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

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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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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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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P05.69 Independent risk factors for pre- and postoperative seizures in meningioma patients identified by logistic regression analysis

Neuro-Oncology ◽

10.1093/neuonc/noy139.395 ◽

2018 ◽

Vol 20 (suppl_3) ◽

pp. iii319-iii319

Author(s):

L Goertz ◽

C Hamisch ◽

N Erdner ◽

H Muders ◽

R Goldbrunner ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Independent Risk Factors

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Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

Frontiers in Medicine ◽

10.3389/fmed.2021.719906 ◽

2021 ◽

Vol 8 ◽

Author(s):

Zhaoli Meng ◽

Wei Fang ◽

Mei Meng ◽

Jicheng Zhang ◽

Qizhi Wang ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Maternal Mortality ◽

Predictive Models ◽

Multivariate Logistic Regression Analysis ◽

Fetal Mortality ◽

Multivariate Logistic Regression ◽

Acute Fatty Liver ◽

Independent Risk Factors

Acute fatty liver of pregnancy (AFLP) is a rare but potentially life-threatening hepatic disorder that leads to considerable maternal and fetal mortality. To explore the risk factors for maternal and fetal mortality in AFLP and develop new predictive models, through this retrospective study, we analyzed the demographic characteristics, clinical symptoms, and laboratory findings of 106 patients with AFLP who were admitted to Shandong Provincial Hospital. Risk factors for maternal and fetal mortality were analyzed by univariate and multivariate logistic regression analysis. The new models based on the multivariate logistic regression analysis and the model for end-stage liver disease (MELD) were tested in AFLP. The receiver operating characteristic curve (ROC) was applied to compare the predictive efficiency, sensitivity, and specificity of the two models. Prenatal nausea (p = 0.037), prolonged prothrombin time (p = 0.003), and elevated serum creatinine (p = 0.003) were independent risk factors for maternal mortality. The ROC curve showed that the area under the curve (AUC) of the MELD was 0.948, with a sensitivity of 100% and a specificity of 83.3%. The AUC of the new model for maternal mortality was 0.926, with a sensitivity of 90% and a specificity of 94.8%. Hepatic encephalopathy (p = 0.016) and thrombocytopenia (p = 0.001) were independent risk factors for fetal mortality. Using the ROC curve, the AUC of the MELD was 0.694, yielding a sensitivity of 68.8% and a specificity of 64.4%. The AUC of the new model for fetal mortality was 0.893, yielding a sensitivity of 100% and a specificity of 73.3%. Both the new predictive model for maternal mortality and the MELD showed good predictive efficacy for maternal mortality in patients with AFLP (AUC = 0.926 and 0.948, respectively), and the new predictive model for fetal mortality was superior to the MELD in predicting fetal mortality (AUC = 0.893 and 0.694, respectively). The two new predictive models were more readily available, less expensive, and easier to implement clinically, especially in low-income countries.

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Re-evaluating the necessity of routine laboratory tests after high tibial osteotomy surgery

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04608-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Hong-bo Li ◽

Si Nie ◽

Min Lan ◽

Xin-gen Liao ◽

Zhi-ming Tang

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

High Tibial Osteotomy ◽

Logistic Regression Analysis ◽

Laboratory Tests ◽

Clinical Treatment ◽

The Body ◽

Tibial Osteotomy ◽

Independent Risk Factors

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.

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Risk factors for composite adverse outcomes of postpartum haemorrhage, Mpilo Central Hospital, Bulawayo, Zimbabwe

F1000Research ◽

10.12688/f1000research.22769.2 ◽

2020 ◽

Vol 9 ◽

pp. 211

Author(s):

Solwayo Ngwenya

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Caesarean Section ◽

Blood Loss ◽

Haemoglobin Level ◽

Postpartum Haemorrhage ◽

Adverse Outcomes ◽

Central Hospital ◽

Independent Risk Factors ◽

Mpilo Central Hospital

Background Globally, primary postpartum haemorrhage continues to cause considerable maternal morbidity and mortality. The aim of this study was to determine the risk factors for composite adverse outcomes of postpartum haemorrhage. The findings could potentially be used to anticipate and prevent composite adverse outcomes of postpartum haemorrhage. Methods This was a retrospective cross-sectional study carried out at Mpilo Central Hospital, a government tertiary referral centre, covering the period 1 July 2016 to 30 November 2019. Participants were included in the study if they had a diagnosis of postpartum haemorrhage. Those variables that had a p<0.2 from the univariate logistic regression analyses were considered for multivariable logistic regression. The association between independent variables and the dependent variable was assessed using odds ratio with 95% confidence intervals, to identify independent risk factors for composite adverse outcomes in PPH. A p< 0.05 was taken as statistically significant. Results The independent risk factors for composite adverse outcomes of postpartum haemorrhage were place of dwelling (AOR 4.57, 95% CI 1.87-11.12, p=0.01), prior Caesarean section (AOR 2.57, 95% CI 1.10-6.00, p=0.03), antepartum haemorrhage (AOR 5.45, 95% CI 2.23-13.27, p<0.0001), antenatal haemoglobin level (AOR 19.64, 95% CI 1.44-268.50, p=0.03), and current delivery by Caesarean section (AOR 10.21, 95% CI 4.39-23.74, p<0.0001). Blood loss was also an independent risk factor for composite adverse outcomes of postpartum haemorrhage with the following blood loss; 1001-1500ml (AOR 9.94, 95% CI 3.68-26.88, p<0.0001), 500-1000ml (AOR 41.27, 95% CI 11.32-150.54, p<0.0001), and 2001ml (AOR 164.77, 95% CI 31.06-874.25, p<0.0001). Conclusion This study found that the independent predictors for composite adverse outcomes of PPH were rural dwelling, prior Caesarean section, antenatal haemoglobin level, current delivery by Caesarean section, and blood loss. In low- and middle-income countries such information could help in increasing clinical vigilance and policy making, and preventing maternal deaths.

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