Which Factors Are Associated with Positive Resection Margin in Pancreatic Ductal Adenocarcinoma for different surgical procedures? Results Based on a Standard Pathological Evaluation System-Retrospective Cohort Study

Abstract Background/Objectives: The present study identified the independent risk factors of R1 resection in pancreaticoduodenectomy (PD) and distal pancreatosplenectomy (DP) for patients with pancreatic ductal adenocarcinoma (PDAC).Methods：Consecutive patients who were operated from 1st December 2017 to 30th December 2018 with curative intent were analyzed retrospectively. A standardized pathological examination with digital whole-mount slide images (DWMSIs) was utilized for the resection margin status. R1 was defined as microscopic tumor infiltration within 1 mm to the resection margin. The potential risk factors of R1 resection for PD and DP were analyzed separately by univariate and multivariate logistic regression analyses. Results：For the 240 patients who underwent PD, and the 146 patients who underwent DP, the R1 resection rates were 30.8% and 35.6 %, respectively. Univariate analysis on risk factors of R1 resection for PD were tumor location, absence of tumor necrosis, N staging, and TNM staging; while those for DP were perineural invasion, T staging, and TNM staging. Multivariate logistic regression analysis showed the location of tumor in the neck and uncinate process, and N1/2 staging were independent risk factors of R1 resection for PD; while those for DP were T3/4 staging. Conclusions：The clarification of the risk factors of R1 resection might clearly make surgeons take better decisions on surgical strategies for different surgical precedures in patients with PDAC.

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Which Factors Are Associated With Positive Resection Margin in Pancreatic Ductal Adenocarcinoma for Different Surgical Procedures? Results Based on a Standard Pathological Evaluation System-retrospective Cohort Study

10.21203/rs.3.rs-111309/v1 ◽

2020 ◽

Author(s):

Bo Li ◽

Shiwei Guo ◽

Xiaohan Shi ◽

Chenming Ni ◽

Suizhi Gao ◽

...

Keyword(s):

Risk Factors ◽

Resection Margin ◽

Tumor Location ◽

Tnm Staging ◽

Uncinate Process ◽

Ductal Adenocarcinoma ◽

Multivariate Logistic Regression ◽

R1 Resection ◽

Independent Risk Factors ◽

N Staging

Abstract Background/Objectives: The present study identified the independent risk factors of R1 resection in pancreaticoduodenectomy (PD) and distal pancreatosplenectomy (DP) for patients with pancreatic ductal adenocarcinoma (PDAC).Methods：Consecutive patients who were operated from December 2017 to 2018 with curative intent were analyzed retrospectively. A standardized pathological examination with digital whole-mount slide images (DWMSIs) was utilized for the resection margin status. R1 was defined as microscopic tumor infiltration within 1 mm to the resection margin. The potential risk factors of R1 resection for PD and DP were analyzed separately by univariate and multivariate logistic regression analyses. Results：For the 240 patients who underwent PD, and the 142 patients who underwent DP, the R1 resection rates were 30.8% and 35.6 %, respectively. Univariate analysis on risk factors of R1 resection for PD were tumor location, absence of tumor necrosis, N staging, TNM staging, and surgical type of PD; while those for DP were nerve invasion, T staging, and TNM staging. Multivariate logistic regression analysis showed that the risk factors of R1 resection for PD were tumor location (neck vs. head; uncinate process vs. head) and N staging, while those for DP were T staging .Conclusions：The location of tumor in the neck and uncinate process, and N1/2 staging were independent risk factors of R1 resection for PD; while those for DP were T3/4 staging.

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Risk Factors and Neurologic Outcomes in Patients With Traumatic Brain Injury and Coagulopathy Within 72 Hours Post-operatively

10.21203/rs.3.rs-145018/v1 ◽

2021 ◽

Author(s):

Tao Chang ◽

Xigang Yan ◽

Chao Zhao ◽

Yufu Zhang ◽

Bao Wang ◽

...

Keyword(s):

Risk Factors ◽

Traumatic Brain Injury ◽

Logistic Regression ◽

Brain Injury ◽

Multivariate Logistic Regression ◽

Neurologic Outcomes ◽

Post Operative Period ◽

Duration Of Surgery ◽

Independent Risk Factors ◽

Time Of Admission

Abstract Background There are few studies on the development and effect of coagulopathy in patients with a traumatic brain injury (TBI) during the early post-operative period. We determined the risk factors and neurologic outcomes of in patients with a TBI and coagulopathy diagnosed by routine laboratory tests within 72 hours post-operatively. Methods The baseline characteristics, intra-operative management, and follow-up results of 462 patients with TBIs were obtained and retrospectively analyzed by multivariate logistic regression from January 2015 to June 2019. Coagulopathy was defined as an activated partial thromboplastin time > 40 seconds, international normalized ratio >1.4, or a platelet count < 100×109 /L.Results Multivariate logistic regression analysis revealed that the Glasgow Coma Scale (GCS) at the time of admission, Injury Severity Score (ISS) at the time of admission, pupil mydriasis, duration of surgery, intra-operative blood loss, and intra-operative crystalloid resuscitation were independent risk factors for patients who developed a coagulopathy post-operatively. There were statistical differences in mortality (p = 0.049), the Glasgow Outcome Scale-Extended (GCS-E; p = 0.024), and the modified Rankin Scale (p = 0.043) between patients with and without coagulopathy 1 week after surgery. Coagulopathy within 72 h after surgery revealed a trend for higher mortality at 1 week (66.7%), 3 months (71.4%), and 6 months (76.2%). Furthermore, coagulopathy and contusion expansion in the early post-operative period were independent risk factors for TBI mortality after surgery. Intra-operative crystalloid resuscitation had a substantial diagnostic accuracy in predicting coagulopathy within 72 h post-operatively (area under the curve [AUC] = 0.972).Conclusion Coagulopathy within 72 h post-operatively in patients with a TBI predicted worse disease progression and unfavorable neurologic outcomes. Hence, we should take practical and reasonable measures to manage these risk factors, which may protect patients with a TBI from post-operative coagulopathy.

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Middle Cerebral Artery-to-Uterine Artery Pulsatility Index Ratio Independently Predicts Adverse Perinatal Outcome in Pregnancies at Term

10.21203/rs.3.rs-51822/v1 ◽

2020 ◽

Author(s):

Sufen Zhou ◽

Hongyan Guo ◽

Heng Liu ◽

Mingqun Li

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Middle Cerebral Artery ◽

Perinatal Outcome ◽

Pulsatility Index ◽

Uterine Artery ◽

Regression Analyses ◽

Multivariate Logistic Regression ◽

Adverse Perinatal Outcome ◽

Independent Risk Factors

Abstract Background: This study aimed to investigate potential predictors, including cerebroplacental ratio (CPR), middle cerebral artery (MCA)/uterine artery pulsatility index (PI) ratio, for adverse perinatal outcome in pregnancies at term.Methods: This was an observational, prospective study of recruited pregnancies at term. An adverse perinatal outcome was set as the primary observational endpoint. The receiver operating characteristic (ROC) curve was plotted to investigate the predictive and cut-off values of risk factors for adverse perinatal outcome. Independent risk factors (maternal, neonatal, prenatal ultrasound and Doppler variables) for adverse perinatal outcome were evaluated by the univariate and multivariate logistic regression analyses.Results: A total of 392 pregnancies at term were included and 19.4% of them had suffered adverse perinatal outcome. CPR (OR: 0.42, 95%CI: 0.20-0.93, P=0.032) and MCA/uterine artery PI ratio (OR: 0.25, 95%CI: 0.16-0.42, P=0.032) were two independent risk factors for adverse perinatal outcome by univariate and multivariate logistic regression analyses.Conclusions: MCA/uterine artery PI ratio is a good predictor of adverse perinatal outcome in pregnancies at term.

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Abnormal Findings on “T1WI or DWI or MRCP:” An Effective Boolean Interpretation Model in Discriminating Small Pancreatic Ductal Adenocarcinoma from Control Group

Journal of Clinical Imaging Science ◽

10.25259/jcis_158_2021 ◽

2021 ◽

Vol 11 ◽

pp. 54

Author(s):

Hainan Ren ◽

Naoko Mori ◽

Minami Hirasawa ◽

Shin Hamada ◽

Shunji Mugikura ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Clinical Practice ◽

Pancreatic Ductal Adenocarcinoma ◽

Logistic Regression Analysis ◽

Multivariate Logistic Regression Analysis ◽

Ductal Adenocarcinoma ◽

Multivariate Logistic Regression ◽

Control Groups ◽

Interpretation Model

Objectives: The objectives of the study was to evaluate the diagnostic performance of findings on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and magnetic resonance cholangiopancreatography (MRCP) separately and to identify an optimal Boolean interpretation model for discriminating patients with small pancreatic ductal adenocarcinoma (PDAC) from control groups in clinical practice. Material and Methods: We retrospectively enrolled 30 patients with surgery confirmed small PDAC (≤20 mm) and 302 patients without pancreatic abnormality between April 2008 and February 2020. The presence of masses was evaluated by T1WI, T2WI, and DWI. Abnormality of the main pancreatic duct (MPD) was evaluated by T2WI and MRCP. Multivariate logistic regression analysis was performed to select significant sequences for discriminating the small PDAC and control groups. Boolean operators “OR” or “AND” were used to construct sequence combinations. Diagnostic performances of these sequences and combinations were evaluated by X2 tests. Results: The sensitivity of T2WI was lowest (20%) for detecting masses. For evaluating MPD abnormality, sensitivity was higher for MRCP than for T2WI (86.7% vs. 53.3%). Multivariate logistic regression analysis showed that T1WI and DWI for detecting the presence of masses and MRCP for evaluating MPD abnormality were significantly associated with differentiation between the two groups (P = 0.0002, P = 0.0484, and P < 0.0001, respectively). Seven combinations were constructed with T1WI, DWI, and MRCP. The combination of findings on “T1WI or DWI or MRCP” achieved the highest sensitivity of 96.7% and negative predictive value of 99.6%. Conclusion: The combination of findings on “T1WI or DWI or MRCP” might be an optimal interpretation model for discriminating small PDAC from control groups in clinical practice.

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In-hospital gastrointestinal bleeding in patients with acute myocardial infarction: incidence, outcomes and risk factors analysis from China Acute Myocardial Infarction Registry

BMJ Open ◽

10.1136/bmjopen-2020-044117 ◽

2021 ◽

Vol 11 (9) ◽

pp. e044117

Author(s):

Wence Shi ◽

Xiaoxue Fan ◽

Jingang Yang ◽

Lin Ni ◽

Shuhong Su ◽

...

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Gastrointestinal Bleeding ◽

Secondary Outcome ◽

Multivariate Logistic Regression ◽

Independent Risk Factors ◽

Receptor Inhibitor

ObjectiveTo investigate the incidence of gastrointestinal bleeding (GIB) in patients with acute myocardial infarction (AMI), clarify the association between adverse clinical outcomes and GIB and identify risk factors for in-hospital GIB after AMI.DesignRetrospective cohort study.Setting108 hospitals across three levels in China.ParticipantsFrom 1 January 2013 to 31 August 2014, after excluding 2659 patients because of incorrect age and missing GIB data, 23 794 patients with AMI from 108 hospitals enrolled in the China Acute Myocardial Infarction Registry were divided into GIB-positive (n=282) and GIB-negative (n=23 512) groups and were compared.Primary and secondary outcome measuresMajor adverse cardiovascular and cerebrovascular events (MACCEs) are a composite of all-cause death, reinfarction and stroke. The association between GIB and endpoints was examined using multivariate logistic regression and Cox proportional hazards models. Independent risk factors associated with GIB were identified using multivariate logistic regression analysis.ResultsThe incidence of in-hospital GIB in patients with AMI was 1.19%. GIB was significantly associated with an increased risk of MACCEs both in-hospital (OR 2.314; p<0.001) and at 2-year follow-up (HR 1.407; p=0.0008). Glycoprotein IIb/IIIa (GPIIb/IIIa) receptor inhibitor, percutaneous coronary intervention (PCI) and thrombolysis were novel independent risk factors for GIB identified in the Chinese AMI population (p<0.05).ConclusionsGIB is associated with both in-hospital and follow-up MACCEs. Gastrointestinal prophylactic treatment should be administered to patients with AMI who receive primary PCI, thrombolytic therapy or GPIIb/IIIa receptor inhibitor.Trial registration numberNCT01874691.

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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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Risk Factors for Mortality in 220 Patients With COVID-19 in Wuhan, China: A Single-Center, Retrospective Study

Ear Nose & Throat Journal ◽

10.1177/0145561320972608 ◽

2020 ◽

pp. 014556132097260

Author(s):

Shuliang Zhou ◽

Sulin Mi ◽

Shuilian Luo ◽

Ying Wang ◽

Bin Ren ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Respiratory Rate ◽

Sofa Score ◽

Laboratory Data ◽

Older Age ◽

Univariable Analysis ◽

Multivariate Logistic Regression ◽

Failure Assessment ◽

Independent Risk Factors

Background: An outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 infection occurred in Wuhan, China, in December 2019. To date, the analysis of fatal cases and the risk factors for death have rarely been reported. Methods: In this study, 220 adult patients with confirmed and suspected COVID-19 were enrolled. Clinical characteristics, laboratory data, treatments, and complications were compared between 168 survivors and 52 nonsurvivors. Univariable analysis and multivariable logistic regression were used to investigate the risk factors for mortality. Results: A total of 220 patients (168 were discharged and 52 died in the hospital) were enrolled in the study. The median age of all patients was 59.5 (47.0-69.0) years, and the median age of patients who died was significantly older than that of patients who survived (70.5 vs 56.0 years, respectively; P < .001). According to multivariate logistic regression, older age (odds ratio: 1.09, 95% CI: 1.03-1.15; P = .001), initial Sequential Organ Failure Assessment (SOFA) score >2 (37.4, 9.4-148.0; P = .011), and respiratory rate >24 per minute (10.89, 1.47-80.67; P = .019) were independent risk factors for mortality. Conclusion: Clinical and laboratory parameters predicting poor prognosis including older age, baseline SOFA score >2, and respiratory rate >24 per minute were identified.

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Development of a prediction model for 1-year poor prognosis in patients with acute ischemic stroke

Journal of Investigative Medicine ◽

10.1136/jim-2018-000883 ◽

2019 ◽

Vol 67 (6) ◽

pp. 957-963 ◽

Cited By ~ 1

Author(s):

Xia Ling ◽

Bo Shen ◽

Kangzhi Li ◽

Lihong Si ◽

Xu Yang

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Ischemic Stroke ◽

Prediction Model ◽

Acute Ischemic Stroke ◽

Poor Prognosis ◽

Multivariate Logistic Regression ◽

Lemeshow Test ◽

Independent Risk Factors ◽

Auc Value

The goals of this study were to develop a new prediction model to predict 1-year poor prognosis (death or modified Rankin scale score of ≥3) in patients with acute ischemic stroke (AIS) and to compare the performance of the new prediction model with other prediction scales. Baseline data of 772 patients with AIS were collected, and univariate and multivariate logistic regression analyses were performed to identify independent risk factors for 1-year poor prognosis in patients with AIS. The area under the receiver operating characteristics curve (AUC) value of the new prediction model and the THRIVE, iScore and ASTRAL scores was compared. The Hosmer-Lemeshow test was used to assess the goodness of fit of the model. We identified 196 (25.4%) patients with poor prognosis at 1-year follow-up, and of these 68 (68/196, 34.7%) had died. Multivariate logistic regression and receiver operating characteristic curve analyses showed that age ≥70 years, consciousness (lethargy or coma), history of stroke or transient ischemic attack, cancer, abnormal fasting blood glucose levels ≥7.0 mmol/L, and National Institutes of Health Stroke Scale score were independent risk factors for 1-year poor prognosis in patients with AIS. Scores were assigned for each variable by rounding off β coefficient to the integer score, and a new prediction model with a maximum total score of 9 points was developed. The AUC value of the new prediction model was higher than the THRIVE score (p<0.05). The χ2 value for the Hosmer-Lemeshow test was 7.337 (p>0.05), suggesting that the prediction model had a good fit. The new prediction model can accurately predict 1-year poor prognosis in Chinese patients with AIS.

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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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LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

10.21203/rs.3.rs-60899/v2 ◽

2020 ◽

Author(s):

Xiao-Xue Zhang ◽

Meng Wei ◽

Lu-Xiang Shang ◽

Yan-Mei Lu ◽

Ling Zhang ◽

...

Keyword(s):

Risk Factors ◽

Atrial Fibrillation ◽

Logistic Regression ◽

Regression Analysis ◽

Roc Curve ◽

Principal Component Regression ◽

Principal Component ◽

Multivariate Logistic Regression ◽

Principal Component Regression Analysis ◽

Independent Risk Factors

Abstract Background: This study explored the relationships between the low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients.Methods: NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS occurred. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS.Results: The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile ( ≥2.73) was 16.23-fold that of the lowest quartile ( < 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients.Conclusions: LDL-C/HDL-C >1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

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