Author
Anuradha Lala
Other affiliations: Cardiovascular Institute of the South, Brigham and Women's Hospital, Mount Sinai Hospital ...read more
Bio: Anuradha Lala is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Medicine & Heart failure. The author has an hindex of 31, co-authored 112 publications receiving 3528 citations. Previous affiliations of Anuradha Lala include Cardiovascular Institute of the South & Brigham and Women's Hospital.
Papers
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TL;DR: The novel coronavirus disease-2019 (COVID-19) has affected nearly every country worldwide and there are anecdotal observations of improved outcomes with systemic anticoagulation (AC), however, the specific outcomes are still unclear.
802 citations
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TL;DR: Cardiovascular disease (CVD) including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes.
532 citations
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TL;DR: Association of AC with in-hospital outcomes and thromboembolic findings on autopsies was associated with lower mortality and intubation among hospitalized COVID-19 patients, although not statistically significant.
373 citations
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TL;DR: This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020 to describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios with mortality.
Abstract: Background Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described. Methods This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.
361 citations
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University of Washington1, Icahn School of Medicine at Mount Sinai2, Henry Ford Health System3, Harvard University4, University of Pennsylvania5, Northwestern University6, The New School7, Cleveland Clinic8, Johns Hopkins University9, University of Illinois at Chicago10, Montefiore Medical Center11, SUNY Downstate Medical Center12, Baylor College of Medicine13, University of Colorado Boulder14, Emory University15, Temple University16
TL;DR: Age and underlying comorbidities rather than immunosuppression intensity-related measures were major drivers of mortality among SOT recipients hospitalized for COVID-19, and multiple measures of immunosppression intensity were not associated with mortality.
Abstract: Background The COVID-19 pandemic has led to significant reductions in transplantation, motivated in part by concerns of disproportionately more severe disease among solid organ transplant (SOT) recipients. However, clinical features, outcomes, and predictors of mortality in SOT recipients are not well-described. Methods We performed a multi-center cohort study of SOT recipients with laboratory-confirmed COVID-19. Data were collected using standardized intake and 28-day follow-up electronic case report forms. Multivariable logistic regression was used to identify risk factors for the primary endpoint, 28-day mortality, among hospitalized patients. Results Four hundred eighty-two SOT recipients from >50 transplant centers were included: 318 (66%) kidney or kidney/pancreas, 73 (15.1%) liver, 57 (11.8%) heart, and 30 (6.2%) lung. Median age was 58 (IQR 46-57), median time post-transplant was 5 years (IQR 2-10), 61% were male, and 92% had ≥1 underlying comorbidity. Among those hospitalized (376 [78%]), 117 (31%) required mechanical ventilation, and 77 (20.5%) died by 28 days after diagnosis. Specific underlying comorbidities (age >65 [aOR 3.0, 95%CI 1.7-5.5, p Conclusions Mortality among SOT recipients hospitalized for COVID-19 was 20.5%. Age and underlying comorbidities rather than immunosuppression intensity-related measures were major drivers of mortality.
306 citations
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01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
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.
4,408 citations
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3,645 citations
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University of Washington1, National Institutes of Health2, Institute for Health Metrics and Evaluation3, Sapienza University of Rome4, Mayo Clinic5, FIU Herbert Wertheim College of Medicine6, Cincinnati Children's Hospital Medical Center7, Boston University8, Essentia Health9, University of Douala10, University of British Columbia11, Medical University of Graz12, Telethon Institute for Child Health Research13, University of Milan14, Cedars-Sinai Medical Center15, Johns Hopkins University16, University of California, San Diego17, University of Michigan18, University of Edinburgh19, University of Texas Southwestern Medical Center20, Queen Mary University of London21, University of Alabama at Birmingham22, Harvard University23, Tufts Medical Center24, All India Institute of Medical Sciences25, Northwestern University26, University of Kentucky27, Casa Sollievo della Sofferenza28, Columbia University29, Icahn School of Medicine at Mount Sinai30, University of Sydney31, University of Cape Town32, Federal University of Rio de Janeiro33, University of Ibadan34, Case Western Reserve University35, Stanford University36, Universidade Federal de Minas Gerais37, The George Institute for Global Health38, Uppsala University39, Dresden University of Technology40, King Fahd Medical City41, Tulane University42, Imperial College London43
TL;DR: CVD burden continues its decades-long rise for almost all countries outside high-income countries, and alarmingly, the age-standardized rate of CVD has begun to rise in some locations where it was previously declining in high- income countries.
3,315 citations
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TL;DR: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...
Abstract: Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...
3,034 citations
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Public Health Research Institute1, Katholieke Universiteit Leuven2, Leiden University3, University of Oxford4, John Radcliffe Hospital5, Keele University6, Medical University of Vienna7, University Medical Center Utrecht8, University College Cork9, University of Pennsylvania10, University of Cologne11, Manchester Academic Health Science Centre12, University of Aberdeen13, RMIT University14, University of Manchester15, University of Amsterdam16, Imperial College London17, University of Ioannina18, Maastricht University Medical Centre19, Humboldt University of Berlin20
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
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.
2,183 citations