Prediction models for diagnosis and prognosis of covid-19 infection : (Record no. 1785)
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| 000 -LEADER | |
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| fixed length control field | 04666nam a22003497a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DOH |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20201120153421.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 201120b ||||| |||| 00| 0 eng d |
| 245 00 - TITLE STATEMENT | |
| Title | Prediction models for diagnosis and prognosis of covid-19 infection : |
| Remainder of title | systematic review and critical appraisal / |
| Statement of responsibility, etc. | Laure Wynants [and fifteen others] |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | Abstract<br/>Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease.<br/><br/>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.<br/><br/>Data sources PubMed and Embase through Ovid, arXiv, medRxiv, and bioRxiv up to 5 May 2020.<br/><br/>Study selection Studies that developed or validated a multivariable covid-19 related prediction model.<br/><br/>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).<br/><br/>Results 14 217 titles were screened, and 107 studies describing 145 prediction models were included. The review identified four models for identifying people at risk in the general population; 91 diagnostic models for detecting covid-19 (60 were based on medical imaging, nine to diagnose disease severity); and 50 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequently reported predictors of diagnosis and prognosis of covid-19 are age, body temperature, lymphocyte count, and lung imaging features. Flu-like symptoms and neutrophil count are frequently predictive in diagnostic models, while comorbidities, sex, C reactive protein, and creatinine are frequent prognostic factors. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.68 to 0.99 in prognostic models. All models were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and vague reporting. Most reports did not include any description of the study population or intended use of the models, and calibration of the model predictions was rarely assessed.<br/><br/>Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Hence, we do not recommend any of these reported prediction models for use in current practice. Immediate sharing of well documented individual participant data from covid-19 studies and collaboration are urgently needed to develop more rigorous prediction models, and validate promising ones. The predictors identified in included models should be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. |
| 580 ## - LINKING ENTRY COMPLEXITY NOTE | |
| Linking entry complexity note | In: BMJ 2020; 369 doi: https://doi.org/10.1136/bmj.m1328 (Published 07 April 2020) |
| 650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Coronavirus Infections |
| General subdivision | diagnosis |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wynants, Laure |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Van Calster, Ben |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bonten, Marc M J |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Collins, Gary S |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Debray, Thomas P A |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | De Vos, Maarten |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Haller, Maria C |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Heinze, Georg |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Moons, Karel G M |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Riley, Richard D |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Schuit, Ewoud |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Smits, Luc J M |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Snell, Kym I E |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Steyerberg, Ewout W |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wallisch, Christine |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Smeden, Maarten van |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf">https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | COVID 19 Resource |
| 999 ## - | |
| -- | 1785 |
| -- | 1785 |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
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| DOH Central Library | DOH Central Library | Electronic Resource Section | 11/20/2020 | COVID-19-000052 | D0001C000052 | 11/20/2020 | 11/20/2020 | COVID 19 Resource |