Abstract WP62: Delayed Appearance of DWI Lesions in Clinically Suspected DWI-Negative Stroke: DWI-CONVERSION Score

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Kitae Kim ◽  
Beom Joon Kim ◽  
Jaewon Huh ◽  
Seong Kyu Yang ◽  
Moon-ku Han ◽  
...  
Keyword(s):  
Validation Cohort ◽  
External Validation ◽  
Stroke Patients ◽  
Derivation Cohort ◽  
C Statistic ◽  
Abcd2 Score ◽  
Medical Histories ◽  
Suspected Stroke ◽  
Comparison Table

Introduction: Our aim was to determine the prevalence and factors associated with delayed appearance of DWI lesion among initially DWI-negative clinically suspected stroke patients in the follow-up DWIs during in-hospital care. Method: Among 5271 patients admitted to stroke unit as clinically suspected stroke/TIA within 7 days from symptom onset in our hospital via ER for 2010~2017, we selected subjects based on the following criteria 1) initial negative DWI (n=827), 2) follow-up DWI within 14 days (n=751). Then, we excluded 57 cases (hemorrhagic cases (n=4), cerebral angiography studies between MRIs (n=53)). Finally-included 694 cases were divided into two cohorts for temporal external validation (2010~2015 (n=488) as derivation; 2016~2017 (n=206) as validation). Results: Of 5271 cases, 827 cases (15.7%) showed initial negative DWI. In 694 finally-included cases, 22.5% (n=156) showed delayed appearance of DWI lesion. In derivation cohort, factors showing significant relationship with positive conversion comprised: medical histories such as atrial fibrillation (aOR 6.17, 3.23-12.01); symptoms including objective hemiparesis (aOR 4.39, 1.90-10.32) (Table 1). These factors were used to construct DWI-CONVERSION score (Table 2a). Its c-statistic was 0.813 in derivation cohort and 0.808 in validation cohort, which is significantly higher than that of ABCD2 score in validation cohort (c-statistic=0.678; P<0.01 for comparison; Table 2b). Conclusion: We identified prevalence and clinical factors significantly associated with delayed appearance of DWI lesions in clinically suspicious stroke patients. DWI-CONVERSION score is a simple tool to predict it.

Prediction of Poststroke Urinary Tract Infection Risk in Immobile Patients Using Machine Learning: Observational Cohort Study (Preprint)

2021 ◽  
Author(s):  
Chen Zhu ◽  
Zidu Xu ◽  
Yaowen Gu ◽  
Si Zheng ◽  
Xiangyu Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Ensemble Learning ◽  
Predictive Models ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Stroke Patients ◽  
Internal Validation ◽  
Derivation Cohort

BACKGROUND Poststroke immobility gets patients more vulnerable to stroke-relevant complications. Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it is still challenging to accurately estimate personal UTI risk due to unclear interaction of various factors and variability of individual characteristics. This calls for more precise and trust-worthy predictive models to assist with potential UTI identification. OBJECTIVE The aim of this study was to develop predictive models for UTI risk identification for immobile stroke patients. A prospective analysis was conducted to evaluate the effectiveness and clinical interpretability of the models. METHODS The data used in this study were collected from the Common Complications of Bedridden Patients and the Construction of Standardized Nursing Intervention Model (CCBPC). Derivation cohort included data of 3982 immobile stroke patients collected during CCBPC-I, from November 1, 2015 to June 30, 2016; external validation cohort included data of 3837 immobile stroke patients collected during CCBPC-II, from November 1, 2016 to July 30, 2017. 6 machine learning models and an ensemble learning model were derived based on 80% of derivation cohort and its effectiveness was evaluated with the remaining 20% of derivation cohort data. We further compared the effectiveness of predictive models in external validation cohort. The performance of logistic regression without regularization was used as a reference. We used Shapley additive explanation values to determine feature importance and examine the clinical significance of prediction models. Shapely values of the factors were calculated to represent the magnitude, prevalence, and direction of their effects, and were further visualized in a summary plot. RESULTS A total of 103(2.59%) patients were diagnosed with UTI in derivation cohort(N=3982); the internal validation cohort (N=797) shared the same incidence. The external validation cohort had a UTI incidence of 1.38% (N=53). Evaluation results showed that the ensemble learning model performed the best in area under the receiver operating characteristic (ROC) curve in internal validation, up to 82.2%; second best in external validation, 80.8%. In addition, the ensemble learning model performed the best sensitivity in both internal and external validation sets (80.9% and 81.1%, respectively). We also identified seven UTI risk factors (pneumonia, glucocorticoid use, female sex, mixed cerebrovascular disease, increased age, prolonged length of stay, and duration of catheterization) contributing most to the predictive model, thus demonstrating the clinical interpretability of model. CONCLUSIONS Our ensemble learning model demonstrated promising performance. Identifying UTI risk and detecting high risk factors among immobile stroke patients would allow more selective and effective use of preventive interventions, thus improving clinical outcomes. Future work should focus on developing a more concise scoring tool and prospectively examining the model in practical use.

scholarly journals Predicting mortality from intracranial hemorrhage in allogeneic hematopoietic stem cell transplantation patients

2021 ◽  
Author(s):  
Xiying Ren ◽  
Qiusha Huang ◽  
Qingyuan Qu ◽  
Xuan Cai ◽  
Hai-Xia Fu ◽  
...  
Keyword(s):  
Stem Cell ◽  
Hematopoietic Stem Cell Transplantation ◽  
Stem Cell Transplantation ◽  
Validation Cohort ◽  
External Validation ◽  
Early Mortality ◽  
Hematopoietic Stem ◽  
Derivation Cohort ◽  
C Statistic ◽  
Grading Scale

Intracranial hemorrhage (ICH) is a rare but fatal central nervous system complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, factors that are predictive of early mortality in patients who develop ICH after undergoing allo-HSCT have not been systemically investigated. From January 2008 to June 2020, 70 allo-HSCT patients with ICH diagnosis formed the derivation cohort. Forty-one allo-HSCT patients with ICH diagnosis were collected from 12 other medical centers during the same period, and they comprised the external validation cohort. We used these 2 cohorts to develop and validate a grading scale that enables the prediction of 30-day mortality from ICH in all-HSCT patients. Four predictors, lactate dehydrogenase level, albumin level, white blood cell count and disease status, were retained in the multivariable logistic regression model, and a simplified grading scale, termed the LAWS score, was developed. The LAWS score was adequately calibrated (Hosmer-Lemeshow test, p&gt;0.05) in both cohorts. It had good discrimination power in both the derivation cohort (C-statistic of 0.859, 95% CI 0.776-0.945) and the external validation cohort (C-statistic of 0.795, 95% CI 0.645-0.945). The LAWS score is the first scoring system capable of predicting the 30-day mortality from ICH in allo-HSCT patients. It showed good performance in identifying allo-HSCT patients at increased risk of early mortality after ICH diagnosis. We anticipate that it would help risk-stratify allo-HSCT patients with ICH and facilitate future studies on developing individualized and novel interventions for patients within different LAWS risk groups.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

Echocardiography for the 10-year prediction of cardiomyopathy in long-term survivors of childhood cancer

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.M Leerink ◽  
H.J.H Van Der Pal ◽  
E.A.M Feijen ◽  
P.G Meregalli ◽  
M.S Pourier ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Median Time ◽  
Cancer Diagnosis ◽  
Predictive Value ◽  
Validation Cohort ◽  
Funding Source ◽  
Derivation Cohort ◽  
After Cancer ◽  
Radiotherapy Dose

Abstract Background Childhood cancer survivors (CCS) treated with anthracyclines and/or chest-directed radiotherapy receive life-long echocardiographic surveillance to detect cardiomyopathy early. Current risk stratification and surveillance frequency recommendations are based on anthracycline- and chest-directed radiotherapy dose. We assessed the added prognostic value of an initial left ventricular ejection fraction (EF) measurement at &gt;5 years after cancer diagnosis. Patients and methods Echocardiographic follow-up was performed in asymptomatic CCS from the Emma Children's Hospital (derivation; n=299; median time after diagnosis, 16.7 years [inter quartile range (IQR) 11.8–23.15]) and from the Radboud University Medical Center (validation; n=218, median time after diagnosis, 17.0 years [IQR 13.0–21.7]) in the Netherlands. CCS with cardiomyopathy at baseline were excluded (n=16). The endpoint was cardiomyopathy, defined as a clinically significant decreased EF (EF&lt;40%). The predictive value of the initial EF at &gt;5 years after cancer diagnosis was analyzed with multivariable Cox regression models in the derivation cohort and the model was validated in the validation cohort. Results The median follow-up after the initial EF was 10.9 years and 8.9 years in the derivation and validation cohort, respectively, with cardiomyopathy developing in 11/299 (3.7%) and 7/218 (3.2%), respectively. Addition of the initial EF on top of anthracycline and chest radiotherapy dose increased the C-index from 0.75 to 0.85 in the derivation cohort and from 0.71 to 0.92 in the validation cohort (p&lt;0.01). The model was well calibrated at 10-year predicted probabilities up to 5%. An initial EF between 40–49% was associated with a hazard ratio of 6.8 (95% CI 1.8–25) for development of cardiomyopathy during follow-up. For those with a predicted 10-year cardiomyopathy probability &lt;3% (76.9% of the derivation cohort and 74.3% of validation cohort) the negative predictive value was &gt;99% in both cohorts. Conclusion The addition of the initial EF &gt;5 years after cancer diagnosis to anthracycline- and chest-directed radiotherapy dose improves the 10-year cardiomyopathy prediction in CCS. Our validated prediction model identifies low-risk survivors in whom the surveillance frequency may be reduced to every 10 years. Calibration in both cohorts Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Dutch Heart Foundation

scholarly journals Development and validation of two SCORE-based cardiovascular risk prediction models for Eastern Europe: a multicohort study

2020 ◽  
Vol 41 (35) ◽  
pp. 3325-3333 ◽  
Author(s):  
Taavi Tillmann ◽  
Kristi Läll ◽  
Oliver Dukes ◽  
Giovanni Veronesi ◽  
Hynek Pikhart ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Models ◽  
Validation Cohort ◽  
European Countries ◽  
Eastern European ◽  
Risk Prediction Models ◽  
Derivation Cohort ◽  
Score Model ◽  
Eastern European Countries

Abstract Aims Cardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model. Methods and results We developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02–0.11] in the derivation cohort and 0.14 (95% CI 0.04–0.25) in the validation cohort. Conclusion Our recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.

scholarly journals Prediction model of bleeding after endoscopic submucosal dissection for early gastric cancer: BEST-J score

Gut ◽  
2020 ◽  
pp. gutjnl-2019-319926 ◽  
Author(s):  
Waku Hatta ◽  
Yosuke Tsuji ◽  
Toshiyuki Yoshio ◽  
Naomi Kakushima ◽  
Shu Hoteya ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer ◽  
Prediction Model ◽  
Endoscopic Submucosal Dissection ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Derivation Cohort ◽  
Submucosal Dissection

ObjectiveBleeding after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) is a frequent adverse event after ESD. We aimed to develop and externally validate a clinically useful prediction model (BEST-J score: Bleeding after ESD Trend from Japan) for bleeding after ESD for EGC.DesignThis retrospective study enrolled patients who underwent ESD for EGC. Patients in the derivation cohort (n=8291) were recruited from 25 institutions, and patients in the external validation cohort (n=2029) were recruited from eight institutions in other areas. In the derivation cohort, weighted points were assigned to predictors of bleeding determined in the multivariate logistic regression analysis and a prediction model was established. External validation of the model was conducted to analyse discrimination and calibration.ResultsA prediction model comprised 10 variables (warfarin, direct oral anticoagulant, chronic kidney disease with haemodialysis, P2Y12 receptor antagonist, aspirin, cilostazol, tumour size >30 mm, lower-third in tumour location, presence of multiple tumours and interruption of each kind of antithrombotic agents). The rates of bleeding after ESD at low-risk (0 to 1 points), intermediate-risk (2 points), high-risk (3 to 4 points) and very high-risk (≥5 points) were 2.8%, 6.1%, 11.4% and 29.7%, respectively. In the external validation cohort, the model showed moderately good discrimination, with a c-statistic of 0.70 (95% CI, 0.64 to 0.76), and good calibration (calibration-in-the-large, 0.05; calibration slope, 1.01).ConclusionsIn this nationwide multicentre study, we derived and externally validated a prediction model for bleeding after ESD. This model may be a good clinical decision-making support tool for ESD in patients with EGC.

scholarly journals Machine learning identifies clinical parameters to predict mortality in patients undergoing transcatheter mitral valve repair

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
E Zweck ◽  
M Spieker ◽  
P Horn ◽  
C Iliadis ◽  
C Metze ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Mitral Valve ◽  
Risk Stratification ◽  
Mitral Valve Repair ◽  
Validation Cohort ◽  
External Validation ◽  
Valve Repair ◽  
Derivation Cohort ◽  
Transcatheter Mitral Valve Repair

Abstract Background Transcatheter Mitral Valve Repair (TMVR) with MitraClip is an important treatment option for patients with severe mitral regurgitation. The lack of appropriate, validated and specific means to risk stratify TMVR patients complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. Purpose We aimed to develop an optimized risk stratification model for TMVR patients using machine learning (ML). Methods We included a total of 1009 TMVR patients from three large university hospitals, of which one (n=317) served as an external validation cohort. The primary endpoint was all-cause 1-year mortality, which was known in 95% of patients. Model performance was assessed using receiver operating characteristics. In the derivation cohort, different ML algorithms, including random forest, logistic regression, support vectors machines, k nearest neighbors, multilayer perceptron, and extreme gradient boosting (XGBoost) were tested using 5-fold cross-validation in the derivation cohort. The final model (Transcatheter MITral Valve Repair MortALIty PredicTion SYstem; MITRALITY) was tested in the validation cohort with respect to existing clinical scores. Results XGBoost was selected as the final algorithm for the MITRALITY Score, using only six baseline clinical features for prediction (in order of predictive importance): blood urea nitrogen, hemoglobin, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY Score's area under the curve (AUC) was 0.783, outperforming existing scores which yielded AUCs of 0.721 and 0.657 at best. 1-year mortality in the MITRALITY Score quartiles across the total cohort was 0.8%, 1.3%, 10.5%, and 54.6%, respectively. Odds of mortality in MITRALITY Score quartile 4 as compared to quartile 1 were 143.02 [34.75; 588.57]. Survival analyses showed that the differences in outcomes between the MITRALITY Score quartiles remained even over a timeframe of 3 years post intervention (log rank: p&lt;0.005). With each increase by 1% in the MITRALITY score, the respective proportional hazard ratio for 3-year survival was 1.06 [1.05, 1.07] (Cox regression, p&lt;0.05). Conclusion The MITRALITY Score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR. These findings may potentially allow for more precise design of future clinical trials, may enable novel treatment strategies tailored to populations of specific risk and thereby serve future daily clinical practice. FUNDunding Acknowledgement Type of funding sources: None. Summary Figure

scholarly journals Prediction of Early Recanalization after Intravenous Thrombolysis in Patients with Large-Vessel Occlusion

2021 ◽  
Vol 23 (2) ◽  
pp. 244-252
Author(s):  
Young Dae Kim ◽  
Hyo Suk Nam ◽  
Joonsang Yoo ◽  
Hyungjong Park ◽  
Sung-Il Sohn ◽  
...  
Keyword(s):  
High Performance ◽  
Validation Cohort ◽  
Intravenous Thrombolysis ◽  
Large Vessel ◽  
Large Vessel Occlusion ◽  
Stroke Patients ◽  
Vessel Occlusion ◽  
Derivation Cohort ◽  
Factors Associated ◽  
Intravenous Tissue Plasminogen Activator

Background and Purpose We aimed to develop a model predicting early recanalization after intravenous tissue plasminogen activator (t-PA) treatment in large-vessel occlusion.Methods Using data from two different multicenter prospective cohorts, we determined the factors associated with early recanalization immediately after t-PA in stroke patients with large-vessel occlusion, and developed and validated a prediction model for early recanalization. Clot volume was semiautomatically measured on thin-section computed tomography using software, and the degree of collaterals was determined using the Tan score. Follow-up angiographic studies were performed immediately after t-PA treatment to assess early recanalization.Results Early recanalization, assessed 61.0±44.7 minutes after t-PA bolus, was achieved in 15.5% (15/97) in the derivation cohort and in 10.5% (8/76) in the validation cohort. Clot volume (odds ratio [OR], 0.979; 95% confidence interval [CI], 0.961 to 0.997; <i>P</i>=0.020) and good collaterals (OR, 6.129; 95% CI, 1.592 to 23.594; <i>P</i>=0.008) were significant factors associated with early recanalization. The area under the curve (AUC) of the model including clot volume was 0.819 (95% CI, 0.720 to 0.917) and 0.842 (95% CI, 0.746 to 0.938) in the derivation and validation cohorts, respectively. The AUC improved when good collaterals were added (derivation cohort: AUC, 0.876; 95% CI, 0.802 to 0.950; <i>P</i>=0.164; validation cohort: AUC, 0.949; 95% CI, 0.886 to 1.000; <i>P</i>=0.036). The integrated discrimination improvement also showed significantly improved prediction (0.097; 95% CI, 0.009 to 0.185; <i>P</i>=0.032).Conclusions The model using clot volume and collaterals predicted early recanalization after intravenous t-PA and had a high performance. This model may aid in determining the recanalization treatment strategy in stroke patients with large-vessel occlusion.

scholarly journals Development and external validation of a prognostic tool for COVID-19 critical disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242953
Author(s):  
Daniel S. Chow ◽  
Justin Glavis-Bloom ◽  
Jennifer E. Soun ◽  
Brent Weinberg ◽  
Theresa Berens Loveless ◽  
...  
Keyword(s):  
Critical Care ◽  
Prognostic Model ◽  
Validation Cohort ◽  
External Validation ◽  
Vital Signs ◽  
Clinical Tool ◽  
Derivation Cohort ◽  
Laboratory Values ◽  
Increased Risk ◽  
Care Capacity

Background The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21–88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27–88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87–1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. Conclusions and relevance We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

scholarly journals BNP/ NT-pro BNP thresholds for the assessment of the prognosis in patients without heart failure

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
S Hendricks ◽  
A A Mahabadi ◽  
L Vogel ◽  
F Al-Rashid ◽  
P Luedike ◽  
...  
Keyword(s):  
Heart Failure ◽  
Natriuretic Peptides ◽  
Validation Cohort ◽  
Skewed Distribution ◽  
Standard Deviation Increase ◽  
Term Survival ◽  
Derivation Cohort ◽  
Heart Failure Patients ◽  
Gender Specific

Abstract Background Natriuretic peptides (BNP/NT-proBNP) are predominantly used for risk stratification, diagnosis and therapeutic monitoring in heart failure patients. A potential value of BNP/NT-proBNP serum levels for the prediction of prognosis in the general population and for non-heart failure patient cohorts is suggested in the literature. However, for non-heart failure patients, no thresholds are established. We aimed to determine cut-off levels that allow prediction of long-term survival in patients without known heart failure. Methods The present analysis is based on a registry of patients undergoing coronary angiography between 2004 and 2019. Patients with existing diagnosis of heart failure or elevated natriuretic peptides (BNP &gt;100pg/nl, NT-proBNP &gt;400pg/nl), with missing follow-up information or without BNP/NT-proBNP levels at admission were excluded. As either BNP or NT-proBNP was available for singular patients and to adjust for the skewed distribution, BNP/NT-proBNP levels ranked based on gender specific percentile from 0 to 99. The cohort was then divided into a derivation and a validation cohort using random sampling. Incidence of death of any cause during follow-up was recorded. In the derivation cohort, cox regression analysis was used to determine the association of natriuretic peptides with incident mortality per 1 standard deviation increase in BNP/NT-proBNP rank. Multivariable models controlled for age, sex, LDL-cholesterol, systolic blood pressure, smoking status, and family history of premature cardiovascular disease. Receiver operating characteristics curve analysis was performed, with corresponding area under the curve, along with Youden's J index assessment, to establish a threshold for prediction of survival. The association of this threshold with incident mortality was tested in the validation cohort. Results Overall, 3,687 patients (age 62.9±12.5 years, 71% male) were included. During a mean follow-up of 2.6±3.4 years, 169 deaths occurred. In the derivation cohort, BNP/NT-proBNP was significantly associated with mortality (Hazard ratio [95% confidence interval]: 1.25 [1.01–1.54], p=0.04). Based on Youden's J index, BNP-thresholds of 9.6 and 29pg/ml and NT-proBNP thresholds of 65 and 77pg/ml for men and women, respectively, were determined. In the derivation cohort, BNP/NT-proBNP levels above these thresholds were significantly associated with increased mortality (2.44 [1.32–4.53], p=0.005). The predictive value of the determined thresholds was confirmed in the validation cohort (2.78 [1.26–6.14], p=0.01). Conclusion We here describe gender-specific BNP/NT-proBNP thresholds that allow prediction of impaired survival in patients without heart failure. Utilization of these thresholds in clinical routine may qualify for risk prediction in non-heart failure cohorts, independent of traditional cardiovascular risk factors. FUNDunding Acknowledgement Type of funding sources: None.

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