Differentiating between benign and malignant breast lesions using dual-energy CT-based model: development and validation.

OBJECTIVES: To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT. MATERIALS AND METHODS: This study prospectively enrolled patients with suspected breast cancer who underwent dual-phase contrast-enhanced DECT from July 2022 to July 2023. Breast lesions were randomly divided into the training and test cohorts at a ratio of 7:3. Clinical characteristics, DECT-based morphological features, and DECT quantitative parameters were collected. Univariate analyses and multivariate logistic regression were performed to determine independent predictors of benign and malignant breast lesions. An individualized model was constructed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the model, whose calibration and clinical usefulness were assessed by calibration curve and decision curve analysis. RESULTS: This study included 200 patients (mean age, 49.9 ± 11.9 years; age range, 22-83 years) with 222 breast lesions. Age, lesion shape, and the effective atomic number (Zeff) in the venous phase were significant independent predictors of breast lesions (all p < 0.05). The discriminative power of the model incorporating these three factors was high, with AUCs of 0.844 (95%CI 0.764-0.925) and 0.791 (95% CI 0.647-0.935) in the training and test cohorts, respectively. The constructed model showed a preferable fitting (all p > 0.05 by the Hosmer-Lemeshow test) and provided enhanced net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts. CONCLUSION: The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT. CRITICAL RELEVANCE STATEMENT: The combination of clinical and morphological characteristics and DECT-derived parameter have the potential to identify benign and malignant breast lesions and it may be useful for incidental breast lesions on DECT to decide if further work-up is needed. KEY POINTS: It is important to characterize incidental breast lesions on DECT for patient management. DECT-based model can differentiate benign and malignant breast lesions with good performance. DECT-based model is a potential tool for distinguishing breast lesions detected on DECT.

Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study.

Objective: To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS. Methods: 183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings. Results: LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations. Conclusion: Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.

A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors.

OBJECTIVE: Preeclampsia (PE) is a pregnancy-related multi-organ disease and a significant cause of incidence rate and mortality of pregnant women and newborns worldwide. Delivery remains the only available treatment for PE. This study aims to establish a dynamic prediction model for PE. METHODS: A total of 737 patients who visited our hospital from January 2021 to June 2022 were identified according to the inclusion and exclusion criteria, forming the primary dataset. Additionally, 176 singleton pregnant women who visited our hospital from July 2022 to November 2022 comprised the verification set. We investigated different gestational weeks of sFlt-1/PLGF (soluble FMS-like tyrosine kinase-1, placental growth factor) ratio combined with maternal characteristics and routine prenatal laboratory results in order to predict PE in each trimester. Multivariate logistic regression was used to establish the prediction model for PE at different gestational weeks. The discrimination, calibration, and clinical validity were utilized to evaluate predictive models as well as models in external validation queues. RESULTS: At 20-24 weeks, the obtained prediction model for PE yielded an area under the curve of 0.568 (95% confidence interval, 0.479-0.657). At 25-29 weeks, the obtained prediction model for PE yielded an area under the curve of 0.773 (95% confidence interval, 0.703-0.842)and 0.731 (95% confidence interval, 0.653-0.809) at 30-34 weeks. After adding maternal factors, uterine artery pulsation index(Ut-IP), and other laboratory indicators to the sFlt-1/PLGF ratio, the predicted performance of PE improved. It found that the AUC improved to 0.826(95% confidence interval, 0.748 ∼ 0.904) at 20-24 weeks, 0.879 (95% confidence interval, 0.823 ∼ 0.935) at 25-29 weeks, and 0.862(95% confidence interval, 0.799 ∼ 0.925) at 30-34 weeks.The calibration plot of the prediction model indicates good predictive accuracy between the predicted probability of PE and the observed probability. Furthermore, decision-curve analysis showed an excellent clinical application value of the models. CONCLUSION: Using the sFlt-1/PLGF ratio combined with multiple factors at 25-29 weeks can effectively predict PE, but the significance of re-examination in late pregnancy is not significant.

Risk stratification and survival time of patients with gram-negative bacillary pneumonia in the intensive care unit.

Introduction: Pneumonia is a common infection in the intensive care unit (ICU), and gram-negative bacilli are the most common bacterial cause. The purpose of the study was to investigate the risk factors for 30-day mortality in patients with gram-negative bacillary pneumonia in the ICU, construct a predictive model, and stratify patients based on risk to assess their short-term survival. Methods: Patients admitted to the ICU with gram-negative bacillary pneumonia at Fujian Medical University Affiliated First Hospital between January 2018 and September 2020 were selected. Patients were divided into deceased and survivor groups based on whether death occurred within 30 days. Multifactorial logistic regression analysis was used to identify independent risk factors for 30-day mortality in these patients, and a predictive nomogram model was constructed based on these factors. Patients were categorized into low-, medium-, and high-risk groups according to the model's predicted probability, and Kaplan-Meier survival curves were plotted to assess short-term survival. Results: The study included 305 patients. Lactic acid (odds ratio [OR], 1.524, 95% CI: 1.057-2.197), tracheal intubation (OR: 4.202, 95% CI: 1.092-16.169), and acute kidney injury (OR:4.776, 95% CI: 1.632-13.978) were identified as independent risk factors for 30-day mortality. A nomogram prediction model was established based on these three factors. Internal validation of the model showed a Hosmer-Lemeshow test result of X2=5.770, P=0.834, and an area under the ROC curve of 0.791 (95% CI: 0.688-0.893). Bootstrap resampling of the original data 1000 times yielded a C-index of 0.791, and a decision curve analysis indicated a high net benefit when the threshold probability was between 15%-90%. The survival time for low-, medium-, and high-risk patients was 30 (30, 30), 30 (16.5, 30), and 17 (11, 27) days, respectively, which were significantly different. Conclusion: Lactic acid, tracheal intubation, and acute kidney injury were independent risk factors for 30-day mortality in patients in the ICU with gram-negative bacillary pneumonia. The predictive model constructed based on these factors showed good predictive performance and helped assess short-term survival, facilitating early intervention and treatment.

Validation of the Saga Fall Injury Risk Model.

Background: Predicting fall injuries can mitigate the sequelae of falls and potentially utilize medical resources effectively. This study aimed to externally validate the accuracy of the Saga Fall Injury Risk Model (SFIRM), consisting of six factors including age, sex, emergency transport, medical referral letter, Bedriddenness Rank, and history of falls, assessed upon admission. Methods: This was a two-center, prospective, observational study. We included inpatients aged 20 years or older in two hospitals, an acute and a chronic care hospital, from October 2018 to September 2019. The predictive performance of the model was evaluated by calculating the area under the curve (AUC), 95% confidence interval (CI), and shrinkage coefficient of the entire study population. The minimum sample size of this study was 2,235 cases. Results: A total of 3,549 patients, with a median age of 78 years, were included in the analysis, and men accounted for 47.9% of all the patients. Among these, 35 (0.99%) had fall injuries. The performance of the SFIRM, as measured by the AUC, was 0.721 (95% CI: 0.662-0.781). The observed fall incidence closely aligned with the predicted incidence calculated using the SFIRM, with a shrinkage coefficient of 0.867. Conclusions: The external validation of the SFIRM in this two-center, prospective study showed good discrimination and calibration. This model can be easily applied upon admission and is valuable for fall injury prediction.

Hemodynamic Impairment of Blood Pressure and Stroke Mechanisms in Symptomatic Intracranial Atherosclerotic Stenosis.

BACKGROUND: Hemodynamic impairment of blood pressure may play a crucial role in determining the mechanisms of stroke in symptomatic intracranial atherosclerotic stenosis). We aimed to elucidate this issue and assess the impacts of modifications to blood pressure on hemodynamic impairment. METHODS: From the Third China National Stroke Registry III, computed fluid dynamics modeling was performed using the Newton-Krylov-Schwarz method in 339 patients with symptomatic intracranial atherosclerotic stenosis during 2015 to 2018. The major exposures were translesional systolic blood pressure (SBP) drop and poststenotic mean arterial pressure (MAP), and the major study outcomes were cortex-involved infarcts and borderzone-involved infarcts, respectively. Multivariate logistic regression models and the bootstrap resampling method were utilized, adjusting for demographics and medical histories. RESULTS: In all, 184 (54.3%) cortex-involved infarcts and 70 (20.6%) borderzone-involved infarcts were identified. In multivariate logistic model, the upper quartile of SBP drop correlated with increased cortex-involved infarcts (odds ratio, 1.92 [95% CI, 1.03-3.57]; bootstrap analysis odds ratio, 2.07 [95% CI, 1.09-3.93]), and the lower quartile of poststenotic MAP may correlate with increased borderzone-involved infarcts (odds ratio, 2.07 [95% CI, 0.95-4.51]; bootstrap analysis odds ratio, 2.38 [95% CI, 1.04-5.45]). Restricted cubic spline analysis revealed a consistent upward trajectory of the relationship between translesional SBP drop and cortex-involved infarcts, while a downward trajectory between poststenotic MAP and borderzone-involved infarcts. SBP drop correlated with poststenotic MAP negatively (rs=-0.765; P<0.001). In generating hemodynamic impairment, simulating blood pressure modifications suggested that ensuring adequate blood pressure to maintain sufficient poststenotic MAP appears preferable to the reverse approach, due to the prolonged plateau period in the association between the translesional SBP drop and cortex-involved infarcts and the relatively short plateau period characterizing the correlation between poststenotic MAP and borderzone-involved infarcts. CONCLUSIONS: This research elucidates the role of hemodynamic impairment of blood pressure in symptomatic intracranial atherosclerotic stenosis-related stroke mechanisms, underscoring the necessity to conduct hemodynamic assessments when managing blood pressure in symptomatic intracranial atherosclerotic stenosis.

Association of Glomerular Filtration Rate Decline With Clinical Outcomes in a Population With Type 2 Diabetes.

Background: While historical rate of decline in kidney function is informally used by clinicians to estimate risk of future adverse clinical outcomes especially kidney failure, in people with type 2 diabetes the epidemiology and independent association of historical eGFR slope on risk is not well described. Objective: Determine the association of eGFR slope and risk of clinically important outcomes. Design Setting and Patients: Observational population-based cohort with type 2 diabetes in Alberta. Measurement and Methods: An Alberta population-based cohort with type 2 diabetes was assembled, characterized, and observed over 1 year (2018) for clinical outcomes of ESKD, first myocardial infarction, first stroke, heart failure, and disease-specific and all-cause hospitalization and mortality. Kidney function was defined using KDIGO criteria using the most recent eGFR and albuminuria measured in the preceding 18 months; annual eGFR slope utilized measurements in the 3 years prior and was parameterized using three methods (percentiles, and linear term with and without missingness indicator). Demographics, laboratory results, medications, and comorbid conditions using validated definitions were described. In addition to descriptive analysis, odds ratios from fully adjusted logistic models regressing outcomes on eGFR slope are reported; the marginal risk of clinical outcomes was also determined. Results: Among 336 376 participants with type 2 diabetes, the median annual eGFR slope was -0.41 mL/min/1.73 m2 (IQR -1.67, 0.62). In fully adjusted models, eGFR slope was independently associated with many adverse clinical outcomes; among those with ≤10th percentile of slope (median -4.71 mL/min/1.73 m2) the OR of kidney failure was 2.22 (95% CI 1.75, 2.82), new stroke 1.23 (1.08, 1.40), heart failure 1.42 (1.27, 1.59), MI 0.98 (0.77, 1.23) all-cause hospitalization 1.31 (1.26, 1.36) and all-cause mortality 1.56 (1.44, 1.68). For every -1 mL/min/1.73 m2 in eGFR slope, the OR of outcomes ranged from 1.01 (0.98, 1.05 for new MI) to 1.09 (1.08, 1.10 for all-cause mortality); findings were significant for 10 of the 13 outcomes considered. Limitations: Causality cannot be established with this study design. Conclusions: These findings support consideration of the rate of eGFR decline in risk stratification and may inform clinicians and policymakers to optimize treatment and inform health care system planning.

Contexte: Bien que les antécédents de déclin de la fonction rénale soient utilisés de manière informelle par les cliniciens pour estimer le risque d'issues cliniques défavorables ­ particulièrement l'insuffisance rénale terminale (IRT) ­ chez les diabétiques de type 2, l'épidémiologie de la pente du DFGe et son association indépendante sur ce risque demeurent mal décrites. Objectif: Examiner l'association entre la pente du DFGe et le risque de résultats d'importance clinique. Sujets et conception de l'étude: Étude de cohorte observationnelle basée sur une population d'Albertains atteints de diabète de type 2. Méthodologie et mesures: Nous avons constitué, caractérisé et observé une cohorte d'Albertains atteints de diabète de type 2 sur une période d'un an (2018) pour les résultats cliniques suivants: IRT, premier infarctus du myocarde (IM), premier AVC, insuffisance cardiaque, ainsi que les hospitalisations et la mortalité liées à la maladie et à toutes causes confondues. La fonction rénale a été définie selon les critères KDIGO à partir des plus récentes valeurs de DFGe et d'albuminurie mesurées dans les 18 mois précédents. La pente annuelle du DFGe a été calculée à partir des mesures effectuées au cours des trois années précédentes et paramétrée selon trois méthodes (percentiles, termes linéaires avec et sans indications de données manquantes). Les données démographiques, les résultats de laboratoire, les médicaments et les comorbidités ont été décrits selon les définitions validées. En plus de l'analyze descriptive, des rapports de cotes (RC) pour les résultats liés au déclin du DFGe ont été établis à l'aide de modèles de régression logistique entièrement ajustés; le risque marginal des résultats cliniques d'intérêt a également été déterminé. Résultats: Parmi les 336 376 diabétiques de type 2 participants, la pente annuelle médiane du DFGe s'établissait à −0,41 ml/min/1,73 m2 (ÉIQ: −1,67 à 0,62). Dans les modèles ajustés, la pente du DFGe a été associée de façon indépendante à plusieurs issues cliniques défavorables. Parmi ceux qui présentaient une pente du DFGe ≤10e percentile (médiane: −4,71 ml/min/1,73 m2), le RC était de 2,22 (IC 95 %: 1,75 à 2,82) pour l'insuffisance rénale; de 1,23 (1,08 à 1,40) pour les nouveaux AVC; de 1,42 (1,27 à 1,59) pour l'insuffisance cardiaque; de 0,98 (0,77 à 1,23) pour les nouveaux IM; de 1,31 (1,26 à 1,36) pour les hospitalisations toutes causes confondues et de 1,56 (1,44 à 1,68) pour la mortalité toutes causes confondues. Pour chaque tranche de - 1 ml/min/1,73 m2 de la pente du DFGe, le RC des résultats cliniques variait de 1,01 (0,98 à 1,05) pour les nouveaux IM à 1,09 (1,08 à 1,10) pour la mortalité toutes causes confondues; les résultats étaient significatifs pour 10 des 13 résultats examinés. Limites: La causalité ne peut pas être établie avec ce plan d'étude. Conclusion: Ces résultats plaident en faveur de la prise en compte du taux de déclin du DFGe dans la stratification du risque. Ils peuvent également aider les cliniciens et les décideurs à optimiser le traitement et à planifier les systèmes de soins de santé.

Cross-border mobility in the Meuse-Rhine Euroregion: impact of COVID-19 border restrictions on everyday activities and visiting social network members.

Introduction: Cross-border mobility (CBM) to visit social network members or for everyday activities is an important part of daily life for citizens in border regions, including the Meuse-Rhine Euroregion (EMR: neighboring regions from the Netherlands, Belgium, and Germany). We assessed changes in CBM during the COVID-19 pandemic and how participants experienced border restrictions. Methods: Impact of COVID-19 on the EMR' is a longitudinal study using comparative cross-border data collection. In 2021, a random sample of the EMR-population was invited for participation in online surveys to assess current and pre-pandemic CBM. Changes in CBM, experience of border restrictions, and associated factors were analyzed using multinomial and multivariable logistic regression analysis. Results: Pre-pandemic, 82% of all 3,543 participants reported any CBM: 31% for social contacts and 79% for everyday activities. Among these, 26% decreased social CBM and 35% decreased CBM for everyday activities by autumn 2021. Negative experience of border restrictions was reported by 45% of participants with pre-pandemic CBM, and was higher (p < 0.05) in Dutch participants (compared to Belgian; aOR= 1.4), cross-border [work] commuters (aOR= 2.2), participants with cross-border social networks of friends, family or acquaintances (aOR= 1.3), and those finding the measures 'limit group size' (aOR= 1.5) and 'minimalize travel' (aOR= 2.0) difficult to adhere to and finding 'minimalize travel' (aOR= 1.6) useless. Discussion: CBM for social contacts and everyday activities was substantial in EMR-citizens, but decreased during the pandemic. Border restrictions were valued as negative by a considerable portion of EMR-citizens, especially when having family or friends across the border. When designing future pandemic control strategies, policy makers should account for the negative impact of CBM restrictions on their citizens.

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Introduction: Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography in routine screening. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to leverage the ML approach in assessing the risk of thyroid nodules based on common clinical features. Methods: Data were sourced from a Chinese cohort undergoing routine physical examinations including thyroid ultrasonography between 2013 and 2023. Models were established to predict the 3-year risk of thyroid nodules based on patients' baseline characteristics and laboratory tests. Four ML algorithms, including logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine, were trained and tested using fivefold cross-validation. The importance of each feature was measured by the permutation score. A nomogram was established to facilitate risk assessment in the clinical settings. Results: The final dataset comprised 4,386 eligible subjects. Thyroid nodules were detected in 54.8% (n=2,404) individuals within the 3-year observation period. All ML models significantly outperformed the baseline regression model, successfully predicting the occurrence of thyroid nodules in approximately two-thirds of individuals. Age, high-density lipoprotein, fasting blood glucose and creatinine levels exhibited the highest impact on the outcome in these models. The nomogram showed consistency and validity, providing greater net benefits for clinical decision-making than other strategies. Conclusion: This study demonstrates the viability of an ML-based approach in predicting the occurrence of thyroid nodules. The findings highlight the potential of ML models in identifying high-risk individuals for personalized screening, thereby guiding the judicious use of ultrasound in this context.

Factors associated with depressive symptoms among cancer patients: a nationwide cross-sectional study.

OBJECTIVE: Research on factors contributing to depressive symptoms in cancer patients at a national level, encompassing a comprehensive set of variables was limited. This study aimed to address this gap by identifying the factors associated with depressive symptoms among cancer patients through a nationwide cross-sectional analysis. METHODS: Various factors, including demographic, socioeconomic, behavioral patterns, general and self-rated health status, chronic conditions, dietary habits, and cancer-related factors, were examined. Data was from the National Health and Nutrition Examination Survey. Univariate and multivariate logistic regression analyses were performed to identify associated factors. The receiver-operating characteristic (ROC) curve was used to evaluate the performance of the logistic model. RESULTS: The findings showed that five sociodemographic factors, two behavioral styles, self-rated health status, comorbid arthritis, two dietary factors and two cancer-related factors were strongly associated with depressive symptoms. Compared with those aged 20-39 years, cancer individuals aged 40-59 years (OR = 0.48, P < 0.05) and those 60 years or older (OR = 0.18, P < 0.05) had lower odds of depression. Positive factors included being never married (OR = 1.98, P < 0.05), widowed, divorced or separated (OR = 1.75, P < 0.05), unemployment (OR = 1.87, P < 0.05), current smoking (OR = 1.84, P < 0.05), inadequate sleep (OR = 1.96, P < 0.05), comorbid arthritis (OR = 1.79, P < 0.05), and poor self-rated health status (OR = 3.53, P < 0.05). No significant association was identified between the Healthy Eating Index 2015 and the Dietary Inflammatory Index with depression (P > 0.05). Shorter cancer diagnosis duration was associated with reduced odds of depression (P < 0.05). The logistic model had an area under the curve of 0.870 (95% CI: 0.846-0.894, P < 0.05). CONCLUSIONS: Cancer patients should receive enhanced family and social support while cultivating a healthy lifestyle and diet. Incorporating plenty of fruits, greens, and beans is highly recommended, along with establishing a comprehensive health management framework.

Risk Prediction Model for Radiation-induced Dermatitis in Patients with Cervical Carcinoma Undergoing Chemoradiotherapy.

PURPOSE: Radiation-induced dermatitis (RD) is a common side-effect of therapeutic ionizing radiation that can severely affect patient quality of life. This study aimed to develop a risk prediction model for the occurrence of RD in patients with cervical carcinoma undergoing chemoradiotherapy using electronic medical records (EMRs). METHODS: Using EMRs, the clinical data of patients who underwent simultaneous radiotherapy and chemotherapy at a tertiary cancer hospital between 2017 and 2022 were retrospectively collected, and the patients were divided into two groups: a training group and a validation group. A predictive model was constructed to predict the development of RD in patients who underwent concurrent radiotherapy and chemotherapy for cervical cancer. Finally, the model's efficacy was validated using a receiver operating characteristic curve. RESULTS: The incidence of radiation dermatitis was 89.5% (560/626) in the entire cohort, 88.6% (388/438) in the training group, and 91.5% (172/188) in the experimental group. The nomogram was established based on the following factors: age, the days between the beginning and conclusion of radiotherapy, the serum albumin after chemoradiotherapy, the use of single or multiple drugs for concurrent chemotherapy, and the total dose of afterloading radiotherapy. Internal and external verification indicated that the model had good discriminatory ability. Overall, the model achieved an area under the receiver operating characteristic curve of .66. CONCLUSIONS: The risk of RD in patients with cervical carcinoma undergoing chemoradiotherapy is high. A risk prediction model can be developed for RD in cervical carcinoma patients undergoing chemoradiotherapy, based on over 5 years of EMR data from a tertiary cancer hospital.

Variability in short-term mortality following repair of ruptured abdominal aortic aneurysms across centers and physicians.

BACKGROUND: Variation in the care management of repairs for ruptured infrarenal abdominal aortic aneurysms between centers and physicians, such as procedural volumes, may explain differences in mortality outcomes. First, we quantified the center and physician variability associated with 30- and 90-day mortality risk after ruptured open surgical repair (rOSR) and ruptured endovascular aneurysm repair (rEVAR). Second, we explored wheter part of this variability was attributable to procedural volume at the center and physician levels. METHODS: Two cohorts including rOSR and rEVAR procedures between 2013 and 2019 were analyzed from the Vascular Quality Initiative database. Thirty- and 90-day all-cause mortality rates were derived from linked Medicare claims data. The median odds ratio (MOR) (median mortality risk from low- to high-risk cluster) and intraclass correlation coefficient (ICC) (variability attributable to each cluster) for 30- and 90-day mortality risks associated with center and physician variability were derived using patient-level adjusted multilevel logistic regression models. Procedural volume was calculated at the center and physician levels and stratified by quartiles. The models were sequentially adjusted for volumes, and the difference in ICCs (without vs with accounting for volume) was calculated to describe the center and physician variability in mortality risk attributable to volumes. RESULTS: We included 450 rOSRs (mean age, 74.5 ± 7.6 years; 23.5% female) and 752 rEVARs (76.4 ± 8.4 years; 26.1% female). After rOSRs, the 30- and 90-day mortality rates were 32.9% and 38.7%, respectively. No variability across centers and physicians was noted (30- and 90-day MORs ≈1 and ICCs ≈0%). Neither center nor physician volume was associated with 30-day (P = .477 and P = .796) or 90-day mortality (P = .098 and P = .559). After rEVAR, the 30- and 90-day mortality rates were 21.3% and 25.5%, respectively. Significant center variability (30-day MOR, 1.82 [95% confidence interval (CI), 1.33-2.22]; ICC, 11% [95% CI, 2%-36%]; and 90-day MOR, 1.76 [95% CI, 1.37-2.09]; ICC, 10% [95% CI, 3%-30%]), but negligeable variability across physicians (30- and 90-day MORs ≈1 and ICCs ≈0%) were noted. Neither center nor physician volume were associated with 30-day (P = .076 and P = .336) or 90-day mortality risk (P = .066 and P = .584). The center variability attributable to procedural volumes was negligeable (difference in ICCs, 1% for 30-day mortality; 0% for 90-day mortality). CONCLUSIONS: Variability in practice from center to center was associated with short-term mortality outcomes in rEVAR, but not for rOSR. Physician variability was not associated with short-term mortality for rOSR or rEVAR. Annualized center and physician volumes did not significantly explain these associations. Further work is needed to identify center-level factors affecting the quality of care and outcomes for ruptured abdominal aortic aneurysms.

Predicting the dynamics of organ failure in patients with acute pancreatitis depending on the mean platelet volume.

Background: The aim of this study is to determine the correlation between the blood serum mean platelet volume (MPV) and the dynamics of the OF course during the early phase in patients with moderately severe and severe acute pancreatitis (AP). Methods: The predetermined criterion was the presence of the OF according to the revised Atlanta criteria 2012 for moderately severe and severe AP. A prospective sample of patients was stratified by severity, and two groups were defined based on MPV. Demographic indicators, comorbidities and clinical outcomes were compared between these groups. Multifactorial analysis determined whether an elevated MPV is independently associated with early OF and other unfavorable outcomes. Results: Out of 108 patients, 20 had moderately severe AP and 88 had severe AP. The blood serum MPV, measured within 72 h of the onset of AP symptoms was lower 11.8 fL in 32 patients and equal to or greater 11.8 fL in 76 patients. Patients with elevated MPV were older (63 vs. 48 years), had obesity (59.2 % vs. 25 %), diabetes mellitus (DM) (51.3 % vs. 12.5 %), ischemic heart disease (70.8 % vs. 28.1 %) and more frequently experienced persistent OF (93.4 % vs. 53.1 %) compared to those with MPV lower 11.8 fL. The incidence of early OF increased proportionally with the severity of MPV (81.6 % vs. 34.4 % in the group with MPV lower 11.8 fL, Ptrend < 0.0001). In multifactorial analysis, adjusted for body mass index and DM, MPV equal to or greater 11.8 fL was independently associated with early OF. Conclusions: Elevated blood serum MPV of patients with AP are independently and proportionally correlated with early organ failure in patients with alcoholic and idiopathic etiology of AP. Key message: The study provides an evaluation of MPV as a prognostic marker for organ failure within the initial 7 days following the onset of acute pancreatitis symptoms. Additionally, alterations in MPV were identified in patients with acute pancreatitis who had diabetes or ischemic heart disease within the first 24 h of hospitalization.

Swirl sign score system: a novel and practical tool for predicting hematoma expansion risk after spontaneous intracerebral haemorrhage.

OBJECTIVE: To methodically analyse the swirl sign and construct a scoring system to predict the risk of hematoma expansion (HE) after spontaneous intracerebral haemorrhage (sICH). METHODS: We analysed 231 of 683 sICH patients with swirl signs on baseline noncontrast CT (NCCT) images. The characteristics of the swirl sign were analysed, including the number, maximum diameter, shape, boundary, minimum CT value of the swirl sign, and the minimum distance from the swirl sign to the edge of the hematoma. In the development cohort, univariate and multivariate analyses were used to identify independent predictors of HE, and logistic regression analysis was used to construct the swirl sign score system. The swirl sign score system was verified in the validation cohort. RESULTS: The number and the minimum CT value of the swirl sign were independent predictors of HE. The swirl sign score system was constructed (2 points for the number of swirl signs >1 and 1 point for the minimum CT value ≤41 Hounsfield units). The area under the curve of the swirl sign score system in predicting HE was 0.773 and 0.770 in the development and validation groups, respectively. CONCLUSIONS: The swirl sign score system is an easy-to-use radiological grading scale that requires only baseline NCCT images to effectively identify subjects at high risk of HE. ADVANCES IN KNOWLEDGE: Our newly developed semiquantitative swirl sign score system greatly improves the ability of swirl sign to predict HE.

Prognostic Impact of Heart Rhythm Shockability Trajectory in Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Study.

BACKGROUND: This study aimed to investigate the association between the temporal transitions in heart rhythms during cardiopulmonary resuscitation (CPR) and outcomes after out-of-hospital cardiac arrest. METHODS: This was an analysis of the prospectively collected databases in 3 academic hospitals in northern and central Taiwan. Adult patients with out-of-hospital cardiac arrest transported by emergency medical service between 2015 and 2022 were included. Favorable neurological recovery and survival to hospital discharge were the primary and secondary outcomes, respectively. Time-specific heart rhythm shockability was defined as the probability of shockable rhythms at a particular time point during CPR. The temporal changes in the time-specific heart rhythm shockability were calculated by group-based trajectory modeling. Multivariable logistic regression analyses were performed to examine the association between the trajectory group and outcomes. Subgroup analyses examined the effects of extracorporeal CPR in different trajectories. RESULTS: The study comprised 2118 patients. The median patient age was 69.1 years, and 1376 (65.0%) patients were male. Three distinct trajectories were identified: high-shockability (52 patients; 2.5%), intermediate-shockability (262 patients; 12.4%), and low-shockability (1804 patients; 85.2%) trajectories. The median proportion of shockable rhythms over the course of CPR for the 3 trajectories was 81.7% (interquartile range, 73.2%-100.0%), 26.7% (interquartile range, 16.7%-37.5%), and 0% (interquartile range, 0%-0%), respectively. The multivariable analysis indicated both intermediate- and high-shockability trajectories were associated with favorable neurological recovery (intermediate-shockability: adjusted odds ratio [aOR], 4.98 [95% CI, 2.34-10.59]; high-shockability: aOR, 5.40 [95% CI, 2.03-14.32]) and survival (intermediate-shockability: aOR, 2.46 [95% CI, 1.44-4.18]; high-shockability: aOR, 2.76 [95% CI, 1.20-6.38]). The subgroup analysis further indicated extracorporeal CPR was significantly associated with favorable neurological outcomes (aOR, 4.06 [95% CI, 1.11-14.81]) only in the intermediate-shockability trajectory. CONCLUSIONS: Heart rhythm shockability trajectories were associated with out-of-hospital cardiac arrest outcomes, which may be a supplementary factor in guiding the allocation of medical resources, such as extracorporeal CPR.

Three-dimensional treatment-planning-based prediction of seed migration to chest after 125I seed brachytherapy for hepatic malignancy.

PURPOSE: To develop and validate risk models incorporating clinical and/or imaging parameters based on three-dimensional treatment-planning systems (3D-TPS) to predict the occurrence of 125I seed migration and the number of migrated seeds <2/≥2 to the chest after brachytherapy for patients with malignant hepatic tumors. METHODS AND MATERIALS: A total of 480 patients diagnosed with malignant liver tumors receiving 125I seed brachytherapy from July 2010 to May 2020 were retrospectively enrolled. Variables included 3D-TPS-based CT parameters, that is, the distance from the seed to the inferior vena cava (DSI), the distance from the seed to the second hepatic portal (DSP) and the angle from the seed to the second hepatic portal (ASP), and patients' clinical characteristics, that is, the number of seed implantation procedures (NSP), the maximum number of implanted seeds one time (MAX) and laboratory parameters within 1 week before treatment. Two sets of logistic regression models incorporating clinical and/or imaging variables were developed to predict the occurrence of seed migration and the number of migrated seeds <2/≥2. Model performance was assessed by ROC analysis and decision curve analysis. RESULTS: Compared with the clinical models, the combined model showed a higher discriminative ability for both the prediction of migration occurrence and number of migrated seeds ≥ 2/<2 to the chest (AUC, 0.879 vs. 0.668, p < 0.05; 0.895 vs. 0.701, p < 0.05). The decision curve analysis results indicated higher net benefits of combined models than clinical models. Variables, including DSI, NSP and pretreatment lymphocyte-to-neutrophil ratio, acted as the most important predictors in combined models. CONCLUSIONS: The proposed combined models based on 3D-TPS improved discriminative abilities for predicting 125I seed migration and number of migrated seeds <2/≥2 to the chest after hepatic brachytherapy, being promising to aid clinical decision-making.

Meta-analysis of the GALAD model for diagnosing primary hepatocellular carcinoma.

BACKGROUND: Ever since the GALAD (gender-age-Lens culinaris agglutinin-reactive alpha-fetoprotein-alpha-fetoprotein-des-gamma-carboxy prothrombin) logistic regression model was established to diagnose hepatocellular carcinoma (HCC), there has been no high-level evidence that evaluates and summarizes it. OBJECTIVE: This meta-analysis was performed to assess the diagnostic ability of the GALAD model. METHODS: The following databases were systematically searched for original diagnostic studies on HCC: PubMed, Embase, Medline, the Web of Science, Cochrane Library, China National Knowledge Infrastructure Wanfang (China), Wiper and the Chinese BioMedical Literature Database. After screening the search results according to our criteria, the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to evaluate the methodologic qualities, and statistical software were used to output the statistics. RESULTS: Ultimately, 10 studies were included and analyzed. The results revealed the pooled sensitivity and specificity of the GALAD model to be 0.86 (95% confidence interval [CI]: 0.82, 0.90) and 0.90 (95% CI: 0.87, 0.92), respectively, for all-stage HCC. The area under the curve (AUC) was 0.94. For early-stage HCC, the pooled sensitivity and specificity of the GALAD model were 0.83 (95% CI: 0.78, 0.87) and 0.81 (95% CI: 0.78, 0.83), respectively. The AUC was 0.90. CONCLUSION: This meta-analysis confirmed that the GALAD model has excellent diagnostic performance for early-stage and all-stage HCC and can maintain high sensitivity and specificity in early-stage HCC. Therefore, the GALAD model is qualified for screening early-stage canceration from chronic liver disease.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings. AIM: This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI. METHODS: This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance. RESULTS: Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results. CONCLUSION: This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

CD4 expression on monocytes correlates with recovery from multiple organ dysfunction syndrome and mortality in patients with septic shock.

Background: To date, the correlation between CD4 on the monocytes (mCD4) expression and the prognosis of patients with septic shock remains unclear. The purpose of this study was to analyze the expression of mCD4 in these patients and further evaluate whether mCD4 expression correlates with either the recovery from multiple organ dysfunction syndrome (MODS) or mortality. Methods: The study participants were recruited from a tertiary general hospital in China (Affiliated Dongyang Hospital of Wenzhou Medical University). Sepsis and septic shock were diagnosed based on the diagnostic criteria of Sepsis-3. MODS was defined as a Sequential Organ Failure Assessment score of at least two organ systems ≥2. Persistent MODS was defined as the continual meeting of the MODS criteria when re-evaluated one week after admission (day 7). A logistic regression model was used to test whether mCD4 was an independent prognostic factor for mortality in patients with septic shock. A paired sample rank sum test was used to examine the correlation between mCD4 expression and MODS recovery. Result: The study recruited 79 patients with septic shock as the study group, 74 patients with sepsis as the disease control group, and 56 volunteers as healthy controls. In the first 24 h after admission (day 1), mCD4 expression was significantly reduced in patients with septic shock compared to healthy controls and patients with sepsis. Moreover, mCD4 expression was an independent prognostic factor for in-hospital and 28 day mortality in patients with septic shock. mCD4 expression did not show significant differences in patients with persistent MODS on day 7 compared to day 1. However, mCD4 expression was significantly higher in patients without persistent MODS on day 7 than on day 1. Conclusion: mCD4 expression is significantly reduced in patients with septic shock, which is an independent prognostic factor for mortality and closely related to recovery from MODS.

Prevalence and Determinants of Low Birth Weight among the Newborns in Dadra and Nagar Haveli: A Community-based Study.

Background: The world is not on track to meet the World Health Assembly (WHA) global target on Low Birth Weight (LBW). To estimate the prevalence and to identify the associated determinants of LBW among the newborns. Material and Methods: We conducted a cross-sectional study among the 364 mothers registered under the all government health facilities of Dadra & Nagar Haveli (DNH) during November 2021 to January 2022. Results: The prevalence of LBW was found to be 39%. On uni-variable logistic regression, live in relationship, caste, weight of mother, Body Mass Index (BMI), weight gain <5 kg in 2nd and 3rd trimester, high-risk pregnancy, complication present in previous pregnancy and preterm delivery, while on multi-variable logistic regression, weight gain <5 kg in 2nd and 3rd trimester (AOR 2, 95% CI 1.007-4.2) and having high-risk pregnancy (AOR 2, 95% CI 1.1-3.0) were found to be the significant predictors of LBW among the newborns. Conclusions: We conclude from the study that the prevalence of low birth weight among the newborn was high. There is a need to address maternal and child health issues like low birth weight, malnutrition and high-risk pregnancy under the RMNCAH+N program through various effective interventions. Future research should evaluate the feasibility of collaborative activities between RMNCAH+N program and the UNICEF in India.