Using sunrise to surmise acute transient patellar dislocation.

To assess which of the formulae for estimating renal function (Cockcroft-Gault (CG), Chronic Kidney Disease Epidemiology Collaboration CKD-EPI) and Modification of Diet in Renal Disease (MDRD)) provides the most accurate prediction of minimum vancomycin concentration (Cmin) and to evaluate whether they can be interchanged to optimise vancomycin dosage. An observational and retrospective study was undertaken in hospitalised adult patients treated with intravenous vancomycin. Patients with serum creatinine (Scr) >2 mg/dL and <0.5 mg/dL, body mass index >40 kg/m2, need for extracorporeal clearance techniques and unstable renal function were excluded. Bayesian analysis was used to obtain individual pharmacokinetic parameters. Vancomycin clearance (CLvan) was calculated by means of CG (eCLvanCG), CKD-EPI (eCLvanCKD-EPI) and MDRD (eCLvanMDRD) and used to obtain Cmin estimates (eCmin). eCmin and observed Cmin were compared using an intraclass correlation coefficient (ICC). A post-hoc analysis by subgroups (age, sex, weight, Scr and estimated glomerular filtration rate (eGFR)) was performed. From each eCLvan, the area under the curve (AUC) was calculated and categorised as AUC <400 mg*hour/L, AUC 400-600 mg*hour/L and AUC >600 mg*hour/L. The kappa coefficient was applied to study AUC concordance. A total of 228 patients (69.3% men) were included. eCminCG had a statistically significant better agreement with Cmin (ICC >0.7) and showed good agreement in almost all subgroups. Patients with Scr >1.1 mg/dL were the only subgroup in which eCminMDRD and eCminCKD-EPI had an adequate ICC with no statistically significant differences compared with eCminCG. eCminMDRD had a similar ICC to eCminCG in the eGFR <60 mL/min and age 46-75 years subgroups. Kappa values showed regular agreement in all subgroups: 0.32 (AUC <400 mg*hour/L), 0.24 (AUC 400-600 mg*hour/L) and 0.41 (AUC >600 mg*hour/L). The CG formula provides the most accurate prediction of vancomycin Cmin. In patients with eGFR <60 mL/min and aged 46-75 years, MDRD also shows a good predictive capacity. However, in low weight and elderly patients, Cmin predictions are superior with CG. Therefore, renal function estimation equations should not be considered interchangeable for vancomycin dose adjustments.

Explainable Boosting Machine Model with a Parallel Ensemble Design Predicts Local Failure for Head and Neck Cancer With Clinical, CT, and Delta CBCT Radiomic Features

Although the rate of pediatric postoperative mortality is low, the development and validation of perioperative risk assessment models have allowed for the stratification of those at highest risk, including the Pediatric Risk Assessment (PRAm) score. The clinical application of such tools requires manual data entry, which may be inaccurate or incomplete, compromise efficiency, and increase physicians' clerical obligations. We aimed to create an electronically derived, automated PRAm score and to evaluate its agreement with the original American College of Surgery National Surgical Quality Improvement Program (ACS NSQIP)-derived and validated score. We performed a retrospective observational study of children <18 years who underwent noncardiac surgery from 2017 through 2021 at Boston Children's Hospital (BCH). An automated PRAm score was developed via electronic derivation of International Classification of Disease (ICD) -9 and -10 codes. The primary outcome was agreement and correlation among PRAm scores obtained via automation, NSQIP data, and manual physician entry from the same BCH cohort. The secondary outcome was discriminatory ability of the 3 PRAm versions. Fleiss Kappa, Spearman correlation (rho), and intraclass correlation coefficient (ICC) and receiver operating characteristic (ROC) curve analyses with area under the curve (AUC) were applied accordingly. Of the 6014 patients with NSQIP and automated PRAm scores (manual scores: n = 5267), the rate of 30-day mortality was 0.18% (n = 11). Agreement and correlation were greater between the NSQIP and automated scores (rho = 0.78; 95% confidence interval [CI], 0.76-0.79; P <.001; ICC = 0.80; 95% CI, 0.79-0.81; Fleiss kappa = 0.66; 95% CI, 0.65-0.67) versus the NSQIP and manual scores (rho = 0.73; 95% CI, 0.71-0.74; P < .001; ICC = 0.78; 95% CI, 0.77-0.79; Fleiss kappa = 0.56; 95% CI, 0.54-0.57). ROC analysis with AUC showed the manual score to have the greatest discrimination (AUC = 0.976; 95% CI, 0.959,0.993) compared to the NSQIP (AUC = 0.904; 95% CI, 0.792-0.999) and automated (AUC = 0.880; 95% CI, 0.769-0.999) scores. Development of an electronically derived, automated PRAm score that maintains good discrimination for 30-day mortality in neonates, infants, and children after noncardiac surgery is feasible. The automated PRAm score may reduce the preoperative clerical workload and provide an efficient and accurate means by which to risk stratify neonatal and pediatric surgical patients with the goal of improving clinical outcomes and resource utilization.

Background: The World Health Organization declared COVID-19 “a pandemic” on March 11, 2020. An essential preventive and screening strategy adopted was temperature screening at various public places such as airports and hospitals with devices like handheld, noncontact infrared thermometers. Before this pandemic, the industrial thermometers have never been used in health-care settings. The study was conducted to compare temperature readings of these industrial-grade infrared noncontact handheld thermometers with a digital thermometer. Materials and Methods: Three Testo® industrial-grade infrared noncontact thermometer (IGNCT) and Dr. Trust digital thermometer were used on 117 individuals for recording temperature. The average of three readings was taken for each instrument by a single investigator. Results: Out of 117 individuals, 14 (11.9%) were febrile. The Pearson correlation values between the digital thermometers varied from 0.3 to 0.42. The reliability, as measured by the intraclass correlation, was poor (0.16-“0.4). The area under the curve for IGNCT devices varied from 76% to 84%. Conclusion: Industrial-grade infrared noncontact temperature (IGNCT) recording devices with its no-touch technique to measure temperature while maintaining distance may be useful in a pandemic situation. However, the higher the area under the curve, the better it is. In a large cohort, 20% will be misclassified, and in absolute terms, they may be translated into large numbers. Hence, there is a case for better devices having an area under the curve of nearly 100% for the screening of population. The role of IGNCT devices for the screening of individuals to identify cases of febrile illness is limited owing to its poor reliability and unacceptable area under the curve for screening large populations.

Hepatic steatosis and iron overload are common in chronic liver diseases and may cause progressive damage if undetected. Liver biopsy, the gold standard, is invasive with limited clinical applicability. This study evaluated the diagnostic performance of three-dimensional (3D) multi-echo chemical shift-encoded magnetic resonance imaging (MRI) for simultaneous non-invasive quantification of hepatic fat and iron content against histopathology. A total of 177 patients with chronic liver disease underwent 3D Multi-Echo Chemical Shift Encoded MRI examination followed by liver biopsy. Proton density fat fraction (PDFF) and R2* values were measured to quantify hepatic fat and iron content, respectively. Histopathological grading served as the reference standard. Diagnostic accuracy, correlation coefficients, and inter-reader reliability were assessed. Receiver operating characteristic (ROC) analysis determined optimal cut-off values for different grades of steatosis and iron content. High repeatability and consistency were observed in measuring PDFF and R2* values [intraclass correlation coefficient (ICC) =0.998 and 0.991, respectively]. Significant correlations were observed between MRI measurements and histopathology for both hepatic fat (r=0.856, P<0.001) and iron content (r=0.617, P<0.001). For steatosis detection, PDFF demonstrated excellent diagnostic accuracy with area under the curves (AUCs) of 0.932, 0.974, and 0.992 for grades ≥1, ≥2, and ≥3, respectively. The R2* value performed comparably in assessing the presence of iron deposition, with an AUC of 0.880. Spearman's correlation analysis demonstrated a significant positive correlation between PDFF and R2* values (r=0.589, P<0.001). 3D Multi-Echo Chemical Shift Encoded MRI demonstrates excellent diagnostic performance with high reproducibility for simultaneous assessment of hepatic steatosis and iron content, showing strong correlations with histopathological findings. This non-invasive technique provides a reliable alternative to liver biopsy and supports its clinical implementation as a standard tool for comprehensive hepatic evaluation.

Purpose Patients with idiopathic Parkinson’s disease (PD) suffer from different non-motor symptoms, including pain. The present study aimed to measure the psychometric properties of the Brief Pain Inventory (BPI) in patients with PD during ON- and OFF-states. Methods We recruited 460 patients with PD and 100 non-PD controls. The pain was assessed by the BPI, King’s Parkinson’s disease Pain Scale (KPPS), Neuropathic Pain Symptom Inventory (NPSI), Visual Analogue Scale-Pain (VAS-pain), and short-form McGill Pain Questionnaire-2 (SF-MPQ-2) in both medication states. Internal consistency and test-retest reliability was examined using Cronbach’s alpha coefficient and intra-class correlation coefficient (ICC). Dimensionality and convergent validity of BPI were also investigated. Diagnostic accuracy and discriminative validity were determined by Receiver Operating Characteristics (ROC) curve analysis and Area Under the Curve (AUC). Results Cronbach’s alpha was satisfactory (α = 0.91–0.97) in both states. The ICC values were 0.85–0.96 in ON- and OFF-state. Factor analysis revealed two factors. A high correlation was obtained between BPI subscales and other scales. AUC >0.91, sensitivity, and specificity> 0.77 were observed for discriminating different pain levels. Furthermore, appropriate diagnostic accuracy was found (AUC, sensitivity, and specificity >0.67) between non-PD control and PD patients. Conclusion The BPI has acceptable psychometric features as well as diagnostic accuracy for patients with PD. Implications for rehabilitation Pain as a non-motor symptom in PD can affect daily and social activities. The BPI is used to assess pain severity and interference in activities. For better treatment, pain should be assessed in off-state like to on-state. BPI has satisfactory reliability and validity in different medication states in PD.

Very early distant metastasis (VEDM) for patients with colorectal cancer (CRC) following surgery suggests failure of local treatment strategy and few biomarkers are available for its effective risk stratification. This study aimed to explore the potential of quantitative dual-energy computed tomography (DECT) spectral parameters and build models to predict VEDM. Consecutive patients suspected of having CRC and with a clinical indication for enhanced CT from April 2021 to July 2022 at a single institution were prospectively enrolled to undertake spectral CT scanning. The spectral features were extracted by two reviewers and intraclass correlation coefficient (ICC) was used for interobserver agreement evaluation. A total of 16 spectral parameters, including unenhanced effective atomic number, triphasic iodine concentrations (ICs)/normalized ICs (NICs)-A/V/E/1/NIC-A/V/E/spectral curve slopes (λ-A/V/E), two arterial enhancement fractions (AEFs), and venous enhancement fraction (VEF), were determined for analysis. Patients with and without VEDM after surgery were matched using propensity score matching (PSM). The diagnostic performance was assessed using the area under the curve (AUC). Models of multiple modalities were generated. In total, 222 patients were included (141 males, age range, 32-83 years) and 13 patients developed VEDM. Interobserver agreement ranged from good to excellent (ICC, 0.773-0.964). A total of three spectral parameters (VEF, λ-V, and 1/NIC-V) exhibited significant discriminatory ability (P<0.05) in predicting VEDM, with AUCs of 0.822 [95% confidence interval (CI): 0.667-0.926], 0.738 (95% CI: 0.573-0.866), and 0.713 (95% CI: 0.546-0.846) and optimal cutoff points of 67.16%, 2.46, and 2.44, respectively. The performance of these spectral parameters was validated in the entire cohort; the combined spectral model showed comparable efficiency to the combined clinical model [AUC, 0.771 (95% CI: 0.622-0.919) vs. 0.779 (95% CI: 0.663-0.894), P>0.05]; the clinical-spectral model achieved further improved AUC of 0.887 (95% CI: 0.812-0.962), which was significantly higher than the combined clinical model (P=0.015), yet not superior to the combined spectral model (P=0.078). Novel spectral parameters showed potential in predicting VEDM in CRC following surgery in this preliminary study, which were closely related with spectral perfusion in the venous phase. However, further studies with larger samples are warranted.

To determine whether radiomics features derived from diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) can improve the differentiation between radiation-induced brain injury (RIBI) and tumor recurrence (TR) in glioma patients. A total of 4199 radiomics features were extracted from conventional MRI, apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) maps, obtained from 96 pathologically confirmed WHO grade 2~4 gliomas with enhancement after standard treatment. The intraclass correlation coefficient (ICC) was used to test segmentation stability between two doctors. Radiomics features were selected using the Mann-Whitney U test, LASSO regression, and RFE algorithms. Four machine learning classifiers were adopted to establish radiomics models. The diagnostic performance of multiparameter, conventional, and single-parameter MRI radiomics models was compared using the area under the curve (AUC). The models were evaluated in the subsequent independent validation set (n = 30). Eight important radiomics features (3 from conventional MRI, 1 from ADC, and 4 from CBF) were selected. Support vector machine (SVM) was chosen as the optimal classifier. The diagnostic performance of the multiparameter MRI radiomics model (AUC 0.96) was higher than that of the conventional MRI (AUC 0.88), ADC (AUC 0.91), and CBF (AUC 0.95) radiomics models. For subgroup analysis, the multiparameter MRI radiomics model showed similar performance, with AUCs of 0.98 in WHO grade 2~3 and 0.96 in WHO grade 4. The incorporation of noninvasive DWI and ASL into the MRI radiomics model improved the diagnostic performance in differentiating RIBI from TR; ASL, especially, played a significant role. • The multiparameter MRI radiomics model was superior to the conventional MRI radiomics model in differentiating glioma recurrence from radiation-induced brain injury. • Diffusion and perfusion MRI could improve the ability of the radiomics model in predicting the progression in patients with glioma. • Arterial spin labeling played an important role in predicting glioma progression using radiomics models.

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality, and transarterial chemoembolization (TACE) is the standard therapy for intermediate-stage disease. However, response to TACE is variable, and reliable quantitative imaging biomarkers are needed to support early treatment decision-making. This study aimed to evaluate the predictive value of the delayed percentage attenuation ratio (DPAR) measured from pre-TACE multiphasic computed tomography (CT) in forecasting early therapeutic response. A retrospective cross-sectional study was conducted involving patients with a definitive diagnosis of HCC who underwent their first TACE session and had complete multiphasic CT imaging before and after treatment. Quantitative washout parameters, delayed percentage attenuation ratio (DPAR), absolute washout (WOAbs), and relative washout (WORel) were measured using standardized region of interest (ROI) placement by three radiologists. Treatment response was assessed four to six weeks post- TACE based on modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria and classified into responders and non-responders. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis, and interobserver reliability was assessed using intraclass correlation coefficient (ICC) and Cohen's κ. A total of 49 HCC patients were included and analyzed. Responders demonstrated significantly higher DPAR values compared with non-responders (median 134.5 vs 113.0; p<0.001). DPAR showed the strongest discriminative performance with an area under the curve (AUC) of 0.898, outperforming WOAbs (AUC 0.689) and WORel (AUC 0.704). The optimal DPAR threshold of ≥120.5 provided 84.4% sensitivity and 88.2% specificity to predict early post-TACE treatment response. Interobserver reliability was excellent for all washout parameters (ICC 0.98–0.99), and agreement for mRECIST classification was also excellent (κ=0.867). In conclusion, pre-TACE DPAR is a robust and reproducible quantitative imaging biomarker that accurately predicts early response to TACE in HCC. A threshold value of ≥120.5 may assist in treatment planning and patient selection in routine clinical practice.

This study aimed to evaluate the diagnostic performances of dual-layer CT (DLCT) for the identification of positive lymph nodes (LNs) in patients with lymphoma and retrospectively included 1165 LNs obtained by biopsy from 78 patients with histologically proven lymphoma, who underwent both pretreatment DLCT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). According to 18F-FDG PET/CT findings as a reference standard, cases were categorized into the LN-negative and LN-positive groups. LNs were then randomly divided at a ratio of 7:3 into the training (n = 809) and validation (n = 356) cohorts. The patients’ clinical characteristics and quantitative parameters including spectral curve slope (λHU), iodine concentration (IC) on arterial phase (AP) and venous phase (VP) images were compared between the LN-negative and LN-positive groups using Chi-square test, t-test or Mann–Whitney U test for categorical variables or quantitative parameters. Multivariate logistic regression analysis with tenfold cross-validation was performed to establish the most efficient predictive model in the training cohort. The area under the curve (AUC) was used to evaluate the diagnostic value of the predictive model, and differences in AUC were determined by the DeLong test. Moreover, the predictive model was validated in the validation cohort. Repeatability analysis was performed for LNs using intraclass correlation coefficients (ICCs). In the training cohort, long diameter (LD) had the highest AUC as an independent factors compared to other parameter in differentiating LN positivity from LN negativity (p = 0.006 to p < 0.001), and the AUC of predictive model jointly involving LD and λHU-AP was significantly elevated (AUC of 0.816, p < 0.001). While the AUC of predictive model in the validation cohort was 0.786. Good to excellent repeatability was observed for all parameters (ICC > 0.75). The combination of DLCT with morphological and functional parameters may represent a potential imaging biomarker for detecting LN positivity in lymphoma.

The King-Devick (K-D) test is used to identify oculomotor impairment after concussion. However, the diagnostic accuracy of the K-D test over time has not been evaluated. To (1) examine the sensitivity and specificity of the K-D test at 0 to 6 hours postinjury, 24 to 48 hours postinjury, the beginning of a return-to-play (RTP) protocol (asymptomatic), unrestricted RTP, and 6 months postconcussion and (2) compare outcomes between athletes with and those without concussion across confounding factors (sex, age, sport contact level, academic year, learning disorder, attention-deficit/hyperactivity disorder, migraine history, concussion history, and test administration mode). Retrospective, cross-sectional design. Multiple institutions in the Concussion Assessment, Research and Education Consortium. A total of 320 athletes with a concussion (162 men, 158 women; age = 19.80 ± 1.41 years) were compared with 1239 total collegiate athletes without a concussion (646 men, 593 women; age = 20.31 ± 1.18 years). We calculated the K-D test time difference (in seconds) by subtracting the baseline from the most recent time. Receiver operator characteristic (ROC) curve and area under the curve (AUC) analyses were used to determine the diagnostic accuracy across time points. We identified cutoff scores and corresponding specificity at both the 80% and 70% sensitivity levels. We repeated ROC with AUC analyses using confounding factors. The K-D test predicted positive results at the 0- to 6-hour (AUC = 0.724, P < .001), 24- to 48-hour (AUC = 0.701, P < .001), RTP (AUC = 0.640, P < .001), and 6-month postconcussion (AUC = 0.615, P < .001) tim points but not at the asymptomatic time point (AUC = 0.513, P = .497). The 0- to 6-hour and 24- to 48-hour time points yielded 80% sensitivity cutoff scores of -2.6 and -3.2 seconds (ie, faster), respectively, but 46% and 41% specificity, respectively. The K-D test had a better AUC when administered using an iPad (AUC = 0.800, 95% CI = 0.747, 0.854) compared with the spiral-bound card system (AUC = 0.646, 95% CI = 0.600, 0.692; P < .001). The diagnostic accuracy of the K-D test was greatest at 0 to 6 hours and 24 to 48 hours postconcussion but declined across subsequent postconcussion time points. The AUCs did not differentiate between groups across confounding factors. Our negative cutoff scores indicated that practice effects contributed to improved performance, requiring athletes to outperform their baseline scores.

Diagnostic accuracy of fibrinogen to differentiate appendicitis from nonspecific abdominal pain in children

ObjectiveTo develop and validate a generalized prediction model that can classify epidermal growth factor receptor (EGFR) mutation status in non–small cell lung cancer patients.MethodsA total of 346 patients (296 in the training cohort and 50 in the validation cohort) from four centers were included in this retrospective study. First, 1085 features were extracted using IBEX from the computed tomography images. The features were screened using the intraclass correlation coefficient, hypothesis tests and least absolute shrinkage and selection operator. Logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) were used to build a radiomics model for classification. The models were evaluated using the following metrics: area under the curve (AUC), calibration curve (CAL), decision curve analysis (DCA), concordance index (C-index), and Brier score.ResultsSixteen features were selected, and models were built using LR, DT, RF, and SVM. In the training cohort, the AUCs was .723, .842, .995, and .883; In the validation cohort, the AUCs were .658, 0567, .88, and .765. RF model with the best AUC, its CAL, C-index (training cohort=.998; validation cohort=.883), and Brier score (training cohort=.007; validation cohort=0.137) showed a satisfactory predictive accuracy; DCA indicated that the RF model has better clinical application value.ConclusionMachine learning models based on computed tomography images can be used to evaluate EGFR status in patients with non–small cell lung cancer, and the RF model outperformed LR, DT, and SVM.

To determine whether single-phase dual-energy CT (DECT) differentiates vascular and nonvascular renal lesions in the portal venous phase (PVP). Optimal iodine threshold was determined and compared to Hounsfield unit (HU) measurements. We retrospectively included 250 patients (266 renal lesions) who underwent a clinically indicated PVP abdominopelvic CT on a rapid-kilovoltage-switching single-source DECT (rsDECT) or a dual-source DECT (dsDECT) scanner. Iodine concentration and HU measurements were calculated by four experienced readers. Diagnostic accuracy was determined using biopsy results and follow-up imaging as reference standard. Area under the curve (AUC) was calculated for each DECT scanner to differentiate vascular from nonvascular lesions and vascular lesions from hemorrhagic/proteinaceous cysts. Univariable and multivariable logistic regression analyses evaluated the association between variables and the presence of vascular lesions. A normalized iodine concentration threshold of 0.25 mg/mL yielded high accuracy in differentiating vascular and nonvascular lesions (AUC 0.93, p < 0.001), with comparable performance to HU measurements (AUC 0.93). Both iodine concentration and HU measurements were independently associated with vascular lesions when adjusted for age, gender, body mass index, and lesion size (AUC 0.95 and 0.95, respectively). When combined, diagnostic performance was higher (AUC 0.96). Both absolute and normalized iodine concentrations performed better than HU measurements (AUC 0.92 vs. AUC 0.87) in differentiating vascular lesions from hemorrhagic/proteinaceous cysts. A single-phase (PVP) DECT scan yields high accuracy to differentiate vascular from nonvascular renal lesions. Iodine concentration showed a slightly higher performance than HU measurements in differentiating vascular lesions from hemorrhagic/proteinaceous cysts. • A single-phase dual-energy CT scan in the portal venous phase differentiates vascular from nonvascular renal lesions with high accuracy (AUC 0.93). • When combined, iodine concentration and HU measurements showed the highest diagnostic performance (AUC 0.96) to differentiate vascular from nonvascular renal lesions. • Compared to HU measurements, iodine concentration showed a slightly higher performance in differentiating vascular lesions from hemorrhagic/proteinaceous cysts.

ObjectiveSeven recombinant viral capsid antigen-IgA (VCA-IgA) ELISA kits are widely used in China, but their diagnostic effects have not been evaluated. In this study, we evaluated whether the diagnostic effects...