scholarly journals Bedside breath tests in children with abdominal pain: a prospective pilot feasibility study

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
David C. Wong ◽  
Samuel D. Relton ◽  
Victoria Lane ◽  
Mohamed Ismail ◽  
Victoria Goss ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Abdominal Pain ◽  
Characteristic Curve ◽  
Primary Diagnosis ◽  
Receiver Operator Characteristic Curve ◽  
Breath Tests ◽  
Paediatric Hospital ◽  
Suspected Appendicitis ◽  
Definitive Method ◽  
Patient Reported

Abstract Background There is no definitive method of accurately diagnosing appendicitis before surgery. We evaluated the feasibility of collecting breath samples in children with abdominal pain and gathered preliminary data on the accuracy of breath tests. Methods We conducted a prospective pilot study at a large tertiary referral paediatric hospital in the UK. We recruited 50 participants with suspected appendicitis, aged between 5 and 15 years. Five had primary diagnosis of appendicitis. The primary outcome was the number of breath samples collected. We also measured the number of samples processed within 2 h and had CO2 ≥ 3.5%. Usability was assessed by patient-reported pain pre- and post-sampling and user-reported sampling difficulty. Logistic regression analysis was used to predict appendicitis and evaluated using the area under the receiver operator characteristic curve (AUROC). Results Samples were collected from all participants. Of the 45 samples, 36 were processed within 2 h. Of the 49 samples, 19 had %CO2 ≥ 3.5%. No difference in patient-reported pain was observed (p = 0.24). Sampling difficulty was associated with patient age (p = 0.004). The logistic regression model had AUROC = 0.86. Conclusions Breath tests are feasible and acceptable to patients presenting with abdominal pain in clinical settings. We demonstrated adequate data collection with no evidence of harm to patients. The AUROC was better than a random classifier; more specific sensors are likely to improve diagnostic performance. Trial registration ClinicalTrials.gov, NCT03248102. Registered 14 Aug 2017.

Value of Fibrinogen to Discriminate Appendicitis from Nonspecific Abdominal Pain in Preschool Children

2019 ◽  
Vol 30 (04) ◽  
pp. 357-363
Author(s):  
Javier Gómez-Veiras ◽  
Ángel Salgado-Barreira ◽  
José Luis Vázquez ◽  
Margarita Montero-Sánchez ◽  
José Ramón Fernández-Lorenzo ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Preschool Children ◽  
Diagnostic Accuracy ◽  
Prothrombin Time ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Suspected Appendicitis ◽  
Uncomplicated Appendicitis ◽  
Good Diagnostic Accuracy

Introduction The aim of this study was to assess the diagnostic value of the biomarker fibrinogen (FB), along with the markers white blood cell (WBC) count, absolute neutrophil count (ANC), and C-reactive protein (CRP), to discriminate appendicitis from nonspecific abdominal pain (NSAP) in preschool children. Materials and Methods We prospectively evaluated all children aged <5 years admitted for suspected appendicitis at an academic pediatric emergency department during 5 years. Diagnostic accuracy of FB (prothrombin time–derived method), WBC, ANC, and CRP were assessed by the area under the curve (AUC) of the receiver-operating characteristic curve. Results A total of 82 patients were enrolled in the study (27 NSAP, 17 uncomplicated, and 38 complicated appendicitides). WBC and ANC had moderate diagnostic accuracy for appendicitis versus NSAP (WBC: AUC 0.66, ANC: AUC 0.67). CRP and FB had good diagnostic accuracy for appendicitis versus NSAP (CRP: AUC 0.78, FB: AUC 0.77). WBC and ANC are not useful to discriminate complicated versus uncomplicated appendicitis (WBC: AUC 0.43, ANC: AUC 0.45). CPR and FB had good diagnostic accuracy for complicated versus uncomplicated appendicitis (CRP: AUC 0.80, FB: AUC 0.73). Conclusion CRP and FB are more useful than WBC and ANC to discriminate appendicitis from NSAP in preschool children. CRP and FB are especially useful to discriminate complicated from uncomplicated appendicitis and NSAP. In a child with suspected appendicitis, a plasma FB level (prothrombin time–derived method) >540 mg/dL is associated with an increased likelihood of complicated appendicitis.

Restenosis and symptom recurrence after endovascular therapy for claudication: Does duplex ultrasound correlate with recurrent claudication?

Vascular ◽  
2014 ◽  
Vol 23 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Douglas W Jones ◽  
Ashley Graham ◽  
Peter H Connolly ◽  
Darren B Schneider ◽  
Andrew J Meltzer
Keyword(s):  
Treatment Failure ◽  
Endovascular Therapy ◽  
Characteristic Curve ◽  
Velocity Ratio ◽  
Duplex Ultrasound ◽  
Receiver Operator Characteristic Curve ◽  
Post Intervention ◽  
Patient Reported ◽  
Institutional Experience ◽  
Symptom Recurrence

After endovascular therapy, duplex ultrasound surveillance to detect restenosis guides clinical decisions and defines treatment failure. However, the correlation between duplex ultrasound and symptom recurrence remains unclear. We reviewed our institutional experience (2007–2010) to identify patients undergoing endovascular therapy for claudication. The association between post-intervention systolic velocity ratio and patient-reported symptom recurrence was determined. We analyzed 183 follow-up visits following treatment in 88 limbs (femoropopliteal (56%) or iliac (44%) arteries). After femoropopliteal intervention, median systolic velocity ratio was higher in patients with symptom recurrence (2.99 symptomatic vs. 1.69 asymptomatic; p < 0.001). Elevated systolic velocity ratio or occlusion correlated with symptom recurrence (area under receiver operator characteristic curve = 0.82 [95% CI 0.74–0.83]), and systolic velocity ratio >2.5 was 71% sensitive and 72% specific for symptom recurrence. After femoropopliteal endovascular therapy for claudication, duplex ultrasound-detected restenosis is highly associated with clinical deterioration. This validates objective criteria for treatment failure in claudicants and suggests that symptom status can serve as a primary indicator of anatomic restenosis.

scholarly journals Electroencephalogram Monitoring of Depth of Anesthesia during Office-Based Anesthesia

2020 ◽  
Author(s):  
Sunil B. Nagaraj ◽  
Pegah Kahali ◽  
Patrick L. Purdon ◽  
Fred E. Shapiro ◽  
M. Brandon Westover
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Characteristic Curve ◽  
Spectral Characteristics ◽  
Receiver Operator Characteristic Curve ◽  
Automated System ◽  
Spectral Features ◽  
Depth Of Anesthesia ◽  
Automated Method

ABSTRACTObjectiveElectroencephalogram (EEG) monitors are often used to monitor depth of general anesthesia. EEG monitoring is less well developed for lighter levels of anesthesia. Here we present an automated method to monitor the depth of anesthesia for office based procedures using EEG spectral features.MethodsWe analyze EEG recordings from 30 patients undergoing sedation using a multimodal anesthesia strategy. Level of sedation during the procedure is coded using the Richmond Agitation and Sedation Scale (RASS). The power spectrum from the frontal EEG is used to infer the level of sedation, by training a logistic regression model with elastic net regularization. Area under the receiver operator characteristic curve (AUC) is used to evaluate how well the automated system distinguishes awake from sedated EEG epochs.ResultsEEG power spectral characteristics vary systematically and consistently across patients with the levels of light anesthesia and relatively healthy patients encountered during office-based anesthesia procedures. The logistic regression model using spectral EEG features distinguishes awake and sedated states with an AUC of 0.85 (± 0.14).ConclusionsOur results demonstrate that frontal EEG spectral features can reliably monitor sedation levels during office based anesthesia.

Comparison of PROMIS Anxiety and Depression, PHQ-8, and GAD-7 to screen for anxiety and depression among patients presenting for spine surgery

2019 ◽  
Vol 30 (4) ◽  
pp. 524-531
Author(s):  
Taylor E. Purvis ◽  
Brian J. Neuman ◽  
Lee H. Riley ◽  
Richard L. Skolasky
Keyword(s):  
Major Depression ◽  
Spine Surgery ◽  
Characteristic Curve ◽  
Depression Scale ◽  
Data Driven ◽  
Anxiety And Depression ◽  
Measurement Information ◽  
Generalized Anxiety ◽  
Cut Points ◽  
Patient Reported

OBJECTIVEIn this paper, the authors demonstrate to spine surgeons the prevalence and severity of anxiety and depression among patients presenting for surgery and explore the relationships between different legacy and Patient-Reported Outcomes Measurement Information System (PROMIS) screening measures.METHODSA total of 512 adult spine surgery patients at a single institution completed the 7-item Generalized Anxiety Disorder questionnaire (GAD-7), 8-item Patient Health Questionnaire (PHQ-8) depression scale, and PROMIS Anxiety and Depression computer-adaptive tests (CATs) preoperatively. Correlation coefficients were calculated between PROMIS scores and GAD-7 and PHQ-8 scores. Published reference tables were used to determine the presence of anxiety or depression using GAD-7 and PHQ-8. Sensitivity and specificity of published guidance on the PROMIS Anxiety and Depression CATs were compared. Guidance from 3 sources was compared: published GAD-7 and PHQ-8 crosswalk tables, American Psychiatric Association scales, and expert clinical consensus. Receiver operator characteristic curves were used to determine data-driven cut-points for PROMIS Anxiety and Depression. Significance was accepted as p < 0.05.RESULTSIn 512 spine surgery patients, anxiety and depression were prevalent preoperatively (5% with any anxiety, 24% with generalized anxiety screen-positive; and 54% with any depression, 24% with probable major depression). Correlations were moderately strong between PROMIS Anxiety and GAD-7 scores (r = 0.72; p < 0.001) and between PROMIS Depression and PHQ-8 scores (r = 0.74; p < 0.001). The observed correlation of the PROMIS Depression score was greater with the PHQ-8 cognitive/affective score (r = 0.766) than with the somatic score (r = 0.601) (p < 0.001). PROMIS Anxiety and Depression CATs were able to detect the presence of generalized anxiety screen-positive (sensitivity, 86.0%; specificity, 81.6%) and of probable major depression (sensitivity, 82.3%; specificity, 81.4%). Receiver operating characteristic curve analysis demonstrated data-driven cut-points for these groups.CONCLUSIONSPROMIS Anxiety and Depression CATs are reliable tools for identifying generalized anxiety screen-positive spine surgery patients and those with probable major depression.

AVALIAÇÃO DE UM INSTRUMENTO DE MENSURAÇÃO DE MORBIDADE PSÍQUICA: ESTUDO DE VALIDAÇÃO DO SELF-REPORTING QUESTIONNAIRE (SRQ-20)

1970 ◽  
Vol 34 (3) ◽  
pp. 544 ◽  
Author(s):  
Kionna Oliveira Bernardes Santos ◽  
Tânia Maria de Araújo ◽  
Paloma de Sousa Pinho ◽  
Ana Cláudia Conceição Silva
Keyword(s):  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Interview ◽  
Receiver Operator Characteristic ◽  
Interview Schedule ◽  
Para Determinar ◽  
Self Reporting Questionnaire

O Self-Reporting Questionnaire (SRQ-20), desenvolvido pela Organização Mundial de Saúde, tem sido utilizado para mensuração de nível de suspeição de transtornos mentais em estudos brasileiros, especialmente em grupos de trabalhadores. O objetivo deste estudo foi avaliar o desempenho do SRQ-20, com base em indicadores de validade (sensibilidade, especificidade, taxa de classificação incorreta e valores preditivos), e determinar o melhor ponto de corte para classificação dos transtornos mentais comuns na população estudada. O estudo incluiu 91 indivíduos selecionados aleatoriamente de um estudo de corte transversal realizado com população residente em áreas urbanas de Feira de Santana (BA). Entrevistas clínicas, realizadas por psicólogas, utilizando o Revised Clinical Interview Schedule (CIS-R), foi adotada como padrão-ouro. Na avaliação do desempenho do SRQ-20 foram estimados indicadores de validade (sensibilidade e especificidade). A curva Receiver Operator Characteristic Curve (ROC) foi utilizada para determinar o melhor ponto de corte para classificação de suspeitos/não suspeitos. O ponto de corte de melhor desempenho foi de 6/7 para a população investigada, revelando desempenho razoável com área sob a curva de 0,789. Os resultados indicam que o SRQ-20 possui característica discriminante regular.

scholarly journals Haemoconcentration as a predictor of severity in acute pancreatitis

2016 ◽  
Vol 4 (1) ◽  
pp. 3-7
Author(s):  
Tanka Prasad Bohara ◽  
Dimindra Karki ◽  
Anuj Parajuli ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Cost Effective ◽  
Receiver Operator Characteristic Curve ◽  
First Episode ◽  
P Value ◽  
Receiver Operator Characteristic ◽  
Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

Application of a Computer Vision Tool for Automated Glottic Tracking to Vocal Fold Paralysis Patients

2021 ◽  
pp. 019459982198960
Author(s):  
Tiffany V. Wang ◽  
Nat Adamian ◽  
Phillip C. Song ◽  
Ramon A. Franco ◽  
Molly N. Huston ◽  
...  
Keyword(s):  
Vocal Fold ◽  
Assessment Tool ◽  
Medical Center ◽  
Characteristic Curve ◽  
Academic Medical Center ◽  
Vocal Fold Paralysis ◽  
Control Group ◽  
Large Patient ◽  
Patient Reported ◽  
Before And After

Objectives (1) Demonstrate true vocal fold (TVF) tracking software (AGATI [Automated Glottic Action Tracking by artificial Intelligence]) as a quantitative assessment of unilateral vocal fold paralysis (UVFP) in a large patient cohort. (2) Correlate patient-reported metrics with AGATI measurements of TVF anterior glottic angles, before and after procedural intervention. Study Design Retrospective cohort study. Setting Academic medical center. Methods AGATI was used to analyze videolaryngoscopy from healthy adults (n = 72) and patients with UVFP (n = 70). Minimum, 3rd percentile, 97th percentile, and maximum anterior glottic angles (AGAs) were computed for each patient. In patients with UVFP, patient-reported outcomes (Voice Handicap Index 10, Dyspnea Index, and Eating Assessment Tool 10) were assessed, before and after procedural intervention (injection or medialization laryngoplasty). A receiver operating characteristic curve for the logistic fit of paralysis vs control group was used to determine AGA cutoff values for defining UVFP. Results Mean (SD) 3rd percentile AGA (in degrees) was 2.67 (3.21) in control and 5.64 (5.42) in patients with UVFP ( P < .001); mean (SD) 97th percentile AGA was 57.08 (11.14) in control and 42.59 (12.37) in patients with UVFP ( P < .001). For patients with UVFP who underwent procedural intervention, the mean 97th percentile AGA decreased by 5 degrees from pre- to postprocedure ( P = .026). The difference between the 97th and 3rd percentile AGA predicted UVFP with 77% sensitivity and 92% specificity ( P < .0001). There was no correlation between AGA measurements and patient-reported outcome scores. Conclusions AGATI demonstrated a difference in AGA measurements between paralysis and control patients. AGATI can predict UVFP with 77% sensitivity and 92% specificity.

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 Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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