Recalibrating the Non-Communicable Diseases Risk Prediction Tools for the Rural Population of Western India

Abstract Background: The present study was conducted to recalibrate the effectiveness of Indian Diabetes Risk Scores (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistically screening for Diabetes Mellitus (DM) and Hypertension (HT) among the patients attending health centres, and to estimate the risk of fatal and non-fatal Cardio-Vascular Diseases (CVDs) using WHO/ISH chartMethods: All the people of age ≥30 years attending the health centers were screened for DM and HT. Weight, height, and waist and hip circumferences were measured and BMI and Waist Hip Ratio (WHR) were calculated. Risk categorization of all participants was done using IDRS, CBAC, and WHO/ISH risk prediction charts. Individuals diagnosed with DM or HT were started on treatment. The data was recorded using Epicollect5 and was analyzed using SPSS v.23 and MedCalc v.19.8. ROC curves were plotted for DM and HT with the IDRS, CBAC score and anthropometric parameters. Sensitivity (SN), specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy and Youden’s index were calculated for different cut-offs of IDRS and CBAC scores.Results: A total of 942 participants were included for the screening, out of them, 6.42 % (95% CI: 4.92-8.20) were diagnosed with DM. Hypertension was detected among 25.7% (95% CI: 22.9-28.5) of the participants. A total of 447 (47.3%) participants were found with IDRS score ≥ 60, and 276 (29.3%) with CBAC score >4. As much as 26.1% were at moderate to higher risk (≥10%) of developing CVDs. Area Under the Curve (AUC) for IDRS in predicting DM was 0.64 (0.58-0.70), with 67.1% SN and 55.2% SP (Youden's Index= 0.22). While the AUC for CBAC was 0.59 (0.53-0.65). For hypertension the both the AUCs were 0.66 (0.62-0.71) and 0.63 (0.59-0.67), respectively.Conclusions: Instead of CBAC, the present study emphasizes the usefulness of IDRS as an excellent tool for screening for both DM and HT. This is the time to expose the hidden part of the NCDs iceberg by having high sensitivity of non-invasive instruments (like IDRS), so, we propose a cut-off value of 50 for the IDRS to screen for diabetes in the rural Indian population.

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A comparison of the 33-item Hypomania Checklist with the 33-item Hypomania Checklist-external assessment for the detection of bipolar disorder in adolescents

International Journal of Bipolar Disorders ◽

10.1186/s40345-021-00246-0 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Xu Chen ◽

Wei Bai ◽

Na Zhao ◽

Sha Sha ◽

Teris Cheung ◽

...

Keyword(s):

Bipolar Disorder ◽

Predictive Value ◽

Area Under The Curve ◽

High Sensitivity ◽

Adverse Outcomes ◽

Clinical Settings ◽

External Assessment ◽

Appropriate Treatment ◽

Youden’S Index ◽

Higher Sensitivity

Abstract Background Adolescents with bipolar disorder (BD) are often misdiagnosed as having major depressive disorder (MDD), which delays appropriate treatment and leads to adverse outcomes. The aim of this study was to compare the performance of the 33-item Hypomania Checklist (HCL-33) with the 33-item Hypomania Checklist- external assessment (HCL-33-EA) in adolescents with BD or MDD. Methods 147 adolescents with BD and 113 adolescents with MDD were consecutively recruited. The HCL-33 and HCL-33-EA were completed by patients and their carers, respectively. The sensitivity, positive predictive value (PPV), specificity, negative predictive value (NPV), and area under the curve (AUC) were calculated and compared between the two instruments, using cut-off values based on the Youden’s index. Results The total scores of the HCL-33 and HCL-33-EA were positively and significantly correlated (rs = 0.309, P < 0.001). Compared to the HCL-33, the HCL-33-EA had higher sensitivity and NPV (HCL-33: sensitivity = 0.58, NPV = 0.53; HCL-33-EA: sensitivity = 0.81, NPV = 0.60), while the HCL-33 had higher specificity and PPV (HCL-33: specificity = 0.61, PPV = 0.66; HCL-33-EA: specificity = 0.37, PPV = 0.63). Conclusion Both the HCL-33 and HCL-33-EA seem to be useful for screening depressed adolescents for BD. The HCL-33-EA would be more appropriate for distinguishing BD from MDD in adolescents due to its high sensitivity in Chinese clinical settings.

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Significance of CAVI, hs-CRP and Homocysteine in subclinical arteriosclerosis among a healthy population in China

Clinical & Investigative Medicine ◽

10.25011/cim.v36i2.19570 ◽

2013 ◽

Vol 36 (2) ◽

pp. 81 ◽

Cited By ~ 1

Author(s):

Jinpeng Zhong ◽

Yonghong Wang ◽

Xiaoling Wang ◽

Fengzeng Li ◽

Yulei Hou ◽

...

Keyword(s):

Predictive Value ◽

Area Under The Curve ◽

High Sensitivity ◽

Intima Media Thickness ◽

Roc Curves ◽

Carotid Intima Media Thickness ◽

Youden Index ◽

Healthy Population ◽

Hs Crp ◽

Sensitivity Specificity

Purpose: The purpose of this study is to evaluate the ability of cardio-ankle vascular index (CAVI), high-sensitivity C-reactive protein (hs-CRP) levels and homocysteine (Hcy) levels to screen for subclinical arteriosclerosis (subAs) in an apparently healthy population, with the view to obtaining an optimal diagnostic marker or profile for subAs. Methods: Subjects (152) undergoing routine health examinations were recruited and divided into two groups: carotid arteriosclerosis (CA) and non-carotid arteriosclerosis (NCA), according to carotid intima-media thickness (CMIT). CAVI was calculated based on blood pressure and pulse wave velocity. Serum hs-CRP and Hcy levels were also measured. A Receiver Operating Characteristic (ROC) curve was plotted to evaluate the efficacy of each in carotid arteriosclerosis screening. Ten parameter combinations, designated W1 to W10, were compared in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: The levels of all three parameters were significantly higher in the CA group, compared with the NCA group. ROC curves showed that the area under the curve (AUC) for CAVI was 0.708 (95%CI: 0.615-0.800), which is significantly larger than that of either hs-CRP (0.622) or Hcy (0.630), respectively (P < 0.001). Maximum sensitivity (100%) and NPV (100%) were attained with W10, while maximum specificity (86.2%) and PPV (46.7%) were obtained with W7. With W9, the maximum Youden index (0.416) was obtained, with a sensitivity of 77.8% and specificity of 63.8%. Conclusions: CAVI is more effective than hs-CRP or Hcy for subAs screening. The optimal profile was obtained with a combination of CAVI and other parameters.

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Evaluation of the clot burden score (cbs) for acute ischemic stroke (ais) in intent-to-treat patients.

Journal Of Cardiovascular, Neurovascular & Stroke ◽

10.32896/cvns.v1n1.11-15 ◽

2019 ◽

Vol 1 (1) ◽

pp. 11-15 ◽

Cited By ~ 1

Author(s):

Sarah Yaziz ◽

Ahmad Sobri Muda ◽

Wan Asyraf Wan Zaidi ◽

Nik Azuan Nik Ismail

Keyword(s):

Ischemic Stroke ◽

Clinical Outcome ◽

Acute Ischemic Stroke ◽

Predictive Value ◽

Area Under The Curve ◽

Rank Correlation ◽

Treatment Decision ◽

Roc Curves ◽

Significant Negative Correlation ◽

Clot Burden

Background : The clot burden score (CBS) is a scoring system used in acute ischemic stroke (AIS) to predict patient outcome and guide treatment decision. However, CBS is not routinely practiced in many institutions. This study aimed to investigate the feasibility of CBS as a relevant predictor of good clinical outcome in AIS cases. Methods: A retrospective data collection and review of AIS patients in a teaching hospital was done from June 2010 until June 2015. Patients were selected following the inclusion and exclusion criteria. These patients were followed up after 90 days of discharge. The Modified Rankin scale (mRS) was used to assess their outcome (functional status). Linear regression Spearman Rank correlation was performed between the CBS and mRS. The quality performance of the correlations was evaluated using Receiver operating characteristic (ROC) curves. Results: A total of 89 patients with AIS were analysed, 67.4% (n=60) male and 32.6% (n=29) female. Twenty-nine (29) patients (33.7%) had a CBS ?6, 6 patients (6.7%) had CBS <6, while 53 patients (59.6%) were deemed clot free. Ninety (90) days post insult, clinical assessment showed that 57 (67.6%) patients were functionally independent, 27 (30.3%) patients functionally dependent, and 5 (5.6%) patients were deceased. Data analysis reported a significant negative correlation (r= -0.611, p<0.001). ROC curves analysis showed an area under the curve of 0.81 at the cut-off point of 6.5. This showed that a CBS of more than 6 predicted a good mRS clinical outcome in AIS patients; with sensitivity of 98.2%, specificity of 53.1%, positive predictive value (PPV) of 76%, and negative predictive value (NPV) of 21%. Conclusion: CBS is a useful additional variable for the management of AIS cases, and should be incorporated into the routine radiological reporting for acute ischemic stroke (AIS) cases.

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Clinical risk scores for the early prediction of severe outcomes in patients hospitalized for COVID-19

Internal and Emergency Medicine ◽

10.1007/s11739-020-02617-4 ◽

2021 ◽

Author(s):

Walter Ageno ◽

◽

Chiara Cogliati ◽

Martina Perego ◽

Domenico Girelli ◽

...

Keyword(s):

Hospital Admission ◽

Predictive Value ◽

Clinical Laboratory ◽

Area Under The Curve ◽

Orotracheal Intubation ◽

Risk Scores ◽

Algorithm Selection ◽

Derivation Cohort ◽

Neutrophil Lymphocyte Ratio ◽

Predictive Risk

AbstractCoronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identification of high-risk COVID-19 patients is crucial. We aimed to derive and validate a simple score for the prediction of severe outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Italian Society of Internal Medicine. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission at five hospitals. Three algorithm selection models were used to construct a predictive risk score: backward Selection, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. Severe outcome was defined as the composite of need for non-invasive ventilation, need for orotracheal intubation, or death. A total of 610 patients were included in the analysis, 313 had a severe outcome. The subset for the derivation analysis included 335 patients, the subset for the validation analysis 275 patients. The LASSO selection identified 6 variables (age, history of coronary heart disease, CRP, AST, D-dimer, and neutrophil/lymphocyte ratio) and resulted in the best performing score with an area under the curve of 0.79 in the derivation cohort and 0.80 in the validation cohort. Using a cut-off of 7 out of 13 points, sensitivity was 0.93, specificity 0.34, positive predictive value 0.59, and negative predictive value 0.82. The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.

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Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

Diagnostic Pathology ◽

10.1186/s13000-021-01184-2 ◽

2022 ◽

Vol 17 (1) ◽

Author(s):

Bachar Alabdullah ◽

Amir Hadji-Ashrafy

Keyword(s):

Gastrointestinal Tract ◽

Predictive Value ◽

Regression Models ◽

Area Under The Curve ◽

Roc Curves ◽

Multivariate Models ◽

Gastrointestinal Carcinoma ◽

Lung Tumours ◽

Napsin A ◽

Sensitivity Specificity

Abstract Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

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Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging

BioMed Research International ◽

10.1155/2020/1763803 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Xiaowen Liang ◽

Jinsui Yu ◽

Jianyi Liao ◽

Zhiyi Chen

Keyword(s):

Predictive Value ◽

Area Under The Curve ◽

Roc Curves ◽

Diagnostic Efficiency ◽

Clinical Value ◽

Data Set ◽

Multiple Organs ◽

Significant Difference ◽

Segmented Images ◽

The Impact

Objective. The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered. This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches. Methods. The training and validation sets comprised randomly selected thyroid and breast nodule images. The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method). A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines. Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves. Results. Among the 4 models, the CNN model using segmented images for classification achieved the best result. For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively. There was no statistically significant difference between the CNN model and the ultrasound guidelines. The combination of the two methods achieved superior diagnostic efficiency compared with their use individually. Conclusions. The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs. The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model. Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.

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Usefulness of 18F-FDG PET/CT in Patients with Cardiac Implantable Electronic Device Suspected of Late Infection

Journal of Clinical Medicine ◽

10.3390/jcm9072246 ◽

2020 ◽

Vol 9 (7) ◽

pp. 2246

Author(s):

Giuseppe Rubini ◽

Cristina Ferrari ◽

Domenico Carretta ◽

Luigi Santacroce ◽

Rossella Ruta ◽

...

Keyword(s):

Predictive Value ◽

Fdg Pet ◽

Large Scale ◽

Electronic Device ◽

High Sensitivity ◽

Roc Curves ◽

Standardized Uptake Values ◽

Pet Ct ◽

18F Fdg ◽

Fdg Pet Ct

The presence of a cardiovascular implantable electronic device (CIED) can be burdened by complications such as late infections that are associated with significant morbidity and mortality and require immediate and effective treatment. The aim of this study was to evaluate the role of 18F-fluorodeoxyglucose positron-emission tomography/computed tomography (18F-FDG PET/CT) in patients with suspected CIED infection. Fifteen patients who performed a 18F-FDG PET/CT for suspicion of CIED infection were retrospectively analyzed; 15 patients, with CIED, that underwent 18F-FDG PET/CT for oncological reasons, were also evaluated. Visual qualitative analysis and semi-quantitative analysis were performed. All patients underwent standard clinical management regardless 18F-FDG PET/CT results. Sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) resulted as 90.91%, 75%, 86.67%, 90.91% and 75% respectively. Maximum standardized uptake values (SUVmax) and semi-quantitative ratio (SQR) were collected and showed differences statistically significant between CIED infected patients and those who were not. Exploratory cut-off values were derived from receiver operating characteristic (ROC) curves for SUVmax (2.56) and SQR (4.15). This study suggests the clinical usefulness of 18F-FDG PET/CT in patients with CIED infection due to its high sensitivity, repeatability and non-invasiveness. It can help the clinicians in decision making, especially in patients with doubtful clinical presentation. Future large-scale and multicentric studies should be conducted to establish precise protocols about 18F-FDG PET/CT performance.

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Precision oncology in AML: validation of the prognostic value of the knowledge bank approach and suggestions for improvement

Journal of Hematology & Oncology ◽

10.1186/s13045-021-01118-x ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Marius Bill ◽

Krzysztof Mrózek ◽

Brian Giacopelli ◽

Jessica Kohlschmidt ◽

Deedra Nicolet ◽

...

Keyword(s):

Predictive Value ◽

Prognostic Value ◽

De Novo ◽

Area Under The Curve ◽

Roc Curves ◽

Precision Oncology ◽

Predictive Values ◽

Mutational Status ◽

Balanced Chromosome Rearrangements ◽

Group B

AbstractRecently, a novel knowledge bank (KB) approach to predict outcomes of individual patients with acute myeloid leukemia (AML) was developed using unbiased machine learning. To validate its prognostic value, we analyzed 1612 adults with de novo AML treated on Cancer and Leukemia Group B front-line trials who had pretreatment clinical, cytogenetics, and mutation data on 81 leukemia/cancer-associated genes available. We used receiver operating characteristic (ROC) curves and the area under the curve (AUC) to evaluate the predictive values of the KB algorithm and other risk classifications. The KB algorithm predicted 3-year overall survival (OS) probability in the entire patient cohort (AUCKB = 0.799), and both younger (< 60 years) (AUCKB = 0.747) and older patients (AUCKB = 0.770). The KB algorithm predicted non-remission death (AUCKB = 0.860) well but was less accurate in predicting relapse death (AUCKB = 0.695) and death in first complete remission (AUCKB = 0.603). The KB algorithm’s 3-year OS predictive value was higher than that of the 2017 European LeukemiaNet (ELN) classification (AUC2017ELN = 0.707, p < 0.001) and 2010 ELN classification (AUC2010ELN = 0.721, p < 0.001) but did not differ significantly from that of the 17-gene stemness score (AUC17-gene = 0.732, p = 0.10). Analysis of additional cytogenetic and molecular markers not included in the KB algorithm revealed that taking into account atypical complex karyotype, infrequent recurrent balanced chromosome rearrangements and mutational status of the SAMHD1, AXL and NOTCH1 genes may improve the KB algorithm. We conclude that the KB algorithm has a high predictive value that is higher than those of the 2017 and 2010 ELN classifications. Inclusion of additional genetic features might refine the KB algorithm.

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Application of Scheimpflug-Based Biomechanical Analyzer and Tomography in Early Detecting of Subclinical Keratoconus in Chinese Patients

10.21203/rs.3.rs-125161/v1 ◽

2020 ◽

Author(s):

Yan Liu ◽

Yu Zhang ◽

Yue-guo Chen

Keyword(s):

Sensitivity And Specificity ◽

Area Under The Curve ◽

High Sensitivity ◽

Roc Curves ◽

Chinese Patients ◽

Youden Index ◽

Biomechanical Property ◽

Surgical Safety ◽

Morphological Index ◽

Diagnostic Capacity

Abstract PurposeTo evaluate the value of Scheimpflug-based biomechanical analyzer combined with tomography in detecting early keratoconus by distinguishing normal eyes from frank keratoconus (KC) and forme frusta keratoconus (FFKC) eyes in Chinese patients. MethodsThis study included 31 bilateral frank keratoconus patients, 27 unilateral clinical manifest keratoconus patients with very asymmetric eyes, and 79 control subjects with normal corneas. Corneal morphological and biomechanical parameters were measured using the Pentacam HR and Corvis ST (OCULUS, Wetzlar, Germany). The diagnostic capacity of computed parameters reflecting corneal biomechanical and morphological traits [including Belin-Ambrósio deviation index (BAD_D), Corvis biomechanical index (CBI) and tomographic and biomechanical index (TBI)] was determined using receiver operating characteristic (ROC) curves and compared by DeLong test. Additionally, the area under the curve (AUC), the best cutoff values, and Youden index for each parameter were reported. The novel corneal stiffness parameter (Stress-Strain Index or SSI) was also compared between KC, FFKC and normal eyes.ResultsEvery morphological and biomechanical index analyzed in this study was significantly different between KC, FFKC and normal eyes (p=0.000). TBI was most valuable for detecting subclinical keratoconus (FFKC eyes) with an AUC of 0.928 (P=0.000), and any forms of corneal ectasia (FFKC and frank KC eyes) with an AUC of 0.966 (P=0.000). The sensitivity and specificity of TBI for detecting FFKC was 97.5% and 77.8%, for detecting any KC was 97.5% and 89.7%, with a cut-off value of 0.375. Morphological index BAD_D and biomechanical index CBI were also very useful in distinguishing any KC eyes from normal eyes with an AUC of 0.965 and 0.934, respectively. SSI was significantly different between KC, FFKC and normal eyes (P=0.000), indicating an independent decrease in corneal stiffness in KC eyes.Conclusion Combination of Scheimpflug-based biomechanical analyzer and tomography could increase the accuracy of detecting early keratoconus in Chinese patients. TBI was the most valuable index for detecting subclinical keratoconus with high sensitivity and specificity. Evaluation of corneal biomechanical property in refractive surgery candidates is helpful to recognize potential keratoconic eyes and increase surgical safety.

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