scholarly journals A Clinical Risk Score to Predict In-hospital Mortality from COVID-19 in South Korea

2021 ◽  
Vol 36 (15) ◽  
Author(s):  
Ae-Young Her ◽  
Youngjune Bhak ◽  
Eun Jung Jun ◽  
Song Lin Yuan ◽  
Scot Garg ◽  
...  
BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e040729 ◽  
Author(s):  
Carlo Fumagalli ◽  
Renzo Rozzini ◽  
Matteo Vannini ◽  
Flaminia Coccia ◽  
Giulia Cesaroni ◽  
...  

ObjectivesSeveral physiological abnormalities that develop during COVID-19 are associated with increased mortality. In the present study, we aimed to develop a clinical risk score to predict the in-hospital mortality in COVID-19 patients, based on a set of variables available soon after the hospitalisation triage.SettingRetrospective cohort study of 516 patients consecutively admitted for COVID-19 to two Italian tertiary hospitals located in Northern and Central Italy were collected from 22 February 2020 (date of first admission) to 10 April 2020.ParticipantsConsecutive patients≥18 years admitted for COVID-19.Main outcome measuresSimple clinical and laboratory findings readily available after triage were compared by patients’ survival status (‘dead’ vs ‘alive’), with the objective of identifying baseline variables associated with mortality. These were used to build a COVID-19 in-hospital mortality risk score (COVID-19MRS).ResultsMean age was 67±13 years (mean±SD), and 66.9% were male. Using Cox regression analysis, tertiles of increasing age (≥75, upper vs <62 years, lower: HR 7.92; p<0.001) and number of chronic diseases (≥4 vs 0–1: HR 2.09; p=0.007), respiratory rate (HR 1.04 per unit increase; p=0.001), PaO2/FiO2 (HR 0.995 per unit increase; p<0.001), serum creatinine (HR 1.34 per unit increase; p<0.001) and platelet count (HR 0.995 per unit increase; p=0.001) were predictors of mortality. All six predictors were used to build the COVID-19MRS (Area Under the Curve 0.90, 95% CI 0.87 to 0.93), which proved to be highly accurate in stratifying patients at low, intermediate and high risk of in-hospital death (p<0.001).ConclusionsThe COVID-19MRS is a rapid, operator-independent and inexpensive clinical tool that objectively predicts mortality in patients with COVID-19. The score could be helpful from triage to guide earlier assignment of COVID-19 patients to the most appropriate level of care.


2020 ◽  
Vol 53 (1) ◽  
pp. 78-86
Author(s):  
Alexandra Halalau ◽  
Zaid Imam ◽  
Patrick Karabon ◽  
Nikhil Mankuzhy ◽  
Aciel Shaheen ◽  
...  

EP Europace ◽  
2013 ◽  
Vol 16 (1) ◽  
pp. 40-46 ◽  
Author(s):  
K. Kraaier ◽  
M. F. Scholten ◽  
J. G. P. Tijssen ◽  
D. A. M. J. Theuns ◽  
L. J. L. M. Jordaens ◽  
...  

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Joanna Shim ◽  
David J Mclernon ◽  
David Hamilton ◽  
Hamish A Simpson ◽  
Marcus Beasley ◽  
...  

2015 ◽  
Vol 2 (1) ◽  
pp. e000060 ◽  
Author(s):  
Shamil Haroon ◽  
Peymane Adab ◽  
Richard D Riley ◽  
Tom Marshall ◽  
Robert Lancashire ◽  
...  

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Daniel M Couri ◽  
Grace Lin ◽  
Tracy L Webster ◽  
Peter A Brady

Introduction: Appropriate selection of patients (pts) with heart failure (HF) who may benefit from cardiac resynchronization therapy (CRT) is difficult. We sought to identify a clinical risk score to better risk stratify patients prior to CRT implantation. Methods: Pts undergoing CRT at Mayo Clinic from 2000 –2005 were included. Multiple clinical variables (age, gender, anemia (Hgb <10g/dL), RF (creatinine clearance ≤ 60ml/min/1.73m 2 ), hyponatremia (Na ≤130mEq/L), elevated BNP level (>500pg/ml), etiology, EF ≤20%, and advanced HF (NYHA functional class III–IV) were assessed with outcomes following CRT. Multivariate analysis was used to determine a clinical risk score. Results: A total of 496 patients (80% males) age 68 ± 12 years (62% ischemic cardiomyopathy, EF 22% ± 8%) were included. In univariate analysis relative risk (RR) was > 1 for RF (RR 1.8, CI 1.3–2.8; p = 0.002), anemia (RR 3.3, CI 1.8 –5.5; p = 0.001), hyponatremia (RR 3.4, CI 1.4 – 6.9; p = 0.008), elevated BNP (RR 2.9, CI 1.6 –5.7; p < 0.001), ischemic cardiomyopathy (ICM) (RR 1.8, CI 1.2–2.7; p < 0.002), EF ≤ 20% (RR 1.5, CI 1.0 –2.1; p = 0.033), and advanced HF (RR 2.5, CI 1.5– 4.9; p < 0.001). Following multivariate analysis RF, anemia, ICM, and advanced HF remained significant predictors of poor outcome (p >0.01 for all). Survival with 3 or more of these clinical risk factors was significantly worse than with less risk factors (p <0.01, Figure ). Conclusions: Pre-implant clinical risk factors including anemia, RF, ICM and advanced HF predict worse outcome following CRT with ≥3 variables predicting >2-fold increased risk of death or heart transplantation. These factors should be considered when selecting pts prior to CRT.


Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Zachary Brumberger ◽  
Mary Branch ◽  
Joseph Rigdon ◽  
Suji Vasu

Introduction: Cardiotoxicity is a well-known risk in breast cancer patients treated with anthracyclines and trastuzumab. Ezaz et al. developed a clinical risk score (CRS) to risk stratify these patients. Despite evidence that African American (AA) race is a significant risk factor for cardiotoxicity, no study has assessed the impact of AA race on this CRS. Here we assess the discrimination ability of the Ezaz et al. CRS with the addition of AA race. Methods: This is a retrospective cohort utilizing a registry of 118 patients with stage I-IV breast cancer treated with anthracyclines and/or trastuzumab. Patients without baseline echocardiography data or with baseline LVEF < 50% were excluded. The CRS from Ezaz et al. consisting of age, adjuvant chemotherapy, coronary artery disease, atrial fibrillation or flutter, diabetes mellitus, hypertension, and renal failure was calculated with the addition of AA race. Cardiotoxicity was defined by an LVEF decline of ≥ 10% to LVEF < 53% from baseline. Results: In our 118 patient cohort, the mean age was 59 years, 23 (20%) AA patients, 65 (55%) patients considered low risk (scores of 0-3) and 53 (45%) considered moderate to high risk (scores ≥4). After a follow up of 3 months to 5 years, 14 (12%) patients developed cardiotoxicity. Table 1 lists the CRS changes in statistical characteristics and predictability with the addition of AA race. In comparing the models, the AUC c-statistic increased from 0.609 to 0.642 (95% CI 0.47-0.75, 95% CI 0.49-0.79 respectively; P value = 0.56) with the addition of AA race ( Figure 1 ). Conclusions: In this study, the Ezaz et al. CRS demonstrated improved discrimination and sensitivity with the addition of AA race. This study suggests AA race improves the predictive ability of the Ezaz et al. CRS. Given the limited size of our study, we promote that this should be hypothesis-driving and encourage further investigation on the path to develop an important risk stratification tool.


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