A Multiparametric Gene-Based Diagnosis Model for HNSCC

2008 ◽  
Vol 139 (2_suppl) ◽  
pp. P88-P89
Author(s):  
Maria J Worsham ◽  
Mei Lu ◽  
Seema Sethi ◽  
Alissa Kapke ◽  
Kang-Mei Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Late Stage ◽  
Tumor Grade ◽  
Healthcare Setting ◽  
Classification And Regression Tree ◽  
Multivariate Logistic Regression Model ◽  
Study Cohort ◽  
Sample Type ◽  
Roc Curve Analysis ◽  
Clinical Variables

Problem A current shortcoming in the diagnosis, prognosis and treatment of HNSCC is a lack of methods that adequately addresses the complexity and diversity of the disease. Diagnostic and prognostic marker systems based on single parameters have generally proven inadequate. Multiparametric methods, which rely on many pieces of information, are ideally suited to the grouping of tumor subtypes, identification of specific patterns of disease progression, and in predicting clinical outcomes. Methods In a retrospective multi-ethnic primary HNSCC cohort drawn from a primary healthcare setting, and constructed through re-review of the primary biopsy, gene alterations (104 genes) and clinical variables (9 histopathology and 3 demographic variables) were evaluated as predictors of stage (TNM, early versus late). Statistical analysis compared logistic regression and Classification and Regression Tree (CART®) analyses to derive the most predictive model, assessed using receiver operating characteristic (ROC) curve analysis. Results Considering all clinical and gene variables for the 360 primary HNSCC study cohort, the multivariate logistic regression model retained only tumor grade, sample type (radical dissection) and their interaction with ROC as 64%. CART® generated a multivariable model with 12 variables: clinical variables of age, pattern of invasion, and tumor grade and gene variables of TP53, F3, TFF1, CDKN2A, KIAA0170, HS222808, TANK, MYC, and UTY1, with an ROC of 0.82%. Conclusion A group of clinical variables in multiparametric combinations with molecular alterations discriminated early and late stage HNSCC. CART® improved the model's performance. Significance Validation of this initial multiparametric strategy for predicting late stage HNSCC comprising several genes and clinical factors, currently underway, should yield a multiparametric, comprehensive genome-wide molecular algorithm integrated with clinical risk factors in order to refine HNSCC diagnosis and prognosis associated with clinical and pathological staging to aid in the clinical management of patients at the earliest stages. Support NIH R01 DE15990.

scholarly journals Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Ru Zhu ◽  
Hua Duan ◽  
Sha Wang ◽  
Lu Gan ◽  
Qian Xu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Endometrial Thickness ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Decision Tree Model ◽  
Classification And Regression Tree ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Tree Model

Objective. To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. A retrospective study. Setting. A tertiary hysteroscopic center at a teaching hospital. Population. Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions. Hysteroscopic adhesion separation surgery and second-look hysteroscopy 3 months later. Measurements and Main Results. Patients’ demographics, clinical indicators, and hysteroscopy data were collected from the electronic database of the hospital. The patients were randomly apportioned to either a training or testing set (332 and 142 patients, respectively). A decision tree model of adhesion recurrence was established with a classification and regression tree algorithm and validated with reference to a multivariate logistic regression model. The decision tree model was constructed based on the training set. The classification node variables were the risk factors for recurrence of IUAs: American Fertility Society score (root node variable), isolation barrier, endometrial thickness, tubal opening, uterine volume, and menstrual volume. The accuracies of the decision tree model and multivariate logistic regression analysis model were 75.35% and 76.06%, respectively, and areas under the receiver operating characteristic curve were 0.763 (95% CI 0.681–0.846) and 0.785 (95% CI 0.702–0.868). Conclusions. The decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions.

Preoperative Circulating MiR-210, a Risk Factor for Postoperative Delirium Among Elderly Patients with Gastric Cancer Undergoing Curative Resection

2020 ◽  
Vol 26 (40) ◽  
pp. 5213-5219
Author(s):  
Yun Chen ◽  
Jinwei Zheng ◽  
Junping Chen
Keyword(s):  
Gastric Cancer ◽  
Logistic Regression ◽  
Risk Factor ◽  
Elderly Patients ◽  
Curative Resection ◽  
Postoperative Delirium ◽  
Area Under The Curve ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Clinicopathologic Characteristics

Background: Postoperative delirium (POD) is a very common complication in elderly patients with gastric cancer (GC) and associated with poor prognosis. MicroRNAs (miRNAs) serve as key post-transcriptional regulators of gene expression via targeting mRNAs and play important roles in the nervous system. This study aimed to investigate the potential predictive role of miRNAs for POD. Methods: Elderly GC patients who were scheduled to undergo elective curative resection were consequently enrolled in this study. POD was assessed at 1 day before surgery and 1-7 days after surgery following the guidance of the 5th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM V, 2013). The demographics, clinicopathologic characteristics and preoperative circulating miRNAs by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were compared between patients with or without POD. Risk factors for POD were assessed via univariate and multivariate logistic regression analyses. Results: A total of 370 participants were enrolled, of which 63 had suffered from POD within postoperative 7 days with an incidence of 17.0%. Preoperative miR-210 was a predictor for POD with an area under the curve (AUC) of 0.921, a cut-off value of 1.67, a sensitivity of 95.11%, and a specificity of 92.06%, (P<0.001). In the multivariate logistic regression model, the relative expression of serum miR-210 was an independent risk factor for POD (OR: 3.37, 95%CI: 1.98–5.87, P=0.003). Conclusions: In conclusion, the present study highlighted that preoperative miR-210 could serve as a potential predictor for POD in elderly GC patients undergoing curative resection.

Acute heart failure: predicting early in-hospital outcomes

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
S Paula ◽  
I Almeida ◽  
H Santos ◽  
H Miranda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Acute Heart Failure ◽  
Roc Curve ◽  
Real Life ◽  
Study Data ◽  
Curve Analysis ◽  
Older Age ◽  
Invasive Ventilation ◽  
Roc Curve Analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (M) of P admitted with AHF. ACTION ICU score is validated to estimate the risk of complications requiring ICU care in non-ST elevation acute coronary syndromes. Objective To validate ACTION-ICU score in AHF and to compare ACTION-ICU to GWTG-HF as predictors of in-hospital M (IHM), early M [1-month mortality (1mM)] and 1-month readmission (1mRA), using real-life data. Methods Based on a single-center retrospective study, data collected from P admitted in the Cardiology department with AHF between 2010 and 2017. P without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used chi-square, non-parametric tests, logistic regression analysis and ROC curve analysis. Results Among the 300 P admitted with AHF included, mean age was 67.4 ± 12.6 years old and 72.7% were male. Systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. ACTION-ICU score was 10.4 ± 2.3 and GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the P needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the P were readmitted 1 month after discharge. Older age (p &lt; 0.001), lower SBP (p = 0,035) and need of inotropes (p &lt; 0.001) were predictors of IHM in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p &lt; 0.001). Older age (OR 1.06, p = 0.002, CI 1.02-1.10), lower SBP (OR 1.01, p = 0.05, CI 1.00-1.02) and lower left ventricle ejection fraction (LVEF) (OR 1.06, p &lt; 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the variables were predictive of IV. LVEF (OR 0.924, p &lt; 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p &lt; 0.001, CI 0.971-0.988), higher urea (OR 1.01, p &lt; 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors of inotropes’ usage. Logistic regression showed that GWTG-HF predicted IHM (OR 1.12, p &lt; 0.001, CI 1.05-1.19), 1mM (OR 1.10, p = 1.10, CI 1.04-1.16) and inotropes’s usage (OR 1.06, p &lt; 0.001, CI 1.03-1.10), however it was not predictive of 1mRA, need of IV or NIV. Similarly, ACTION-ICU predicted IHM (OR 1.51, p = 0.02, CI 1.158-1.977), 1mM (OR 1.45, p = 0.002, CI 1.15-1.81) and inotropes’ usage (OR 1.22, p = 0.002, CI 1.08-1.39), but not 1mRA, the need of IV or NIV. ROC curve analysis revealed that GWTG-HF score performed better than ACTION-ICU regarding IHM (AUC 0.774, CI 0.46-0-90 vs AUC 0.731, CI 0.59-0.88) and 1mM (AUC 0.727, CI 0.60-0.85 vs AUC 0.707, CI 0.58-0.84). Conclusion In our population, both scores were able to predict IHM, 1mM and inotropes’s usage.

scholarly journals Serum surfactant protein D in COVID-19 is elevated and correlated with disease severity

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming Tong ◽  
Ying Xiong ◽  
Chen Zhu ◽  
Hong Xu ◽  
Qing Zheng ◽  
...  
Keyword(s):  
Acute Phase ◽  
Early Stage ◽  
Recovery Period ◽  
Serum Levels ◽  
Surfactant Protein ◽  
Mean Value ◽  
Multivariate Logistic Regression Model ◽  
Surfactant Protein D ◽  
Multivariate Logistic Regression ◽  
Roc Curve Analysis

Abstract Background The serum surfactant protein D (SP-D) level is suggested to be a useful biomarker for acute lung injuries and acute respiratory distress syndrome. Whether the serum SP-D level could identify the severity of coronavirus disease 2019 (COVID-19) in the early stage has not been elucidated. Methods We performed an observational study on 39 laboratory-confirmed COVID-19 patients from The Fourth People’s Hospital of Yiyang, Hunan, China. Receiver operating characteristic (ROC) curve analysis, correlation analysis, and multivariate logistic regression model analysis were performed. Results In the acute phase, the serum levels of SP-D were elevated significantly in severe COVID-19 patients than in mild cases (mean value ± standard deviation (SD), 449.7 ± 125.8 vs 245.9 ± 90.0 ng/mL, P<0.001), while the serum levels of SP-D in the recovery period were decreased dramatically than that in the acute phase (mean value ± SD, 129.5 ± 51.7 vs 292.9 ± 130.7 ng/ml, P<0.001), and so were for the stratified patients. The chest CT imaging scores were considerably higher in the severe group compared with those in the mild group (median value, 10.0 vs 9.0, P = 0.011), while markedly lower in the recovery period than those in the acute phase (median value, 2.0 vs 9.0, P<0.001), and so were for the stratified patients. ROC curve analysis revealed that areas under the curve of lymphocyte counts (LYM), C-reaction protein (CRP), erythrocyte sedimentation rate (ESR), interleukin-6 (IL-6), and SP-D for severe COVID-19 were 0.719, 0.833, 0.817, 0.837, and 0.922, respectively. Correlation analysis showed that the SP-D levels were negatively correlated with LYM (r = − 0.320, P = 0.047), while positively correlated with CRP (r = 0.658, P<0.001), IL-6 (r = 0.471, P = 0.002), the duration of nucleic acid of throat swab turning negative (r = 0.668, P<0.001), chest CT imaging score on admission (r = 0.695, P<0.001) and length of stay (r = 0.420, P = 0.008). Multivariate logistic regression model analysis showed that age (P = 0.041, OR = 1.093) and SP-D (P = 0.008, OR = 1.018) were risk factors for severe COVID-19. Conclusions Elevated serum SP-D level was a potential biomarker for the severity of COVID-19; this may be useful in identifying patients whose condition worsens at an early stage.

scholarly journals The second pregnancy has no effect in the incidence of macrosomia: a cross-sectional survey in two western Chinese regions

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Li Luo ◽  
Huan Zeng ◽  
Mao Zeng ◽  
Xueqing Liu ◽  
Xianglong Xu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Western China ◽  
Fetal Macrosomia ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Cross Sectional ◽  
Second Child ◽  
Child Policy ◽  
Significant Difference ◽  
The Impact

Abstract Background After the implementation of the universal two-child policy in China, the increase in parity has led to an increase in adverse pregnancy outcomes. The impact of one and two fetuses on the incidence of fetal macrosomia has not been fully confirmed in China. This study aimed to explore the differences in the incidence of fetal macrosomia in first and second pregnancies in Western China after the implementation of the universal two-child policy. Methods A total of 1598 pregnant women from three hospitals were investigated by means of a cross-sectional study from August 2017 to January 2018. Participants were recruited by convenience and divided into first and second pregnancy groups. These groups included 1094 primiparas and 504 women giving birth to their second child. Univariate and multivariate logistic regression analyses were performed to discuss the differences in the incidence of fetal macrosomia in first and second pregnancies. Results No significant difference was found in the incidence of macrosomia in the first pregnancy group (7.2%) and the second pregnancy group (7.1%). In the second-time pregnant mothers, no significant association was found between the macrosomia of the second child (5.5%) and that of the first child (4.7%). The multivariate logistic regression model showed that mothers older than 30 years are not likely to give birth to children with macrosomia (odds ratio (OR) 0.6, 95% confidence interval (CI) 0.4,0.9). Conclusions The incidence of macrosomia in Western China is might not be affected by the birth of the second child and is not increased by low parity.

scholarly journals Soluble cytotoxic T-lymphocyte–associated antigen 4 (sCTLA-4) as a potential biomarker for diagnosis and evaluation of the prognosis in Glioma

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiajia Liu ◽  
Xiaoyi Tian ◽  
Yan Wang ◽  
Xixiong Kang ◽  
Wenqi Song
Keyword(s):  
T Lymphocyte ◽  
Roc Curve ◽  
Cytotoxic T Lymphocyte ◽  
Tumor Grade ◽  
Curve Analysis ◽  
Enzyme Linked Immunosorbent Assay ◽  
Low Grade ◽  
High Grade ◽  
Healthy Individuals ◽  
Roc Curve Analysis

Abstract Background The cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) is widely considered as a pivotal immune checkpoint molecule to suppress antitumor immunity. However, the significance of soluble CTLA-4 (sCTLA-4) remains unclear in the patients with brain glioma. Here we aimed to investigate the significance of serum sCTLA-4 levels as a noninvasive biomarker for diagnosis and evaluation of the prognosis in glioma patients. Methods In this study, the levels of sCTLA-4 in serum from 50 patients diagnosed with different grade gliomas including preoperative and postoperative, and 50 healthy individuals were measured by an enzyme-linked immunosorbent assay (ELISA). And then ROC curve analysis and survival analyses were performed to explore the clinical significance of sCTLA-4. Results Serum sCTLA-4 levels were significantly increased in patients with glioma compared to that of healthy individuals, and which was also positively correlated with the tumor grade. ROC curve analysis showed that the best cutoff value for sCTLA-4 for glioma is 112.1 pg/ml, as well as the sensitivity and specificity with 82.0 and 78.0%, respectively, and a cut-off value of 220.43 pg/ml was best distinguished in patients between low-grade glioma group and high-grade glioma group with sensitivity 73.1% and specificity 79.2%. Survival analysis revealed that the patients with high sCTLA-4 levels (> 189.64 pg/ml) had shorter progression-free survival (PFS) compared to those with low sCTLA-4 levels (≤189.64 pg/ml). In the univariate analysis, elder, high-grade tumor, high sCTLA-4 levels and high Ki-67 index were significantly associated with shorter PFS. In the multivariate analysis, sCTLA-4 levels and tumor grade remained an independent prognostic factor. Conclusion These findings indicated that serum sCTLA-4 levels play a critical role in the pathogenesis and development of glioma, which might become a valuable predictive biomarker for supplementary diagnosis and evaluation of the progress and prognosis in glioma.

scholarly journals Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

scholarly journals Right choice of a method for determination of cut-off values: A statistical tool for a diagnostic test

2014 ◽  
Vol 5 (3) ◽  
pp. 30-34 ◽  
Author(s):  
Balkishan Sharma ◽  
Ravikant Jain
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Traditional Method ◽  
Statistical Technique ◽  
Curve Analysis ◽  
Operating Characteristics ◽  
Roc Curve Analysis

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296      

The incidence of pelvic lymph node metastasis by FIGO staging for patients with adequately surgically staged endometrial adenocarcinoma of endometrioid histology

2008 ◽  
Vol 18 (2) ◽  
pp. 269-273 ◽  
Author(s):  
D. S. Chi ◽  
R. R. Barakat ◽  
M. J. Palayekar ◽  
D. A. Levine ◽  
Y. Sonoda ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Myometrial Invasion ◽  
Tumor Grade ◽  
Pelvic Lymph Node ◽  
Study Cohort ◽  
Node Metastasis ◽  
Figo Staging ◽  
Pelvic Lymph Node Metastasis

The seminal Gynecologic Oncology Group study on surgical pathologic spread patterns of endometrial cancer demonstrated the risk of pelvic lymph node metastasis for clinical stage I endometrial cancer based on tumor grade and thirds of myometrial invasion. However, the FIGO staging system assigns surgical stage by categorizing depth of myometrial invasion in halves. The objective of this study was to determine the incidence of pelvic lymph node metastasis in endometrial cancer based on tumor grade and myometrial invasion as per the current FIGO staging system. We reviewed the records of all patients who underwent primary surgical staging for clinical stage I endometrial cancer at our institution between May 1993 and November 2005. To make the study cohort as homogeneous as possible, we included only cases of endometrioid histology. We also included only patients who had adequate staging, which was defined as a total hysterectomy with removal of at least eight pelvic lymph nodes. During the study period, 1036 patients underwent primary surgery for endometrial cancer. The study cohort was composed of the 349 patients who met study inclusion criteria. Distribution of tumor grade was as follows: grade 1, 80 (23%); grade 2, 182 (52%); and grade 3, 87 (25%). Overall, 30 patients (9%) had pelvic lymph node metastasis. The incidence of pelvic lymph node metastasis in relation to tumor grade and depth of myometrial invasion (none, inner half, and outer half) was as follows: grade 1–0%, 0%, and 0%, respectively; grade 2–4%, 10%, and 17%, respectively; and grade 3–0%, 7%, and 28%, respectively. We determined the incidence of pelvic nodal metastasis in a large cohort of endometrial cancer patients of uniform histologic subtype in relation to tumor grade and a one-half myometrial invasion cutoff. These data are more applicable to current surgical practice than the previously described one-third myometrial invasion cutoff results.

Abstract P630: Detection of Atrial Fibrillation by Prolonged Cardiac Monitoring in Cryptogenic and Known Stroke Etiology

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Michal krawczyk ◽  
Sebastián Fridman ◽  
Maria Bres Bullrich ◽  
Palak Shah ◽  
Juan C Vargas-Gonzalez ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Cryptogenic Stroke ◽  
Left Atrial Volume ◽  
Cardiac Monitoring ◽  
Volume Index ◽  
Multivariate Logistic Regression Model ◽  
Stroke Patients ◽  
Cross Sectional ◽  
Therapeutic Decisions

Introduction: Approximately 25% of strokes are classified as cryptogenic (CS), while greater than 50% have an identifiable or ‘known’ etiology (KS). Several studies have demonstrated that prolonged cardiac monitoring (PCM) after cryptogenic stroke substantially increases the detection of atrial fibrillation (AF), but the yield of PCM in KS stroke is unknown. As a result, the majority of guidelines recommend restricting PCM to patients with cryptogenic stroke. If the detection of AF in KS is no different to cryptogenic stroke, it would suggest that this group too would similarly benefit from PCM, with the potential to impact therapeutic decisions (e.g. initiating anticoagulation). Methods: In a cross-sectional study, we compared AF detection by PCM (minimum of 48 hrs) between CS and KS patients without a previous diagnosis of AF. We developed a multivariate logistic regression model by including known and significant clinical, echocardiographic, and radiological factors known to be associated with the detection of AF. We reported results as odds ratios (OR) and 95% confidence intervals (95% CI). Results: We included 561 ischemic stroke patients, 376 with CS and 185 with KS. The median duration of PCM was 167h for CS and 48h for KS. AF was detected in 30 of 376 (8%) CS patients, and 20 of 185 (7.9%) KS patients. Age, history of thyroid disease, clinical presentation of dysarthria, wake-up stroke, and left atrial volume index on echocardiography were significantly associated with a new diagnosis of AF after stroke in the univariable analysis and were thus included in the logistic regression analysis. Additionally, duration of PCM was included in the multivariate model. After adjustment for potential confounders, AF detection by PCM was not significantly higher for CS than KS (OR 0.95, 95% CI 0.25-3.32, P=0.94). Conclusion: To the best of our knowledge this is the first study directly comparing the incidence of AF between CS and KS as the pre-specified primary outcome. Our findings suggest that CS and KS patients have similar rates of AF detection by PCM. Future prospective research is required to confirm these findings and to determine the cost-effectiveness of PCM in non-cryptogenic stroke patients.

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