Introduction Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical and societal-economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19). Methods EMBASE, PubMed/Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th, 2020. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I 2 , T 2 , and Cochrane Q, sensitivity analyses, and assessed publication bias. Results 148 studies met the inclusion criteria for the systematic review and meta-analysis with 12′149 patients (5′739 female) and a median age of 47.0 [35.0–64.6] years. 617 patients died from COVID-19 and its complication. 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78–1.72]; p < 0.001), being male (RR = 1.32 [1.13–1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48–1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially ( I 2 ; range: 1.5–98.2%). Publication bias was only found in eight studies (Egger's test: p < 0.05). Conclusions Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19.

Weis (1) (2)*, Aline Cuénod (3) (4), Bastian Rieck (1) (2), Felipe Llinares-López (1) (2), Olivier Dubuis (8), Susanne Graf (7), Claudia Lang (8), Michael Oberle (9), Maximilian Brackmann (10), Kirstine K. Søgaard (3) (4), Michael Osthoff (5) (6), Karsten Borgwardt (1) (2)*+, Adrian Egli (3) (4)*+

Background: The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the field of microbiology by facilitating precise and rapid species identification. Recently, machine learning techniques have been leveraged to maximally exploit the information contained in MALDI-TOF MS, with the ultimate goal to refine species identification and streamline antimicrobial resistance determination. Objectives: The aim was to systematically review and evaluate studies employing machine learning for the analysis of MALDI-TOF mass spectra. Data sources: Using PubMed/Medline, Scopus and Web of Science, we searched the existing literature for machine learning-supported applications of MALDI-TOF mass spectra for microbial species and antimicrobial susceptibility identification. Study eligibility criteria: Original research studies using machine learning to exploit MALDI-TOF mass spectra for microbial specie and antimicrobial susceptibility identification were included. Studies focusing on single proteins and peptides, case studies and review articles were excluded. Methods: A systematic review according to the PRISMA guidelines was performed and a quality assessment of the machine learning models conducted. Results: From the 36 studies that met our inclusion criteria, 27 employed machine learning for species identification and nine for antimicrobial susceptibility testing. Support Vector Machines, Genetic Algorithms, Artificial Neural Networks and Quick Classifiers were the most frequently used machine learning algorithms. The quality of the studies ranged between poor and very good. The majority of the studies reported how to interpret the predictors (88.89%) and suggested possible clinical applications of the developed algorithm (100%), but only four studies (11.11%) validated machine learning algorithms on external datasets. Conclusions: A growing number of studies utilize machine learning to optimize the analysis of MALDI-TOF mass spectra. This review, however, demonstrates that there are certain shortcomings of current machine learning-supported approaches that have to be addressed to make them widely available and incorporated them in the clinical routine.