Diagnostic value of preoperative computed tomography (CT-scan) in salivary gland tumors: a 14-year retrospective analysis

BackgroundProvisional major criteria for the diagnosis of very early systemic sclerosis (VESSc) proposed of EULAR Scleroderma Trial and Research Group (EUSTAR) are Raynaud’s phenomenon (RP), positive autoantibodies (antinuclear, anticentromere-ACA, antitopoisomerase...

To explore the value of lactic acid (Lac), procalcitonin (PCT), sequential organ failure assessment (SOFA) score and acute physiology and chronic health evaluation II (APACHE II) score in assessing the severity and predicting the prognosis in sepsis shock. A retrospectively study was conducted. Patients with septic shock hospitalized in the department of critical care medicine of the Affiliated Hospital of Jining Medical University from April 2015 to June 2019 were enrrolled. The patient's gender, age, body mass index (BMI), infection site, organ dysfunction status; Lac, PCT, C-reactive protein (CRP), heart rate and body temperature immediately after admission to the intensive care unit (ICU); APACHE II and SOFA scores within 24 hours, and 28-day prognosis were collected. According to the 28-day prognosis, the patients with septic shock were divided into the survival group and the death group, and the differences in the indicators between the groups were compared. Multivariate Logistic regression analysis was used to screen the risk factors of 28-day death in patients with septic shock; receiver operating characteristic curve (ROC curve) was used to analyze the value of Lac, PCT, SOFA, APACHE II, and age in predicting the 28-day prognosis of patients with septic shock. A total of 303 septic shock patients were enrolled, of which 124 cases survived and 179 died on the 28th day, and the 28-day mortality was 59.08%. (1) Compared with the survival group, patients in the death group were older (years old: 66.58±15.22 vs. 61.15±15.68), APACHE II, SOFA, proportion of lung infections, Lac increased [APACHE II score: 22.79±7.62 vs. 17.98±6.88, SOFA score: 9.42±3.51 vs. 5.65±1.59, proportion of lung infections: 53.63% (96/179) vs. 39.52% (49/124), Lac (mmol/L): 5.10±3.72 vs. 3.71±2.56], oxygenation index (PaO2/FiO2) and ICU body temperature decreased [PaO2/FiO2 (mmHg, 1 mmHg = 0.133 kPa): 198.94±80.15 vs. 220.68±72.06, ICU body temperature (centigrade): 37.47±1.08 vs. 37.80±1.14], and the differences were statistically significant (all P < 0.05). (2) Multivariate Logistic regression analysis results: after adjusted for potential confounding factors, APACHE II, PCT, Lac, age and SOFA were independent risk factors for death in patients with septic shock [APACHE II: odds ratio (OR) =1.05, 95% confidence interval (95%CI) was 1.01-1.10, P = 0.039; PCT: OR = 0.99, 95%CI was 0.98-1.00, P =0.012; Lac: OR = 1.23, 95%CI was 1.08-1.40, P = 0.002; age: OR = 1.03, 95%CI was 1.01-1.05, P = 0.009; SOFA score: OR =1.88, 95%CI was 1.59-2.22, P < 0.001]. (3) ROC curve analysis showed that APACHE II, Lac, age and SOFA could predict the prognosis of patients with septic shock [APACHE II: the area under the ROC curve (AUC) = 0.682 4, 95%CI was 0.621 7-0.743 1, P = 0.000; when the best cut-off value was 18.500, its sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 72.63%, 54.84%, 69.89%, 58.12%, 1.608 1 and 0.499 2, respectively. Lac: AUC = 0.604 5, 95%CI was 0.540 8-0.668 2, P = 0.002; when the best cut-off value was 3.550 mmol/L, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 50.84%, 73.39%, 73.39%, 50.94%, 1.910 3 and 0.669 9, respectively. Age: AUC = 0.599 1, 95%CI was 0.535 4-0.662 7, P = 0.003; when the best cut-off value was 72.500 years old, the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 42.46%, 75.00%, 71.03%, 47.45%, 1.698 3 and 0.767 2, respectively. SOFA: AUC = 0.822 3, 95%CI was 0.776 7-0.867 9, P = 0.000; when the best cut-off value was 7.500, its sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 68.72%, 87.90%, 89.13%, 66.06%, 5.680 4, 0.355 9 respectively]. The combined prediction had a good sensitivity (72.63%) and specificity (84.86%), and AUC (0.876 5) was higher than that of a single variable, suggested that the multivariate combination was more accurate in predicting the short-term outcome of septic shock. Lac, PCT, SOFA score, APACHE II score and age were independent risk factors for death in patients with septic shock, and the accuracy of Lac, SOFA score, APACHE II score and age in predicting short-term prognosis of septic shock was better than that of single variable, and the diagnostic value was higher.

A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario

Comparison of NAFLD fibrosis score and BARD score in predicting fibrosis in nonalcoholic fatty liver disease

Objective To evaluate the effect of the Thyroid Imaging Report and Data System proposed by American Radiological Society (ACR-TIRADS) for differential diagnosis in thyroid nodules, and compare ACR-TIRADS to the TIRADS proposed by Kwak et al.(K-TIRADS) and the ultrasound-based risk stratification system evaluated by American Thyroid Association (ATA-Risk Stratification). Methods The clinical data of 1 760 patients with 1 912 thyroid nodules from 8 hospitals in Jiangsu province were retrospectively analysed. All of them were categorized based on ultrasound-based risk stratification systems. The ROC curve was established to assess and compare the diagnostic value of the systems. Results The area under the ROC curve (AUC) of ACR-TIRADS was 0.830, with high sensitivity and negative predictive value (86.9% and 87.5%, respectively), and relatively low specificity and positive predictive value (64.1% and 62.9%, respectively). The sensitivity and specificity of K-TIRADS were up to 84.9% and 76.1%, respectively. The AUC of ATA-Risk Stratification was 0.852, with relatively high specificity (83.4%), and low sensitivity (79.4%). There were significant differences in the AUC among the three ultrasound-based risk stratification systems, of which K-TIRADS was the highest (P<0.001). There was no significant difference in sensitivity of ACR-TIRADS and K-TIRADS (P=0.137), but significantly higher than that of ATA-Risk Stratification (P<0.001). There were significant differences in the specificity among the three systems, of which ATA-Risk Stratification was the highest (P<0.001). In addition, there were 109 nodules (5.7%) couldn′t be classified based on ATA-Risk Stratification, with high malignancy rate of 31.2%. Conclusions The diagnostic efficiency of ACR-TIRADS is good, but lower than K-TIRADS and ATA-Risk Stratification. ACR-TIRADS has the highest sensitivity, and ATA-Risk Stratification has the highest specificity, while the overall diagnostic efficiency of K-TIRADS is the best. Key words: Ultrasonography; Thyroid nodule; Thyroid imaging reporting and data system; Ultrasound-based risk stratification; Differential diagnosis

Acute appendicitis is one of the most frequent surgical emergencies worldwide. Early and accurate diagnosis is crucial to prevent complications such as perforation or unnecessary surgeries. Clinical scoring systems, such as the YASH score, have been developed to aid in diagnosis; however, their accuracy requires validation in different populations. Objective: To evaluate the diagnostic accuracy of the YASH scoring system in predicting acute appendicitis by assessing its sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Methods: A prospective observational study was conducted at Fazaia Ruth Pfau Medical College, PAF Hospital, Faisal, Karachi, from August 1, 2023, to July 1, 2024. Ethical approval was obtained from the institutional review board. A total of 100 patients aged 10 years and above of both genders presenting with suspected acute appendicitis (symptoms including right lower quadrant pain, fever, nausea, vomiting, and tenderness on examination) were enrolled using a non-probability consecutive sampling technique. Diagnostic performance of the YASH score was evaluated against surgical and histopathological outcomes. Sensitivity, specificity, PPV, NPV, and area under the ROC curve (AUC) were calculated using standard statistical methods. Results: The YASH scoring system showed a sensitivity of 89.04%, indicating high accuracy in identifying true appendicitis cases. The specificity was 85.19%, signifying good ability to exclude non-appendicitis cases. The PPV and NPV were consistent with the diagnostic trend. ROC curve analysis yielded an AUC value of 0.912, confirming strong overall performance in predicting acute appendicitis. Conclusion: The YASH scoring system demonstrated high diagnostic accuracy in predicting acute appendicitis with excellent sensitivity, specificity, and AUC. It can be reliably utilized as a clinical decision-making tool, especially in resource-limited settings where imaging may not be readily available.

Locally advanced rectal cancer: Value of ADC mapping in prediction of tumor response to radiochemotherapy

To scan the protein mass spectra in the sera and bronchoalveolar lavage fluid (BALF) from patients with peripheral lung cancer, screen out the differential proteins, and explore the clinical significance of the differential proteins. SELDI-TOF-MS was used to detect the protein mass spectra and to screen out the differential proteins in the sera and BALF collected before and after lung biopsy in 20 patients with peripheral lung cancer and 20 patients with benign pulmonary diseases. The differential proteins were analyzed and the initial diagnostic models were set up. (1) There were 6 differential protein peaks in the sera of the 2 groups (P < 0.05). The protein with a mass/charge ratio (M/Z) of 6637 was selected to establish the diagnostic model. The sensitivity of diagnosing peripheral lung cancer was 70% (14/20), the specificity 90% (18/20), the accuracy 80% (32/40), the positive predictive value (PV+) 88% (14/16), the negative predictive value (PV-) 75% (18/24), and the area under the ROC curve (AUC) was 0.73. (2) There were 11 differential protein peaks in the BALF collected before lung cancer biopsy of the 2 groups (P < 0.05). The protein with a M/Z of 7982 was selected to establish the diagnostic model. The sensitivity of diagnosing peripheral lung cancer was 85% (17/20), the specificity 90% (18/20), the accuracy 88% (35/40), the PV+ 89% (17/19), the PV- 86% (18/21), and the AUC was 0.94. (3) There were 14 differential protein peaks in the BALF collected after lung cancer biopsy of the 2 groups (P < 0.05). The protein with a M/Z of 7671 was selected to establish the diagnostic model. The sensitivity of diagnosing peripheral lung cancer was 85% (17/20), the specificity 100% (20/20), the accuracy 93% (37/40), the PV+ 100% (17/17), the PV- 87% (20/23), and the AUC was 0.93. There were more differential proteins in BALF as compared with sera. There were more differential proteins in the BALF collected after lung biopsy as compared to that before lung biopsy. The AUC of the diagnostic models set up by proteins in BALF collected before and after lung biopsy were all above 0.9 and showed higher efficiency for the diagnosis of peripheral lung cancer as compared to proteins in sera. These differential proteins may be better tumor markers for the diagnosis of peripheral lung cancer at the early stage.

BackgroundMeasurement of serum human epidermal growth factor receptor-2 (HER-2/neu) levels might play an essential role as a diagnostic/screening marker for the early selection of therapeutic approaches and predict prognosis in breast cancer patients. We aimed to undertake a systematic review and meta-analysis focusing on the diagnostic/screening value of serum HER-2 levels in comparison to routine methods.MethodsWe performed a systematic search via PubMed, Scopus, Cochrane-Library, and Web of Science databases for human diagnostic studies reporting the levels of serum HER-2 in breast cancer patients, which was confirmed using the histopathological examination. Meta-analyses were carried out for sensitivity, specificity, accuracy, area under the ROC curve (AUC), positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR).ResultsFourteen studies entered into this investigation. The meta-analysis indicated the low sensitivity for serum HER2 levels (Sensitivity: 53.05, 95%CI 40.82–65.28), but reasonable specificity of 79.27 (95%CI 73.02–85.51), accuracy of 72.06 (95%CI 67.04–77.08) and AUC of 0.79 (95%CI 0.66–0.92). We also found a significant differences for PPV (PPV: 56.18, 95%CI 44.16–68.20), NPV (NPV: 76.93, 95%CI 69.56–84.31), PLR (PLR: 2.10, 95%CI 1.69–2.50) and NLR (NLR: 0.58, 95%CI 0.44–0.71).ConclusionOur findings revealed that although serum HER-2 levels showed low se nsitivity for breast cancer diagnosis, its specificity, accuracy and AUC were reasonable. Hence, it seems that the measurement of serum HER-2 levels can play a significant role as a verification test for initial negative screening test results, especially in low-income regions due to its cost-effectiveness and ease of implementation.

Introduction:Asthma exacerbations are frequent causes of pediatric hospital admissions. We sought to develop a time series algorithm to forecast next-day daily asthma hospitalizations.Methods:Daily hospitalizations for asthma were collected at Cincinnati Children’s from January 1, 2016, to December 31, 2023. We evaluated Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Prophet, and Ensemble models to forecast next-day asthma hospitalizations validated on 2023 data, considering varying historical training data lengths. Forecasts were calibrated to identify days exceeding a 5% high-risk threshold of historical totals and considered multiple validation years and years before and during the COVID-19 pandemic.Results:A total of 5,593 hospital admissions were recorded for asthma. Over 2,922 days, 166 days met the 5% high-risk threshold equating to 6 or more admissions. The Ensemble (Median Absolute Percentage Error (MAPE): 46.7%; Positive Predictive Value (PPV): 0.278; Negative Predictive Value (NPV): 0.942; Area Under the ROC Curve (AUC): 0.740; Sensitivity: 0.800; Specificity: 0.656) model achieved higher accuracy of high-risk days than ARIMA (MAPE: 46.5%; PPV: 0.278; NPV: 0.942; AUC: 0.709; Sensitivity: 0.760; Specificity: 0.571), ETS (MAPE: 47.2%; PPV: 0.222; NPV: 0.939; AUC: 0.711; Sensitivity: 0.800; Specificity: 0.668), and Prophet (MAPE: 48.9%; PPV: 0.444; NPV: 0.951; AUC: 0.732; Sensitivity: 0.680; Specificity: 0.741) models.Conclusions:Our Ensemble model of mean predictions from ARIMA, ETS, and Prophet models was the most accurate in forecasting future asthma hospitalizations. Integrating forecasting techniques with clinical operations could enable proactive prevention through enhanced population care management.

To determine the most appropriate nutritional assessment tool for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI from four nutritional assessment tools including PNI, GNRI, CONUT, and BMI. Consecutive cases diagnosed with acute coronary syndrome (ACS) and underwent percutaneous coronary intervention (PCI) in the Department of Cardiovascular Medicine of the Air force characteristic medical center from 1 January 2020 to 1 April 2022 were retrospectively collected. The basic clinical characteristics and relevant test and examination indexes were collected uniformly, and the cases were divided into the MACE group (174 cases) and the non-MACE group (372 cases) according to whether a major adverse cardiovascular event (MACE) had occurred within 1 year. Predictive models were constructed to assess the nutritional status of patients with the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling nutritional status (CONUT) scores, and Body Mass Index (BMI), respectively, and to analyze their relationship with prognosis. The incremental value of the four nutritional assessment tools in predicting risk was compared using the Integrated Discriminant Improvement (IDI) and the net reclassification improvement (NRI). The predictive effect of each model on the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI was assessed using area under the ROC curve (AUC), calibration curves, decision analysis curves, and clinical impact curves; comparative analyses were performed. Among the four nutritional assessment tools, the area under the curve (AUC) was significantly higher for the PNI (AUC: 0.798, 95%CI 0.755–0.840 P < 0.001) and GNRI (AUC: 0.760, 95%CI 0.715–0.804 P < 0.001) than for the CONUT (AUC: 0.719,95%CI 0.673–0.765 P < 0.001) and BMI (AUC: 0.576, 95%CI 0.522–0.630 P < 0.001). The positive predictive value (PPV) of PNI: 67.67% was better than GNRI, CONUT, and BMI, and the negative predictive value (NPV): of 83.90% was better than CONUT and BMI and similar to the NPV of GNRI. The PNI, GNRI, and CONUT were compared with BMI, respectively. The PNI had the most significant improvement in the Integrated Discriminant Improvement Index (IDI) (IDI: 0.1732, P < 0.001); the PNI also had the most significant improvement in the Net Reclassification Index (NRI) (NRI: 0.8185, P < 0.001). In addition, of the four nutritional assessment tools used in this study, the PNI was more appropriate for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI.

In order to explore the regression equation for the prediction model of subarachnoid hemorrhage and cerebral vasospasm, the nomogram prediction model of SCVS occurrence was established. This study is a retrospective analysis of 125 cases of aSAH admitted to a hospital; the patients were divided into SCVS group and non-SCVS group. Select SIRI as a simple and reliable marker of inflammation, analyze its correlation with SCVS and its predictive value, and analyze the predictive value of SIRI to SCVS through ROC curve. Based on the SIRI inflammation level and other related risk factors, a nomogram prediction model for the occurrence of SCVS was built. The experimental results show that the SIRI level of patients in the SCVS group was significantly higher than that of the non-SCVS group, and logistic regression analysis found that SIRI is an independent risk factor for SCVS. SIRI = 3.63 × 109/L is the best cutoff value for diagnosing the occurrence of SCVS. When TC = 2.24 mmol/L and SIRI = 3.63 × 10%/L, its Youden Index is the largest (0.312, 0.296) and is the best cutoff value for predicting the occurrence of SCVS; at the same time, its prediction accuracy (area under the ROC curve (AUC)), sensitivity, specificity, the positive predictive value, and negative predictive value are 0.743, 72.70%, 80.10%, 77.53%, and 94.24% and 0.725, 70.60%, 76.90%, 73.49%, and 93.59%. Nomogram prediction model establishment and evaluation combined with the results of multifactor analysis are used to build an individual nomogram prediction model. The model has good prediction consistency (C-index = 0.685, P < 0.01). ROC analysis results showed that the model that combined SIRI and other standard variables (AUC = 0.896, 95% CI was 0.803-0.929, P < 0.001) was better than the model that did not combine SIRI (AUC = 0.859, 95% CI was 0.759-0.912, P < 0.001) and the model based only on SIRI (AUC = 0.725, 95% CI was 0.586-0.793, P = 0.001) has better predictive value for SCVS. Joint SIRI will optimize the prediction performance of the nomogram model and improve the early recognition and screening capabilities of SCVS.

IntroductionYoung children commonly wheeze but only some have asthma later in life. Asthma prediction tools have poor predictive performance and few have been validated. We aimed to develop a robust...

Objective To investigate the efficacy and clinical value of ultrasound elastography (UE) combined with liquid-based cytology test (LCT) in the diagnosis of thyroid micronodular lesions, and to provide new ideas for the diagnosis of thyroid micronodular lesions. Methods UE examination and ultrasound-guided fine needle aspiration LCT were performed in 58 cases of thyroid micronodular lesions. The UE scores and LCT examination results were summarized. Logistic regression and area under the ROC curve (AUC) and its 95% confidence interval (CI) were used to evaluate the efficacy and clinical value of the two techniques in the accurate diagnosis of thyroid micronodular lesions. Results The histopathological diagnosis was used as the gold standard. Thereby, the AUC of ultrasound elastography combined with liquid-based cytology test was 0.84 (95%CI: 0.69~0.99) . The diagnostic sensitivity was 96.87%, and the specificity was 64.64%. The positive predictive value was 92.86%, and the negative predictive value was 79.02%. Conclusion The combination of UE and LCT is significant in the diagnosis of thyroid micronodular lesions, and it justifies widespread use. Key words: Thyroid; Ultrasonography, doppler, color; Liquid-based cytology test

Body fat assessment is needed in individuals with HIV. The objective was to identify the discriminatory capacity of the abdominal skinfold (ASF) tricipital skinfold (TSF), subscapular fold (SSF), calf skinfold (CSF), body adiposity index (BAI), body mass index, conicity index (IC), mid-upper arm circumference (MUAC), waist circumference (WC), perimeter of neck (PN) and waist-to-height ratio (WHtR) for high body fat in children and adolescents with HIV, compared Dual energy X-ray absorptiometry (DXA) and air displacement plethysmography (ADP). Descriptive study, cross - sectional study, with 65 children and adolescents with HIV by vertical transmission. Body fat was measured by DXA and ADP. Measures were measured by international standardization. The diagnostic properties for high body fat were assessed by area under the ROC curve (AUC). For boys, having DXA as a reference for fat, ASF (AUC: 0.920), TSF (AUC: 0.792), SSF (AUC: 0.766), CSF (AUC: 0.866), BAI satisfactory discriminatory capacity. With ADP as the reference method, ASF (AUC: 0.920), TSF (AUC: 0.921), SSF (AUC: 0.766), CSF (AUC: 0.901), BAI (AUC: 0.756) and BMI (AUC: 0.699) presented satisfactory results. For girls, having DXA as a reference for fat, ASF (AUC: 0.838), TSF (AUC: 0.842), SSF (AUC: 0.840), CSF (AUC: 0.887), BAI (AUC: 0.846), and BMI (AUC: 0.859) presented satisfactory discriminatory capacity. Assuming ADP as a reference for fat, ASF (AUC [AUC: 0.799], TSF [AUC: 0.825], SSF [AUC: 0.767], CSF [AUC: 0.897], BAI 0.788), were satisfactory. The ASF, TSF, SSF, CSF, BAI and BMI anthropometric indicators may be suggested as the most suitable for the detection of high body fat in children and adolescents with HIV.