortality with delayed diagnosis is likely explained by case severity bias. Economically, timely diagnosis consistently achieves comparable health outcomes at a lower cost.\u003c/p\u003e\u003cp\u003eThe inverse association between delayed diagnosis and mortality is best explained by case severity bias. Infants with severe or complex cardiac lesions typically present with prominent clinical signs, prompting early detection, yet face a higher risk of early mortality despite prompt treatment. In contrast, infants with milder or less apparent lesions are more likely to experience diagnostic delay but have inherently better short-term survival. The absence of an association between high-risk scores and mortality reinforces that the score predicts diagnostic delay rather than intrinsic disease severity or survival probability.\u003c/p\u003e\u003cp\u003eEconomically, the finding that timely diagnosis reduces overall costs, even with higher individual treatment costs for survivors, is consistent with international evidence. Early detection of CCHD has been shown to reduce the need for emergency interventions, prevent severe complications, and optimize resource utilization, ultimately resulting in more efficient and less costly care pathways.\u003c/p\u003e\u003cp\u003eThe risk factors for delayed CCHD diagnosis identified in this study are consistent with barriers reported internationally. Studies from the United States and Pakistan have highlighted nontertiary hospital delivery, isolated CCHD, low socioeconomic status, and rural residence as contributors to delayed diagnosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Maternal illiteracy and low socioeconomic status have also been implicated in delayed detection in Ethiopia and Pakistan, whereas a study from Massachusetts reported no significant socioeconomic disparities, likely reflecting the strengths of its health system[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDelayed detection also varies by type of CCHD[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Coarctation of the aorta is frequently diagnosed late[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and in our Indonesian cohort, cyanotic CCHD was an independent factor for delayed diagnosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], whereas acyanotic CCHD was associated with longer delays in Pakistan[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Ethiopian studies have noted maternal illiteracy as a predictor[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and US studies have shown that infants born in lower-level nurseries (Levels I and II) are more prone to late CCHD detection than are those born in Level III facilities[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral factors unique to Indonesia contribute to diagnostic delays. Many deliveries are performed by midwives, who often lack adequate training in CCHD recognition, and neonatal CCHD screening is not yet a routine practice[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There is a shortage of pediatric cardiac programs and specialists, and general pediatricians frequently lack training in pediatric cardiology, similar to findings in Pakistan and Ethiopia. The referral system remains suboptimal, delaying access to tertiary care, whereas social and cultural barriers, such as health illiteracy, stigma, cultural beliefs, and reliance on traditional remedies, further postpone medical attention[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe economic analysis revealed that the timely diagnosis of CCHD is a cost-saving strategy that achieves equal health outcomes at a lower cost. This result aligns with findings from other CCHD screening studies. Pulse oximetry screening in the United States reported ICERs of approximately \u003cspan\u003e$\u003c/span\u003e40,385 per life-year gained and \u003cspan\u003e$\u003c/span\u003e24,677 per timely detected case[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies in the United Kingdom reported ICERs ranging from \u0026pound;1,489 to \u0026pound;24,000 per infant, all of which are considered cost-effective[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In China, clinical assessment alone is highly cost-effective (Int\u003cspan\u003e$\u003c/span\u003e7,528 per DALY averted)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], whereas a combined approach of pulse oximetry plus clinical evaluation, although initially more expensive, offers better health outcomes and potential dominance with improved access[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Colombia reported that pulse oximetry was cost-effective at \u003cspan\u003e$\u003c/span\u003e100 per infant but not at a higher survival threshold (US\u003cspan\u003e$\u003c/span\u003e26,292 per DALY)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], whereas Canadian studies reported a 92.3% probability of cost-effectiveness at basic thresholds[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDebate exists regarding appropriate cost-effectiveness thresholds in low- and middle-income countries, often cited as one to three times GDP per capita[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with some recommending lower thresholds. However, our finding that timely diagnosis is cost-saving bypasses this debate, as it provides clear economic benefits regardless of threshold selection[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study is the first to develop and validate a clinical risk score specifically for delayed CCHD diagnosis in Indonesia, addressing a critical gap in regions with unique healthcare challenges. It also provides context-specific cost-effectiveness data showing timely diagnosis as a cost-saving strategy in a resource-limited setting. Although the single-center retrospective design limits direct numerical generalizability[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the identified risk factors, including socioeconomic status, rural residence, and low birth weight, are common in many low- and middle-income countries and suggest that this framework has broader relevance[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe risk score enables early identification of infants likely to experience diagnostic delays, allowing targeted referral and screening interventions. However, diagnostic delay itself was not linked to increased mortality, likely reflecting case severity bias, where more severe lesions are detected early yet carry higher intrinsic mortality risk. Timely diagnosis has proven cost-saving even with higher treatment costs, supporting investments in early detection strategies, the integration of risk-based and physiological screening, and health system interventions to reduce socioeconomic and geographic disparities.\u003c/p\u003e\u003cp\u003eThese findings have several clinical implications. The risk score provides a practical tool for identifying infants at risk of delayed CCHD diagnosis during birth hospitalization or early postnatal examinations, supporting timely referral and diagnostic workup[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. An emphasis on risk factors such as rural residence and low socioeconomic status can help target interventions to reduce disparities and promote equity in pediatric cardiac care[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The score can also complement physiological screening methods such as pulse oximetry, improving the detection of asymptomatic newborns and those with CCHDs that may not cause immediate hypoxemia[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a policy perspective, timely diagnosis as a cost-saving strategy supports the prioritization of early CCHD detection within Indonesia\u0026rsquo;s national insurance program (JKN). This would increase equitable access to cardiac care and reduce out-of-pocket costs[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Implementation requires investment in healthcare professional training, especially for midwives and general practitioners; expanded diagnostic capacity, including echocardiography; and enhanced telemedicine support for remote areas[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Policies explicitly addressing disparities associated with rural residence and low socioeconomic status are also crucial for reducing inequities in pediatric cardiac care[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has several limitations. The lower mortality observed among infants with delayed diagnoses likely reflects case severity bias, as more severe cases are detected earlier but experience poorer outcomes despite treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The retrospective design limited variable availability, such as detailed prenatal history, and introduced the risk of misclassification regarding the timing of diagnosis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, the analysis focused primarily on early mortality and DALYs, without including long-term morbidity or survival outcomes[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFuture research should focus on external validation of the risk score in diverse clinical settings to establish its generalizability and robustness. Studies exploring the integrated use of this risk score with physiological screening methods, including pulse oximetry and prenatal screening, could enhance early detection strategies. Additionally, future economic evaluations should incorporate quality of life measures, such as quality-adjusted life years (QALYs) and lifetime cost data, to provide a more comprehensive assessment of the benefits of early CCHD detection.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed and validated a practical risk score to identify infants at risk for delayed diagnosis of critical congenital heart disease on the basis of easily obtainable demographic and clinical variables. Although the score effectively stratified diagnostic delay risk, it did not predict mortality, highlighting that delayed diagnosis is influenced by healthcare access factors rather than intrinsic disease severity. Syndromic features and low socioeconomic status were independently associated with increased mortality risk. Economic evaluation demonstrated that timely diagnosis is a cost-saving strategy, even when early treatment incurs higher individual costs. These findings support early detection initiatives, the integration of risk-based screening with physiological methods, and targeted health policies to reduce diagnostic inequities and improve resource allocation for pediatric cardiac care in resource-limited settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCHD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCritical congenital heart disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCongenital heart disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDALY\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisability adjusted life year\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIncremental cost effectiveness ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eJKN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eJaminan Kesehatan Nasional\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBI-JKN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePenerima Bantuan Iuran \u0026ndash; Jaminan Kesehatan Nasional\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eINA-CBG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIndonesian Case-Based Groups.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Islamic Development Bank for its support to Sardjito Hospital through Project IDN 1031.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIKM conceived and designed the study. MTW, ERS, IRM, and IHZ collected and analyzed the data. IKM, MTW, and IHZ contributed to data interpretation. IKM, NA, SN, and N supervised the study. IHZ drafted the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Islamic Development Bank (Project IDN 1031).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was granted by the Medical and Health Research Ethics Committee, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada (No. KE/FK/1783/EC/2024). As the study utilized retrospective anonymized data, individual informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSingh Y. Diagnosis and management of critical congenital heart defects in infants. \u003cem\u003ePaediatr Child Health\u003c/em\u003e. 2022;32:332\u0026ndash;8. https://doi.org/10.1016/j.paed.2022.07.003\u003c/li\u003e\n\u003cli\u003eWillim HA, Cristianto, Supit AI. 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Characteristic and survival analysis of infants with critical congenital heart disease. \u003cem\u003eKesmas\u003c/em\u003e. 2025;20:8\u0026ndash;14. https://doi.org/10.7454/kesmas.v20i1.2041\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Critical congenital heart disease, delayed diagnosis, risk score, mortality, cost-effectiveness","lastPublishedDoi":"10.21203/rs.3.rs-7463764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7463764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDelayed diagnosis of critical congenital heart disease (CCHD) is associated with increased morbidity, mortality, and healthcare costs, particularly in low- and middle-income countries. Reliable predictive tools for the early identification of infants at risk for delayed diagnosis are unavailable. This study aimed to develop and validate a clinical risk score for predicting delayed CCHD diagnosis, assess its relationship with mortality, and evaluate the cost effectiveness of timely compared with delayed diagnosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort of 871 children with echocardiography-confirmed CCHD (2019\u0026ndash;2024) was analyzed. Predictors of delayed diagnosis were identified through multivariable logistic regression and converted into an additive point-based risk score. Model discrimination was evaluated via the area under the receiver operating characteristic curve (AUC), with Youden\u0026rsquo;s index used to identify the optimal threshold. Mortality associations were analyzed via adjusted logistic regression. Economic evaluation compared timely and delayed diagnoses on the basis of direct medical costs and disability-adjusted life years (DALYs) from the healthcare provider perspective, with one-way sensitivity analysis performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDelayed diagnosis occurred in 72.44% of the patients. Syndromic features, low birth weight, rural residence, and low socioeconomic status independently predict delay. The risk score achieved moderate discriminatory performance (AUC 0.66; 95% CI 0.62\u0026ndash;0.70), and a cutoff of \u0026ge;\u0026thinsp;5 points identified 42% of infants as high risk (sensitivity 59% (95% CI 0.55\u0026ndash;0.63), specificity 64% (95% CI 0.58\u0026ndash;0.70)). High-risk classification was not associated with mortality (aOR 1.01; 95% CI 0.52\u0026ndash;1.98), whereas delayed diagnosis was associated with lower mortality (aOR 0.36; 95% CI 0.26\u0026ndash;0.49). Timely diagnosis resulted in lower overall expected costs despite similar DALYs, producing an incremental cost-effectiveness ratio of -IDR388,897 per DALY averted.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eA simple, clinically applicable risk score can identify infants at risk of delayed CCHD diagnosis. Although delayed diagnosis does not predict mortality, timely diagnosis reduces overall healthcare costs, reinforcing the value of early detection and equitable access to pediatric cardiac care in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Predicting Delayed Diagnosis in Critical Congenital Heart Disease: Risk Score Development and Economic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:23:59","doi":"10.21203/rs.3.rs-7463764/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-05T13:49:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T02:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176335813414896863386100886437223849930","date":"2025-10-15T15:56:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71792184137595043410547230555734816561","date":"2025-10-14T14:23:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T01:26:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95114120308424178996998557812701284034","date":"2025-10-12T19:22:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143734675258908042904945981129235169532","date":"2025-10-09T23:34:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T07:40:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-15T09:01:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T07:08:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-13T07:08:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-08-26T13:59:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc15cfcd-535c-4429-84db-50d3bdd86fcf","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T19:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:23:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7463764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7463764","identity":"rs-7463764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}