scholarly journals Who Is at Risk for Postdischarge Nausea and Vomiting after Ambulatory Surgery?

2012 ◽  
Vol 117 (3) ◽  
pp. 475-486 ◽  
Author(s):  
Christian C. Apfel ◽  
Beverly K. Philip ◽  
Ozlem S. Cakmakkaya ◽  
Ashley Shilling ◽  
Yun-Ying Shi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Ambulatory Surgery ◽  
Characteristic Curve ◽  
Nausea And Vomiting ◽  
Validation Dataset ◽  
Postanesthesia Care Unit ◽  
Operating Characteristic Curve

Background About one in four patients suffers from postoperative nausea and vomiting. Fortunately, risk scores have been developed to better manage this outcome in hospitalized patients, but there is currently no risk score for postdischarge nausea and vomiting (PDNV) in ambulatory surgical patients. Methods We conducted a prospective multicenter study of 2,170 adults undergoing general anesthesia at ambulatory surgery centers in the United States from 2007 to 2008. PDNV was assessed from discharge until the end of the second postoperative day. Logistic regression analysis was applied to a development dataset and the area under the receiver operating characteristic curve was calculated in a validation dataset. Results The overall incidence of PDNV was 37%. Logistic regression analysis of the development dataset (n=1,913) identified five independent predictors (odds ratio; 95% CI): female gender (1.54; 1.22 to 1.94), age less than 50 yr (2.17; 1.75 to 2.69), history of nausea and/or vomiting after previous anesthesia (1.50; 1.19 to 1.88), opioid administration in the postanesthesia care unit (1.93; 1.53 to 2.43), and nausea in the postanesthesia care unit (3.14; 2.44-4.04). In the validation dataset (n=257), zero, one, two, three, four, and five of these factors were associated with a PDNV incidence of 7%, 20%, 28%, 53%, 60%, and 89%, respectively, and an area under the receiver operating characteristic curve of 0.72 (0.69 to 0.73). Conclusions PDNV affects a substantial number of patients after ambulatory surgery. We developed and validated a simplified risk score to identify patients who would benefit from long-acting prophylactic antiemetics at discharge from the ambulatory care center.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

scholarly journals A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation

2018 ◽  
Vol 26 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Muhammad Faisal ◽  
Andy Scally ◽  
Robin Howes ◽  
Kevin Beatson ◽  
Donald Richardson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Operating Characteristic Curve

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n = 24,696) and compared the performance of these models in data from another hospital ( n = 13,477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

scholarly journals Postoperative Ultrasound, Unassisted Maturation, and Subsequent Primary Patency of Arteriovenous Fistulas

2018 ◽  
Vol 13 (9) ◽  
pp. 1364-1372 ◽  
Author(s):  
Crystal A. Farrington ◽  
Michelle L. Robbin ◽  
Timmy Lee ◽  
Jill Barker-Finkel ◽  
Michael Allon
Keyword(s):  
Blood Flow ◽  
Logistic Regression ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariable Logistic Regression ◽  
Ultrasound Measurements ◽  
Operating Characteristic Curve

Background and objectivesPostoperative ultrasound is commonly used to assess arteriovenous fistula (AVF) maturation for hemodialysis, but its utility for predicting unassisted AVF maturation or primary AVF patency for hemodialysis has not been well defined. This study assessed the predictive value of postoperative AVF ultrasound measurements for these clinical AVF outcomes.Design, setting, participants, & measurementsWe queried a prospective vascular access database to identify 246 patients on catheter-dependent hemodialysis who underwent AVF creation between 2010 and 2016 and obtained a postoperative ultrasound within 90 days. Multivariable logistic regression was used to evaluate the association of clinical characteristics and postoperative ultrasound measurements with unassisted AVF maturation. A receiver operating characteristic curve estimated the predictive value of these factors for unassisted AVF maturation. Finally, multivariable survival analysis was used to identify factors associated with primary AVF patency in patients with unassisted AVF maturation.ResultsUnassisted AVF maturation occurred in 121 out of 246 patients (49%), assisted maturation in 55 patients (22%), and failure to mature in 70 patients (28%). Using multivariable logistic regression, unassisted AVF maturation was associated with AVF blood flow (odds ratio [OR], 1.30; 95% confidence interval [95% CI], 1.18 to 1.45 per 100 ml/min increase; P<0.001), forearm location (OR, 0.37; 95% CI, 0.08 to 1.78; P=0.21), presence of stenosis (OR, 0.45; 95% CI, 0.23 to 0.88; P=0.02); AVF depth (OR, 0.88; 95% CI, 0.77 to 1.00 per 1 mm increase; P=0.05), and AVF location interaction with depth (OR, 0.50; 95% CI, 0.28 to 0.84; P=0.02). The area under the receiver operating characteristic curve, using all these factors, was 0.84 (95% CI, 0.79 to 0.89; P<0.001). Primary AVF patency in patients with unassisted maturation was associated only with AVF diameter (hazard ratio, 0.84; 95% CI, 0.76 to 0.94 per 1 mm increase; P=0.002).ConclusionsUnassisted AVF maturation is predicted by AVF blood flow, location, depth, and stenosis. AVF patency after unassisted maturation is predicted only by the postoperative AVF diameter.

Diagnostic studies

2020 ◽  
pp. 389-404
Author(s):  
Janet L. Peacock ◽  
Philip J. Peacock
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Clinical Practice ◽  
Receiver Operating Characteristic Curve ◽  
Characteristic Curve ◽  
Diagnostic Testing ◽  
Predictive Values ◽  
Post Test ◽  
Operating Characteristic Curve ◽  
Sensitivity Specificity

This chapter describes how statistical methods are used in diagnostic testing to obtain different measures of a test’s performance. It describes how to calculate sensitivity, specificity, and positive and negative predictive values, and shows the relevance of the pre- and post-test odds and the likelihood ratio in evaluating a test in clinical practice. The chapter also describes the receiver operating characteristic curve and shows how this links with logistic regression analysis. All methods are illustrated with examples.

scholarly journals Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score

BMJ ◽  
2020 ◽  
pp. m3339 ◽  
Author(s):  
Stephen R Knight ◽  
Antonia Ho ◽  
Riinu Pius ◽  
Iain Buchan ◽  
Gail Carson ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Risk Stratification ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Validation Dataset ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Objective To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). Design Prospective observational cohort study. Setting International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium—ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020 . Participants Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. Main outcome measure In-hospital mortality. Results 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. Study registration ISRCTN66726260

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

The gut hormone GLP-2 predicts cardiovascular risk in patients with acute myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
F Kahles ◽  
R.W Mertens ◽  
M.V Rueckbeil ◽  
M.C Arrivas ◽  
J Moellmann ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Acute Myocardial Infarction ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Funding Source ◽  
Cardiovascular Prognosis ◽  
Operating Characteristic Curve ◽  
Hs Crp

Abstract Background GLP-1 and GLP-2 (glucagon-like peptide-1/2) are gut derived hormones that are co-secreted from intestinal L-cells in response to food intake. While GLP-1 is known to induce postprandial insulin secretion, GLP-2 enhances intestinal nutrient absorption and is clinically used for the treatment of patients with short bowel syndrome. The relevance of the GLP-2 system for cardiovascular disease is unknown. Purpose The aim of this study was to assess the predictive capacity of GLP-2 for cardiovascular prognosis in patients with myocardial infarction. Methods Total GLP-2 levels, NT-proBNP concentrations and the Global Registry of Acute Coronary Events (GRACE) score were assessed at time of admission in 918 patients with myocardial infarction, among them 597 patients with NSTEMI and 321 with STEMI. The primary composite outcome of the study was the first occurrence of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke (3-P-MACE) with a median follow-up of 311 days. Results Kaplan-Meier survival plots (separated by the median of GLP-2 with a cut-off value of 4.4 ng/mL) and univariable cox regression analyses found GLP-2 values to be associated with adverse outcome (logarithmized GLP-2 values HR: 2.87; 95% CI: 1.75–4.68; p&lt;0.0001). Further adjustment for age, sex, smoking, hypertension, hypercholesterolemia, diabetes mellitus, family history of cardiovascular disease, hs-Troponin T, NT-proBNP and hs-CRP levels did not affect the association of GLP-2 with poor prognosis (logarithmized GLP-2 values HR: 2.96; 95% CI: 1.38–6.34; p=0.0053). Receiver operating characteristic curve (ROC) analyses illustrated that GLP-2 is a strong indicator for cardiovascular events and proved to be comparable to other established risk markers (area under the curve of the combined endpoint at 6 months; GLP-2: 0.72; hs-Troponin: 0.56; NT-proBNP: 0.70; hs-CRP: 0.62). Adjustment of the GRACE risk estimate by GLP-2 increased the area under the receiver-operating characteristic curve for the combined triple endpoint after 6 months from 0.70 (GRACE) to 0.75 (GRACE + GLP-2) in NSTEMI patients. Addition of GLP-2 to a model containing GRACE and NT-proBNP led to a further improvement in model performance (increase in AUC from 0.72 for GRACE + NT-proBNP to 0.77 for GRACE + NT-proBNP + GLP-2). Conclusions In patients admitted with acute myocardial infarction, GLP-2 levels are associated with adverse cardiovascular prognosis. This demonstrates a strong yet not appreciated crosstalk between the heart and the gut with relevance for cardiovascular outcome. Future studies are needed to further explore this crosstalk with the possibility of new treatment avenues for cardiovascular disease. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): German Society of Cardiology (DGK), German Research Foundation (DFG)

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