scholarly journals Pilot Study: Detection of Gastric Cancer From Exhaled Air Analyzed With an Electronic Nose in Chinese Patients

2018 ◽  
Vol 25 (5) ◽  
pp. 429-434 ◽  
Author(s):  
Valérie N. E. Schuermans ◽  
Ziyu Li ◽  
Audrey C. H. M. Jongen ◽  
Zhouqiao Wu ◽  
Jinyao Shi ◽  
...  
Keyword(s):  
Pilot Study ◽  
Electronic Nose ◽  
Predictive Value ◽  
Binary Data ◽  
Characteristic Curve ◽  
Chinese Patients ◽  
Control Group ◽  
P Value ◽  
Predictive Values ◽  
Exhaled Air

The aim of this pilot study is to investigate the ability of an electronic nose (e-nose) to distinguish malignant gastric histology from healthy controls in exhaled breath. In a period of 3 weeks, all preoperative gastric carcinoma (GC) patients (n = 16) in the Beijing Oncology Hospital were asked to participate in the study. The control group (n = 28) consisted of family members screened by endoscopy and healthy volunteers. The e-nose consists of 3 sensors with which volatile organic compounds in the exhaled air react. Real-time analysis takes place within the e-nose, and binary data are exported and interpreted by an artificial neuronal network. This is a self-learning computational system. The inclusion rate of the study was 100%. Baseline characteristics differed significantly only for age: the average age of the patient group was 57 years and that of the healthy control group 37 years ( P value = .000). Weight loss was the only significant different symptom ( P value = .040). A total of 16 patients and 28 controls were included; 13 proved to be true positive and 20 proved to be true negative. The receiver operating characteristic curve showed a sensitivity of 81% and a specificity of 71%, with an accuracy of 75%. These results give a positive predictive value of 62% and a negative predictive value of 87%. This pilot study shows that the e-nose has the capability of diagnosing GC based on exhaled air, with promising predictive values for a screening purpose.

scholarly journals The Predictive Values of Lymphocytopenia in West Darfur Patients with Malaria

2022 ◽  
Vol 17 (2) ◽  
pp. 198-210
Author(s):  
Salma Mohammed Abotaha ◽  
Amira Ahmed Khalid Humeida (MBBs- MD)
Keyword(s):  
Predictive Value ◽  
Lymphocyte Count ◽  
Absolute Lymphocyte Count ◽  
World Health ◽  
Control Group ◽  
P Value ◽  
Case Group ◽  
Predictive Values ◽  
The World ◽  
The Mean

Background: In Sudan malaria is most commonly caused by infection with plasmodium falciparum, although by p.vivax. Malaria causes the most dangerous and highest rates of complication and mortality. Most malaria cases in 2018 were reported by the world health organization (WHO) in the African region(213 million cases of malaria or 93% of all malaria cases in the world and 70% is 5 years or younger). Objectives: The aim of this study was to measure and compare the mean of absolute lymphocyte count in malaria patients and control groups, and to determine positive and negative predictive values of lymphocytopenia in malaria patients. Methods: It was conducted on 100 subjects with malaria as cases and 100 subjects without malaria as controls, at EL Genina Hospital after obtaining the ethical approval and the subjects' consent. It was done by testing the CBC, differential counts, and absolute lymphocyte count then determining the means and p-values. The positive and negative predictive values were also determined. Results: It was found that the mean of TWBC count in the case group was(7,13109/l), and (7,84109/l)in the control group, the p-value was (0.150). The mean of lymphocytes differential in the case group was (20.73%)and (33.96%)in the control group, the p-value was (0.000). While the mean of the absolute lymphocytes counts in the case group was (1.39109/l), it was (2.56109/l)in the control group, with a p-value (0.000). This p-value indicated that there was significant lymphocytopenia in malaria patients. The positive predictive value was 83% and the negative predictive value was 69%. Conclusion: This study concluded that there was no significant lymphocytopenia in malaria patients and that lymphocytopenia cannot be used as the key hematological indicator of malaria infection. 

scholarly journals Age-specific cut-off levels of anti-Müllerian hormone can be used as diagnostic markers for polycystic ovary syndrome

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fahimeh Ramezani Tehrani ◽  
Maryam Rahmati ◽  
Fatemeh Mahboobifard ◽  
Faezeh Firouzi ◽  
Nazanin Hashemi ◽  
...  
Keyword(s):  
Bayesian Method ◽  
Polycystic Ovary ◽  
Characteristic Curve ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Control Group ◽  
Age Group ◽  
Predictive Values ◽  
Pcos Group

Abstract Background The majority of available studies on the AMH thresholds were not age-specific and performed the receiver operating characteristic curve (ROC) analysis, based on variations in sensitivity and specificity rather than positive and negative predictive values (PPV and NPV, respectively), which are more clinically applicable. Moreover, all of these studies used a pre-specified age categorization to report the age-specific cut-off values of AMH. Methods A total of 803 women, including 303 PCOS patients and 500 eumenorrheic non-hirsute control women, were enrolled in the present study. The PCOS group included PCOS women, aged 20–40 years, who were referred to the Reproductive Endocrinology Research Center, Tehran, Iran. The Rotterdam consensus criteria were used for diagnosis of PCOS. The control group was selected among women, aged 20–40 years, who participated in Tehran Lipid and Glucose cohort Study (TLGS). Generalized additive models (GAMs) were used to identify the optimal cut-off points for various age categories. The cut-off levels of AMH in different age categories were estimated, using the Bayesian method. Main results and the role of chance Two optimal cut-off levels of AMH (ng/ml) were identified at the age of 27 and 35 years, based on GAMs. The cut-off levels for the prediction of PCOS in the age categories of 20–27, 27–35, and 35–40 years were 5.7 (95 % CI: 5.48–6.19), 4.55 (95 % CI: 4.52–4.64), and 3.72 (95 % CI: 3.55–3.80), respectively. Based on the Bayesian method, the PPV and NPV of these cut-off levels were as follows: PPV = 0.98 (95 % CI: 0.96–0.99) and NPV = 0.40 (95 % CI: 0.30–0.51) for the age group of 20–27 years; PPV = 0.96 (95 % CI: 0.91–0.99) and NPV = 0.82 (95 % CI: 0.78–0.86) for the age group of 27–35 years; and PPV = 0.86 (95 % CI: 0.80–0.94) and NPV = 0.96 (95 % CI: 0.93–0.98) for the age group of 35–40 years. Conclusions Application of age-specific cut-off levels of AMH, according to the GAMs and Bayesian method, could elegantly assess the value of AMH in discriminating PCOS patients in all age categories.

scholarly journals Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

2021 ◽  
Author(s):  
Nikita Sushentsev ◽  
Leonardo Rundo ◽  
Oleg Blyuss ◽  
Tatiana Nazarenko ◽  
Aleksandr Suvorov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Cancer Progression ◽  
Predictive Value ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Control Group ◽  
P Value ◽  
Lasso Regression

Abstract Objectives To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). Methods The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5–16 years’ experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong’s test. Results The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34–0.77). Conclusions PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. Key Points • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

scholarly journals Assessment of the Role of the Anti-Mullerian Hormone, Luteinizing Hormone/Follicle Stimulating Hormone Ratio in the Diagnosis of Polycystic Ovary Syndrome in Sudanese Women

2018 ◽  
Vol 6 (7) ◽  
pp. 1244-1247
Author(s):  
Hafza Tola ◽  
Mohammed Abbas ◽  
Elsir Abu Alhassan ◽  
Nassr Eldin Shrif ◽  
Mohammed Rida
Keyword(s):  
Luteinizing Hormone ◽  
Polycystic Ovary Syndrome ◽  
Predictive Value ◽  
Polycystic Ovary ◽  
Test Group ◽  
Control Group ◽  
P Value ◽  
Diagnostic Efficiency ◽  
Ovary Syndrome ◽  
Stimulating Hormone

BACKGROUND: The diagnosis of polycystic ovary syndrome (PCOS) is not an easy procedure, as the signs and symptoms are heterogeneous and of undefined aetiology.AIM: This study is aimed to evaluate serum anti-Mullerian hormone (AMH) level and luteinizing hormone (LH)/folic stimulating hormone (FSH) ratio in women with PCOS in Sudan and to assess the diagnostic efficiency for the diagnosis of PCOS.METHODS: In a case-control study, Serum AMH, LH, FSH was measured in the early follicular phase from Sudanese patients (N = 230) with PCOS and100 controls. The LH/FSH ratio was calculated, and its diagnostic power was evaluated by receiver operating characteristic curves.RESULTS: The means of serum AMH, serum LH level and LH/FSH ratio of the test, were significantly increased in the test group compared to the control group (P-value < 0.000). The AMH sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were found to be 83%, 99%, 99%, and 72% respectively. Serum AMH was considered adequate measures for the diagnosis of PCOS; its level showed an area under the ROC curve of 0.98 (95% confidence, P-value < 0.000). The best compromise between 98% specificity and 90% sensitivity was obtained with a cut-off value of 3.3 ng/mL for PCOS diagnosis. There was no correlation between age, body mass index (BMI) and AMH level in the test group.CONCLUSIONS: The Serum AMH level and LH/FSH ratio were higher in patients than in control. However AMH level has better discriminative power and good diagnostic potency for the diagnosis of the PCOS among Sudanese women.

Association of Bacteria Species with Positive Predictive Value of Bacteria Isolated from Cerebrospinal Fluid Cultures: A Retrospective Cohort Study

2021 ◽  
Author(s):  
Yi Ye ◽  
Guanghui Zheng ◽  
Yueyue Kong ◽  
Jiawei Ma ◽  
Guojun Zhang ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Cohort Study ◽  
Positive Predictive Value ◽  
Retrospective Cohort Study ◽  
Predictive Value ◽  
Retrospective Cohort ◽  
Klebsiella Oxytoca ◽  
P Value ◽  
Coagulase Negative Staphylococci ◽  
Predictive Values

Abstract Background: Previous studies discuss the positive predictive value through whether the bacteria are coagulase-negative staphylococci. The view may need to be updated. The aim was to evaluate the positive predictive value of different bacteria species isolated from cerebrospinal fluid cultures and discuss the rationality to view coagulase-negative staphylococci as a group.Methods: This retrospective cohort study recruit all adults with positive cerebrospinal fluid cultures sampled by lumbar puncture 2012-2020 in the Department of Neurosurgery. The exposure was bacteria species, and the outcome was positive predictive value. An episode was defined as a patient with one bacteria. When episodes with a bacteria species reached five, the bacteria species was analyzed specifically. The positive predictive value was defined as the incidence of isolated-bacteria-related infected episodes. The isolated-bacteria-related infected episode was defined as the patient was with clinical features of bacterial meningitis, and the improvement was related to sensitive antibacterial agents. Then the differences of the positive predictive value of different bacteria in all specific bacteria species, coagulase-negative staphylococci, and non-coagulase-negative staphylococci bacteria were calculated, respectively. The results were statistically significant when P-value <.05.Results: 1180 episodes from 1133 patients with 79 bacteria were studied; the positive predictive value was 54.3%. The bacteria included 67 bacteria species, ten bacteria genus, viridans streptococci, and unclassified coagulase-negative staphylococci. Twenty-four specific bacteria species were analyzed. The range of positive predictive values of them was 29.4%-100.0% (P<.0001). The positive predictive value for Enterobacter aerogenes, Pseudomonas aeruginosa, Enterobacter cloacae, and Klebsiella oxytoca was the highest, while the positive predictive value for Staphylococcus cohnii was the lowest. Moreover, 767 (65.0%) were with coagulase-negative staphylococci, the positive predictive value was 46.4%, and the range was 29.4%-85.7% (P=.0020); 413 (35.0%) were with non-coagulase-negative staphylococci bacteria, the positive predictive value was 69.0%, and the range was 40.0%-100.0% (P<.0001).Conclusions: This study suggests that the positive predictive value of different bacteria species is different. It is more reasonable to discuss the positive predictive value of bacteria isolated from cerebrospinal fluid cultures through the bacteria species rather than whether they are coagulase-negative staphylococci.Trial registration: This is a retrospective study without interventions on participants.

scholarly journals Early identification of patients at risk of acute severe pancreatitis with Systemic Inflammatory Response Syndrome score at admission

2019 ◽  
Vol 8 (2) ◽  
pp. 92-95
Author(s):  
Uttam Laudari ◽  
Abishek Thapa ◽  
Tanka Prasad Bohara ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Systemic Inflammatory Response Syndrome ◽  
Inflammatory Response ◽  
Predictive Value ◽  
College Teaching ◽  
Systemic Inflammatory Response ◽  
P Value ◽  
Predictive Values ◽  
Acute Severe Pancreatitis ◽  
Severe Pancreatitis

Background: Clinically, the Systemic Inflammatory Response Syndrome (SIRS) is identified by two or more symptoms including fever or hypothermia, tachycardia, tachypnea and change in blood leukocyte count. The relationship between SIRS symptoms at the time of presentation and severity of pancreatitis is yet to be determined though progression of SIRS in subsequent days has already been correlated.Objectives: To determine the severity of pancreatitis with SIRS score at the time of admission.Methodology: A retrospective cohort study of patients admitted to Department of Surgery, Kathmandu Medical College Teaching Hospital (KMCTH) with diagnosis of Acute Pancreatitis (AP) from December 2014 to January 2016 was conducted. Clinical, biochemical and imaging data were collected from the medical record section. Patients with diagnosis of acute pancreatitis as per Revised Atlanta Classification 2012 were included in the study. SIRS score at time of admission was correlated with Modified Marshall scoring system for organ dysfunction. Patients were grouped into severe and nonsevere group. Sensitivity, specificity and predictive values of SIRS score at admission for organ failure were calculated.Results: Among the 41 patients admitted with diagnosis of acute pancreatitis irrespective of cause, the sensitivity of SIRS score ≥2 at admission in predicting severe pancreatitis was 60 %, specificity was 20%, positive predictive value was 28% and negative predictive value was 20 %, with p-value of 0.52 and odds ratio of 1.6 (CI: 0.376-6.808).Conclusion: SIRS score at admission cannot be solely used in predicting acute severe pancreatitis. Patients can be stratified in resource deficit setting for timely referral to tertiary centre.

scholarly journals An assessment of chromosomal alterations detected by fluorescence in situ hybridisation in pancreatobiliary tract malignancy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaohong Pu ◽  
Hongwei Zheng ◽  
Xin Yang ◽  
Qing Ye ◽  
Zhiwen Fan ◽  
...  
Keyword(s):  
Predictive Value ◽  
In Situ Hybridisation ◽  
False Negative ◽  
Chinese Patients ◽  
Fluorescence In Situ Hybridisation ◽  
Predictive Values ◽  
Formalin Fixed Paraffin Embedded ◽  
9P21 Locus ◽  
Sensitivity Specificity

Abstract Background Using fluorescence in situ hybridisation (FISH) to detect any gain of chromosomes 3, 7, or 17 and loss of the 9p21 locus has been proven to be sensitive in the diagnosis of pancreatobiliary tumors. However, both genetic and environmental factors contribute to the pathogenesis of pancreatobiliary tumors. Therefore, it is unknown whether this method is suitable for Chinese patients with pancreatobiliary tumors. This study aims to compare the sensitivity, specificity, predictive values and accuracy of cytology, ERCP/MRCP and FISH based on Chinese patients with pancreatobiliary tumors,and to analyze differences between brushing-based and formalin-fixed paraffin-embedded (FFPE)-based FISH. Methods A total of 66 brush cytology specimens obtained during ERCP were detected by FISH and cytology test respectively to compare the sensitivity, specificity, predictive values and accuracy. Besides, FFPE-based FISH was performed on 46 corresponding paraffin sections of pancreatobiliary tumors obtained by surgical resection. Results Our findings demonstrate that FISH greatly improves diagnostic sensitivity and negative predictive value compared to ERCP/MRCP and cytology without much reduction in specificity and positive predictive value. However, our results also indicate that FFPE-based FISH could not effectively identify the false-negative of brushing-based FISH. Conclusions We believe that FISH can effectively distinguish true positive and false positive results of cytological or radiological suspicions of malignancy. However, FFPE-based FISH still does not precisely recognize the false-negative of brushing-based FISH. Both cytology-based and PPFE-based FISH had limitation in some specimens.

scholarly journals Validity of ICD-10-CM codes for determination of diabetes type for persons with youth-onset type 1 and type 2 diabetes

2019 ◽  
Vol 7 (1) ◽  
pp. e000547 ◽  
Author(s):  
Gloria C Chi ◽  
Xia Li ◽  
Sara Y Tartof ◽  
Jeff M Slezak ◽  
Corinna Koebnick ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Predictive Value ◽  
Gold Standard ◽  
Characteristic Curve ◽  
Diabetes Type ◽  
Predictive Values ◽  
Icd 10

ObjectiveDiagnosis codes might be used for diabetes surveillance if they accurately distinguish diabetes type. We assessed the validity ofInternational Classification of Disease, 10th Revision, Clinical Modification(ICD-10-CM) codes to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) among health plan members with youth-onset (diagnosis age <20 years) diabetes.Research design and methods. Diabetes case identification and abstraction of diabetes type was done as part of the SEARCH for Diabetes in Youth Study. The gold standard for diabetes type is the physician-assigned diabetes type documented in patients’ medical records. Using all healthcare encounters with ICD-10-CM codes for diabetes, we summarized codes within each encounter and determined diabetes type using percent of encounters classified as T2DM. We chose 50% as the threshold from a receiver operating characteristic curve because this threshold yielded the largest Youden’s index. Persons with ≥50% T2DM-coded encounters were classified as having T2DM. Otherwise, persons were classified as having T1DM. We calculated sensitivity, specificity, positive and negative predictive values, and accuracy overall and by demographic characteristics.ResultsAccording to the gold standard, 1911 persons had T1DM and 652 persons had T2DM (mean age (SD): 19.1 (6.5) years). We obtained 90.6% (95% CI 88.4% to 92.9%) sensitivity, 96.3% (95% CI 95.4% to 97.1%) specificity, 89.3% (95% CI 86.9% to 91.6%) positive predictive value, 96.8% (95% CI 96.0% to 97.6%) negative predictive value, and 94.8% (95% CI 94.0% to 95.7%) accuracy for discriminating T2DM from T1DM.ConclusionsICD-10-CM codes can accurately classify diabetes type for persons with youth-onset diabetes, showing promise for rapid, cost-efficient diabetes surveillance.

Usefulness of the ACTEL Score to Predict Atrial Fibrillation in Patients with Cryptogenic Stroke

Cardiology ◽  
2020 ◽  
Vol 145 (3) ◽  
pp. 168-177 ◽  
Author(s):  
Antonio Muscari ◽  
Pietro Barone ◽  
Luca Faccioli ◽  
Marco Ghinelli ◽  
Marco Pastore Trossello ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Cryptogenic Stroke ◽  
Left Ventricular ◽  
Control Group ◽  
End Diastolic Volume ◽  
Electrocardiogram Monitoring

Introduction: To assess the probability of undetected atrial fibrillation (AF) in patients with ischemic stroke, we previously compared patients who were first diagnosed with AF with patients with large or small artery disease and obtained the MrWALLETS 8-item scoring system. In the present study, we utilized cryptogenic strokes (CS) as the control group, as AF is normally sought among CS patients. Methods: We retrospectively examined 191 ischemic stroke patients (72.5 ± 12.6 years), 68 with first diagnosed AF and 123 with CS, who had undergone 2 brain CT scans, echocardiography, carotid/vertebral ultrasound, continuous electrocardiogram monitoring and anamnestic/laboratory search for cardiovascular risk factors. Results: In logistic regression, 5 variables were independently associated with AF, forming the “ACTEL” score: Age ≥75 years (OR 2.42, 95% CI 1.18–4.96, p = 0.02; +1 point); hyperCholesterolemia (OR 0.38, 95% CI 0.18–0.78, p = 0.009; –1 point); Tricuspid regurgitation ≥ mild-to-moderate (OR 4.99, 95% CI 1.63–15.27, p = 0.005; +1 point); left ventricular End-diastolic volume <65 mL (OR 7.43, 95% CI 2.44–22.6, p = 0.0004; +1 point); Left atrium ≥4 cm (OR 4.57, 95% CI 1.97–10.62, p = 0.0004; +1 point). The algebraic sum of these points may range from –1 to +4. For AF identification, the area under the receiver operating characteristic curve was 0.80 (95% CI 0.73–0.87). With a cutoff of ≥2, positive predictive value was 80.8%, specificity 92.7% and sensitivity 55.9%. Conclusions: The ACTEL score, a simplified and improved version of the MrWALLETS score, allows the identification of patients with first diagnosed AF, in the context of CSs, with a high positive predictive value.

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