scholarly journals Implementation of Multivariate Logistic Regression Model for Cerebral Palsy Identification using Prenatal, Perinatal Risk Factors

2021 ◽  
Vol 1085 (1) ◽  
pp. 012015
Author(s):  
K. Muthureka ◽  
U. Srinivasulu Reddy ◽  
B. Janet
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Cerebral Palsy ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Perinatal Risk Factors ◽  
Perinatal Risk

scholarly journals 282. Risk Factors for Mortality in Severe COVID-19 Patients Admitted to the Intensive Care Unit: A Retrospective Single-Center Study in Saudi Arabia

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S246-S247
Author(s):  
Sherif Khattab ◽  
Souad AlMuthree ◽  
Mohamed Bakry ◽  
Noha Ibraheem ◽  
Omar Alghamdi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Logistic Regression ◽  
Regression Model ◽  
Sofa Score ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors ◽  
Cox Proportional Hazard Regression

Abstract Background The first case of COVID-19 in the Kingdom of Saudi Arabia (KSA) was reported in March 2020. This study aims to describe the overall mortality in the ICU during the COVID-19 pandemic and to determine independent risk factors for overall survival & 29 days mortality. Methods This is a retrospective single-center study; data for adult patients admitted to the ICU with COVID-19 between 1st March 2020 to 31st December 2020 were extracted and reviewed. Overall survival was described using Kaplan-Meier curves with reporting of median overall survival and 29 days survival estimates. Multivariate analysis was performed using Cox proportional hazards model and multivariate logistic regression model. Figure 1. Study flow chart Table 1. Demographic characteristics categorized by Gender Results Eligible subjects were 209 (Figure 1) and subjects demographics are summarized in (Table1). Observed death events were 82 (39.2% of the total cohort), 61% of deaths reached at 2 weeks of ICU stay (n.= 50), median overall survival (OS) was reached at day 23, as shown in (Figure 2). The multivariate Cox proportional hazard regression analysis (Figure 3) showed elevated SOFA score [aHR= 1.10, P < 0.001] and Vasopressors [aHR= 3.23, P= 0.002] as independent risk factors for overall ICU mortality. Independent protective factors were: Systemic corticosteroids use (P= 0.019), Insulin use (P= 0.026) and Liposomal Amphotericin B (LAMB) use (P= 0.019). For mortality at day 29, the multivariate logistic regression model (Figure. 4) showed elevated SOFA score (P= 0.005), any need for ventilation escalation after ICU admission (P= 0.014), Ribavirin use (P=0.016) and Vasopressors use ( P< 0.001) as independent risk factors. Angiotensin-Converting Enzyme inhibitors (ACEi) use was a protective factor (P=0.025). Figure 2. Overall Survival (OS) for patients admitted to the ICU due to COVID-19 - Kaplan Meier (KM) Figure 3. Multivariate Cox proportional hazard regression model for factors associated with overall mortality in patients admitted to the ICU due to COVID-19 Figure 4. Multivariate logistic regression model for factors associated with 29 days mortality in patients admitted to the ICU due to COVID-19 Conclusion SOFA score and vasopressors are independent predictors for overall survival and 29-day mortality in the ICU. The need for ventilation escalation after ICU admission appeared to lead to poor prognosis in regard to 29-day mortality only. Systemic corticosteroids are lifesaving, further studies are required to confirm the observed clinical benefits with insulin, LAMB and ACEi use in the ICU and to investigate any hazardous impact of ribavirin on COVID-19 outcomes. Study limitations Residual confounding of other measured and/or unobserved factors cannot be ruled out. Disclosures Sherif Khattab, BPharm, Gilead Sciences (Employee, Shareholder) Mohamed Bakry, MBBCh, Gilead Sciences (Employee)Roche Pharma (Employee)

scholarly journals Nomogram for Predicting the Postoperative Venous Thromboembolism in Spinal Metastasis Tumor: A Multicenter Retrospective Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao-ran Zhang ◽  
Ming-you Xu ◽  
Xiong-gang Yang ◽  
Feng Wang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

IntroductionVenous thromboembolism can be divided into deep vein thrombosis and pulmonary embolism. These diseases are a major factor affecting the clinical prognosis of patients and can lead to the death of these patients. Unfortunately, the literature on the risk factors of venous thromboembolism after surgery for spine metastatic bone lesions are rare, and no predictive model has been established.MethodsWe retrospectively analyzed 411 cancer patients who underwent metastatic spinal tumor surgery at our institution between 2009 and 2019. The outcome variable of the current study is venous thromboembolism that occurred within 90 days of surgery. In order to identify the risk factors for venous thromboembolism, a univariate logistic regression analysis was performed first, and then variables significant at the P value less than 0.2 were included in a multivariate logistic regression analysis. Finally, a nomogram model was established using the independent risk factors.ResultsIn the multivariate logistic regression model, four independent risk factors for venous thromboembolism were further screened out, including preoperative Frankel score (OR=2.68, 95% CI 1.78-4.04, P=0.001), blood transfusion (OR=3.11, 95% CI 1.61-6.02, P=0.041), Charlson comorbidity index (OR=2.01, 95% CI 1.27-3.17, P=0.013; OR=2.29, 95% CI 1.25-4.20, P=0.017), and operative time (OR=1.36, 95% CI 1.14-1.63, P=0.001). On the basis of the four independent influencing factors screened out by multivariate logistic regression model, a nomogram prediction model was established. Both training sample and validation sample showed that the predicted probability of the nomogram had a strong correlation with the actual situation.ConclusionThe prediction model for postoperative VTE developed by our team provides clinicians with a simple method that can be used to calculate the VTE risk of patients at the bedside, and can help clinicians make evidence-based judgments on when to use intervention measures. In clinical practice, the simplicity of this predictive model has great practical value.

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 Maternal age as a risk factor for cerebral palsy

2021 ◽  
Vol 84 (2) ◽  
pp. 117-131
Author(s):  
Marta Sternal ◽  
Barbara Kwiatkowska ◽  
Krzysztof Borysławski ◽  
Agnieszka Tomaszewska
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Cerebral Palsy ◽  
Regression Model ◽  
Maternal Age ◽  
Logistic Regression Model ◽  
Significant Risk ◽  
Control Group ◽  
Logistic Regression Models ◽  
Pregnancy And Childbirth

Abstract The relationship between maternal age and the occurrence of cerebral palsy is still highly controversial. The aim of the study was to examine the effect of maternal age on the risk of CP development, taking into account all significant risk factors and the division into single, twin, full-term, and pre-term pregnancies. The survey covered 278 children with CP attending selected educational institutions in Poland. The control group consisted of data collected from the medical records of 435 children born at Limanowa county hospital, Poland. The analyses included socio-economic factors, factors related to pregnancy and childbirth, and factors related to the presence of comorbidities and diseases in the child. Constructed logistic regression models were used for statistical analyses. For all age categories included in the estimated models (assessing the effect of demographic factors on the development of CP), only the category of ≤24 years of age (in the group of all children) was significant. It was estimated that in this mother’s age category, the risk of CP is lower (OR 0.6, 95% CI: 0.3–1.0) in comparison to mothers aged 25-29 (p = 0.03). However, estimation with the use of a complex logistic regression model did not show any significant effect of maternal age on the incidence of CP in groups from different pregnancies types. It became apparent that maternal age is a weak predictor of CP, insignificant in the final logistic regression model. It seems correct to assume that the studies conducted so far, showing a significant effect of maternal age in this respect, may be associated with bias in the estimators used to assess the risk of CP due to the fact that other important risk factors for CP development were not included in the research.

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