Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Abstract

words)Objective To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.Design Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24 th March 2020. Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text. Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST.Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results 2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China.Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in

Factors Affecting COVID-19 Outcomes in Cancer Patients: A First Report From Guy's Cancer Center in London

“…Zhang et al also reported that recent treatment within 14 days was associated with an increased risk of developing severe events (28 days) (5). This difference with our observations may be attributed to different definitions of severe events, as it was not entirely clear how these were defined by Zhang et al As highlighted by Wynants et al in their assessment of current statistical models published for COVID-19 (19), there is a need for consistent use of outcome definitions. However, our observations of a positive association with CRP levels is in line with most COVID-19 studies published to date (32).…”

“…Moreover, the case series from New York analyzed which patient characteristics are associated with COVID-19 death, but only made a comparison with noncancer patients (14,15). The first results of the COVID- 19 and Cancer Consortium provide insights from a large cohort in terms of COVID-19 mortality, though a wide variety of institutions with different COVID-19 testing procedures were included (16). In addition, recently published prognostic studies in COVID-19 positive patients have been judged to be at high risk of bias, mainly due to 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 limited information on model building strategies used (19).…”

Comorbilidad y factores pronósticos al ingreso en una cohorte COVID-19 de un hospital general

“…En la extensa literatura generada ya sobre COVID-19, se han identificado factores pronósticos de mala evolución y mortalidad. Una revisión de estudios pronósticos demostró que la mayoría presentan limitaciones metodológicas 3 : excluyen una proporción significativa de pacientes que no alcanzan el evento resultado (muerte o alta), recogen variables en distintos momentos 4 , se basan en información del TAC de tórax (no siempre disponible al ingreso) 5,6 , o incluyen variables de difícil obtención 7 . Desde las primeras series publicadas se evidenció que la edad avanzada y la comorbilidad conllevan mayor riesgo de mortalidad 8---10 .…”

A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study

“…Many studies explored the diagnostic or prognostic value of various factors including age, sex, CT scan, biochemical and haematological parameters (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13). Most studies were however geographically limited, had high risk for bias, and had no validation cohort (14). C-reactive protein, ferritin, Ddimer, albumin, urea nitrogen, bilirubin and lactate dehydrogenase (LDH) levels are cited as indirect indicators of the presence and severity of COVID-19 (11,13,(15)(16)(17)(18)(19)(20), as are complete blood count (CBC) and differential count (DIFF) changes, specifically lymphopenia, neutrophilia, high neutrophil-to-lymphocyte ratio (NLR) and thrombocytopenia (15,(21)(22)(23)(24)(25).…”