Sex-based differences in outcomes for adult patients presenting to the emergency department with a concussion

2021 ◽  
pp. 1-10
Author(s):  
Brian H. Rowe ◽  
Esther H. Yang ◽  
Lindsay A. Gaudet ◽  
Leeor Eliyahu ◽  
Daniela R. Junqueira ◽  
...  
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Motor Vehicle ◽  
Adult Population ◽  
Treatment Options ◽  
Multivariate Logistic Regression Model ◽  
Adult Women ◽  
Multivariate Logistic Regression ◽  
Persistent Symptoms ◽  
Postconcussion Symptoms

OBJECTIVE Patients with concussion frequently present to the emergency department (ED). Studies of athletes and children indicate that concussion symptoms are often more severe and prolonged in females compared with males. Given infrequent study of concussion symptoms in the general adult population, the authors conducted a sex-based comparison of patients with concussion. METHODS Adults (≥ 17 years of age) presenting with concussion to one of three urban Canadian EDs were recruited. Discharged patients were contacted by telephone 30 and 90 days later to capture the extent of persistent postconcussion symptoms using the Rivermead Post Concussion Symptoms Questionnaire (RPQ). A multivariate logistic regression model for persistent symptoms that included biological sex was developed. RESULTS Overall, 250 patients were included; 131 (52%) were women, and the median age of women was significantly higher than that of men (40 vs 32 years). Women had higher RPQ scores at baseline (p < 0.001) and the 30-day follow-up (p = 0.001); this difference resolved by 90 days. The multivariate logistic regression identified that women, patients having a history of sleep disorder, and those presenting to the ED with concussions after a motor vehicle collision were more likely to experience persistent symptoms. CONCLUSIONS In a community concussion sample, inconsequential demographic differences existed between adult women and men on ED presentation. Based on self-reported and objective outcomes, work and daily activities may be more affected by concussion and persistent postconcussion symptoms for women than men. Further analysis of these differences is required to identify different treatment options and ensure adequate care and management of injury.

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.

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.

Identification of characteristics associated with long-term survival in patients with metastatic urothelial carcinoma (mUC) who received durvalumab (D) with or without tremelimumab (T) in clinical studies.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 441-441
Author(s):  
Marie Alt ◽  
Carlos Stecca ◽  
Shaum Kabadi ◽  
Benga Kazeem ◽  
Srikala S. Sridhar
Keyword(s):  
Logistic Regression ◽  
Phase I ◽  
Regression Model ◽  
Clinical Studies ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Term Survival ◽  
Long Term Survival

441 Background: Immune checkpoint inhibitors (ICI) have changed the landscape of mUC, yet outcomes are variable as some patients (pts) do not respond to treatment while others have a durable response. To optimally select pts who may derive benefit from ICIs, predictive factors are required. This retrospective, post-hoc analysis evaluated pt characteristics to determine differences between short and long-term survivors among pts with mUC who received D (anti–PD-L1) with or without T (anti–CTLA-4) in two clinical studies. Methods: Pts with platinum-refractory mUC who received D monotherapy in the phase I/II study 1108 (10 mg/kg Q2W, up to 12 mo) or D+T in the phase I study 10 (D at 20 mg/kg + T at 1 mg/kg Q4W for 4 mo, then D at 10 mg/kg Q2W for 12 mo) were included. Pt characteristics, tumor characteristics, radiological assessments, and biological assessments were collected. The primary outcome measure was long-term overall survival (OS). Pts were categorized as OS ≥2 yrs (from 1st dose of study drug) or OS <2 yrs. A univariate analysis was conducted on each baseline characteristic to assess independent associations with long-term OS; a multivariate logistic regression model was employed including each variable with a p-value ≤0.1 as factors or covariates. Results: A total of 367 pts with mUC were included in the analysis: 88 (24.0%) had OS ≥2 yrs (range: 2.09–4.99) and 279 (76.0%) had OS <2 yrs (range: 0.03–1.98). Pts with OS ≥2 yrs had a significantly higher objective response rates than those with OS <2 yrs (71.6% vs 5.7%; p<0.0001) and a significantly longer duration of response (median 2.3 yrs vs 0.39 yrs; p<0.0001). The characteristics included in the multivariate logistic regression model are listed in the Table. Long-term OS was significantly associated with ECOG PS, PD-L1 status, baseline hemoglobin level, and baseline absolute neutrophils count. Conclusions: Our analyses show that several characteristics, including tumor response to treatment, are associated with long-term OS for pts with mUC treated with D or D+T. Further investigation into these and other characteristics may provide additional insights into long-term survival outcomes with ICIs. [Table: see text]

scholarly journals A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study

2020 ◽  
Vol 8 (2) ◽  
pp. e001314
Author(s):  
Chao Liu ◽  
Li Li ◽  
Kehan Song ◽  
Zhi-Ying Zhan ◽  
Yi Yao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Solid Tumors ◽  
Neutrophil Count ◽  
Mortality Risk ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Lemeshow Test ◽  
Wbc Count

BackgroundIndividualized prediction of mortality risk can inform the treatment strategy for patients with COVID-19 and solid tumors and potentially improve patient outcomes. We aimed to develop a nomogram for predicting in-hospital mortality of patients with COVID-19 with solid tumors.MethodsWe enrolled patients with COVID-19 with solid tumors admitted to 32 hospitals in China between December 17, 2020, and March 18, 2020. A multivariate logistic regression model was constructed via stepwise regression analysis, and a nomogram was subsequently developed based on the fitted multivariate logistic regression model. Discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve (AUC) for the model and by bootstrap resampling, a Hosmer-Lemeshow test, and visual inspection of the calibration curve.ResultsThere were 216 patients with COVID-19 with solid tumors included in the present study, of whom 37 (17%) died and the other 179 all recovered from COVID-19 and were discharged. The median age of the enrolled patients was 63.0 years and 113 (52.3%) were men. Multivariate logistic regression revealed that increasing age (OR=1.08, 95% CI 1.00 to 1.16), receipt of antitumor treatment within 3 months before COVID-19 (OR=28.65, 95% CI 3.54 to 231.97), peripheral white blood cell (WBC) count ≥6.93 ×109/L (OR=14.52, 95% CI 2.45 to 86.14), derived neutrophil-to-lymphocyte ratio (dNLR; neutrophil count/(WBC count minus neutrophil count)) ≥4.19 (OR=18.99, 95% CI 3.58 to 100.65), and dyspnea on admission (OR=20.38, 95% CI 3.55 to 117.02) were associated with elevated mortality risk. The performance of the established nomogram was satisfactory, with an AUC of 0.953 (95% CI 0.908 to 0.997) for the model, non-significant findings on the Hosmer-Lemeshow test, and rough agreement between predicted and observed probabilities as suggested in calibration curves. The sensitivity and specificity of the model were 86.4% and 92.5%.ConclusionIncreasing age, receipt of antitumor treatment within 3 months before COVID-19 diagnosis, elevated WBC count and dNLR, and having dyspnea on admission were independent risk factors for mortality among patients with COVID-19 and solid tumors. The nomogram based on these factors accurately predicted mortality risk for individual patients.

Does radiation therapy increase late cardiac death in patients with left breast cancer?

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 1127-1127
Author(s):  
Chi Lin ◽  
Kyle A. Denniston ◽  
Mary E. Charlton
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Logistic Regression ◽  
Radiation Therapy ◽  
Cardiac Death ◽  
Lower Risk ◽  
Logistic Regression Model ◽  
Left Breast ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression

1127 Background: The objective of this study was to evaluate the effect of external beam radiation therapy (RT) on late cardiac death (CD) in patients with left breast cancer. Methods: A total of 529,246 patients who were diagnosed with adenocarcinoma of the breast between 1983 and 2004 and survived ≥ 5 years were identified from the SEER database. After excluding patients who were male, had right breast cancer, received brachytherapy or had missing data, 163,894 patients remained. Examined risk factors for CD include age (≤49/50-59/60-69/70-100), race (white/non-white), stage (In situ/local/regional/distant), breast subsite (nipple and areola/inner quadrant/outer quadrant), diagnosis year (1983-1993/1994-2004), surgery status (none/less than mastectomy/mastectomy) and RT. Time to CD was evaluated using the Kaplan-Meier method. A multivariate logistic regression model was used to evaluate factors associated with the use of RT and the Cox Proportional Hazards model was used to evaluate risk factors for CD. Results: A multivariate logistic regression model revealed that patients who received RT tended to be younger, white, more recently diagnosed, have inner quadrant and more advanced disease and undergo less than mastectomy. Median overall survival for patients with RT was significantly longer than those without RT (263 vs. 226 months, Log-Rank p < .0001). RT group had a lower risk of CD than no-RT group (Log-Rank p < .0001). Median time to CD was not reached in either group. The probability of CD was increased with increasing age and stage, and decreased with more recent diagnosis year and after mastectomy. Cox model found RT to be associated with lower probability of CD (HR 0.66, 95% CI 0.62-0.70), after adjusting for age, stage, surgery status and diagnosis year. Race and breast subsite were not associated with CD. Conclusions: Patients with left breast cancer who survived ≥ 5 years and received RT had a lower risk of cardiac death than those who did not. The cause of this difference is unclear but suggests influence from an uninvestigated factor, potentially the increased use of cardiotoxic chemotherapy or other cardiovascular comorbidity in those patients not receiving RT. Continued study, accounting for such factors, is warranted.

scholarly journals Early Prediction of the Carbapenem Resistance Gram-negative Bacteria Carriage in Intensive Care Unit using Machine-Learning

2020 ◽  
Author(s):  
Qiqiang Liang ◽  
Qinyu Zhao ◽  
Xin Xu ◽  
Yu Zhou ◽  
Man Huang
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Carbapenem Resistance ◽  
Multivariate Logistic Regression Model ◽  
Gram Negative Bacteria ◽  
Multivariate Logistic Regression ◽  
Gram Negative ◽  
Better Than

Abstract Background The prevention and control of carbapenem-resistance gram-negative bacteria (CR-GNB) is the difficulty and focus for clinicians in the intensive care unit (ICU). This study construct a CR-GNB carriage prediction model in order to predict the CR-GNB incidence in one week. Methods The database is comprised of nearly 10,000 patients. the model is constructed by the multivariate logistic regression model and three machine learning algorithms. Then we choose the optimal model and verify the accuracy by daily predicted and recorded the occurrence of CR-GNB of all patients admitted for 4 months. Results There are 1385 patients with positive CR-GNB cultures and 1535 negative patients in this study. Forty-five variables have statistical significant differences. We include the 17 variables in the multivariate logistic regression model and build three machine learning models for all variables. In terms of accuracy and the area under the receiver operating characteristic (AUROC) curve, the random forest is better than XGBoost and multivariate logistic regression model, and better than decision tree model (accuracy: 84% >82%>81%>72%), (AUROC: 0.9089 > 0.8947 ≈ 0.8987 > 0.7845). In the 4-month prospective study, 81 cases were predicted to be positive in CR-GNB culture within 7 days, 146 cases were predicted to be negative, 86 cases were positive, and 120 cases were negative, with an overall accuracy of 84% and AUROC of 91.98%. Conclusions Prediction models by machine learning can predict the occurrence of CR-GNB colonization or infection within a week period, and can real-time predict and guide medical staff to identify high-risk groups more accurately.

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