A comparative evaluation of bioequivalence of Gan & Lee glargine U300 and Toujeo® in Chinese healthy male participants.

Background: To assess the bioequivalence between Gan & Lee (GL) glargine U300 and Toujeo® regarding pharmacokinetics (PK), pharmacodynamics (PD), and safety in Chinese healthy male participants. Methods: A single-center, randomized, double-blind, single-dose, two-preparation, two-sequence, four-cycle repeated crossover design study was performed to compare GL glargine U300 and Toujeo® in 40 healthy participants. The primary PK endpoints were the area under the curve of glargine metabolites, M1 concentration from 0 to 24 hours (AUC0-24h), and the maximum glargine concentration within 24 hours post-dose (Cmax). The primary PD endpoints were the area under the glucose infusion rate (GIR) curve from 0 to 24 hours (AUCGIR.0-24h) and the maximum GIR within 24 hours post-dose (GIRmax). Results: GL Glargine U300 demonstrated comparable PK parameters (AUC0-24h, Cmax, AUC0-12h, and AUC12-24h of M1) and PD responses [AUCGIR.0-24h, GIRmax, AUCGIR.0-12h, and AUCGIR.12-24h] to those of Toujeo®, as indicated by 90% confidence intervals ranging from 80% to 125%. No significant disparities in safety profiles were observed between the two treatment groups, and there were no reported instances of serious adverse events. Conclusion: The PK, PD, and safety of GL glargine U300 were bioequivalent to that of Toujeo®. Clinical trial registration: https://www.chinadrugtrials.org.cn/, identifier CTR20212419.

Machine learning in diagnostic support in medical emergency departments.

Diagnosing patients in the medical emergency department is complex and this is expected to increase in many countries due to an ageing population. In this study we investigate the feasibility of training machine learning algorithms to assist physicians handling the complex situation in the medical emergency departments. This is expected to reduce diagnostic errors and improve patient logistics and outcome. We included a total of 9,190 consecutive patient admissions diagnosed and treated in two hospitals in this cohort study. Patients had a biochemical workup including blood and urine analyses on clinical decision totaling 260 analyses. After adding nurse-registered data we trained 19 machine learning algorithms on a random 80% sample of the patients and validated the results on the remaining 20%. We trained algorithms for 19 different patient outcomes including the main outcomes death in 7 (Area under the Curve (AUC) 91.4%) and 30 days (AUC 91.3%) and safe-discharge(AUC 87.3%). The various algorithms obtained areas under the Receiver Operating Characteristics -curves in the range of 71.8-96.3% in the holdout cohort (68.3-98.2% in the training cohort). Performing this list of biochemical analyses at admission also reduced the number of subsequent venipunctures within 24 h from patient admittance by 22%. We have shown that it is possible to develop a list of machine-learning algorithms with high AUC for use in medical emergency departments. Moreover, the study showed that it is possible to reduce the number of venipunctures in this cohort.

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

Purpose: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data. Methods: Cross-sectional study of children aged five to 18 years who underwent biometry and autorefraction before and after cycloplegia. Myopia was defined as cycloplegic spherical equivalent refraction (SER) ≤-0.5 Diopter (D). Models were evaluated for predicting SER using R2 and mean absolute error (MAE) and myopia status using area under the receiver operating characteristic (ROC) curve (AUC). Best-performing models were further evaluated using sensitivity/specificity and comparison of observed versus predicted myopia prevalence rate overall and in each age group. Independent data sets were used for training (n = 1938) and validation (n = 1476). Results: In the validation dataset, ML models predicted cycloplegic SER with high R2 (0.913-0.935) and low MAE (0.393-0.480 D). The AUC for predicting myopia was high (0.984-0.987). The best-performing model for SER (XGBoost) had high sensitivity and specificity (91.1% and 97.2%). Random forest (RF), the best-performing model for myopia, had high sensitivity and specificity (92.2% and 96.9%). Within each age group, difference between predicted and actual myopia prevalence was within 4%. Conclusions: Using noncycloplegic refractive error and ocular biometric data, ML models performed well for predicting cycloplegic SER and myopia status. When measuring cycloplegic SER is not feasible, ML may provide a useful tool for estimating cycloplegic SER and myopia prevalence rate in epidemiological studies. Translational Relevance: Using ML to predict cycloplegic refraction based on noncycloplegic data is a powerful tool for large, population-based studies of refractive error.

Focal liver lesion diagnosis with deep learning and multistage CT imaging.

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.

Tree-based ensemble machine learning models in the prediction of acute respiratory distress syndrome following cardiac surgery: a multicenter cohort study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) after cardiac surgery is a severe respiratory complication with high mortality and morbidity. Traditional clinical approaches may lead to under recognition of this heterogeneous syndrome, potentially resulting in diagnosis delay. This study aims to develop and external validate seven machine learning (ML) models, trained on electronic health records data, for predicting ARDS after cardiac surgery. METHODS: This multicenter, observational cohort study included patients who underwent cardiac surgery in the training and testing cohorts (data from Nanjing First Hospital), as well as those patients who had cardiac surgery in a validation cohort (data from Shanghai General Hospital). The number of important features was determined using the sliding windows sequential forward feature selection method (SWSFS). We developed a set of tree-based ML models, including Decision Tree, GBDT, AdaBoost, XGBoost, LightGBM, Random Forest, and Deep Forest. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and Brier score. The SHapley Additive exPlanation (SHAP) techinque was employed to interpret the ML model. Furthermore, a comparison was made between the ML models and traditional scoring systems. ARDS is defined according to the Berlin definition. RESULTS: A total of 1996 patients who had cardiac surgery were included in the study. The top five important features identified by the SWSFS were chronic obstructive pulmonary disease, preoperative albumin, central venous pressure_T4, cardiopulmonary bypass time, and left ventricular ejection fraction. Among the seven ML models, Deep Forest demonstrated the best performance, with an AUC of 0.882 and a Brier score of 0.809 in the validation cohort. Notably, the SHAP values effectively illustrated the contribution of the 13 features attributed to the model output and the individual feature's effect on model prediction. In addition, the ensemble ML models demonstrated better performance than the other six traditional scoring systems. CONCLUSIONS: Our study identified 13 important features and provided multiple ML models to enhance the risk stratification for ARDS after cardiac surgery. Using these predictors and ML models might provide a basis for early diagnostic and preventive strategies in the perioperative management of ARDS patients.

Detecting Visual Field Worsening From Optic Nerve Head and Macular Optical Coherence Tomography Thickness Measurements.

Purpose: Compare the use of optic disc and macular optical coherence tomography measurements to predict glaucomatous visual field (VF) worsening. Methods: Machine learning and statistical models were trained on 924 eyes (924 patients) with circumpapillary retinal nerve fiber layer (cp-RNFL) or ganglion cell inner plexiform layer (GC-IPL) thickness measurements. The probability of 24-2 VF worsening was predicted using both trend-based and event-based progression definitions of VF worsening. Additionally, the cp-RNFL and GC-IPL predictions were combined to produce a combined prediction. A held-out test set of 617 eyes was used to calculate the area under the curve (AUC) to compare cp-RNFL, GC-IPL, and combined predictions. Results: The AUCs for cp-RNFL, GC-IPL, and combined predictions with the statistical and machine learning models were 0.72, 0.69, 0.73, and 0.78, 0.75, 0.81, respectively, when using trend-based analysis as ground truth. The differences in performance between the cp-RNFL, GC-IPL, and combined predictions were not statistically significant. AUCs were highest in glaucoma suspects using cp-RNFL predictions and highest in moderate/advanced glaucoma using GC-IPL predictions. The AUCs for the statistical and machine learning models were 0.63, 0.68, 0.69, and 0.72, 0.69, 0.73, respectively, when using event-based analysis. AUCs decreased with increasing disease severity for all predictions. Conclusions: cp-RNFL and GC-IPL similarly predicted VF worsening overall, but cp-RNFL performed best in early glaucoma stages and GC-IPL in later stages. Combining both did not enhance detection significantly. Translational Relevance: cp-RNFL best predicted trend-based 24-2 VF progression in early-stage disease, while GC-IPL best predicted progression in late-stage disease. Combining both features led to minimal improvement in predicting progression.

Diagnostic and Prognostic Value of Methyltransferase Like 13 in Hepatocellular Carcinoma Based on Bioinformatics Analysis.

BACKGROUND/AIMS:  Hepatocellular carcinoma (HCC) is one of the cancers with the highest incidence and mortality rates. This study aims to explore the diagnostic and prognostic utility of methyltransferase like 13 (METTL13) in patients with HCC via bioinformatics analysis. MATERIALS AND METHODS:  We obtained mRNA data of HCC from the database of the Cancer Genome Atlas (TCGA), drawing survival curve by R 4.2.1 software. Cox regression analysis was conducted based on tumor stage and METTL13 expression. The GSE114564 dataset was chosen from the Gene Expression Omnibus. The differences in serum METTL13 levels between the groups of early HCC (eHCC) and non-cancer controls were evaluated. Using a receiver operating characteristic curve, we calculated the area under the curve (AUC) of serum METTL13 for diagnosing eHCC. RESULTS:  A total of 225 cases with HCC were screened from TCGA, and 29 cases were normal controls. The results showed that the METTL13 expression in the HCC group was higher than that in the normal controls (P <.001). The univariate [hazard ratio (HR) = 1.895, P = .006] and multivariate Cox regression (HR = 1.702, P = .037) analyses showed that high METTL13 expression reduced overall survival in HCC. Serum METTL13 levels were higher in the eHCC group than in the non-cancer controls (P = .008). The optimum AUC for predicting eHCC by serum METTL13 was 0.7091. CONCLUSION:  Serum METTL13 has a moderate diagnostic value for eHCC. High METTL13 expression is correlated with a worse prognosis in patients with HCC. Methyltransferase like 13 possesses the potential to be a novel biomarker for HCC.

Value of Lung Ultrasound Sonography B-Lines Quantification as a Marker of Heart Failure in COPD Exacerbation.

Introduction: Identifying heart failure (HF) in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) can be challenging. Lung ultrasound sonography (LUS) B-lines quantification has recently gained a large place in the diagnosis of HF, but its diagnostic performance in AECOPD remains poorly studied. Purpose: This study aimed to assess the contribution of LUS B-lines score (LUS score) in the diagnosis of HF in AECOPD patients. Patients and methods: This is a prospective cross-sectional multicenter cohort study including patients admitted to the emergency department for AECOPD. All included patients underwent LUS. A lung ultrasound score (LUS score) based on B-lines calculation was assessed. A cardiac origin of dyspnea was retained for a LUS score greater than 15. HF diagnosis was based on clinical examination, pro-brain natriuretic peptide levels, and echocardiographic findings. The LUS score diagnostic performance was assessed by receiver operating characteristic (ROC) curve, sensitivity, specificity, and likelihood ratio at the best cutoffs. Results: We included 380 patients, mean age was 68±11.6 years, sex ratio (M/F) 1.96. Patients were divided into two groups: the HF group [n=157 (41.4%)] and the non-HF group [n=223 (58.6%)]. Mean LUS score was higher in the HF group (26.8±8.4 vs 15.3±7.1; p<0.001). The mean LUS score in the HF patients with reduced LVEF was 29.2±8.7, and was 24.5±7.6 in the HF patients with preserved LVEF. LUS score area under ROC curve for the diagnosis of HF was 0.71 [0.65-0.76]. The best sensitivity (89% [85.9-92,1]) was observed at the threshold of 5; the best specificity (85% [81.4-88.6]) was observed at the threshold of 30. Correlation between LUS score and E/E' ratio was good (R=0.46, p=0.0001). Conclusion: Our results suggest that LUS score could be helpful and should be considered in the diagnostic approach of HF in AECOPD patients, at least as a ruling in test.

A graph-learning based model for automatic diagnosis of Sjögren's syndrome on digital pathological images: a multicentre cohort study.

BACKGROUND: Sjögren's Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations. METHODS: The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts. FINDINGS: CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM's diagnostic accuracy is closer to skilled pathologists compared to beginners. INTERPRETATION: Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.

BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis.

BACKGROUND: Nosocomial infections (NIs) frequently occur and adversely impact prognosis for hospitalized patients with cirrhosis. This study aims to develop and validate two machine learning models for NIs and in-hospital mortality risk prediction. METHODS: The Prediction of Nosocomial Infection and Prognosis in Cirrhotic patients (PIPC) study included hospitalized patients with cirrhosis at the Qingchun Campus of the First Affiliated Hospital of Zhejiang University. We then assessed several machine learning algorithms to construct predictive models for NIs and prognosis. We validated the best-performing models with bootstrapping techniques and an external validation dataset. The accuracy of the predictions was evaluated through sensitivity, specificity, predictive values, and likelihood ratios, while predictive robustness was examined through subgroup analyses and comparisons between models. RESULTS: We enrolled 1,297 patients into derivation cohort and 496 patients into external validation cohort. Among the six algorithms assessed, the Random Forest algorithm performed best. For NIs, the PIPC-NI model achieved an area under the curve (AUC) of 0.784 (95% confidence interval [CI] 0.741-0.826), a sensitivity of 0.712, and a specificity of 0.702. For in-hospital mortality, the PIPC- mortality model achieved an AUC of 0.793 (95% CI 0.749-0.836), a sensitivity of 0.769, and a specificity of 0.701. Moreover, our PIPC models demonstrated superior predictive performance compared to the existing MELD, MELD-Na, and Child-Pugh scores. CONCLUSIONS: The PIPC models showed good predictive power and may facilitate healthcare providers in easily assessing the risk of NIs and prognosis among hospitalized patients with cirrhosis.

Diagnostic prediction models for bacterial meningitis in children with a suspected central nervous system infection: a systematic review and prospective validation study.

OBJECTIVES: Diagnostic prediction models exist to assess the probability of bacterial meningitis (BM) in paediatric patients with suspected meningitis. To evaluate the diagnostic accuracy of these models in a broad population of children suspected of a central nervous system (CNS) infection, we performed external validation. METHODS: We performed a systematic literature review in Medline to identify articles on the development, refinement or validation of a prediction model for BM, and validated these models in a prospective cohort of children aged 0-18 years old suspected of a CNS infection. PRIMARY AND SECONDARY OUTCOME MEASURES: We calculated sensitivity, specificity, predictive values, the area under the receiver operating characteristic curve (AUC) and evaluated calibration of the models for diagnosis of BM. RESULTS: In total, 23 prediction models were validated in a cohort of 450 patients suspected of a CNS infection included between 2012 and 2015. In 75 patients (17%), the final diagnosis was a CNS infection including 30 with BM (7%). AUCs ranged from 0.69 to 0.94 (median 0.83, interquartile range [IQR] 0.79-0.87) overall, from 0.74 to 0.96 (median 0.89, IQR 0.82-0.92) in children aged ≥28 days and from 0.58 to 0.91 (median 0.79, IQR 0.75-0.82) in neonates. CONCLUSIONS: Prediction models show good to excellent test characteristics for excluding BM in children and can be of help in the diagnostic workup of paediatric patients with a suspected CNS infection, but cannot replace a thorough history, physical examination and ancillary testing.

Comparison of Glaucoma Detection Performance of Binocular Perimetry Screening Program Using imo Perimetry With Frequency Doubling Technology.

Purpose: We aimed to preliminarily compare the glaucoma detection accuracy of a head-mounted binocular visual perimeter "imo" screening program (ISP) with that of frequency doubling technology (FDT). Methods: This multicenter, diagnostic accuracy study based on prospectively collected data included 76 non-glaucoma (including pre-perimetric glaucoma) eyes and 92 glaucomatous eyes from patients visiting two hospitals. Patients underwent ISP and FDT (C-20-1 screening program) on the same day. Diagnostic efficacy was evaluated using receiver operating characteristic curves and areas under the curve (AUCs). In addition, we compared the ISP and FDT testing times. Results: AUC values for ISP versus FDT were as follows: (1) mild-stage glaucoma (mean deviation [MD] > -6 dB), 0.82 (95% confidence interval [CI], 0.75-0.88) versus 0.76 (95% CI, 0.68-0.83); moderate-stage glaucoma (-6 dB ≥ MD ≥ -12 dB), 0.98 (95% CI, 0.95-1.00) versus 0.96 (95% CI, 0.93-1.00); and advanced-stage glaucoma (-12 dB > MD), 1.00 (95% CI, 1.00-1.00) versus 0.99 (95% CI, 0.98-1.00). In addition, mild-stage glaucoma was classified into two stages (MD > -3 D) and (-3 D ≥ MD > -6 D). AUC values were 0.81 (95% CI, 0.73-0.88) versus 0.76 (95% CI, 0.68-0.84) for MD > -3 D and 0.86 (95% CI, 0.77-0.94) versus 0.73 (95% CI, 0.61-0.86) for -3 D ≥ MD > -6 D. The testing time for the ISP was significantly shorter than that of FDT for all glaucoma stages (P < 0.001). Conclusions: The ISP demonstrates non-inferiority in detecting glaucoma and has a shorter testing time compared with FDT. These findings provide evidence for applied further studies on large-scale population-based glaucoma screening. Translational Relevance: Our study provides a non-inferior and quicker glaucoma screening than existing tools.

Pharmacokinetics, Tolerability, and Safety of Esmethadone in Subjects with Chronic Kidney Disease or Hepatic Impairment.

BACKGROUND AND OBJECTIVES: Esmethadone (dextromethadone; d-methadone; S-methadone (+)-methadone; REL-1017) is a low potency N-methyl-D-aspartate (NMDA) receptor channel blocker that showed a rapid and sustained adjunctive antidepressant effects in patients with major depressive disorder with inadequate response to ongoing serotonergic antidepressant treatment. Previous studies indicated that esmethadone is partially excreted by the kidney (53.9% of the dose) and by the liver (39.1% of the dose). METHODS: Here we studied the pharmacokinetics and safety of esmethadone after a single oral dose of 25 mg in subjects with different stages of kidney and liver impairment. RESULTS: In subjects with a mild and moderate decrease in glomerular fraction rate (GFR), esmethadone Cmax and AUC0-inf values did not differ compared with healthy subjects. In patients with severe renal impairment, the ratios of Cmax and AUC0-inf values compared with healthy subjects were above 100% (138.22-176.85%) and, while modest, these increases reached statistical significance. In subjects with end stage renal disease (ESRD) undergoing intermittent hemodialysis (IHD), Cmax and AUC0-inf values were not statistically different compared with healthy subjects. IHD did not modified plasma total esmethadone concentrations in blood exiting versus entering the dialyzer. Dose adjustment is not warranted in subjects with mild-to-moderate impaired renal function. Dose reduction may be considered for select patients with severe renal disfunction. In subjects with mild-or-moderate hepatic impairment, Cmax and AUC0-inf were approximately 20-30% lower compared with healthy controls. The drug free fraction increased with the severity of hepatic impairment, from 5.4% in healthy controls to 8.3% in subjects with moderate hepatic impairment. CONCLUSION: Mild and moderate hepatic impairment has a minimal to modest impact on exposure to total or unbound esmethadone and dose adjustments are not warranted in subjects with mild and moderate hepatic impairment. Administration of esmethadone was well tolerated in healthy adult subjects, in subjects with mild or moderate hepatic impairment, and in subjects with mild moderate or severe renal impairment, including patients with ESRF undergoing dialysis.

Area under the inspiratory flow-volume curve (AIN): Proposed normative values.

RATIONALE: Area under expiratory flow-volume curve (AEX) has been shown to be a valuable functional measurement in respiratory physiology. Area under inspiratory flow-volume loop (AIN) also shows promise in characterizing upper and/or lower airflow obstruction. OBJECTIVES: we aimed here to develop normative reference values for AIN, able to ascertain deviations from normal. METHODS: We analyzed AIN in 4,980 spirometry tests recorded in non-smoking, healthy individuals in the Pulmonary Function Testing Laboratory. RESULTS: The mean (95% confidence interval, CI), standard deviation and median (25th-75th interquartile range) AIN were 16.05 (15.79-16.31), 9.08 and 14.72 (9.12-21.42) L2·sec-1, respectively. The mean (95% CI) and standard deviation of the best-trial measurements for square root of AIN (Sqrt AIN) were 3.84 (3.81-3.87) and 1.14; 4.15 (4.12-4.18) and 1.03 in men, and 2.68 (2.63-2.72) and 0.72 L·sec-1/2 in women. The mean (standard deviation) of pre- and post-bronchodilator Sqrt AIN were 3.71 (1.17) and 3.81 (1.19) L·sec-1/2, respectively. The mean (95% CI), standard deviation and lowest 5th percentile (lower limit of normal, LLN) of Sqrt AIN/Sqrt AEX (%) were 101.3 (100.82-101.88), 18.7, and 71.8%; stratified by gender, it was 102.2 (101.6-102.8), 18.6, and 72.8% in men, and 98 (96.9-99.2), 18.8, and 68.6% in women, respectively. CONCLUSIONS: The availability of area under the inspiratory flow-volume curve (AIN) and the derived indices offers a promising opportunity to assess upper airway disease (e.g., involvement of larynx, trachea or major bronchi), especially because some of these measurements appear to be independent of age, race, height, and weight.

Association of Severe Vitamin D Deficiency with Hospitalization in the Previous Year in Hospitalized Exacerbated COPD Patients.

Purpose: Vitamin D deficiency (VDD, 25-hydroxyvitamin D < 20 ng/mL) has been reported associated with exacerbation of chronic obstructive pulmonary disease (COPD) but sometimes controversial. Research on severe vitamin D deficiency (SVDD, 25-hydroxyvitamin D < 10 ng/mL) in exacerbation of COPD is limited. Patients and Methods: We performed a retrospective observational study in 134 hospitalized exacerbated COPD patients. 25-hydroxyvitamin D was modeled as a continuous or dichotomized (cutoff value: 10 or 20 ng/mL) variable to evaluate the association of SVDD with hospitalization in the previous year. Receiver operator characteristic (ROC) analysis was performed to find the optimal cut-off value of 25-hydroxyvitamin D. Results: In total 23% of the patients had SVDD. SVDD was more prevalent in women, and SVDD group tended to have lower blood eosinophils counts. 25-hydroxyvitamin D level was significantly lower in patients who were hospitalized in the previous year (13.6 vs 16.7 ng/mL, P = 0.044), and the prevalence of SVDD was higher (38.0% vs 14.3%, P = 0.002). SVDD was independently associated with hospitalization in the previous year [odds ratio (OR) 4.34, 95% CI 1.61-11.72, P = 0.004] in hospitalized exacerbated COPD patients, whereas continuous 25-hydroxyvitamin D and VDD were not (P = 0.1, P = 0.9, separately). The ROC curve yielded an area under the curve of 0.60 (95% CI 0.50-0.71) with an optimal 25-hydroxyvitamin D cutoff of 10.4 ng/mL. Conclusion: SVDD probably showed a more stable association with hospitalization in the previous year in hospitalized exacerbated COPD patients. Reasons for lower eosinophil counts in SVDD group needed further exploration.

Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation.

OBJECTIVE: Cellulitis is the most common cause of skin-related hospitalisations, and the mortality of patients with sepsis remains high. Some stratification models have been developed, but their performance in external validation has been unsatisfactory. This study was designed to develop and compare different models for predicting patients with cellulitis developing sepsis during hospitalisation. DESIGN: This is a retrospective cohort study. SETTING: This study included both the development and the external-validation phases from two independent large cohorts internationally. PARTICIPANTS AND METHODS: A total of 6695 patients with cellulitis in the Medical Information Mart for Intensive care (MIMIC)-IV database were used to develop models with different machine-learning algorithms. The best models were selected and then externally validated in 2506 patients with cellulitis from the YiduCloud database of our university. The performances and robustness of selected models were further compared in the external-validation group by area under the curve (AUC), diagnostic accuracy, sensitivity, specificity and diagnostic OR. PRIMARY OUTCOME MEASURES: The primary outcome of interest in this study was the development based on the Sepsis-3.0 criteria during hospitalisation. RESULTS: Patient characteristics were significantly different between the two groups. In internal validation, XGBoost was the best model, with an AUC of 0.780, and AdaBoost was the worst model, with an AUC of 0.585. In external validation, the AUC of the artificial neural network (ANN) model was the highest, 0.830, while the AUC of the logistic regression (LR) model was the lowest, 0.792. The AUC values changed less in the boosting and ANN models than in the LR model when variables were deleted. CONCLUSIONS: Boosting and neural network models performed slightly better than the LR model and were more robust in complex clinical situations. The results could provide a tool for clinicians to detect hospitalised patients with cellulitis developing sepsis early.

The effect of itraconazole, a strong CYP3A4 inhibitor, on the pharmacokinetics of the first-in-class ACKR3/CXCR7 antagonist, ACT-1004-1239.

Cytochrome P450 (CYP) 3A4 is an enzyme involved in the metabolism of many drugs that are currently on the market and is therefore a key player in drug-drug interactions (DDIs). ACT-1004-1239 is a potent and selective, first-in-class ACKR3/CXRC7 antagonist being developed as a treatment for demyelinating diseases including multiple sclerosis. Based on the human absorption, distribution, metabolism, and excretion (ADME) study results, ACT-1004-1239 is predominantly metabolized by CYP3A4. This study investigated the effect of the strong CYP3A4 inhibitor, itraconazole, on the pharmacokinetics of single-dose ACT-1004-1239 in healthy male subjects. In the open-label, fixed-sequence DDI study, a total of 16 subjects were treated. Each subject received a single dose of 10 mg ACT-1004-1239 (Treatment A) in the first period followed by concomitant administration of multiple doses of 200 mg itraconazole and a single dose of 10 mg ACT-1004-1239 in the second period. We report a median of difference in tmax (90% confidence interval, CI) of 0.5 h (0.0, 1.0) comparing both treatments. The geometric mean ratio (GMR) (90% CI) of Cmax and AUC0-∞ was 2.16 (1.89, 2.47) and 2.77 (2.55, 3.00), respectively. The GMR (90% CI) of t1/2 was 1.46 (1.26, 1.70). Both treatments were well-tolerated with an identical incidence in subjects reporting treatment-emergent adverse events (TEAE). The most frequently reported TEAEs were headache and nausea. In conclusion, ACT-1004-1239 is classified as a moderately sensitive CYP3A4 substrate (i.e., increase of AUC ≥2- to <5-fold), and this should be considered in further clinical studies if CYP3A4 inhibitors are concomitantly administered.

Reduced hepatic impairment study to evaluate pharmacokinetics and safety of zavegepant and to inform dosing recommendation for hepatic impairment.

Zavegepant, a high-affinity, selective, small-molecule calcitonin gene-related peptide (CGRP) receptor antagonist, is approved in the United States for acute treatment of migraine in adults. The effects of moderate hepatic impairment (8 participants with Child-Pugh score 7-9 points) on the pharmacokinetics of a single 10-mg intranasal dose of zavegepant versus eight matched participants with normal hepatic function were evaluated in a phase I study. Pharmacokinetic sampling determined total and unbound plasma zavegepant concentrations. Moderate hepatic impairment increased the exposure of total zavegepant (~2-fold increase in AUC0-inf and 16% increase in Cmax) versus normal hepatic function, which is not considered clinically meaningful. The geometric least squares mean ratios (moderate impairment/normal) of plasma zavegepant AUC0-inf and Cmax were 193% (90% confidence interval [CI]: 112, 333; p = 0.051) and 116% (90% CI: 69, 195; p = 0.630), respectively. The geometric mean fraction unbound of zavegepant was similar for participants with moderate hepatic impairment (0.13; coefficient of variation [CV] 13.71%) versus those with normal hepatic function (0.11; CV 21.43%). Similar exposure findings were observed with unbound zavegepant versus normal hepatic function (~2.3-fold increase in AUC0-inf and 39% increase in Cmax). One treatment-emergent adverse event (mild, treatment-related headache) was reported in a participant with normal hepatic function. No dosage adjustment of intranasal zavegepant is required in adults with mild or moderate hepatic impairment.

Sample size calculation for comparing two ROC curves.

Biomarkers are key components of personalized medicine. In this paper, we consider biomarkers taking continuous values that are associated with disease status, called case and control. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. Oftentimes, two biomarkers are collected from each subject to test if one has a larger AUC than the other. We propose a simple non-parametric statistical test for comparing the performance of two biomarkers. We also present a simple sample size calculation method for this test statistic. Our sample size formula requires specification of AUC values (or the standardized effect size of each biomarker between cases and controls together with the correlation coefficient between two biomarkers), prevalence of cases in the study population, type I error rate, and power. Through simulations, we show that the testing on two biomarkers controls type I error rate accurately and the proposed sample size closely maintains specified statistical power.