Diagnostic Score Model Research Articles

Risk factors for venous ulceration in patients with varicose veins of lower extremities.

The aim of this case-control study was to explore the potential risk factors for venous ulceration in patients with varicose veins of lower extremities and to establish a simplified diagnostic score model. Seventy subjects with varicose veins of lower extremities and venous ulceration were compared with 1164 controls with varicose veins of lower extremities and no history of venous ulceration. Stepwise multivariate logistic regression analysis was used to identify the risk factors for venous ulceration. The steps in developing the diagnostic score model were based on the Framingham Heart study. The area under the receiver operating characteristic curve (AUC) was calculated to assess the diagnostic ability of the diagnostic score model. Multivariate analysis showed that men, overweight, obesity, longer duration varicose veins, deep venous valve insufficiency, low lymphocyte counts, and high fibrinogen content were independently associated with an increased risk of venous ulceration. The AUC for the diagnostic score model was 0.75, which indicated good discriminatory ability. Special attention should be paid to the high-risk group of patients with lower extremity varicose veins. The diagnostic score model might be a useful screening tool for clinicians, policy makers, and patients.

Development of a diagnostic score using FeNO and symptoms to predict asthma

BackgroundFractional exhaled nitric oxide (FeNO) is known as effective for ruling-in asthma. The diagnostic value might be increased in combination with clinical signs and symptoms (CSS). The aim was to develop a new model for ruling-in and ruling-out asthma. MethodsDiagnostic multi-centre study in three practices of pneumologists in Germany. Whole-body plethysmography was combined with bronchodilation tests or bronchial provocation as diagnostic reference standard. Follow-up was performed after 3 months. An expert committee evaluated test results, symptoms, and course of disease for the final diagnosis. Relevant CSS known from guidelines were used to enable combinatorial development of decision rules. Outcomes of multiple logistic regression modeling were translated into a diagnostic score and internally validated by ten-fold cross validation. Results308 patients with complete follow-up were included. 186 (60.4%) were female, average age was 44.7 years and 161 (52.5%) had asthma. The average area under the receiver operating curve (AUC) of the diagnostic score was 0.755 (interquartile range 0.721–0.814). Allergic rhinitis, wheezing, dyspnea on exertion, coughing attacks at night, and awakening by shortness of breath were leading symptoms for ruling-in asthma. Frequent coughing and frequent respiratory infections were leading symptoms for ruling-out. The combination of FeNO and CSS allowed ruling-in asthma with a probability of up to 99%, and ruling-out with a post-test probability down to 9%. ConclusionThe diagnostic scoring model increased the diagnostic value of FeNO in combination with CSS. The new decision rule allowed to rule-in asthma with high certainty, and also to rule-out with acceptable certainty.

A diagnostic scoring model of ENKTCL in the nose-Waldeyer's ring based on logistic regression: Differential diagnosis from DLBCL.

To establish a logistic regression model based on CT and MRI imaging features and Epstein-Barr (EB) virus nucleic acid to develop a diagnostic score model to differentiate extranodal NK/T nasal type (ENKTCL) from diffuse large B cell lymphoma (DLBCL). This study population was obtained from two independent hospitals. A total of 89 patients with ENKTCL (n = 36) or DLBCL (n = 53) from January 2013 to May 2021 were analyzed retrospectively as the training cohort, and 61 patients (ENKTCL=27; DLBCL=34) from Jun 2021 to Dec 2022 were enrolled as the validation cohort. All patients underwent CT/MR enhanced examination and EB virus nucleic acid test within 2 weeks before surgery. Clinical features, imaging features and EB virus nucleic acid results were analyzed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors of ENKTCL and establish a predictive model. Independent predictors were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the predictive model and score model. We searched for significant clinical characteristics, imaging characteristics and EB virus nucleic acid and constructed the scoring system via multivariate logistic regression and converted regression coefficients to weighted scores. The independent predictors for ENKTCL diagnosis in multivariate logistic regression analysis, including site of disease (nose), edge of lesion (blurred), T2WI (high signal), gyrus like changes, EB virus nucleic acid (positive), and the weighted score of regression coefficient was 2, 3, 4, 3, 4 points. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. The AUC of the scoring model in the training cohort were 0.925 (95% CI, 0.906-0.990) and the cutoff point was 5 points. In the validation cohort, the AUC was 0.959 (95% CI, 0.915-1.000) and the cutoff value was 6 points. Four score ranges were as follows: 0-6 points for very low probability of ENKTCL, 7-9 points for low probability; 10-11 points for middle probability; 12-16 points for very high probability. The diagnostic score model of ENKTCL based on Logistic regression model which combined with imaging features and EB virus nucleic acid. The scoring system was convenient, practical and could significantly improve the diagnostic accuracy of ENKTCL and the differential diagnosis of ENKTCL from DLBCL.

Machine Learning Logistic Regression Model for Early Decision Making in Referral of Children with Cervical Lymphadenopathy Suspected of Lymphoma

While cervical lymphadenopathy is common in children, a decision model for detecting high-grade lymphoma is lacking. Previously reported individual lymphoma-predicting factors and multivariate models were not sufficiently discriminative for clinical application. To develop a diagnostic scoring tool, we collected data from all children with cervical lymphadenopathy referred to our national pediatric oncology center within 30 months (n = 182). Thirty-nine putative lymphoma-predictive factors were investigated. The outcome groups were classical Hodgkin lymphoma (cHL), nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL), non-Hodgkin lymphoma (NHL), other malignancies, and a benign group. We integrated the best univariate predicting factors into a multivariate, machine learning model. Logistic regression allocated each variable a weighing factor. The model was tested in a different patient cohort (n = 60). We report a 12-factor diagnostic model with a sensitivity of 95% (95% CI 89-98%) and a specificity of 88% (95% CI 77-94%) for detecting cHL and NHL. Our 12-factor diagnostic scoring model is highly sensitive and specific in detecting high-grade lymphomas in children with cervical lymphadenopathy. It may enable fast referral to a pediatric oncologist in patients with high-grade lymphoma and may reduce the number of referrals and unnecessary invasive procedures in children with benign lymphadenopathy.

Small hyperattenuating adrenal nodules in patients with lung cancer: Differentiation of metastases from adenomas on biphasic contrast-enhanced computed tomography.

The objective of this study was to evaluate the value of biphasic contrast-enhanced computed tomography (CECT) in the differential diagnosis of metastasis and lipid-poor adenomas (LPAs) in lung cancer patients with unilateral small hyperattenuating adrenal nodule. This retrospective study included 241 lung cancer patients with unilateral small hyperattenuating adrenal nodule (metastases, 123; LPAs, 118). All patients underwent plain chest or abdominal computed tomography (CT) scan and biphasic CECT scan, including arterial and venous phases. Qualitative and quantitative clinical and radiological characteristics of the two groups were compared using univariate analysis. An original diagnostic model was developed using multivariable logistic regression, and then, according to odds ratio (OR) of the risk factors of metastases, a diagnostic scoring model was developed. The areas under the receiver operating characteristic curves (AUCs) of the two diagnostic models were compared by DeLong test. Compared with LAPs, metastases were older and showed more frequently irregular in shape and cystic degeneration/necrosis (all p < 0.05). Enhancement ratios on venous (ERV) and arterial (ERA) phase of LAPs were noticeably higher than that of metastases, whereas CT values in unenhanced phase (UP) of LPAs were noticeably lower than that of metastases (all p < 0.05). Compared with LAPs, the proportions of male and III/IV clinical stage and small-cell lung cancer (SCLL) were significantly higher for metastases (all p < 0.05). As for peak enhancement phase, LPAs showed relatively faster wash-in and earlier wash-out enhancement pattern than metastases (p < 0.001). Multivariate analysis revealed age ≥ 59.5 years (OR: 2.269; p = 0.04), male (OR: 3.511; p = 0.002), CT values in UP ≥ 27.5 HU (OR: 6.968; p < 0.001), cystic degeneration/necrosis (OR: 3.076; p = 0.031), ERV ≤ 1.44 (OR: 4.835; p < 0.001), venous phase or equally enhanced (OR: 16.907; p < 0.001 or OR: 14.036; p < 0.001), and clinical stage II or III or IV (OR: 3.550; p = 0.208 or OR: 17.535; p = 0.002 or OR: 20.241; p = 0.001) were risk factors for diagnosis of metastases. AUCs of the original diagnostic model and the diagnostic scoring model for metastases were 0.919 (0.883-0.955) and 0.914 (0.880-0.948), respectively. There was no statistical significance of AUC between the two diagnostic model (p = 0.644). Biphasic CECT performed well diagnostic ability in differentiating metastases from LAPs. The diagnostic scoring model is easy to popularize due to simplicity and convenience.

Clinical value of thyroid related hormones combined with neutrophil to lymphocyte ratio in patients with nonalcoholic fatty liver disease

In sufferers with nonalcoholic fatty liver disease (NAFLD), the differences of thyroid associated hormones and neutrophil to lymphocyte ratio (NLR) in different liver pathological groups have been compared. Patients with NAFLD diagnosed by liver biopsy in our hospital from July 2012 to February 2019 were selected. All subjects were divided into nonalcoholic steatohepatitis (NASH) team and non-NASH group, no/mild fibrosis group (F0-1) and significant fibrosis group (F2-4). The differences of thyroid related hormones and NLR in these groups were in contrast, respectively. For the TSH, we conducted further evaluation based on gender. The TSH and NLR in NASH patients were significantly higher than non-NASH patients, but there was no considerable difference in free triiodothyronine (FT3) and free thyroxine (FT4) between the 2 groups. In the gender-based subgroup analysis, the variations of TSH between the 2 groups were nonetheless statistically significant (P < .05). The TSH and NLR in the significant fibrosis group were higher than these in the non/mild liver fibrosis group, and the differences were statistically significant (P < .05), but there was no large difference in FT3 and FT4 between the 2 groups (P > .05). In addition, in the gender-based subgroup analysis and further multivariable analysis, the variations of TSH between the 2 groups were still statistically significant (P < .05). In this study, we found that serum thyroid stimulating hormone (TSH) and neutrophil to lymphocyte ratio (NLR) were closely associated to the severity of NAFLD, suggesting that this simple available laboratory index may additionally be incorporated into the future noninvasive diagnostic scoring model to predict the incidence of NASH and the degree of fibrosis.

Development and validation of a sepsis diagnostic scoring model for neonates with suspected sepsis.

BackgroundWe aimed to develop and validate a diagnostic model for sepsis among neonates evaluated for suspected sepsis, by incorporating thromboelastometry parameters, maternal/neonatal risk factors, clinical signs/symptoms and laboratory results.MethodsThis retrospective cohort study included 291 neonates with presumed sepsis, hospitalized in a NICU, from 07/2014 to 07/2021. Laboratory tests were obtained on disease onset and prior to initiating antibiotic therapy. Τhromboelastometry extrinsically activated (EXTEM) assay was performed simultaneously and Tοllner and nSOFA scores were calculated. Sepsis diagnosis was the outcome variable. A 10-fold cross-validation least absolute shrinkage and selection operator logit regression procedure was applied to derive the final multivariable score. Clinical utility was evaluated by decision curve analysis.ResultsGestational age, CRP, considerable skin discoloration, liver enlargement, neutrophil left shift, and EXTEM A10, were identified as the strongest predictors and included in the Neonatal Sepsis Diagnostic (NeoSeD) model. NeoSeD score demonstrated excellent discrimination capacity for sepsis and septic shock with an AUC: 0.918 (95% CI, 0.884–0.952) and 0.974 (95% CI, 0.958–0.989) respectively, which was significantly higher compared to Töllner and nSOFA scores.ConclusionsThe NeoSeD score is simple, accurate, practical, and may contribute to a timely diagnosis of sepsis in neonates with suspected sepsis. External validation in multinational cohorts is necessary before clinical application.

Preoperative determination of pathological grades of primary single HCC: development and validation of a scoring model.

This study aimed to establish a reliable diagnostic score model for the preoperative determination of pathological grade in HCC based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI and biochemical indicators. In this retrospective study, we analyzed 139 patients with HCC who underwent Gd-EOB-DTPA MRI between 2014 and 2020, including an establishment cohort of 76 patients and a validation cohort of 63 patients. Based on the imaging features demonstrated on Gd-EOB-DTPA MRI images and biochemical indicators of the establishment cohort, a scoring model based on logistic regression was developed, and compared with postoperative pathological findings in terms of effective determination of pathological grade. The validity of the scoring model was assessed by ROC curves and an independent external validation cohort. Three parameters related to pathological grades were identified, including maximum diameter of the tumor, peritumoral hypointensity in the hepatobiliary phase, and [alkaline phosphatase (U/L) + gamma glutamyl transpeptidase (U/L)]/ lymphocyte count (× 109/L) (AGLR) ratios. Based on these three parameters, a scoring model was developed. ROC curve showed that a score of > 5 was set as the threshold for determining pathological grades with accuracy, sensitivity, specificity, PPV, and NPV of 89.5%, 75.0%, 95.1%, 85.7%, and 90.7%, respectively. The study provided the groundwork for a promising and easily implementable scoring model for preoperative determination of HCC pathological grades, for which further validation should be pursued.

Construction of a Comprehensive Diagnostic Scoring Model for Prostate Cancer Based on a Novel Six-Gene Panel.

Accumulating evidence indicates that the N6-methyladenosine (m6A) modification plays a critical role in human cancers. Given the current understanding of m6A modification, this process is believed to be dynamically regulated by m6A regulators. Although the discovery of m6A regulators has greatly enhanced our understanding of the mechanism underlying m6A modification in cancers, the function and role of m6A in the context of prostate cancer (PCa) remain unclear. Here, we aimed to establish a comprehensive diagnostic scoring model that can act as a complement to prostate-specific antigen (PSA) screening. To achieve this, we first drew the landscape of m6A regulators and constructed a LASSO-Cox model using three risk genes (METTL14, HNRNP2AB1, and YTHDF2). Particularly, METTL14 expression was found to be significantly related to overall survival, tumor T stage, relapse rate, and tumor microenvironment of PCa patients, showing that it has important prognostic value. Furthermore, for the sake of improving the predictive ability, we presented a comprehensive diagnostic scoring model based on a novel 6-gene panel by combining with genes found in our previous study, and its application potential was further validated by the whole TCGA and ICGC cohorts. Our study provides additional clues and insights regarding the treatment and diagnosis of PCa patients.

Prediction of microvascular invasion in HCC by a scoring model combining Gd-EOB-DTPA MRI and biochemical indicators.

This study aimed to establish a reliable diagnostic scoring model for the preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) and biochemical indicators. This retrospective study included 129 patients with HCC at our hospital from 2014 to 2020. Based on the intratumoral and peritumoral features on Gd-EOB-DTPA MRI and biochemical indicators, a scoring model was developed for preoperative prediction of MVI, and examined for diagnostic efficacy according to postoperative pathological results. The scoring model was further externally validated in an independent cohort of 63 HCC patients. Logistic regression analysis was performed to identify five parameters related to MVI, including maximum tumor diameter, peritumoral low intensity in the hepatobiliary phase, incomplete capsule, apparent diffusion coefficient (ADC), and [alkaline phosphatase (ALP) (U/L) + gamma-glutamyl transpeptidase (GGT) (U/L)] / lymphocyte count (× 109/L) ratio (AGLR). Based on these five parameters, a scoring model was developed, and the accuracy, sensitivity, specificity, PPV, and NPV in predicting MVI were 93.6%, 94.7%, 93.2%, 85.7%, and 97.6%, respectively, with a score > 8 set as the threshold. The scoring model based on Gd-EOB-DTPA MRI and biochemical indicators provides a reliable tool for preoperative prediction of MVI in HCC patients. • The scoring model based on Gd-EOB-DTPA MRI and biochemical indicators is practical for preoperative prediction of MVI in HCC patients. • AGLR is an independent risk factor for MVI. • The scoring model could help implement more appropriate interventions, potentially leading to precise and individualized treatments based on the biological characteristics of the tumor.

The Use of Cognitive Diagnostic Modeling in the Assessment of Computational Thinking

Computational thinking (CT) is a set of cognitive skills that every child should acquire. K–12 classrooms are expected to provide students opportunities (tasks) to think computationally. We introduce a CT competency assessment for middle school students. The assessment design process started by establishing a cognitive model of CT domain mastery, in which three broad skill types were identified to represent CT competency. After multiple-choice item prototypes were written, pilot tested, and revised, 15 of them were finally selected to be administered to 564 students in two middle schools in the Midwestern United States. Using a cognitive diagnostic scoring model, mastery classifications for each student were determined that can be used diagnostically by teachers as a pretest and, perhaps in the future, to compare the outcomes of CT instructional programs. The results inform an initial understanding of typical learning progressions in CT at the middle school level.

Differentiation of lipid-poor adenoma from pheochromocytoma on biphasic contrast-enhanced CT.

To evaluate the diagnostic performance of biphasic contrast-enhanced CT in differentiation of lipid-poor adenomas from pheochromocytomas. 129 patients with 132 lipid-poor adenomas and 93 patients with 97 pheochromocytomas confirmed by pathology were included in this retrospective study. Patients underwent unenhanced abdominal CT scan followed by arterial and venous phase. Quantitative and qualitative imaging features were compared between the two groups using univariate analysis. Risk factors for pheochromocytomas were evaluated by multivariate logistic regression analysis and a diagnostic scoring model was established based on odd ratio (OR) of the risk factors. Pheochromocytomas were larger and showed cystic degeneration more frequently compared with lipid-poor adenomas (p < 0.01). No significant difference was found in peak enhancement phase between the two groups (p = 0.348). Attenuation values on unenhanced phase (CTU), arterial phase (CTA), and venous phase (CTV) of pheochromocytomas were significantly higher than that of lipid-poor adenomas while enhancement ratio on arterial and venous phase (ERA, ERV) of pheochromocytomas was significantly lower than that of lipid-poor adenomas (all p < 0.05). Multivariate analysis revealed lesion size > 29mm (OR: 5.74; 95% CI 2.51-13.16; p < 0.001), CTA > 81 HU (OR: 2.54; 95% CI 1.04-6.17; p = 0.04), CTV > 97 HU (OR: 11.19; 95% CI 3.21-38.97; p < 0.001), ERV ≤ 1.5 (OR: 20.23; 95% CI 6.30-64.87; p < 0.001), and the presence of cystic degeneration (OR: 6.22, 95% CI 1.74-22.25; p = 0.005) were risk factors for pheochromocytomas. The diagnostic scoring model yielded an area under the curve (AUC) of 0.911. Biphasic contrast-enhanced CT showed good diagnostic performance in differentiation of lipid-poor adenomas from pheochromocytomas.

Scoring model of convex probe endobronchial ultrasound multimodal imaging in differentiating benign and malignant lung lesions.

BackgroundConvex probe endobronchial ultrasound images can reflect the morphology, blood flow status and stiffness of the lesions. Endobronchial ultrasound multimodal imaging has great value for the diagnosis of intrathoracic lymph nodes. This study aimed to analyze the application of endobronchial ultrasound multimodal imaging on lung lesions.MethodsPatients undergoing endobronchial ultrasound-guided transbronchial needle aspiration in Shanghai Chest Hospital from July 2018 to December 2019 were retrospectively enrolled. Nine grayscale features (long and short axes, margin, shape, lobulation sign, echogenicity, necrosis, liquefaction, calcification, and air-bronchogram), blood flow volume and elastography five-score method were analyzed to explore the best diagnostic method. The gold standard for diagnosing lesions depends on the histological and cytopathological findings of endobronchial ultrasound-guided transbronchial needle aspiration, transthoracic biopsy, resected sample of lesions, microbiological examination or clinical follow-up of at least 6 months.ResultsEndobronchial ultrasound multimodal imaging of 97 malignant lung lesions and 19 benign lung lesions from 116 patients were analyzed. There were statistically significant differences in distinct margin, presence of lobulation sign, presence of necrosis, and elastography grading score 4–5 between malignant and benign lung lesions, among which presence of lobulation sign and elastography grading score 4–5 were independent predictors. A diagnostic scoring model was then constructed based on the above four features, and when two or more features were present, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy for malignant lung lesions prediction were 92.78%, 57.89%, 91.84%, 61.11% and 87.07%, respectively.ConclusionsThe combination of endobronchial ultrasound grayscale and elastography has potential value for malignant and benign lung lesions differentiation. The diagnostic scoring model established in this study needs further validation to guide the malignant and benign diagnosis of lung lesions.

A scoring model for radiologic diagnosis of gastric leiomyomas (GLMs) with contrast-enhanced computed tomography (CE-CT): Differential diagnosis from gastrointestinal stromal tumors (GISTs)

ObjectivesTo investigate CT findings and develop a diagnostic score model to differentiate GLMs from GISTs. MethodsThis retrospective study included 109 patients with pathologically confirmed GLMs (n = 46) and GISTs (n = 63) from January 2013 to August 2018 who received CE-CT before surgery. Demographic and radiological features was collected, including lesion location, contour, presence or absence of intralesional necrosis and ulceration, growth pattern, whether the tumor involved EGJ, the long diameter (LD) /the short diameter (SD) ratio, pattern and degree of lesion enhancement. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors and establish a predictive model. Independent predictors for GLMs were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the model. Overall score distribution was divided into four groups to show differentiating probability of GLMs from GISTs. ResultsFive CT features were the independent predictors for GLMs diagnosis in multivariate logistic regression analysis, including esophagogastric junction (EGJ) involvement (OR, 367.9; 95 % CI, 5.8–23302.8; P = 0.005), absence of necrosis (OR, 11.9; 95 % CI, 1.0−138. 1; P = 0.048) and ulceration (OR, 151.9; 95 % CI, 1.4–16899.6; P = 0.037), degree of enhancement (OR, 9.3; 95 % CI, 3.2–27.4; P < 0.001), and long diameter/ short diameter (LD/SD) ratio (OR,170.9; 95 % CI, 8.4–3493.4; P = 0.001). At a cutoff of 9 points, AUC for this score model was 0.95, with 95.65 % sensitivity, 79.37 % specificity, 77.19 % PPV, 96.15 % NPV and 86.24 % diagnostic accuracy. An increasing trend was showed in diagnostic probability of GLMs among four groups based on the score (P < 0.001). ConclusionsThe newly designed scoring system is reliable and easy-to-use for GLMs diagnosis by distinguishing from GISTs, including EGJ involvement, absence of ulceration and necrosis, mild enhancement and high LD/SD ratio. The overall score of model ranged from 1 to 17 points, which was divided into 4 groups: 1–7 points, 7–10 points, 10–13 points and 13–17 points, with a diagnostic probability of GLMs 0%, 45 %, 83 % and 100 %, respectively.

Construction of a Comprehensive Diagnostic Scoring Model for Prostate Cancer Based on Six Gene-Panel

Accumulating evidence indicates that the N6-methyladenosine (m6A) modification plays a critical role in human cancers.Given the current understanding of m6A modification, this process is believed to be dynamically regulated by m6A regulators. Although the discovery of m6A regulators has greatly enhanced our understanding of the mechanism underlying m6A modification in cancers, the function and role of m6A in the context of prostate cancer (PCa) remains unclear.Here, we aimed to establish a comprehensive diagnostic scoring model that can be used to prostate-specific antigen (PSA) screening.To achieve this, we first drew the landscape of m6A regulators and constructed a LASSO-Cox model using 3 risk genes (METTL14, HNRNP2AB1 and YTHDF2). Particularly,METTL14 expression was found to be significantly related to overall survival, tumor T stage and relapse rate, showing that METTL14 has important prognostic value. Furthermore, for the sake of improving the predictive ability, we presented a comprehensive diagnostic scoring model based on 6 gene-panel by combining with genes found in our previous study.In conclusion, our study provides additional clues and insights regarding the treatment and diagnosis of PCa patients. Funding Statement: This work was supported by grants from the National Natural Science Foundation of China (31870928, Project-recepient: Q.L; 81770494, Project-recepient: Q. X.). Declaration of Interests: The authors declare that they have no conflicts of interest.

Leucocyte Count Does Not Improve the Diagnostic Performance of a Diagnostic Score (DS) in Distinguishing Acute Appendicitis (AA) from Nonspecific Abdominal Pain (NSAP).

Although, acute appendicitis (AA) and nonspecific abdominal pain (NSAP) are the most common diagnoses among secondary care patients with acute abdominal pain, the diagnostic performance of leucocyte count (LC) in DS (Diagnostic Score) model is rarely considered. As an extension of the World Organisation of Gastro-Enterology Research Committee (OMGE) acute abdominal pain study, 1,333 patients presenting with acute abdominal pain were included in the study. The clinical history and diagnostic symptoms (n=22), signs (n=14) and tests (n=3) in each patient were recorded in detail, and the collected data were related with the final diagnoses of the patients. In the ROC comparison test, there was no statistically significant difference in the performance of DSLC- (DS without LC) and DSLC+ (DS with LC). The highest sensitivities of the DSLC- and DSLC+ tests for detecting AA were 86% (95%CI=81-90%) and 87% (95%CI=82-91%), respectively. The highest specificities of the DSLC- and DSLC+ tests for detecting AA were 98% (95%CI=97-99%) and 98% (95%CI=96-99%), respectively. DS could assist the clinician in differentiating AA from NSAP and other causes of acute abdominal pain. Importantly, LC does not improve the diagnostic performance of a DS in AA.

Diagnostic, progressive and prognostic performance of m6A methylation RNA regulators in lung adenocarcinoma.

Background: N6-methyladenosine (m6A) RNA methylation is dynamically and reversibly regulated by methyl-transferases ("writers"), binding proteins ("readers"), and demethylases ("erasers"). The m6A is restored to adenosine and thus to achieve demethylation modification. The abnormality of m6A epigenetic modification in cancer has been increasingly attended. However, we are rarely aware of its diagnostic, progressive and prognostic performance in lung adenocarcinoma (LUAD).Methods and Results: The expression of 13 widely reported m6A RNA regulators in LUAD and normal samples were systematically analyzed. There were 12 m6A RNA methylation genes displaying aberrant expressions, and an 11-gene diagnostic score model was finally built (Diagnostic score =0.033*KIAA1429+0.116*HNRNPC+0.115*RBM15-0.067* METTL3-0.048*ZC3H13-0.221*WTAP+0.213*YTHDF1-0.132*YTHDC1-0.135* FTO+0.078*YTHDF2+0.014*ALKBH5). Receiver operating characteristic (ROC) analysis was performed to demonstrate superiority of the diagnostic score model (Area under the curve (AUC) was 0.996 of training cohort, P<0.0001; AUC was 0.971 of one validation cohort-GSE75037, P<0.0001; AUC was 0.878 of another validation cohort-GSE63459, P<0.0001). In both training and validation cohorts, YTHDC2 was associated with tumor stage (P<0.01), while HNRNPC was up expressed in progressed tumor (P<0.05). Besides, WTAP, RBM15, KIAA1429, YTHDF1, and YTHDF2 were all up expressed for TP53 mutation. Furthermore, using least absolute shrinkage and selection operator (lasso) regression analysis, a ten-gene risk score model was built. Risk score=0.169*ALKBH5-0.159*FTO+0.581*HNRNPC-0.348* YTHDF2-0.265*YTHDF1-0.123*YTHDC2+0.434*RBM15+0.143*KIAA1429-0.200*WTAP-0.310*METTL3. There existed correlation between the risk score and TNM stage (P<0.01), lymph node stage (P<0.05), gender (P<0.05), living status (P<0.001). Univariate and multivariate Cox regression analyses of relevant clinicopathological characters and the risk score revealed risk score was an independent risk factor of lung adenocarcinoma (HR: 2.181, 95%CI (1.594-2.984), P<0.001). Finally, a nomogram was built to facilitate clinicians to predict outcome.Conclusions: m6A epigenetic modification took part in the progression, and provided auxiliary diagnosis and prognosis of LUAD.

Hypocalcemia, hypochloremia, and eosinopenia as clinical predictors of leptospirosis: A retrospective study

BackgroundUnderestimation of leptospirosis cases is happening in many countries. The most common factor of underreporting is misdiagnosis. Considering the limitations of direct detection of pathogen and serological diagnosis for leptospirosis, clinical features and blood tests though non-specific are usually referred in making presumptive diagnosis to decide disease management. MethodsIn this single-centre retrospective study, comparative analysis on clinical presentations and laboratory findings was performed between confirmed leptospirosis versus non-leptospirosis cases. ResultsIn multivariate logistic regression evidenced by a Hosmer-Lemeshow significance value of 0.979 and Nagelkerke R square of 0.426, the predictors of a leptospirosis case are hypocalcemia (calcium <2.10mmol/L), hypochloremia (chloride <98mmol/L), and eosinopenia (absolute eosinophil count <0.040×109/L). The proposed diagnostic scoring model has a discriminatory power with area under the curve (AUC) 0.761 (p<0.001). A score value of 6 reflected a sensitivity of 0.762, specificity of 0.655, a positive predictive value of 0.38, negative predictive value of 0.91, a positive likelihood ratios of 2.21, and a negative likelihood ratios of 0.36. ConclusionWith further validation in clinical settings, implementation of this diagnostic scoring model is helpful to manage presumed leptospirosis especially in the absence of leptospirosis confirmatory tests.

Validation of a diagnostic score model for the prediction of Legionella pneumophila pneumonia

BackgroundCommunity-acquired pneumonia (CAP) due to Legionella has a high mortality rate in patients who do not receive adequate antibiotic therapy. In a previous study, we developed a simple Legionella Score to distinguish patients with Legionella and non-Legionella pneumonia based on clinical information at diagnosis. In the present study, we validated this Legionella Score for the presumptive diagnosis of Legionella CAP. MethodsThis validation cohort included 109 patients with Legionella CAP and 683 patients with non-Legionella CAP. The Legionella Score includes six parameters by assigning one point for each of the following items: being male, absence of cough, dyspnea, C-reactive protein (CRP) ≥ 18 mg/dL, lactate dehydrogenase (LDH) ≥ 260 U/L, and sodium < 134 mmol/L. ResultsWhen the Legionella CAP and non-Legionella CAP were compared by univariate analysis, most of the evaluated symptoms and laboratory test results differed substantially. The six parameters that were used for the Legionella Score also indicated clear differences between the Legionella and non-Legionella CAP. All Legionella patients had a score of 2 points or higher. The median Legionella Scores were 4 in the Legionella CAP cases and 2 in the non-Legionella CAP cases. A receiver operating characteristics curve showed that the area under the curve was 0.93. The proposed best cutoff, total score ≥3, had sensitivity of 93% and specificity of 75%. ConclusionOur Legionella Score was shown to have good diagnostic ability with a positive likelihood of 3.7 and a negative likelihood of 0.10.

A Diagnostic Scoring System to Distinguish Precocious Puberty from Premature Thelarche based on Clinical and Laboratory Findings

Background: The diagnosis of patients who present with early pubertal signs can pose a challenge. Objectives: The aim of this study was to design a new diagnostic scoring system based on clinical and laboratory findings to distinguish precocious puberty (PP) from premature thelarche (PT) among girls diagnosed with early puberty. Methods: We enrolled 267 girls (PP, n = 164; PT, n = 103) previously diagnosed with early puberty using GnRH test, retrospectively. A diagnostic scoring model (M) was constructed with five variables and their cut off values were determined for scoring: age of onset (6.5 years), bone age (BA)/choronologic age (CA) ratio (≥ 1.1), estradiol (12 pg/ml), uterine length (32 mm) and ovarian volume (1.09 cm3). Overall score was determined for model. The model was also applied prospectively to a second girls population (n = 86) that showed signs of early puberty. Results: The sensitivity and specificity of model was 89.6% and 87.4%, respectively. In the cohort group, the sensitivity and specificity of model was 90% and 89.4%, prospectively. Conclusions: A diagnostic scoring system, based on clinical and laboratory findings can be an alternative or complementary method for the differential diagnosis of early puberty.