OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

Objective To study the expression of PI3K and MMP-7 in esophageal carcinoma and the rela-tionship between the expression of PI3K and MMP-7 and carcinogenesis and progression of esophageal carcinoma. Methods PI3K and MMP-7 expression were detected in 24 normal esophageal mucosa,94 primary tumor tissues with SP immunohistochemistal method. Results There were significant differences of PI3K and MMP-7 expressions between esophageal carcinoma and normal mucesa epithelium ( all P < 0.01 ) [71.28% (67/94) vs 4.17% ( 1/24 ) and 52.13% (49/94) vs 0% (0/24)]. There were significant correlations between PI3K expression and the degrees of differentiation,invasive depth,clinical staging and the metastasis of lymph node (all P <0.01 ). The positive ex-pression rate of MMP-7 had the relationship with metastasis of lymph node (P < 0.05 ), the degrees of differentiation ( P < 0.05 ) invasive depth ( P < 0.01 ), and clinical staging( P < 0.05 ) . There was a positive relationship between PI3K and MMP-7 expression in this study (r = 0. 232 ,P = 0.025). Conlusions PBK and MMP-7 play an impor-tant roles in carcinogenesis and progression of esophageal carcinoma and can be used as valuable biomarkers to eval-uate biological characteristics in esophageal squamous cell carcinoma.