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Identifying Risk Factors for Complicated Post-operative Course in Tetralogy of Fallot Using a Machine Learning Approach.

ABSTRACT: Introduction: Tetralogy of Fallot (TOF) repair is associated with excellent operative survival. However, a subset of patients experiences post-operative complications, which can significantly alter the early and late post-operative course. We utilized a machine learning approach to identify risk factors for post-operative complications after TOF repair. Methods: We conducted a single-center prospective cohort study of children <2 years of age with TOF undergoing surgical repair. The outcome was occurrence of post-operative cardiac complications, measured between TOF repair and hospital discharge or death. Predictors included patient, operative, and echocardiographic variables, including pre-operative right ventricular strain and fractional area change as measures of right ventricular function. Gradient-boosted quantile regression models (GBM) determined predictors of post-operative complications. Cross-validated GBMs were implemented with and without a filtering stage non-parametric regression model to select a subset of clinically meaningful predictors. Sensitivity analysis with gradient-boosted Poisson regression models was used to examine if the same predictors were identified in the subset of patients with at least one complication. Results: Of the 162 subjects enrolled between March 2012 and May 2018, 43 (26.5%) had at least one post-operative cardiac complication. The most frequent complications were arrhythmia requiring treatment (N = 22, 13.6%), cardiac catheterization (N = 17, 10.5%), and extracorporeal membrane oxygenation (ECMO) (N = 11, 6.8%). Fifty-six variables were used in the machine learning analysis, of which there were 21 predictors that were already identified from the first-stage regression. Duration of cardiopulmonary bypass (CPB) was the highest ranked predictor in all models. Other predictors included gestational age, pre-operative right ventricular (RV) global longitudinal strain, pulmonary valve Z-score, and immediate post-operative arterial oxygen level. Sensitivity analysis identified similar predictors, confirming the robustness of these findings across models. Conclusions: Cardiac complications after TOF repair are prevalent in a quarter of patients. A prolonged surgery remains an important predictor of post-operative complications; however, other perioperative factors are likewise important, including pre-operative right ventricular remodeling. This study identifies potential opportunities to optimize the surgical repair for TOF to diminish post-operative complications and secure improved clinical outcomes. Efforts toward optimizing pre-operative ventricular remodeling might mitigate post-operative complications and help reduce future morbidity.

SUBMITTER: Faerber JA

PROVIDER: S-EPMC8339319 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIntra-cardiac repair for tetralogy of Fallot has some degree of residual right ventricular outflow tract (RVOT) obstruction. However, the measurement of this gradient intra-operatively might get affected by the depth of anesthesia which is important for the long-term outcome.AimsThe primary aim was to compare intraoperative RVOT gradient post repair under two different anesthetic depths of 1% and 2% end-tidal sevoflurane. The secondary objective was to follow up the changes in RVOT gradient till 1 month postoperatively. Design: Observational study. Setting : Advanced Cardiac Centre of PGIMER, Chandigarh.MethodsFollowing intracardiac repair, RVOT gradient was measured directly by placing needle into the right ventricle and pulmonary artery at sevoflurane 1%, and subsequently, at 2% end.tidal concentration while maintaining hemodynamic stability. These gradients were also measured using transesophageal echocardiography (TEE) (ClinicalTrials.gov NCT03234582).ResultsTwenty-one patients were included in this study that had intra-cardiac repair, of which pulmonary annulus was preserved for 15 cases. Mean RVOT gradients measured invasively and by TEE at end-tidal sevoflurane concentration of 1% and 2% were not significantly different (6.67 ± 4.16 mmHg vs. 6.76 ± 3.82 mmHg, P > 0.05 invasively and 13.01 ± 7.40 mmHg vs. 12.53 ± 7.11 mmHg, P > 0.05 by TEE, respectively). RVOT gradient measured by trans-thoracic echocardiography (TTE) postoperatively at the time of extubation and during follow-up at 1 month showed significant reduction (11.37 ± 6.00 mmHg, P < 0.05 and 9.23 ± 4.92 mmHg, P < 0.01 respectively). Six patients who underwent repair with transannular patch had significant pulmonary regurgitation (PR) following surgery, with no significant change in PR severity or RVOT gradient on increasing anesthetic depth.ConclusionsPostoperative RVOT gradient was not altered by changing depth of anesthesia provided systemic blood pressure was maintained. One month postrepair RVOT gradients were significantly reduced as compared to the intraoperative values.

Project description:A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1, and NLRP1) that help predict sepsis severity or mortality. While these genes have been previously associated with sepsis mortality, in this work, we show that these genes are also implicated in complex disease courses, even among survivors. The discovery of eight novel genetic biomarkers related to the overactive innate immune system, including neutrophil function, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.

Project description:OBJECTIVES:Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts. DESIGN:We performed clustering analysis using data from patients' hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population. SETTING:Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members. PATIENTS:ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables. CONCLUSIONS:A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients' shared needs.

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Identifying Risk Factors for Complicated Post-operative Course in Tetralogy of Fallot Using a Machine Learning Approach.

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