Objective: To review and appraise the validity and usefulness of published and preprint reports of prediction models for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital or dying with the disease. Design: Living systematic review and critical appraisal by the covid-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources: PubMed and Embase through Ovid, up to 17 February 2021, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection: Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results: 126 978 titles were screened, and 412 studies describing 731 new prediction models or validations were included. Of these 731, 125 were diagnostic models (including 75 based on medical imaging) and the remaining 606 were prognostic models for either identifying those at risk of covid-19 in the general population (13 models) or predicting diverse outcomes in those individuals with confirmed covid-19 (593 models). Owing to the widespread availability of diagnostic testing capacity after the summer of 2020, this living review has now focused on the prognostic models. Of these, 29 had low risk of bias, 32 had unclear risk of bias, and 545 had high risk of bias. The most common causes for high risk of bias were inadequate sample sizes (n=408, 67%) and inappropriate or incomplete evaluation of model performance (n=338, 56%). 381 models were newly developed, and 225 were external validations of existing models. The reported C indexes varied between 0.77 and 0.93 in development studies with low risk of bias, and between 0.56 and 0.78 in external validations with low risk of bias. The Qcovid models, the PRIEST score, Carr’s model, the ISARIC4C Deterioration model, and the Xie model showed adequate predictive performance in studies at low risk of bias. Details on all reviewed models are publicly available at https://www.covprecise.org/ . Conclusion: Prediction models for covid-19 entered the academic literature to support medical decision making at unprecedented speed and in large numbers. Most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic. Models with low risk of bias should be validated before clinical implementation, preferably through collaborative efforts to also allow an investigation of the heterogeneity in their performance across various populations and settings. Methodological guidance, as provided in this paper, should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction modellers should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration: Protocol https://osf.io/ehc47/ , registration https://osf.io/wy245 . Readers’ note: This article is the final version of a living systematic review that has been updated over the past two years to reflect emerging evidence. This version is update 4 of the original article published on 7 April 2020 ( BMJ 2020;369:m1328). Previous updates can be found as data supplements ( https://www.bmj.com/content/369/bmj.m1328/related#datasupp ). When citing this paper please consider adding the update number and date of access for clarity.

Variance between studies in a meta-analysis will exist. This heterogeneity may be of clinical, methodological or statistical origin. The last of these is quantified by the I(2) -statistic. We investigated, using simulated studies, the accuracy of I(2) in the assessment of heterogeneity and the effects of heterogeneity on the predictive value of meta-analyses. The relevance of quantifying I(2) was determined according to the likely presence of heterogeneity between studies (low, high, or unknown) and the calculated I(2) (low or high). The findings were illustrated by published meta-analyses of selective digestive decontamination and weaning protocols. As expected, I(2) increases and the likelihood of drawing correct inferences from a meta-analysis decreases with increasing heterogeneity. With low levels of heterogeneity, I(2) does not appear to be predictive of the accuracy of the meta-analysis result. With high levels of heterogeneity, even meta-analyses with low I(2) -values have low predictive values. Most commonly, the level of heterogeneity in a meta-analysis will be unknown. In these scenarios, I(2) determination may help to identify estimates with low predictive values (high I(2) ). In this situation, the results of a meta-analysis will be unreliable. With low I(2) -values and unknown levels of heterogeneity, predictive values of pooled estimates may range extensively, and findings should be interpreted with caution. In conclusion, quantifying statistical heterogeneity through I(2) -statistics is only helpful when the amount of clinical heterogeneity is unknown and I(2) is high. Objective methods to quantify the levels of clinical and methodological heterogeneity are urgently needed to allow reliable determination of the accuracy of meta-analyses.

Introduction: A clinical suspicion of infection is mandatory for diagnosing sepsis in patients with a systemic inflammatory response syndrome. Yet, the accuracy of categorizing critically ill patients presenting to the intensive care unit (ICU) as being infected or not is unknown. We therefore assessed the likelihood of infection in patients who were treated for sepsis upon admission to the ICU, and quantified the association between plausibility of infection and mortality. Methods: We studied a cohort of critically ill patients admitted with clinically suspected sepsis to two tertiary ICUs in the Netherlands between January 2011 and December 2013. The likelihood of infection was categorized as none, possible, probable or definite by post-hoc assessment. We used multivariable competing risks survival analyses to determine the association of the plausibility of infection with mortality. Results: Among 2579 patients treated for sepsis, 13% had a post-hoc infection likelihood of “none”, and an additional 30% of only “possible”. These percentages were largely similar for different suspected sites of infection. In crude analyses, the likelihood of infection was associated with increased length of stay and complications. In multivariable analysis, patients with an unlikely infection had a higher mortality rate compared to patients with a definite infection (subdistribution hazard ratio 1.23; 95% confidence interval 1.03-1.49). Conclusions: This study is the first prospective analysis to show that the clinical diagnosis of sepsis upon ICU admission corresponds poorly with the presence of infection on post-hoc assessment. A higher likelihood of infection does not adversely influence outcome in this population. Trial registration: ClinicalTrials.gov NCT01905033. Registered 11 July 2013.

The Gram-positive bacterium Enterococcus faecium is an important cause of nosocomial infections in immunocompromized patients. We present a pyrosequencing-based comparative genome analysis of seven E. faecium strains that were isolated from various sources. In the genomes of clinical isolates several antibiotic resistance genes were identified, including the vanA transposon that confers resistance to vancomycin in two strains. A functional comparison between E. faecium and the related opportunistic pathogen E. faecalis based on differences in the presence of protein families, revealed divergence in plant carbohydrate metabolic pathways and oxidative stress defense mechanisms. The E. faecium pan-genome was estimated to be essentially unlimited in size, indicating that E. faecium can efficiently acquire and incorporate exogenous DNA in its gene pool. One of the most prominent sources of genomic diversity consists of bacteriophages that have integrated in the genome. The CRISPR-Cas system, which contributes to immunity against bacteriophage infection in prokaryotes, is not present in the sequenced strains. Three sequenced isolates carry the esp gene, which is involved in urinary tract infections and biofilm formation. The esp gene is located on a large pathogenicity island (PAI), which is between 64 and 104 kb in size. Conjugation experiments showed that the entire esp PAI can be transferred horizontally and inserts in a site-specific manner. Genes involved in environmental persistence, colonization and virulence can easily be aquired by E. faecium. This will make the development of successful treatment strategies targeted against this organism a challenge for years to come.

Risk factors for the development of ventilator-associated pneumonia (VAP), as identified in epidemiological studies, have provided a basis for testable interventions in randomized trials. We describe how these results have influenced patient treatment. Single interventions in patients undergoing intubation have focused on either reducing aspiration of oropharyngeal secretions, modulation of colonization (in either the oropharynx, the stomach, or the whole digestive tract), use of systemic antimicrobial prophylaxis, or ventilator circuit changes. More recently, multiple simultaneously implemented interventions have been used. In general, routine measures to decrease oropharyngeal aspiration and antibiotic-containing prevention strategies appear to be the most effective, and the latter were associated with improved rates of patient survival in recent trials. These benefits must be balanced against the widespread fear of emergence of antibiotic resistance. In hospital settings with low baseline levels of antibiotic resistance, however, the benefits to patient outcome may outweigh this fear of resistance. In settings with high levels of antibiotic resistance, combined approaches of non-antibiotic using strategies and education programs might be most beneficial.