🚨 NEW UPDATE 🚨 living systematic review of diagnosis and prognosis models related to COVID-19 Now 145 models reviewed and appraised: reporting and conduct remains very poor almost without exception (but a few potentially useful models too)
Our NEW systematic review article about diagnosis and prognosis models related to COVID-19 is out now in 📢 This review will be regularly updated in the coming months. Watch this space📢 1/n
Excited to see the first update of our systematic review of diagnosis and prognosis models related to COVID-19 Very grateful to for giving us the opportunity to make this a living review
Individual risk prediction models for #COVID19: widely available, but often poorly done and reported. Predictions at high risk of bias. Urgently needed: sharing of good quality data and methodological expertise!
Waste in #COVID19 research (). Preprints contributing to this research waste - many are poorly reported, with flawed studies & irresponsible dissemination. We observed this in our review of covid19 prediction models ()
Prediction models for covid-19 are quickly entering the academic literature to support medical decision making. But new research indicates that proposed models are poorly reported, at high risk of bias and their reported performance is probably optimistic
The 31 #COVID19 predictive models for diagnosis and prognosis systematically reviewed: "poorly reported, at high risk of bias, and their reported performance is probably optimistic" and collaborators #AI/ML
NEW UPDATE to our systematic review of diagnostic and prognostic models for COVID-19, covering an additional 76 studies Working hard to keep this effort going with our wonderful team: already reviewing 69 newly published studies.
Reading a #covid19 prediction model paper as part of the update to . n=53, 5 outcome events, 43 predictors, 6 machine learning methods, 10-fold CV, no meaningful performance measures (no discrimination/calibration) => unusable research. #researchwaste
Our rapid response to the promising COVID-19 prognosis model recently published in : Formal risk of bias assessment will follow soon in our living systematic review of prognosis and diagnosis COVID-19 models:
#COVID19 modelling produces estimates that "are poorly reported, at high risk of bias, and probably optimistic." #BMJResearch Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
NEW PAPER in (led by ) critically appraising #COVID19 diagnostic & prognostic prediction models (). Findings: studies at high risk of bias + poorly reported. Good quality data sharing & methods expertise needed.
Key issues in causal observational studies (Els Goetghebeur’s talk at ) are very similar to some of the key problems we identified in our systematic review and critical appraisal of covid-19 prediction modeling studies (). A thread.
News article misleadingly suggests #AI has demonstrated diagnostic success (). Our review which is cited () is very clear - performance is optimistic/misleading - none are recommended - need better and further evaluation in new data.
#COVID19 modelling produces estimates that "are poorly reported, at high risk of bias, and probably optimistic." #BMJResearch Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
Pleased to say our first update to the #covid diagnostic & prognostic prediction models living review is now online in the (). 66 models developed in 95 days - all at high risk of bias.
Excellent review and critical appraisal of prediction models for diagnosis and prognosis of covid-19 infection in , highlighting high risk of bias in published models (including ML models for diagnosis from CT scans).
We contacted to ask for manuscripts that detailed the development and validation of their prediction tools back in April for including in our review They refused (as we report)! But indeed, this doesn't look good:
New study shows poor reporting of "Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal" (). Studies should adhere to the TRIPOD reporting guideline &
How valid and useful are prediction models for diagnosis and prognosis of covid-19? This living systematic review has been updated this week
Three #covid reviews: diagnostic & prognostic models () [n=51], diagnostic antibody tests () [n=57], diagnostic serological tests () [n=40]. All with the same conclusions - primary studies at high risk of bias.
Editorial in by "Prediction models for diagnosis and prognosis in Covid-19" () accompanying our recent systematic review ()
Our editorial is now published in BMJ: "Prediction models for diagnosis and prognosis in Covid-19" () accompanying the recent systematic review by et al () 1/2
1/2 A rapid systematic review just came out in the BMJ. It clearly shows why implementing A.I. into the clinical practice is going to be so hard without evidence-based data.
Many epidemiologists, (bio)statisticians), health data scientists, and other methodologists have conducted & attentively, critically accompanied research reports of #COVID19 & wrote papers why certain things are not as simple and valid as they appear. 1/6
They also seem to forget that the development of a risk score to make stratifications is difficult work and requires validation. Something that has not be done successfully so far, as described in the same journal (but not referenced)
Tvärsäkra uttalanden baserade på osäkra modeller. Se
Beware! “unreliable predictions could cause more harm than benefit in guiding clinical decisions”- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
UPDATE: systematic review & critical appraisal of COVID prediction models. Now includes 107 studies and 145 prediction models. Same message - all at high risk of bias 'we do not recommend any of these reported prediction models for use in current practice'
"Proposed prediction models for prognosis of covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic."
Also, #LivingSystematicReviews in methodology, on prediction models for #COVID19 Important to look for the protocols for methods and risk of bias assessment. In short, pls include in reviews of reviews 5/5
EXCELLENT REVIEW: COVID-19: PREDICTION MODELS 145 Models: - High risk of bias - Nonrepresentative selection of control patients - Only 27 carried out an external validation, and calibration was rarely assessed.
[#FridayWiMLDSPaper curated by ] "#Prediction models for diagnosis and prognosis of #COVID19 infection: systematic review and critical appraisal" by & others 📜 #WiMLDSParis #WiMLDS
COVID 19 PREDICTION MODELS REVIEW / CRITICAL APPRAISAL 66 Models: All to have high risk of bias owing to a combination of: 1)- Poor reporting 2)- Poor methodological conduct: For participant selection Predictor description Statistical methods used.
"Prediction models for covid are quickly entering the academic literature to support med decision making at a time when they are urgently needed...proposed models are poorly reported, at high risk of bias, their reported performance is probably optimistic"
A+ On Prediction Model Theater "31 proposed prediction models are poorly reported, at high risk of bias, and their reported performance is probably optimistic." "unreliable predictions could cause more harm than benefit in guiding clinical decisions"
145 risk prediction models for individuals to use on covid so far. Great to see a living systematic review of them all in the BMJ.
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal review of 145 models found standards were ... low
"Prediction models for covid-19 are ... poorly reported, at high risk of bias, and their reported performance is probably optimistic ... The predictors identified in included studies could be considered as candidate predictors for new models" #Covid19
Review indicates proposed predictn models 4 Covid-19 r poorly reported, at high risk of bias & their reported performance is probably optimistic. et al do not recommend any of these reported prediction models to be used in current practice
Predictions could support better care for individuals and communities dealing with #COVID19. Despite the urgency not done carefully though these models could carry serious risk of harm or exacerbating biases
Interesting, via From - "Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal" "biased data compromised all of the 31 models analyzed"... “none can be recommended for clinical use"
#COVID19 modelling produces estimates that 'are likely to be optimistic and misleading'. They are prone to bias and the modelling is incomplete. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal via et al