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Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

ABSTRACT: Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for predicting mortality. In order to compare the significance of clinical features dynamically, we further applied the random forest (RF) model to nine selected clinical parameters acquired at baseline and day 3 independently. An RF model trained using clinical data collected at day 3 showed improved performance and prognostication efficacy (area under the curve [AUC]: 0.84, 95% CI: 0.78-0.89) compared to baseline with an AUC value of 0.72 (95% CI: 0.65-0.78). Mean airway pressure (MAP), bicarbonate, age, platelet count, albumin, heart rate, and glucose were the most significant clinical indicators associated with mortality at day 3. Thus, clinical features collected early (day 3) improved performance of integrative ML models with better prognostication for mortality. Among these, MAP represented the most important feature for ARDS patients' early risk stratification.

SUBMITTER: Ding N

PROVIDER: S-EPMC11294575 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Ding Ning N Nath Tanmay T Damarla Mahendra M Gao Li L Hassoun Paul M PM

Scientific reports 20240801 1

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for pre ...[more]

PMID: 39090217

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Project description:BackgroundAlthough offering the best chance of potential cure for patients with localized perihilar cholangiocarcinoma (pCCA), resection has been associated with high morbidity and sometimes poor long-term outcomes due to recurrence. We sought to develop a predictive model to identify individuals at high risk for very early recurrence (VER) after curative-intent surgery for pCCA.MethodsPatients who underwent curative-intent surgery for pCCA between 2000-2023 were identified from a multi-institutional database. An eXtreme Gradient Boosting (XGBoost) model was developed to estimate the risk of VER, defined as recurrence within 6 months after resection. The relative importance of clinicopathologic factors was determined using SHapley Additive exPlanations (SHAP) values.ResultsAmong 434 patients undergoing curative-intent resection for pCCA, 65 (15.0%) patients developed VER. Median overall survival (OS) among patients with and without VER was 8.4 [interquartile range (IQR) 6.6-11.3] versus 38.5 (IQR 31.9-45.7) months (P<0.001). An XGBoost model was able to stratify patients relative to the risk of VER [low-risk: 6-month recurrence-free survival (RFS) 94.6% vs. intermediate-risk: 6-month RFS 88.3% vs. high-risk: 6-month RFS 40.0%; P<0.001]. Similarly, 3-year OS incrementally worsened based on VER risk (low-risk: 75.3% vs. intermediate-risk: 19.5% vs. high-risk: 4.6%; P<0.001). The SHAP algorithm identified age, preoperative carbohydrate antigen 19-9 (CA19-9) levels, tumor size and differentiation/grade, as well as lymph node metastasis as the five most important predictors of VER. The predictive accuracy of the model was good in the training [c-index: 0.74, 95% confidence interval (CI): 0.67-0.81] and internal validation (c-index: 0.77, 95% CI: 0.71-0.83) cohorts. An easy-to-use risk calculator for VER was developed and made available online at: https://junkawashima.shinyapps.io/VER_hilar/.ConclusionsA novel, machine learning based model was able to predict accurately the chance of VER after curative-intent resection of pCCA. In turn, the tool may help surgeons in the selection of patients likely to benefit the most from resection, as well as counsel individuals about the anticipated risk of recurrence in the early post-operative period.

Dataset Information

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Publications

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

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