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Open accessJournal ArticleDOI: 10.7326/M20-6754

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.

02 Mar 2021-Annals of Internal Medicine (American College of Physicians)-Vol. 174, Iss: 6, pp 777-785
Abstract: Background Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. Objective To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. Design Retrospective observational cohort study. Settings Five hospitals in Maryland and Washington, D.C. Patients Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. Measurements A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. Results Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. Limitation The SCARP tool was developed by using data from a single health system. Conclusion Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. Primary funding source Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

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Open accessJournal ArticleDOI: 10.1007/S11739-021-02742-8
Elena Corradini, Paolo Ventura, Walter Ageno1, Chiara Cogliati2  +27 moreInstitutions (15)
Abstract: During the COVID-19 2020 outbreak, a large body of data has been provided on general management and outcomes of hospitalized COVID-19 patients. Yet, relatively little is known on characteristics and outcome of patients managed in Internal Medicine Units (IMU). To address this gap, the Italian Society of Internal Medicine has conducted a nationwide cohort multicentre study on death outcome in adult COVID-19 patients admitted and managed in IMU. This study assessed 3044 COVID-19 patients at 41 referral hospitals across Italy from February 3rd to May 8th 2020. Demographics, comorbidities, organ dysfunction, treatment, and outcomes including death were assessed. During the study period, 697 patients (22.9%) were transferred to intensive care units, and 351 died in IMU (death rate 14.9%). At admission, factors independently associated with in-hospital mortality were age (OR 2.46, p = 0.000), productive cough (OR 2.04, p = 0.000), pre-existing chronic heart failure (OR 1.58, p = 0.017) and chronic obstructive pulmonary disease (OR 1.17, p = 0.048), the number of comorbidities (OR 1.34, p = 0.000) and polypharmacy (OR 1.20, p = 0.000). Of note, up to 40% of elderly patients did not report fever at admission. Decreasing PaO2/FiO2 ratio at admission was strongly inversely associated with survival. The use of conventional oxygen supplementation increased with the number of pre-existing comorbidities, but it did not associate with better survival in patients with PaO2/FiO2 ratio < 100. The latter, significantly benefited by the early use of non-invasive mechanical ventilation. Our study identified PaO2/FiO2 ratio at admission and comorbidity as the main alert signs to inform clinical decisions and resource allocation in non-critically ill COVID-19 patients admitted to IMU.

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Topics: Intensive care (55%), Cohort study (53%), Mortality rate (52%) ... read more

5 Citations


Open accessJournal ArticleDOI: 10.1177/21501327211018559
Sanjeev Nanda1, Audry Chacin Suarez1, Loren Toussaint2, Ann Vincent1  +10 moreInstitutions (2)
Abstract: PurposeThe purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes.PatientsPatients from this study are f...

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Topics: Body mass index (59%), Framingham Risk Score (52%)

2 Citations


Open accessJournal ArticleDOI: 10.1002/JCSM.12789
Abstract: Background: Strength and muscle mass are predictors of relevant clinical outcomes in critically ill patients, but in hospitalized patients with COVID-19, it remains to be determined. In this prospective observational study, we investigated whether muscle strength or muscle mass are predictive of hospital length of stay (LOS) in patients with moderate to severe COVID-19 patients. Methods: We evaluated prospectively 196 patients at hospital admission for muscle mass and strength. Ten patients did not test positive for SARS-CoV-2 during hospitalization and were excluded from the analyses. Results: The sample comprised patients of both sexes (50% male) with a mean age (SD) of 59 (±15) years, body mass index of 29.5 (±6.9) kg/m2. The prevalence of current smoking patients was 24.7%, and more prevalent coexisting conditions were hypertension (67.7%), obesity (40.9%), and type 2 diabetes (36.0%). Mean (SD) LOS was 8.6 days (7.7); 17.0% of the patients required intensive care; 3.8% used invasive mechanical ventilation; and 6.6% died during the hospitalization period. The crude hazard ratio (HR) for LOS was greatest for handgrip strength comparing the strongest versus other patients (1.47 [95% CI: 1.07–2.03; P = 0.019]). Evidence of an association between increased handgrip strength and shorter hospital stay was also identified when handgrip strength was standardized according to the sex-specific mean and standard deviation (1.23 [95% CI: 1.06–1.43; P = 0.007]). Mean LOS was shorter for the strongest patients (7.5 ± 6.1 days) versus others (9.2 ± 8.4 days). Evidence of associations were also present for vastus lateralis cross-sectional area. The crude HR identified shorter hospital stay for patients with greater sex-specific standardized values (1.20 [95% CI: 1.03–1.39; P = 0.016]). Evidence was also obtained associating longer hospital stays for patients with the lowest values for vastus lateralis cross-sectional area (0.63 [95% CI: 0.46–0.88; P = 0.006). Mean LOS for the patients with the lowest muscle cross-sectional area was longer (10.8 ± 8.8 days) versus others (7.7 ± 7.2 days). The magnitude of associations for handgrip strength and vastus lateralis cross-sectional area remained consistent and statistically significant after adjusting for other covariates. Conclusions: Muscle strength and mass assessed upon hospital admission are predictors of LOS in patients with moderate to severe COVID-19, which stresses the value of muscle health in prognosis of this disease.

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Topics: Intensive care (54%)

1 Citations


Open accessPosted ContentDOI: 10.1101/2021.03.30.21254578
31 Mar 2021-medRxiv
Abstract: ImportanceStrength and muscle mass are predictors of relevant clinical outcomes in critically ill patients, but in hospitalized patients with COVID-19 remains to be determined. ObjectiveTo investigate whether muscle strength or muscle mass are predictive of hospital length of stay (LOS) in patients with moderate to severe COVID-19. DesignProspective observational study. SettingClinical Hospital of the School of Medicine of the University of Sao Paulo. ParticipantsOne hundred ninety-six patients were evaluated. Ten patients did not test positive for SARS-CoV-2 during hospitalization and were excluded from the analyses. The sample comprised patients of both sexes (50% male) with a mean age (SD) of 59 ({+/-}15) years, body mass index of 29.5 ({+/-}6.9) kg/m2. The prevalence of current smoking patients was 24.7%, and more prevalent coexisting conditions were hypertension (67.7%), obesity (40.9%), and type 2 diabetes (36.0%). Mean (SD) LOS was 8.6 days (7.7); 17.0% of the patients required intensive care; 3.8% used invasive mechanical ventilation; and 6.6% died during the hospitalization period. Main outcomeThe outcome was LOS, defined as time from hospital admission to medical discharge. ResultsThe crude Hazard Ratio (HR) for LOS was greatest for handgrip strength comparing the strongest vs. other patients (1.54 [95%CI: 1.12 - 2.12; p = 0.008]). Evidence of an association between increased handgrip strength and shorter hospital stay was also identified when handgrip strength was standardized according to the sex-specific mean and standard deviation (1.23 [95%CI: 1.06 - 1.19; p = 0.008]). The magnitude of these associations remained consistent and statistically significant after adjusting for other covariates. Mean LOS was shorter for the strongest patients (7.5 {+/-} 6.1 days) vs. others (9.2 {+/-} 8.4 days). Evidence of associations were also present for vastus lateralis cross-sectional area. The crude HR identified shorter hospital stay for patients with greater sex-specific standardized values (1.17 [95%CI: 1.01 - 1.36; p = 0.037]); however, we found increased uncertainty in the estimate with the addition of other covariates (1.18 [95%CI: 0.97 - 1.43; p = 0.092]). Evidence was also obtained associating longer hospital stays for patients with the lowest values for vastus lateralis cross-sectional area (0.69 [95%CI: 0.50 - 0.95; p = 0.025). Mean LOS for the patients with the lowest muscle cross-sectional area was longer (10.8 {+/-} 8.8 days) vs. others (7.7 {+/-} 7.2 days). Conclusions and RelevanceMuscle strength and mass assessed upon hospital admission are predictors of LOS in patients with moderate to severe COVID-19, which stresses the value of muscle health in prognosis of this disease. FundingThe authors acknowledge the support by the Brazilian National Council for Scientific and Technological Development (CNPq - grant 301571/2017-1). H.R. and B.G. are supported by grants from the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq 428242/2018-9; 301571/2017-1; 301914/2017-6). B.G. is also supported by a grant from the Sao Paulo Research Foundation (FAPESP 2017/13552-2). Key pointsO_ST_ABSQuestionC_ST_ABSDo muscle strength and muscle mass predict hospital length of stay (LOS) in patients with moderate to severe COVID-19 patients? FindingsIn this prospective observational study that included 186 hospitalized patients with moderate to severe COVID-19, we observed that LOS was shorter among patients in the highest tertile of strength (assessed by handgrip) vs. those in the mid/lowest tertiles (crude Hazard Ratio [HR]: 1.54, 95%CI: 1.12-2.12). In addition, LOS was longer among patients in the lowest tertile of muscle cross-sectional area (assessed by ultrasound imaging) vs. those in the mid/highest tertiles (HR: 0.69, 95%CI: 0.50 - 0.95). MeaningMuscle strength and mass assessed on hospital admission are predictors of LOS in patients with moderate to severe COVID-19, suggesting that muscle health may be protective in this disease.

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Topics: Intensive care (54%)

1 Citations


Open accessJournal ArticleDOI: 10.1007/S11606-021-06924-0
Justin R Kingery1, Paul Martin1, Ben R Baer1, Laura C. Pinheiro1  +13 moreInstitutions (1)
Abstract: The clinical course of COVID-19 includes multiple disease phases. Data describing post-hospital discharge outcomes may provide insight into disease course. Studies describing post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate the incidence of outcomes. To determine 30-day post-hospitalization outcomes following COVID-19 infection. Retrospective cohort study Quaternary referral hospital and community hospital in New York City. COVID-19 infected patients discharged alive from the emergency department (ED) or hospital between March 3 and May 15, 2020. Outcomes included return to an ED, re-hospitalization, and mortality within 30 days of hospital discharge. Thirty-day follow-up data were successfully collected on 94.6% of eligible patients. Among 1344 patients, 16.5% returned to an ED, 9.8% were re-hospitalized, and 2.4% died. Among patients who returned to the ED, 50.0% (108/216) went to a different hospital from the hospital of the index presentation, and 61.1% (132/216) of those who returned were re-hospitalized. In Cox models adjusted for variables selected using the lasso method, age (HR 1.01 per year [95% CI 1.00–1.02]), diabetes (1.54 [1.06–2.23]), and the need for inpatient dialysis (3.78 [2.23–6.43]) during the index presentation were independently associated with a higher re-hospitalization rate. Older age (HR 1.08 [1.05–1.11]) and Asian race (2.89 [1.27–6.61]) were significantly associated with mortality. Among patients discharged alive following their index presentation for COVID-19, risk for returning to a hospital within 30 days of discharge was substantial. These patients merit close post-discharge follow-up to optimize outcomes.

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Open accessJournal ArticleDOI: 10.1016/S0140-6736(20)30566-3
Fei Zhou1, Ting Yu, Ronghui Du, Guohui Fan2  +16 moreInstitutions (5)
28 Mar 2020-The Lancet
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.

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Topics: Cohort study (56%), Retrospective cohort study (56%), Odds ratio (53%) ... read more

15,279 Citations


Open accessJournal ArticleDOI: 10.1016/S1473-3099(20)30120-1
Ensheng Dong1, Hongru Du1, Lauren Gardner1Institutions (1)
Abstract: The outbreak of the 2019 novel coronavirus disease (COVID-19) has induced a considerable degree of fear, emotional stress and anxiety among individuals around t

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Topics: Dashboard (business) (62%), Web application (53%)

5,397 Citations


Open accessJournal ArticleDOI: 10.1001/JAMA.2020.6775
26 May 2020-JAMA
Abstract: Importance There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19). Objective To describe the clinical characteristics and outcomes of patients with COVID-19 hospitalized in a US health care system. Design, Setting, and Participants Case series of patients with COVID-19 admitted to 12 hospitals in New York City, Long Island, and Westchester County, New York, within the Northwell Health system. The study included all sequentially hospitalized patients between March 1, 2020, and April 4, 2020, inclusive of these dates. Exposures Confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample among patients requiring admission. Main Outcomes and Measures Clinical outcomes during hospitalization, such as invasive mechanical ventilation, kidney replacement therapy, and death. Demographics, baseline comorbidities, presenting vital signs, and test results were also collected. Results A total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female). The most common comorbidities were hypertension (3026; 56.6%), obesity (1737; 41.7%), and diabetes (1808; 33.8%). At triage, 30.7% of patients were febrile, 17.3% had a respiratory rate greater than 24 breaths/min, and 27.8% received supplemental oxygen. The rate of respiratory virus co-infection was 2.1%. Outcomes were assessed for 2634 patients who were discharged or had died at the study end point. During hospitalization, 373 patients (14.2%) (median age, 68 years [IQR, 56-78]; 33.5% female) were treated in the intensive care unit care, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died. As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital. The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5) for readmitted patients. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR, 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1). Conclusions and Relevance This case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.

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Topics: Interquartile range (55%), Respiratory virus (51%)

5,140 Citations


Open accessJournal ArticleDOI: 10.1038/S41586-020-2521-4
08 Jul 2020-Nature
Abstract: Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY-a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53-1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29-1.69) and 1.45 (1.32-1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly.

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Topics: Cohort study (53%), Hazard ratio (51%), Risk assessment (50%)

2,257 Citations


Open accessJournal ArticleDOI: 10.1038/BJC.2014.639
07 Jan 2015-BMJ
Abstract: Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health-care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

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1,503 Citations


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