External validation of the prognostic index in acute paraquat poisoning

2015 ◽  
Vol 35 (4) ◽  
pp. 366-370 ◽  
Author(s):  
DS Kim ◽  
C Kang ◽  
DH Kim ◽  
SC Kim ◽  
SH Lee ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Prognostic Index ◽  
Mortality Rates ◽  
Prognostic Indicators ◽  
Group A ◽  
Operating Characteristic Curve ◽  
Group B ◽  
Overall Mortality

Objective: Some studies have evaluated the prognostic indicators associated with acute paraquat (PQ) poisoning. In this study, we externally validated the Yamaguchi index, which showed a good prognostic relevance in predicting the outcome of PQ poisoning. Methods: A retrospective analysis of 297 patients was performed. The Yamaguchi index was calculated using the following equation: Eq1 = (K+ × HCO3−)/(Creatinine × 0.088)(mEq/L) against time from PQ ingestion ( T). The patients were divided into three groups: group A: Eq1 > 1500 − 399 × log T, group B: 930 − 399 × log T < Eq1 ≤ 1500 − 399 × log T, and group C: Eq1 ≤ 930 − 399 × log T). Results: The overall mortality rate was 65.3% (194 of 297). The mortality rates of the three groups stratified by the Yamaguchi index were 7.1% (2 of 28), 22.4% (15 of 67), and 87.6% (177 of 202). The area under the receiver–operating characteristic curve for predicting mortality from the external validation of the Yamaguchi index was 0.842 (95% confidence interval: 0.795–0.882). Conclusion: The Yamaguchi index is a reliable prognostic factor and could be helpful in predicting mortality due to PQ poisoning.

scholarly journals A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children

2020 ◽  
Vol 31 (6) ◽  
pp. 1348-1357 ◽  
Author(s):  
Ibrahim Sandokji ◽  
Yu Yamamoto ◽  
Aditya Biswas ◽  
Tanima Arora ◽  
Ugochukwu Ugwuowo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Health Record ◽  
Hospitalized Children ◽  
Operating Characteristic Curve ◽  
Electronic Health

BackgroundTimely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges.MethodsWe retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay.ResultsAmong 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points.ConclusionsUsing various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.

scholarly journals Validation and Adaptation of the Multidimensional Prognostic Index in an Older Australian Cohort

2019 ◽  
Vol 8 (11) ◽  
pp. 1820 ◽  
Author(s):  
Kimberley Bryant ◽  
Michael J. Sorich ◽  
Richard J. Woodman ◽  
Arduino A. Mangoni
Keyword(s):  
Operating Characteristic ◽  
Reference Group ◽  
Characteristic Curve ◽  
Prognostic Index ◽  
Adverse Outcomes ◽  
Moderate Risk ◽  
Australian Cohort ◽  
Operating Characteristic Curve ◽  
Simple Logistic ◽  
Discriminatory Performance

Background and aims: The Multidimensional Prognostic Index (MPI), an objective and quantifiable tool based on the Comprehensive Geriatric Assessment, has been shown to predict adverse outcomes in European cohorts. We conducted a validation study of the original MPI, and of adapted versions that accounted for the use of specific drugs and cultural diversity in the assessment of cognition, in older Australians. Methods: The capacity of the MPI to predict 12-month mortality was assessed in 697 patients (median age: 80 years; interquartile range: 72–86) admitted to a metropolitan teaching hospital between September 2015 and February 2017. Results: In simple logistic regression analysis, the MPI was associated with 12-month mortality (Low risk: OR reference group; moderate risk: OR 2.50, 95% CI: 1.67–3.75; high risk: OR 4.24, 95% CI: 2.28–7.88). The area under the receiver operating characteristic curve (AUC) for the unadjusted MPI was 0.61 (0.57–0.65) and 0.64 (95% CI: 0.59–0.68) with age and sex adjusted. The adapted versions of the MPI did not significantly change the AUC of the original MPI. Conclusion: The original and adapted MPI were strongly associated with 12-month mortality in an Australian cohort. However, the discriminatory performance was lower than that reported in European studies.

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 External validation of the Practical Risk Chart for the prediction of delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage

2017 ◽  
Vol 126 (5) ◽  
pp. 1530-1536 ◽  
Author(s):  
Paul M. Foreman ◽  
Michelle H. Chua ◽  
Mark R. Harrigan ◽  
Winfield S. Fisher ◽  
R. Shane Tubbs ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Operating Characteristic ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Characteristic Curve ◽  
External Validation ◽  
Delayed Cerebral Ischemia ◽  
Model Discrimination ◽  
Operating Characteristic Curve ◽  
Best Fit

OBJECTIVEDelayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH) occurs in approximately 30% of patients. The Practical Risk Chart was developed to predict DCI based on admission characteristics; the authors seek to externally validate and critically appraise this prediction tool.METHODSA prospective cohort of aSAH patients was used to externally validate the previously published Practical Risk Chart. The model consists of 4 variables: clinical condition on admission, amount of cisternal and intraventricular blood on CT, and age. External validity was assessed using logistic regression. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC).RESULTSIn a cohort of 125 patients with aSAH, the Practical Risk Chart adequately predicted DCI, with an AUC of 0.66 (95% CI 0.55–0.77). Clinical grade on admission and amount of intracranial blood on CT were the strongest predictors of DCI and clinical vasospasm. The best-fit model used a combination of the Hunt and Hess grade and the modified Fisher scale to yield an AUC of 0.76 (95% CI 0.675–0.85) and 0.70 (95% CI 0.602–0.8) for the prediction of DCI and clinical vasospasm, respectively.CONCLUSIONSThe Practical Risk Chart adequately predicts the risk of DCI following aSAH. However, the best-fit model represents a simpler stratification scheme, using only the Hunt and Hess grade and the modified Fisher scale, and produces a comparable AUC.

Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans

2021 ◽  
pp. 746-757
Author(s):  
Rajesh P. Shah ◽  
Heather M. Selby ◽  
Pritam Mukherjee ◽  
Shefali Verma ◽  
Peiyi Xie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Support Vector ◽  
Support Vector Classifier ◽  
Computed Tomography Scans ◽  
Group A ◽  
Group B

PURPOSE Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size. MATERIALS AND METHODS Computed tomography scans from a single institution were selected and resampled to 1 × 1 × 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance. RESULTS A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B. CONCLUSION A machine learning radiomics model may help differentiate SCLC from other lung lesions.

scholarly journals External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

2021 ◽  
Author(s):  
Benjamin S. Wessler ◽  
Jason Nelson ◽  
Jinny G. Park ◽  
Hannah McGinnes ◽  
Gaurav Gulati ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
External Validation ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Operating Characteristic Curve ◽  
Performance Heterogeneity

Background: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. Methods: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. Results: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0–94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th–75th percentile [interquartile range (IQR)], 0.66–0.79), representing a median percent decrease in discrimination of −11.1% (IQR, −32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was −3.7% (IQR, −13.2 to 3.1) for closely related validations (n=123), −9.0 (IQR, −27.6 to 3.9) for related validations (n=862), and −17.2% (IQR, −42.3 to 0) for distantly related validations (n=717; P <0.001). Conclusions: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

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.

Three plasma-based microRNAs as potent diagnostic biomarkers for endometrial cancer

2021 ◽  
pp. 1-12
Author(s):  
Xingchen Fan ◽  
Minmin Cao ◽  
Cheng Liu ◽  
Cheng Zhang ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Diagnostic Value ◽  
Diagnostic Biomarkers ◽  
Non Invasive ◽  
Polymerase Chain ◽  
Public Datasets ◽  
Plasma Mirnas

BACKGROUND: MicroRNAs (miRNAs), with noticeable stability and unique expression pattern in plasma of patients with various diseases, are powerful non-invasive biomarkers for cancer detection including endometrial cancer (EC). OBJECTIVE: The objective of this study was to identify promising miRNA biomarkers in plasma to assist the clinical screening of EC. METHODS: A total of 93 EC and 79 normal control (NC) plasma samples were analyzed using Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) in this four-stage experiment. The receiver operating characteristic curve (ROC) analysis was conducted to evaluate the diagnostic value. Additionally, the expression features of the identified miRNAs were further explored in tissues and plasma exosomes samples. RESULTS: The expression of miR-142-3p, miR-146a-5p, and miR-151a-5p was significantly overexpressed in the plasma of EC patients compared with NCs. Areas under the ROC curve of the 3-miRNA signature were 0.729, 0.751, and 0.789 for the training, testing, and external validation phases, respectively. The diagnostic performance of the identified signature proved to be stable in the three public datasets and superior to the other miRNA biomarkers in EC diagnosis. Moreover, the expression of miR-151a-5p was significantly elevated in EC plasma exosomes. CONCLUSIONS: A signature consisting of 3 plasma miRNAs was identified and showed potential for the non-invasive diagnosis of EC.

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