Maria C Haller

Bio: Maria C Haller is an academic researcher from Medical University of Vienna. The author has contributed to research in topics: Transplantation & Kidney transplantation. The author has an hindex of 17, co-authored 31 publications receiving 2299 citations. Previous affiliations of Maria C Haller include Ghent University Hospital & Renal Association.

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.

Steroid avoidance or withdrawal for kidney transplant recipients

Abstract: Background Steroid-sparing strategies have been attempted in recent decades to avoid morbidity from long-term steroid intake among kidney transplant recipients. Previous systematic reviews of steroid withdrawal after kidney transplantation have shown a significant increase in acute rejection. There are various protocols to withdraw steroids after kidney transplantation and their possible benefits or harms are subject to systematic review. This is an update of a review first published in 2009. Objectives To evaluate the benefits and harms of steroid withdrawal or avoidance for kidney transplant recipients. Search methods We searched the Cochrane Kidney and Transplant Specialised Register to 15 February 2016 through contact with the Information Specialist using search terms relevant to this review. Selection criteria All randomised and quasi-randomised controlled trials (RCTs) in which steroids were avoided or withdrawn at any time point after kidney transplantation were included. Data collection and analysis Assessment of risk of bias and data extraction was performed by two authors independently and disagreement resolved by discussion. Statistical analyses were performed using the random-effects model and dichotomous outcomes were reported as relative risk (RR) and continuous outcomes as mean difference (MD) with 95% confidence intervals. Main results We included 48 studies (224 reports) that involved 7803 randomised participants. Of these, three studies were conducted in children (346 participants). The 2009 review included 30 studies (94 reports, 5949 participants). Risk of bias was assessed as low for sequence generation in 19 studies and allocation concealment in 14 studies. Incomplete outcome data were adequately addressed in 22 studies and 37 were free of selective reporting. The 48 included studies evaluated three different comparisons: steroid avoidance or withdrawal compared with steroid maintenance, and steroid avoidance compared with steroid withdrawal. For the adult studies there was no significant difference in patient mortality either in studies comparing steroid withdrawal versus steroid maintenance (10 studies, 1913 participants, death at one year post transplantation: RR 0.68, 95% CI 0.36 to 1.30) or in studies comparing steroid avoidance versus steroid maintenance (10 studies, 1462 participants, death at one year after transplantation: RR 0.96, 95% CI 0.52 to 1.80). Similarly no significant difference in graft loss was found comparing steroid withdrawal versus steroid maintenance (8 studies, 1817 participants, graft loss excluding death with functioning graft at one year after transplantation: RR 1.17, 95% CI 0.72 to 1.92) and comparing steroid avoidance versus steroid maintenance (7 studies, 1211 participants, graft loss excluding death with functioning graft at one year after transplantation: RR 1.09, 95% CI 0.64 to 1.86). The risk of acute rejection significantly increased in patients treated with steroids for less than 14 days after transplantation (7 studies, 835 participants: RR 1.58, 95% CI 1.08 to 2.30) and in patients who were withdrawn from steroids at a later time point after transplantation (10 studies, 1913 participants, RR 1.77, 95% CI 1.20 to 2.61). There was no evidence to suggest a difference in harmful events, such as infection and malignancy, in adult kidney transplant recipients. The effect of steroid withdrawal in children is unclear. Authors' conclusions This updated review increases the evidence that steroid avoidance and withdrawal after kidney transplantation significantly increase the risk of acute rejection. There was no evidence to suggest a difference in patient mortality or graft loss up to five year after transplantation, but long-term consequences of steroid avoidance and withdrawal remain unclear until today, because prospective long-term studies have not been conducted.

Cost-effectiveness analysis of renal replacement therapy in Austria.

Abstract: Background. Providing renal replacement therapy (RRT) for end-stage renal disease patients is resource intensive. Despite growing financial pressure in health care systems worldwide, cost-effectiveness studies of RRT modalities are scarce. Methods. We developed a Markov model of costs, quality of life and survival to compare three different assignment strategies to chronic RRT in Europe. Results. Mean annual treatment costs for haemodialysis were V43 600 during the first 12 months, V40 000 between 13 and 24 months and V40 600 beyond 25 months after initiation of treatment. Mean annual treatment costs for peritoneal dialysis were V25 900 during the first 12 months, V15 300 between 13 and 24 months and V20 500 beyond 25 months. Mean annual therapy costs for a kidney transplantation during the first 12 months were V50 900 from a living donor, V51 000 from a deceased donor, V17 200 between 13 and 24 months and V12 900 beyond 25 months after engraftment. Over the next 10 years in Austria with a population of 8 million people, increased assignment to peritoneal dialysis of 20% incident patients saved V26 million with a discount rate of 3% and gained 839 quality-adjusted life years (QALYs); additionally, increasing renal transplants to 10% from live donations saved V38 million discounted and gained 2242 QALYs. Conclusions. Live donor renal transplantation is cost effective and associated with increase in QALYs. Therefore, preemptive live kidney transplantation should be promoted from a fiscal as well as medical point of view.

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

Abstract: Objective: To review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, or (ii) future complications in individuals already diagnosed with COVID-19. Any models to identify subjects at risk for COVID-19 in the general population were also included. Design: Rapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection. Data sources: PubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 13th March 2020. Study selection: Studies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles and abstracts. Data extraction: Data 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: 1916 titles were screened. Of these, 15 studies describing 19 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; nine diagnostic models to detect COVID-19 infection in symptomatic individuals (seven of which were deep learning models for COVID-19 diagnosis utilising computed tomography (CT) results); and seven prognostic models for predicting mortality risk, or length of hospital stay. None of the 15 studies used data on COVID-19 cases outside of China. Predictors included in more than one of the 19 models were: age, sex, comorbidities, C-reactive protein, lymphocyte markers (percentage or neutrophil-to-lymphocyte ratio), lactate dehydrogenase, and features derived from CT images. Reported 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 5 of the 9 diagnostic models), and from 0.90 to 0.98 in those for prognosis (reported for 4 of the 7 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. Conclusion: COVID-19 related prediction models for diagnosis and prognosis are quickly entering the academic literature through publications and preprint reports, aiming to support medical decision making in a time where this is needed urgently. Many models were poorly reported and all appraised as high risk of bias. We call for immediate sharing of the individual participant data from COVID-19 studies worldwide to support collaborative efforts in building more rigously developed and validated COVID-19 related prediction models. The predictors identified in current studies should be considered for potential inclusion in new models. We also stress the need to adhere to methodological standards when developing and evaluating COVID-19 related predictions models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions about COVID-19 in the current pandemic.

Dialysis Vintage and Outcomes after Kidney Transplantation: A Retrospective Cohort Study

Abstract: Background and objectives Historically, length of pretransplant dialysis was associated with premature graft loss and mortality after kidney transplantation, but with recent advancements in RRT it is unclear whether this negative association still exists. Design, setting, participants, &measurements This is a retrospective cohort study evaluating 6979 first kidney allograft recipients from the Austrian Registry transplanted between 1990 and 2013. Duration of pretransplant dialysis treatment was used as categoric predictor classified by tertiles of the distribution of time on dialysis. A separate category for pre-emptive transplantation was added and defined as kidney transplantation without any dialysis preceding the transplant. Outcomes were death-censored graft loss, all-cause mortality, and the composite of both. Results Median duration of follow-up was 8.2 years, and 1866 graft losses and 2407 deaths occurred during the study period. Pre-emptive transplantation was associated with a lower risk of graft loss (hazard ratio, 0.76; 95% confidence interval, 0.59 to 0.98), but not in subgroup analyses excluding living transplants and transplants performed since 2000. The association between dialysis duration and graft loss did not depend on the year of transplantation ( P =0.40) or donor source ( P =0.92). Longer waiting time on dialysis was not associated with a higher rate of graft loss, but the rate of death was higher in patients on pretransplant dialysis for >1.5 years (hazard ratio, 1.62; 95% confidence interval, 1.43 to 1.83) compared with pretransplant dialysis for Conclusions Our findings support the evidence that pre-emptive transplantation is associated with superior graft survival compared with pretransplant dialysis, although this association was weaker in transplants performed since 2000. However, our analysis shows that length of dialysis was no longer associated with a higher rate of graft loss, although longer waiting times on dialysis were still associated with a higher rate of death.

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.