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  • 1
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S24-S25
    Abstract: The long-term health effects after SARS-CoV-2 infection remain poorly understood. We evaluated health and healthcare usage after SARS-CoV-2 infection via surveys and longitudinal electronic medical record (EMR) review within the Military Health System (MHS). Methods We studied MHS beneficiaries enrolled in the Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) cohort from March to December 2020. COVID-19 illness symptom severity and duration were derived from surveys initiated in late 2020. In addition, multi-year healthcare encounter history before and after onset of COVID-19 symptoms was collected from the MHS EMR. Odds of organ-system clinical diagnoses within the 3 months pre- and post-symptom onset were calculated using generalized linear models, controlling for age, sex, and race, and including participant as a random effect. Results 1,015 participants were included who were SARS-CoV-2 positive, symptomatic, and had 3-month follow-up data available in the EMR (Table 1). 625 of these participants had survey data collected more than 28 days post-symptom onset, among whom 17% and 6% reported persistent symptoms at 28-84 days, and 85+ days, respectively. 9.6% had not resumed normal activities by one month. The most frequently reported symptoms persisting beyond 28 days were dyspnea, loss of smell and/or taste, fatigue, and exercise intolerance (Figure 1A). When compared with the period 61 to 90 days prior to symptom onset, the first month post-symptom onset period was associated with increases of pulmonary (aOR = 57, 95% CI 28-112), renal (aOR = 29, 95% CI 10-84), cardiovascular (aOR = 7, 95% CI 5-11), and neurological diagnoses (aOR = 3, 95% CI 2-4) (Figures 1B and 1C). Cardiovascular disease diagnoses remained elevated through 3 months (aOR = 2, 95% CI 1-3). Table 1. Characteristics of SARS-CoV-2+ EPICC participants, and illness duration among those with 28+ days post-symptom onset survey data collection. Figure 1 Fig1A. Symptoms reported by EPICC participants with illnesses longer than 28 days; 1B. Percent of participants with organ system specific diagnoses on each day, 90 days pre- and post-symptom onset; 1C. Odds of organ system specific diagnoses within each month, +/- 3 months of symptom onset, were calculated using generalized linear models, controlling for age, sex, and race and included participants as a random effect. Odds shown are relative to the earliest period included in the model, 61-90 days before onset. Conclusion In this MHS cohort, a significant proportion of participants had persistent symptoms and cardiovascular disease diagnoses 3 months after COVID-19 illness onset. These findings emphasize the long-term morbidity of COVID-19 and the importance of mitigating SARS-CoV-2 infections. Further analyses will evaluate demographic, clinical, and biomarker predictors of medium-to-long term organ-specific post-acute sequelae. Disclosures Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work)) Ryan C. Maves, MD, EMD Serono (Advisor or Review Panel member)Heron Therapeutics (Advisor or Review Panel member) David A. Lindholm, MD, American Board of Internal Medicine (Individual(s) Involved: Self): Member of Auxiliary R & D Infectious Disease Item-Writer Task Force. No financial support received. No exam questions will be disclosed ., Other Financial or Material Support
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2757767-3
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  • 2
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S756-S757
    Abstract: The Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED) is a multicenter study assessing influenza vaccine effectiveness in active duty service members, retirees, and dependents. PAIVED recently completed its third year and offers a unique opportunity to examine influenza-like illness (ILI) trends prior to and during the COVID-19 pandemic in a prospective, well-defined cohort. Methods During the 2018-19, 2019-20, and 2020-21 influenza seasons, PAIVED enrolled DoD beneficiaries presenting for annual influenza vaccination. After collecting baseline demographic data, participants were randomized to receive egg-based, cell-based, or recombinant-derived influenza vaccine. Weekly throughout the influenza season of enrollment, participants were surveyed electronically for ILI, defined as (1) having cough or sore throat, plus (2) feeling feverish/having chills or having body aches/fatigue. Participants with ILI completed a daily symptom diary for seven days and submitted a nasal swab for pathogen detection. Results Over the three seasons, there were 10,656 PAIVED participants: 1514 (14.2%) in 2018-19, 5876 (55.1%) in 2019-20, and 3266 (30.6%) in 2020-21. The majority were male (68-73% per year) with a mean age of 34±14.8 years at enrollment. 2266 participants reported a total of 2673 unique ILIs. The highest percentage of participants with ILI was in 2019-20 (28.2%), versus 19.6% in 2018-19 and 9.6% in 2020-21. Figure 1 depicts the percent of individuals reporting ILI by week of the season for each of the PAIVED seasons. Notably, after March 21, 2020, the weekly incidence of participants reporting ILI never exceeded 1%. Figure 1. Percent of PAIVED participants reporting ILI by week of season. Conclusion The low incidence of reported ILI in PAIVED participants during the COVID-19 pandemic is consistent with national influenza surveillance reports of influenza and outpatient ILI activity, suggesting that mitigation measures taken to reduce transmission of SARS-CoV-2 reduced the spread of other respiratory viruses. Disclaimer Disclosures Ryan C. Maves, MD, EMD Serono (Advisor or Review Panel member)Heron Therapeutics (Advisor or Review Panel member) Jitu Modi, MD, GSK (Speaker’s Bureau)
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2757767-3
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  • 3
    In: Chest, Elsevier BV, Vol. 161, No. 2 ( 2022-02), p. 429-447
    Type of Medium: Online Resource
    ISSN: 0012-3692
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2007244-2
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  • 4
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S273-S273
    Abstract: The risk factors of venous thromboembolism (VTE) in COVID-19 warrant further study. We leveraged a cohort in the Military Health System (MHS) to identify clinical and virological predictors of incident deep venous thrombosis (DVT), pulmonary embolism (PE), and other VTE within 90-days after COVID-19 onset. Methods PCR or serologically-confirmed SARS-CoV-2 infected MHS beneficiaries were enrolled via nine military treatment facilities (MTF) through April 2021. Case characteristics were derived from interview and review of the electronic medical record (EMR) through one-year follow-up in outpatients and inpatients. qPCR was performed on upper respiratory swab specimens collected post-enrollment to estimate SARS-CoV-2 viral load. The frequency of incident DVT, PE, or other VTE by 90-days post-COVID-19 onset were ascertained by ICD-10 code. Correlates of 90-day VTE were determined through multivariate logistic regression, including age and sampling-time-adjusted log10-SARS-CoV-2 GE/reaction as a priori predictors in addition to other demographic and clinical covariates which were selected through stepwise regression. Results 1473 participants with SARS-CoV-2 infection were enrolled through April 2021. 21% of study participants were inpatients; the mean age was 41 years (SD = 17.0 years). The median Charlson Comorbidity Index score was 0 (IQR = 0 - 1, range = 0 - 13). 27 (1.8%) had a prior history of VTE. Mean maximum viral load observed was 1.65 x 107 genome equivalents/reaction. 36 (2.4%) of all SARS-CoV-2 cases (including inpatients and outpatients), 29 (9.5%) of COVID-19 inpatients, and 7 (0.6%) of outpatients received an ICD-10 diagnosis of any VTE within 90 days after COVID-19 onset. Logistic regression identified hospitalization (aOR = 11.1, p = 0.003) and prior VTE (aOR = 6.2 , p = 0.009) as independent predictors of VTE within 90 days of symptom onset. Neither age (aOR = 1.0, p = 0.50), other demographic covariates, other comorbidities, nor SARS-CoV-2 viral load (aOR = 1.1, p = 0.60) were associated with 90-day VTE. Conclusion VTE was relatively frequent in this MHS cohort. SARS-CoV-2 viral load did not increase the odds of 90-day VTE. Rather, being hospitalized for SARS-CoV-2 and prior VTE history remained the strongest predictors of this complication. Disclosures Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work)) Ryan C. Maves, MD, EMD Serono (Advisor or Review Panel member)Heron Therapeutics (Advisor or Review Panel member) David A. Lindholm, MD, American Board of Internal Medicine (Individual(s) Involved: Self): Member of Auxiliary R & D Infectious Disease Item-Writer Task Force. No financial support received. No exam questions will be disclosed ., Other Financial or Material Support David Tribble, M.D., DrPH, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work))
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2757767-3
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  • 5
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S440-S441
    Abstract: Self-collection of mid-nasal swabs (SCNS) at home is a convenient alternative to health-care worker-collected nasal swabs (HCWC) for determining the pathogen-specific epidemiology of influenza-like illness (ILI). We evaluated the compliance and performance characteristics of SCNS vs. HCWC for respiratory pathogens during 2019-2020 flu season. Methods Adult Military Health System (MHS) beneficiaries were enrolled in an influenza vaccine effectiveness trial (PAIVED). Following vaccination, subjects were instructed on SCNS and completion of a symptom diary and were contacted weekly to ascertain ILI symptoms (fever, sore throat, and/or cough). In the event of an ILI, subjects completed the symptom diary and SCNS and were scheduled a clinic visit for HCWC. Swabs were tested with the Luminex NxTAG® Respiratory Pathogen Panel. We evaluated compliance with swab collection, positive percent agreement (PPA) of SCNS using PCR detection from either HCWC or SCNS as the reference standard, and agreement between paired swabs using the Cohen Kappa coefficient (Κ). Results 1808 ILI were reported by 972 participants enrolled during the study period. Compliance with HCWC was higher than SCNS (58% [1042] vs. 42% [766] ; p & lt; 0.001). SCNS were associated with a shorter interval from symptom onset (median: 4 days [IQR:2-6 days] vs. clinic collect: 7 days [IQR:4-9 days] ; p & lt; 0.001). 663 paired swabs were available for 609 participants (Table 1). The overall detection rate was higher in SCNS (36%) than HCWC (26%; p & lt; 0.001) (Figure 1). The overall PPA was 85.7% and a PPA of approximately 80% of greater was observed for influenza, rhino/enterovirus, parainfluenza and respiratory syncytial virus. Agreement between paired swabs was poor due to the lower detection rates in HCWC. Table 1. Demographics and swab collection data for 609 participants who provided 663 paired swabs Figure 1. Detection by pathogen in 663 paired swabs Conclusion SCNS were associated with higher detection rates compared to HCWC, likely due to the shorter interval between symptom onset and swab collection. Strategies to improve compliance with SCNS and minimize the interval between symptom onset and swab collection are needed to optimize detection of respiratory pathogens in this MHS cohort. Disclosures Ryan C. Maves, MD, EMD Serono (Advisor or Review Panel member)Heron Therapeutics (Advisor or Review Panel member) Jitu Modi, MD, GSK (Speaker's Bureau)
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2757767-3
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  • 6
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S126-S127
    Abstract: The SARS-CoV-2 pandemic has spotlighted respiratory infections and the value of effective vaccines. The SARS-CoV-2 vaccine has been remarkably effective; however, influenza vaccine effectiveness has been reported to be lower among active duty military populations than in the general public (18% vs 36%). The Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED) study compares 3 FDA-licensed influenza vaccine types (egg-based, cell-based, and recombinant) to assess differences in immunogenicity and effectiveness in adults. Methods Participants in the 3rd year of PAIVED (2020/21 influenza season) were enrolled from October 2020 through January 2021. Participants received weekly surveys about influenza-like-illnesses (ILI) experienced in the past week; if they reported an ILI, they were queried about symptom duration and severity, and asked to self-collect a nasal swab and dried blood sample. Four weeks later, more information about symptom duration and illness burden was obtained via telephone interview, and the participant collected a second blood sample. Results PAIVED year 3 enrolled 3,269 participants (Table 1). 278 participants reported 1 ILI , while 60 reported 2 ILIs, and 18 reported 3 ILIs. No pathogen was identified for most processed ILI samples (78%); the most common viruses were SARS-CoV-2 (25, 12%), rhinovirus (24, 12%), and seasonal coronaviruses (4, 2%). No influenza has been identified thus far. Among those participants who had convalescent ILI visits (275), the median duration of the reported ILIs was 9 days (IQR 5, 15), with a median of 4 days (IQR 2, 7) of limited activity, and 2 days (IQR 0, 3) with fever. Three individuals were hospitalized. Conclusion There have been relatively low rates of ILI identified in this study during this season, with only 11% of the participants reporting an ILI so far, consistent with low rates of non-COVID-19 ILI reported elsewhere during the current pandemic. We anticipate some influenza cases may be identified as more samples are processed. Planned analyses include calculating comparative influenza vaccine effectiveness to inform future vaccine purchasing decisions, as well as comparing serological response to the different vaccines. Disclosures Ryan C. Maves, MD, EMD Serono (Advisor or Review Panel member)Heron Therapeutics (Advisor or Review Panel member) Jitu Modi, MD, GSK (Speaker’s Bureau)
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2757767-3
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  • 7
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S331-S332
    Abstract: The novel coronavirus disease 2019 (COVID-19) pandemic remains a global challenge. Accurate COVID-19 prognosis remains an important aspect of clinical management. While many prognostic systems have been proposed, most are derived from analyses of individual symptoms or biomarkers. Here, we take a machine learning approach to first identify discrete clusters of early stage-symptoms which may delineate groups with distinct symptom phenotypes. We then sought to identify whether these groups correlate with subsequent disease severity. Methods The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study with data and biospecimens collected from nine military treatment facilities over 1 year of follow-up. Demographic and clinical characteristics were measured with interviews and electronic medical record review. Early symptoms by organ-domain were measured by FLU-PRO-plus surveys collected for 14 days post-enrollment, with surveys completed a median 14.5 (Interquartile Range, IQR = 13) days post-symptom onset. Using these FLU-PRO-plus responses, we applied principal component analysis followed by unsupervised machine learning algorithm k-means to identify groups with distinct clusters of symptoms. We then fit multivariate logistic regression models to determine how these early-symptom clusters correlated with hospitalization risk after controlling for age, sex, race, and obesity. Results Using SARS-CoV-2 positive participants (n = 1137) from the EPICC cohort (Figure 1), we transformed reported symptoms into domains and identified three groups of participants with distinct clusters of symptoms. Logistic regression demonstrated that cluster-2 was associated with an approximately three-fold increased odds [3.01 (95% CI: 2-4.52); P & lt; 0.001] of hospitalization which remained significant after controlling for other factors [2.97 (95% CI: 1.88-4.69); P & lt; 0.001]. (A) Baseline characteristics of SARS-CoV-2 positive participants. (B) Heatmap comparing FLU-PRO response in each participant. (C) Principal component analysis followed by k-means clustering identified three groups of participants. (D) Crude and adjusted association of identified cluster with hospitalization. Conclusion Our findings have identified three distinct groups with early-symptom phenotypes. With further validation of the clusters’ significance, this tool could be used to improve COVID-19 prognosis in a precision medicine framework and may assist in patient triaging and clinical decision-making. Disclaimer Disclosures David A. Lindholm, MD, American Board of Internal Medicine (Individual(s) Involved: Self): Member of Auxiliary R & D Infectious Disease Item-Writer Task Force. No financial support received. No exam questions will be disclosed ., Other Financial or Material Support Ryan C. Maves, MD, EMD Serono (Advisor or Review Panel member)Heron Therapeutics (Advisor or Review Panel member) Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work))
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2757767-3
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  • 8
    In: JAMA Network Open, American Medical Association (AMA), Vol. 6, No. 7 ( 2023-07-13), p. e2323349-
    Abstract: Current data identifying COVID-19 risk factors lack standardized outcomes and insufficiently control for confounders. Objective To identify risk factors associated with COVID-19, severe COVID-19, and SARS-CoV-2 infection. Design, Setting, and Participants This secondary cross-protocol analysis included 4 multicenter, international, randomized, blinded, placebo-controlled, COVID-19 vaccine efficacy trials with harmonized protocols established by the COVID-19 Prevention Network. Individual-level data from participants randomized to receive placebo within each trial were combined and analyzed. Enrollment began July 2020 and the last data cutoff was in July 2021. Participants included adults in stable health, at risk for SARS-CoV-2, and assigned to the placebo group within each vaccine trial. Data were analyzed from April 2022 to February 2023. Exposures Comorbid conditions, demographic factors, and SARS-CoV-2 exposure risk at the time of enrollment. Main Outcomes and Measures Coprimary outcomes were COVID-19 and severe COVID-19. Multivariate Cox proportional regression models estimated adjusted hazard ratios (aHRs) and 95% CIs for baseline covariates, accounting for trial, region, and calendar time. Secondary outcomes included severe COVID-19 among people with COVID-19, subclinical SARS-CoV-2 infection, and SARS-CoV-2 infection. Results A total of 57 692 participants (median [range] age, 51 [18-95] years; 11 720 participants [20.3%] aged ≥65 years; 31 058 participants [53.8%] assigned male at birth) were included. The analysis population included 3270 American Indian or Alaska Native participants (5.7%), 7849 Black or African American participants (13.6%), 17 678 Hispanic or Latino participants (30.6%), and 40 745 White participants (70.6%). Annualized incidence was 13.9% (95% CI, 13.3%-14.4%) for COVID-19 and 2.0% (95% CI, 1.8%-2.2%) for severe COVID-19. Factors associated with increased rates of COVID-19 included workplace exposure (high vs low: aHR, 1.35 [95% CI, 1.16-1.58]; medium vs low: aHR, 1.41 [95% CI, 1.21-1.65] ; P   & amp;lt; .001) and living condition risk (very high vs low risk: aHR, 1.41 [95% CI, 1.21-1.66]; medium vs low risk: aHR, 1.19 [95% CI, 1.08-1.32] ; P   & amp;lt; .001). Factors associated with decreased rates of COVID-19 included previous SARS-CoV-2 infection (aHR, 0.13 [95% CI, 0.09-0.19]; P   & amp;lt; .001), age 65 years or older (aHR vs age & amp;lt;65 years, 0.57 [95% CI, 0.50-0.64]; P   & amp;lt; .001) and Black or African American race (aHR vs White race, 0.78 [95% CI, 0.67-0.91]; P  = .002). Factors associated with increased rates of severe COVID-19 included race (American Indian or Alaska Native vs White: aHR, 2.61 [95% CI, 1.85-3.69]; multiracial vs White: aHR, 2.19 [95% CI, 1.50-3.20] ; P   & amp;lt; .001), diabetes (aHR, 1.54 [95% CI, 1.14-2.08]; P  = .005) and at least 2 comorbidities (aHR vs none, 1.39 [95% CI, 1.09-1.76]; P  = .008). In analyses restricted to participants who contracted COVID-19, increased severe COVID-19 rates were associated with age 65 years or older (aHR vs & amp;lt;65 years, 1.75 [95% CI, 1.32-2.31]; P   & amp;lt; .001), race (American Indian or Alaska Native vs White: aHR, 1.98 [95% CI, 1.38-2.83]; Black or African American vs White: aHR, 1.49 [95% CI, 1.03-2.14] ; multiracial: aHR, 1.81 [95% CI, 1.21-2.69]; overall P  = .001), body mass index (aHR per 1-unit increase, 1.03 [95% CI, 1.01-1.04]; P  = .001), and diabetes (aHR, 1.85 [95% CI, 1.37-2.49]; P   & amp;lt; .001). Previous SARS-CoV-2 infection was associated with decreased severe COVID-19 rates (aHR, 0.04 [95% CI, 0.01-0.14]; P   & amp;lt; .001). Conclusions and Relevance In this secondary cross-protocol analysis of 4 randomized clinical trials, exposure and demographic factors had the strongest associations with outcomes; results could inform mitigation strategies for SARS-CoV-2 and viruses with comparable epidemiological characteristics.
    Type of Medium: Online Resource
    ISSN: 2574-3805
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2023
    detail.hit.zdb_id: 2931249-8
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  • 9
    In: eBioMedicine, Elsevier BV, Vol. 96 ( 2023-10), p. 104799-
    Type of Medium: Online Resource
    ISSN: 2352-3964
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2799017-5
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  • 10
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. 12 ( 2021-12-01)
    Abstract: Early recognition of high-risk patients with coronavirus disease 2019 (COVID-19) may improve outcomes. Although many predictive scoring systems exist, their complexity may limit utility in COVID-19. We assessed the prognostic performance of the National Early Warning Score (NEWS) and an age-based modification (NEWS+age) among hospitalized COVID-19 patients enrolled in a prospective, multicenter US Military Health System (MHS) observational cohort study. Methods Hospitalized adults with confirmed COVID-19 not requiring invasive mechanical ventilation at admission and with a baseline NEWS were included. We analyzed each scoring system’s ability to predict key clinical outcomes, including progression to invasive ventilation or death, stratified by baseline severity (low [0–3], medium [4–6] , and high [≥7]). Results Among 184 included participants, those with low baseline NEWS had significantly shorter hospitalizations (P  & lt; .01) and lower maximum illness severity (P  & lt; .001). Most (80.2%) of low NEWS vs 15.8% of high NEWS participants required no or at most low-flow oxygen supplementation. Low NEWS (≤3) had a negative predictive value of 97.2% for progression to invasive ventilation or death; a high NEWS (≥7) had high specificity (93.1%) but low positive predictive value (42.1%) for such progression. NEWS+age performed similarly to NEWS at predicting invasive ventilation or death (NEWS+age: area under the receiver operating characteristics curve [AUROC], 0.69; 95% CI, 0.65–0.73; NEWS: AUROC, 0.70; 95% CI, 0.66–0.75). Conclusions NEWS and NEWS+age showed similar test characteristics in an MHS COVID-19 cohort. Notably, low baseline scores had an excellent negative predictive value. Given their easy applicability, these scoring systems may be useful in resource-limited settings to identify COVID-19 patients who are unlikely to progress to critical illness.
    Type of Medium: Online Resource
    ISSN: 2328-8957
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2757767-3
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