Forward Stepwise Logistic Regression Research Articles

What role do socioeconomic and clinical factors play in disease activity states in rheumatoid arthritis? Data from a large UK early inflammatory arthritis audit

BackgroundPersistently active rheumatoid arthritis (pactiveRA) may be due to the interplay between biological and non-biological factors. The role of socioeconomic factors remains unclear.ObjectivesTo explore which biological and non-biological factors associate...

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables

ObjectiveDiabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario.MethodsIn this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People’s Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People’s Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction.ResultsThe risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively.ConclusionThe MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Dynamic change of metabolic dysfunction-associated steatotic liver disease in patients with hepatitis C virus infection after achieving sustained virologic response with direct-acting antivirals.

Information on the dynamics of metabolic dysfunction-associated steatotic liver disease (MASLD) among hepatitis C virus patients achieving sustained virologic response (SVR12) with direct-acting antivirals (DAAs) is limited. We enrolled 1512 eligible participants in this prospective study. MASLD was defined by a controlled attenuation parameter (CAP) of ≥248dB/m utilizing vibration-controlled transient elastography in conjunction with presence of ≥1 cardiometabolic risk factor. The distribution of MASLD and the changes in CAP were evaluated before treatment and at SVR12. Forward stepwise logistic regression analyses were performed to determine factors significantly associated with the regression or emergence of MASLD. The prevalence of MASLD decreased from 45.0% before treatment to 36.1% at SVR12. Among 681 participants with MASLD before treatment, 144 (21%) exhibited MASLD regression at SVR12. Conversely, among 831 participants without MASLD before treatment, 9 (1.1%) developed MASLD at SVR12. Absence of type 2 diabetes (T2D) [odds ratio (OR): 1.73, 95% confidence interval (CI): 1.13-2.65, p = 0.011], age > 50years (OR: 1.73, 95% CI: 1.11-2.68, p = 0.015), and alanine transaminase (ALT) ≤ 2 times the upper limit of normal (ULN) (OR: 1.56; 95% CI: 1.03-2.37, p = 0.035) were associated with the regression of MASLD. Presence of T2D was associated with the emergence of MASLD (OR: 5.83, 95% CI: 1.51-22.56, p = 0.011). The prevalence of MASLD decreased after achieving SVR12 with DAAs. Patients with pre-existing T2D showed a diminished probability of MASLD regression and a heightened risk of MASLD emergence post-SVR12.

Outcomes of pregnancies complicated by cirrhosis: a retrospective cohort study

BackgroundAlthough pregnancy complicated by liver cirrhosis is rare, women with cirrhosis experience increased adverse pregnancy outcomes. This study aimed to evaluate pregnancy outcomes in women with liver cirrhosis and develop a predictive model using maternal factors for preterm birth in such pregnancies.MethodsA retrospective analysis was conducted on pregnancy outcomes of a cirrhosis group (n = 43) and a non-cirrhosis group (n = 172) in a university hospital between 2010 and 2022. Logistic regression evaluated pregnancy outcomes, and a forward stepwise logistic regression model was designed to predict preterm birth in pregnant women with cirrhosis. The model's predictive performance was evaluated using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC).ResultsThe incidence of cirrhosis during pregnancy was 0.06% (50/81,554). Pregnant women with cirrhosis faced increased risks of cesarean section, preterm birth, intrahepatic cholestasis of pregnancy, thrombocytopenia, and postpartum hemorrhage. In pregnant women with cirrhosis, preterm birth risk significantly increased at an incidence rate of 46.51% (20/43). According to the prediction model, the key predictors of preterm birth in pregnant women with cirrhosis were intrahepatic cholestasis of pregnancy and total bilirubin. The model demonstrated accurate prediction, with an AUC of 0.847, yielding a model accuracy of 81.4%.ConclusionsPregnant women with cirrhosis face a heightened risk of adverse obstetric outcomes, particularly an increased incidence of preterm birth. The preliminary evidence shows that the regression model established in our study can use the identified key predictors to predict preterm birth in pregnant women with cirrhosis, with high accuracy.

Establishment and validation of a nomogram clinical prediction model for osteoporosis in senile patients with type 2 diabetes mellitus

This study aimed to develop a predictive nomogram model to estimate the odds of osteoporosis (OP) in elderly patients with type 2 diabetes mellitus (T2DM) and validate its prediction efficiency. The hospitalized elderly patients with T2DM from the Affiliated Hospital of North Sichuan Medical University between July 2022 and March 2023 were included in this study. We sorted them into the model group and the validation group with a ratio of 7:3 randomly. The selection operator regression (LASSO) algorithm was utilized to select the optimal matching factors, which were then included in a multifactorial forward stepwise logistic regression to determine independent influencing factors and develop a nomogram. The discrimination, accuracy, and clinical efficacy of the nomogram model were analyzed utilizing the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve analysis (DCA). A total of 379 study participants were included in this study. Gender (OR = 8.801, 95% CI 4.695–16.499), Geriatric Nutritional Risk Index (GNRI) < 98 (OR = 4.698, 95% CI 2.416–9.135), serum calcium (Ca) (OR = 0.023, 95% CI 0.003–0.154), glycated hemoglobin (HbA1c) (OR = 1.181, 95% CI 1.055–1.322), duration of diabetes (OR = 1.076, 95% CI 1.034–1.119), and serum creatinine (SCr) (OR = 0.984, 95% CI 0.975–0.993) were identified as independent influencing factors for DOP occurrence in the elderly. The area under the curve (AUC) of the nomogram model was 0.844 (95% CI 0.797–0.89) in the modeling group and 0.878 (95% CI 0.814–0.942) in the validation group. The nomogram clinical prediction model was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. The nomogram model established in this study has good discrimination and accuracy, allowing for intuitive and individualized analysis of the risk of DOP occurrence in elderly individuals. It can identify high-risk populations and facilitate the development of effective preventive measures.

Predictive Factors for the Formation of Viable Embryos in Subfertile Patients with Diminished Ovarian Reserve: A Clinical Prediction Study

This study aims to construct and validate a nomogram for predicting blastocyst formation in patients with diminished ovarian reserve (DOR) during in vitro fertilization (IVF) procedures. A retrospective analysis was conducted on 445 DOR patients who underwent in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) at the Reproductive Center of Yulin Maternal and Child Health Hospital from January 2019 to January 2023. A total of 1016 embryos were cultured for blastocyst formation, of which 487 were usable blastocysts and 529 did not form usable blastocysts. The embryos were randomly divided into a training set (711 embryos) and a validation set (305 embryos). Relevant factors were initially identified through univariate logistic regression analysis based on the training set, followed by multivariate logistic regression analysis to establish a nomogram model. The prediction model was then calibrated and validated. Multivariate stepwise forward logistic regression analysis showed that female age, normal fertilization status, embryo grade on D2, and embryo grade on D3 were independent predictors of blastocyst formation in DOR patients. The Hosmer–Lemeshow test indicated no statistical difference between the predicted probabilities of blastocyst formation and actual blastocyst formation (P > 0.05). These results suggest that female age, normal fertilization status, embryo grade on D2, and embryo grade on D3 are independent predictors of blastocyst formation in DOR patients. The clinical prediction nomogram constructed from these factors has good predictive value and clinical utility and can provide a basis for clinical prognosis, intervention, and the formulation of individualized medical plans.

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

AimPatients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China.MethodsInitial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance.ResultsSix key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725).ConclusionFor patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.

Effect of variable selection strategy on the predictive models for adverse pregnancy outcomes of pre-eclampsia: A retrospective study

Objectives: The improvement of prediction for adverse pregnancy outcomes is quite essential to the women suffering from pre-eclampsia, while the collection of predictive indicators is the prerequisite. The traditional knowledge-based strategy for variable selection confronts challenge referring to dataset with high-dimensional or unfamiliar data. In this study, we employed five different automatic variable selection methods to screen out influential indicators, and evaluated the performance of constructed predictive models. Methods: Seven hundreds and thirty-three Han-Chinese women were enrolled and 56 clinical and laboratory variables were recorded. After grouping based on binary pregnancy outcomes, statistical description and analysis were performed. Then, utilizing forward stepwise logistic regression (FSLR) as the reference method, another four variable selection strategies were included for filtering contributing variables as the predictive subsets, respectively. Finally, the logistic regression prediction models were constructed by the five subsets and evaluated by the receiver operator characteristic curve. Results: The variables confirmed statistical significance between the adverse and satisfactory outcomes groups did not overlap with the variables selected by selection strategies. “Platelet” and “Creatinine clearance rate” were the most influential indicator to predict adverse maternal outcome, while “Birth weight of neonates” was the best indicator for predicting adverse neonatal outcome. In average, the predictive models for neonatal outcomes achieved better performance than models for maternal outcomes. “Mutual information” and “Recursive feature elimination” were the best strategy under current dataset and study design. Conclusions: Variable selection strategies may provide an alternative approach besides picking influential indicators by statistical significance. Future work will focus on applying different variable selection methods to the high-dimensional dataset, which includes novel or unfamiliar variables. This aims to identify the most appropriate collection of predictors that can enhance prediction ability and clinical decision-making.

Predicting Major Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion.

The National Cardiovascular Data Registry Left Atrial Appendage Occlusion Registry (LAAO) includes the vast majority of transcatheter LAAO procedures performed in the United States. The objective of this study was to develop a model predicting adverse events among patients undergoing LAAO with Watchman FLX. Data from 41 001 LAAO procedures with Watchman FLX from July 2020 to September 2021 were used to develop and validate a model predicting in-hospital major adverse events. Randomly selected development (70%, n=28 530) and validation (30%, n=12 471) cohorts were analyzed with 1000 bootstrapped samples, using forward stepwise logistic regression to create the final model. A simplified bedside risk score was also developed using this model. Increased age, female sex, low preprocedure hemoglobin, no prior attempt at atrial fibrillation termination, and increased fall risk most strongly predicted in-hospital major adverse events and were included in the final model along with other clinically relevant variables. The median in-hospital risk-standardized adverse event rate was 1.50% (range, 1.03%-2.84%; interquartile range, 1.42%-1.64%). The model demonstrated moderate discrimination (development C-index, 0.67 [95% CI, 0.65-0.70] and validation C-index, 0.66 [95% CI, 0.62-0.70]) with good calibration. The simplified risk score was well calibrated with risk of in-hospital major adverse events ranging from 0.26% to 3.90% for a score of 0 to 8, respectively. A transcatheter LAAO risk model using National Cardiovascular Data Registry and LAAO Registry data can predict in-hospital major adverse events, demonstrated consistency across hospitals and can be used for quality improvement efforts. A simple bedside risk score was similarly predictive and may inform shared decision-making.

Improved early risk stratification of deep sternal wound infection risk after coronary artery bypass grafting

BackgroundDeep sternal wound infection (DSWI) following open heart surgery is associated with excessive morbidity and mortality. Contemporary DSWI risk prediction models aim at identifying high-risk patients with varying complexity and performance characteristics. We aimed to optimize the DSWI risk factor set and to identify additional risk factors for early postoperative detection of patients prone to DSWI.MethodsSingle-centre retrospective analysis of patients with isolated multivessel coronary artery disease undergoing myocardial revascularization at Paracelsus Medical University Nuremberg between 2007 and 2022 was performed to identify risk factors for DSWI. Three data sets were created to examine preoperative, intraoperative, and early postoperative parameters, constituting the “Baseline”, the “Improved Baseline” and the “Extended” models. The “Extended” data set included risk factors that had not been analysed before. Univariable and stepwise forward multiple logistic regression analyses were performed for each respective set of variables.ResultsFrom 5221 patients, 179 (3.4%) developed DSWI. The “Extended” model performed best, with the area under the curve (AUC) of 0.80, 95%-CI: [0.76, 0.83]. Pleural effusion requiring intervention, postoperative delirium, preoperative hospital stay > 24 h, and the use of fibrin sealant were new independent predictors of DSWI in addition to age, Diabetes Mellitus on insulin, Body Mass Index, peripheral artery disease, mediastinal re-exploration, bilateral internal mammary harvesting, acute kidney injury and blood transfusions.ConclusionsThe “Extended” regression model with the short-term postoperative complications significantly improved DSWI risk discrimination after surgical revascularization. Short preoperative stay, prevention of postoperative delirium, protocols reducing the need for evacuation of effusion and restrictive use of fibrin sealant for sternal closure facilitate DSWI reduction.Trial registrationThe registered retrospective study was registered at the study centre and approved by the Institutional Review Board of Paracelsus Medical University Nuremberg (IRB-2019-005).

Assessing the optimal MRI descriptors to diagnose Ménière's disease and the added value of analysing the vestibular aqueduct.

To evaluate the diagnostic performance and reliability of MRI descriptors used for the detection of Ménière's disease (MD) on delayed post-gadolinium MRI. To determine which combination of descriptors should be optimally applied and whether analysis of the vestibular aqueduct (VA) contributes to the diagnosis. This retrospective single centre case-control study evaluated delayed post-gadolinium MRI of patients with Ménièriform symptoms examined consecutively between Dec 2017 and March 2023. Two observers evaluated 17 MRI descriptors of MD and quantified perilymphatic enhancement (PLE) in the cochlea. Definite MD ears according to the 2015 Barany Society criteria were compared to control ears. Cohen's kappa and diagnostic odds ratio (DORs) were calculated for each descriptor. Forward stepwise logistic regression determined which combination of MRI descriptors would best predict MD ears, and the area under the receiver operating characteristic curve for this model was measured. A total of 227 patients (mean age 48.3 ± 14.6, 99 men) with 96 definite MD and 78 control ears were evaluated. The presence of saccular abnormality (absent, as large as or confluent with the utricle) performed best with a DOR of 292.6 (95% confidence interval (CI), 38.305-2235.058). All VA descriptors demonstrated excellent reliability and with DORs of 7.761 (95% CI, 3.517-17.125) to 18.1 (95% CI, 8.445-39.170). Combining these saccular abnormalities with asymmetric cochlear PLE and an incompletely visualised VA correctly classified 90.2% of cases (sensitivity 84.4%, specificity 97.4%, AUC 0.938). Either absent, enlarged or confluent saccules are the best predictors of MD. Incomplete visualisation of the VA adds value to the diagnosis. A number of different MRI descriptors have been proposed for the diagnosis of Ménière's disease, but by establishing the optimally performing MRI features and highlighting new useful descriptors, there is an opportunity to improve the diagnostic performance of Ménière's disease imaging. • A comprehensive range of existing and novel vestibular aqueduct delayed post-gadolinium MRI descriptors were compared for their diagnostic performance in Ménière's disease. • Saccular abnormality (absent, confluent with or larger than the utricle) is a reliable descriptor and is the optimal individual MRI predictor of Ménière's disease. • The presence of this saccule descriptor or asymmetric perilymphatic enhancement and incomplete vestibular aqueduct visualisation will optimise the MRI diagnosis of Ménière's disease.

Risk factors and an early predictive model for Kawasaki disease shock syndrome in Chinese children

BackgroundKawasaki disease shock syndrome (KDSS), though rare, has increased risk for cardiovascular complications. Early diagnosis is crucial to improve the prognosis of KDSS patients. Our study aimed to identify risk factors and construct a predictive model for KDSS.MethodsThis case-control study was conducted from June, 2015 to July, 2023 in two children’s hospitals in China. Children initially diagnosed with KDSS and children with Kawasaki disease (KD) without shock were matched at a ratio of 1:4 by using the propensity score method. Laboratory results obtained prior to shock syndrome and treatment with intravenous immunoglobulin were recorded to predict the onset of KDSS. Univariable logistic regression and forward stepwise logistic regression were used to select significant and independent risk factors associated with KDSS.ResultsAfter matching by age and gender, 73 KDSS and 292 KD patients without shock formed the development dataset; 40 KDSS and 160 KD patients without shock formed the validation dataset. Interleukin-10 (IL-10) > reference value, platelet counts (PLT) < 260 × 109/L, C-reactive protein (CRP) > 80 mg/ml, procalcitonin (PCT) > 1ng/ml, and albumin (Alb) < 35 g/L were independent risk factors for KDSS. The nomogram model including the above five indicators had area under the curves (AUCs) of 0.91(95% CI: 0.87–0.94) and 0.90 (95% CI: 0.71–0.86) in the development and validation datasets, with a specificity and sensitivity of 80% and 86%, 66% and 77%, respectively. Calibration curves showed good predictive accuracy of the nomogram. Decision curve analyses revealed the predictive model has application value.ConclusionsThis study identified IL-10, PLT, CRP, PCT and Alb as risk factors for KDSS. The nomogram model can effectively predict the occurrence of KDSS in Chinese children. It will facilitate pediatricians in early diagnosis, which is essential to the prevention of cardiovascular complications.

Abstract TP134: White Matter Disease and Patterns of Infarction: A Single Center Study

Background: Patterns of acute cerebral infarction can often reveal the underlying mechanism of ischemic injury. Despite the strong overlap between vascular risk factors and embolic infarct mechanisms (with multifocal infarct patterns), acute multifocal subcortical infarcts may be related to microangiopathic changes. Methods: A prospective stroke registry was queried for patients with acute infarction in &gt;1 vascular territory on diffusion-weighted imaging (unrelated to any endovascular procedure). The odds of a multi-infarct subcortical pattern (vs. cortical pattern) were estimated for patients with higher Fazekas grade (2-3) versus low grade (0-1) patients using forward stepwise logistic regression with greatest minimization of corrected Akaike information criterion including all demographic covariates. Results: Out of 746 patients with acute infarcts and complete covariate data, 226 patients (30.3%) had a multi-infarct pattern, of whom 113 (50.0%) had high grade (Fazekas 2-3) white matter disease. Compared to patients with grade 0-1 Fazekas score, those with grade 2-3 more frequently had multifocal subcortical infarcts (4.4% vs. 10%, p=0.01). Following multivariable adjustment, the odds of multifocal subcortical infarcts were greater for patients with higher Fazekas grades (adjusted odds ratio [OR] 2.9, 95% confidence interval [CI] 1.3-6.7). The higher odds of multifocal subcortical infarcts were observed in a stepwise fashion for each Fazekas grade when compared to grade 0 (adjusted OR for grade 3 10.1, 95% CI 1.2-84; grade 2 OR 9.4, 95% 1.2-75.6; grade 1 OR 4.9, 95% 0.6-40.4). Conclusions: In this single center analysis we found that white matter burden is correlated with subcortical patterns of multifocal cerebral infarction. This suggests such infarcts may be caused by uncontrolled vascular risk factors rather than a proximal embolic source when chronic white matter disease is present.

Abstract TP146: Collateral Status-Based Model for Hemorrhagic Transformation in Successfully Recanalized Medium Vessel Occlusions

Introduction: Collateral status imaging parameters are associated with predicting hemorrhagic transformation (HT) in acute ischemic stroke caused by large vessel occlusions. The purpose of this investigation is to examine the association between imaging biomarkers of poor collateral status and HT occurrence in distal medium vessel occlusion (DMVO) stroke patients. Methods: Data from two comprehensive stroke centers between 2019 and 2023 were retrospectively reviewed for acute ischemic stroke patients with M2-M4 segment middle cerebral artery occlusions. For inclusion, patients must have been successfully recanalized by mechanical thrombectomy with or without intravenous thrombolytic and ultimate modified thrombolysis in cerebral infarction (mTICI) score 2B/2C/3. Occurrence of HT was determined from post-interventional imaging, and HT subtype was defined by the ECASS 2 criteria. Significant univariate predictors were included in a forward stepwise logistic regression for outcome of HT, and adjusted odds ratios (aOR) were reported. Results: Of 60 patients, 17 (28.3%) patients had HT. Average age was 74 [IQR 64-81] years, 34 (56.7%) were female. The multivariate model (area under curve = 0.93) had three significant predictors: diabetes mellitus (aOR 36.5, 95% CI 4.9-272.2, p&lt;0.001); proximal versus distal occlusion (reference: distal, aOR 0.06, 95% CI 0-0.4, p=0.006); CBV index (aOR 0.0002, 95% CI 0-0.1, p=0.007). Conclusions: Lower CBV index (a marker of poor collateral status), presence of distal occlusions, and history of diabetes mellitus are significant predictors associated with increased odds of HT in successfully recanalized DMVO stroke patients. Larger studies are necessary to further validate our findings.

Development and validation a nomogram for predicting new-onset postoperative atrial fibrillation following pulmonary resection

BackgroundThe new-onset postoperative atrial fibrillation (NOPAF) following pulmonary resection is a common clinical concern. The aim of this study was to construct a nomogram to intuitively predict the risk of NOPAF and offered protective treatments.MethodsPatients who underwent pulmonary resection between January 2018 and December 2020 were consecutively enrolled. Forward stepwise multivariable logistic regression analyses were used to screen independent predictors, and a derived nomogram model was built. The model performance was evaluated in terms of calibration, discrimination and clinical utility and validated with bootstrap resampling.ResultsA total of 3583 patients who met the research criteria were recruited for this study. The incidence of NOPAF was 1.507% (54/3583). A nomogram, composed of five independent predictors, namely age, admission heart rate, extent of resection, laterality, percent maximum ventilation volume per minute (%MVV), was constructed. The concordance index (C-index) was 0.811. The nomogram showed substantial discriminative ability, with an area under the receiver operating characteristic curve of 0.811 (95% CI 0.758-0.864). Moreover, the model shows prominent calibration performance and higher net clinical benefits.ConclusionWe developed a novel nomogram that can predict the risk of NOPAF following pulmonary resection, which may assist clinicians predict the individual probability of NOPAF and perform available prophylaxis. By using bootstrap resampling for validation, the optimal discrimination and calibration were demonstrated, indicating that the nomogram may have clinical practicality.

Dual-layer spectral detector CT: A noninvasive preoperative tool for predicting histopathological differentiation in pancreatic ductal adenocarcinoma

PurposeTo predict histopathological differentiation grades in patients with pancreatic ductal adenocarcinoma (PDAC) before surgery with quantitative and qualitative variables obtained from dual-layer spectral detector CT (DLCT). MethodsTotally 128 patients with histopathologically confirmed PDAC and preoperative DLCT were retrospectively enrolled and categorized into the low-grade (LG) (well and moderately differentiated, n = 82) and high-grade (HG) (poorly differentiated, n = 46) subgroups. Both conventional and spectral variables for PDAC were measured. The ratio of iodine concentration (IC) values in arterial phase(AP) and venous phase (VP) was defined as iodine enhancement fraction_AP/VP (IEF_AP/VP). Necrosis was visually assessed on both conventional CT images (necrosis_con) and virtual mono-energetic images (VMIs) at 40 keV (necrosis_40keV). Forward stepwise logistic regression method was conducted to perform univariable and multivariable analysis. Receiver operating characteristic (ROC) curves and the DeLong method were used to evaluate and compare the efficiencies of variables in predicting tumor grade. ResultsNecrosis_con (odds ratio [OR] = 2.84, 95% confidence interval [CI]: 1.13–7.13; p < 0.001) was an independent predictor among conventional variables, and necrosis_40keV (OR = 5.82, 95% CI: 1.98–17.11; p = 0.001) and IEF_AP/VP (OR = 1.12, 95% CI:1.07–1.17; p < 0.001) were independent predictors among spectral variables for distinguishing LG PDAC from HG PDAC. IEF_AP/VP (AUC = 0.754, p = 0.016) and combination model (AUC = 0.812, p < 0.001) had better predictive performances than necrosis_con (AUC = 0.580). The combination model yielded the highest sensitivity (72%) and accuracy (79%), while IEF_AP/VP exhibited the highest specificity (89%). ConclusionVariables derived from DLCT have the potential to preoperatively evaluate PDAC tumor grade. Furthermore, spectral variables and their combination exhibited superior predictive performances than conventional CT variables.

Akut pankreatitli hastalarda akut pankreatik ve peripankreatik sıvı gelişiminde prediktif faktörler

Aims: Acute fluid collections after acute pancreatitis carries risk of serious complications as infected pseudocyst and Wall off Necrose development. Hence, it is important to predict the development of acute fluid collections for treatment and management of acute pancreatitis. In this study, it is aimed to investigate predictive factors for development of acute fluid collections in patients with acute pancreatitis. &#x0D; Methods: Total of 438 patients diagnosed with acute pancreatitis were screened. According to the Revised Atlanta Classification fluid development after acute pancreatitis was determined and the relationship between fluid development and the hematological/biochemical parameters of the patients at the time of admission was investigated. The best cut-off point of laboratory measurements for fluid development was determined by ROC analysis and the factors that may be most decisive in distinguishing between the patients with and without fluid development were determined by multivariate forward stepwise logistic regression analysis.&#x0D; Results: It is found that developing acute fluid collections after acute pancreatitis was higher in patients with younger age and male gender. Also the risk of developing acute fluid collections after acute pancreatitis was found to be 6.2 times higher in patients with CRP/Albumin ratio greater than 1.09; 2.5 times higher in patients with ALP below 199.5 U/L; 1.9 times higher in patients with WBC greater than 11,6 ×10⁹/L and 1.5 times higher in patients with PLR above 197.1. Also the risk of developing acute necrotic collections after acute pancreatitis was 3 times higher in patients with serum calcium level below 8,6 mg/dl.&#x0D; Conclusion: It has been determined that, presence of high CRP/albumin ratio, high NLR and low serum ALP level can be used as an indicator in predicting acute pancreatic and peripancreatic fluid development.

Predictive Factors Associated with Chronic Neck Pain in Patients with Cervical Degenerative Disease: A Retrospective Cohort Study.

To explore the predictive factors of neck pain (NP) in patients with cervical degenerative disease by retrospectively analyzing their occupational and demographic characteristics and to provide a valuable reference for preventing and treating chronic NP. We retrospectively reviewed the occupational and demographic data of patients with cervical degenerative disease who had undergone anterior cervical surgery between June 2021 and December 2022 at our center. The patients were divided into NP and no-NP groups based on whether they had chronic NP before surgery. Relevant occupational and demographic data from all patients were statistically analyzed, and all variables were made categorical. Forward stepwise logistic regression models were constructed for preoperative chronic neck pain to explore the possible risk factors associated with chronic neck pain. The differences in smoking, being an office worker, BMI, and disease types between NP and no-NP groups were statistically significant. In contrast, there were no statistically significant in age, sex, academic level, duration, and degeneration grade between the two groups. Moreover, further logistic regression analysis indicated that smoking, being an office worker, having an abnormal BMI, and cervical spondylotic radiculopathy (CSR) were related to chronic neck pain. The present study indicated that smoking, being an office worker, having an abnormal BMI, and CSR were predisposing risk factors for NP associated with cervical degenerative disease. Although intervertebral disc degeneration is the pathology basis of NP, the degeneration grade was not related to the occurrence of NP in our current study. Therefore, quitting smoking, avoiding sedentariness, and maintaining a normal BMI may prevent NP to some extent.

Association of Lung Ultrasound B-Lines with Left Ventricular Diastolic Function in Clinically Euvolemic Haemodialysis Patients

Introduction: Left ventricular diastolic dysfunction (LVDD) frequently occurs in haemodialysis patients and is associated with adverse outcomes. Lung ultrasound (LUS) has been recently proposed for the quantification of extravascular lung water through assessment of B-lines. LUS findings and their relationship with LVDD in clinically euvolemic haemodialysis patients were investigated in this study. Methods: Echocardiography and LUS examinations were performed on each patient. Multivariate linear regression and forward stepwise logistic regression were performed to determine the relationship between B-lines and LVDD. A receiver operating characteristic (ROC) curve with area under the curve (AUC) was calculated to determine the accuracy of B-lines for evaluating LVDD. Results: A total of 119 patients were enrolled. The number of B-lines was statistically related to echocardiographic parameters (LAVI, LVEDVI, E/A, and E/e′) of diastolic function, while the relationship between B-lines and LVEF disappeared after adjusting for potential confounding factors. Additionally, compared with the mild B-line group (B-lines: <14), the moderate (B-lines: 14–30) and severe B-line groups (B-lines: >30) were associated with an increased risk of LVDD (OR 24.344, 95% CI 4.854–122.084, p < 0.001, and OR 94.552, 95% CI 9.617–929.022, p < 0.001, respectively). Furthermore, the AUC of the ROC curve for B-lines predicting LVDD was 0.845, and the cut-off of B-lines was 14.5 (sensitivity 64.91%, specificity 93.55%). Conclusion: LUS B-lines were closely associated with left ventricular diastolic function in clinically euvolemic haemodialysis patients. Moreover, our findings suggested a B-line ≥14.5 as a reliable cut-off value for identifying patients with LVDD. LUS B-lines may be used as a novel indicator for evaluating LVDD.

Risk Factor Profile and Hospital Outcomes in Patients ≤ 45 Years vs. &gt;45 Years of Age Presenting with Acute ST-Segment Elevation Myocardial Infarction

Objectives: In Pakistan, cardiovascular risk factors for acute myocardial infarction are increasing. There are few studies available on atherosclerotic risk factors in young patients and its outcome The purpose of this study was to compare pattern of traditional CVD risk factors, hospital mortality and major adverse cardiac events at discharge in young versus old patients (≤45years and above 45 years age respectively) admitted with STEMI and undergoing primary PCI.&#x0D; Methodology: This retrospective cohort study was conducted on consecutive patients presenting with STEMI between June 2013 and June 2018 in a single centre cardiac only tertiary care setup. Institute’s registry is fashioned along and is affiliated with US NCDR. Patients with cardiac arrest or cardiogenic shock at arrival were excluded. MACE was defined as post PCI cardiac arrest or cardiogenic shock, heart failure, major bleed or hospital death. Models were built using stepwise forward logistic regression method.&#x0D; Results: Total of 5343 patients were admitted with STEMI during study period, after exclusion data of 1642 patients were analyzed. Among young patients, the frequency of the male gender, any tobacco use and family history of premature coronary artery disease were significantly higher (all p values &lt; 0.001). While in older STEMI patients frequency of heart failure, hypertension, prior myocardial infarction, diabetes mellitus ,abnormal creatinine clearance (&lt;90 mL/min/1.73 m²) , multi-vessel coronary disease, advanced Killip class and higher body mass index was more prevalent (all P&lt;0.001). Among young patients advanced Killip class and femoral access site(all P values &lt;0.001), diabetes mellitus (p=0.03), abnormal creatinine clearance(p=0.04),and left ventricular ejection fraction less than 40%(p=0.01), were more significant in-hospital mortality predictor whereas advanced Killip class, Left ventricular ejection fraction less than 40% male gender(p=0.04), diabetes mellitus(p=0.001),femoral access site(p=0.001), and Left ventricular ejection fraction less than 40% were more significant MACE predictors (all P&lt;0.001).&#x0D; Conclusion: There were no significant difference in hospital mortality and MACE among young versus old STEMI patients when adjusted for diabetes mellitus, abnormal creatinine clearance (&lt;90 mL/min/1.73 m²), multi-vessel coronary disease, advanced Killip class. Tobacco use is the main modifiable risk factors for young patients with STEMI. Surprisingly, being a woman and having a positive family history with premature coronary artery disease were protective.