Explainable Boosting Machine approach identifies risk factors for acute renal failure.

BACKGROUND: Risk stratification and outcome prediction are crucial for intensive care resource planning. In addressing the large data sets of intensive care unit (ICU) patients, we employed the Explainable Boosting Machine (EBM), a novel machine learning model, to identify determinants of acute kidney injury (AKI) in these patients. AKI significantly impacts outcomes in the critically ill. METHODS: An analysis of 3572 ICU patients was conducted. Variables such as average central venous pressure (CVP), mean arterial pressure (MAP), age, gender, and comorbidities were examined. This analysis combined traditional statistical methods with the EBM to gain a detailed understanding of AKI risk factors. RESULTS: Our analysis revealed chronic kidney disease, heart failure, arrhythmias, liver disease, and anemia as significant comorbidities influencing AKI risk, with liver disease and anemia being particularly impactful. Surgical factors were also key; lower GI surgery heightened AKI risk, while neurosurgery was associated with a reduced risk. EBM identified four crucial variables affecting AKI prediction: anemia, liver disease, and average CVP increased AKI risk, whereas neurosurgery decreased it. Age was a progressive risk factor, with risk escalating after the age of 50 years. Hemodynamic instability, marked by a MAP below 65 mmHg, was strongly linked to AKI, showcasing a threshold effect at 60 mmHg. Intriguingly, average CVP was a significant predictor, with a critical threshold at 10.7 mmHg. CONCLUSION: Using an Explainable Boosting Machine enhance the precision in AKI risk factors in ICU patients, providing a more nuanced understanding of known AKI risks. This approach allows for refined predictive modeling of AKI, effectively overcoming the limitations of traditional statistical models.

Comparative analysis of intraoral radiographs with variation of tube angulation to detect insufficient crown margins.

Variations in angulation of the x-ray tube affect the appearance of insufficient approximal crown margins on intraoral radiographs. This study examines the impact of such angular variation on the assessment of digital radiographs using three different X-ray tubes--Heliodent DS (Sirona), Gendex Expert DC (KaVo Dental) and Focus (KaVo Dental)--as well as the Gendex Visualix eHD CCD sensor (KaVo Dental). The test specimens, crowned teeth 46 from two mandibles provided by the Institute of Anatomy and Cell Biology, were examined with each tube. The results indicate great differences in the angles indicative of insufficient crown margins on X-ray images. Because of beam divergence and the crown marginal gap, the length and width of which frequently varies, it is difficult to infer any optimum angle from the data. This leads to the conclusion that at present, it is not possible to establish ideal angles for visualization of insufficient approximal crown margins.