scholarly journals Clinical Prediction Scores for Type 1 Cardiorenal Syndrome Derived and Validated in Chinese Cohorts

2014 ◽  
Vol 5 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Hong Cheng ◽  
Yi-pu Chen
Keyword(s):  
Coronary Angiography ◽  
Risk Score ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
External Validation ◽  
Multivariate Logistic Regression Analysis ◽  
Cardiorenal Syndrome ◽  
Chinese Patients ◽  
Risk Scores

Type 1 cardiorenal syndrome is one of the major diseases threatening human life in China. The incidence of acute kidney injury (AKI) associated with acute heart failure (AHF), acute myocardial infarction (AMI), cardiac surgery, and coronary angiography has been reported to be 32.2, 14.7, 40.2, and 4.5%, respectively. In the past 2 years, we derived and validated 4 risk scores for the prediction of AKI associated with the above acute heart diseases as well as for examination and treatment in Chinese cohorts. A univariable comparison and a subsequent multivariate logistic regression analysis of the potential predictive variables of AKI in the derivation set were conducted and used to establish the prediction scores, which were then verified in the validation set. The area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow goodness-of-fit statistic test were performed to assess the discrimination and calibration of the prediction scores, respectively. These 4 prediction scores all showed adequate discrimination (area under the ROC curve, ≥0.70) and good calibration (p > 0.05). Both Forman's risk score (for AKI associated with AHF) and Mehran's risk score (for AKI associated with coronary angiography) are widely applied around the world. The external validation of these 2 risk scores was performed in our patients, but their discriminative power was quite low (area under the ROC curve, 0.65 and 0.57, respectively). Therefore, these prediction scores derived from Chinese cohorts might be more accurate than those derived from different races when they are applied in Chinese patients. © 2014 S. Karger AG, Basel

P953Comparison of the original and updated ACTION risk scores for predicting in-hospital and one-year mortality in patients with acute myocardial infarction undergoing primary PCI

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
D Jelic ◽  
Z Mehmedbegovic ◽  
D Milasinovic ◽  
V Dedovic ◽  
V Zobenica ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Risk Score ◽  
Goodness Of Fit ◽  
External Validation ◽  
Risk Scores ◽  
P Value ◽  
Goodness Of Fit Test ◽  
Catheterization Laboratory ◽  
One Year

Abstract Background The Acute Coronary Treatment and Intervention Outcomes Network (ACTION) Registry-Get With The Guidelines (GWTG) AMI mortality model and risk score (ACTION) were introduced in 2011 to predict in-hospital mortality. In 2016 score was updated to enable a more accurate assessment, but, up-to-date, external validation in direct comparison was not performed. Purpose We aimed to externally validate and compare the prognostic value of original and updated ACTION score for in-hospital and one-year mortality. Method From a prospective electronic registry of a high-volume catheterization laboratory in a period from January 2009 to December 2017, a total of 5615 consecutive patients who underwent pPCI were available for analysis. For each patient, original (O-) and updated (U-) ACTION scores were calculated using required clinical and angiographic characteristics. In-hospital and one-year mortality (follow-up available for 91%) were assessed. Calibration and discrimination of the three risk models were evaluated by the Hosmer-Lemeshow (H-L) goodness-of-fit test and C-statistic, respectively. Results Mortality rates for in-hospital and one-year mortality were 4.2% and 9.6%, respectively. Both scores showed good model calibration as assessed by the H-L test and very good discriminative power for in-hospital and one-year mortality as assessed by C-statistics (Table 1 & Figure 1). Net reclassification index (NRI=1.06) showed that 48% of patients with in-hospital event and 58% without event, had their risk recalculated with U-ACTION with Integrated Discrimination Improvement slope 9.1% higher than in first model. Table 1 Risk score H-L H-L p value AUC 95% CI p value AUC 95% CI Significant p value O-ACTION 9.4 0.3 0.829 0.819 to 0.839 p<0.0001 0.781 0.769 to 0.792 p<0.0001 U-ACTION 10.9 0.2 0.918 0.911 to 0.925 0.838 0.827 to 0.848 Figure 1 Conclusion Updated ACTION score enables better prediction of in-hospital and one-year mortality in patients undergoing pPCI for acute myocardial infarction, thus it can be used preferentially over the original ACTION score for assessment of short and long-term mortality risks of this population.

Risk of Conversion to Arthroplasty After Hip Arthroscopy: Validation of a Published Risk Score Using an Independent, Prospectively Collected Database

2021 ◽  
pp. 036354652199382
Author(s):  
Mario Hevesi ◽  
Devin P. Leland ◽  
Philip J. Rosinsky ◽  
Ajay C. Lall ◽  
Benjamin G. Domb ◽  
...  
Keyword(s):  
Risk Score ◽  
Roc Curve ◽  
Hip Arthroscopy ◽  
External Validation ◽  
Area Under The Curve ◽  
Harris Hip Score ◽  
Brier Score ◽  
Level Of Evidence ◽  
Center Edge ◽  
Modified Harris Hip Score

Background: Hip arthroscopy is rapidly advancing and increasingly commonly performed. The most common surgery after arthroscopy is total hip arthroplasty (THA), which unfortunately occurs within 2 years of arthroscopy in up to 10% of patients. Predictive models for conversion to THA, such as that proposed by Redmond et al, have potentially substantial value in perioperative counseling and decreasing early arthroscopy failures; however, these models need to be externally validated to demonstrate broad applicability. Purpose: To utilize an independent, prospectively collected database to externally validate a previously published risk calculator by determining its accuracy in predicting conversion of hip arthroscopy to THA at a minimum 2-year follow-up. Study Design: Cohort study (diagnosis); Level of evidence, 1. Methods: Hip arthroscopies performed at a single center between November 2015 and March 2017 were reviewed. Patients were assessed pre- and intraoperatively for components of the THA risk score studied—namely, age, modified Harris Hip Score, lateral center-edge angle, revision procedure, femoral version, and femoral and acetabular Outerbridge scores—and followed for a minimum of 2 years. Conversion to THA was determined along with the risk score’s receiver operating characteristic (ROC) curve and Brier score calibration characteristics. Results: A total of 187 patients (43 men, 144 women, mean age, 36.0 ± 12.4 years) underwent hip arthroscopy and were followed for a mean of 2.9 ± 0.85 years (range, 2.0-5.5 years), with 13 patients (7%) converting to THA at a mean of 1.6 ± 0.9 years. Patients who converted to THA had a mean predicted arthroplasty risk of 22.6% ± 12.0%, compared with patients who remained arthroplasty-free with a predicted risk of 4.6% ± 5.3% ( P < .01). The Brier score for the calculator was 0.04 ( P = .53), which was not statistically different from ideal calibration, and the calculator demonstrated a satisfactory area under the curve of 0.894 ( P < .001). Conclusion: This external validation study supported our hypothesis in that the THA risk score described by Redmond et al was found to accurately predict which patients undergoing hip arthroscopy were at risk for converting to subsequent arthroplasty, with satisfactory discriminatory, ROC curve, and Brier score calibration characteristics. These findings are important in that they provide surgeons with validated tools to identify the patients at greatest risk for failure after hip arthroscopy and assist in perioperative counseling and decision making.

scholarly journals “Get with the Guidelines Heart Failure Risk Score” for mortality prediction in patients undergoing MitraClip

2021 ◽  
Author(s):  
Christos Iliadis ◽  
Maximilian Spieker ◽  
Refik Kavsur ◽  
Clemens Metze ◽  
Martin Hellmich ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Goodness Of Fit ◽  
Left Ventricular ◽  
Risk Scores ◽  
Left Ventricular Ejection ◽  
Goodness Of Fit Test ◽  
Ventricular Ejection Fraction ◽  
Failure Risk ◽  
Get With The Guidelines

Abstract Background Reliable risk scores in patients undergoing transcatheter edge-to-edge mitral valve repair (TMVR) are lacking. Heart failure is common in these patients, and risk scores derived from heart failure populations might help stratify TMVR patients. Methods Consecutive patients from three Heart Centers undergoing TMVR were enrolled to investigate the association of the “Get with the Guidelines Heart Failure Risk Score” (comprising the variables systolic blood pressure, urea nitrogen, blood sodium, age, heart rate, race, history of chronic obstructive lung disease) with all-cause mortality. Results Among 815 patients with available data 177 patients died during a median follow-up time of 365 days. Estimated 1-year mortality by quartiles of the score (0–37; 38–42, 43–46 and more than 46 points) was 6%, 10%, 23% and 30%, respectively (p < 0.001), with good concordance between observed and predicted mortality rates (goodness of fit test p = 0.46). Every increase of one score point was associated with a 9% increase in the hazard of mortality (95% CI 1.06–1.11%, p < 0.001). The score was associated with long-term mortality independently of left ventricular ejection fraction, NYHA class and NTproBNP, and was equally predictive in primary and secondary mitral regurgitation. Conclusion The “Get with the Guidelines Heart Failure Risk Score” showed a strong association with mortality in patients undergoing TMVR with additive information beyond traditional risk factors. Given the routinely available variables included in this score, application is easy and broadly possible. Graphic abstract

scholarly journals Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: results from the prospective TEDDY study

2018 ◽  
Vol 56 (9) ◽  
pp. 602-605 ◽  
Author(s):  
Andreas Beyerlein ◽  
Ezio Bonifacio ◽  
Kendra Vehik ◽  
Markus Hippich ◽  
Christiane Winkler ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Factors ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Islet Autoimmunity ◽  
Islet Autoantibody ◽  
Clinical Type

BackgroundProgression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.MethodsIn 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression.ResultsIslet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93).ConclusionsGenetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes.

Development and external validation of a novel risk score to identify insignificant prostate cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 5073-5073
Author(s):  
Lorenzo Dutto ◽  
Jorn H. Witt ◽  
Katarina Urbanova ◽  
Christian Wagner ◽  
Andreas Schuette ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Risk Score ◽  
External Validation ◽  
Tumour Volume ◽  
Risk Scores ◽  
Clinical Predictors ◽  
Predictive Tools ◽  
Tertiary Referral Center ◽  
Insignificant Prostate Cancer

5073 Background: Active surveillance is increasingly used for insignificant prostate cancer (PCa). In order to identify suitable patients, risk scores have been developed which use pre-operative factors. We evaluated the accuracy of 9 separate tools developed to identify patients harbouring insignificant PCa in 2613 patients who underwent radical prostatectomy for Gleason 3+3 PCa. We have developed and validated a novel risk score to correctly identify insignificant PCa for use in unscreened patient cohorts using non-dichotomised clinical predictors. Methods: 2799 patients who would have been candidates for AS (Gleason score 6 only) patients underwent robotic radical prostatectomy between 2006 and 2016 at a tertiary referral center. The volume and grade of tumour in the resected prostate was analysed. Inignificant PCa was defined as Gleason 3+3 only, index tumour volume <1.3 cm3 , total tumour volume <2.5 cm3 (updated ERSPC definition). 2613 patients were included in the final analysis. We computed the accuracy (specificity, sensitivity and area under the curve (AUC) of the receiver operator characteristic) of 9 predictive tools. Multivariate logistic regression with elastic net regularisation was used to develop a novel tool to predict insignificant prostate cancer using age at diagnosis, baseline PSA, TRUS volume, clinical T-stage, number of positive cores and percentage of positive cores as predictors. This tool was validated in an external cohort of 441 unscreened patients undergoing surgery for Gleason 6 PCa. Results: All of the predefined tools rated poorly as predictors of insignificant disease as none of them reached the required AUC threshold of 0.7. The new tool performed well in training and validation cohorts. Conclusions: Pre-existing predictive tools to identify indolent PCa have a poor predictive value when applied to an unscreened cohort of patients. Our novel tool shows good predictive power for insignificant PCa in this population in training and validation cohorts. The inherent selection bias due to analysis of a surgical cohort is acknowledged. [Table: see text]

scholarly journals Predictive model for in-hospital mortality following cardiogenic shock

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
F Garcia-Rodeja Arias ◽  
M Perez Dominguez ◽  
J Martinon Martinez ◽  
J M Garcia Acuna ◽  
C Abou Joch Casas ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Cardiogenic Shock ◽  
Predictive Model ◽  
Risk Score ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Simplified Model ◽  
P Value ◽  
Final Model ◽  
Risk Of Death

Abstract Introduction and objectives Cardiogenic shock is a condition caused by reduced cardiac output and hypotension, resulting in end-organ damage and multiorgan failure. Although prognosis has been improved in recent years, this state is still associated with high morbidity and mortality. The aim of our study was to perform a predictive model for in-hospital mortality that allows stratifying the risk of death in patients with cardiogenic shock. Methods This is a retrospective analysis from a prospective registry, that included 135 patients from one Spanish Universitary Hospital between 2011 and 2020. Multivariate analysis was performed among those variables with significant association with short-term outcome of univariate analysis with a p-value &lt;0.2. Those variables which had a p-value &gt;0.1 in the multivariable analysis were excluded of the final model. Our method was assessed using the area under the ROC-curve (AUC). Goodness of fit was tested using Hosmer-Lemeshow statistic test. Finally, we performed a risk score using the pondered weight of the coefficients of a simplified model created after categorizing the continuous quantitative variables included in the final model, giving a maximum of 16 points and creating three categories of risk. Results The in-hospital mortality rate was 41.5%, the average of age was 74.2 years, 35.6% were females and acute coronary syndrome (ACS) was the main cause of shock (60.7%). Mitral regurgitation (moderate-severe), age, ACS etiology, NT-proBNP, blood hemoglobin and lactate at admission were included in the final model. Risk-adjustment model had good accuracy in predicting in-hospital mortality (AUC 0.85; 95% CI 0,78–0,90) and the goodness of fit test was p-value&gt;0.10. According to the risk score made with the simplified model, these patients were stratified into three categories: low (scores 0–6), intermediate (scores 7–10), and high (scores 11–16) risk with observed mortality of 12.9%, 49.1% and 87.5% respectively (p&lt;0,001). Conclusions Our predictive model using six variables, shows good discernment for in-hospital mortality and the risk score has identified three groups with significant differences in prognosis. This model could help in guiding treatments and clinical decision-making, so it needs external validation and to be compared with other models already published. FUNDunding Acknowledgement Type of funding sources: None. ROC curve Risk Score

scholarly journals External validity of a model to predict postoperative atrial fibrillation after thoracic surgery

2019 ◽  
Vol 57 (5) ◽  
pp. 874-880 ◽  
Author(s):  
Heather Smith ◽  
Heidi Li ◽  
Olivier Brandts-Longtin ◽  
Ching Yeung ◽  
Donna Maziak ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Thoracic Surgery ◽  
Risk Score ◽  
External Validation ◽  
Postoperative Atrial Fibrillation ◽  
Risk Scores ◽  
Incidence Risk ◽  
Lung Resections ◽  
Mean Heart Rate

Abstract OBJECTIVES A prediction model developed by Passman et al. stratifies patients’ risk of postoperative atrial fibrillation (POAF) after major non-cardiac thoracic surgery using 3 simple factors (sex, age and preoperative resting heart rate). The model has neither undergone external validation nor proven to be relevant in current thoracic surgery practice. METHODS A retrospective single-centre analysis of all patients who underwent major non-cardiac thoracic surgery (2008–2017) with prospective documentation of incidence and severity of POAF was used for external validation of Passman’s derivation sample (published in 2005 with 856 patients). The model calibration was assessed by evaluating the incidence of POAF and patients’ risk scores (0–6). RESULTS A total of 2054 patients were included. Among them, POAF occurred in 164 (7.9%), compared to 147 (17.2%) in Passman’s study. Differences in our sample compared to Passman’s sample included mean heart rate (75.7 vs 73.7 bpm, P &lt; 0.001), proportion of patients with hypertension (46.1 vs 29.4%, P &lt; 0.001), proportion of extensive lung resections, particularly pneumonectomy (6.1 vs 21%, P &lt; 0.001) and proportion of minimally invasive surgeries (56.6% vs 0%). The model demonstrated a positive correlation between risk scores and POAF incidence (risk score 1.2% vs 6.16%). CONCLUSIONS The POAF model demonstrated good calibration in our population, despite a lower overall incidence of POAF compared to the derivation study. POAF rates were higher among patients with a higher risk score and undergoing procedures with greater intrathoracic dissection. This tool may be useful in identifying patients who are at risk of POAF when undergoing major thoracic surgery and may, therefore, benefit from targeted prophylactic therapy.

scholarly journals Development and external validation of a novel nomogram for screening Chinese Lynch syndrome: based on a multicenter, population study

2021 ◽  
Vol 13 ◽  
pp. 175883592110232
Author(s):  
Mengyuan Yang ◽  
Dan Li ◽  
Wu Jiang ◽  
Lizhen Zhu ◽  
Haixing Ju ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lynch Syndrome ◽  
Mismatch Repair ◽  
Roc Curve ◽  
External Validation ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Screening Model ◽  
Pathogenic Variants

Background: This multicenter study aimed to reveal the genetic spectrum of colorectal cancer (CRC) with deficient mismatch repair (dMMR) and build a screening model for Lynch syndrome (LS). Methods: Through the immunohistochemical (IHC) screening of mismatch repair protein results in postoperative CRC patients, 311 dMMR cases, whose germline and somatic variants were detected using the ColonCore panel, were collected. Univariate and multivariate logistic regression analysis was performed on the clinical characteristics of these dMMR individuals, and a clinical nomogram, incorporating statistically significant factors identified using multivariate logistic regression analysis, was constructed to predict the probability of LS. The model was validated externally by an independent cohort. Results: In total, 311 CRC patients with IHC dMMR included 95 identified MMR germline variant (LS) cases and 216 cases without pathogenic or likely pathogenic variants in MMR genes (non-Lynch-associated dMMR). Of the 95 individuals, approximately 51.6%, 28.4%, 14.7%, and 5.3% cases carried germline MLH1, MSH2, MSH6, and PMS2 pathogenic or likely pathogenic variants, respectively. A novel nomogram was then built to predict the probability of LS for CRC patients with dMMR intuitively. The receiver operating characteristic (ROC) curve informed that this nomogram-based screening model could identify LS with a higher specificity and sensitivity with an area under curve (AUC) of 0.87 than current screening criteria based on family history. In the external validation cohort, the AUC of the ROC curve reached 0.804, inferring the screening model’s universal applicability. We recommend that dMMR-CRC patients with a probability of LS greater than 0.435 should receive a further germline sequencing. Conclusion: This novel screening model based on the clinical characteristic differences between LS and non-Lynch-associated dMMR may assist clinicians to preliminarily screen LS and refer susceptible patients to experienced specialists.

scholarly journals Bleeding risk prediction in elderly patients managed invasively for Acute Coronary Syndromes: external validation of the PRECISE-DAPT and PARIS risk scores

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C Montalto ◽  
G Crimi ◽  
N Morici ◽  
L Piatti ◽  
D Grosseto ◽  
...  
Keyword(s):  
High Risk ◽  
Elderly Patients ◽  
Risk Score ◽  
External Validation ◽  
Bleeding Risk ◽  
Risk Scores ◽  
Risk Category ◽  
Net Benefit ◽  
Discrimination Power ◽  
Risk Categories

Abstract Background Tailoring Dual Antiplatelet Therapy (DAPT) to each patient bleeding and ischemic risk profile is a majopr challenge in everyday clinical practice. As the elderlies were underrepresented in validation cohorts of bleeding risk scores, their generalizability in this context is uncertain. Purpose We sought to assess the clinical utility of the PRECISE-DAPT and PARIS bleeding risk scores to elderly patients suffering from ACS and undergoing invasive management. Methods Our external validation cohort included 1,883 patients older &gt;74 years admitted for ACS and treated with PCI from 3 multicenter, randomized trials. Bleeding risk scores were calculated on a patient-level and subjects were stratified into risk categories according to each risk score definition. Results After a median follow-up of 365 days, patients in the high-risk categories according to the PRECISE-DAPT score experienced a higher rate of BARC 3–5 bleedings (log rank p=0.002) while this was not observed for those in the high-risk category according to the PARIS risk score (log rank p=0.3). Both scores had a moderate discriminative power (c-statistics 0.70 and 0.64, respectively) and calibration was accurate for both risk scores (all χ2&gt;0.05), but PARIS risk score was associated to a greater overestimation of the risk (mean D observed-predicted probability = −0.65 for PRECISE DAPT and −4.62 for PARIS, p=0.02; Figure 1). Decision curve analysis was in favor of the PRECISE-DAPT score up to a risk threshold of 2%. A sensitivity showed that calibration and discrimination power was moderate for both risk scores also after including BARC 2 events. Conclusion In the setting of older adults managed invasively for ACS both the PARIS and the PRECISE-DAPT scores were moderately accurate in predicting bleeding risk. However, the use of the PRECISE-DAPT is associated with better performance and a higher net benefit. Figure 1 Funding Acknowledgement Type of funding source: None

scholarly journals Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jiandong Zhou ◽  
Sharen Lee ◽  
Xiansong Wang ◽  
Yi Li ◽  
William Ka Kei Wu ◽  
...  
Keyword(s):  
Risk Score ◽  
Primary Outcome ◽  
Troponin I ◽  
Cox Regression ◽  
External Validation ◽  
Density Lipoprotein ◽  
Adverse Outcomes ◽  
Public Hospitals ◽  
Risk Scores ◽  
Test Results

AbstractRecent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

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