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

ObjectiveTo 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. DesignRapid 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 24th 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. Results2696 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 infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count. Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies 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, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed. ConclusionCOVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models. Systematic review registration protocolosf.io/ehc47/, registration: osf.io/wy245 Summary boxesO_ST_ABSWhat is already known on this topicC_ST_ABS- The sharp recent increase in COVID-19 infections has put a strain on healthcare systems worldwide, necessitating efficient early detection, diagnosis of patients suspected of the infection and prognostication of COVID-19 confirmed cases. - Viral nucleic acid testing and chest CT are standard methods for diagnosing COVID-19, but are time-consuming. - Earlier reports suggest that the elderly, patients with comorbidity (COPD, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after COVID-19 infection. What this study adds- We identified three models to predict hospital admission from pneumonia and other events (as a proxy for COVID-19 pneumonia) in the general population. - We identified 18 diagnostic models for COVID-19 detection in symptomatic patients. - 13 of these were machine learning models based on CT images. - We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe or critical state, and two aimed to predict a hospital stay of more than 10 days from admission. - Included studies were poorly reported compromising their subsequent appraisal, and recommendation for use in daily practice. All studies were appraised at high risk of bias, raising concern that the models may be flawed and perform poorly when applied in practice, such that their predictions may be unreliable.

Irish public opinion on assisted human reproduction services: Contemporary assessments from a national sample / 대한생식의학회지

OBJECTIVE: To measure Irish opinion on a range of assisted human reproduction (AHR) treatments. METHODS: A nationally representative sample of Irish adults (n=1,003) were anonymously sampled by telephone survey. RESULTS: Most participants (77%) agreed that any fertility services offered internationally should also be available in Ireland, although only a small minority of the general Irish population had personal familiarity with AHR or infertility. This sample finds substantial agreement (63%) that the Government of Ireland should introduce legislation covering AHR. The range of support for gamete donation in Ireland ranged from 53% to 83%, depending on how donor privacy and disclosure policies are presented. For example, donation where the donor agrees to be contacted by the child born following donation, and anonymous donation where donor privacy is completely protected by law were supported by 68% and 66%, respectively. The least popular (53%) donor gamete treatment type appeared to be donation where the donor consents to be involved in the future life of any child born as a result of donor fertility treatment. Respondents in social class ABC1 (58%), age 18 to 24 (62%), age 25 to 34 (60%), or without children (61%) were more likely to favour this donor treatment policy in our sample. CONCLUSION: This is the first nationwide assessment of Irish public opinion on the advanced reproductive technologies since 2005. Access to a wide range of AHR treatment was supported by all subgroups studied. Public opinion concerning specific types of AHR treatment varied, yet general support for the need for national AHR legislation was reported by 63% of this national sample. Contemporary views on AHR remain largely consistent with the Commission for Assisted Human Reproduction recommendations from 2005, although further research is needed to clarify exactly how popular opinion on these issues has changed. It appears that legislation allowing for the full range of donation options (and not mandating disclosure of donor identity at a stipulated age) would better align with current Irish public opinion.

Array comparative genomic hybridization screening in IVF significantly reduces number of embryos available for cryopreservation / 대한생식의학회지

OBJECTIVE: During IVF, non-transferred embryos are usually selected for cryopreservation on the basis of morphological criteria. This investigation evaluated an application for array comparative genomic hybridization (aCGH) in assessment of surplus embryos prior to cryopreservation. METHODS: First-time IVF patients undergoing elective single embryo transfer and having at least one extra non-transferred embryo suitable for cryopreservation were offered enrollment in the study. Patients were randomized into two groups: Patients in group A (n=55) had embryos assessed first by morphology and then by aCGH, performed on cells obtained from trophectoderm biopsy on post-fertilization day 5. Only euploid embryos were designated for cryopreservation. Patients in group B (n=48) had embryos assessed by morphology alone, with only good morphology embryos considered suitable for cryopreservation. RESULTS: Among biopsied embryos in group A (n=425), euploidy was confirmed in 226 (53.1%). After fresh single embryo transfer, 64 (28.3%) surplus euploid embryos were cryopreserved for 51 patients (92.7%). In group B, 389 good morphology blastocysts were identified and a single top quality blastocyst was selected for fresh transfer. All group B patients (48/48) had at least one blastocyst remaining for cryopreservation. A total of 157 (40.4%) blastocysts were frozen in this group, a significantly larger proportion than was cryopreserved in group A (p=0.017, by chi-squared analysis). CONCLUSION: While aCGH and subsequent frozen embryo transfer are currently used to screen embryos, this is the first investigation to quantify the impact of aCGH specifically on embryo cryopreservation. Incorporation of aCGH screening significantly reduced the total number of cryopreserved blastocysts compared to when suitability for freezing was determined by morphology only. IVF patients should be counseled that the benefits of aCGH screening will likely come at the cost of sharply limiting the number of surplus embryos available for cryopreservation.