Nina Kreuzberger

Bio: Nina Kreuzberger is an academic researcher from University of Cologne. The author has contributed to research in topics: Medicine & Adverse effect. The author has an hindex of 4, co-authored 10 publications receiving 1407 citations.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: 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 covid-19 infection or being admitted to hospital 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 1 July 2020, 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 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear 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 unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. 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 almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. 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 model authors 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 a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 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.

SARS-CoV-2-neutralising monoclonal antibodies for treatment of COVID-19

Abstract: Background Monoclonal antibodies (mAbs) are laboratory‐produced molecules derived from the B cells of an infected host. They are being investigated as a potential therapy for coronavirus disease 2019 (COVID‐19). Objectives To assess the effectiveness and safety of SARS‐CoV‐2‐neutralising mAbs for treating patients with COVID‐19, compared to an active comparator, placebo, or no intervention. To maintain the currency of the evidence, we will use a living systematic review approach. A secondary objective is to track newly developed SARS‐CoV‐2‐targeting mAbs from first tests in humans onwards. Search methods We searched MEDLINE, Embase, the Cochrane COVID‐19 Study Register, and three other databases on 17 June 2021. We also checked references, searched citations, and contacted study authors to identify additional studies. Between submission and publication, we conducted a shortened randomised controlled trial (RCT)‐only search on 30 July 2021. Selection criteria We included studies that evaluated SARS‐CoV‐2‐neutralising mAbs, alone or combined, compared to an active comparator, placebo, or no intervention, to treat people with COVID‐19. We excluded studies on prophylactic use of SARS‐CoV‐2‐neutralising mAbs. Data collection and analysis Two authors independently assessed search results, extracted data, and assessed risk of bias using the Cochrane risk of bias tool (RoB2). Prioritised outcomes were all‐cause mortality by days 30 and 60, clinical progression, quality of life, admission to hospital, adverse events (AEs), and serious adverse events (SAEs). We rated the certainty of evidence using GRADE. Main results We identified six RCTs that provided results from 17,495 participants with planned completion dates between July 2021 and December 2031. Target sample sizes varied from 1020 to 10,000 participants. Average age was 42 to 53 years across four studies of non‐hospitalised participants, and 61 years in two studies of hospitalised participants. Non‐hospitalised individuals with COVID‐19 Four studies evaluated single agents bamlanivimab (N = 465), sotrovimab (N = 868), regdanvimab (N = 307), and combinations of bamlanivimab/etesevimab (N = 1035), and casirivimab/imdevimab (N = 799). We did not identify data for mortality at 60 days or quality of life. Our certainty of the evidence is low for all outcomes due to too few events (very serious imprecision). Bamlanivimab compared to placebo No deaths occurred in the study by day 29. There were nine people admitted to hospital by day 29 out of 156 in the placebo group compared with one out of 101 in the group treated with 0.7 g bamlanivimab (risk ratio (RR) 0.17, 95% confidence interval (CI) 0.02 to 1.33), 2 from 107 in the group treated with 2.8 g (RR 0.32, 95% CI 0.07 to 1.47) and 2 from 101 in the group treated with 7.0 g (RR 0.34, 95% CI 0.08 to 1.56). Treatment with 0.7 g, 2.8 g and 7.0 g bamlanivimab may have similar rates of AEs as placebo (RR 0.99, 95% CI 0.66 to 1.50; RR 0.90, 95% CI 0.59 to 1.38; RR 0.81, 95% CI 0.52 to 1.27). The effect on SAEs is uncertain. Clinical progression/improvement of symptoms or development of severe symptoms were not reported. Bamlanivimab/etesevimab compared to placebo There were 10 deaths in the placebo group and none in bamlanivimab/etesevimab group by day 30 (RR 0.05, 95% CI 0.00 to 0.81). Bamlanivimab/etesevimab may decrease hospital admission by day 29 (RR 0.30, 95% CI 0.16 to 0.59), may result in a slight increase in any grade AEs (RR 1.15, 95% CI 0.83 to 1.59) and may increase SAEs (RR 1.40, 95% CI 0.45 to 4.37). Clinical progression/improvement of symptoms or development of severe symptoms were not reported. Casirivimab/imdevimab compared to placebo Casirivimab/imdevimab may reduce hospital admissions or death (2.4 g: RR 0.43, 95% CI 0.08 to 2.19; 8.0 g: RR 0.21, 95% CI 0.02 to 1.79). We are uncertain of the effect on grades 3‐4 AEs (2.4 g: RR 0.76, 95% CI 0.17 to 3.37; 8.0 g: RR 0.50, 95% CI 0.09 to 2.73) and SAEs (2.4 g: RR 0.68, 95% CI 0.19 to 2.37; 8.0 g: RR 0.34, 95% CI 0.07 to 1.65). Mortality by day 30 and clinical progression/improvement of symptoms or development of severe symptoms were not reported. Sotrovimab compared to placebo We are uncertain whether sotrovimab has an effect on mortality (RR 0.33, 95% CI 0.01 to 8.18) and invasive mechanical ventilation (IMV) requirement or death (RR 0.14, 95% CI 0.01 to 2.76). Treatment with sotrovimab may reduce the number of participants with oxygen requirement (RR 0.11, 95 % CI 0.02 to 0.45), hospital admission or death by day 30 (RR 0.14, 95% CI 0.04 to 0.48), grades 3‐4 AEs (RR 0.26, 95% CI 0.12 to 0.60), SAEs (RR 0.27, 95% CI 0.12 to 0.63) and may have little or no effect on any grade AEs (RR 0.87, 95% CI 0.66 to 1.16). Regdanvimab compared to placebo Treatment with either dose (40 or 80 mg/kg) compared with placebo may decrease hospital admissions or death (RR 0.45, 95% CI 0.14 to 1.42; RR 0.56, 95% CI 0.19 to 1.60, 206 participants), but may increase grades 3‐4 AEs (RR 2.62, 95% CI 0.52 to 13.12; RR 2.00, 95% CI 0.37 to 10.70). 80 mg/kg may reduce any grade AEs (RR 0.79, 95% CI 0.52 to 1.22) but 40 mg/kg may have little to no effect (RR 0.96, 95% CI 0.64 to 1.43). There were too few events to allow meaningful judgment for the outcomes mortality by 30 days, IMV requirement, and SAEs. Hospitalised individuals with COVID‐19 Two studies evaluating bamlanivimab as a single agent (N = 314) and casirivimab/imdevimab as a combination therapy (N = 9785) were included. Bamlanivimab compared to placebo We are uncertain whether bamlanivimab has an effect on mortality by day 30 (RR 1.39, 95% CI 0.40 to 4.83) and SAEs by day 28 (RR 0.93, 95% CI 0.27 to 3.14). Bamlanivimab may have little to no effect on time to hospital discharge (HR 0.97, 95% CI 0.78 to 1.20) and mortality by day 90 (HR 1.09, 95% CI 0.49 to 2.43). The effect of bamlanivimab on the development of severe symptoms at day 5 (RR 1.17, 95% CI 0.75 to 1.85) is uncertain. Bamlanivimab may increase grades 3‐4 AEs at day 28 (RR 1.27, 95% CI 0.81 to 1.98). We assessed the evidence as low certainty for all outcomes due to serious imprecision, and very low certainty for severe symptoms because of additional concerns about indirectness. Casirivimab/imdevimab with usual care compared to usual care alone Treatment with casirivimab/imdevimab compared to usual care probably has little or no effect on mortality by day 30 (RR 0.94, 95% CI 0.87 to 1.02), IMV requirement or death (RR 0.96, 95% CI 0.90 to 1.04), nor alive at hospital discharge by day 30 (RR 1.01, 95% CI 0.98 to 1.04). We assessed the evidence as moderate certainty due to study limitations (lack of blinding). AEs and SAEs were not reported. Authors' conclusions The evidence for each comparison is based on single studies. None of these measured quality of life. Our certainty in the evidence for all non‐hospitalised individuals is low, and for hospitalised individuals is very low to moderate. We consider the current evidence insufficient to draw meaningful conclusions regarding treatment with SARS‐CoV‐2‐neutralising mAbs. Further studies and long‐term data from the existing studies are needed to confirm or refute these initial findings, and to understand how the emergence of SARS‐CoV‐2 variants may impact the effectiveness of SARS‐CoV‐2‐neutralising mAbs. Publication of the 36 ongoing studies may resolve uncertainties about the effectiveness and safety of SARS‐CoV‐2‐neutralising mAbs for the treatment of COVID‐19 and possible subgroup differences.

Clinical Practice Guidelines From the Association for the Advancement of Blood and Biotherapies (AABB): COVID-19 Convalescent Plasma

Abstract: The Association for the Advancement of Blood and Biotherapies developed clinical practice guidelines for the appropriate use of COVID-19 convalescent plasma. This article describes the evidence and rationale for recommendations for inpatient and outpatient treatment and prophylaxis.

Interim PET‐results for prognosis in adults with Hodgkin lymphoma: a systematic review and meta‐analysis of prognostic factor studies

Abstract: Background Hodgkin lymphoma (HL) is one of the most common haematological malignancies in young adults and, with cure rates of 90%, has become curable for the majority of individuals. Positron emission tomography (PET) is an imaging tool used to monitor a tumour's metabolic activity, stage and progression. Interim PET during chemotherapy has been posited as a prognostic factor in individuals with HL to distinguish between those with a poor prognosis and those with a better prognosis. This distinction is important to inform decision-making on the clinical pathway of individuals with HL. Objectives To determine whether in previously untreated adults with HL receiving first-line therapy, interim PET scan results can distinguish between those with a poor prognosis and those with a better prognosis, and thereby predict survival outcomes in each group. Search methods We searched MEDLINE, Embase, CENTRAL and conference proceedings up until April 2019. We also searched one trial registry (ClinicalTrials.gov). Selection criteria We included retrospective and prospective studies evaluating interim PET scans in a minimum of 10 individuals with HL (all stages) undergoing first-line therapy. Interim PET was defined as conducted during therapy (after one, two, three or four treatment cycles). The minimum follow-up period was at least 12 months. We excluded studies if the trial design allowed treatment modification based on the interim PET scan results. Data collection and analysis We developed a data extraction form according to the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Two teams of two review authors independently screened the studies, extracted data on overall survival (OS), progression-free survival (PFS) and PET-associated adverse events (AEs), assessed risk of bias (per outcome) according to the Quality in Prognosis Studies (QUIPS) tool, and assessed the certainty of the evidence (GRADE). We contacted investigators to obtain missing information and data. Main results Our literature search yielded 11,277 results. In total, we included 23 studies (99 references) with 7335 newly-diagnosed individuals with classic HL (all stages). Participants in 16 studies underwent (interim) PET combined with computed tomography (PET-CT), compared to PET only in the remaining seven studies. The standard chemotherapy regimen included ABVD (16) studies, compared to BEACOPP or other regimens (seven studies). Most studies (N = 21) conducted interim PET scans after two cycles (PET2) of chemotherapy, although PET1, PET3 and PET4 were also reported in some studies. In the meta-analyses, we used PET2 data if available as we wanted to ensure homogeneity between studies. In most studies interim PET scan results were evaluated according to the Deauville 5-point scale (N = 12). Eight studies were not included in meta-analyses due to missing information and/or data; results were reported narratively. For the remaining studies, we pooled the unadjusted hazard ratio (HR). The timing of the outcome measurement was after two or three years (the median follow-up time ranged from 22 to 65 months) in the pooled studies. Eight studies explored the independent prognostic ability of interim PET by adjusting for other established prognostic factors (e.g. disease stage, B symptoms). We did not pool the results because the multivariable analyses adjusted for a different set of factors in each study. Overall survival Twelve (out of 23) studies reported OS. Six of these were assessed as low risk of bias in all of the first four domains of QUIPS (study participation, study attrition, prognostic factor measurement and outcome measurement). The other six studies were assessed as unclear, moderate or high risk of bias in at least one of these four domains. Four studies were assessed as low risk, and eight studies as high risk of bias for the domain other prognostic factors (covariates). Nine studies were assessed as low risk, and three studies as high risk of bias for the domain 'statistical analysis and reporting'. We pooled nine studies with 1802 participants. Participants with HL who have a negative interim PET scan result probably have a large advantage in OS compared to those with a positive interim PET scan result (unadjusted HR 5.09, 95% confidence interval (CI) 2.64 to 9.81, I² = 44%, moderate-certainty evidence). In absolute values, this means that 900 out of 1000 participants with a negative interim PET scan result will probably survive longer than three years compared to 585 (95% CI 356 to 757) out of 1000 participants with a positive result. Adjusted results from two studies also indicate an independent prognostic value of interim PET scan results (moderate-certainty evidence). Progression-free survival Twenty-one studies reported PFS. Eleven out of 21 were assessed as low risk of bias in the first four domains. The remaining were assessed as unclear, moderate or high risk of bias in at least one of the four domains. Eleven studies were assessed as low risk, and ten studies as high risk of bias for the domain other prognostic factors (covariates). Eight studies were assessed as high risk, thirteen as low risk of bias for statistical analysis and reporting. We pooled 14 studies with 2079 participants. Participants who have a negative interim PET scan result may have an advantage in PFS compared to those with a positive interim PET scan result, but the evidence is very uncertain (unadjusted HR 4.90, 95% CI 3.47 to 6.90, I² = 45%, very low-certainty evidence). This means that 850 out of 1000 participants with a negative interim PET scan result may be progression-free longer than three years compared to 451 (95% CI 326 to 569) out of 1000 participants with a positive result. Adjusted results (not pooled) from eight studies also indicate that there may be an independent prognostic value of interim PET scan results (low-certainty evidence). PET-associated adverse events No study measured PET-associated AEs. Authors' conclusions This review provides moderate-certainty evidence that interim PET scan results predict OS, and very low-certainty evidence that interim PET scan results predict progression-free survival in treated individuals with HL. This evidence is primarily based on unadjusted data. More studies are needed to test the adjusted prognostic ability of interim PET against established prognostic factors.

Prognostic models for newly‐diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta‐analysis

Abstract: Copyright © 2020 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd. Background: Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL. Objectives: To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances. Search methods: We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies. Selection criteria: We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design. Data collection and analysis: We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models. Main results: From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies:. CLL International Prognostic Index (CLL-IPI). This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score. Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score. The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77. Authors' conclusions: Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Coronavirus Disease 2019–COVID-19

Abstract: SUMMARYIn recent decades, several new diseases have emerged in different geographical areas, with pathogens including Ebola virus, Zika virus, Nipah virus, and coronaviruses (CoVs). Recently, a new type of viral infection emerged in Wuhan City, China, and initial genomic sequencing data of this virus do not match with previously sequenced CoVs, suggesting a novel CoV strain (2019-nCoV), which has now been termed severe acute respiratory syndrome CoV-2 (SARS-CoV-2). Although coronavirus disease 2019 (COVID-19) is suspected to originate from an animal host (zoonotic origin) followed by human-to-human transmission, the possibility of other routes should not be ruled out. Compared to diseases caused by previously known human CoVs, COVID-19 shows less severe pathogenesis but higher transmission competence, as is evident from the continuously increasing number of confirmed cases globally. Compared to other emerging viruses, such as Ebola virus, avian H7N9, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV-2 has shown relatively low pathogenicity and moderate transmissibility. Codon usage studies suggest that this novel virus has been transferred from an animal source, such as bats. Early diagnosis by real-time PCR and next-generation sequencing has facilitated the identification of the pathogen at an early stage. Since no antiviral drug or vaccine exists to treat or prevent SARS-CoV-2, potential therapeutic strategies that are currently being evaluated predominantly stem from previous experience with treating SARS-CoV, MERS-CoV, and other emerging viral diseases. In this review, we address epidemiological, diagnostic, clinical, and therapeutic aspects, including perspectives of vaccines and preventive measures that have already been globally recommended to counter this pandemic virus.

Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.

Abstract: OBJECTIVE:To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). DESIGN:Prospective observational cohort study. SETTING:International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium-ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020. PARTICIPANTS: Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. MAIN OUTCOME MEASURE:In-hospital mortality. RESULTS:35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). CONCLUSIONS:An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. STUDY REGISTRATION:ISRCTN66726260.

Digital technologies in the public-health response to COVID-19.

Abstract: Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.