P3794MOODS: a novel risk score to identify patients with atrial fibrillation and sleep apnoea

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
K Kadhim ◽  
A Elliott ◽  
M Middeldorp ◽  
J Hendriks ◽  
C Gallagher ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Risk Score ◽  
Clinical Characteristics ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Positive Likelihood Ratio ◽  
Resource Utilisation ◽  
Operating Characteristics ◽  
Derivation Cohort ◽  
Sleep Studies

Abstract Background Sleep-disordered breathing (SDB) is an important risk factor for developing atrial fibrillation (AF), and treatment of concomitant SDB can improve AF rhythm outcomes. Diagnosis of SDB requires sleep studies which can pose a significant time and resource burden. We sought to develop a prediction score based on clinical characteristics that can help identify AF patients who require further assessment for SDB. Methods Prospectively-collected data for 442 consecutive patients treated for AF from 2009 to 2017 were analysed. All patients were considered candidates for rhythm-control and therefore referred for sleep studies. The diagnosis of SDB was confirmed using in-lab polysomnography and classified using the apnoea-hypopnoea-index (AHI), with cut-offs of ≥15/hr and ≥30/hr indicating moderate-to-severe and severe SDB respectively. Patients treated up to 2015 formed the derivation cohort (n=311) and the remainder (n=113) formed the validation cohort. Multivariate logistic regression analysis was used to identify clinical variables predictive of moderate-to-severe SDB. A risk score model was developed based on regression coefficients and tested using receiver-operating-characteristics analyses on the validation cohort. Results Overall, mean age was 60±11 years, mean body mass index (BMI) was 30±5 kg/m2 and 69% were men. The prevalence of moderate-to-severe SDB was 33.7%. There were no significant differences in baseline characteristics between the derivation and validation cohorts. Male gender (score=1), overweight (BMI: 25–29 kg/m2, score=2), obesity (BMI≥30 kg/m2, score=3), diabetes (score=1), and stroke (score=2) were significantly independently predictive of moderate-to-severe SDB and formulated the score. The score performed well to predict moderate-to-severe SDB with a C-statistic of 0.73 (95% CI: 0.67–0.79, P<0.001) in the derivation cohort, and 0.67 (95% CI: 0.57–0.77, P<0.001) in the validation cohort. As a rule-out test, a score of ≤3 had a negative predictive value of 77% for moderate-to-severe SDB (91% for severe SDB). A score of ≥4 had an intermediate positive likelihood ratio (PLR) of 2 for moderate-to-severe SDB (2.2 for severe SDB), while a score of ≥5 had a high PLR of 6.5 and 6.8 for moderate-to-severe SDB and severe SDB respectively. Sensitivity and specificity table Conclusion A novel risk score comprising clinical characteristics can identify patients with AF likely to benefit from further assessment for SDB. Application of this model may aid optimise resource utilisation and facilitate timely patient care.

scholarly journals Risk Score for Prediction of 10-Year Atrial Fibrillation: A Community-Based Study

2018 ◽  
Vol 118 (09) ◽  
pp. 1556-1563 ◽  
Author(s):  
Doron Aronson ◽  
Varda Shalev ◽  
Rachel Katz ◽  
Gabriel Chodick ◽  
Diab Mutlak
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Risk Score ◽  
Proportional Hazards Model ◽  
Validation Cohort ◽  
Cox Proportional Hazards Model ◽  
Net Benefit ◽  
Derivation Cohort ◽  
Risk Equation ◽  
History Of

Purpose We used a large real-world data from community settings to develop and validate a 10-year risk score for new-onset atrial fibrillation (AF) and calculate its net benefit performance. Methods Multivariable Cox proportional hazards model was used to estimate effects of risk factors in the derivation cohort (n = 96,778) and to derive a risk equation. Measures of calibration and discrimination were calculated in the validation cohort (n = 48,404). Results Cumulative AF incidence rates for both the derivation and validation cohorts were 5.8% at 10 years. The final models included the following variables: age, sex, body mass index, history of treated hypertension, systolic blood pressure ≥ 160 mm Hg, chronic lung disease, history of myocardial infarction, history of peripheral arterial disease, heart failure and history of an inflammatory disease. There was a 27-fold difference (1.0% vs. 27.2%) in AF risk between the lowest (–1) and the highest (9) sum score. The c-statistic was 0.743 (95% confidence interval [CI], 0.737–0.749) for the derivation cohort and 0.749 (95% CI, 0.741–0.759) in the validation cohort. The risk equation was well calibrated, with predicted risks closely matching observed risks. Decision curve analysis displayed consistent positive net benefit of using the AF risk score for decision thresholds between 1 and 25% 10-year AF risk. Conclusion We provide a simple score for the prediction of 10-year risk for AF. The score can be used to select patients at highest risk for treatments of modifiable risk factors, monitoring for sub-clinical AF detection or for clinical trials of primary prevention of AF.

scholarly journals The Admit-AF risk score: a clinical risk score for predicting hospital admissions in patients with atrial fibrillation

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
P Meyre ◽  
S Aeschbacher ◽  
S Blum ◽  
M Coslovsky ◽  
J.H Beer ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
High Risk ◽  
Risk Score ◽  
Hospital Admissions ◽  
Validation Cohort ◽  
Funding Source ◽  
Prediction Tool ◽  
Derivation Cohort

Abstract Background Patients with atrial fibrillation (AF) have a high risk of hospital admissions, but there is no validated prediction tool to identify those at highest risk. Purpose To develop and externally validate a risk score for all-cause hospital admissions in patients with AF. Methods We used a prospective cohort of 2387 patients with established AF as derivation cohort. Independent risk factors were selected from a broad range of variables using the least absolute shrinkage and selection operator (LASSO) method fit to a Cox regression model. The developed risk score was externally validated in a separate prospective, multicenter cohort of 1300 AF patients. Results In the derivation cohort, 891 patients (37.3%) were admitted to the hospital over a median follow-up 2.0 years. In the validation cohort, hospital admissions occurred in 719 patients (55.3%) during a median follow-up 1.9 years. The most important predictors for admission were age (75–79 years: adjusted hazard ratio [aHR], 1.33; 95% confidence interval [95% CI], 1.00–1.77; 80–84 years: aHR, 1.51; 95% CI, 1.12–2.03; ≥85 years: aHR, 1.88; 95% CI, 1.35–2.61), prior pulmonary vein isolation (aHR, 0.74; 95% CI, 0.60–0.90), hypertension (aHR, 1.16; 95% CI, 0.99–1.36), diabetes (aHR, 1.38; 95% CI, 1.17–1.62), coronary heart disease (aHR, 1.18; 95% CI, 1.02–1.37), prior stroke/TIA (aHR, 1.28; 95% CI, 1.10–1.50), heart failure (aHR, 1.21; 95% CI, 1.04–1.41), peripheral artery disease (aHR, 1.31; 95% CI, 1.06–1.63), cancer (aHR, 1.33; 95% CI, 1.13–1.57), renal failure (aHR, 1.18, 95% CI, 1.01–1.38), and previous falls (aHR, 1.44; 95% CI, 1.16–1.78). A risk score with these variables was well calibrated, and achieved a C-index of 0.64 in the derivation and 0.59 in the validation cohort. Conclusions Multiple risk factors were associated with hospital admissions in AF patients. This prediction tool selects high-risk patients who may benefit from preventive interventions. The Admit-AF risk score Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): The Swiss National Science Foundation (Grant numbers 33CS30_1148474 and 33CS30_177520), the Foundation for Cardiovascular Research Basel and the University of Basel

Abstract 17048: Dederivation and Validation of a Simple Heart Risk Score in 608,544 Thai Adults: HCUR Study

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Parinya Chamnan ◽  
Weera Mahawanakul ◽  
Prasert Boongird ◽  
Wannee Nitiyanant ◽  
Wichai Aekplakorn ◽  
...  
Keyword(s):  
General Population ◽  
Risk Score ◽  
Retrospective Cohort ◽  
Validation Cohort ◽  
Information Criteria ◽  
Global Model ◽  
Derivation Cohort ◽  
Risk Equation ◽  
Risk Algorithm ◽  
Incident Cases

Introduction: Most heart risk prediction equations were developed in Western populations. These risk scores are likely to perform less well in Asian populations, who have different background risk. Hypothesis: This study aimed to develop and validate a new risk algorithm for estimating 5-year risk of developing coronary heart disease (CHD) in a large retrospective cohort of Thai general population. Methods: This retrospective cohort was derived from the linkage of 2006 health checks data with diagnostic information from electronic health records of 608,544 men and women aged 20 years and above residing in Ubon Ratchathani. It was randomly and evenly divided into the derivation and validation cohorts. An outcome of interest was first recorded diagnosis of CHD over a period of 6 years between January 2006 and December 2012. A Cox proportional hazards model was used to estimate effects of risk factors on CHD risk and to derive a risk equation in the derivation cohort. Measures of discrimination, global model fits and calibration were calculated in the validation cohort. Results: The derivation cohort comprised of 304,272 individuals, who contributed 1,757,369 person-years of follow-up and 1,272 incident cases of CHD, while the validation cohort comprised of 304,272 individuals (1,757,312 person-years), with 1,290 incident cases of stroke. The risk equation was 0.0580 x Age (years) + 0.5739 x Sex (Male=1) + 0.3850 x Hypertension (present=1) + 0.7080 x Diabetes (present=1) + 0.0386 x Body mass index (kg/m 2 ) + 0.2117 x Central obesity (present=1) - 0.1389 (if exercise 1-2 days/week) or -0.3975 (if exercise 3-5 days/week) or - 0.5598 (if exercise >5 days/week). The stroke risk equation had a reasonably good discriminatory ability in the validation cohort with the area under the receiver operating characteristic curve of 0.790 (95%CI 0.779-0.801). The risk equation had good global model fit as measured by Bayesian information criteria. The Gronnesby and Borgan test showed good calibration, with chi-square statistic of 809.45 (p<0.001). Conclusions: This simple heart risk score is the first risk algorithm to estimate the 5-year risk of CHD in a Thai general population. The risk score does not need laboratory tests and can therefore be used in clinical settings and by the public.

Derivation and Validation of a Prediction Tool for Venous Thromboembolism: A VERITY Registry Study.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 697-697 ◽  
Author(s):  
Roopen Arya ◽  
Shankaranarayana Paneesha ◽  
Aidan McManus ◽  
Nick Parsons ◽  
Nicholas Scriven ◽  
...  
Keyword(s):  
Risk Factors ◽  
Venous Thromboembolism ◽  
Risk Score ◽  
Validation Cohort ◽  
Accurate Estimation ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Derivation Cohort ◽  
Hormonal Factor ◽  
Electronic Alerts

Abstract Accurate estimation of risk for venous thromboembolism (VTE) may help clinicians assess prophylaxis needs. Only empirical algorithms and risk scores have been described; an empirical risk score (‘Kucher’) based on 8 VTE risk factors (cancer, prior VTE, hypercoagulability, surgery, age>75 yrs, BMI>29, bed rest, hormonal factor) using electronic alerts improved hospitalized patient outcome (NEJM2005;352:969–77). We wished to develop a multivariate regression model for VTE risk, based on Kucher, and validate its performance. The initial derivation cohort consisted of patients enrolled in ‘VERITY’, a multicentre VTE treatment registry for whom the endpoint of VTE and all 8 risk factors were known. Initial univariate analysis (n=5928; 32.4% with diagnosis of VTE) suggested VTE risk was not accounted for by the 8 factors; an additional 3 were added (leg paralysis, smoking, IV drug use [IVD]). The final derivation cohort was 5241 patients (32.0% with VTE) with complete risk data. The validation cohort (n=915) was derived from a database of 928 consecutively enrolled patients at a single DVT clinic. Model parameters were estimated using the statistical package ‘R’ using a stepwise selection procedure to choose the optimal number of main effects and pair-wise interactions. This showed that advanced age (estimated odds ratio [OR]=2.8, p<0.001); inpatient (OR=3.0, p<0.001); surgery (OR=3.1, p<0.001); prior VTE (OR=2.9, p<0.001); leg paralysis (OR=3.8, p<0.001); cancer (OR=5.3, p<0.001); IVD (OR=14.3, p<0.001); smoking (OR=1.2, p=0.009); and thrombophilia (OR=2.8; p<0.001) increased the risk of VTE. Obesity (OR=0.7; p<0.001) increased the VTE risk only in patients with a hormonal factor (OR=2.0, p=0.007). Backward stepwise regression showed prior VTE as the most important factor followed by cancer, IVD, surgery, inpatient, age, leg paralysis, hormonal factor, obesity, thrombophilia and smoking. Expressing the parameter estimates in terms of probabilities defines a risk score model for VTE. Using the model, the receiver operating characteristic (ROC) curve (see figure) area under the curve (AUC) was estimated as 0.720 (95% CI, 0.705–0.735) for the model (dashed line), indicating a good diagnostic test significantly better (p<0.001) than Kucher (AUC=0.617, 95% CI, 0.599–0.634)(solid line). For the validation cohort, AUC was estimated as 0.678 (95% CI, 0.635–0.721) for the model, which was not significantly different from AUC for the full dataset used for model development, and was 0.587 (95% CI, 0.542–0.632) for Kucher. This model to predict individual patient risk of VTE may contribute to decision making regarding prophylaxis in clinical practice. Figure Figure

scholarly journals Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study

2019 ◽  
Vol 7 (1) ◽  
pp. e000735 ◽  
Author(s):  
Dahai Yu ◽  
Jin Shang ◽  
Yamei Cai ◽  
Zheng Wang ◽  
Xiaoxue Zhang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Nephropathy ◽  
Risk Score ◽  
Cardiovascular Mortality ◽  
Validation Cohort ◽  
External Validation ◽  
Cardiovascular Death ◽  
Individual Risk ◽  
Derivation Cohort

ObjectiveTo derive, and externally validate, a risk score for cardiovascular death among patients with type 2 diabetes and newly diagnosed diabetic nephropathy (DN).Research design and methodsTwo independent prospective cohorts with type 2 diabetes were used to develop and externally validate the risk score. The derivation cohort comprised 2282 patients with an incident, clinical diagnosis of DN. The validation cohort includes 950 patients with incident, biopsy-proven diagnosis of DN. The outcome was cardiovascular death within 2 years of the diagnosis of DN. Logistic regression was applied to derive the risk score for cardiovascular death from the derivation cohort, which was externally validated in the validation cohort. The score was also estimated by applying the United Kingdom Prospective Diabetes Study (UKPDS) risk score in the external validation cohort.ResultsThe 2-year cardiovascular mortality was 12.05% and 11.79% in the derivation cohort and validation cohort, respectively. Traditional predictors including age, gender, body mass index, blood pressures, glucose, lipid profiles alongside novel laboratory test items covering five test panels (liver function, serum electrolytes, thyroid function, blood coagulation and blood count) were included in the final model.C-statistics was 0.736 (95% CI 0.731 to 0.740) and 0.747 (95% CI 0.737 to 0.756) in the derivation cohort and validation cohort, respectively. The calibration slope was 0.993 (95% CI 0.974 to 1.013) and 1.000 (95% CI 0.981 to 1.020) in the derivation cohort and validation cohort, respectively.The UKPDS risk score substantially underestimated cardiovascular mortality.ConclusionsA new risk score based on routine clinical measurements that quantified individual risk of cardiovascular death was developed and externally validated. Compared with the UKPDS risk score, which underestimated the cardiovascular disease risk, the new score is a more specific tool for patients with type 2 diabetes and DN. The score could work as a tool to identify individuals at the highest risk of cardiovascular death among those with DN.

Development and Validation of a Risk Score to Predict the First Hip Fracture in the Oldest Old: A Retrospective Cohort Study

2019 ◽  
Vol 75 (5) ◽  
pp. 980-986 ◽  
Author(s):  
Ming-Tuen Lam ◽  
Chor-Wing Sing ◽  
Gloria H Y Li ◽  
Annie W C Kung ◽  
Kathryn C B Tan ◽  
...  
Keyword(s):  
Hong Kong ◽  
Hip Fracture ◽  
Fracture Risk ◽  
Risk Score ◽  
Validation Cohort ◽  
Oldest Old ◽  
Community Dwelling ◽  
Risk Scores ◽  
Derivation Cohort

Abstract Background To evaluate whether the common risk factors and risk scores (FRAX, QFracture, and Garvan) can predict hip fracture in the oldest old (defined as people aged 80 and older) and to develop an oldest-old-specific 10-year hip fracture prediction risk algorithm. Methods Subjects aged 80 years and older without history of hip fracture were studied. For the derivation cohort (N = 251, mean age = 83), participants were enrolled with a median follow-up time of 8.9 years. For the validation cohort (N = 599, mean age = 85), outpatients were enrolled with a median follow-up of 2.6 years. A five-factor risk score (the Hong Kong Osteoporosis Study [HKOS] score) for incident hip fracture was derived and validated, and its predictive accuracy was evaluated and compared with other risk scores. Results In the derivation cohort, the C-statistics were .65, .61, .65, .76, and .78 for FRAX with bone mineral density (BMD), FRAX without BMD, QFracture, Garvan, and the HKOS score, respectively. The category-less net reclassification index and integrated discrimination improvement of the HKOS score showed a better reclassification of hip fracture than FRAX and QFracture (all p &lt; .001) but not Garvan, while Garvan, but not HKOS score, showed a significant over-estimation in fracture risk (Hosmer–Lemeshow test p &lt; .001). In the validation cohort, the HKOS score had a C-statistic of .81 and a considerable agreement between expected and observed fracture risk in calibration. Conclusion The HKOS score can predict 10-year incident hip fracture among the oldest old in Hong Kong. The score may be useful in identifying the oldest old patients at risk of hip fracture in both community-dwelling and hospital settings.

Predictive factors of recurrent atrial tachyarrhythmia after multiple procedures of radiofrequency catheter ablation for paroxysmal atrial fibrillation: Kansai Plus Atrial Fibrillation Registry (KPAF)

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Tanaka ◽  
S Shizuta ◽  
K Inoue ◽  
A Kobori ◽  
K Kaitani ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Paroxysmal Atrial Fibrillation ◽  
Scoring System ◽  
Radiofrequency Catheter Ablation ◽  
Validation Cohort ◽  
Left Ventricular Ejection ◽  
Atrial Tachyarrhythmia ◽  
Derivation Cohort ◽  
Stage 4

Abstract Background The predictors of arrhythmia recurrence after radiofrequency catheter ablation (RFCA) for paroxysmal atrial fibrillation (PAF) have not yet been fully evaluated. Purpose The aim of this study was to develop and validate a risk scoring system to predict the incidence of recurrence of atrial tachyarrhythmia after the final RFCA for PAF. Methods The study population consisted of 3223 consecutive patients undergoing first-time RFCA for PAF from November 2011 to March 2014 in 26 cardiovascular centers in Japan who were enrolled in the Kansai Plus Atrial Fibrillation (KPAF) registry. We developed a scoring system in a derivation cohort with 2149 patients and assessed its reproducibility in a validation cohort with 1074 patients. The primary endpoint was recurrent atrial tachyarrhythmia lasting for ≥30 seconds after 91 days post the final ablation. Results During a median follow-up period of 3.1 years, 404 (18.8%) patients of the derivation cohort had AF recurrence after the final RFCA. The baseline patient characteristics of the derivation cohort were as follows: mean age 64.7 years, male 1480 (68.9%), mean body mass index (BMI) 23.6 kg/m2, hypertension 1122 (52.2%), prior heart failure 182 (8.5%), diabetes mellitus 203 (9.5%), prior stroke and/or transient ischemic attack 21 (1.0%), prior vascular disease 209 (9.7%), prior valvular disease 105 (4.9%), median CHADS2 score 1.1, median CHA2DS2-VASc score 2.1, mean number of ineffective antiarrhythmic drugs (AAD) 0.80, median duration of history of AF episodes 2.1 years, mean left atrial diameter (LAD) 38.2 mm, mean left ventricular ejection fraction (LVEF) 65.3%, and mean eGFR 68.7 mL/min/1.73m2. There was no significant difference in the baseline characteristics between derivation and validation cohorts. The results of the multivariate logistic regression models identified 5 independent variables of recurrent atrial tachyarrhythmia after the final RFCA: female (odds ratio (OR) = 1.45, p=0.0017), BMI &lt;25 kg/m2 (OR=1.40, p=0.0081), duration of AF history 3 years≤ (OR=1.39, p&lt;0.0034), chronic kidney disease (CKD) (OR=2.1, p=0.005, for stage 2/3CKD, OR=2.6, p=0.018 for stage 4/5 CKD), and LVEF (OR=2.1, p=0.039 for LVEF &lt;50%, OR=1.5, p=0.022 for LVEF 50–60%). The predictive score for each factor was 3 points for CKD stage 4/5, 2 for CKD stage2/3 and LVEF &lt;50%, and 1for the others (11 points in total). The arrhythmia-free rates after the final RCFA in the derivation cohort according to the score were as follows: 0–2 points = 91.7%, 3–4 = 80.7%, 5&lt; = 72.6%, respectively. The similar results were reproduced in the validation cohort (Figure 1). Conclusion Our newly developed scoring system, composed of female, BMI, AF duration, CKD, and LVEF, could reproducibly predict arrhythmia recurrence after the final RFCA for PAF. Funding Acknowledgement Type of funding source: None

scholarly journals Development and validation of a predictive score for ICU delirium in critically ill patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huijuan Zhang ◽  
Jing Yuan ◽  
Qun Chen ◽  
Yingya Cao ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Predictive Score ◽  
Clinical Indicators ◽  
Derivation Cohort ◽  
Significant Difference ◽  
History Of ◽  
Icu Delirium ◽  
Risk Predictors ◽  
Development And Validation

Abstract Background The incidence of delirium in intensive care unit (ICU) patients is high and associated with a poor prognosis. We validated the risk factors of delirium to identify relevant early and predictive clinical indicators and developed an optimized model. Methods In the derivation cohort, 223 patients were assigned to two groups (with or without delirium) based on the CAM-ICU results. Multivariate logistic regression analysis was conducted to identify independent risk predictors, and the accuracy of the predictors was then validated in a prospective cohort of 81 patients. Results A total of 304 patients were included: 223 in the derivation group and 81 in the validation group, 64(21.1%)developed delirium. The model consisted of six predictors assessed at ICU admission: history of hypertension (RR = 4.367; P = 0.020), hypoxaemia (RR = 3.382; P = 0.018), use of benzodiazepines (RR = 5.503; P = 0.013), deep sedation (RR = 3.339; P = 0.048), sepsis (RR = 3.480; P = 0.018) and mechanical ventilation (RR = 3.547; P = 0.037). The mathematical model predicted ICU delirium with an accuracy of 0.862 (P < 0.001) in the derivation cohort and 0.739 (P < 0.001) in the validation cohort. No significant difference was found between the predicted and observed cases of ICU delirium in the validation cohort (P > 0.05). Conclusions Patients’ risk of delirium can be predicted at admission using the early prediction score, allowing the implementation of early preventive interventions aimed to reduce the incidence and severity of ICU delirium.

scholarly journals A novel predictive model based on inflammatory markers to assess the prognosis of patients with HBV-related acute-on-chronic liver failure：a retrospective cohort study

2020 ◽  
Author(s):  
Li Qiang ◽  
Jiao Qin ◽  
Changfeng Sun ◽  
Yunjian Sheng ◽  
Wen Chen ◽  
...  
Keyword(s):  
Liver Failure ◽  
Predictive Value ◽  
Inflammatory Markers ◽  
Validation Cohort ◽  
Meld Score ◽  
Chronic Liver ◽  
Assessment Model ◽  
Operating Characteristics ◽  
Distribution Width ◽  
Derivation Cohort

Abstract Background: Systemic inflammatory response is closely related to the development and prognosis of liver failure. This study aimed to establish a new model combing the inflammatory markers including neutrophil/lymphocyte ratio (NLR) and red blood cell distribution width (RDW) with several hematological testing indicators to assess the prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). Methods: A derivation cohort with 421 patients and a validation cohort with 156 patients were recruited from three hospitals. Retrospectively collecting their clinical data and laboratory testing indicators. Medcalc-15.10 software was employed for Data analyses.Results: Multivariate analysis indicated that RDW, NLR, INR, TBIL and Cr were risk factors for 90-day mortality in patients with HBV-ACLF. The risk assessment model isCOXRNTIC=0.053×RDW+0.027×NLR+0.003×TBIL+0.317×INR+0.003×Cr (RNTIC) with a cut-off value of 3.08 (sensitivity: 77.89%, specificity: 86.04%). The area under the receiver operating characteristics curve (AUC) of the RNTIC was 0.873 [95%CI(0.837–0.903)], better than the predictive value of MELD score [0.732, 95%CI(0.687–0.774)], MELD-Na [0.714, 95%CI(0.668-0.757)], CTP[0.703, 95%CI(0.657-0.747)]. In the validation cohort, RNTIC also performed a better prediction value than MELD score, MELD-Na and CTP with the AUC of [0.845, 95%CI(0.778-0.898)], [0.768, 95%CI (0.694-0.832)], [0.759, 95%CI(0.684-0.824)] and [0.718, 95%CI(0.641-0.787)] respectively. Conclusions: The inflammatory markers RDW and NLR could be used as independent predictors of 90-day mortality in patients with HBV-ACLF. Compared with MELD score, RNTIC had a more powerful predictive value for prognosis of patients with HBV-ACLF.

Abstract P520: Infarct Growth Rate is an Independent Predictor of Poor Outcome and Mortality After Mechanical Thrombectomy With Successful Reperfusion

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Darko Quispe-Orozco ◽  
Joel M Sequeiros ◽  
Cynthia Zevallos ◽  
Mudassir Farooqui ◽  
Cindy Khanh Nguyen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Growth Rate ◽  
Mechanical Thrombectomy ◽  
Independent Predictor ◽  
Infarct Volume ◽  
Multivariate Logistic Regression Analysis ◽  
Functional Independence ◽  
Collateral Flow ◽  
Operating Characteristics ◽  
Poor Outcome

Introduction: Despite treatment with mechanical thrombectomy (MT), some patients fail to regain functional independence at 90 days. The growth of the ischemic core varies across patients, and likely reflects differences in collateral flow and ischemic tolerance. In this study, we sought establish the optimal infarct growth rate (IGR) threshold to differentiate between slow and fast progressors and assess its ability to predict poor outcome. Methods: We retrospectively identified patients with anterior large-vessel occlusion (LVO) stroke with successful MT (mTICI ≥ 2b) at two comprehensive stroke centers. Final infarct volume (FIV) was calculated from post-MT Diffusion-weighted MRI. Assuming relative stability of the FIV after successful reperfusion, we defined IGR as [FIV (ml)] / [Time from stroke onset to reperfusion (hours)]. Good clinical outcome was defined as a modified Rankin scale score (mRS) ≤2. We used Receiver Operating Characteristics (ROC) analysis to calculate the optimal IGR threshold with high specificity for predicting a poor outcome. Multivariate logistic regression analysis was performed to evaluate the association of fast progressors (IGR ≥ 7.14 ml/h) on the poor functional outcome and mortality. Results: Of the 212 patients (age 68 ± 15, 51% female, NIHSS 15 ± 7) included, 110 (51.8%) patients had a poor outcome. The median IGR was significantly higher in patients with poor compared to good outcome (7 ml/h vs. 3.1 ml/h, p<0.001). An IGR ≥ 7.14 ml/h showed a sensitivity of 0.49 and a specificity of 0.7 to predict a poor outcome with an area under the ROC curve of 0.65 (95% CI, 0.58-0.73). IGR ≥ 7.14 ml/h was an independent predictor of poor outcome (OR 2.2, 95% CI 1.1-4.6, p=0.036) and mortality (OR 4.2, 95% CI 1.8-10.6, p=0.001) after adjusting for age, sex, atrial fibrillation, NIHSS and ASPECTS. Ordinal regression showed that the odds of having better outcomes decrease 60% in fast progressors (OR 0.40, 95% CI: 0.22-0.70, p=0.001) after adjusting for age, sex, atrial fibrillation, NIHSS, and ASPECTS. Conclusions: IGR is an independent predictor of poor outcome and mortality in patients with successful MT. Early identification of this population might help to institute therapeutic strategies of accelerating reperfusion and slowing the IGR.

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