scholarly journals External validation of the 4C Mortality Score for patients with COVID-19 and pre-existing cardiovascular diseases/risk factors

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e052708
Author(s):  
Shunsuke Kuroda ◽  
Shingo Matsumoto ◽  
Takahide Sano ◽  
Takeshi Kitai ◽  
Taishi Yonetsu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Missing Values ◽  
Prediction Models ◽  
Invasive Mechanical Ventilation ◽  
Composite Endpoint ◽  
Risk Scores ◽  
Coronary Risk Factors ◽  
Discriminatory Ability ◽  
Hospitalised Patients

ObjectivesPredictive algorithms to inform risk management decisions are needed for patients with COVID-19, although the traditional risk scores have not been adequately assessed in Asian patients. We aimed to evaluate the performance of a COVID-19-specific prediction model, the 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score, along with other conventional critical care risk models in Japanese nationwide registry data.DesignRetrospective cohort study.Setting and participantsHospitalised patients with COVID-19 and cardiovascular disease or coronary risk factors from January to May 2020 in 49 hospitals in Japan.Main outcome measuresTwo different types of outcomes, in-hospital mortality and a composite outcome, defined as the need for invasive mechanical ventilation and mortality.ResultsThe risk scores for 693 patients were tested by predicting in-hospital mortality for all patients and composite endpoint among those not intubated at baseline (n=659). The number of events was 108 (15.6%) for mortality and 178 (27.0%) for composite endpoints. After missing values were multiply imputed, the performance of the 4C Mortality Score was assessed and compared with three prediction models that have shown good discriminatory ability (RISE UP score, A-DROP score and the Rapid Emergency Medicine Score (REMS)). The area under the receiver operating characteristic curve (AUC) for the 4C Mortality Score was 0.84 (95% CI 0.80 to 0.88) for in-hospital mortality and 0.78 (95% CI 0.74 to 0.81) for the composite endpoint. It showed greater discriminatory ability compared with other scores, except for the RISE UP score, for predicting in-hospital mortality (AUC: 0.82, 95% CI 0.78 to 0.86). Similarly, the 4C Mortality Score showed a positive net reclassification improvement index over the A-DROP and REMS for mortality and over all three scores for the composite endpoint. The 4C Mortality Score model showed good calibration, regardless of outcome.ConclusionsThe 4C Mortality Score performed well in an independent external COVID-19 cohort and may enable appropriate disposition of patients and allocation of medical resources.Trial registration number UMIN000040598.

scholarly journals Coronary artery surgery in women compared with men: analysis of coronary risk factors and in-hospital mortality in a single centre.

Heart ◽  
1994 ◽  
Vol 71 (5) ◽  
pp. 408-412 ◽  
Author(s):  
M. Barbir ◽  
F. Lazem ◽  
C. Ilsley ◽  
A. Mitchell ◽  
A. Khaghani ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Hospital Mortality ◽  
Coronary Risk Factors ◽  
Coronary Risk ◽  
Single Centre ◽  
Coronary Artery Surgery

scholarly journals Interim Analysis of Risk Factors for Severe Outcomes among a Cohort of Hospitalized Adults Identified through the U.S. Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET)

2020 ◽  
Author(s):  
Lindsay Kim ◽  
Shikha Garg ◽  
Alissa O'Halloran ◽  
Michael Whitaker ◽  
Huong Pham ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Invasive Mechanical Ventilation ◽  
The United States ◽  
Surveillance Network ◽  
Adjusted Risk ◽  
Factors Associated ◽  
Icu Admission ◽  
Male Sex ◽  
Race Ethnicity

Background: As of May 15, 2020, the United States has reported the greatest number of coronavirus disease 2019 (COVID-19) cases and deaths globally. Objective: To describe risk factors for severe outcomes among adults hospitalized with COVID-19. Design: Cohort study of patients identified through the Coronavirus Disease 2019-Associated Hospitalization Surveillance Network. Setting: 154 acute care hospitals in 74 counties in 13 states. Patients: 2491 patients hospitalized with laboratory-confirmed COVID-19 during March 1-May 2, 2020. Measurements: Age, sex, race/ethnicity, and underlying medical conditions. Results: Ninety-two percent of patients had at least 1 underlying condition; 32% required intensive care unit (ICU) admission; 19% invasive mechanical ventilation; 15% vasopressors; and 17% died during hospitalization. Independent factors associated with ICU admission included ages 50-64, 65-74, 75-84 and 85+ years versus 18-39 years (adjusted risk ratio (aRR) 1.53, 1.65, 1.84 and 1.43, respectively); male sex (aRR 1.34); obesity (aRR 1.31); immunosuppression (aRR 1.29); and diabetes (aRR 1.13). Independent factors associated with in-hospital mortality included ages 50-64, 65-74, 75-84 and 85+ years versus 18-39 years (aRR 3.11, 5.77, 7.67 and 10.98, respectively); male sex (aRR 1.30); immunosuppression (aRR 1.39); renal disease (aRR 1.33); chronic lung disease (aRR 1.31); cardiovascular disease (aRR 1.28); neurologic disorders (aRR 1.25); and diabetes (aRR 1.19). Race/ethnicity was not associated with either ICU admission or death. Limitation: Data were limited to patients who were discharged or died in-hospital and had complete chart abstractions; patients who were still hospitalized or did not have accessible medical records were excluded. Conclusion: In-hospital mortality for COVID-19 increased markedly with increasing age. These data help to characterize persons at highest risk for severe COVID-19-associated outcomes and define target groups for prevention and treatment strategies.

scholarly journals Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection

2020 ◽  
Author(s):  
Jacob McPadden ◽  
Frederick Warner ◽  
H. Patrick Young ◽  
Nathan C. Hurley ◽  
Rebecca A. Pulk ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Ethnic Groups ◽  
Treatment Guidelines ◽  
Invasive Mechanical Ventilation ◽  
Respiratory Syndrome Virus ◽  
Data Repository ◽  
Discharge Disposition ◽  
Discharged Patients ◽  
Male Sex

AbstractObjectiveSevere acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2.DesignThis was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository.SettingYale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas.PopulationsThe study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020.Main outcome and performance measuresPrimary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support.ResultsOf the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups.ConclusionsThis observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

scholarly journals Azithromycin in Hospitalised Patients with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

2020 ◽  
Author(s):  
Peter W Horby ◽  
Alistair Roddick ◽  
Enti Spata ◽  
Natalie Staplin ◽  
Jonathan R Emberson ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Usual Care ◽  
Rate Ratio ◽  
Invasive Mechanical Ventilation ◽  
Composite Endpoint ◽  
Standard Of Care ◽  
Open Label ◽  
Hospitalised Patients ◽  
Randomised Controlled ◽  
To Receive

Background: Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatory actions. We evaluated the efficacy and safety of azithromycin in hospitalised patients with COVID-19. Methods: In this randomised, controlled, open-label, adaptive platform trial, several possible treatments were compared with usual care in patients hospitalised with COVID-19 in the UK. Eligible and consenting patients were randomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once daily by mouth or intravenously for 10 days or until discharge (or one of the other treatment arms). Patients were twice as likely to be randomised to usual care as to any of the active treatment groups. The primary outcome was 28-day mortality. Findings: Between 7 April and 27 November 2020, 2582 patients were randomly allocated to receive azithromycin and 5182 patients to receive usual care alone. Overall, 496 (19%) patients allocated to azithromycin and 997 (19%) patients allocated to usual care died within 28 days (rate ratio 1.00; 95% confidence interval [CI] 0.90-1.12; p=0.99). Consistent results were seen in all pre-specified subgroups of patients. There was no difference in duration of hospitalisation (median 12 days vs. 13 days) or the proportion of patients discharged from hospital alive within 28 days (60% vs. 59%; rate ratio 1.03; 95% CI 0.97-1.10; p=0.29). Among those not on invasive mechanical ventilation at baseline, there was no difference in the proportion meeting the composite endpoint of invasive mechanical ventilation or death (21% vs. 22%; risk ratio 0.97; 95% CI 0.89-1.07; p=0.54). Interpretation: In patients hospitalised with COVID-19, azithromycin did not provide any clinical benefit. Azithromycin use in patients hospitalised with COVID-19 should be restricted to patients where there is a clear antimicrobial indication.

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

Abstract 15875: Prediction of Intensive Care Unit Admission Among Patients Hospitalized for COVID-19: The Intermountain Coronavirus ICU Risk Model (CORONA-ICU)

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Heidi T May ◽  
Joseph B Muhlestein ◽  
Benjamin D Horne ◽  
Kirk U Knowlton ◽  
Tami L Bair ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Beta Blockers ◽  
Risk Scores ◽  
Channel Blockers ◽  
Icu Patients

Background: Treatment for COVID-19 has created surges in hospitalizations, intensive care unit (ICU) admissions, and the need for advanced medical therapy and equipment, including ventilators. Identifying patients early on who are at risk for more intensive hospital resource use and poor outcomes could result in shorter hospital stays, lower costs, and improved outcomes. Therefore, we created clinical risk scores (CORONA-ICU and -ICU+) to predict ICU admission among patients hospitalized for COVID-19. Methods: Intermountain Healthcare patients who tested positive for SARS-CoV-2 and were hospitalized between March 4, 2020 and June 8, 2020 were studied. Derivation of CORONA-ICU risk score models used weightings of commonly collected risk factors and medicines. The primary outcome was admission to the ICU during hospitalization, and secondary outcomes included death and ventilator use. Results: A total of 451 patients were hospitalized for a SARS-CoV-2 positive infection, and 191 (42.4%) required admission to the ICU. Patients admitted to the ICU were older (58.2 vs. 53.6 years), more often male (61.3% vs. 48.5%), and had higher rates of hyperlipidemia, hypertension, diabetes, and peripheral arterial disease. ICU patients more often took ACE inhibitors, beta-blockers, calcium channel blockers, diuretics, and statins. Table 1 shows variables that were evaluated and included in the CORONA-ICU risk prediction models. Models adding medications (CORONA-ICU+) improved risk-prediction. Though not created to predict death and ventilator use, these models did so with high accuracy (Table 2). Conclusion: The CORONA-ICU and -ICU+ models, composed of commonly collected risk factors without or with medications, were shown to be highly predictive of ICU admissions, death, and ventilator use. These models can be efficiently derived and effectively identify high-risk patients who require more careful observation and increased use of advanced medical therapies.

Risk of venous thromboembolism and primary prophylaxis in cancer

2012 ◽  
Vol 32 (02) ◽  
pp. 132-137 ◽  
Author(s):  
C. Ay ◽  
I. Pabinger
Keyword(s):  
Risk Factors ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Cancer Patients ◽  
Prediction Models ◽  
Critical Evaluation ◽  
Primary Prophylaxis ◽  
Vte Prophylaxis ◽  
Related Risk ◽  
Hospitalised Patients

SummaryVenous thromboembolism (VTE) is a common complication in patients with cancer that causes significant morbidity and mortality. Several patient-, tumour-and treatment-related risk factors for VTE in cancer patients have been identified. An effective and safe thromboprophylaxis in cancer patients at high risk of VTE is desirable. Recently, the identification of potential biomarkers and the development of risk scoring models for prediction of cancer-associated VTE have been published. Whether primary VTE prophylaxis based on risk assessment through these biomarkers and risk prediction models might be useful, is currently not yet known. However, thromboprophylaxis is clearly indicated in high-risk situations. While VTE prophylaxis is recommended in cancer patients undergoing surgery and in hospitalised patients with acute disease, studies in ambulatory cancer patients are still rare and evidence for primary VTE prophylaxis is currently limited. In this review, risk factors associated with VTE in cancer patients and current approaches of thromboprophylaxis in different settings, specifically in ambulatory cancer patients are subjected to a critical evaluation.

scholarly journals Risk factors for mortality of adult patients with COVID-19 hospitalised in an emerging country: a cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e050321
Author(s):  
Alfonso M Cueto-Manzano ◽  
María C Espinel-Bermúdez ◽  
Sandra O Hernández-González ◽  
Enrique Rojas-Campos ◽  
Arnulfo H Nava-Zavala ◽  
...  
Keyword(s):  
Risk Factors ◽  
Kidney Disease ◽  
High Frequency ◽  
Kidney Diseases ◽  
Invasive Mechanical Ventilation ◽  
Rank Test ◽  
Triage System ◽  
Manchester Triage System ◽  
Hospitalised Patients ◽  
Emerging Country

ObjectiveTo describe mortality of in-hospital patients with COVID-19 and compare risk factors between survivors and non-survivors.DesignProspective cohort of adult inpatients.SettingTertiary healthcare teaching hospital in Guadalajara, Mexico.ParticipantsAll patients with confirmed COVID-19 hospitalised from 25 March to 7 September 2020 were included. End of study: 7 November 2020.Primary outcome measuresPatient survival analysed by the Kaplan-Meier method and comparison of factors by the log-rank test. Mortality risk factors analysed by multivariate Cox’s proportional-hazard model.ResultsOne thousand ten patients included: 386 (38%) died, 618 (61%) alive at discharge and six (0.6%) remained hospitalised. There was predominance of men (63%) and high frequency of overweight–obesity (71%); hypertension (54%); diabetes (40%); and lung (9%), cardiovascular (8%) and kidney diseases (11%); all of them significantly more frequent in non-survivors. Overweight–obesity was not different between groups, but severity of disease (Manchester Triage System and quick Sequential Organ Failure Assessment) was significantly worse in non-survivors, who were also significantly older (65 vs 45 years, respectively) and had haematological, biochemical, coagulation and inflammatory biomarkers more altered than survivors. Mortality predictors were invasive mechanical ventilation (IMV; OR 3.31, p<0.0001), admission to intensive care unit (ICU; OR 2.18, p<0.0001), age (OR 1.02, p<0.0001), Manchester Triage System (urgent OR 1.44, p=0.02; immediate/very urgent OR 2.02, p=0.004), baseline C reactive protein (CRP; OR 1.002, p=0.009) and antecedent of kidney disease (OR 1.58, p=0.04)ConclusionsMortality in hospitalised patients with COVID-19 in this emerging country centre seemed to be higher than in developed countries. Patients displayed a high frequency of risk factors for poor outcome, but the need for IMV, ICU admission, older age, more severe disease at admission, antecedent of kidney disease and higher CRP levels significantly predicted mortality.

scholarly journals Targeting lung cancer screening to individuals at greatest risk: the role of genetic factors

2021 ◽  
pp. jmedgenet-2020-107399
Author(s):  
Mikey B Lebrett ◽  
Emma J Crosbie ◽  
Miriam J Smith ◽  
Emma R Woodward ◽  
D Gareth Evans ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Genetic Risk ◽  
Prediction Models ◽  
Late Onset ◽  
Lung Cancer Screening ◽  
Genetic Risk Factors ◽  
Smoking History ◽  
Risk Scores

Lung cancer (LC) is the most common global cancer. An individual’s risk of developing LC is mediated by an array of factors, including family history of the disease. Considerable research into genetic risk factors for LC has taken place in recent years, with both low-penetrance and high-penetrance variants implicated in increasing or decreasing a person’s risk of the disease. LC is the leading cause of cancer death worldwide; poor survival is driven by late onset of non-specific symptoms, resulting in late-stage diagnoses. Evidence for the efficacy of screening in detecting cancer earlier, thereby reducing lung-cancer specific mortality, is now well established. To ensure the cost-effectiveness of a screening programme and to limit the potential harms to participants, a risk threshold for screening eligibility is required. Risk prediction models (RPMs), which provide an individual’s personal risk of LC over a particular period based on a large number of risk factors, may improve the selection of high-risk individuals for LC screening when compared with generalised eligibility criteria that only consider smoking history and age. No currently used RPM integrates genetic risk factors into its calculation of risk. This review provides an overview of the evidence for LC screening, screening related harms and the use of RPMs in screening cohort selection. It gives a synopsis of the known genetic risk factors for lung cancer and discusses the evidence for including them in RPMs, focusing in particular on the use of polygenic risk scores to increase the accuracy of targeted lung cancer screening.

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