External validation of a rapid, non-invasive tool for periodontitis screening in a medical care setting

Abstract Objectives Medical professionals should advise their patients to visit a dentist if necessary. Due to the lack of time and knowledge, screening for periodontitis is often not done. To alleviate this problem, a screening model for total (own teeth/gum health, gum treatment, loose teeth, mouthwash use, and age)/severe periodontitis (gum treatment, loose teeth, tooth appearance, mouthwash use, age, and sex) in a medical care setting was developed in the Academic Center of Dentistry Amsterdam (ACTA) [1]. The purpose of the present study was to externally validate this tool in an outpatient medical setting. Materials and methods Patients were requited in an outpatient medical setting as the validation cohort. The self-reported oral health questionnaire was conducted, demographic data were collected, and periodontal examination was performed. Algorithm discrimination was expressed as the area under the receiver operating characteristic curve (AUROCC). Sensitivity, specificity, and positive and negative predictive values were calculated. Calibration plots were made. Results For predicting total periodontitis, the AUROCC was 0.59 with a sensitivity of 49% and specificity of 68%. The PPV was 57% and the NPV scored 55%. For predicting severe periodontitis, the AUROCC was 0.73 with a sensitivity of 71% and specificity of 63%. The PPV was 39% and the NPV 87%. Conclusions The performance of the algorithm for severe periodontitis is found to be sufficient in the current medical study population. Further external validation of periodontitis algorithms in non-dental school populations is recommended. Clinical relevance Because general physicians are obligated to screen patients for periodontitis, it is our general goal that they can use a prediction model in medical settings without an oral examination.

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External Validation of Different Scoring Systems for Suspected Choledocholithiasis

Digestive Surgery ◽

10.1159/000495696 ◽

2019 ◽

Vol 36 (6) ◽

pp. 530-538

Author(s):

Nicolò Tamini ◽

Davide Paolo Bernasconi ◽

Luca Gianotti

Keyword(s):

Operating Characteristic ◽

Bile Duct Stone ◽

Characteristic Curve ◽

External Validation ◽

Area Under The Curve ◽

Scoring Systems ◽

Youden Index ◽

Predictive Values ◽

Sensitivity Specificity ◽

Symptoms And Signs

Aim of the Study: The diagnosis of choledocholithiasis is challenging. Previously published scoring systems designed to calculate the risk of choledocholithiasis were evaluated to appraise the diagnostic performance. Patients and Methods: Data of patients who were admitted between 2013 and 2015 with the following characteristics were retrieved: bile stone-related symptoms and signs, and indication to laparoscopic cholecystectomy. To validate and appraise the performance of the 6 scoring systems, the acknowledged domains of each metrics were applied to the present cohort. Sensitivity, specificity, positive, negative predictive, Youden index, and receiver operating characteristic curve with the area under the curve (AUC) values of the scores were calculated. Results: Two-hundred patients were analyzed. The highest sensitivity and specificity were obtained from the Menezes’ (96.6%) and Telem’s (99.3%) metrics respectively. The Telem’s and Menezes’ scores had the best positive (75.0%) and negative (96.4%) predictive values respectively. The best accuracy, as computed by the Youden index and AUC, was found for the Soltan’s scoring system (0.628 and 0.88, respectively). Conclusion: The available scoring systems are precise only in identifying patients with a negligible risk of common bile duct stone, but overall insufficiently accurate to suggest the routine use in clinical practice.

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A descriptive and validation study of a predictive model of severity of SARS-COV-2 infection

Advances in Laboratory Medicine / Avances en Medicina de Laboratorio ◽

10.1515/almed-2021-0039 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Yolanda Villena-Ortiz ◽

Marina Giralt ◽

Laura Castellote-Bellés ◽

Rosa M. Lopez-Martínez ◽

Luisa Martinez-Sanchez ◽

...

Keyword(s):

Emergency Department ◽

Predictive Model ◽

Clinical Laboratory ◽

Demographic Data ◽

Severe Disease ◽

External Validation ◽

Predictive Values ◽

Reactive Protein ◽

Predictive Variables ◽

Moderate Disease

Abstract Objectives The strain the SARS-COV-2 pandemic is putting on hospitals requires that predictive values are identified for a rapid triage and management of patients at a higher risk of developing severe COVID-19. We developed and validated a prognostic model of COVID-19 severity. Methods A descriptive, comparative study of patients with positive vs. negative PCR-RT for SARS-COV-2 and of patients who developed moderate vs. severe COVID-19 was conducted. The model was built based on analytical and demographic data and comorbidities of patients seen in an Emergency Department with symptoms consistent with COVID-19. A logistic regression model was designed from data of the COVID-19-positive cohort. Results The sample was composed of 410 COVID-positive patients (303 with moderate disease and 107 with severe disease) and 81 COVID-negative patients. The predictive variables identified included lactate dehydrogenase, C-reactive protein, total proteins, urea, and platelets. Internal calibration showed an area under the ROC curve (AUC) of 0.88 (CI 95%: 0.85–0.92), with a rate of correct classifications of 85.2% for a cut-off value of 0.5. External validation (100 patients) yielded an AUC of 0.79 (95% CI: 0.71–0.89), with a rate of correct classifications of 73%. Conclusions The predictive model identifies patients at a higher risk of developing severe COVID-19 at Emergency Department, with a first blood test and common parameters used in a clinical laboratory. This model may be a valuable tool for clinical planning and decision-making.

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Validity and diagnostic accuracy of the Luganda version of the 9-item and 2-item Patient Health Questionnaire for detecting major depressive disorder in rural Uganda

Global Mental Health ◽

10.1017/gmh.2016.14 ◽

2016 ◽

Vol 3 ◽

Cited By ~ 15

Author(s):

J. E. M. Nakku ◽

S. D. Rathod ◽

D. Kizza ◽

E. Breuer ◽

K. Mutyaba ◽

...

Keyword(s):

Primary Care ◽

Diagnostic Accuracy ◽

Care Setting ◽

Primary Care Setting ◽

Characteristic Curve ◽

Screening Tools ◽

Idioms Of Distress ◽

Future Research ◽

Predictive Values ◽

Patient Health

Background.The prevalence of depression in rural Ugandan communities is high and yet detection and treatment of depression in the primary care setting is suboptimal. Short valid depression screening measures may improve detection of depression. We describe the validation of the Luganda translated nine- and two-item Patient Health Questionnaires (PHQ-9 and PHQ-2) as screening tools for depression in two rural primary care facilities in Eastern Uganda.Methods.A total of 1407 adult respondents were screened consecutively using the nine-item Luganda PHQ. Of these 212 were randomly selected to respond to the Mini International Neuropsychiatric Interview diagnostic questionnaire. Descriptive statistics for respondents’ demographic characteristics and PHQ scores were generated. The sensitivity, specificity and positive predictive values (PPVs), and area under the ROC curve were determined for both the PHQ-9 and PHQ-2.Results.The optimum trade-off between sensitivity and PPV was at a cut-off of ≧5. The weighted area under the receiver Operating Characteristic curve was 0.74 (95% CI 0.60–0.89) and 0.68 (95% CI 0.54–0.82) for PHQ-9 and PHQ-2, respectively.Conclusion.The Luganda translation of the PHQ-9 was found to be modestly useful in detecting depression. The PHQ-9 performed only slightly better than the PHQ-2 in this rural Ugandan Primary care setting. Future research could improve on diagnostic accuracy by considering the idioms of distress among Luganda speakers, and revising the PHQ-9 accordingly. The usefulness of the PHQ-2 in this rural population should be viewed with caution.

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A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

10.1101/2020.03.24.20042119 ◽

2020 ◽

Cited By ~ 4

Author(s):

Ying Zhou ◽

Zhen Yang ◽

Yanan Guo ◽

Shuang Geng ◽

Shan Gao ◽

...

Keyword(s):

Risk Factors ◽

Disease Severity ◽

Predictive Value ◽

Demographic Data ◽

Characteristic Curve ◽

Severe Pneumonia ◽

Internal Validation ◽

Predictive Values ◽

Radiological Data ◽

D Dimer

AbstractBackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.MethodsPatients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared using the Statistical Package for the Social Sciences (IBM Corp., Armonk, NY, USA) to explore clinical characteristics and risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.ResultsA total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age [odds ratio (OR): 1.059, 95% confidence interval (CI): 1.036–1.082; p < 0.001], N/L (OR: 1.322, 95% CI: 1.180–1.481; p < 0.001), CRP (OR: 1.231, 95% CI: 1.129–1.341; p = 0.002), and D-dimer (OR: 1.059, 95% CI: 1.013–1.107; p = 0.011). We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. The sensitivity and specificity were 89.47% and 67.42%. In the training sets, the negative and positive predictive values were 93.80% and 41.32%, respectively, with a specificity of 70.76% and a sensitivity of 89.87%.ConclusionsA product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

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A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

10.21203/rs.3.rs-29566/v1 ◽

2020 ◽

Cited By ~ 4

Author(s):

Ying Zhou ◽

Zhen Yang ◽

Yanan Guo ◽

Shuang Geng ◽

Shan Gao ◽

...

Keyword(s):

Risk Factors ◽

Disease Severity ◽

Predictive Value ◽

Demographic Data ◽

Characteristic Curve ◽

Severe Pneumonia ◽

Internal Validation ◽

Predictive Values ◽

Radiological Data ◽

D Dimer

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.Methods Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared to explore risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.Results A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age, N/L, CRP and D-dimer. We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. In the training sets, the negative and positive predictive values were 93.80% and 41.32%.Conclusions A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

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Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

Otolaryngology ◽

10.1177/0194599820939071 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1156-1165

Author(s):

Juan Xiao ◽

Qiang Xiao ◽

Wei Cong ◽

Ting Li ◽

Shouluan Ding ◽

...

Keyword(s):

Thyroid Nodules ◽

Characteristic Curve ◽

Area Under The Curve ◽

Diagnostic Study ◽

Diagnostic Efficiency ◽

Training Set ◽

Multivariable Logistic Regression Model ◽

Predictive Values ◽

Validation Set ◽

Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

Scientific Reports ◽

10.1038/s41598-021-81408-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jaeseung Shin ◽

Joon Seok Lim ◽

Yong-Min Huh ◽

Jie-Hyun Kim ◽

Woo Jin Hyung ◽

...

Keyword(s):

Gastric Cancer ◽

Advanced Gastric Cancer ◽

Cox Regression ◽

Characteristic Curve ◽

External Validation ◽

Locally Advanced ◽

Clinical Model ◽

Locally Advanced Gastric Cancer ◽

Contrast Enhanced Ct ◽

Preoperative Ct

AbstractThis study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.

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

Cancer Biomarkers ◽

10.3233/cbm-200972 ◽

2021 ◽

pp. 1-12

Author(s):

Xingchen Fan ◽

Minmin Cao ◽

Cheng Liu ◽

Cheng Zhang ◽

Chunyu Li ◽

...

Keyword(s):

Endometrial Cancer ◽

Operating Characteristic ◽

Characteristic Curve ◽

External Validation ◽

Diagnostic Value ◽

Diagnostic Biomarkers ◽

Non Invasive ◽

Polymerase Chain ◽

Public Datasets ◽

Plasma Mirnas

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

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Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽

10.3390/diagnostics11061127 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1127

Author(s):

Ji Hyung Nam ◽

Dong Jun Oh ◽

Sumin Lee ◽

Hyun Joo Song ◽

Yun Jeong Lim

Keyword(s):

Deep Learning ◽

Small Bowel ◽

Scoring System ◽

Operating Characteristic ◽

Clinical Evidence ◽

Learning Algorithm ◽

Characteristic Curve ◽

External Validation ◽

Test Results ◽

Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

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Predictive value of the neutrophil to lymphocyte ratio for disease deterioration and serious adverse outcomes in patients with COVID-19: a prospective cohort study

BMC Infectious Diseases ◽

10.1186/s12879-021-05796-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhi-Yong Zeng ◽

Shao-Dan Feng ◽

Gong-Ping Chen ◽

Jiang-Nan Wu

Keyword(s):

Cohort Study ◽

Hospital Admission ◽

Clinical Outcomes ◽

Prospective Cohort Study ◽

Prospective Cohort ◽

Characteristic Curve ◽

Adverse Outcomes ◽

Neutrophil To Lymphocyte Ratio ◽

Predictive Values ◽

Lymphocyte Ratio

Abstract Background Early identification of patients who are at high risk of poor clinical outcomes is of great importance in saving the lives of patients with novel coronavirus disease 2019 (COVID-19) in the context of limited medical resources. Objective To evaluate the value of the neutrophil to lymphocyte ratio (NLR), calculated at hospital admission and in isolation, for the prediction of the subsequent presence of disease progression and serious clinical outcomes (e.g., shock, death). Methods We designed a prospective cohort study of 352 hospitalized patients with COVID-19 between January 9 and February 26, 2020, in Yichang City, Hubei Province. Patients with an NLR equal to or higher than the cutoff value derived from the receiver operating characteristic curve method were classified as the exposed group. The primary outcome was disease deterioration, defined as an increase of the clinical disease severity classification during hospitalization (e.g., moderate to severe/critical; severe to critical). The secondary outcomes were shock and death during the treatment. Results During the follow-up period, 51 (14.5%) patients’ conditions deteriorated, 15 patients (4.3%) had complicated septic shock, and 15 patients (4.3%) died. The NLR was higher in patients with deterioration than in those without deterioration (median: 5.33 vs. 2.14, P < 0.001), and higher in patients with serious clinical outcomes than in those without serious clinical outcomes (shock vs. no shock: 6.19 vs. 2.25, P < 0.001; death vs. survival: 7.19 vs. 2.25, P < 0.001). The NLR measured at hospital admission had high value in predicting subsequent disease deterioration, shock and death (all the areas under the curve > 0.80). The sensitivity of an NLR ≥ 2.6937 for predicting subsequent disease deterioration, shock and death was 82.0% (95% confidence interval, 69.0 to 91.0), 93.3% (68.0 to 100), and 92.9% (66.0 to 100), and the corresponding negative predictive values were 95.7% (93.0 to 99.2), 99.5% (98.6 to 100) and 99.5% (98.6 to 100), respectively. Conclusions The NLR measured at admission and in isolation can be used to effectively predict the subsequent presence of disease deterioration and serious clinical outcomes in patients with COVID-19.

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