Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Occurrence and Exposure Assessment of Nickel in Zhejiang Province, China.

Nickel (Ni) is a silver-white metal with high antioxidative properties, often existing in a bivalent form in the environment. Despite being the fifth most abundant metal on Earth, anthropogenic activities, including industrial processes, have elevated Ni levels in environmental media. This study investigated Ni contamination in various food groups in Zhejiang Province, China, mainly focusing on Ni levels in beans, vegetables, aquatic foods, meat products, cereal products, and fruits. A total of 2628 samples were collected and analyzed. Beans exhibited the highest Ni content in all samples. The overall detection rate of Ni was 86.5%, with variation among food categories. For plant-origin foods, legumes had the highest Ni concentration while for animal-origin foods, shellfish showed the highest median Ni concentration. The results indicate generally acceptable Ni exposure levels among Zhejiang residents, except for children aged 0-6. Beans were identified as the primary contributor to high Ni exposure risk. The paper suggests monitoring Ni contamination in food, especially for vulnerable populations, and provides insights into exposure risks in different age groups.

A prediction model of cognitive impairment risk in elderly illiterate Chinese women.

Objective: To establish and validate a targeted model for the prediction of cognitive impairment in elderly illiterate Chinese women. Methods: 1864 participants in the 2011-2014 cohort and 1,060 participants in the 2014-2018 cohort from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were included in this study. The Chinese version of the Mini-Mental State Examination (MMSE) was used to measure cognitive function. Demographics and lifestyle information were collected to construct a risk prediction model by a restricted cubic spline Cox regression. The discrimination and accuracy of the model were assessed by the area under the curve (AUC) and the concordance index, respectively. Results: A total of seven critical variables were included in the final prediction model for cognitive impairment risk, including age, MMSE score, waist-to-height ratio (WHtR), psychological score, activities of daily living (ADL), instrumental abilities of daily living (IADL), and frequency of tooth brushing. The internal and external validation AUCs were 0.8 and 0.74, respectively; and the receiver operating characteristic (ROC) curves indicated good performance ability of the constructed model. Conclusion: A feasible model to explore the factors influencing cognitive impairment in elderly illiterate women in China and to identify the elders at high risk was successfully constructed.