Development and Validation of a Radiomics Nomogram for Prognosis Prediction of Patients with Acute Paraquat Poisoning: A Retrospective Cohort Study

Abstract

Objective. To evaluate the efficiency of a radiomics model in predicting the prognosis of patients with acute paraquat poisoning (APP). Materials and Methods. Chest computed tomography images and clinical data of 80 patients with APP were obtained from November 2014 to October 2017, which were randomly assigned to a primary group and a validation group by a ratio of 7 : 3, and then the radiomics features were extracted from the whole lung. Principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were used to select the features and establish the radiomics signature (Rad-score). Multivariate logistic regression analysis was used to establish a radiomics prediction model incorporating the Rad-score and clinical risk factors; the model was represented by nomogram. The performance of the nomogram was confirmed by its discrimination and calibration. Result. The area under the ROC curve of operation was 0.942 and 0.865, respectively, in the primary and validation datasets. The sensitivity and specificity were 0.864 and 0.914 and 0.778 and 0.929, and the prediction accuracy rates were 89.5% and 87%, respectively. Predictors included in the individualized predictive nomograms include the Rad-score, blood paraquat concentration, creatine kinase, and serum creatinine. The AUC of the nomogram was 0.973 and 0.944 in the primary and validation datasets, and the sensitivity and specificity were 0.943 and 0.955, respectively, in the primary dataset and 0.889 and 0.929 in the validation dataset, and the prediction accuracy was 94.7% and 91.3%, respectively. Conclusion. The radiomics nomogram incorporates the radiomics signature and hematological laboratory data, which can be conveniently used to facilitate the individualized prediction of the prognosis of APP patients.

Development and validation of a prognostic nomogram for predicting in-hospital mortality of patients with acute paraquat poisoning

“…1); among them, 360/724 patients (49.7%) died. In terms of demographics, there were 325/724 (44.9%) males, with a median age of 33 (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42) years. Among 506/724 patients in the training group, 44.1% were male, with a median age of 29 years, and 253/506 (50%) patients died.…”

“…Among 506/724 patients in the training group, 44.1% were male, with a median age of 29 years, and 253/506 (50%) patients died. Of the 218/724 patients in the validation group, 46.8% were male, with a median age of 31 (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42) years, and 107/218 (49.1%) patients died. No statistical significant differences were observed between the two groups (P > 0.05) (Table 1).…”

Utility of Clinical Variables for Deciding Palliative Care in Paraquat Poisoning: A Retrospective Study

“… 8 Previous literature also revealed that liver and renal dysfunction, serum anion gap above 25.5 mmoL/L, and computerized tomography of the thorax were predictors of outcomes in PP. 9 , 10 …”

Comparison of Machine Learning Models in Predicting In-hospital Mortality of Patients with Acute Paraquat Poisoning

“…Despite its widespread use as an herbicide, PQ's high toxicity and limited detoxi cation options have resulted in PQ poisoning becoming a major public health concern [2]. Numerous scholars have conducted studies on APP prediction models [3][4][5][6][7][8][9][10]. For instance, Chen et al utilized Support Vector Machine (SVM) to predict the prognosis of 79 APP patients using routine blood indices [7].…”