A Novel Risk Index to Predict Waiting Times for Pediatric Heart Transplant Candidates

A Novel Risk Index to Predict Waiting Times for Pediatric Heart Transplant Candidates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Risk Index to Predict Waiting Times for Pediatric Heart Transplant Candidates Bhavana Kunisetty, Ashley Montgomery, Chase Robinson, Abbas Rana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7152075/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Heart transplantation is the definitive treatment for many pediatric patients with cardiac conditions. However, significant variability exists in waitlist durations, influenced by multiple factors that remain poorly understood. This lack of research hampers the development of standardized methods to predict and transparently communicate wait times to patients and families. Methods: This study aimed to create an index to predict pediatric heart transplant waitlist durations using data from 7,856 patients in the Organ Procurement and Transplantation Network (OPTN) database. Significant variables associated with waitlist times were identified through univariate and multivariable Cox regression analyses (p < 0.05), incorporated into a risk index with points assigned according to multivariable odds ratios, and evaluated for predictive accuracy using receiver operating characteristic (ROC) curve analysis. Results: Our study identified 8 factors from multivariable analysis to significantly impact pediatric heart transplant waitlist durations. After utilizing these factors to create the risk index, ROC analysis to evaluate the predictive capabilities of the index resulted in a c-statistic of 0.81 (95% Confidence Interval: 0.801-0.835). Conclusion: Our findings highlight key determinants of waitlist times and demonstrate the potential of a predictive index to improve transparency, guide clinical decision-making, and manage patient and family expectations. Pediatric Heart Transplant Pediatric Heart Failure Waitlist Stratification Organ Allocation Figures Figure 1 Figure 2 Figure 3 Introduction The pediatric population faces a range of critical cardiac conditions, including end-stage cardiomyopathy, severe congenital heart defects, valvular abnormalities, and persistent, life-threatening arrhythmias [ 1 ]. Despite significant advancements in surgical techniques, perioperative care, and medical therapies, these treatments often serve as temporary management for critically failing hearts [ 2 , 3 ]. Thus, for many, heart transplantation remains the definitive, gold-standard treatment for survival [ 4 ]. In infants, congenital heart defects such as hypoplastic left heart syndrome and Fontan circulation failure are the leading indications, while dilated cardiomyopathy predominates in older children [ 5 – 7 ]. For these patients, heart transplantation offers a transformative opportunity for long-term survival and improved quality of life. Once a physician determines that a young patient is a suitable candidate, the patient is registered for the heart transplant waitlist. Then, a suitable donor is matched, after which an appropriate transplant procedure can be performed [ 8 ]. Several factors influence the waitlist duration and the probability of finding an appropriate cardiac donor [ 9 ]. However, there remains a notable lack of research exploring these influencing factors, and thus no standardized approach exists to predict wait times. This lack of predictability is especially concerning given that many patients on the waitlist are in advanced stages of cardiac disease, where the need for a transplant is both urgent and critical. Furthermore, the absence of clear waitlist timelines significantly exacerbates challenges faced by patients, their families, and healthcare providers [ 10 , 11 ]. Our study aims to develop a predictive index for waitlist times in pediatric heart transplant candidates. This index is based on a thorough analysis of the OPTN database, identifying key variables that are strongly associated with variations in waitlist times. By assigning a predictive score to each transplant candidate, this tool has the potential to offer a clear and transparent path through the waitlist process. Methods Study Population This study performed a retrospective analysis of pediatric heart transplant waitlist data utilizing de-identified data provided by the United Network for Organ Sharing (UNOS) database. Inclusion criteria were patients under the age of 18 listed for a heart transplant between January 1, 2000 and December 31, 2020. Exclusion criteria included patients aged 18 or older at listing, individuals outside of the time range (January 1, 2000 to December 31, 2020), and patients with multiple organ listings. After applying these criteria, the final cohort included 7,856 pediatric patients. Statistical Analysis We conducted the statistical analysis for the study utilizing STATA BE 18.5 (Stata Corp, College Station, TX). Univariate and multivariable Cox regression analyses were performed to identify factors that significantly impact waitlist time. Only significant factors (p-value < 0.05) found in univariate analysis were then included in the multivariable analysis. The outcome of interest for the study was transplantation at 1 year. Therefore, for odds ratios greater than 1.0, this signifies an increase in the “risk” of receiving a transplantation at 1 year. Consequently, variables with an odds ratio less than 1.0 signify a decreased “risk” of receiving a transplantation at 1 year. Data Entry and Missing Data The entry rate for each variable is included in Table 1 . For continuous variables with missing entries, we performed the predictive mean matching imputation method. The following variables were imputed: initial weight (0.34% missing entry completion), initial serum albumin (9.65% missing entry completion), and initial height (1.13% missing entry completion). Table 1 Demographics Factors Entry Completion (%) Prevalence (%) Number of Patients Median Days on Waitlist Standard Deviation Diagnosis: Congenital Heart Defect with Surgery 100 29.81 2,342 66.5 240 Diagnosis: Dilated myopathy (idiopathic) 25.36 1,992 43 258 Diagnosis: Congenital Heart Defect: prior surgery unknown 7.52 591 41 268 Diagnosis: Restrictive myopathy idiopathic 4.89 384 75.5 378 Diagnosis: CHD 43.16 3,391 60 234 Previous transplant: 1 100 5.59 439 67 272 Previous transplant: 2 0.29 23 41 115 Recipient age: <2 100 37.30 2,930 50 207 Recipient Age: 2–10 24.61 1,933 79 321 Recipient Age: 15–18 16.01 1,258 47 257 Recipient Weight: ≤ 3 kg 99.66 3.70 290 51 56 Recipient Weight: 3–4 kg 8.25 648 42 260 Recipient Weight: 4–5 kg 5.44 427 50 173 Recipient Weight: 5–6 kg 4.96 389 49 225 Recipient Weight: 6–7 kg 4.15 326 54 92 Recipient Weight: 7–8 kg 3.37 264 51 331 Recipient Weight: 8–9 kg 3.17 249 56.5 188 Recipient Weight: 9–10 kg 2.93 230 59 186 Blood Type: A 100 37.12 2,916 43 215 Blood Type: B 13.30 1,045 49 263 Blood Type: O 45.52 3,576 66 280 Blood Type: AB 4.06 319 32 111 Serum albumin: ≤ 2.0 90.35 2.47 194 47 318 Serum albumin: 2.0-2.5 5.60 440 42 136 Serum albumin: 2.5-3.0 14.12 1,109 40 119 Creatinine: 1.5-2.0 95.96 1.16 91 25 144 Creatinine: ≥2.0 1.07 84 34.5 249 Life support: yes 99.98 59.66 4,687 39 170 Diabetes 99.97 1.69 133 38 492 Recipient Height: ≥ 172 98.87 8.20 644 44 272 Recipient Height: ≤ 88cm 40.03 3,145 51 210 African American Race 100% 20.05 1,575 49 212 Payment Method: private insurance 99.97 47.06 3,696 52 263 Payment method: Medicaid 43.67 3,430 53.5 239 UNOS Region: 1 100% 3.34 262 69.5 216 UNOS Region: 2 8.12 638 61 310 UNOS Region: 3 15.73 1,236 48 238 UNOS Region: 4 8.45 664 67.5 357 UNOS Region: 5 16.09 1,264 60 226 UNOS Region: 6 3.11 244 54 140 UNOS Region: 7 8.81 692 49 365 UNOS Region: 8 10.51 826 50 161 UNOS Region: 9 7.10 558 33 125 UNOS Region: 10 7.10 558 55 242 UNOS Region: 11 11.63 914 52 207 Gender: Male 100% 55.04 4,324 52 259 Identifying Risk Factors Patient-related variables were included in the analysis if they were recorded at the time of listing (e.g., diagnosis, blood type, and demographic data) or prior to listing (e.g., ethnicity, insurance type). Variables reflecting donor characteristics or post-transplant factors were excluded. The final dataset contained 14 variables, such as clinical and demographic factors, diagnoses, and laboratory values recorded at listing. Univariate and Multivariable Analysis The variables included in the univariate analysis are shown in Table 2 . Groupings for continuous variables were constructed based on clinical judgment. The following variables were included in the univariate analysis: diagnosis, previous transplant, age, weight, blood type, total serum albumin, creatinine, life support, diabetes, height, African American race, payment method, UNOS region, and male sex. Significant variables from the univariate analysis (p-value < 0.05) were then included in the multivariable analysis. Table 2 Univariate Logistic Regression Results Factors Odds Ratio Confidence Interval p-value Reference Diagnosis: Congenital Heart Defect with Surgery 0.66 (0.552-0.800) < 0.001 Diagnosis: other Diagnosis: Dilated myopathy (idiopathic) 2.47 (1.893–3.231) <0.001 Diagnosis: Congenital Heart Defect: prior surgery unknown 0.74 (0.549–1.016) 0.064 Diagnosis: Restrictive myopathy idiopathic 0.44 (0.327–0.614) < 0.001 Diagnosis: CHD 0.77 (0.646–0.926) 0.005 Previous transplant: 1 0.50 (0.368–0.680) 2 Previous transplant: 2 1.52 (0.205–11.341) 0.680 Recipient age: <2 4.53 (3.465–5.922) < 0.001 Recipient Age: 10–15 Recipient Age: 2–10 0.47 (0.396–0.574) < 0.001 Recipient Age: 15–18 0.63 (0.508–0.784) < 0.001 Recipient Weight: ≤ 3 kg 20.93 (2.924-148.975) 0.002 Recipient Weight: ≥ 10 kg Recipient Weight: 3–4 kg 12.12 (4.519–32.551) <0.001 Recipient Weight: 4–5 kg 6.14 (2.531–14.903) < 0.001 Recipient Weight: 5–6 kg 3.03 (1.559–5.921) 0.001 Recipient Weight: 6–7 kg 2.83 (1.396–5.747) 0.004 Recipient Weight: 7–8 kg 1.01 (0.613–1.666) 0.966 Recipient Weight: 8–9 kg 1.51 (0.822–2.792) 0.182 Recipient Weight: 9–10 kg 1.95 (0.958–3.973) 0.065 Blood Type: A 1.60 (1.310–1.955) < 0.001 Blood Type: B 1.25 (0.945–1.674) 0.115 Blood Type: O 0.53 (0.442–0.639) < 0.001 Blood Type: AB 3.18 (1.495–6.763) 0.003 Serum albumin: ≤ 2.0 1.15 (0.625–2.139) 0.643 Serum albumin: ≥ 3.0 Serum albumin: 2.0-2.5 1.88 (1.136–3.134) 0.014 Serum albumin: 2.5-3.0 2.47 (1.728–3.542) <0.001 Creatinine: 1.5-2.0 1.51 (0.553–4.138) 0.420 Creatinine: ≤ 1.5 Creatinine: ≥2.0 1.38 (0.507–3.809) 0.522 Life support: yes 9.91 (7.717–12.747) <0.001 Life support: no Diabetes 1.25 (0.581–2.693) 0.566 No history of diabetes Recipient Height: ≥ 172 0.64 (0.487- 0.857) 0.002 Recipient Height: 88–172 Recipient Height: ≤ 88cm 3.36 (2.661–4.244) < 0.001 African American Race 1.32 (1.041–1.693) 0.022 Race: other Payment Method: private insurance 0.81 (0.682–0.978) 0.028 Payment method: other Payment method: Medicaid 1.21 (1.014–1.465) 0.035 UNOS Region: 1 0.88 (0.548–1.419) 0.605 UNOS Region: 2 0.79 (0.589–1.082) 0.147 UNOS Region: 3 1.37 (1.043–1.801) 0.023 UNOS Region: 4 0.61 (0.469–0.814) 0.001 UNOS Region: 5 0.92 (0.728–1.175) 0.524 UNOS Region: 6 1.05 (0.623-1.800) 0.831 UNOS Region: 7 0.70 (0.531–0.933) 0.015 UNOS Region: 8 1.08 (0.802–1.465) 0.599 UNOS Region: 9 3.05 (1.750–5.338) < 0.001 UNOS Region: 10 1.03 (0.728–1.479) 0.837 UNOS Region: 11 1.11 (0.835- 1.494) 0.456 Gender: male 0.90 (0.753–1.083) 0.275 Gender: female The variables included in the multivariable analysis include: diagnosis: congenital heart defect with surgery, dilated myopathy (idiopathic), restrictive myopathy (idiopathic), CHD; previous transplant: 1; recipient age: <2, 2–10, 15–18; recipient weight: ≤3, 3–4, 4–5, 5–6, 6–7; blood type: A, O, AB; albumin: 2.0–2.5, 2.5–3.0; life support; recipient height: ≥172, ≤ 88 cm; African American race; insurance type: private, Medicaid; and UNOS region: 3, 4, 7, and 9. Risk Index Using significant variables from the multivariate analysis, a risk index was constructed. Each factor was assigned a set number of points equal to the odds ratio for the variable found in multivariable analysis. One point was given to each risk factor for every 1% increase in the chance of transplantation, and one point was deducted from each risk factor for every 1% decrease in the chance of transplantation. For example, a variable with an odds ratio of 1.21 would be assigned 21 points. Furthermore, a variable with an odds ratio of 0.61 would be assigned − 39 points. After constructing the score index, 3 risk categories were created: high (bottom 33% percentile), medium, and low (upper 33% percentile). Using receiver operating curve (ROC) analysis, the predictive capabilities of the score index in predicting transplantation and wait times was assessed. Results Univariate and Multivariable Analysis Table 2 shows the results from the univariate analysis, and Table 3 shows the results from the multivariable analysis. The following factors were found to be significant in the multivariable analysis: diagnosis: dilated myopathy (idiopathic), recipient age 2–10, weight (kg): ≤3, 3–4, and 4–5, blood type O and AB, albumin 2.5–3.0, life support, recipient height ≥ 172 cm, and UNOS Region 4, 7, and 9. Table 3 Multivariable Logistic Regression Results Factors Odds Ratio Confidence Interval p-value Diagnosis: Congenital Heart Defect with Surgery 0.93 (0.668–1.307) 0.694 Diagnosis: Dilated myopathy (idiopathic) 1.40 (1.00-1.96) 0.047 Diagnosis: Restrictive myopathy (idiopathic) 0.71 (0.489–1.057) 0.094 Diagnosis: CHD 0.70 (0.484–1.015) 0.060 Previous transplant: 1 0.70 (0.486–1.022) 0.065 Recipient age: <2 1.43 (0.811–2.552) 0.212 Recipient Age: 2–10 0.74 (0.580–0.960) 0.023 Recipient Age: 15–18 0.94 (0.701–1.260) 0.683 Recipient Weight: ≤ 3 kg 10.23 (1.374–76.225) 0.023 Recipient Weight: 3–4 kg 5.18 (1.801–14.903) 0.002 Recipient Weight: 4–5 kg 2.87 (1.100-7.493) 0.031 Recipient Weight: 5–6 kg 1.55 (0.727–3.335) 0.253 Recipient Weight: 6–7 kg 1.59 (0.719–3.521) 0.252 Blood Type: A 1.19 (0.854–1.664) 0.300 Blood Type: O 0.53 (0.395–0.733) < 0.001 Blood Type: AB 2.52 (1.116–5.699) 0.026 Serum albumin: 2.0-2.5 1.22 (0.715–2.109) 0.456 Serum albumin: 2.5-3.0 1.47 (1.009–2.161) 0.044 Life support: yes 7.06 (5.439–9.167) < 0.001 Recipient Height: ≥ 172 cm 0.56 (0.401–0.799) 0.001 Recipient Height: ≤ 88 cm 1.06 (0.692–1.631) 0.780 African American Race 1.19 (0.918–1.560) 0.183 Payment Method: private insurance 0.93 (0.665–1.323) 0.716 Payment method: Medicaid 1.03 (0.726–1.475) 0.845 UNOS Region: 3 1.21 (0.899–1.634) 0.205 UNOS Region: 4 0.72 (0.538–0.990) 0.043 UNOS Region: 7 0.68 (0.503–0.937) 0.018 UNOS Region: 9 2.55 (1.431–4.551) 0.002 The top three significant factors associated with shorter waitlist times in the multivariable analysis were: recipient weight ≤ 3 kg (HR: 10.23, p = 0.023), life support use (HR: 7.06, p < 0.001), and recipient weight 3–4 kg (HR: 5.18, p = 0.002). The top three significant factors associated with longer waitlist times were: blood type O (HR: 0.53, p < 0.001), recipient height ≥ 172 cm (HR: 0.56, p = 0.001), and UNOS Region 7 (HR: 0.68, p = 0.018). Risk Index Significant factors identified in the multivariate analysis were used to create the risk score. Table 4 displays all factors included in the development of the risk score, along with the points assigned to each. Scores for each patient were calculated by summing the points corresponding to the risk factors present, resulting in a cumulative total score. Table 4 Points Awarded for Wait Time Score Index Factors Odds Ratio Points Awarded Diagnosis: Dilated myopathy (idiopathic) 1.40 (1.00-1.968) 40 Recipient Age: 2–10 0.74 (0.580–0.960) -26 Recipient Weight: ≤ 3 kg 10.23 (1.374–76.225) 902 Recipient Weight: 3–4 kg 5.18 (1.801–14.903) 418 Recipient Weight: 4–5 kg 2.87 (1.100-7.493) 187 Blood Type: O 0.53 (0.395–0.733) -47 Blood Type: AB 2.52 (1.116–5.699) 152 Serum albumin: 2.5-3.0 1.47 (1.009–2.161) 47 Life support: yes 7.06 (5.439–9.167) 606 Recipient Height: ≥ 172 cm 0.56 (0.401–0.799) -44 UNOS Region: 4 0.72 (0.538–0.990) -28 UNOS Region: 7 0.68 (0.503–0.937) -32 UNOS Region: 9 2.55 (1.431–4.551) 155 Statement and Declarations Duplicate/Prior/Overlapping Publication or Submission This article has not been published previously as an abstract or an electronic preprint. Risk groups were divided into tertiles based on the likelihood of transplantation: Tertile 1 (≤ 47), Tertile 2 (47–606), and Tertile 3 (≥ 606). A Kaplan-Meier curve was generated and stratified by risk group. The results are presented in Fig. 1 . Furthermore, the mean wait time for each tertile was calculated, and the results are displayed in Fig. 2 . When examining transplant outcomes at 1 year, the mean wait time for Tertile 1 was 214 days, for Tertile 2 was 95 days, and for Tertile 3 was 60 days. ROC Analysis Using ROC analysis, we assessed the predictive capability of the index for the likelihood of transplantation. The ROC value for the index was 0.81 (95% confidence interval [CI]: 0.801–0.835). The results are displayed in Fig. 3 . Discussion In this study, we developed a risk index using eight variables to predict waitlist duration for pediatric heart transplant candidates, utilizing a comprehensive set of factors from the UNOS database. After univariate and multivariate analyses, the factors found to be most significant included: dilated myopathy (idiopathic), recipient age (2–10), weight (≤ 3, 3–4, and 4–5 kg), blood type (O and AB), albumin (2.5–3.0), life support, recipient height (≥ 172 cm), and UNOS Regions (4, 7, and 9). Our study identifies clinically significant risk factors impacting wait times for pediatric heart transplant recipients and stratifies patients based on their point totals. Our novel risk index demonstrated strong predictive capabilities, with a final c-statistic of 0.81 via ROC analysis (95% Confidence Interval [CI]: 0.801–0.835), offering a novel tool to improve wait-time transparency. Some studies have previously identified factors influencing waitlist times such as height and region, yet there remains a paucity of research stratifying these factors [ 7 , 8 ]. The lack of a standardized estimator, has left this cohort in a state of uncertainty with limited guidance on their expected wait time [ 13 ]. This is crucial for many reasons. First, pediatric cardiac transplant candidates are often critically ill and vulnerable to the risks associated with arbitrary wait times and medical complications [ 12 ]. This can include experiencing deteriorating health due to inadequate management and the added stress of prolonged waitlist time uncertainty [ 11 ]. Secondly, families are left to cope with uncertainty, resulting in substantial emotional, financial, and physical strain [ 10 ]. Finally, healthcare providers face difficulties in making informed clinical decisions amidst unclear waitlist timelines. A precise, data-driven approach to estimating waitlist times could better prepare patients and their families and inform more tailored clinical decision-making. Furthermore, our index includes known factors to significantly impact wait list survival. For example, our study found the following factors to be significantly associated with shorter waitlist times : recipient weight ≤ 3 kg (HR: 10.23, p = 0.023) and life support use (HR: 7.06, p < 0.001). This is crucial as the prior literature consistently identifies low birth weight as a critical predictor of disease progression and mortality in pediatric heart disease. Studies indicate that low birth weight is correlated with underdeveloped organs, increased risk of complications and infections, greater likelihood of requiring extracorporeal membrane oxygenation (ECMO), and a higher association with congenital heart disease, which without prior surgical intervention, significantly increases mortality risk [ 14 ]. Mah et al. found that infants weighing less than 3 kg had a higher hazard ratio for waitlist mortality (HR 1.4) [ 15 ]. Similarly, Butts et al. reported that infants with a small body surface area (BSA) below 0.3 m² face higher waitlist mortality [ 12 ]. Several studies have demonstrated the prioritization of patients with low birth weight on the organ waitlist. One analysis of the United Network for Organ Sharing (UNOS) database reported that infants weighing less than 2.5 kg had a median waitlist time of 28 days, compared to 42 days for patients weighing more than 4 kg [ 14 ]. Another variable highly correlated with short wait times is patient life support use. These patients are often categorized under higher urgency statuses (e.g., Status 1 or Status 2). This finding aligns with literature reporting prioritization of patients on temporary mechanical circulatory support devices (tMCS), such as veno-arterial extracorporeal membrane oxygenation (VA-ECMO) or intra-aortic balloon pumps. One study reported that patients on VA-ECMO had a median waitlist time of 5 days compared to 31 days for those not on such support under the revised allocation system [ 16 ]. In 2016, changes in allocation policy further prioritized transplantation for patients on tMCS. The incidence of transplantation for VA-ECMO-supported patients increased to 81.5% under the new system, compared to 43.0% under the previous organ allocation system [ 17 ]. Our study also found the following factors found in the multivariable analysis to be associated with longer waitlist times : blood type O, recipient height top 8th percentile (≥ 172 cm), and UNOS Region 7. All of these factors have been found to be associated with wait times in previous studies. For example, pediatric patients with blood type O often experience higher than normal wait times due to the universal high demand for blood type O. Additionally, taller pediatric patients may face longer waitlist times due to the limited availability of appropriately sized donor hearts. Finally, UNOS Region 7, which includes states such as Iowa, Kansas, Missouri, and Nebraska, has historically demonstrated regional variations in donor availability and differences in listing practices [ 18 – 20 ]. Other organ transplant studies have similarly demonstrated significant geographic variation in waitlist times based on the recipient’s OPTN region. Davies et al. found that mean waitlist time for pediatric heart transplantation varied across OPTN regions, ranging from 91.0 ± 163 days (Region 6) to 248.1 ± 493 days (Region 4), with individual waitlist mortality varying from 6.9% (Region 1) to 19.2% (Region 5) [ 21 ]. Similarly, Benvenuto et al. reported that lung transplant candidates in regions with low local lung availability had significantly worse waitlist outcomes [ 22 ]. These findings underscore the critical role that geography plays in equitable access to transplants and highlight the need for allocation strategies that bridge access disparities. With the ability to predict individualized wait times, clinicians can tailor management strategies accordingly to help alleviate the physical complications and emotional burden that unclear waitlist times produce [ 23 , 24 ]. Limitations This study presents an effective predictive index and provides insights into factors influencing pediatric heart transplant wait times, but several limitations should be considered. This analysis relies on retrospective data from the UNOS database and is therefore limited by the accuracy and completeness of recorded information. Although we incorporated multiple imputation to address missing data, confounding variables may still remain. Additionally, the UNOS database includes only U.S.-based data, thus our model may have limited applicability to other organ allocation systems in international cohorts. While a number of variables were included in our univariate and multivariate analyses, future work could expand this study by including a more comprehensive set of variables, such as hemodynamic parameters, immunologic and laboratory profiles, socioeconomic factors, and regional differences in organ availability. These factors were of interest to our study but were not included due to database limitations. Additionally, although the risk index developed in this study demonstrated strong predictive performance, it was based on traditional statistical models. More complex interactions among the recorded data may exist, and could be better identified through advanced computational approaches such as machine learning. Conclusion Our study generated a novel risk index predicting wait times for pediatric heart transplant recipients. The index incorporated eight significant factors found to impact transplantation within one year of being placed on the waitlist. The index generated predictive capabilities of c-statistic = 0.81, showing its promising potential as a valuable tool for estimating waitlist times in pediatric heart transplant candidates. Clinically, this risk index may help improve the accuracy of wait time estimates, identify patients at risk for prolonged wait times, and guide interim clinical management. Declarations Duplicate/Prior/Overlapping Publication or Submission This article has not been published previously as an abstract or an electronic preprint. Competing Interests All authors certify that they have no affiliation with or involvement in any organization or entity with any financial or non-financial conflicts of interest to disclose to Pediatric Cardiology that are directly or indirectly related to the work submitted for publication. No honorarium, grant, or other form of payment was given to produce the manuscript. Funding No funding was received to assist with the preparation of this manuscript. Ethics Approval The study utilized deidentified patient information from the public database, United Network for Organ Sharing. IRB approval was not needed for this study. Informed Consent Due to using de-identified patient information, patient consent and IRB approval were both waived. Author Contribution All authors contributed to the study design, literature review, data analysis, and writing of the manuscript. All authors reviewed and approved the manuscript. Data Availability The data in this study was obtained from the de-identified data provided by the United Network for Organ Sharing (UNOS) database. The link to this database is as follows: https://optn.transplant.hrsa.gov/data/view-data-reports/request-data/ References Martens S, Tie H, Kehl HG et al (2023) Heart transplantation surgery in children and young adults with congenital heart disease. 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Ann Thorac Surg 61(2):570–575. 10.1016/0003-4975(95)01031-9 Nilsson K, Westas M, Andersson G, Johansson P, Lundgren J (2022) Waiting for kidney transplantation from deceased donors: Experiences and support needs during the waiting time -A qualitative study. Patient Educ Couns 105(7):2422–2428. 10.1016/j.pec.2022.02.016 Tong A, Hanson CS, Chapman JR et al (2015) Suspended in a paradox’-patient attitudes to wait-listing for kidney transplantation: systematic review and thematic synthesis of qualitative studies. Transpl Int 28(7):771–787. 10.1111/tri.12575 Butts RJ, Toombs L, Kirklin JK et al (2024) Waitlist Outcomes for Pediatric Heart Transplantation in the Current Era: An Analysis of the Pediatric Heart Transplant Society Database. Circulation 150(5):362–373. 10.1161/CIRCULATIONAHA.123.068189 Almond CSD, Thiagarajan RR, Piercey GE et al (2009) Waiting List Mortality Among Children Listed for Heart Transplantation in the United States. Circulation 119(5):717–727. 10.1161/CIRCULATIONAHA.108.815712 Rizwan R, Zafar F, Chin C, Tweddell J, Bryant R, Morales D (2018) Listing Low-Weight or Ill Infants for Heart Transplantation: Is It Prudent? Ann Thorac Surg 106(4):1189–1196. 10.1016/j.athoracsur.2018.06.004 Mah D, Singh TP, Thiagarajan RR et al (2009) Incidence and Risk Factors for Mortality in Infants Awaiting Heart Transplantation in the USA. J Heart Lung Transpl 28(12):1292–1298. 10.1016/j.healun.2009.06.013 Topkara VK, Sayer GT, Clerkin KJ et al (2022) Recovery With Temporary Mechanical Circulatory Support While Waitlisted for Heart Transplantation. J Am Coll Cardiol 79(9):900–913. 10.1016/j.jacc.2021.12.022 Heidenreich PA, Bozkurt B, Aguilar D et al (2022) 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 145(18). 10.1161/CIR.0000000000001063 Eapen S, Nordan T, Critsinelis AC et al (2024) Blood type O heart transplant candidates have longer waitlist time and higher delisting under the new allocation system. J Thorac Cardiovasc Surg 167(1):231–240e7. 10.1016/j.jtcvs.2022.07.029 Joyce DL, Lahr BD, Joyce LD, Kushwaha SS, Daly RC (2018) Prediction Model for Wait Times in Cardiac Transplantation. ASAIO J 64(5):680–685. 10.1097/MAT.0000000000000706 Kauffman H (1999) Determinants of waiting time for heart transplants in the United States. J Heart Lung Transpl 18(5):414–419. 10.1016/S1053-2498(98)00062-X Davies RR, Haldeman S, Pizarro C (2013) Regional Variation in Survival Before and After Pediatric Heart Transplantation—An Analysis of The UNOS Database. Am J Transpl 13(7):1817–1829. 10.1111/ajt.12259 Benvenuto LJ, Anderson DR, Kim HP et al (2018) Geographic disparities in donor lung supply and lung transplant waitlist outcomes: A cohort study. Am J Transpl 18(6):1471–1480. 10.1111/ajt.14630 Karataş H, Balas Ş (2024) The liminal experience of awaiting for a cadaveric kidney donation: I would not wish it on even my enemy! Soc Sci Med 363:117466. 10.1016/j.socscimed.2024.117466 Anthony SJ, Annunziato RA, Fairey E, Kelly VL, So S, Wray J (2014) Waiting for transplant: Physical, psychosocial, and nutritional status considerations for pediatric candidates and implications for care. Pediatr Transpl 18(5):423–434. 10.1111/petr.12305 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7152075","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499557288,"identity":"d85277f2-afb7-4a57-863f-679f85bcaac7","order_by":0,"name":"Bhavana Kunisetty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACAwkoA0QfALPYe8AUYwORWoB8njMkaIHwJXLwazGXbn4m8XOHHYPkjNyDB39U/KkzuPn24KcbDDayGw5g12I555iZZO+ZZAZpibyEwzxnDCQMbuclS+cwpBnj0mJwI8HYgLeNmUFOIsfgMGMbSEuOGXMOw+FE3FrSPxv+basHazn48x9Qy80zIC3/8WjJMXzM23YY6LAcgwO8DUAtN3hAWg7g1GI5I6fwsWzbcR7JnjcGh3mOGUvOPAPyi0Gy8UwcWswl0jccfNtWLSdxPMf4448aOX6+42cPfs6psJPtw6EFBnjgLAWwSgP8ylGBfAMpqkfBKBgFo2AkAACWHV9HqQG34wAAAABJRU5ErkJggg==","orcid":"","institution":"Baylor College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Bhavana","middleName":"","lastName":"Kunisetty","suffix":""},{"id":499557290,"identity":"e2ac37dc-76f3-4d2b-a7fd-d0d1305315b9","order_by":1,"name":"Ashley Montgomery","email":"","orcid":"","institution":"Baylor College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ashley","middleName":"","lastName":"Montgomery","suffix":""},{"id":499557292,"identity":"0ad83ea3-d23e-4c49-8af9-a846731b98f0","order_by":2,"name":"Chase Robinson","email":"","orcid":"","institution":"Baylor College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chase","middleName":"","lastName":"Robinson","suffix":""},{"id":499557295,"identity":"0b34a0be-8fa5-428b-90dd-d8b04e969e2a","order_by":3,"name":"Abbas Rana","email":"","orcid":"","institution":"Baylor College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Rana","suffix":""}],"badges":[],"createdAt":"2025-07-17 20:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7152075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7152075/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89396225,"identity":"a798c9e6-23c5-4058-865f-cb71e0576c30","added_by":"auto","created_at":"2025-08-19 13:39:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":483116,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan Meier Curve\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7152075/v1/481ef770bde2c1c459f3b88b.png"},{"id":89396223,"identity":"80bfaa1f-3c55-4482-9f91-ec3bc22d7a7d","added_by":"auto","created_at":"2025-08-19 13:39:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":484151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean Waiting Time\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7152075/v1/8c825891eae2cbc73353da15.png"},{"id":89397456,"identity":"d968e0f1-88f7-4f09-ac01-50afe02d1f9c","added_by":"auto","created_at":"2025-08-19 13:47:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":423946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eROC Analysis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7152075/v1/6e0a7090a525948ab70e6a0b.png"},{"id":91511288,"identity":"02986f8d-3070-4b3d-9430-cdf5ce64194f","added_by":"auto","created_at":"2025-09-17 08:47:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1679607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7152075/v1/c1df9198-4585-4c56-9b12-78f748188a28.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Risk Index to Predict Waiting Times for Pediatric Heart Transplant Candidates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe pediatric population faces a range of critical cardiac conditions, including end-stage cardiomyopathy, severe congenital heart defects, valvular abnormalities, and persistent, life-threatening arrhythmias [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant advancements in surgical techniques, perioperative care, and medical therapies, these treatments often serve as temporary management for critically failing hearts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, for many, heart transplantation remains the definitive, gold-standard treatment for survival [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In infants, congenital heart defects such as hypoplastic left heart syndrome and Fontan circulation failure are the leading indications, while dilated cardiomyopathy predominates in older children [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For these patients, heart transplantation offers a transformative opportunity for long-term survival and improved quality of life.\u003c/p\u003e\u003cp\u003eOnce a physician determines that a young patient is a suitable candidate, the patient is registered for the heart transplant waitlist. Then, a suitable donor is matched, after which an appropriate transplant procedure can be performed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Several factors influence the waitlist duration and the probability of finding an appropriate cardiac donor [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, there remains a notable lack of research exploring these influencing factors, and thus no standardized approach exists to predict wait times. This lack of predictability is especially concerning given that many patients on the waitlist are in advanced stages of cardiac disease, where the need for a transplant is both urgent and critical. Furthermore, the absence of clear waitlist timelines significantly exacerbates challenges faced by patients, their families, and healthcare providers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study aims to develop a predictive index for waitlist times in pediatric heart transplant candidates. This index is based on a thorough analysis of the OPTN database, identifying key variables that are strongly associated with variations in waitlist times. By assigning a predictive score to each transplant candidate, this tool has the potential to offer a clear and transparent path through the waitlist process.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study performed a retrospective analysis of pediatric heart transplant waitlist data utilizing de-identified data provided by the United Network for Organ Sharing (UNOS) database. Inclusion criteria were patients under the age of 18 listed for a heart transplant between January 1, 2000 and December 31, 2020. Exclusion criteria included patients aged 18 or older at listing, individuals outside of the time range (January 1, 2000 to December 31, 2020), and patients with multiple organ listings. After applying these criteria, the final cohort included 7,856 pediatric patients.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe conducted the statistical analysis for the study utilizing STATA BE 18.5 (Stata Corp, College Station, TX). Univariate and multivariable Cox regression analyses were performed to identify factors that significantly impact waitlist time. Only significant factors (p-value \u0026lt; 0.05) found in univariate analysis were then included in the multivariable analysis. The outcome of interest for the study was transplantation at 1 year. Therefore, for odds ratios greater than 1.0, this signifies an increase in the “risk” of receiving a transplantation at 1 year. Consequently, variables with an odds ratio less than 1.0 signify a decreased “risk” of receiving a transplantation at 1 year.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Entry and Missing Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe entry rate for each variable is included in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For continuous variables with missing entries, we performed the predictive mean matching imputation method. The following variables were imputed: initial weight (0.34% missing entry completion), initial serum albumin (9.65% missing entry completion), and initial height (1.13% missing entry completion).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eDemographics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEntry Completion (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrevalence (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of Patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedian Days on Waitlist\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Congenital Heart Defect with Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Dilated myopathy (idiopathic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Congenital Heart Defect: prior surgery unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Restrictive myopathy idiopathic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: CHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious transplant: 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious transplant: 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient age: \u0026lt;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Age: 2–10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Age: 15–18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e257\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: ≤ 3 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e99.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 3–4 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 4–5 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 5–6 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 6–7 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 7–8 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 8–9 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 9–10 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: AB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: ≤ 2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e90.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: 2.0-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: 2.5-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine: 1.5-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e95.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine: ≥2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife support: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4,687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e492\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Height: ≥ 172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e98.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Height: ≤ 88cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfrican American Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayment Method: private insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e99.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayment method: Medicaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"10\" rowspan=\"11\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender: Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4,324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentifying Risk Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatient-related variables were included in the analysis if they were recorded at the time of listing (e.g., diagnosis, blood type, and demographic data) or prior to listing (e.g., ethnicity, insurance type). Variables reflecting donor characteristics or post-transplant factors were excluded. The final dataset contained 14 variables, such as clinical and demographic factors, diagnoses, and laboratory values recorded at listing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariate and Multivariable Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe variables included in the univariate analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Groupings for continuous variables were constructed based on clinical judgment. The following variables were included in the univariate analysis: diagnosis, previous transplant, age, weight, blood type, total serum albumin, creatinine, life support, diabetes, height, African American race, payment method, UNOS region, and male sex. Significant variables from the univariate analysis (p-value \u0026lt; 0.05) were then included in the multivariable analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eUnivariate Logistic Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\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\u003eConfidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Congenital Heart Defect with Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.552-0.800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDiagnosis: other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Dilated myopathy (idiopathic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.893–3.231)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Congenital Heart Defect: prior surgery unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.549–1.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Restrictive myopathy idiopathic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.327–0.614)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: CHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.646–0.926)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious transplant: 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.368–0.680)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePrevious transplant: \u0026gt;2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious transplant: 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.205–11.341)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient age: \u0026lt;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.465–5.922)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRecipient Age: 10–15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Age: 2–10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.396–0.574)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Age: 15–18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.508–0.784)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: ≤ 3 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.924-148.975)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eRecipient Weight: ≥ 10 kg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 3–4 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.519–32.551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 4–5 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.531–14.903)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 5–6 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.559–5.921)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 6–7 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.396–5.747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 7–8 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.613–1.666)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 8–9 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.822–2.792)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 9–10 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.958–3.973)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.310–1.955)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\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\u003eBlood Type: B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.945–1.674)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.115\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\u003eBlood Type: O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.442–0.639)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\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\u003eBlood Type: AB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.495–6.763)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\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\u003eSerum albumin: ≤ 2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.625–2.139)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSerum albumin: ≥ 3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: 2.0-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.136–3.134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: 2.5-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.728–3.542)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine: 1.5-2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.553–4.138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCreatinine: ≤ 1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine: ≥2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.507–3.809)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife support: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.717–12.747)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLife support: no\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.581–2.693)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo history of diabetes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Height: ≥ 172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.487- 0.857)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRecipient Height: 88–172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Height: ≤ 88cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.661–4.244)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfrican American Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.041–1.693)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRace: other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayment Method: private insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.682–0.978)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePayment method: other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayment method: Medicaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.014–1.465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.548–1.419)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.605\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\u003eUNOS Region: 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.589–1.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.147\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\u003eUNOS Region: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.043–1.801)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\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\u003eUNOS Region: 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.469–0.814)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\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\u003eUNOS Region: 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.728–1.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.524\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\u003eUNOS Region: 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.623-1.800)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.831\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\u003eUNOS Region: 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.531–0.933)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\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\u003eUNOS Region: 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.802–1.465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.599\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\u003eUNOS Region: 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.750–5.338)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\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\u003eUNOS Region: 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.728–1.479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.837\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\u003eUNOS Region: 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.835- 1.494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.456\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\u003eGender: male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.753–1.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGender: female\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe variables included in the multivariable analysis include: diagnosis: congenital heart defect with surgery, dilated myopathy (idiopathic), restrictive myopathy (idiopathic), CHD; previous transplant: 1; recipient age: \u0026lt;2, 2–10, 15–18; recipient weight: ≤3, 3–4, 4–5, 5–6, 6–7; blood type: A, O, AB; albumin: 2.0–2.5, 2.5–3.0; life support; recipient height: ≥172, ≤ 88 cm; African American race; insurance type: private, Medicaid; and UNOS region: 3, 4, 7, and 9.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk Index\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing significant variables from the multivariate analysis, a risk index was constructed. Each factor was assigned a set number of points equal to the odds ratio for the variable found in multivariable analysis. One point was given to each risk factor for every 1% increase in the chance of transplantation, and one point was deducted from each risk factor for every 1% decrease in the chance of transplantation. For example, a variable with an odds ratio of 1.21 would be assigned 21 points. Furthermore, a variable with an odds ratio of 0.61 would be assigned − 39 points. After constructing the score index, 3 risk categories were created: high (bottom 33% percentile), medium, and low (upper 33% percentile). Using receiver operating curve (ROC) analysis, the predictive capabilities of the score index in predicting transplantation and wait times was assessed.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eUnivariate and Multivariable Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results from the univariate analysis, and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results from the multivariable analysis. The following factors were found to be significant in the multivariable analysis: diagnosis: dilated myopathy (idiopathic), recipient age 2\u0026ndash;10, weight (kg): \u0026le;3, 3\u0026ndash;4, and 4\u0026ndash;5, blood type O and AB, albumin 2.5\u0026ndash;3.0, life support, recipient height\u0026thinsp;\u0026ge;\u0026thinsp;172 cm, and UNOS Region 4, 7, and 9.\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\u003eMultivariable Logistic Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\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\u003eConfidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003eDiagnosis: Congenital Heart Defect with Surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.668\u0026ndash;1.307)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.694\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Dilated myopathy (idiopathic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.00-1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Restrictive myopathy (idiopathic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.489\u0026ndash;1.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: CHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.484\u0026ndash;1.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious transplant: 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.486\u0026ndash;1.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient age: \u0026lt;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.811\u0026ndash;2.552)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Age: 2\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.580\u0026ndash;0.960)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Age: 15\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.701\u0026ndash;1.260)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.683\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: \u0026le; 3 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.374\u0026ndash;76.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 3\u0026ndash;4 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.801\u0026ndash;14.903)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 4\u0026ndash;5 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.100-7.493)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 5\u0026ndash;6 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.727\u0026ndash;3.335)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 6\u0026ndash;7 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.719\u0026ndash;3.521)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.854\u0026ndash;1.664)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.395\u0026ndash;0.733)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: AB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.116\u0026ndash;5.699)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: 2.0-2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.715\u0026ndash;2.109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: 2.5-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.009\u0026ndash;2.161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife support: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(5.439\u0026ndash;9.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Height: \u0026ge; 172 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.401\u0026ndash;0.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Height: \u0026le; 88 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.692\u0026ndash;1.631)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfrican American Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.918\u0026ndash;1.560)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayment Method: private insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.665\u0026ndash;1.323)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayment method: Medicaid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.726\u0026ndash;1.475)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.899\u0026ndash;1.634)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.538\u0026ndash;0.990)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(0.503\u0026ndash;0.937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.431\u0026ndash;4.551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\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 top three significant factors associated with \u003cb\u003eshorter waitlist times\u003c/b\u003e in the multivariable analysis were: recipient weight\u0026thinsp;\u0026le;\u0026thinsp;3 kg (HR: 10.23, p\u0026thinsp;=\u0026thinsp;0.023), life support use (HR: 7.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and recipient weight 3\u0026ndash;4 kg (HR: 5.18, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003cp\u003eThe top three significant factors associated with \u003cb\u003elonger waitlist times\u003c/b\u003e were: blood type O (HR: 0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), recipient height\u0026thinsp;\u0026ge;\u0026thinsp;172 cm (HR: 0.56, p\u0026thinsp;=\u0026thinsp;0.001), and UNOS Region 7 (HR: 0.68, p\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk Index\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSignificant factors identified in the multivariate analysis were used to create the risk score. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays all factors included in the development of the risk score, along with the points assigned to each. Scores for each patient were calculated by summing the points corresponding to the risk factors present, resulting in a cumulative total score.\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\u003ePoints Awarded for Wait Time Score Index\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\u003eFactors\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\u003ePoints Awarded\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis: Dilated myopathy (idiopathic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40 (1.00-1.968)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Age: 2\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74 (0.580\u0026ndash;0.960)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: \u0026le; 3 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.23 (1.374\u0026ndash;76.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e902\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 3\u0026ndash;4 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.18 (1.801\u0026ndash;14.903)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Weight: 4\u0026ndash;5 kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.87 (1.100-7.493)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.53 (0.395\u0026ndash;0.733)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Type: AB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.52 (1.116\u0026ndash;5.699)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin: 2.5-3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47 (1.009\u0026ndash;2.161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife support: yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.06 (5.439\u0026ndash;9.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecipient Height: \u0026ge; 172 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.56 (0.401\u0026ndash;0.799)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72 (0.538\u0026ndash;0.990)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68 (0.503\u0026ndash;0.937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNOS Region: 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.55 (1.431\u0026ndash;4.551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eStatement and Declarations\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eDuplicate/Prior/Overlapping Publication or Submission\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eThis article has not been published previously as an abstract or an electronic preprint.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRisk groups were divided into tertiles based on the likelihood of transplantation: Tertile 1 (\u0026le;\u0026thinsp;47), Tertile 2 (47\u0026ndash;606), and Tertile 3 (\u0026ge;\u0026thinsp;606). A Kaplan-Meier curve was generated and stratified by risk group. The results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Furthermore, the mean wait time for each tertile was calculated, and the results are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. When examining transplant outcomes at 1 year, the mean wait time for Tertile 1 was 214 days, for Tertile 2 was 95 days, and for Tertile 3 was 60 days.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eROC Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing ROC analysis, we assessed the predictive capability of the index for the likelihood of transplantation. The ROC value for the index was 0.81 (95% confidence interval [CI]: 0.801\u0026ndash;0.835). The results are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed a risk index using eight variables to predict waitlist duration for pediatric heart transplant candidates, utilizing a comprehensive set of factors from the UNOS database. After univariate and multivariate analyses, the factors found to be most significant included: dilated myopathy (idiopathic), recipient age (2\u0026ndash;10), weight (\u0026le;\u0026thinsp;3, 3\u0026ndash;4, and 4\u0026ndash;5 kg), blood type (O and AB), albumin (2.5\u0026ndash;3.0), life support, recipient height (\u0026ge;\u0026thinsp;172 cm), and UNOS Regions (4, 7, and 9). Our study identifies clinically significant risk factors impacting wait times for pediatric heart transplant recipients and stratifies patients based on their point totals. Our novel risk index demonstrated strong predictive capabilities, with a final c-statistic of 0.81 via ROC analysis (95% Confidence Interval [CI]: 0.801\u0026ndash;0.835), offering a novel tool to improve wait-time transparency.\u003c/p\u003e\u003cp\u003eSome studies have previously identified factors influencing waitlist times such as height and region, yet there remains a paucity of research stratifying these factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The lack of a standardized estimator, has left this cohort in a state of uncertainty with limited guidance on their expected wait time [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This is crucial for many reasons. First, pediatric cardiac transplant candidates are often critically ill and vulnerable to the risks associated with arbitrary wait times and medical complications [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This can include experiencing deteriorating health due to inadequate management and the added stress of prolonged waitlist time uncertainty [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Secondly, families are left to cope with uncertainty, resulting in substantial emotional, financial, and physical strain [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Finally, healthcare providers face difficulties in making informed clinical decisions amidst unclear waitlist timelines. A precise, data-driven approach to estimating waitlist times could better prepare patients and their families and inform more tailored clinical decision-making.\u003c/p\u003e\u003cp\u003eFurthermore, our index includes known factors to significantly impact wait list survival. For example, our study found the following factors to be significantly associated with \u003cb\u003eshorter waitlist times\u003c/b\u003e: recipient weight\u0026thinsp;\u0026le;\u0026thinsp;3 kg (HR: 10.23, p\u0026thinsp;=\u0026thinsp;0.023) and life support use (HR: 7.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This is crucial as the prior literature consistently identifies low birth weight as a critical predictor of disease progression and mortality in pediatric heart disease. Studies indicate that low birth weight is correlated with underdeveloped organs, increased risk of complications and infections, greater likelihood of requiring extracorporeal membrane oxygenation (ECMO), and a higher association with congenital heart disease, which without prior surgical intervention, significantly increases mortality risk [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Mah et al. found that infants weighing less than 3 kg had a higher hazard ratio for waitlist mortality (HR 1.4) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, Butts et al. reported that infants with a small body surface area (BSA) below 0.3 m\u0026sup2; face higher waitlist mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Several studies have demonstrated the prioritization of patients with low birth weight on the organ waitlist. One analysis of the United Network for Organ Sharing (UNOS) database reported that infants weighing less than 2.5 kg had a median waitlist time of 28 days, compared to 42 days for patients weighing more than 4 kg [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another variable highly correlated with short wait times is patient life support use. These patients are often categorized under higher urgency statuses (e.g., Status 1 or Status 2). This finding aligns with literature reporting prioritization of patients on temporary mechanical circulatory support devices (tMCS), such as veno-arterial extracorporeal membrane oxygenation (VA-ECMO) or intra-aortic balloon pumps. One study reported that patients on VA-ECMO had a median waitlist time of 5 days compared to 31 days for those not on such support under the revised allocation system [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In 2016, changes in allocation policy further prioritized transplantation for patients on tMCS. The incidence of transplantation for VA-ECMO-supported patients increased to 81.5% under the new system, compared to 43.0% under the previous organ allocation system [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study also found the following factors found in the multivariable analysis to be associated with \u003cb\u003elonger waitlist times\u003c/b\u003e: blood type O, recipient height top 8th percentile (\u0026ge;\u0026thinsp;172 cm), and UNOS Region 7. All of these factors have been found to be associated with wait times in previous studies. For example, pediatric patients with blood type O often experience higher than normal wait times due to the universal high demand for blood type O. Additionally, taller pediatric patients may face longer waitlist times due to the limited availability of appropriately sized donor hearts. Finally, UNOS Region 7, which includes states such as Iowa, Kansas, Missouri, and Nebraska, has historically demonstrated regional variations in donor availability and differences in listing practices [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Other organ transplant studies have similarly demonstrated significant geographic variation in waitlist times based on the recipient\u0026rsquo;s OPTN region. Davies et al. found that mean waitlist time for pediatric heart transplantation varied across OPTN regions, ranging from 91.0\u0026thinsp;\u0026plusmn;\u0026thinsp;163 days (Region 6) to 248.1\u0026thinsp;\u0026plusmn;\u0026thinsp;493 days (Region 4), with individual waitlist mortality varying from 6.9% (Region 1) to 19.2% (Region 5) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, Benvenuto et al. reported that lung transplant candidates in regions with low local lung availability had significantly worse waitlist outcomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings underscore the critical role that geography plays in equitable access to transplants and highlight the need for allocation strategies that bridge access disparities. With the ability to predict individualized wait times, clinicians can tailor management strategies accordingly to help alleviate the physical complications and emotional burden that unclear waitlist times produce [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study presents an effective predictive index and provides insights into factors influencing pediatric heart transplant wait times, but several limitations should be considered.\u003c/p\u003e\u003cp\u003eThis analysis relies on retrospective data from the UNOS database and is therefore limited by the accuracy and completeness of recorded information. Although we incorporated multiple imputation to address missing data, confounding variables may still remain. Additionally, the UNOS database includes only U.S.-based data, thus our model may have limited applicability to other organ allocation systems in international cohorts. While a number of variables were included in our univariate and multivariate analyses, future work could expand this study by including a more comprehensive set of variables, such as hemodynamic parameters, immunologic and laboratory profiles, socioeconomic factors, and regional differences in organ availability. These factors were of interest to our study but were not included due to database limitations. Additionally, although the risk index developed in this study demonstrated strong predictive performance, it was based on traditional statistical models. More complex interactions among the recorded data may exist, and could be better identified through advanced computational approaches such as machine learning.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study generated a novel risk index predicting wait times for pediatric heart transplant recipients. The index incorporated eight significant factors found to impact transplantation within one year of being placed on the waitlist. The index generated predictive capabilities of c-statistic\u0026thinsp;=\u0026thinsp;0.81, showing its promising potential as a valuable tool for estimating waitlist times in pediatric heart transplant candidates. Clinically, this risk index may help improve the accuracy of wait time estimates, identify patients at risk for prolonged wait times, and guide interim clinical management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDuplicate/Prior/Overlapping Publication or Submission\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article has not been published previously as an abstract or an electronic preprint.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliation with or involvement in any organization or entity with any financial or non-financial conflicts of interest to disclose to Pediatric Cardiology that are directly or indirectly related to the work submitted for publication. No honorarium, grant, or other form of payment was given to produce the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilized deidentified patient information from the public database, United Network for Organ Sharing. IRB approval was not needed for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed Consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to using de-identified patient information, patient consent and IRB approval were both waived.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study design, literature review, data analysis, and writing of the manuscript. All authors reviewed and approved the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data in this study was obtained from the de-identified data provided by the United Network for Organ Sharing (UNOS) database. The link to this database is as follows: https://optn.transplant.hrsa.gov/data/view-data-reports/request-data/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartens S, Tie H, Kehl HG et al (2023) Heart transplantation surgery in children and young adults with congenital heart disease. J Cardiothorac Surg 18(1):342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13019-023-02461-5\u003c/span\u003e\u003cspan address=\"10.1186/s13019-023-02461-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYarlagadda VV, Maeda K, Zhang Y et al (2017) Temporary Circulatory Support in U.S. Children Awaiting Heart Transplantation. J Am Coll Cardiol 70(18):2250\u0026ndash;2260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2017.08.072\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2017.08.072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLipshultz SE, Adams MJ, Colan SD et al (2013) Long-term Cardiovascular Toxicity in Children, Adolescents, and Young Adults Who Receive Cancer Therapy: Pathophysiology, Course, Monitoring, Management, Prevention, and Research Directions: A Scientific Statement From the American Heart Association. 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Pediatr Transpl 18(5):423\u0026ndash;434. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/petr.12305\u003c/span\u003e\u003cspan address=\"10.1111/petr.12305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pediatric Heart Transplant, Pediatric Heart Failure, Waitlist Stratification, Organ Allocation","lastPublishedDoi":"10.21203/rs.3.rs-7152075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7152075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Heart transplantation is the definitive treatment for many pediatric patients with cardiac conditions. However, significant variability exists in waitlist durations, influenced by multiple factors that remain poorly understood. This lack of research hampers the development of standardized methods to predict and transparently communicate wait times to patients and families.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study aimed to create an index to predict pediatric heart transplant waitlist durations using data from 7,856 patients in the Organ Procurement and Transplantation Network (OPTN) database. Significant variables associated with waitlist times were identified through univariate and multivariable Cox regression analyses (p \u0026lt; 0.05), incorporated into a risk index with points assigned according to multivariable odds ratios, and evaluated for predictive accuracy using receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOur study identified 8 factors from multivariable analysis to significantly impact pediatric heart transplant waitlist durations. After utilizing these factors to create the risk index, ROC analysis to evaluate the predictive capabilities of the index resulted in a c-statistic of 0.81 (95% Confidence Interval: 0.801-0.835).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur findings highlight key determinants of waitlist times and demonstrate the potential of a predictive index to improve transparency, guide clinical decision-making, and manage patient and family expectations.\u003c/p\u003e","manuscriptTitle":"A Novel Risk Index to Predict Waiting Times for Pediatric Heart Transplant Candidates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 13:39:52","doi":"10.21203/rs.3.rs-7152075/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"290ecc65-9ae0-4722-b647-a1a8da8deb5c","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T08:39:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 13:39:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7152075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7152075","identity":"rs-7152075","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}