Search

Objective: Clinical data on which artificial intelligence (AI) algorithms are trained and tested provide the basis to improve diagnosis or treatment of infectious diseases (ID). We aimed to identify important data for ID research to prioritise efforts being undertaken in AI programmes., Methods: We searched for 1,000 articlesfrom high-impact ID journals on PubMed, selecting 288 of the latest articles from 10 top journals. We classified them into structured or unstructured data. Variables were homogenised and grouped into the following categories: epidemiology, admission, demographics, comorbidities, clinical manifestations, laboratory, microbiology, other diagnoses, treatment, outcomes and other non-categorizable variables., Results: 4,488 individual variables were collected, from the 288 articles. 3,670 (81.8%) variables were classified as structured data whilst 818 (18.2%) as unstructured data. From the structured data, 2,319 (63.2%) variables were classified as direct-retrievable from electronic health records-whilst 1,351 (36.8%) were indirect. The most frequent unstructured data were related to clinical manifestations and were repeated across articles. Data on demographics, comorbidities and microbiology constituted the most frequent group of variables., Conclusions: This article identified that structured variables have comprised the most important data in research to generate knowledge in the field of ID. Extracting these data should be a priority when a medical centre intends to start an AI programme for ID. We also documented that the most important unstructured data in this field are those related to clinical manifestations. Such data could easily undergo some structuring with the use of semi-structured medical records focusing on a few symptoms., (©The Author 2023. Published by Sociedad Española de Quimioterapia. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0/).)

Invasive fungal infection often complicates patients with severe viral infection, especially those admitted to critical care units. Severe SARS-CoV-2 infection has been no exception and a significant association with Aspergillus spp. has been documented, resulting in high patient mortality. In this summary we describe the clinical presentation, the underlying diseases most commonly linked with this association, radiological manifestations and therapeutic management of CAPA.

Objective: Controversial results on remdesivir efficacy have been reported. We aimed to report our real-life experience with the use of remdesivir from its availability in Spain., Methods: We performed a descriptive study of all patients admitted for ≥48 hours with confirmed COVID-19 who received remdesivir between the 1st of July and the 30th of September 2020., Results: A total of 123 patients out of 242 admitted with COVID-19 at our hospital (50.8%) received remdesivir. Median age was 58 years, 61% were males and 56.9 % received at least one anti-inflammatory treatment. No adverse events requiring remdesivir discontinuation were reported. The need of intensive care unit admission, mechanical ventilation and 30-days mortality were 19.5%, 7.3% and 4.1%, respectively., Conclusions: In our real-life experience, the use of remdesivir in hospitalized patients with COVID-19 was associated with a low mortality rate and good safety profile., (©The Author 2021. Published by Sociedad Española de Quimioterapia. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0/).)