Development and internal validation of an early postoperative nomogram for predicting prolonged intensive care unit stay after primary complete repair of tetralogy of Fallot in children
|更新时间:2026-10-23
|
Development and internal validation of an early postoperative nomogram for predicting prolonged intensive care unit stay after primary complete repair of tetralogy of Fallot in children
Journal of Chongqing Medical UniversityPages: 1-9(2026)
作者机构:
空军军医大学第一附属医院心血管外科,西安 710032
作者简介:
基金信息:
DOI:
CLC:R114
Received:14 July 2026,
Revised:2026-09-17,
Accepted:20 September 2026,
Published:23 October 2026
稿件说明:
移动端阅览
Rao Min,Zhu Junming,Lü Xiangni.Development and internal validation of an early postoperative nomogram for predicting prolonged intensive care unit stay after primary complete repair of tetralogy of Fallot in children[J].Journal of Chongqing Medical University,
DOI:
Rao Min,Zhu Junming,Lü Xiangni.Development and internal validation of an early postoperative nomogram for predicting prolonged intensive care unit stay after primary complete repair of tetralogy of Fallot in children[J].Journal of Chongqing Medical University,DOI:10.13406/j.cnki.cyxb..
Development and internal validation of an early postoperative nomogram for predicting prolonged intensive care unit stay after primary complete repair of tetralogy of Fallot in children
=1.29,95%CI=1.02~1.63]与ICU延长滞留概率升高相关。模型表观受试者工作特征曲线下面积(area under the receiver operating characteristic curve,AUC)为0.736;重复嵌套交叉验证AUC为0.707(95%CI=0.653~0.759),Brier评分为0.196,校准截距为0.036(95%CI=-0.263~0.366),校准斜率为1.057(95%CI=0.750~1.425)。敏感性复合结局模型的重复嵌套交叉验证AUC为0.701(95%CI=0.647~0.754)。
To investigate predictors of prolonged intensive care unit(ICU) stay after primary complete repair of tetralogy of Fallot in children and develop an early postoperative risk prediction model for use at ICU admission.
Methods
2
This single-center retrospective cohort study consecutively screened 783 children who underwent surgical treatment for tetralogy of Fallot during the study period. Of these,343 were excluded because they did not meet the inclusion criteria or met the exclusion criteria,and 19 were excluded because their postoperative ICU stay or in-hospital outcome could not be ascertained. The final analysis included 421 children. Based on previous studies and the postoperative ICU management pathway at our center,an ICU stay of ≥72 h was prespecified as the primary outcome. Secondary outcomes included an ICU stay of ≥96 h and in-hospital mortality. A composite outcome of in-hospital mortality or an ICU stay of ≥72 h was evaluated in a sensitivity analysis. The prediction time point was defined as ICU admission after completion of surgery. Ten prespecified candidate predictors were simultaneously entered into an L
2
-penalized logistic regression model,with the penalty parameter selected by cross-validation. A nomogram was constructed using the penalized coefficients and recalibration intercept. Model discrimination,overall prediction error,and calibration were evaluated using five repeated runs of nested five-fold cross-validation and 500 bootstrap resamples.
Results
2
Of the 421 children,142 experienced a prolonged ICU stay,corresponding to an incidence of 33.7%. L
2
-penalized logistic regression showed that greater weight at surgery was associated with a lower probability of prolonged ICU stay(odds ratio[
OR
]=0.83,95%CI=0.73-0.95). Endotracheal intubation before arrival in the operating room(
OR
=2.36,95%CI=1.18-4.88),longer
cardiopulmonary bypass time (per 10-min increase:
OR
=1.12,95%CI=1.03-1.22),higher lactate concentration at ICU admission(per 1 mmol/L increase:
OR
=1.41,95%CI=1.08-1.88),higher vasoactive-inotropic score at ICU admission (per 5-point increase:
OR
=1.72,95%CI=1.23-2.52),and a higher grade of residual lesions assessed by transesophageal echocardiography before leaving the operating room(per 1-grade increase:
OR
=1.29,95%CI:1.02-1.63) were associated with a higher probability of prolonged ICU stay. The apparent area under the receiver operating characteristic curve(AUC) was 0.736 for the model. The AUC of repeated nested cross-validation was 0.707(95%CI=0.653-0.759),with a Brier score of 0.196,a calibration intercept of 0.036 (95%CI:-0.263 to 0.366),and a calibration slope of 1.057(95%CI=0.750-1.425). For the sensitivity analysis model using the composite outcome,the AUC of repeated nested cross-validation was 0.701(95%CI=0.647-0.754).
Conclusion
2
The L
2
-penalized logistic regression model based on information available at ICU admission demonstrated moderate discrimination during internal validation,although uncertainty remained. The model should be regarded as an exploratory early postoperative risk prediction tool for use at ICU admission. Because the prediction time point is ICU admission,the model does not support preoperative decision-making or preoperative ICU resource planning. Until independent temporal or external validation has been completed,the model should not be used to adjust monitoring intensity or guide clinical decision-making.
关键词
Keywords
references
Horenstein MS , Diaz-Frias J , Guillaume M . Tetralogy of Fallot [M ] //StatPearls. Treasure Island(FL):StatPearls Publishing, 2026 . [2026‑10‑14] . https://www.statpearls.com/articlelibrary/viewarticle/25473/ https://www.statpearls.com/articlelibrary/viewarticle/25473/ .
Gagliardi MF , Micaglio E , Micheletti A , et al . Tetralogy of Fallot:genetic,epigenetic and clinical insights into a multifactorial congenital heart disease [J ] . Genes(Basel) , 2026 , 17 ( 2 ): 181 .
Song HL , Zhang HJ . Comparative analysis of mid‑term outcomes of one‑stage complete repair of tetralogy of Fallot with two surgical procedures [J ] . J Hebei Med Univ , 2023 , 44 ( 8 ): 905 ‑ 909,914 .
Adesanya AM , Best KE , Coats L , et al . Predictors of post-operative hospital length of stay following complete repair of tetralogy of Fallot in a pediatric cohort in the North of England [J ] . Pediatr Cardiol , 2024 , 45 ( 1 ): 92 - 99 .
Kesumarini D , Widyastuti Y , Boom CE , et al . Risk factors associated with prolonged mechanical ventilation and length of stay after repair of tetralogy of Fallot [J ] . World J Pediatr Congenit Heart Surg , 2024 , 15 ( 1 ): 81 - 88 .
Mercer-Rosa L , Elci OU , DeCost G , et al . Predictors of length of hospital stay after complete repair for tetralogy of Fallot:a prospective cohort study [J ] . J Am Heart Assoc , 2018 , 7 ( 11 ): e008719 .
Liu H , Zheng SQ , Li XY , et al . Derivation and validation of a nomogram to predict in-hospital complications in children with tetralogy of Fallot repaired at an older age [J ] . J Am Heart Assoc , 2019 , 8 ( 21 ): e013388 .
Pagowska-Klimek I , Pychynska-Pokorska M , Krajewski W , et al . Predictors of long intensive care unit stay following cardiac surgery in children [J ] . Eur J Cardiothorac Surg , 2011 , 40 ( 1 ): 179 - 184 .
Kanazawa T , Egi M , Shimizu K , et al . Intraoperative change of lactate level is associated with postoperative outcomes in pediatric cardiac surgery patients:retrospective observational study [J ] . BMC Anesthesiol , 2015 , 15 : 29 .
Gaies MG , Jeffries HE , Niebler RA , et al . Vasoactive-inotropic score is associated with outcome after infant cardiac surgery:an analysis from the Pediatric Cardiac Critical Care Consortium and Virtual PICU System Registries [J ] . Pediatr Crit Care Med , 2014 , 15 ( 6 ): 529 - 537 .
Nathan M , Levine JC , Van Rompay MI , et al . Impact of major residual lesions on outcomes after surgery for congenital heart disease [J ] . J Am Coll Cardiol , 2021 , 77 ( 19 ): 2382 - 2394 .
Wu X , An R , Luo QP , et al . Effect of preoperative pulse oximeter oxygen saturation on postoperative prolonged mechanical ventilation in patients with tetralogy of Fallot [J ] . Front Cardiovasc Med , 2022 , 9 : 967240 .
Song YA , Wen C , Pan Y , et al . Development and validation of a predictive model for low cardiac output syndrome after surgical repair of tetralogy of Fallot [J ] . Transl Pediatr , 2023 , 12 ( 6 ): 1110 - 1120 .
Collins GS , Dhiman P , Ma J , et al . Evaluation of clinical prediction models(part 1):from development to external validation [J ] . BMJ , 2024 , 384 : e074819 .
Efthimiou O , Seo M , Chalkou K , et al . Developing clinical prediction models:a step-by-step guide [J ] . BMJ , 2024 , 386 : e078276 .
Riley RD , Archer L , Snell KIE , et al . Evaluation of clinical prediction models(part 2):how to undertake an external validation study [J ] . BMJ , 2024 , 384 : e074820 .
Collins GS , Moons KGM , Dhiman P , et al . TRIPOD+AI statement:updated guidance for reporting clinical prediction models that use regression or machine learning methods [J ] . BMJ , 2024 , 385 : e078378 .