scholarly journals A New Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaodong Ding ◽  
Yiqin Wang ◽  
Yiming Hao ◽  
Yi Lv ◽  
Rui Chen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pulse Rate ◽  
Euclidean Distance ◽  
Operating Characteristic ◽  
Pulse Wave ◽  
Characteristic Curve ◽  
Signal Quality ◽  
Training Set ◽  
Pulse Rate Variability ◽  
Operating Characteristic Curve

Background. Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality. Objective. The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability. Methods. During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes. Results. The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal. Conclusion. This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

scholarly journals A nomogram to predict unfavourable outcome in patients receiving oral anticoagulants for atrial fibrillation after stroke

2020 ◽  
Vol 5 (4) ◽  
pp. 384-393
Author(s):  
Manuel Cappellari ◽  
David J Seiffge ◽  
Masatoshi Koga ◽  
Maurizio Paciaroni ◽  
Stefano Forlivesi ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Vitamin K ◽  
Oral Anticoagulant ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Vitamin K Antagonist ◽  
Direct Oral Anticoagulant ◽  
Unfavourable Outcome ◽  
Stroke Patients ◽  
Operating Characteristic Curve

Introduction It is unknown whether the type of treatment (direct oral anticoagulant versus vitamin K antagonist) and the time of treatment introduction (early versus late) may affect the functional outcome in stroke patients with atrial fibrillation. We aimed to develop and validate a nomogram model including direct oral anticoagulant/vitamin K antagonist and early/late oral anticoagulant introduction for predicting the probability of unfavourable outcome after stroke in atrial fibrillation-patients. Patients and Methods We conducted an individual patient data analysis of four prospective studies. Unfavourable functional outcome was defined as three-month modified Rankin Scale score 3 -6. To generate the nomogram, five independent predictors including age (<65 years, reference; 65--79; or 80), National Institutes of Health Stroke Scale score (0--5 points, reference; 6--15; 16--25; or >25), acute revascularisation treatments (yes, reference, or no), direct oral anticoagulant (reference) or vitamin K antagonist, and early (7 days, reference) or late (8--30) anticoagulant introduction entered into a final logistic regression model. The discriminative performance of the model was assessed by using the area under the receiver operating characteristic curve. Results A total of 2102 patients with complete data for generating the nomogram was randomly dichotomised into training ( n = 1553) and test ( n = 549) sets. The area under the receiver operating characteristic curve was 0.822 (95% confidence interval, CI: 0.800--0.844) in the training set and 0.803 (95% CI: 0.764--0.842) in the test set. The model was adequately calibrated (9.852; p = 0.276 for the Hosmer--Lemeshow test). Discussion and Conclusion Our nomogram is the first model including type of oral anticoagulant and time of treatment introduction to predict the probability of three-month unfavourable outcome in a large multicentre cohort of stroke patients with atrial fibrillation.

scholarly journals A clinical scoring system for pediatric hand-foot-mouth disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Huang ◽  
Li Deng ◽  
Liping Jia ◽  
Runan Zhu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Test Set ◽  
Hand Foot Mouth Disease ◽  
Clinical Scoring ◽  
Exposure History ◽  
Operating Characteristic Curve

Abstract Background The aim of the present study was to develop a clinical scoring system for the diagnosis of hand-foot-mouth disease (HFMD) with improved accuracy. Methods A retrospective analysis was performed on standardized patient history and clinical examination data obtained from 1435 pediatric patients under the age of three years who presented with acute rash illness and underwent enterovirus nucleic acid detection. Patients were then divided into the HFMD (1094 patients) group or non-HFMD (341 patients) group based on a positive or a negative result from the assay, respectively. We then divided the data into a training set (1004 cases, 70%) and a test set (431 cases, 30%) using a random number method. Multivariate logistic regression was performed on 15 clinical variables (e.g. age, exposure history, number of rash spots in a single body region) to identify variables highly predictive of a positive diagnosis in the training set. Using the variables with high impact on the diagnostic accuracy, we generated a scoring system for predicting HFMD and subsequently evaluated this system in the test set by receiver operating characteristic curve (ROC curve). Results Using the logistic model, we identified seven clinical variables (age, exposure history, and rash density at specific regions of the body) to be included into the scoring system. The final scores ranged from − 5 to 24 (higher scores positively predicted HFMD diagnosis). Through our training set, a cutoff score of 7 resulted in a sensitivity of 0.76 and specificity of 0.68. The area under the receiver operating characteristic curve (AUC) was 0.804 (95% confidence interval [CI]: 0.773–0.835) (P < 0.001). Using the test set, we obtained an AUC of 0.76 (95% CI: 0.710–0.810) with a sensitivity of 0.76 and a specificity of 0.62. These results from the test set were consistent with those from the training set. Conclusions This study establishes an objective scoring system for the diagnosis of typical and atypical HFMD using measures accessible through routine clinical encounters. Due to the accuracy and sensitivity achieved by this scoring system, it can be employed as a rapid, low-cost method for establishing diagnoses in children with acute rash illness.

scholarly journals IER-SICH Nomogram to Predict Symptomatic Intracerebral Hemorrhage After Thrombectomy for Stroke

Stroke ◽  
2019 ◽  
Vol 50 (4) ◽  
pp. 909-916 ◽  
Author(s):  
Manuel Cappellari ◽  
Salvatore Mangiafico ◽  
Valentina Saia ◽  
Giovanni Pracucci ◽  
Sergio Nappini ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Test Set ◽  
Symptomatic Intracerebral Hemorrhage ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background and Purpose— As a reliable scoring system to detect the risk of symptomatic intracerebral hemorrhage after thrombectomy for ischemic stroke is not yet available, we developed a nomogram for predicting symptomatic intracerebral hemorrhage in patients with large vessel occlusion in the anterior circulation who received bridging of thrombectomy with intravenous thrombolysis (training set), and to validate the model by using a cohort of patients treated with direct thrombectomy (test set). Methods— We conducted a cohort study on prospectively collected data from 3714 patients enrolled in the IER (Italian Registry of Endovascular Stroke Treatment in Acute Stroke). Symptomatic intracerebral hemorrhage was defined as any type of intracerebral hemorrhage with increase of ≥4 National Institutes of Health Stroke Scale score points from baseline ≤24 hours or death. Based on multivariate logistic models, the nomogram was generated. We assessed the discriminative performance by using the area under the receiver operating characteristic curve. Results— National Institutes of Health Stroke Scale score, onset-to-end procedure time, age, unsuccessful recanalization, and Careggi collateral score composed the IER-SICH nomogram. After removing Careggi collateral score from the first model, a second model including Alberta Stroke Program Early CT Score was developed. The area under the receiver operating characteristic curve of the IER-SICH nomogram was 0.778 in the training set (n=492) and 0.709 in the test set (n=399). The area under the receiver operating characteristic curve of the second model was 0.733 in the training set (n=988) and 0.685 in the test set (n=779). Conclusions— The IER-SICH nomogram is the first model developed and validated for predicting symptomatic intracerebral hemorrhage after thrombectomy. It may provide indications on early identification of patients for more or less postprocedural intensive management.

scholarly journals Potential Metabolite Markers of Multiple Myeloma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3163-3163
Author(s):  
Jiliang Xia ◽  
Jingyu Zhang ◽  
Xuan Wu ◽  
ShiLian Chen ◽  
Jingchao Lin ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Confidence Interval ◽  
Peripheral Blood ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Petroselinic Acid ◽  
Characteristic Curve ◽  
Training Set ◽  
Operating Characteristic Curve ◽  
Testing Set

Abstract E-mail: [email protected] Background: Metabolism reprogramming is one of ten features in cancer. It is well known that metabolites in tumor microenvironment contribute to the survival and proliferation of cancer cells. Currently, a lack of detailed information about the metabolites profiling in bone marrow microenvironment limits us to understand the roles of metabolites associated with multiple myeloma(MM) and its diagnosis and treatment. Here we report a serum untargeted metabolomics study of MM patients, together with healthy donors(HD), with the aim of discovering metabolite markers associated with MM. Materials and Methods: Gas chromatography-time-of-flight mass spectrometry (GC-TOFMS)-based metabolomics was used to analyze 140 serum subjects, including 81 bone marrow subjects(22 HD, 59 MM patients) and 59 peripheral blood subjects(27 HD, 32 MM patients). The bone marrow subjects were divided into training set(11 HD, 32 MM patients) and testing set(11 HD, 27 MM patients). SIMCA-14.1 software package was used to visualize the metabolite alterations between MM patient and HD through Principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). Both the T-test and the receiver operating characteristic curve(ROC) analysis were performed by SPSS software. Metabolites in serum with higher fold change(FC) and variable importance in the projection(VIP) value(VIP > 1.5, P < 0.05 and FC > 1.5, P < 0.05, FDR < 0.05) were considered as biomarker candidates. Results: A total of 117 and 123 metabolites were annotated from the detected spectral features in bone marrow serum subjects derived from training set and testing set, respectively. Based on multivariate statistical analysis(PCA and OPLS-DA) and univariate statistical analysis(T-test), a panel of 6 and 10 metabolites were identified as differential metabolites(VIP > 1.5, P < 0.05 and FC > 1.5, P < 0.05, FDR < 0.05) between MM patients and HD in training set and testing set, respectively, among of which 5 metabolites were found significantly altered in both sets. Creatinine and glycine were significantly elevated in MM patients compared with HD, while fatty acid consists of palmitic acid, petroselinic acid and stearic acida were found decreased in MM patients compared with HD. ROC analysis of these 5 metabolites resulted in an area under the receiver operating characteristic curve (AUC) of 0.922(95% confidence interval=0.748-1) in the training set and 0.923(95% confidence interval=0.853-1) in the testing set. Furthermore, the diagnostic potential of the metabolite signatures was assessed in peripheral blood subjects. Consistent with bone marrow subjects, metabolite signatures were significantly changed(VIP > 1.5, P < 0.05 and FC > 1.5, P < 0.05, FDR < 0.05) in peripheral blood subjects derived from MM patients compared with HD. The AUC of this metabolites signatures was 0.901(95% confidence interval=0.748-1) in peripheral blood subjects, implying that this panel of metabolites could be of potential clinical significance for the diagnosis of MM. Conclusion: We conclude that a panel of 5 metabolites, including creatinine, glycine, palmitic acid, petroselinic acid and stearic acid, in serum has great potential in discriminating MM patient from HD. This metabolite signatures provides a novel and promising molecular diagnostic approach for the detection of MM. Disclosures No relevant conflicts of interest to declare.

scholarly journals Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record

2020 ◽  
Vol 13 (10) ◽  
Author(s):  
Rashmee U. Shah ◽  
R. Kannan Mutharasan ◽  
Faraz S. Ahmad ◽  
Anna G. Rosenblatt ◽  
Hawkins C. Gay ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Language Processing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Operating Characteristic Curve ◽  
Validation Set ◽  
Natural Language Processing Algorithm ◽  
Receiver Operating

Background: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone. Methods and Results: We created 3 data sets from patients with at least one AF billing code from 2010 to 2017: a training set (n=886), an internal validation set from site no. 1 (n=285), and an external validation set from site no. 2 (n=276). A team of clinicians reviewed and adjudicated patients as AF present or absent, which served as the reference standard. We trained 54 algorithms to classify each patient, varying the model, number of features, number of stop words, and the method used to create the feature set. The algorithm with the highest F-score (the harmonic mean of sensitivity and positive predictive value) in the training set was applied to the validation sets. F-scores and area under the receiver operating characteristic curves were compared between site no. 1 and site no. 2 using bootstrapping. Adjudicated AF prevalence was 75.1% at site no. 1 and 86.2% at site no. 2. Among 54 algorithms, the best performing model was logistic regression, using 1000 features, 100 stop words, and term frequency-inverse document frequency method to create the feature set, with sensitivity 92.8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the training set. The performance at site no. 1 was sensitivity 92.5%, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91. The performance at site no. 2 was sensitivity 89.5%, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80. The F-score was lower at site no. 2 compared with site no. 1 (92.5% [SD, 1.1%] versus 94.2% [SD, 1.1%]; P <0.001). Conclusions: We developed a natural language processing algorithm to identify patients with AF using text alone, with >90% F-score at 2 separate sites. This approach allows better use of the clinical narrative and creates an opportunity for precise, high-throughput cohort identification.

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

The gut hormone GLP-2 predicts cardiovascular risk in patients with acute myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
F Kahles ◽  
R.W Mertens ◽  
M.V Rueckbeil ◽  
M.C Arrivas ◽  
J Moellmann ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Acute Myocardial Infarction ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Funding Source ◽  
Cardiovascular Prognosis ◽  
Operating Characteristic Curve ◽  
Hs Crp

Abstract Background GLP-1 and GLP-2 (glucagon-like peptide-1/2) are gut derived hormones that are co-secreted from intestinal L-cells in response to food intake. While GLP-1 is known to induce postprandial insulin secretion, GLP-2 enhances intestinal nutrient absorption and is clinically used for the treatment of patients with short bowel syndrome. The relevance of the GLP-2 system for cardiovascular disease is unknown. Purpose The aim of this study was to assess the predictive capacity of GLP-2 for cardiovascular prognosis in patients with myocardial infarction. Methods Total GLP-2 levels, NT-proBNP concentrations and the Global Registry of Acute Coronary Events (GRACE) score were assessed at time of admission in 918 patients with myocardial infarction, among them 597 patients with NSTEMI and 321 with STEMI. The primary composite outcome of the study was the first occurrence of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke (3-P-MACE) with a median follow-up of 311 days. Results Kaplan-Meier survival plots (separated by the median of GLP-2 with a cut-off value of 4.4 ng/mL) and univariable cox regression analyses found GLP-2 values to be associated with adverse outcome (logarithmized GLP-2 values HR: 2.87; 95% CI: 1.75–4.68; p&lt;0.0001). Further adjustment for age, sex, smoking, hypertension, hypercholesterolemia, diabetes mellitus, family history of cardiovascular disease, hs-Troponin T, NT-proBNP and hs-CRP levels did not affect the association of GLP-2 with poor prognosis (logarithmized GLP-2 values HR: 2.96; 95% CI: 1.38–6.34; p=0.0053). Receiver operating characteristic curve (ROC) analyses illustrated that GLP-2 is a strong indicator for cardiovascular events and proved to be comparable to other established risk markers (area under the curve of the combined endpoint at 6 months; GLP-2: 0.72; hs-Troponin: 0.56; NT-proBNP: 0.70; hs-CRP: 0.62). Adjustment of the GRACE risk estimate by GLP-2 increased the area under the receiver-operating characteristic curve for the combined triple endpoint after 6 months from 0.70 (GRACE) to 0.75 (GRACE + GLP-2) in NSTEMI patients. Addition of GLP-2 to a model containing GRACE and NT-proBNP led to a further improvement in model performance (increase in AUC from 0.72 for GRACE + NT-proBNP to 0.77 for GRACE + NT-proBNP + GLP-2). Conclusions In patients admitted with acute myocardial infarction, GLP-2 levels are associated with adverse cardiovascular prognosis. This demonstrates a strong yet not appreciated crosstalk between the heart and the gut with relevance for cardiovascular outcome. Future studies are needed to further explore this crosstalk with the possibility of new treatment avenues for cardiovascular disease. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): German Society of Cardiology (DGK), German Research Foundation (DFG)

Sign in / Sign up

Export Citation Format

Share Document