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

Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach

Background

Inflammatory bowel disease (IBD) is a chronic, incurable gastrointestinal disease without a gold standard for diagnosis. This study aimed to develop predictive models for diagnosing IBD, Crohn's disease (CD), and Ulcerative colitis (UC) by combining two approaches: machine learning (ML) and traditional nomogram models.

Methods

 Cohorts 1 and 2 comprised data from the UK Biobank (UKB), and the First Hospital of Jilin University, respectively, which represented the initial laboratory tests upon admission for 1135 and 237 CD patients, 2192 and 326 UC patients, and 1798 and 298 non-IBD patients. Cohorts 1 and 2 were used to create predictive models. The parameters of the machine learning model established by Cohorts 1 and 2 were merged, and nomogram models were developed using Logistic regression. Cohort 3 collected initial laboratory tests from 117 CD patients, 197 UC patients, and 241 non IBD patients at a tertiary hospital in different regions of China for external testing of three nomogram models.

Results

For Cohort 1, ML-IBD-1, ML-CD-1 and ML-UC-1 models developed using the LightGBM algorithm demonstrated exceptional discrimination (ML-IBD-1: AUC = 0.788; ML-CD-1: AUC = 0.772; ML-UC-1: AUC = 0.841). For Cohort 2, ML-IBD-2, ML-CD-2, and ML-UC-2 models developed using XGBoost and Logistic Regression algorithms demonstrated exceptional discrimination (ML-IBD-2: AUC = 0.894; ML-CD-2: AUC = 0.932; ML-UC-2: AUC = 0.778). The nomogram model exhibits good diagnostic capability (nomogram-IBD: AUC=0.778, 95% CI (0.688-0.868); nomogram-CD: AUC=0.744, 95% CI (0.710-0.778); nomogram-UC, AUC=0.702, 95% CI (0.591-0.814)). The predictive ability of the three models was validated in cohort 3 (nomogram-IBD: AUC=0.758, 95% CI (0.683-0.832); nomogram-CD: AUC=0.791, 95% CI (0.717-0.865); nomogram-UC, AUC=0.817, 95% CI (0.702-0.932)).

Conclusion

This study utilized three cohorts and developed risk prediction models for IBD, CD, and UC with good diagnostic capability, based on conventional laboratory data using ML and nomogram.

The prognostic value of red blood cell distribution width for mortality in intracranial hemorrhage: A systematic review and meta-analysis

BACKGROUND

Some studies have reported conflicting results regarding the prognostic value of red blood cell distribution width (RDW) for patients with intracranial hemorrhage (ICH). This meta-analysis aims to investigate the association between RDW and all-cause mortality in ICH.

METHODS

We systematically searched the following databases, including PubMed, EMBASE, Cochrane library, and Web of Science, for all studies assessing the prognostic value of mortality in patients with ICH from inception to December 2023. We calculated pooled odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

A total of 7 studies evaluated the association of RDW and all-cause mortality. A higher RDW levels were significantly associated with all-cause mortality (OR = 1.52; 95% CI = 1.22 to 1.89; P = .0002; I2 = 76%).

CONCLUSION

Therefore, RDW is a valuable prognostic marker for the risk of all-cause mortality in patients with intracranial hemorrhage.

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

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.

OBJECTIVE

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 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.

RESULTS

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.

CONCLUSION

The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.

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

Objective

Peripheral 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.

Methods

A 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.

Results

Multi-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.

Conclusion

PBRPs-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.

The Predictive Value of Different Laboratory Indicators Based on the 2018 Tokyo Guidelines for the Severity of Acute Cholangitis

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.