A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study.

BACKGROUND: Acute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes. OBJECTIVE: The primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes. METHODS: We developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older. RESULTS: The developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy. CONCLUSIONS: NAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials.

PURPOSE: Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is included in the International Myeloma Working Group (IMWG) imaging guidelines for the work-up at diagnosis and the follow-up of multiple myeloma (MM) notably because it is a reliable tool as a predictor of prognosis. Nevertheless, none of the published studies focusing on the prognostic value of PET-derived features at baseline consider tumor heterogeneity, which could be of high importance in MM. The aim of this study was to evaluate the prognostic value of baseline PET-derived features in transplant-eligible newly diagnosed (TEND) MM patients enrolled in two prospective independent European randomized phase III trials using an innovative statistical random survival forest (RSF) approach. METHODS: Imaging ancillary studies of IFM/DFCI2009 and EMN02/HO95 trials formed part of the present analysis (IMAJEM and EMN02/HO95, respectively). Among all patients initially enrolled in these studies, those with a positive baseline FDG-PET/CT imaging and focal bone lesions (FLs) and/or extramedullary disease (EMD) were included in the present analysis. A total of 17 image features (visual and quantitative, reflecting whole imaging characteristics) and 5 clinical/histopathological parameters were collected. The statistical analysis was conducted using two RSF approaches (train/validation + test and additional nested cross-validation) to predict progression-free survival (PFS). RESULTS: One hundred thirty-nine patients were considered for this study. The final model based on the first RSF (train/validation + test) approach selected 3 features (treatment arm, hemoglobin, and SUVmaxBone Marrow (BM)) among the 22 involved initially, and two risk groups of patients (good and poor prognosis) could be defined with a mean hazard ratio of 4.3 ± 1.5 and a mean log-rank p value of 0.01 ± 0.01. The additional RSF (nested cross-validation) analysis highlighted the robustness of the proposed model across different splits of the dataset. Indeed, the first features selected using the train/validation + test approach remained the first ones over the folds with the nested approach. CONCLUSION: We proposed a new prognosis model for TEND MM patients at diagnosis based on two RSF approaches. TRIAL REGISTRATION: IMAJEM: NCT01309334 and EMN02/HO95: NCT01134484.

Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis.

PURPOSE: Multiple myeloma (MM) is a bone marrow cancer that accounts for 10% of all hematological malignancies. It has been reported that FDG PET imaging provides prognostic information for both baseline and therapeutic follow-up of MM patients using visual analysis. In this study, we aim to develop a computer-assisted method based on PET quantitative image features to assist diagnoses and treatment decisions for MM patients. METHODS: Our proposed model relies on a two-stage method with Random Survival Forest (RFS) and variable importance (VIMP) for both feature selection and prediction. The targeted variable for prediction is the progression-free survival (PFS). We consider texture-based (radiomics), conventional (e.g., SUVmax) and clinical biomarkers. We evaluate PFS predictions in terms of C-index and final prognosis separation in two risk groups, from a database of 66 patients who were part of the prospective multi-centric french IMAJEM study. RESULTS: Our method (VIMP + RSF) provides better results (1-C-index of 0.36) than conventional methods such as Lasso-Cox and gradient-boosting Cox (0.48 and 0.56, respectively). We experimentally proved the interest of using selection (0.61 for RSF without selection) and showed that VIMP selection is more stable and gives better results than minimal depth and variable hunting (0.47 and 0.43). The approach gives better prognosis group separation (a p value of 0.05 against 0.11 to 0.4 for others). CONCLUSION: Our results confirm the predictive value of radiomics for MM patients, in particular, they demonstrate that quantitative/heterogeneity image-based features reduce the error of the predicted progression. To our knowledge, this is the first work using RFS on PET images for the progression prediction of MM patients. Moreover, we provide an analysis of the feature selection process, which points toward the identification of clinically relevant biomarkers.