The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests

Rahman, Saqib; Walker, Robert; Maynard, Nick; Trudgill, Nigel; Crosby, Tom; Cromwell, David; Underwood, Tim

doi:10.1097/sla.0000000000004794

Cited by 40 publications

(29 citation statements)

References 45 publications

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“…The relation between variables and OS probably is nonlinear for patients after LTx, and that may be the reason why the RSF model showed a better performance. In this study, we confirmed the superiority of the RSF model for LTx recipients compared with the Cox model, consistent with previous studies . Moreover, we also tried stepwise selection to determine the modeling factors for the Cox regression model.…”

Section: Discussionsupporting

confidence: 89%

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

et al. 2023

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ImportanceAlthough numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.ObjectiveTo develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm.Design, Setting, and ParticipantsThis retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regression model was set as a benchmark. The integrated area under the curve (iAUC) and integrated Brier score (iBS) were applied to assess model performance in the test set. Data were analyzed from January 2017 to December 2019.Main Outcomes And MeasuresOverall survival in patients after LTx.ResultsA total of 504 patients were eligible for this study, consisting of 353 patients in the training set (mean [SD] age, 55.03 [12.78] years; 235 [66.6%] male patients) and 151 patients in the test set (mean [SD] age, 56.79 [10.95] years; 99 [65.6%] male patients). According to the variable importance of each factor, 16 were selected for the final RSF model, and postoperative extracorporeal membrane oxygenation time was identified as the most valuable factor. The RSF model had excellent performance with an iAUC of 0.879 (95% CI, 0.832-0.921) and an iBS of 0.130 (95% CI, 0.106-0.154). The Cox regression model fitted by the same modeling factors to the RSF model was significantly inferior to the RSF model with an iAUC of 0.658 (95% CI, 0.572-0.747; P &lt; .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P &lt; .001). According to the RSF model predictions, the patients after LTx were stratified into 2 prognostic groups displaying significant difference, with mean overall survival of 52.91 months (95% CI, 48.51-57.32) and 14.83 months (95% CI, 9.44-20.22; log-rank P &lt; .001), respectively.Conclusions and relevanceIn this prognostic study, the findings first demonstrated that RSF could provide more accurate overall survival prediction and remarkable prognostic stratification than the Cox regression model for patients after LTx.

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Section: Discussionsupporting

confidence: 89%

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

et al. 2023

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“…Chi-square tests were performed for categorical variables (e.g., gender) and differences in non-normally distributed numerical data using Wilcoxon rank-sum test between subgroups. To screen robust features, Elastic Net [a combination of Ridge and least absolute shrinkage and selection operator (LASSO) method], Random Forest, Boruta, and extreme gradient boosting (XGBoost) analyses were performed to select the most important subgroup-relevant features by calculating the importance score for each variable (Engebretsen and Bohlin, 2019;Johnson et al, 2020;Yperman et al, 2020;Colen et al, 2021;Rahman et al, 2021). With the remaining variables, the predictors were incorporated into a multivariable logistic regression model while adjusting for potential confounders including Age and Gender, considering the effect of participant structure on statistical results.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer’s Disease Based on Chemiluminescence Immunoassay for Total Tau in Plasma

Zhang

Wang

Dai

et al. 2022

Front. Aging Neurosci.

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BackgroundPredicting amnestic mild cognitive impairment (aMCI) in conversion and Alzheimer’s disease (AD) remains a daunting task. Standard diagnostic procedures for AD population are reliant on neuroimaging features (positron emission tomography, PET), cerebrospinal fluid (CSF) biomarkers (Aβ1-42, T-tau, P-tau), which are expensive or require invasive sampling. The blood-based biomarkers offer the opportunity to provide an alternative approach for easy diagnosis of AD, which would be a less invasive and cost-effective screening tool than currently approved CSF or amyloid β positron emission tomography (PET) biomarkers.MethodsWe developed and validated a sensitive and selective immunoassay for total Tau in plasma. Robust signatures were obtained based on several clinical features selected by multiple machine learning algorithms between the three participant groups. Subsequently, a well-fitted nomogram was constructed and validated, integrating clinical factors and total Tau concentration. The predictive performance was evaluated according to the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. Decision curve analysis and calibration curves are used to evaluate the net benefit of nomograms in clinical decision-making.ResultsUnder optimum conditions, chemiluminescence analysis (CLIA) displays a desirable dynamic range within Tau concentration from 7.80 to 250 pg/mL with readily achieved higher performances (LOD: 5.16 pg/mL). In the discovery cohort, the discrimination between the three well-defined participant groups according to Tau concentration was in consistent agreement with clinical diagnosis (AD vs. non-MCI: AUC = 0.799; aMCI vs. non-MCI: AUC = 0.691; AD vs. aMCI: AUC = 0.670). Multiple machine learning algorithms identified Age, Gender, EMPG, Tau, ALB, HCY, VB12, and/or Glu as robust signatures. A nomogram integrated total Tau concentration and clinical factors provided better predictive performance (AD vs. non-MCI: AUC = 0.960, AD vs. aMCI: AUC = 0.813 in discovery cohort; AD vs. non-MCI: AUC = 0.938, AD vs. aMCI: AUC = 0.754 in validation cohort).ConclusionThe developed assay and a satisfactory nomogram model hold promising clinical potential for early diagnosis of aMCI and AD participants.

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“…The Random Forest (RF) algorithm created decision trees with split points (nodes) as a result of recursive partitioning of the data into groups; tree branching was empirically based on variables that maximized outcome differences between daughter nodes until each tree was fully extended. The individual predicted probability was then derived using the average across all generated trees 21 . Specifically, an RF classification model was used to identify patient‐specific predictors of TTS = 0 adjusting for all patient, facility, tumor, and treatment characteristics 22 .…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence to investigate predictors and prognostic impact of time to surgery in colon cancer

Alaimo

Moazzam

Woldesenbet

et al. 2023

Journal of Surgical Oncology

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Background and Objectives The role of time to surgery (TTS) for long‐term outcomes in colon cancer (CC) remains ill‐defined. We sought to utilize artificial intelligence (AI) to characterize the drivers of TTS and its prognostic impact. Methods The National Cancer Database was utilized to identify patients diagnosed with non‐metastatic CC between 2004 and 2018. AI models were employed to rank the importance of several sociodemographic, facility, and tumor characteristics in determining TTS, and postoperative survival. Results Among 518 983 patients, 137 902 (26.6%) received intraoperative diagnosis of CC (TTS = 0), while 381 081 (74.4%) underwent elective surgery (TTS > 0) with median TTS of 19.0 days (interquartile range [IQR]: 7.0−33.0). An AI model, identified tumor stage, receipt of adequate lymphadenectomy, histologic grade, lymphovascular invasion, and insurance status as the most important variables associated with TTS = 0. Conversely, the type and location of treating facility and receipt of adjuvant therapy were among the most important variables for TTS > 0. Notably, TTS was among the most important variables associated with survival, and TTS > 3 weeks was associated with an incremental increase in mortality risk. Conclusions The identification of factors associated with TTS can help stratify patients most likely to suffer poor outcomes due to prolonged TTS, as well as guide quality improvement initiatives related to timely surgical care.

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The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests

Cited by 40 publications

References 45 publications

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer’s Disease Based on Chemiluminescence Immunoassay for Total Tau in Plasma

Artificial intelligence to investigate predictors and prognostic impact of time to surgery in colon cancer

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