ObjectivePeripheral blood routine parameters (PBRPs) are simple and easily acquired markers to identify ulcerative colitis (UC) and Crohn's disease (CD) and reveal the severity, whereas the diagnostic performance of individual PBRP is limited. We, therefore used four machine learning (ML) models to evaluate the diagnostic and predictive values of PBRPs for UC and CD.MethodsA retrospective study was conducted by collecting the PBRPs of 414 inflammatory bowel disease (IBD) patients, 423 healthy controls (HCs), and 344 non-IBD intestinal diseases (non-IBD) patients. We used approximately 70 % of the PBRPs data from both patients and HCs for training, 30 % for testing, and another group for external verification. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnosis and prediction performance of these four ML models.ResultsMulti-layer perceptron artificial neural network model (MLP-ANN) yielded the highest diagnostic performance than the other three models in six subgroups in the training set, which is helpful for discriminating IBD and HCs, UC and CD, active CD and remissive CD, active UC and remissive UC, non-IBD and HCs, and IBD and non-IBD with the AUC of 1.00, 0.988, 0.942, 1.00, 0.986, and 0.97 in the testing set, as well as the AUC of 1.00, 1.00, 0.773, 0.904, 1.00 and 0.992 in the external validation set.ConclusionPBRPs-based MLP-ANN model exhibited good performance in discriminating between UC and CD and revealing the disease activity; however, a larger sample size and more models need to be considered for further research. 

Utility of four machine learning approaches for identifying ulcerative colitis and Crohn's disease

(1) Background: The RDW-CV has predictive value in many pregnancy-related conditions. Re-search question: Can the RDW-CV be utilized predictively in pregnant patients with thrombo-philia? Aim of the study: To carry out a detailed analysis of the RDW-CV in pregnant patients with treated thrombophilia as compared with pregnant patients without thrombophilia who de-livered via cesarean section at term. (2) Methods: We studied 160 pregnant patients, including 80 pregnant patients with treated thrombophilia in the study group, and another 80 patients of a similar age and parity. The patients were referred to our hospital for delivery at term by means of a cesarean section between 1/10/2017 and 1/12/2021. Every patient received a sonogram during the first 1–2 days after the cesarean section, and their uterine evaluation was interpreted using the PUUS (Postpartum Uterine Ultrasonographic Scale). (3) Results: Our PUUS≥1 pregnant and postpartum patients with treated thrombophilia were the only group with an RDW-CV value over 14. No deep vein thrombosis was observed during hospitalization, showing that the antico-agulant treatment was necessary and effective. An RDW-CV of 14.5 was the highest value in the Rh-negative group of pregnant patients with treated thrombophilia. An RDW-CV of 14.48 was the highest level in the Rh-negative postpartum patients with treated thrombophilia. (4) Conclu-sion: An RDW-CV of ≥14 can predict uterine hematometra (PUUS≥1). The RDW-CV values were higher in Rh-negative patients. 

The Predictive Value of RDW-CV in Pregnant Patients with Treated Thrombophilia Who Delivered via Cesarean Section at Term

Abstract BACKGROUND : Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6%-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment. ONJECTIVE : The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients. STUDY DESIGN : Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The data set after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed by using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated. RESULTS : A total of 111 APIP were included. In all APIP patients, biliary pancreatitis was the most important reason (62.1%). The levels of serum amylase, creatinine, albumin, triglyceride, high density lipoprotein cholesteroland apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability. CONCLUSION : The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination and clinical practicability. 

Acute pancreatitis in pregnancy: A propensity score matching analysis and dynamic nomogram for risk assessment

Background This study evaluated the predictive value of different laboratory indicators for the severity of acute cholangitis (AC) according to the 2018 Tokyo Guidelines. Objectives We enrolled consecutive patients with a diagnosis of AC from June 2016 to May 2021. Serum procalcitonin (PCT) and C-reactive protein (CRP) levels, white blood cell counts, the neutrophil–lymphocyte ratio, and the platelet–lymphocyte ratio (PLR) were compared according to the severity of AC. Results In total, 293 patients were enrolled in this study (mild, n = 172; moderate, n = 68; severe, n = 53). In receiver operating characteristic analyses, CRP was the best biomarker for differentiating mild and moderate AC (area under the curve [AUC] = 0.66, 95% confidence interval [CI] = 0.58–0.74). PCT was the best biomarker for differentiating mild and severe AC (AUC = 0.80, 95% CI = 0.74–0.86). Blood culture was performed in 117 patients (39.93%), 53 of whom (45.30%) had positive results. Regarding blood culture positivity, PLR was most predictive (AUC = 0.85, 95% CI = 0.78–0.92). Conclusions PCT can be used as a reliable predictor of severe AC. CRP was most predictive of moderate AC, whereas PLR was most predictive of blood culture positivity. 

The predictive value of different laboratory indicators based on the 2018 tokyo guidelines for the severity of acute cholangitis

Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6–18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment. The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients. Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated. A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability. The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.

Acute Pancreatitis in Pregnancy: A Propensity Score Matching Analysis and Dynamic Nomogram for Risk Assessment.

Acute pancreatitis in pregnancy (APIP) with a high risk of death is extremely harmful to mother and fetus. There are few models specifically designed to assess the severity of APIP. Our study aimed to establish a clinical model for early prediction of severity of APIP.A retrospective study in a total of 188 patients with APIP was enrolled. The hematological indicators, IAP (intra-abdominal pressure) and clinical data were obtained for statistical analysis and prediction model construction.According to univariate and multivariate logistic regression analysis, we found that red cell distribution width (RDW), neutrophil-lymphocyte ratio (NLR) and Intra-abdominal pressure (IAP) are prediction indexes of the severity in APIP (p-value < 0.05). Our novel clinical prediction model was created by based on the above three risk factors and showed superior predictive power in primary cohort (AUC = 0.895) and validation cohort (AUC = 0.863). A nomogram for severe acute pancreatitis in pregnancy (SAPIP) was created based on the three indicators. The nomogram was well-calibrated.RDW, NLR and IAP were the independent risk factors of APIP. Our clinical prediction model of severity in APIP based on RDW, NLR and IAP with predictive evaluation is accurate and effective. 

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A novel clinical prediction model of severity based on red cell distribution width, neutrophil-lymphocyte ratio and intra-abdominal pressure in acute pancreatitis in pregnancy

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A novel clinical prediction model of severity based on red cell distribution width, neutrophil-lymphocyte ratio and intra-abdominal pressure in acute pancreatitis in pregnancy