A clinical predictive model for risk stratification of patients with severe acute lower gastrointestinal bleeding

Background Lower gastrointestinal bleeding (LGIB) is a common presentation of surgical admissions, imposing a significant burden on healthcare costs and resources. There is a paucity of standardised clinical predictive tools available for the initial assessment and risk stratification of patients with LGIB. We propose a simple clinical scoring model to prognosticate patients at risk of severe LGIB and an algorithm to guide management of such patients. Methods A retrospective cohort study was conducted, identifying consecutive patients admitted to our institution for LGIB over a 1-year period. Baseline demographics, clinical parameters at initial presentation and treatment interventions were recorded. Multivariate logistic regression was performed to identify factors predictive of severe LGIB. A clinical management algorithm was developed to discriminate between patients requiring admission, and to guide endoscopic, angiographic and/or surgical intervention. Results 226/649 (34.8%) patients had severe LGIB. Six variables were entered into a clinical predictive model for risk stratification of LGIB: Tachycardia (HR ≥ 100), hypotension (SBP < 90 mmHg), anaemia (Hb < 9 g/dL), metabolic acidosis, use of antiplatelet/anticoagulants, and active per-rectal bleeding. The optimum cut-off score of ≥ 1 had a sensitivity of 91.9%, specificity of 39.8%, and positive and negative predictive Values of 45% and 90.2%, respectively, for predicting severe LGIB. The area under curve (AUC) was 0.77. Conclusion Early diagnosis and management of severe LGIB remains a challenge for the acute care surgeon. The predictive model described comprises objective clinical parameters routinely obtained at initial triage to guide risk stratification, disposition and inpatient management of patients.

Risk factors analysis and establishment of a clinical predictive model for recurrence after first attack of myasthenia gravis: a single-center retrospective study

Background Myasthenia gravis (MG) is a rare and recurrent disease. The purpose of this study was to investigate the risk factors for relapse in MG patients after their first attack and establish a clinical predictive model. We conducted a retrospective study of 86 MG patients, followed and reviewed the clinical data of patients from the first onset to the first relapse, including age of onset, site of first symptom, MGFA at onset, thymoma, surgical resection of the thymoma, infection history, irregular drug use, combination of other autoimmune diseases, AChR antibody, and anti-Musk antibody, etc. The R software was used for statistical analysis. Univariate analysis and multivariate analysis were used to analyze risk factors. The clinical predictive model was established by Logistic regression analysis. Results Within 2 years after the first attack, 61.2% of MG patients relapsed. MGFA at onset, irregular drug use and infection history were independent risk factors for MG relapse within 2 years after the first attack ( p < 0.05). The clinical prediction model has good discrimination and calibration. Conclusion The relapse of MG is affected by a variety of factors. The clinical predictive model that was established in this study can help clinicians predict the probability of relapse in MG patients, identify early high-risk relapse patients, and serve for high-quality clinical management.

A Clinical Predictive Model of Progesterone Elevation on the Day of HCG Administration Was Establish Based on the Basal P, BMI and Ovarian Induction Regimen

Background The progesterone elevation (PE) on the day of human chorionic gonadotrophin (hCG) is associated with a significant decrease in the probability of clinical pregnancy after fresh embryo transfer. The goal of this study was to develop a nomogram to explore the baseline indexes to predict the occurrence of PE (0.9ng/ml) on the day of HCG administration before IVF/ICSI treatment.Methods The patients who were performed a GnRH agonist or antagonist pituitary suppression protocol during controlled ovulation stimulation(COS) in reproductive center of Changzhou maternal and child health care hospital from 2017-2019 were included. The nomogram was built from all participants.Results Three variates significantly associated with the PE occurrence on the HCG administration of infertility women were the type ovulation regiment, basal P and BMI. This predictive model showed good calibration and discriminatory abilities, with an area under the curve (AUC) of 0.639（95%CI 0.596~0.681）. Hosmer and Leme show test were performed for evaluating both calibration and discrimination and confirmed our nomogram is a user-friendly graphical representation of the model (χ2= 4.750，P=0.784)Conclusion The nomogram presents graphically association factors (type ovulation regiment, basal P and BMI) and prediction models which can offer simple and useful guidance to avoid the PE occurrence on the HCG administration for clinicians and infertility patients.

A Clinical Predictive Model for Risk Stratification of Patients with Severe Acute Lower Gastrointestinal Bleeding

Background: Lower Gastro-Intestinal Bleeding (LGIB) is a common presentation of surgical admissions, imposing a significant burden on healthcare costs and resources. There is a paucity of standardised clinical predictive tools available for the initial assessment and risk stratification of patients with LGIB. We propose a simple clinical scoring model to prognosticate patients at risk of severe LGIB and an algorithm to guide management of such patients.Methods: A retrospective cohort study was conducted, identifying consecutive patients admitted to our institution for LGIB over a 1-year period. Baseline demographics, clinical parameters at initial presentation and treatment interventions were recorded. Severe LGIB was the primary outcome measure. Multivariate logistic regression was performed to identify factors predictive of severe LGIB. A clinical management algorithm was developed to discriminate between patients requiring admission, and to guide endoscopic, angiographic and/or surgical intervention.Results: 226/649 (34.8%) patients had severe LGIB. Six variables were entered into a clinical predictive model for risk stratification of LGIB: Tachycardia (HR>100), hypotension (SBP<90mmHg), anemia (Hb<9g/dL), metabolic acidosis, use of antiplatelet/anticoagulants, and active per-rectal bleeding. The optimum cut-off score of >1 had a sensitivity of 91.9%, specificity of 39.8%, and Positive and Negative Predictive Values of 45% and 90.2% respectively for predicting severe LGIB. The Area Under Curve (AUC) was 0.77.BConclusion: Early diagnosis and management of severe LGIB remains a challenge for the acute care surgeon. The predictive model described comprises objective clinical parameters routinely obtained at initial triage to guide risk stratification, disposition and inpatient management of patients.