BackgroundWe aimed to develop and validate a prediction model for in‐hospital complications in children with tetralogy of Fallot repaired at an older age.Methods and ResultsA total of 513 pediatric patients from the Tianjin data set formed a derivation cohort, and 158 pediatric patients from the Hefei and Xiamen data sets formed validation cohorts. We applied least absolute shrinkage and selection operator analysis for variable selection and logistic regression coefficients for risk scoring. We classified patients into different risk categorizations by threshold analysis and investigated the association with in‐hospital complications using logistic regression. In‐hospital complications were defined as death, need for extensive pharmacologic support (vasoactive‐inotrope score of ≥20), and need for mechanical circulatory support. We developed a nomogram based on risk classifier and independent baseline variables using a multivariable logistic model. Based on risk scores weighted by 11 preoperative and 4 intraoperative selected variables, we classified patients as low, intermediate, and high risk in the derivation cohort. With reference to the low‐risk group, the intermediate‐ and high‐risk groups conferred significantly higher in‐hospital complication risks (adjusted odds ratio: 2.721 [95% CI, 1.267–5.841], P=0.0102; 9.297 [95% CI, 4.601–18.786], P<0.0001). A nomogram integrating the ARIAR‐Risk classifier (absolute and relative low risk, intermediate risk, and aggressive and refractory high risk) with age and mean blood pressure showed good discrimination and goodness‐of‐fit for derivation (area under the receiver operating characteristic curve: 0.785 [95% CI, 0.731–0.839]; Hosmer‐Lemeshow test, P=0.544) and external validation (area under the receiver operating characteristic curve: 0.759 [95% CI, 0.636–0.881]; Hosmer‐Lemeshow test, P=0.508).ConclusionsA risk‐classifier–oriented nomogram is a reliable prediction model for in‐hospital complications in children with tetralogy of Fallot repaired at an older age, and strengthens risk/benefit–based decision‐making.
Aim
The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications.
Methods
A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohort at a ratio of 7:3 for the development and validation of risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analysis were performed by risk stratifications (low versus middle-high risk) and arch repair strategies (proximal versus extensive arch repair).
Results
Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisted of platelet-leukocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, hemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability (AUROC 0.930 [95% CI 0.906-0.954] and 0.954 [95%CI 0.930-0.977] in the derivation and validation cohort, respectively). Compared with proximal arch repair, extensive repair was associated with similar mortality risk among patients at low risk (OR 1.838 [95%CI 0.559, 6.038]P = 0.316), but associated with higher mortality risk among patients at middle-high risk (OR 2.007 [95%CI 1.460, 2.757]P < 0.0001)
Conclusion
In ATAAD patients, simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.
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