Wu YL, Jing YL, Liu WH, Gong XY, Che L, Xue JY, Li TY, Jiang L, Huang XY, Yu WL, Weng YQ. Nomogram for predicting myocardial injury in pediatric patients undergoing living donor liver transplantation for biliary atresia. World J Gastrointest Surg 2025; 17(4): 103263 [DOI: 10.4240/wjgs.v17.i4.103263]
Corresponding Author of This Article
Yi-Qi Weng, PhD, Chief Physician, Professor, Department of Anesthesiology, Tianjin First Central Hospital, No. 24 Fukang Road, Nankai District, Tianjin 300192, China. wyq2023@nankai.edu.cn
Research Domain of This Article
Anesthesiology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Yu-Li Wu, Wei-Hua Liu, Lu Che, Xiao-Yu Huang, Wen-Li Yu, Yi-Qi Weng, Department of Anesthesiology, Tianjin First Central Hospital, Tianjin 300192, China
Yong-Le Jing, Department of Cardiology, Tianjin First Central Hospital, Tianjin 300192, China
Xin-Yuan Gong, Department of Science and Education, Tianjin First Central Hospital, Tianjin 300192, China
Jing-Yi Xue, Tian-Ying Li, Lei Jiang, School of Medicine, Nankai University, Tianjin 300071, China
Author contributions: Wu YL, Jing YL, and Weng YQ conceived the manuscript; Liu WH and Che L wrote and prepared the tables and figures; Gong XY reviewed the statistical methods; Xue JY and Li TY coordinated and supervised data collection; Jiang L and Huang XY performed the statistical analysis; Yu WL and Weng YQ critically reviewed the manuscript for important intellectual content. Yu WL and Weng YQ contributed equally to this manuscript. All authors have read and agreed to the published version of the manuscript.
Supported by Tianjin Health Research Project, No. TJWJ2024QN037; Research Empowerment-Medical Research and Application Fund Project, No. BHCF-KYFN-2024004; and the Young Talent Program of Tianjin First Central Hospital.
Institutional review board statement: This research received approval from the ethics committee of Tianjin First Central Hospital, No. 2022DZX02.
Informed consent statement: Since this was a retrospective study with the inability to access to the patients, the Ethics Committee granted a waiver for the requirement of informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: All data generated or analyzed during this study are included in this published article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yi-Qi Weng, PhD, Chief Physician, Professor, Department of Anesthesiology, Tianjin First Central Hospital, No. 24 Fukang Road, Nankai District, Tianjin 300192, China. wyq2023@nankai.edu.cn
Received: November 21, 2024 Revised: December 28, 2024 Accepted: February 27, 2025 Published online: April 27, 2025 Processing time: 128 Days and 15.6 Hours
Abstract
BACKGROUND
Myocardial injury is common during liver transplantation and is associated with poor outcomes. The development of a reliable prediction system for this type of injury is crucial for reducing the incidence of cardiac complications in children receiving living donor liver transplantation (LDLT). However, establishing a practical myocardial injury prediction system for children with biliary atresia remains a considerable challenge.
AIM
To create and validate a nomogram model for predicting myocardial injury in children with biliary atresia who received LDLT.
METHODS
Clinical data from pediatric patients who received LDLT for biliary atresia between November, 2019 and January, 2022 were retrospectively analyzed. The complete dataset was randomly partitioned into a training set and a validation set at a ratio of 7:3. Least absolute shrinkage and selection operator regression was used to preliminarily screen out the predictors of myocardial injury. The prediction model was established via multivariable logistic regression and presented in the form of a nomogram.
RESULTS
This study included 321 patients, 150 (46.7%) of whom had myocardial injury. The participants were randomly allocated into two groups: A training group consisting of 225 patients and a validation group comprising 96 patients. The predictors in this nomogram included the preoperative neutrophil-to-lymphocyte ratio, high sensitivity C-reactive protein level, pediatric end-stage liver disease score and postreperfusion syndrome. The area under the curve for predicting myocardial injury was 0.865 in the training set and 0.856 in the validation set. The calibration curve revealed that the predicted values were very close to the actual values in the two sets. Decision curve analysis revealed that the prediction model offered a favorable net benefit.
CONCLUSION
The nomogram developed in this study effectively predicts myocardial injury in pediatric LDLT patients, showing good accuracy and potential for clinical application.
Core Tip: Myocardial injury is common during liver transplantation and is associated with a poor prognosis. Clinical data from pediatric patients who underwent living donor liver transplantation for biliary atresia were retrospectively analyzed. The prediction model was established via multivariable logistic regression and presented in the form of a nomogram. The predictors in this nomogram included the preoperative neutrophil-to-lymphocyte ratio, high sensitivity C-reactive protein level, pediatric end-stage liver disease score and postreperfusion syndrome. This nomogram effectively predicts myocardial injury during pediatric living donor liver transplantation, demonstrating strong discrimination, accuracy, and promising potential for clinical application.
Citation: Wu YL, Jing YL, Liu WH, Gong XY, Che L, Xue JY, Li TY, Jiang L, Huang XY, Yu WL, Weng YQ. Nomogram for predicting myocardial injury in pediatric patients undergoing living donor liver transplantation for biliary atresia. World J Gastrointest Surg 2025; 17(4): 103263
Living donor liver transplantation (LDLT) is the main treatment for biliary atresia in children. Hepatic ischemia/reperfusion occurs after opening of the hepatic portal vein. In addition to causing liver damage, it also damages remote organs, including the heart, lungs, and kidneys[1-4]. This pathophysiological change poses enormous challenges to clinicians. A study from University of California, Los Angeles Medical Center revealed that 40.4% of adult liver transplant patients experienced myocardial injury during surgery, and their survival rate was lower than that of patients without such injury[1]. Previous research has also indicated that more than 40% of children receiving liver transplants experience perioperative myocardial damage, which leads to postoperative acute lung injury and a prolonged stay in the intensive care unit (ICU)[5]. Children with myocardial damage have extended stays in the ICU after surgery, experience delayed recovery of transplanted liver function, and have a significantly lower 1-year survival rate[6]. Therefore, establishing an effective myocardial injury prediction system, identifying high-risk patients, and formulating early myocardial injury prevention and control strategies are particularly important for reducing the incidence of cardiac complications in children and improving their prognosis.
Studies have shown that deep learning models pretrained on labeled 12-lead electrocardiogram can predict myocardial injury[7], but these models are ineffective in pediatric patients. Some plasma biomarkers, such as caspase-3, C-C motif chemokine ligand 2, vascular endothelial growth factor, copeptin, and miR-223, have clinical potential to predict myocardial damage early[8-10], but these markers are relatively new and usually cannot be detected by conventional analyzers, requiring specialized detection methods (such as enzyme linked immunosorbent assay or polymerase chain reaction), resulting in a lack of clinical implementation. At present, establishing a practical myocardial injury prediction system for children with biliary atresia remains a considerable challenge. The purpose of this study was to describe our clinical experience of LDLT in pediatric patients with biliary atresia, establish and verify a predictive nomogram model for myocardial injury, and provide a reference for the formulation of prevention and treatment strategies for myocardial injury associated with LDLT.
MATERIALS AND METHODS
Patients
This retrospective study was approved by the Institutional Review Committee at Tianjin First Central Hospital. Cases of pediatric patients (aged < 18 years) who underwent LDLT at this hospital between November 1, 2019 and January 1, 2022 were included in the present study. Children with a diagnosis other than biliary atresia, those with congenital heart disease, those who underwent liver retransplantation, and those with incomplete data were excluded.
Anesthesia and surgical procedures
All pediatric patients received midazolam, fentanyl, propofol, and rocuronium for general anesthesia induction. Anesthesia was maintained with propofol, remifentanil, fentanyl, cisatracurium, and sevoflurane. Mean arterial pressure, electrocardiogram, pulse oximetry, and central venous pressure were monitored during the operation. Ringer’s acetate and albumin were administered as part of the fluid therapy regimen. Blood products or coagulation drugs were transfused according to the results of viscoelastic coagulation function monitoring. Acid-base and electrolyte balances were modulated on the basis of the blood gas values. The donors underwent left hepatectomy, and during transplantation, the transplanted liver was perfused with histidine-tryptophan-ketoglutarate solution. The recipients underwent piggyback liver transplantation without venous bypass and were admitted to the ICU for further treatment.
Clinical materials
We randomly divided the entire dataset at a ratio of 7:3 between the training cohort and the validation cohort. Baseline data, including age, sex, weight, height, the QTc interval, the left ventricular ejection fraction, high sensitivity C-reactive protein (hsCRP), hemoglobin, blood platelet counts, the platelet-to-lymphocyte ratio, aspartate aminotransferase (AST), alanine aminotransferase, albumin, total bilirubin (TB), creatinine, the neutrophil-to-lymphocyte ratio (NLR), the international normalized ratio, and the pediatric end-stage liver disease (PELD) score, were retrospectively extracted. All test results were obtained 3 days prior to transplantation. The donor-related parameters collected included graft cold ischemia time (CIT) and graft weight. The clinical parameters collected during the operation included vital signs, laboratory tests, anhepatic phase time, anesthesia time, surgical time, urine volume, blood loss, red blood cell transfusion volume, plasma transfusion volume and fluid infusion volume. The incidence of postreperfusion syndrome (PRS) was recorded. Using the above indicators as independent variables, factors that independently contribute to myocardial injury were screened through R language. The parameters for postoperative observation included the one-year survival rate; duration of ventilation; length of stay in the ICU; total hospital stay; peak TB, AST, and alanine aminotransferase levels during the initial week following LDLT; and the incidence of acute kidney injury.
Definition
In accordance with previous studies[5,6], myocardial injury in this study was defined as a cardiac troponin I (cTnI) level ≥ 0.07 ng/mL at the end of surgery. The definition of PRS was as follows: During the initial five minutes following liver graft reperfusion, the mean arterial pressure decreased by more than 30% in comparison to that at the end of the anhepatic phase, and this reduction persisted for a minimum duration of one minute[11]. The assessment of acute kidney injury was conducted in accordance with the criteria established by the guidelines[12].
Statistical analysis
Statistical analyses were performed using SPSS 27.0 and R software (version 4.3.2). A P value < 0.05 indicated statistical significance. The normality of continuous variables was evaluated using the Shapiro-Wilk test in conjunction with Q-Q plots. Normally distributed data are presented herein as mean ± SDs and were analyzed with t tests. Conversely, data that did not adhere to a normal distribution are presented as medians with interquartile ranges and were analyzed using Mann-Whitney U tests. Categorical variables are presented as counts and percentages and were analyzed using the Pearson χ2 test. The area under the curve (AUC) was calculated via receiver operating characteristic (ROC) curve methodology. Least absolute shrinkage and selection operator regression was conducted with the “glmnet” package, multivariable logistic regression with “glm”, and a nomogram was built using “rms”. ROC curves were generated using “pROC”, the Hosmer-Lemeshow test with “ResourceSelection”, calibration curves with “rms”, and decision curve analysis curves with “dcurves”.
RESULTS
Patient screening and study design
During this study, 358 pediatric patients underwent LDLT. Fourteen individuals who were diagnosed with metabolic diseases, 2 individuals with Alagille syndrome, 2 individuals with Langerhans cell hyperplasia, 1 individual with a cavernous portal vein, 1 individual who needed liver retransplantation, 5 individuals with congenital heart disease, and 12 individuals whose data were incomplete were excluded. A cohort of 321 pediatric patients diagnosed with biliary atresia who underwent LDLT were included. Please refer to Figure 1 for the flow chart.
Figure 1 Flow chart of pediatric patients.
LASSO: Least absolute shrinkage and selection operator.
Patient characteristics
As shown in Table 1, a total of 321 children who underwent LDLT were enrolled in the study, 150 (46.7%) of whom experienced intraoperative myocardial injury. The duration of postoperative stay in the ICU [3.0 (2.0-3.4) vs 2.0 (2.0-3.0) days, P = 0.044], peak TB [92 (66-123) vs 78 (50-115) μmol/L, P = 0.004], and AST [758 (506-1395) vs 666 (448-1180) U/L, P = 0.027] levels during the initial week following LDLT in the myocardial injury group were significantly greater than those in the nonmyocardial injury group. Patients in the myocardial injury group had a significantly lower 1-year survival rate than their counterparts in the nonmyocardial injury group did (92.0% vs 98.2%, P = 0.017). All enrolled patients were randomly divided into a training set (n = 225) and a validation set (n = 96) at a ratio of 7:3. There were no significant differences in the clinical variables between the training and validation sets (Table 2).
Table 1 Demographic characteristics and outcomes, n (%).
Total (n = 321)
Non-myocardial injury (n = 171)
Myocardial injury (n = 150)
P value
Preoperative clinical data
Age (month)
8.0 (6.0, 12.0)
9.0 (6.0, 16.5)
7.0 (6.0, 12.0)
0.002
Male gender
155 (48.3)
85 (49.7)
70 (46.7)
0.666
Weight (kg)
7.4 (6.5, 10.0)
7.9 (6.5, 11.0)
7.0 (6.5, 8.3)
0.003
Height (cm)
67 (63, 78)
70 (64, 82)
66 (63, 71)
0.002
QTc (ms)
405 (387, 423)
404 (387, 423)
406 (387, 430)
0.483
LVEF (%)
64 (62, 67)
64 (62, 66)
65 (62, 67)
0.254
NLR
0.78 (0.50, 1.29)
0.56 (0.42, 0.96)
1.10 (0.74, 1.88)
< 0.001
PLR
39.5 (28.2, 55.2)
38.4 (27.4, 51.0)
40.7 (28.9, 57.6)
0.067
hsCRP (mg/L)
5.31 (2.26, 12.78)
3.36 (0.92, 7.19)
9.66 (5.09, 26.43)
< 0.001
Albumin (g/L)
34.3 (32.0, 38.3)
35.0 (32.9, 39.5)
33.6 (31.0, 36.5)
0.001
TB (μmol/L)
225 (77, 317)
172 (39, 278)
258 (153, 352)
< 0.001
AST (U/L)
190 (119, 303)
191(113, 330)
186 (124, 290)
0.656
ALT (U/L)
113 (71, 174)
119 (65, 197)
110 (74, 153)
0.565
Creatinine (μmol/L)
13.0 (11.0, 16.0)
14.0 (12.0, 16.0)
12.0 (10.0, 15.0)
0.017
Hemoglobin (g/L), mean ± SD
93 ± 16
95 ± 17
90 ± 15
0.011
Platelet (109/L)
190 (127, 255)
190 (126, 235)
190 (130, 262)
0.378
INR
1.38 (1.13, 1.82)
1.25 (1.06, 1.66)
1.54 (1.32, 1.92)
< 0.001
PELD
< 0.001
< 14.5
103 (32.1)
82 (48.0)
21 (14.0)
≥ 14.5
218 (67.9)
89 (52.0)
129 (86.0)
Graft-related data
Graft Weight (g)
244 (214, 280)
246 (214, 277)
244 (217, 280)
0.814
Graft CIT (minute)
86 (66, 112)
85 (66, 109)
86 (67, 115)
0.275
Intraoperative clinical data
Laboratory test results before reperfusion
pH, mean ± SD
7.388 ± 0.067
7.391 ± 0.068
7.384 ± 0.067
0.379
Base excess (mmol/L)
-4.40 (-6.40, -2.40)
-4.40 (-6.25, -1.90)
-4.40 (-6.65, -3.00)
0.338
Lactate (mmol/L)
2.60 (2.10, 3.40)
2.50 (1.90, 3.10)
2.70 (2.20, 3.70)
0.001
Hemoglobin (g/L)
82 (73, 92)
84 (75, 93)
81 (73, 91)
0.219
K (mmol/L)
3.70 (3.40, 4.10)
3.70 (3.50, 4.20)
3.70 (3.40, 4.07)
0.405
Ca (mmol/L)
1.09 (1.01, 1.16)
1.09 (1.01, 1.16)
1.08 (1.01, 1.16)
0.827
Vital signs before reperfusion
Heart rate (beats/minute), mean ± SD
117 ± 13
119 ± 14
115 ± 12
0.029
MAP (mmHg)
58 (52, 66)
59 (54, 67)
56 (51, 64)
0.014
CVP (mmHg)
5 (3, 7)
5 (4, 7)
5 (3, 7)
0.821
Temperature (°C)
36.5 (35.9, 37.0)
36.5 (35.9, 37.0)
36.5 (35.8, 37.0)
0.528
Anhepatic phase time (minute)
48 (41, 58)
48 (40, 56)
49 (41, 62)
0.141
PRS
< 0.001
No
182 (56.7)
124 (72.5)
58 (38.7)
Yes
139 (43.3)
47 (27.5)
92 (61.3)
Data at the end of surgery
Blood loss (mL)
300 (200, 400)
300 (200, 400)
300 (200, 400)
0.415
RBCs transfusion (units)
2.0 (2.0, 3.0)
2.0 (1.5, 3.0)
2.0 (2.0, 3.0)
0.069
FFP infusion (mL)
0 (0, 150)
0 (0, 100)
0 (0, 200)
0.005
Fluid infusion (mL)
1387 (1100, 1700)
1387 (1100, 1770)
1384 (1103, 1671)
0.784
Urine volume (mL)
400 (280, 600)
400 (300, 600)
400 (240, 600)
0.120
Surgical time (minute)
540 (490, 600)
540 (495, 600)
542 (481, 590)
0.730
Anaesthesia time (minute)
615 (560, 650)
615 (565, 650)
616 (555, 655)
0.650
Postoperative clinical data
Ventilation time (minute)
205 (136, 340)
230 (147, 349)
185 (133, 320)
0.193
ICU stay (day)
2.5 (2.0, 3.0)
2.0 (2.0, 3.0)
3.0 (2.0, 3.4)
0.044
AKI
0.545
No
240 (74.8)
125 (73.1)
115 (76.7)
Yes
81 (25.2)
46 (26.9)
35 (23.3)
Liver function tests during the initial week following LDLT
Peak TB (μmol/L)
84 (56, 117)
78 (50, 115)
92 (66, 123)
0.004
Peak AST (U/L)
697 (485, 1290)
666 (448, 1180)
758 (506, 1395)
0.027
Peak ALT (U/L)
586 (404, 1089)
585 (372, 1076)
592 (415, 1138)
0.247
Hospital stay (day)
21 (17, 27)
21 (17, 30)
21 (17, 25)
0.158
1-year survival rate
306 (95.3)
168 (98.2)
138 (92.0)
0.017
Table 2 Clinical data of patients in the training and validation sets, n (%).
Training set (n = 225)
Validation set (n = 96)
P value
Preoperative clinical data
Age (month)
8.0 (6.0, 12.0)
9.0 (6.0, 16.3)
0.434
Male gender
108 (48.0)
47 (49.0)
0.972
Weight (kg)
7.3 (6.5, 9.5)
7.5 (6.5, 10.0)
0.742
Height (cm)
67 (64, 76)
67 (63, 79)
0.621
QTc (ms)
405 (387, 423)
407 (387, 422)
0.862
LVEF (%)
64 (62, 67)
65 (62, 67)
0.693
NLR
0.79 (0.51, 1.28)
0.78 (0.49, 1.43)
0.878
PLR
39.5 (27.5, 55.0)
42.5 (30.3, 55.2)
0.111
hsCRP (mg/L)
5.31 (2.53, 13.93)
5.04 (1.95, 10.63)
0.262
Albumin (g/L)
34.5 (32.0, 38.3)
34.3 (31.7, 38.3)
0.338
TB (μmol/L)
226 (78, 317)
223 (65, 314)
0.372
AST (U/L)
190 (123, 306)
191 (108, 294)
0.488
ALT (U/L)
115 (68, 179)
107 (72, 154)
0.680
Creatinine (μmol/L)
13.0 (11.0, 16.0)
13.0 (11.0, 15.0)
0.933
Hemoglobin (g/L), mean ± SD
93 ± 16
93 ± 17
0.916
Platelet (109/L)
190 (128, 250)
184 (118, 265)
0.451
INR
1.40 (1.13, 1.82)
1.38 (1.13, 1.83)
0.953
PELD score
0.937
< 14.5
73 (32.4)
30 (31.3)
≥ 14.5
152 (67.6)
66 (68.8)
Graft-related data
Graft weight (g)
244 (218, 277)
246 (212, 281)
0.849
Graft CIT (minute)
85 (66, 112)
88 (68, 119)
0.556
Intraoperative clinical data
Laboratory test results before reperfusion
pH, mean ± SD
7.39 ± 0.07
7.38 ± 0.07
0.223
Base excess (mmol/L)
-4.40 (-6.20, -2.20)
-4.40 (-6.82, -2.85)
0.630
Lactate (mmol/L)
2.60 (2.00, 3.40)
2.70 (2.20, 3.40)
0.503
Hemoglobin (g/L)
84 (74, 92)
81 (73, 92)
0.572
K (mmol/L)
3.80 (3.50, 4.10)
3.65 (3.40, 4.03)
0.139
Ca (mmol/L)
1.09 (1.01, 1.17)
1.08 (1.01, 1.14)
0.306
Vital signs before reperfusion
Heart rate (beats/minute), mean ± SD
116 ± 13
118 ± 12
0.202
MAP (mmHg)
59 (52, 66)
58 (53, 64)
0.736
CVP (mmHg)
5 (3, 7)
5 (3, 7)
0.539
Temperature (°C)
36.5 (35.9, 37.0)
36.5 (36.0, 37.1)
0.570
Anhepatic phase duration (minute)
49 (41, 59)
46 (40, 55)
0.063
PRS
0.611
No
125 (55.6)
57 (59.4)
Yes
100 (44.4)
39 (40.6)
Data at the end of surgery
Blood loss (mL)
300 (230, 400)
300 (200, 400)
0.079
RBCs transfusion (units)
2.0 (2.0, 3.0)
2.0 (2.0, 2.5)
0.117
FFP infusion (mL)
0 (0, 150)
0 (0, 135)
0.650
Fluid infusion (mL)
1400 (1109, 1745)
1334 (1100, 1652)
0.403
Urine volume (mL)
400 (260, 600)
425 (300, 600)
0.628
Surgical time (minute)
540 (480, 596)
550 (504, 601)
0.070
Anaesthesia time (minute)
615 (555, 649)
625 (579, 663)
0.066
Myocardial injury
105 (46.7)
45 (46.9)
0.973
Postoperative clinical data
Ventilation time (minute)
205 (135, 320)
208 (139, 350)
0.729
ICU stay (day)
2.0 (2.0, 3.0)
3.0 (2.0, 3.1)
0.101
AKI
0.839
No
167 (74.2)
73 (76.0)
Yes
58 (25.8)
23 (24.0)
Liver function tests during the initial week following LDLT
Peak TB (μmol/L)
83 (56, 121)
86 (56, 111)
0.700
Peak AST (U/L)
699 (483, 1333)
691 (497, 1220)
0.762
Peak ALT (U/L)
588 (408, 1084)
579 (385, 1096)
0.603
Hospital stay (day)
21 (17, 26)
21 (16, 29)
0.609
One-year survival rate
215 (95.6)
91 (94.8)
0.776
Predictors of myocardial injury
Taking the occurrence of myocardial injury as the dependent variable and the 39 selected indicators as independent variables, the optimal penalty coefficient was determined through 10-fold cross-verification, and its corresponding Lambda.1se value was 0.06345441. Four variables were selected: Preoperative NLR, hsCRP level, PELD score and PRS (as illustrated in Figure 2). The four variables identified through least absolute shrinkage and selection operator were incorporated into a multivariable logistic regression analysis. The results demonstrated that all four indicators functioned as independent risk factors linked to myocardial injury in children receiving liver transplantation (P < 0.05). The details are listed in Table 3.
Figure 2 Least absolute shrinkage and selection operator regression.
A: Coefficient curve of independent variables; B: Selection of the best independent variables by least absolute shrinkage and selection operator regression and 10-fold cross validation.
Table 3 Multivariable logistic regression analysis of risk factors for myocardial injury.
Risk factors
OR
95%CI
P value
PELD ≥ 14.5
4.441
2.066-10.120
< 0.001
hsCRP
1.085
1.045-1.133
< 0.001
NLR
3.380
1.766-7.282
0.001
PRS
4.662
2.330-9.676
< 0.001
Nomogram construction for myocardial injury during LDLT
As shown in Figure 3, a nomogram prediction model was established on the basis of the results of multivariable logistic regression analysis. The results of the predictive analysis revealed that the AUC for predicting the occurrence of myocardial injury in the training set was 0.865 (95% confidence interval: 0.819-0.912), the optimal cutoff value was 0.410, the specificity was 79.2%, the sensitivity was 80.0%, and the discriminatory ability was good (Figure 4A). The Hosmer-Lemeshow test (χ2 = 10.006, P = 0.2646) indicated a good fit for the training set. Bootstrap sampling with 1000 iterations confirmed strong alignment between the predicted and actual risks (Figure 5A).
Figure 4 Receiver operating characteristic curves for predicting myocardial injury in pediatric patients in the training and validation sets.
A: Receiver operating characteristic curves for predicting myocardial injury in pediatric patients in the training set; B: Receiver operating characteristic curves for predicting myocardial injury in pediatric patients in the validation set. AUC: Area under the curve; CI: Confidence interval.
Figure 5 Calibration curves for the training and validation sets.
A: Calibration curves for the training set; B: Calibration curves for the validation set.
Calibration verification of the nomogram
A total of 96 patients were included in the validation set, 45 (46.9%) of whom had myocardial injury. In the validation set, the AUC for predicting myocardial injury was 0.856, with a 95% confidence interval ranging from 0.783 to 0.928. This model demonstrated an optimal cutoff value of 0.585, a specificity of 94.1% and a sensitivity of 66.7%, as shown in Figure 4B. The Hosmer-Lemeshow test yielded χ2 = 12.389, P = 0.1347, indicating that the model fit was good. Internal calibration performed via 1000 bootstrap resamplings revealed that the predicted values were very close to the actual values (Figure 5B).
Analysis of the clinical practicality of the prediction model
In the decision curve analysis, the green horizontal lines signify the scenario of no intervention, where the net benefit is zero, whereas the red lines represent the intervention applied to all patients (Figure 6). The predictive model demonstrated a substantial net benefit, with a broad high-risk threshold probability range observed in both the training and validation groups, highlighting its clinical usefulness.
Figure 6 Clinical decision curve analysis for the training and validation sets.
A: Clinical decision curve analysis for the training set; B: Clinical decision curve analysis for the validation set.
DISCUSSION
CTnI is an important marker for the early diagnosis of myocardial injury[13,14]. When cardiomyocytes are damaged, their concentration in the blood increases, indicating myocardial injury. In the pediatric population, troponins are strongly correlated with the extent of myocardial damage and can be used as predictors of subsequent cardiac recovery and mortality[15]. From Dou et al’s study[16], we can observe the change trend of cTnI levels in children during LDLT: It increases during the operation, increases 8-12 times at the end of the operation, reaches the peak value, and returns to the pretransplantation level 3 days after the operation. In accordance with Sheng et al’s study[5] and the instrument manufacturer’s instructions, a cTnI concentration ≥ 0.07 ng/mL indicated myocardial injury. The study revealed that 150 patients (46.7%) experienced myocardial injury during surgery, and the independent risk factors for this injury included a high NLR, high hsCRP level, high PELD score and the occurrence of PRS. Many factors, such as severe preoperative conditions, severe fluctuations in circulation during surgery and inflammatory reactions in the body, may lead to myocardial injury.
The NLR, which is the ratio of the neutrophil count to the lymphocyte count in peripheral blood, is a readily available laboratory indicator and a marker of systemic inflammation[17]. Cirrhosis and portal hypertension are often accompanied by bacterial translocation and endotoxemia, which can induce an increase in blood neutrophil counts and a decrease in total lymphocyte counts, as well as elevated levels of systemic proinflammatory cytokines, which can lead to cardiac dysfunction[18,19]. Studies have shown that the main cause of liver transplantation-related myocardial injury is the inflammatory response[20,21]. Bezinover et al[22] demonstrated that the massive release of tumor necrosis factor alpha in the early stage of reperfusion of the new liver could lead to hemodynamic instability and a heightened requirement for norepinephrine. Marfella et al[23] reported that tumor necrosis factor alpha could induce the overexpression of nitric oxide synthase in myocardial cells, leading to massive production of nitric oxide, reducing the response of myofilaments to Ca2+, and thus exerting a negative inotropic effect on the myocardium. A study by Nylec et al[24] revealed that the NLR is a risk factor for increased graft loss and mortality in adult orthotopic liver transplant patients. The increase in the NLR before surgery in patients with cirrhosis may indicate endotoxemia and an excessive inflammatory response in the body, which is related not only to myocardial damage but also to a poor prognosis.
CRP serves as a highly sensitive biomarker for systemic inflammation and is a good indicator of the severity of myocardial damage[25]. CRP plays a proinflammatory role by activating the complement system and stimulating natural killer cells[26]. In rat liver ischemia/reperfusion studies, CRP was found to have activated C3 in hepatocytes, indicating that it is a key mediator of classical complement activation[27]. Higher hsCRP levels are closely associated with lower systolic blood pressure and the development of cardiac complications[28]. Elevated hsCRP levels before liver transplantation are associated with increased cardiovascular events after liver transplantation[29].
The PELD score serves as a vital tool for assessing the severity and future outlook of end-stage liver disease in children[30]. A retrospective analysis conducted by Sheng et al[31] involving 112 children with biliary atresia who underwent LDLT revealed that the preoperative PELD score was positively correlated with the intraoperative increase in cardiac troponin I levels. Specifically, higher PELD scores were associated with increased serum cardiac troponin I concentrations during surgery. Oh et al[32] reported that a PELD score greater than 25 independently predicts graft function loss after pediatric LDLT. Pan et al’s study revealed that the PELD score was an independent predictor of 1-year and 3-year survival[33]. Patients with high PELD scores are more likely to suffer from intraoperative myocardial injury and therefore have a poor prognosis.
Living-donor liver transplantation is the main treatment for biliary atresia in children. The transition from the anhepatic stage to the neohepatic stage is the most critical period of surgery. During this phase, abrupt and significant hemodynamic alterations may occur, resulting in decreased myocardial contractility, sustained hypotension, bradycardia, or severe arrhythmias. These adverse events are typical features of PRS[34-36]. PRS is recognized as one of the most prevalent complications associated with liver transplantation, with an incidence rate ranging from approximately 12% to 55%[36-38]. This syndrome may result in a slower healing process for transplanted liver functions, longer hospital stays, and an increased risk of mortality, thereby significantly impacting the postoperative quality of life of pediatric patients[36,38]. PRS can be caused by multiple factors, such as metabolic acidosis, hyperkalemia, hypocalcemia, hypothermia, inflammatory mediators, increased CIT and increased severity of liver disease[11,39-42]. PRS prevention and treatment strategies should be actively adopted to reduce the risk of myocardial injury. It is essential to improve the patient’s overall condition before surgery by implementing measures such as infection control, nutritional support, and the correction of anemia and hypoproteinemia. During the operation, we should optimize the patient’s blood volume status, take active temperature protection measures, administer anti-inflammatory medications, correct acid-base imbalances and electrolyte disturbances, shorten the CIT as much as possible, and apply pressors before reperfusion. The above measures may be practical strategies for managing PRS.
There is no related research on the establishment of a predictive nomogram model for myocardial injury in pediatric liver transplantation through a large-sample retrospective study, so this preliminary exploratory study is valuable. Compared with the conventional logistic regression model, the nomogram model offers greater simplicity, enhanced intuitiveness, and increased practical utility in clinical settings. Importantly, this study had a small sample size, lacked external validation, and excluded some patients because of incomplete data, potentially causing selection bias and limitations. In addition, anesthesia management varies across centers, which may bias the results of single-center studies. Therefore, future analyses should be further verified using multicenter large sample data. While the current model provides a robust prediction of myocardial injury on the basis of a set of preoperative and intraoperative variables, there are numerous opportunities to refine and expand its predictive power by incorporating additional predictors. Future studies can explore the role of advanced biomarkers, imaging findings, and other novel technologies that could further increase the model’s accuracy and clinical utility.
CONCLUSION
The nomogram developed in this study effectively predicts myocardial injury in pediatric LDLT, showing good accuracy and potential for clinical application.
ACKNOWLEDGEMENTS
We thank our colleagues who have cooperated in this study.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade C, Grade C
Novelty: Grade B, Grade B
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade B, Grade B
P-Reviewer: Wang PP S-Editor: Wang JJ L-Editor: A P-Editor: Zhang XD
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