Incomplete stent apposition of low-profile visualized intraluminal support stents in the treatment of cerebral aneurysms

ObjectiveThis study retrospectively analyzed the risk factors, management strategies, and complications of incomplete stent apposition (ISA) of low-profile visualized intraluminal support (LVIS) stents after initial deployment in the treatment of cerebral aneurysms.MethodsThe clinical characteristics of ISA or wall apposition (WA) of LVIS stent after initial deployment were analyzed. The risk factors of ISA were identified using univariate logistic regression analysis and multivariate logistic regression analysis. The clinical characteristics of ISA following different management strategies were also shown.ResultsThe retrospective study enrolled 303 patients with 315 LVIS stent-assisted aneurysms. Fifty-nine patients with 59 stents showed ISA after initial deployment. At the end of the study, the presence of ISA was only observed in eight patients (2.5%). The stent-subtended arc angle (>90) and the aneurysm of the internal carotid artery (ICA) were associated with ISA. The stent-subtended arc angle (>90) and stent size (4.5*20 mm) were independent risk factors of ISA. The incidence of thromboembolic events in the ISA group was significantly higher than that in the WA group. After the treatment of ISA, there was no significant difference in good outcomes between patients with ISA and those with WA after initial deployment.ConclusionsISA is more likely to occur at tortuous vessels. The stent-subtended arc angle (>90) and LVIS size (4.5*20 mm) were independent risk factors of ISA. ISA led to significantly increased incidence of thromboembolic events. However, ISA after initial deployment did not affect the patient's prognosis.

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Monocytes as an Early Predictor for Patients with Acute Paraquat Poisoning: A Retrospective Analysis

BioMed Research International ◽

10.1155/2019/6360459 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Yong Zhao ◽

Ya Qi Song ◽

Jie Gao ◽

Shun Yi Feng ◽

Yong Li

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Receiver Operator Characteristic ◽

Monocyte Count ◽

Central Hospital ◽

Predictive Values ◽

Kaplan Meier ◽

Independent Risk Factors ◽

Early Predictor

Background. The predictive values of monocytes in the prognosis of patients with acute paraquat (PQ) poisoning are unclear. This retrospective study investigated the predictive values of monocytes in the prognosis of patients with acute PQ poisoning. Methods. Adult patients who suffered from acute PQ poisoning in the emergency care unit of Cangzhou Central Hospital from May 2012 to December 2018 were enrolled. The patients were divided into groups, namely, survival and nonsurvival, according to a 90-day prognosis. Moreover, correlation, logistic regression, receiver-operator characteristic (ROC), and Kaplan–Meier curve analyses were applied to evaluate the monocyte values used to predict the prognosis of patients with acute PQ poisoning. Result. Among the 109 patients, 45 survived within 90 days after the poisoning, resulting in a 41.28% survival rate. The monocyte count of the nonsurvivors was significantly higher than that of the survivors (P< 0.001). Correlation analysis showed that monocyte count positively correlated with plasma PQ concentration (r= 0.413; P< 0.001) and negatively correlated with survival time (r= 0.512; P< 0.001) and 90-day survival (r= 0.503; P< 0.001). Logistic regression analysis showed that elevated monocytes were the independent risk factors for the 90-day survival. The area under the ROC curve of the monocyte count used to predict the 90-day survival was 0.826 (95% CI: 0.751–0.904), the optimal cut-off was 0.51×109/L, sensitivity was 73.4%, and specificity was 86.7%. Conclusion. This study demonstrated that elevated monocyte count is a useful early predictor of 90-day survival in patients with acute PQ poisoning. However, further studies are warranted to draw firm conclusions.

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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

10.2196/preprints.19588 ◽

2020 ◽

Author(s):

Tao Fan ◽

Bo Hao ◽

Shuo Yang ◽

Bo Shen ◽

Zhixin Huang ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Clinical Characteristics ◽

Cox Regression ◽

Disease Onset ◽

Lasso Regression ◽

Prediction Ability ◽

Calibration Curves ◽

Independent Risk Factors ◽

Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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Risk Factors for Silent Cerebral Infarcts in a Pediatric Sickle Cell Anemia (SCA) Cohort

Blood ◽

10.1182/blood.v112.11.2487.2487 ◽

2008 ◽

Vol 112 (11) ◽

pp. 2487-2487 ◽

Cited By ~ 1

Author(s):

Francoise Bernaudin ◽

Suzanne Verlhac ◽

Annie Kamdem ◽

Cécile Arnaud ◽

Lena Coïc ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Risk Factor ◽

Logistic Regression Analysis ◽

G6pd Deficiency ◽

Significant Risk ◽

Significant Risk Factor ◽

History Of ◽

Independent Risk Factors

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.

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Association of Biomarkers Related to Preoperative Inflammatory and Coagulation with Postoperative In-Hospital Deaths in Patients with Type A Acute Aortic Dissection

10.21203/rs.3.rs-261063/v1 ◽

2021 ◽

Author(s):

Ming Li ◽

Haifeng Sun ◽

Suochun Xu ◽

Yang Yan ◽

Haichen Wang ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Multivariate Logistic Regression Analysis ◽

Type A ◽

Hospital Deaths ◽

Independent Risk Factors ◽

D Dimer

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.

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Intramural Ectopic Pregnancy: Clinical Characteristics, Risk Factors for Uterine Rupture and Hysterectomy

Frontiers in Medicine ◽

10.3389/fmed.2021.769627 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiaoran Chen ◽

Lingyun Gao ◽

Hongna Yu ◽

Meijuan Liu ◽

Shujun Kong ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Ectopic Pregnancy ◽

Uterine Rupture ◽

Clinical Characteristics ◽

Treatment Options ◽

Management Strategies ◽

Treatment Strategies ◽

Expectant Management ◽

Preoperative Imaging

Background: Intramural ectopic pregnancy is defined as the gestational sac (GS) is entirely within the myometrium, separate from the endometrial cavity and fallopian tubes, which is unsustainable and potentially life-threatening. The data investigating the clinical characteristics, management strategy, and fertility outcomes after treatment of intramural ectopic pregnancies are very limited due to its extreme rarity.Methods: To investigate the clinical characteristics, treatment options, and fertility outcomes in patients with intramural ectopic pregnancy, a retrospective study included 56 patients was conducted. We also used logistic regression to identify potential risk factors for uterine rupture and hysterectomy in these patients.Results: The mean age of patients was 31.1 years, with an average gestational age (GA) of 10.0 weeks, and the majority of the patient cohort (83.9%) had uterine or endometrial surgical history. 55.4% of the intramural pregnancy was diagnosed by preoperative imaging examination and 67.7% was detected by ultrasound. There was no dominant predisposed zone of the GS. Common treatment strategies included laparotomy surgery (41.1%) and laparoscopic surgery (35.7%), followed by methotrexate (7.1%) and expectant management (5.4%). Uterine rupture occurred in 9 patients and 8 patients underwent a hysterectomy, but no maternal demise was found. Logistic regression showed that a GA >10 weeks predicted a significantly higher risk of uterine rupture (Odds ratio [OR] 8.000, 95% confidence interval [CI] 1.456–43.966, P = 0.017) and hysterectomy (OR 12.333, 95% CI 2.125–71.565, P = 0.005), and GS located in the fundus also predicted higher probability of uterine rupture (OR 7.000,95% CI 1.271–38.543, P = 0.025). Among the ten patients who had a desire for fertility, 6 of them succeeded and 4 of them successfully delivered with a GA ≥ 34 weeks.Conclusion: GA > 10 weeks was the risk factor for both uterine rupture and hysterectomy, while patients with GS located in the uterine fundus had a significantly higher risk of uterine rupture. The fertility outcomes were moderate after treatment. The management strategies should be individualized according to disease conditions and the desire for fertility, and early diagnosis is essential for optimizing clinical outcomes.

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A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

Frontiers in Pediatrics ◽

10.3389/fped.2021.756095 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jie Liu ◽

Jian Zhang ◽

Haodong Huang ◽

Yunting Wang ◽

Zuyue Zhang ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Retrospective Study ◽

Kawasaki Disease ◽

Prediction Model ◽

Prediction Models ◽

Ivig Resistance ◽

Independent Risk Factors

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.

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Nomogram for Predicting Persistent Organ Failure with Acute Pancreatitis in Pregnancy

10.21203/rs.3.rs-1223472/v1 ◽

2022 ◽

Author(s):

Chengcheng Sheng ◽

Zongxu Xu ◽

Jun Wang

Keyword(s):

Risk Factors ◽

Acute Pancreatitis ◽

Logistic Regression ◽

Regression Analysis ◽

Organ Failure ◽

Multivariate Logistic Regression Analysis ◽

Concordance Index ◽

Multivariate Logistic Regression ◽

Independent Risk Factors ◽

In Pregnancy

Abstract Background: Acute pancreatitis in pregnancy (APIP) with persistent organ failure (POF) poses a high risk of death for mother and fetus. This study sought to create a nomogram model for early prediction of POF with APIP patients.Methods: We conducted a cross-sectional study on APIP patients with organ failure (OF) between January 2012 and March 2021 in a university hospital. 131 patients were collected. Their clinical courses and pregnancy outcomes were obtained. Risk factors for POF were identified by univariate and multivariate logistic regression analysis. Prediction models with POF were built and nomogram was plotted. The performance of the nomogram was evaluated by using a bootstrapped-concordance index and calibration plots.Results: Hypertriglyceridemia was the most common etiology in this group of APIP patients, which accounted for 50% of transient organ failure (TOF) and 72.3% of POF. All in-hospital maternal death was in the POF group (P<0.05), which also had a significantly higher perinatal mortality rate than the TOF group (P<0.05). Univariate and multivariate logistic regression analysis determined that lactate dehydrogenase, triglycerides, serum creatinine, and procalcitonin were independent risk factors for predicting POF in APIP. A nomogram for POF was created by using the four indicators. The area under the curve was 0.875 (95% confidence interval 0.80–0.95). The nomogram had a bootstrapped-concordance index of 0.85 and was well-calibrated.Conclusions: Hypertriglyceridemia was the leading cause of organ failure-related APIP. Lactate dehydrogenase, triglycerides, serum creatinine, and procalcitonin were the independent risk factors of POF in APIP. Our nomogram model showed an effective prediction of POF with the four indicators in APIP patients.

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Diabetes and Younger Age Are Vital and Independent Risk Factors for Acute Pancreatitis in Patients with Severe Hypertriglyceridemia

BioMed Research International ◽

10.1155/2019/2620750 ◽

2019 ◽

Vol 2019 ◽

pp. 1-6

Author(s):

Qiang Li ◽

Chaoqun Hou ◽

Yunpeng Peng ◽

Xiaole Zhu ◽

Chenyuan Shi ◽

...

Keyword(s):

Risk Factors ◽

Acute Pancreatitis ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Multivariate Logistic Regression Analysis ◽

Severe Hypertriglyceridemia ◽

Younger Age ◽

History Of ◽

Independent Risk Factors

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.

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Increased pretreatment C-reactive protein-to-albumin ratio predicts severe coronavirus disease 2019

10.21203/rs.3.rs-31723/v2 ◽

2020 ◽

Author(s):

Xiaoyue Wang ◽

Yan Xu ◽

Huang Huang ◽

Desheng Jiang ◽

Chunlei Zhou ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Early Warning ◽

Warning Signs ◽

C Reactive Protein ◽

Albumin Ratio ◽

Reactive Protein ◽

Independent Risk Factors

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.

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