Body mass index and the prevalence of high-risk colorectal adenomas in a population undergoing screening colonoscopy in Alberta, Canada.

PURPOSE: There is limited evidence regarding body mass index (BMI) as an early marker of high-risk adenoma (HRA) at the time of screening colonoscopy. Because high-risk adenomas (HRA) can develop into colorectal cancer (CRC), BMI could serve as an important clinical predictor of future risk of CRC. METHODS: We examined data from 1831 adults undergoing screening colonoscopy at the Forzani & MacPhail Colon Cancer Screening Center in Alberta, Canada. We fit multivariable logistic regression models to examine the association between BMI and HRA. Non-linear relationships for BMI on HRA were also evaluated using restricted cubic splines. RESULTS: The mean BMI in patients with HRA was 28.2 kg/m2 compared to 27.4 kg/m2 in patients without adenomas (t test: p = 0.003). In the adjusted models, those with a BMI over 30 kg/m2 had 1.45 (95% CI 1.05-2.00) times the odds of HRA detected during colonoscopy compared to those with a BMI below 25 kg/m2. Examining BMI as continuous, the odds of HRA were 1.20 (95% CI 1.04-1.37) times higher for every 5 kg/m2 increase in BMI. CONCLUSION: The findings of this study suggest that excess body mass is associated with higher risk of HRA among a screening population and may be useful an early marker of future disease.

The effects of potentially traumatic events on the recovery from pre-existing anxiety and depression symptomatology and the risk of PTSD.

AIM: The extent to which recent potentially traumatic events (PTEs) hinder the recovery from pre-existing mental health problems is largely unknown. The same applies to the extent to which non-recovery from pre-existing mental health problems increases the risk of posttraumatic stress disorder (PTSD). The aim of the present study is to gain insight in these effects. METHODS: Data were extracted from six annual surveys of the Dutch population-based Victims in Modern Society (VICTIMS) study. Of the adult respondents who participated in two subsequent surveys (labeled T1 and T2, n = 6942), those with severe anxiety and depression symptoms (ADS) at T1 (n = 487) were selected. We distinguished respondents exposed to PTEs (PTE-group, n = 162) and not exposed to PTEs (comparison group, n = 325) between T1 and T2. We applied five indicators of recovery [based on the Reliable Change Index (RCI), degrees of symptom reduction, and the cut-off score at T2]. Differences in the recovery from ADS and probable PTSD at T2 were examined using multivariate logistic regression. RESULTS: The PTE group less often recovered from severe ADS between T1 and T2 than the comparison group according to all five indicators of recovery, while controlling for 11 different variables (0.40 ≤ adjusted OR's ≤ 0.66). Those in the PTE group who did not recover, considerably more often suffered from probable PTSD at T2 (63%-82%) than those who did recover (0%-29%; 8.96 ≤ adjusted OR ≤ 26.33). CONCLUSION: Recent potentially traumatic events hinder the recovery from pre-existing anxiety and depression symptomatology and thereby increase the risk of probable PTSD.

Risk Factors for Postoperative Refractive Error in New-Generation Intraocular Lens Calculation Formulas.

Purpose: We examined differences in the accuracy of three intraocular lens (IOL) calculation formulas: the traditional Sanders-Retzlaff-Kraff/Theoretical (SRK/T) formula; the Barrett Universal II (BU II) formula, which is a new-generation IOL calculation formula; and the postoperative spherical equivalent prediction using artificial intelligence and linear algorithms developed by Debellemanière, Gatinel, and Saad formula (PEARL-DGS [PEARL]) formula, and evaluated factors that cause postoperative refractive error (PE). Patients and Methods: The study included 205 patients (205 eyes) with a mean age of 75.2 ± 8.7 years who underwent cataract surgery at our institution from December 2018 to October 2023. The PE of the three IOL calculation formulas was calculated and compared. Multivariate logistic regression analysis was performed with a PE higher than ±0.50 D as the dependent variable, and age, sex, axial length (AL), mean keratometry (mean K), anterior chamber depth (ACD), lens thickness (LT), and white-to-white (WTW) as independent variables. Results: The mean PE (ME) ± standard deviation of the SRK/T, BU II, and PEARL formulas was 0.11 ± 0.52, 0.11 ± 0.50, and 0.21 ± 0.50 D, respectively. MEs of the three IOL calculation formulas were significantly different from 0 (p < 0.01). The median absolute error (MedAE) was not significantly different among the three IOL calculation formulas (p = 0.83). The percentage of PE within ±0.50 D was not significantly different among the three IOL calculation formulas (p = 0.13). Multivariate logistic regression analysis showed that the significantly associated factors with PE higher than ±0.50 D were AL, ACD, and LT for the SRK/T formula, sex and LT for the BU II formula, and LT for the PEARL formula (all p < 0.05). Conclusion: In the BU II and PEARL formulas, AL was excluded as a factor affecting PE, indicating that LT was a risk factor.

A prediction model based on computed tomography characteristics for identifying malignant from benign sub-centimeter solid pulmonary nodules.

Background: Distinguishing benign from malignant sub-centimeter solid pulmonary nodules (SSPNs) continues to be challenging in clinical practice. Earlier diagnosis is crucial for improving patient survival and prognosis. This study aimed to investigate the risk factors of malignant SSPNs and establish and validate a prediction model based on computed tomography (CT) characteristics to assist in their early diagnosis. Methods: A total of 261 consecutive participants with 261 SSPNs were retrospectively recruited between January 2012 and July 2023 from National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (Center 1), including 161 malignant lesions and 100 benign lesions. Patients were randomly assigned to the training set (n=183) and validation set (n=78) according to a 7:3 ratio. Malignant nodules were confirmed by pathology; and benign nodules were confirmed by follow-up or pathology. Clinical data and CT features were collected to estimate the independent predictors of malignancy of SSPN with multivariate logistic analysis. A clinical prediction model was subsequently established by logistic regression. Furthermore, an additional 69 consecutive patients with 69 SSPNs from The Fourth Hospital of Hebei Medical University (Center 2) between January 2022 and December 2022 were retrospectively included as an external cohort to validate the predictive efficacy of the model. The performance of the prediction model was assessed by sensitivity, specificity, and the area under the receiver operating characteristic curve. Results: There were 113 (61.7%), 48 (61.5%) and 28 (40.6%) malignant SSPNs in the training, internal and external validation sets, respectively. Multivariate logistic analysis revealed four independent predictors of malignant SSPNs: tumor-lung interface (P=0.002), spiculation (P=0.04), air bronchogram (P=0.047), and invisible at the mediastinal window (P=0.003). The area under the curve (AUC) for the prediction model in the training set was 0.875 [95% confidence interval (CI): 0.818, 0.933]; and the sensitivity and specificity were 94.7% and 68.6%, respectively. The AUCs in the internal and external validation set were (0.781; 95% CI: 0.664, 0.897) and (0.873; 95% CI: 0.791, 0.955), respectively; the sensitivity and specificity were 66.7% and 83.3% for the internal validation data, and 100.0% and 61.0% for the external validation data, respectively. Conclusions: The prediction model based on CT characteristics could be helpful for distinguishing malignant SSPNs from benign ones.

Multiple Logistic Regression Model for Assessing the Risk Factors of Traffic Accidents: Khon Kaen Model.

BACKGROUND: Thailand has consistently held the highest global ranking in traffic accidents since 2017, with Khon Kaen displaying the highest mortality rate in the Department of Disease Control Region 7. OBJECTIVES: This study aims to utilize Injury Surveillance (IS) data to identify risk factors associated with emergency room (ER) outcomes at the Emergency Department of Khon Kaen hospital in Khon Kaen Municipality. METHODS: Data from the Injury Surveillance system's (IS system) database were collected, focusing on severity outcomes, time of events, and risk behaviors from January 1, 2008, to December 31, 2021. Data analysis was conducted using the R program, employing the Chi-square or independent T test to compare results and analyze associations between potential risk factors and ER outcomes. Multiple logistic regression (MLR) was used for classification analysis, and a confusion matrix was applied to evaluate the performance of the models. RESULTS: MLR analysis revealed that being male, age, alcohol consumption, and nighttime driving were more likely to increase the probability of severity outcomes. CONCLUSION: Being male, age, alcohol consumption, and nighttime driving are identified as potential risk factors contributing to the development of severity outcomes following traffic accidents.

Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT.

OBJECTIVES: To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents. MATERIALS AND METHODS: This retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs. RESULTS: The study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance. CONCLUSION: Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions. CLINICAL RELEVANCE STATEMENT: The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses. KEY POINTS: Distinguishing benign from malignant liver lesions with non-contrast-enhanced CT would be desirable. This model, incorporating slope K, effective Z, and blood quantification, distinguished benign from malignant liver lesions. Non-contrast-enhanced DECT has benefits, particularly in patients with an iodine allergy, renal failure, or asthma.

Implementation of Patient-Reported Outcomes in a Medical Oncology Setting (the iPROMOS Study): Type II Hybrid Implementation Study.

BACKGROUND: Clinical trials have demonstrated that patient-reported outcome measures (PROMs) can improve mortality and morbidity outcomes when used in clinical practice. OBJECTIVE: This study aimed to prospectively investigate the implementation of PROMs in routine oncology. Outcomes measured included improved symptom detection, clinical response to symptom information, and health service outcomes. METHODS: Two of 12 eligible clinics were randomized to implement symptom PROMs in a medical oncology outpatient department in Australia. Randomization was carried out at the clinic level. Patients in control clinics continued with usual care; those in intervention clinics completed a symptom PROM at presentation. This was a pilot study investigating symptom detection, using binary logistic models, and clinical response to PROMs investigated using multiple regression models. RESULTS: A total of 461 patient encounters were included, consisting of 242 encounters in the control and 222 in the intervention condition. Patients in these clinics most commonly had head and neck, lung, prostate, breast, or colorectal cancer and were seen in the clinic for surveillance and oral or systemic treatments for curative, metastatic, or palliative cancer care pathways. Compared with control encounters, the proportion of symptoms detected increased in intervention encounters (odds ratio 1.05, 95% CI 0.99-1.11; P=.08). The odds of receiving supportive care, demonstrated by nonroutine allied health review, increased in the intervention compared with control encounters (odds ratio 3.54, 95% CI 1.26-9.90; P=.02). CONCLUSIONS: Implementation of PROMs in routine care did not significantly improve symptom detection but increased the likelihood of nonroutine allied health reviews for supportive care. Larger studies are needed to investigate health service outcomes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000398202; https://tinyurl.com/3cxbemy4.

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.

Risk Factors for Unplanned Higher-Level Re-Amputation and Mortality after Lower Extremity Amputation in Chronic Limb-Threatening Ischemia.

Background: The factors associated with unplanned higher-level re-amputation (UHRA) and one-year mortality among patients with chronic limb-threatening ischemia (CLTI) after lower extremity amputation are poorly understood. Methods: This was a single-center retrospective study of patients who underwent amputations for CLTI between 2014 and 2017. Unadjusted bivariate analyses and adjusted odds ratios (AOR) from logistic regression models were used to assess associations between pre-amputation risk factors and outcomes (UHRA and one-year mortality). Results: We obtained data on 203 amputations from 182 patients (median age 65 years [interquartile range (IQR) 57, 75]; 70.7% males), including 118 (58.1%) toe, 20 (9.9%) transmetatarsal (TMA), 37 (18.2%) below-knee (BKA), and 28 (13.8%) amputations at or above the knee. Median follow-up was 285 days (IQR 62, 1348). Thirty-six limbs (17.7%) had a UHRA, and the majority of these (72.2%) were following index forefoot amputations. Risk factors for UHRA included non-ambulatory status (AOR 6.74, 95% confidence interval (CI) 1.74-26.18; p < 0.10) and toe pressure < 30 mm Hg (AOR 4.89, 95% CI 1.52-15.78; p < 0.01). One-year mortality was 17.2% (n = 32), and risk factors included coronary artery disease (AOR 3.93, 95% CI 1.56-9.87; p < 0.05), congestive heart failure (AOR 4.90, 95% CI 1.96-12.29; p = 0.001), end-stage renal disease (AOR 7.54, 95% CI 3.10-18.34; p < 0.001), and non-independent ambulation (AOR 4.31, 95% CI 1.20-15.49; p = 0.03). Male sex was associated with a reduced odds of death at 1 year (AOR 0.37, 95% CI 0.15-0.89; p < 0.05). UHRA was not associated with one-year mortality. Conclusions: Rates of UHRA after toe amputations and TMA are high despite revascularization and one-year mortality is high among patients with CLTI requiring amputation.

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.

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.

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.

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.

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.

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.

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.

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.