scholarly journals Development and Validation of a Score for Prediction of Postoperative Respiratory Complications

2013 ◽  
Vol 118 (6) ◽  
pp. 1276-1285 ◽  
Author(s):  
Britta Brueckmann ◽  
Jose L. Villa-Uribe ◽  
Brian T. Bateman ◽  
Martina Grosse-Sundrup ◽  
Dean R. Hess ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Multivariable Logistic Regression Analysis ◽  
Respiratory Complications ◽  
Billing Data ◽  
Entire Cohort ◽  
Postoperative Respiratory Complications ◽  
History Of ◽  
American Society

Abstract Background: Postoperative respiratory failure is associated with increased morbidity and mortality, as well as high costs of hospital care. Methods: Using electronic anesthesia records, billing data, and chart review, the authors developed and validated a score predicting reintubation in the hospital after primary extubation in the operating room, leading to unplanned mechanical ventilation within the first 3 postoperative days. Using multivariable logistic regression analysis, independent predictors were determined and a score postulated and validated. Results: In the entire cohort (n = 33,769 surgical cases within 29,924 patients), reintubation occurred in 137 cases (0.41%). Of those, 16%, (n = 22) died subsequently, whereas the mortality in patients who were not reintubated was 0.26% (P < 0.0001). Independent predictors for reintubation were: American Society of Anesthesiologist Score 3 or more, emergency surgery, high-risk surgical service, history of congestive heart failure, and chronic pulmonary disease. A point value of 3, 3, 2, 2, and 1 were assigned to these predictors, respectively, based on their β coefficient in the predictive model. The score yielded a calculated area under the curve of 0.81, whereas each point increment was associated with a 1.7-fold (odds ratio: 1.72 [95% CI, 1.55–1.91]) increase in the odds for reintubation in the training dataset. Using the validation dataset (n = 16,884), the score had an area under the curve of 0.80 and similar estimated probabilities for reintubation. Conclusion: The authors developed and validated a score for the prediction of postoperative respiratory complications, a simple, 11-point score that can be used preoperatively by anesthesiologists to predict severe postoperative respiratory complications.

scholarly journals Establishment of a clinical nomogram model to predict the progression of COVID-19 to severe disease

2020 ◽  
Author(s):  
changli tu ◽  
Guojie Wang ◽  
Cuiyan Tan ◽  
Meizhu Chen ◽  
Zijun Xiang ◽  
...  
Keyword(s):  
Disease Progression ◽  
Selection Process ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Efficient Manner ◽  
And Performance ◽  
Sensitivity Specificity ◽  
D Dimer

Abstract Background Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aimed to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely, efficient manner. Methods This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed with the discrimination curve. Results Risk factors, including demographics data, symptoms, laboratory and image findings were recorded for the 202 patients. Eight of the 52 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. Sensitivity, specificity and AUC were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset. Conclusions We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.

scholarly journals The validation of threshold decision ruls and calculator for APhiG algoritm for clarification of prostate cancer staging before treatment

2020 ◽  
Vol 16 (1) ◽  
pp. 43-53
Author(s):  
N. S. Sergeeva ◽  
T. E. Skachkova ◽  
N. V. Marshutina ◽  
K. M. Nushko ◽  
I. M. Shevchuk ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Rules ◽  
Area Under The Curve ◽  
Cancer Staging ◽  
Curve Analysis ◽  
Training Dataset ◽  
Validation Dataset ◽  
Prostate Cancer Staging

Background. We have previously described an algorithm APhiG (Age of patients, Prostate health index and Gleason score), for staging of prostate cancer before treatment. The algorithm was developed by logistic regression on a training dataset and validated on a validation dataset (VD). Objective. Validation of threshold decision rules and a program for APhiG calculation on the VD.Materials and methods. ROC curve analysis on VD (83 cases).Results and conclusion. It was shown that sensitivity, specificity, positive and negative predictive value, diagnostic accuracy threshold decision rules and area under the curve (AUC) for APhiG in the VD (n = 83) not significantly different from those indicators in the training dataset (n = 337), which was the basis for the algorithm APhiG development.

scholarly journals Development of an algorithm for assessing fall risk in a Japanese inpatient population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoko Nakanishi ◽  
Tokunori Ikeda ◽  
Taishi Nakamura ◽  
Yoshinori Yamanouchi ◽  
Akira Chikamoto ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fall Risk ◽  
Correlation Coefficients ◽  
Lower Limbs ◽  
Training Dataset ◽  
Validation Dataset ◽  
Risk Level ◽  
Multivariable Logistic Regression Analysis ◽  
Fall Risk Factors ◽  
Inpatient Population

AbstractFalling is a representative incident in hospitalization and can cause serious complications. In this study, we constructed an algorithm that nurses can use to easily recognize essential fall risk factors and appropriately perform an assessment. A total of 56,911 inpatients (non-fall, 56,673; fall; 238) hospitalized between October 2017 and September 2018 were used for the training dataset. Correlation coefficients, multivariable logistic regression analysis, and decision tree analysis were performed using 36 fall risk factors identified from inpatients. An algorithm was generated combining nine essential fall risk factors (delirium, fall history, use of a walking aid, stagger, impaired judgment/comprehension, muscle weakness of the lower limbs, night urination, use of sleeping drug, and presence of infusion route/tube). Moreover, fall risk level was conveniently classified into four groups (extra-high, high, moderate, and low) according to the priority of fall risk. Finally, we confirmed the reliability of the algorithm using a validation dataset that comprised 57,929 inpatients (non-fall, 57,695; fall, 234) hospitalized between October 2018 and September 2019. Using the newly created algorithm, clinical staff including nurses may be able to appropriately evaluate fall risk level and provide preventive interventions for individual inpatients.

scholarly journals Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models

2019 ◽  
Vol 9 (1) ◽  
pp. 171 ◽  
Author(s):  
Wei Chen ◽  
Zenghui Sun ◽  
Jichang Han
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Weights Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset

The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.

scholarly journals Do gliosarcomas have distinct imaging features on routine MRI?

2021 ◽  
pp. 197140092110123
Author(s):  
Christoph J Maurer ◽  
Irina Mader ◽  
Felix Joachimski ◽  
Ori Staszewski ◽  
Bruno Märkl ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Multivariate Analysis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Imaging Features ◽  
Age And Sex

Purpose The aim of this study was the development and external validation of a logistic regression model to differentiate gliosarcoma (GSC) and glioblastoma multiforme (GBM) on standard MR imaging. Methods A univariate and multivariate analysis was carried out of a logistic regression model to discriminate patients histologically diagnosed with primary GSC and an age and sex-matched group of patients with primary GBM on presurgical MRI with external validation. Results In total, 56 patients with GSC and 56 patients with GBM were included. Evidence of haemorrhage suggested the diagnosis of GSC, whereas cystic components and pial as well as ependymal invasion were more commonly observed in GBM patients. The logistic regression model yielded a mean area under the curve (AUC) of 0.919 on the training dataset and of 0.746 on the validation dataset. The accuracy in the validation dataset was 0.67 with a sensitivity of 0.85 and a specificity of 0.5. Conclusions Although some imaging criteria suggest the diagnosis of GSC or GBM, differentiation between these two tumour entities on standard MRI alone is not feasible.

scholarly journals Predicting the Presence of Targetable Molecular Alteration(s) With Clinico-metabolic 18F-FDG PET Radiomics in Non-Asian Lung Adenocarcinoma Patients.

2021 ◽  
Author(s):  
Nicolas Aide ◽  
Kathleen Weys ◽  
Charline LASNON
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Analysis ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Smoking History ◽  
Training Dataset ◽  
Validation Dataset ◽  
Molecular Alteration ◽  
Molecular Alterations ◽  
Ajcc Stage

Abstract PurposeTo investigate if combining clinical characteristics with pre-therapeutic 18F-FDG PET radiomics could predict the presence of molecular alteration(s) in key molecular targets in lung adenocarcinoma in order to screen patients who are more likely to benefit from a tumoral molecular analysis. MethodsThis non-interventional mono-centric study prospectively included patients with newly-diagnosed lung adenocarcinoma referred for baseline PET and who had tumoral molecular analyses for the following targets: EGFR, BRAF, KRAS, NRAS, MET, STK11, PIK3CA, ALK and ROS1. Tumoral volumes of interest were analysed using LifeX software. A logistic regression was performed, including sex, age, smoking history, AJCC stage and thirty-one PET variables. A validation process was used by randomly splitting the data in training and validation datasets.ResultsEighty-seven patients were analysed. Forty-seven patients (54.0%) had at least one molecular alteration. Using the training dataset (n=67), five variables were included in the logit model: age, sex, AJCC stage, correlation_GLCM and GLNU_GLZLM. More molecular alterations were observed in women: 88.0% in women versus 40.3% in men (p<0.0001). Other clinical and PET variables were not different between patients with and without molecular alterations. There was a moderate correlation between correlation_GLCM and GLNU_GLZLM (p <0.0001, ρ = 0.591). The ROC analysis for molecular alteration prediction using this model found an area under the curve equal to 0.891 (p<0.0001). A cut-off value set to 0.38 led to a sensitivity of 97.4%, a negative predictive value of 80.4% and a LR+ equal to 3.1. When applying this cut-off value in the validation dataset of patients (n=20), the test presented a sensitivity equal to 88.9%, a NPV equal to 87.5% and a LR+ = 2.4. ConclusionsA clinico-metabolic 18F-FDG PET phenotype allows detecting key molecular target alterations with high sensitivity and NPV thus opens the way to the selection of patients for molecular analysis.

scholarly journals A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

2021 ◽  
Author(s):  
Xue Lin ◽  
Sheng Zhao ◽  
Huijie Jiang ◽  
Fucang Jia ◽  
Guisheng Wang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Goodness Of Fit ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Locally Advanced ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Prospective Analysis ◽  
Clinical Value

Abstract Purpose To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer. Methods A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram. Results The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987). Conclusion A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

scholarly journals Development and validation of a predictive model of drug-resistant genetic generalized epilepsy

Neurology ◽  
2020 ◽  
Vol 95 (15) ◽  
pp. e2150-e2160 ◽  
Author(s):  
Hyunmi Choi ◽  
Kamil Detyniecki ◽  
Carl Bazil ◽  
Suzanne Thornton ◽  
Peter Crosta ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Seizure Type ◽  
Drug Resistant ◽  
Psychiatric Condition ◽  
Catamenial Epilepsy ◽  
Increased Risk ◽  
History Of ◽  
Generalized Epilepsy

ObjectiveTo develop and validate a clinical prediction model for antiepileptic drug (AED)–resistant genetic generalized epilepsy (GGE).MethodWe performed a case-control study of patients with and without drug-resistant GGE, nested within ongoing longitudinal observational studies of AED response at 2 tertiary epilepsy centers. Using a validation dataset, we tested the predictive performance of 3 candidate models, developed from a training dataset. We then tested the candidate models' predictive ability on an external testing dataset.ResultsOf 5,189 patients in the ongoing longitudinal study, 121 met criteria for AED-resistant GGE and 468 met criteria for AED-responsive GGE. There were 66 patients with GGE in the external dataset, of whom 17 were cases. Catamenial epilepsy, history of a psychiatric condition, and seizure types were strongly related with drug-resistant GGE case status. Compared to women without catamenial epilepsy, women with catamenial epilepsy had about a fourfold increased risk for AED resistance. The calibration of 3 models, assessing the agreement between observed outcomes and predictions, was adequate. Discriminative ability, as measured with area under the receiver operating characteristic curve (AUC), ranged from 0.58 to 0.65.ConclusionCatamenial epilepsy, history of a psychiatric condition, and the seizure type combination of generalized tonic clonic, myoclonic, and absence seizures are negative prognostic factors of drug-resistant GGE. The AUC of 0.6 is not consistent with truly effective separation of the groups, suggesting other unmeasured variables may need to be considered in future studies to improve predictability.

Fluoroscopically guided lumbar spine interlaminar and transforaminal epidural injections: inadvertent intravascular injection

2020 ◽  
Vol 61 (11) ◽  
pp. 1534-1540
Author(s):  
Jad S Husseini ◽  
F Joseph Simeone ◽  
Steven J Staffa ◽  
William E Palmer ◽  
Connie Y Chang
Keyword(s):  
Lumbar Spine ◽  
Multivariable Logistic Regression Analysis ◽  
Intravascular Injection ◽  
Entire Cohort ◽  
Epidural Injections ◽  
Lumbar Level ◽  
Transforaminal Approach ◽  
Increased Risk ◽  
Prior Surgery ◽  
History Of

Background Inadvertent intravascular injection is a rare but catastrophic complication of lumbar epidural injections. Purpose To determine risk factors for inadvertent intravascular injection in fluoroscopically guided lumbar spine epidural injections. Material and Methods A total of 212 patients who presented for lumbar interlaminar or transforaminal injection were prospectively enrolled. Patient demographics, history of surgery, injection side, site and approach, and volume of contrast injected were recorded. Results There were 89 (42%) interlaminar and 123 (58%) transforaminal injections. For 36 (17%) patients, there had been surgery at the injected or adjacent lumbar level. There were 25 (12%) inadvertent intravascular injections, with an incidence of 2/93 (2%) for interlaminar and 23/119 (19%) for transforaminal injections. The patients with inadvertent intravascular injection were older ( P = 0.017) and had prior surgery at or adjacent to the level of injection ( P < 0.0001). Transforaminal approach had a higher intravasation rate than interlaminar injections, both when comparing the entire cohort ( P = 0.0001) and only patients without prior surgery ( P = 0.01). In multivariable logistic regression analysis, transforaminal injections (odds ratio [OR] 9.77, 95% confidence interval [CI] 2.14–44.6, P = 0.003) and prior surgery at or adjacent to the level of injection (OR 5.71, 95% CI 2.15–15.15, P < 0.001) were independently associated with increased risk of inadvertent intravascular injections. Conclusion Inadvertent intravascular injection occurred in 12% of our lumbar injection cohort and is more common with transforaminal injections, in older patients, and with prior lumbar surgery at or adjacent to the level of injection.

scholarly journals An online diabetic retinopathy screening tool for patients with type 2 diabetes

2021 ◽  
Vol 14 (11) ◽  
pp. 1748-1755
Author(s):  
Wan-Yue Li ◽  
◽  
Ya-Nan Song ◽  
Ling Luo ◽  
Chuang Nie ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Screening Tool ◽  
External Validation ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Validation Dataset ◽  
Internal Validation ◽  
Diabetic Retinopathy Screening

AIM: To develop a useful diabetic retinopathy (DR) screening tool for patients with type 2 diabetes mellitus (T2DM). METHODS: A DR prediction model based on the Logistic regression algorithm was established on the development dataset containing 778 samples (randomly assigned to the training dataset and the internal validation dataset at a ratio of 7:3). The generalization capability of the model was assessed using an external validation dataset containing 128 samples. The DR risk calculator was developed through WeChat Developer Tools using JavaScript, which was embedded in the WeChat Mini Program. RESULTS: The model revealed risk factors (duration of diabetes, diabetic nephropathy, and creatinine level) and protective factors (annual DR screening and hyperlipidemia) for DR. In the internal and external validation, the recall ratios of the model were 0.92 and 0.89, respectively, and the area under the curve values were 0.82 and 0.70, respectively. CONCLUSION: The DR screening tool integrates education, risk prediction, and medical advice function, which could help clinicians in conducting DR risk assessments and providing recommendations for ophthalmic referral to increase the DR screening rate among patients with T2DM.

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