Analysis of Independent Risk Factors and Establishment of Diagnostic Prediction Model for Perineural Invasion in Gastric Cancer Based on Logistic Regression Analysis

2021 ◽  
Vol 11 (07) ◽  
pp. 2951-2960
Author(s):  
恺鹏 李
Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2487-2487 ◽  
Author(s):  
Francoise Bernaudin ◽  
Suzanne Verlhac ◽  
Annie Kamdem ◽  
Cécile Arnaud ◽  
Lena Coïc ◽  
...  

Abstract Background Silent infarcts are associated with impaired cognitive functioning and have been shown to be predictors of stroke (Miller ST J Pediatr 2001). Until now, reported risk factors for silent infarcts were low pain event rate, history of seizures, high leukocyte count and Sen bS haplotype (Kinney TR Pediatrics 1999). Here, we seek to define the prevalence and risk factors of silent infarcts in the Créteil SCA pediatric cohort comprising patients assessed at least yearly by transcranial doppler (TCD) since 1992, and by MRI/MRA. Methods This study retrospectively analyzed data from the Créteil cohort stroke-free SS/Sb0 children (280; 134 F, 146 M), according to institutional review board. Time-averaged mean of maximum velocities higher than 200 cm/sec were considered as abnormal, resulting in initiation of a transfusion program (TP). A switch to hydroxyurea was proposed to patients with normalized velocities (< 170 cm/sec) and normal MRA on TP, although TP was re-initiated in case of abnormal velocities recurrence. Patients with “conditional” velocities (170–199 cm/sec) were assessed by TCD 4 times yearly. Alpha genes and beta-globin haplotypes were determined. Baseline biological parameters (G6PD activity; WBC, PMN, Reticulocytes, Platelets counts; Hemoglobin, Hematocrit, HbF, LDH levels; MCV; SpO2) were obtained a minimum of 3 months away from a transfusion, one month from a painful episode, after 12 months of age, before the first TCD, and always before therapy intensification. Results. Patients were followed for a total of 2139 patient-years. Alpha-Thal was present in 114/254 patients (45%) and 27/241 (11.2%) had G6PD deficiency. Beta genotype, available in 240 patients, was BaBa in 102 (42.5%), BeBe in 54 (22.5%), SeSe in 19 (7.9%) and “other” in 65 (27.1%); TCD was abnormal in 52 of 280 patients (18.6%). MRA showed stenoses in 30 of 226 evaluated patients (13.3%) while MRI demonstrated presence of silent infarcts in 81/280 patients (28.9%). Abnormal TCD (p<0.001), G6PD deficiency (p=0.008), high LDH (p=0.03), and low Hb (p=0.026) were significant risk factors for stenoses by univariate analysis while multivariate analysis retained only abnormal TCD as a significant risk factor for stenoses ([OR= 10.6, 95% CI (4.6–24.4)]; p<0.001). Univariate logistic regression analysis showed that the risk of silent infarcts was not related to alpha-Thal, beta genotype, abnormal TCD, WBC, PMN, platelets, reticulocyte counts, MCV, LDH level, HbF %, pain or ACS rates but was significantly associated with stenoses detected by MRA (p<0.001), gender (male; p=0.04), G6PD deficiency (p=0.05), low Hb (p=0.016) and Hct (p=0.012). Multivariate logistic regression analysis showed that gender ([OR= 2.1, 95% CI (1.03–4.27)]; p=0.042), low Hb ([OR= 1.4, 95% CI (1.0–1.1)]; p=0.05) and stenoses ([OR= 4.8, 95% CI (1.88–12.28)]; p=0.001) were all significant independent risk factors for silent infarcts. The presence of stenoses was the only significant risk factor for silent infarcts in patients with a history of abnormal TCD ([OR= 5.9, 95% CI (1.6–21.7)]; p=0.008). Conclusion We recently showed that G6PD deficiency, absence of alpha-Thal, and hemolysis are independent significant risk factors for abnormal TCD in stroke-free SCA patients (Bernaudin et al, Blood, 2008, in press). Here, we report that an abnormal TCD is the most significant risk factor for stenoses and, expanding previous studies, we demonstrate that stenoses, low Hb and gender are significant independent risk factors for silent infarcts.


2021 ◽  
Author(s):  
Ming Li ◽  
Haifeng Sun ◽  
Suochun Xu ◽  
Yang Yan ◽  
Haichen Wang ◽  
...  

Abstract Background: The aim of this study was to analyze the predictive value of biomarkers related to preoperative inflammatory and coagulation in the prognosis of patients with type A acute aortic dissection (AAD). Methods: A total of 206 patients with type A AAD who had received surgical treatment were enrolled. Patients were divided into two groups according to whether they died during hospitalization. Peripheral blood samples were collected before anesthesia induction. Preoperative levels of D-dimer, fibrinogen (FIB), platelet (PLT), white blood cells (WBC) and neutrophil (NEU) between the two groups were compared. Univariate and multivariate logistic regression analysis were utilized to identify the independent risk factors for postoperative in-hospital deaths of patients with type A AAD. Receiver operating characteristic (ROC) curve were used to analyze the predictive value of D-dimer, FIB, PLT, WBC, NEU and CRP in the prognosis of the patients. Results: Univariate logistic regression analysis showed that the P values of the five parameters including D-dimer, FIB, PLT, WBC and NEU were all less than 0.1, which may be risk factors for postoperative in-hospital deaths of patients with type A AAD. Further multivariate logistic regression analysis indicated that higher preoperative D-dimer and WBC levels were independent risk factors for in-hospital deaths of patients with type A AAD. ROC curve analysis indicated that FIB+PLT combination is provided with the highest predictive value for in-hospital deaths.Conclusion: Both preoperative D-dimer and WBC in patients with type A AAD may be used as independent risk factors for the prognosis of such patients. Combined use of FIB and PLT may improve the accuracy and accessibility of clinical prognostic assessment.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichuang Lian ◽  
Yafang Li ◽  
Wenyi Wang ◽  
Wei Ding ◽  
Zongxin Niu ◽  
...  

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.


2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liu ◽  
Jian Zhang ◽  
Haodong Huang ◽  
Yunting Wang ◽  
Zuyue Zhang ◽  
...  

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.


Author(s):  
Masaru Samura ◽  
Naoki Hirose ◽  
Takenori Kurata ◽  
Keisuke Takada ◽  
Fumio Nagumo ◽  
...  

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%–<25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.


2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Qiang Li ◽  
Chaoqun Hou ◽  
Yunpeng Peng ◽  
Xiaole Zhu ◽  
Chenyuan Shi ◽  
...  

Background. The incidence of hypertriglyceridemia-induced acute pancreatitis (HIAP) is increasing worldwide, and now it is the third leading cause of acute pancreatitis in the United States. But, there are only 5% of patients with severe hypertriglyceridemia (>1000 mg/dl) which might generate acute pancreatitis. In order to explore which part of the patients is easy to develop into pancreatitis, a case-control study was performed by us to consider which patient population tend to develop acute pancreatitis in patients with severe hypertriglyceridemia. To perform a retrospective case-control study, we identified severe hypertriglyceridemia patients without AP (HNAP) and with HIAP with a fasting triglyceride level of >1000 mg/dl from The First Affiliated Hospital of Nanjing Medical University during January 1, 2014, to December 31, 2016. Baseline patient characteristics, comorbidities, and risk factors were recorded and evaluated by the univariate and multivariate logistic regression analysis for HIAP and HNAP patients. A total of 124 patients with severe hypertriglyceridemia were included in this study; of which, 62 patients were in the HIAP group and 62 were in the HNAP group. Univariate logistic regression analysis showed that there was no gender difference in both groups; however, there were more younger patients in the HIAP group than in the HNAP group (P value < 0.001), and the HIAP group had low level of high-density lipoprotein compared to the HNAP group (P<0.05). Meanwhile, the presence of pancreatitis was associated with higher level of glycemia and a history of diabetes (P<0.05). Multivariate logistic regression analysis indicated that a history of diabetes and younger age were independent risk factors for acute pancreatitis in patients with severe hypertriglyceridemia. Uncontrolled diabetes and younger age are potential risk factors in patients with severe hypertriglyceridemia to develop acute pancreatitis.


2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Yan Xu ◽  
Huang Huang ◽  
Desheng Jiang ◽  
Chunlei Zhou ◽  
...  

Abstract Objective The aim of this study was to identify early warning signs for severe coronavirus disease 2019 (COVID-19). Methods We retrospectively analysed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center, comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values were determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factors for severe COVID-19. Results The patients with mild and severe COVID-19 had significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) ( P =0.000; P =0.008; P=0.000; P =0.000). The severity of COVID-19 was positively correlated with comorbid cancer, age, NLR, and CAR ( P <0.005). Multivariate logistic regression analysis showed that age, the NLR and the CAR were independent risk factors for severe COVID-19 (OR=1.086, P =0.008; OR=1.512, P =0.007; OR=17.652, P =0.001). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.


2018 ◽  
Vol 20 (suppl_3) ◽  
pp. iii319-iii319
Author(s):  
L Goertz ◽  
C Hamisch ◽  
N Erdner ◽  
H Muders ◽  
R Goldbrunner ◽  
...  

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hong-bo Li ◽  
Si Nie ◽  
Min Lan ◽  
Xin-gen Liao ◽  
Zhi-ming Tang

Abstract Background To assess the utility of routine postoperative laboratory tests for patients undergoing high tibial osteotomy (HTO) surgery. Methods The associations between clinical risk factors and postoperative clinical treatment were analyzed. Additionally, a logistic regression analysis was performed to detect independent risk factors for patients requiring postoperative clinical treatment. Results A total of 482 patients with symptomatic isolated medial compartment osteoarthritis from January 2015 to May 2020 were included in the present study and underwent examination by the full set of postoperative laboratory tests within 3 days after HTO surgery. However, only a small proportion of the patients with anemia (3.9 %), hypoalbuminemia (4.1 %), and abnormal serum potassium levels (3.5 %) required clinical intervention after surgery. Binary logistic regression analysis showed that the body mass index (BMI), preoperative hemoglobin level, estimated blood loss and operative duration were independent risk factors for postoperative blood transfusion in patients who underwent HTO surgery, and factors associated with albumin supplementation were female sex and preoperative albumin level. In addition, these results indicated that preoperative potassium was potential risk factor for patients who required potassium supplementation postoperatively. Conclusions Based on the analysis, we conclude that routinely ordering postoperative laboratory tests after HTO surgery is unnecessary. However, for patients with identified risk factors, routine postoperative laboratory tests are still needed.


2021 ◽  
Author(s):  
Huifeng Wang ◽  
Zhiling Zhao ◽  
Zhao-hui Tong

Abstract Background: To investigate the independent risk factors for sepsis and the prognostic indicators of sepsis-related mortality to guide clinical practice.Methods: Adult patients diagnosed with sepsis in the respiratory intensive care unit (RICU), emergency ICU (EICU), and surgical ICU (SICU) of Beijing Chao-Yang Hospital, Capital Medical University, from January 2016 to April 2021 were enrolled. Comorbidities, complications, and laboratory indicators were retrospectively analyzed. Variables with a p value < 0.05 in the univariate analysis were entered into multivariate logistic regression analysis to identify the independent risk factors for sepsis. Receiver operating characteristic curve (ROC) analysis was used for those variables with P < 0.05 in multivariate regression to evaluate the fit of the predictive model and its prognostic efficacy. Results: A total of 123 adult patients with sepsis were enrolled, with 80 males and 43 females and a mean age of 61.56 ± 17.12 years. Acute respiratory distress syndrome (ARDS) occurred in 84 patients (68.3%), acute kidney injury (AKI) occurred in 28 patients (22.8%), acute myocardial injury (AMI) occurred in 6 patients (4.9%), disseminated intravascular coagulation (DIC) occurred in 14 patients (11.4%), septic shock occurred in 40 patients (32.5%), and 41 patients (33.3%) died. Multivariate logistic regression analysis showed that mean arterial pressure (MAP), acute physiology and chronic health evaluation II (APACHE II) score, albumin level, and the presence of DIC were independent risk factors for sepsis (P < 0.05). The area under the ROC curve for the model including MAP, albumin, and APACHE II score was the highest at 0.890.Conclusion: The MAP, APACHE II score, albumin level, and DIC were independent risk factors for sepsis. The inclusion of the MAP, albumin level, and APACHE II score in the model yielded the most accurate prediction of the risk of mortality.


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