Lactate to Albumin Ratio and Mortality in Patients with Severe Coronavirus Disease-2019 Admitted to an Intensive Care Unit.

This study sought to evaluate the effectiveness of lactate/albumin ratio for ICU mortality prediction in a large cohort of patients with severe Coronavirus Disease-2019 (COVID-19) admitted to an intensive care unit (ICU). This is a single-center retrospective cohort study of prospectively collected data derived from the COVID-19 dataset for all critically ill patients admitted to an academic ICU. Data were used to determine the relation between lactate/albumin ratio and other laboratory parameters measured on the first day of the ICU stay and to evaluate the prognostic performance for ICU mortality prediction.

Prediction of recurrence risk in endometrial cancer with multimodal deep learning.

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials.

Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials.

Background: Numerous studies have shown that older women with endometrial cancer have a higher risk of recurrence and cancer-related death. However, it remains unclear whether older age is a causal prognostic factor, or whether other risk factors become increasingly common with age. We aimed to address this question with a unique multimethod study design using state-of-the-art statistical and causal inference techniques on datasets of three large, randomised trials.