Predicting acute kidney injury onset with a random forest algorithm using electronic medical records of COVID-19 patients: the CRACoV-AKI model.

INTRODUCTION: Acute kidney injury (AKI) is a serious and common complication of SARS­CoV­2 infection. Most risk assessment tools for AKI have been developed in the intensive care unit or in elderly populations. As the COVID­19 pandemic is transitioning into an endemic phase, there is an unmet need for prognostic scores tailored to the population of patients hospitalized for this disease. OBJECTIVES: We aimed to develop a robust predictive model for the occurrence of AKI in hospitalized patients with COVID­19. PATIENTS AND METHODS: Electronic medical records of all adult inpatients admitted between March 2020 and January 2022 were extracted from the database of a large, tertiary care center with a reference status in Lesser Poland. We screened 5806 patients with SARS­CoV­2 infection confirmed with a polymerase chain reaction test. After excluding individuals with lacking data on serum creatinine levels and those with a mild disease course (<7 days of inpatient care), a total of 4630 records were considered. Data were randomly split into training (n = 3462) and test (n = 1168) sets. A random forest model was tuned with feature engineering based on expert advice and metrics evaluated in nested cross­validation to reduce bias. RESULTS: Nested cross­validation yielded an area under the curve ranging between 0.793 and 0.807, and an average performance of 0.798. Model explanation techniques from a global perspective suggested that a need for respiratory support, chronic kidney disease, and procalcitonin concentration were among the most important variables in permutation tests. CONCLUSIONS: The CRACoV­AKI model enables AKI risk stratification among hospitalized patients with COVID­19. Machine learning-based tools may thus offer additional decision­making support for specialist providers.

Comparison of TLC and Different Micro TLC Techniques in Analysis of Tropane Alkaloids and Their Derivatives Mixture from Datura Inoxia Mill. Extract.

Planar chromatography is a very useful tool for analysis of wide range of different mixtures. Thanks to its possibility for rapid separation of large number of samples simultaneously, low solvent consumption and ability to analyse rough material allow to receive precise and reliable results in short time and low cost. Miniaturization of planar techniques brings a lot of advantages, such as shortening distance and time of chromatogram development, and further lowering of solvent consumption. Besides, it often allows to improve separation parameters and raise efficiency of chromatographic system. In this paper, ability of analysis of tropane alkaloids mixture from Datura Inoxia Mill. extract using conventional TLC technique with five micro TLC techniques (short distance TLC, HPTLC, UTLC, OPLC and ETLC) in maximally closed chromatographic conditions was compared in order to present abilities of micro TLC techniques in plant material analysis.