Lowest Akaike Information Criterion Research Articles

Relationship between parasitism with milk yield and body weight of Mafriwal cattle

Mafriwal cattle have good characteristics, but parasitism effects on their production have never been explored. This study aimed to determine the relationship between phenotypic markers of parasitism, namely packed cell volume (PCV), peripheral eosinophils count (PEC), nematode eggs (EPG), and coccidia oocyst (OPG) with the milk yield and body weight of Mafriwal cattle. Fecal and blood samples were collected from 242 cattle between 2021 and 2022. PCV and PEC were obtained from blood samples, while EPG and OPG were obtained from fecal samples. PCR was conducted to identify haemoparasite species. Milk yield data and the body weight of calves were also recorded. Spearman’s rank correlation showed that body weight has a negative correlation with PCV (r=-0.4; P<0.01) and a positive correlation with PEC (r=0.39; P<0.01) in 2022. Multiple linear regression models were used to determine the effect of phenotypic markers and year of sampling on milk yield and body weight. The best models were selected based on the lowest Akaike Information Criterion. Milk yield decreased significantly (P<0.001) from 2021 to 2022. An increase in PEC led to a significant reduction (P<0.001) in milk yield. Conversely, body weight increased significantly (P<0.001) from 2021 to 2022 and an increase in PCV caused a significant (P<0.001) reduction in body weight. Elevated EPG and OPG led to significant (P<0.01) reductions in body weight but the interaction between EPG and OPG has a significant positive (P<0.001) impact on body weight. Overall, the findings suggest that milk yield and body weight of Mafriwal cattle were significantly affected by parasitism thus necessitating the implementation of parasite control approaches.

Constructing and Validating Estimation Models for Individual-Tree Aboveground Biomass of Salix suchowensis in China

Biomass serves as a crucial indicator of plant productivity, and the development of biomass models has become an efficient way for estimating tree biomass production rapidly and accurately. This study aimed to develop a rapid and accurate model to estimate the individual aboveground biomass of Salix suchowensis. Growth parameters, including plant height (PH), ground diameter (GD), number of first branches (NFB), number of second branches (NSB) and aboveground fresh biomass weight (FW), were measured from 892 destructive sample trees. Correlation analysis indicated that GD had higher positive correlations with FW than PH, NFB and NSB. According to the biological features and field observations of S. suchowensis, the samples were classified into three categories: single-stemmed type, first-branched type and second-branched type. Based on the field measurement data, regression models were constructed separately between FW and each growth trait (PH, GD, NFB and NSB) using linear and nonlinear regression functions (linear, exponential and power). Then, multiple power regression and multiple linear regression were conducted to estimate the fresh biomass of three types of S. suchowensis. For the single-stemmed plant type, model M1 with GD as the single parameter had the highest adj R2, outperforming the other models. Among the 16 constructed biomass-estimating equations for the first-branched plant type, model M32 FW = 0.010371 × PH1.15862 × GD1.250581 × NFB0.190707 was found to have the best fit, with the highest coefficient of determination (adj R2 = 0.6627) and lowest Akaike Information Criterion (AIC = 5997.3081). When it comes to the second-branched plant type, the best-fitting equation was proved to be the multiple power model M43 with PH, GD, NFB and NSB as parameters, which had the highest adj R2 value and best-fitting effect. The stability and reliability of the models were confirmed by the F-test, repeated k-fold cross-validation and paired-sample t-tests. The models developed in this study could provide efficient tools for accurately estimating the total aboveground biomass for S. suchowensis at individual tree levels. The results of this study could also be useful for optimizing the economic productivity of shrub willow plantations.

Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement.

The objective of this investigation is to explore the capability of baseline 18F-FDG PET/CT radiomics to predict the prognosis of diffuse large B-cell lymphoma (DLBCL) with extranodal involvement (ENI). 126 patients diagnosed with DLBCL with ENI were included in the cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to refine the optimum subset from the 1328 features. Cox regression analyses were employed to discern significant clinical variables and conventional PET parameters, which were then employed with radiomics score to develop combined model for predicting both progression-free survival (PFS) and overall survival (OS). The fitness and the predictive capability of the models were assessed via the Akaike information criterion (AIC) and concordance index (C-index). 62 patients experienced disease recurrence or progression and 28 patients ultimately died. The combined model exhibited a lower AIC value compared to the radiomics model and SDmax/clinical variables for both PFS (507.101 vs. 510.658 vs. 525.506) and OS (215.667 vs. 230.556 vs. 219.313), respectively. The C-indices of the combined model, radiomics model, and SDmax/clinical variables were 0.724, 0.704, and 0.615 for PFS, and 0.842, 0.744, and 0.792 for OS, respectively. Kaplan--Meier curves showed significantly higher rates of relapse and mortality among patients classified as high-risk compared to those classified as low-risk (all P < 0.05). The combined model of clinical variables, conventional PET parameters, and baseline PET/CT radiomics features demonstrates a higher accuracy in predicting the prognosis of DLBCL with ENI.

Observational Constraints on the Parameters of Hořava–Lifshitz Gravity

AbstractThis study investigates the accelerated cosmic expansion within the Hořava–Lifshitz (HL) Model. To constrain the cosmological parameters of this model, 17 Baryon Acoustic Oscillation points, 31 Cosmic Chronometer points, 40 Type Ia Supernovae points, 24 quasar Hubble diagram points, and 162 Gamma Ray Bursts points, along with the latest Hubble constant measurement (R22) are incorporated. is treated as a free parameter to extract and using late‐time datasets, aiming for optimal fitting values in each model. Treating as free improves precision, reduces bias, and enhances dataset compatibility. The obtained values of and are compared to the model, showing consistency with previous estimates from Planck and SDSS studies. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) favor the Hořava–Lifshitz model, with the model having the lowest AIC. Additionally, and analyses are conducted to assess model preference. Validation using the reduced statistic indicates satisfactory fits for the Hořava–Lifshitz model, while recognizing as the preferred model. Extensions of the analysis warrant further investigation.

P-285 Serum progesterone level may not impact ongoing pregnancy rate (OPR) in patients with endometriosis undergoing frozen-warmed-blastocyst transfer employing hormone replacement treatment (HRT) with subcutaneous progesterone

Abstract Study question Do serum progesterone (P4) levels on frozen embryo transfer (FET)-1 day impact OPRs in patients with endometriosis undergoing HRT-FET with subcutaneous progesterone (sc P)? Summary answer In HRT cycles employing sc P, serum P4 levels on FET-1 day do not affect OPRs in patients with or without endometriosis. What is known already In HRT-FET cycles employing vaginal P, reproductive outcomes are improved by serum P4 monitoring and employing a rescue protocol, if needed. However, there is paucity of data for the need of serum P4 monitoring in HRT cycles employing sc P. Endometriosis is associated with P4 resistance. There is only one study evaluating the impact of serum P4 levels on reproductive outcomes in patients with endometriosis; in this study lacking a control group and employing vaginal P gel 90 mg/12 hours supplemented by daily 50 mg intramuscular P, a threshold of 37.1 ng/ml has been reported for improved live birth rates. Study design, size, duration Retrospective cohort study. 451 consecutive patients undergoing their first HRT- vitrified warmed blastocyst transfer during November 2021-June 2023 at Anatolia IVF Centre were included. 44 patients had endometriosis with surgical and/or ultrasound findings and the remaining 407 patients did not harbor endometriosis. The presence of adenomyosis was an exclusion criterion. Following 10-12 days of 6 mg/day estradiol valereate use, sc P 25 mg/12hours (Prolutex, Ibsa) was administered. The primary outcome measure was OPR. Participants/materials, setting, methods Serum P4 levels were measured 4-5 hours after morning administration of sc P on FET-1 day. The impact of serum P4 on OPRs was assessed at various percentiles adjusting for female age, body mass index, prior birth, preimplantation genetic testing for aneuploidy, number/grade of blastocysts transferred. Multivariate regression models were performed in the whole cohort incorporating polynomial P4 terms and endometriosis interactions; optimal model for OPR prediction was selected using the lowest Akaike Information Criteria. Main results and the role of chance Patients with (27/44, 61.4%) and without (214/407, 52.6%) endometriosis had comparable OPRs. In the endometriosis group, the 10th, 50th, and 90th percentiles of serum P4 were 15.6, 25.1, and 35.4 ng/ml, respectively, compared to 14.9, 23.5, and 35.6 ng/ml in the non-endometriosis group. In the endometriosis group, the OPRs were 60.0%, 66.7%, 56.3%, and 60.0% for the &amp;lt;10p, 10-50p, 50-90p, &amp;gt;90p groups, respectively. These figures were 46.3%, 50.3%, 56.8%, and 51.2%, in the non-endometriosis group, respectively. In the endometriosis group, taking the 50-90p group as the reference, the adjusted odds ratios (aORs) for OPR of &amp;lt; 10p, 10-50p, and &amp;gt;90p groups were 1.24 [0.03, 50.45], 1.77 [0.31, 10.24], and 2.08 [0.05, 80.23], respectively. In the non-endometriosis group, the respective aORs were 0.85 [0.40, 1.79], 0.80 [0.49, 1.29], and 0.72 [0.34, 1.50]. In the multivariate regression analysis to determine the optimal model, neither serum P4 levels (aOR for linear term: 1.11 [0.98, 1.26]; for quadratic term: 0.998 [0.996, 1.00]) nor the diagnosis of endometriosis (OR: 1.48 [0.74, 2.98]) significantly predicted ongoing pregnancy after adjusting for confounders. Additionally, the optimal model did not include the interaction term, suggesting that endometriosis does not significantly modify the impact of serum P4 levels on OPRs. Limitations, reasons for caution The study’s limitations include retrospective study design, a limited sample size of the endometriosis cohort, absence of laparoscopy for endometriosis exclusion in the non-endometriosis group, and lack of data on endometriosis phenotypes. Wider implications of the findings In HRT-FET cycles employing sc P 25 mg/12hours neither serum P4 nor the presence of endometriosis have an impact on OPRs. Endometriosis may not modify the impact of serum P4 on OPRs suggesting a lack of P resistance in such cycles or the overcoming of P resistance by this regimen. Trial registration number not applicable

30-Day Risk Score for Mortality and Stroke in Patients with Carotid Artery Stenosis Using Artificial Intelligence Based Carotid Plaque Morphology

The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology. Patients with a CT angiography head/neck between 2010-2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each CT was used to create a 3D image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with receiver operating characteristic curves (AUC). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke. We created three models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, PAD, hyperlipidemia, BMI, COPD, and history of TIA/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, perivascular adipose tissue, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC. Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model have a 20% chance of stroke/death. Further prospective studies are needed to validate our findings.

Application of Geographically Weighted Logistic Regression in Modeling the Human Development Index in East Java

Main Objectives: pinpoint the factors influencing HDI, taking into consideration location and spatial factors. Problems: The Human Development Index (HDI) in East Java often fails to reflect actual conditions accurately, as disparities exist among districts and cities, with some falling below government expectations. Novelty: GWLR extends logistics regression by incorporating spatial factors, allowing for the identification of regional differences and influential factors affecting HDI based on actual data. Methods: To address this issue, the Geographically Weighted Logistic Regression (GWLR) method is employed. The independent variables used are Expected Years of Schooling (X1), Open Unemployment Rate (X2), and Morbidity Rate ( X3) in 2021, while dependent variable is the Human Development Index (Y). Finding/Results: The study reveals that GWLR provides a superior model compared to Ordinary Logistic Regression, indicated by a lower Akaike Information Criterion (AIC) of 28.72. Additionally, the GWLR model with Fixed Gaussian Kernel weights outperforms other weighting methods. At 90% confidence level, the significant variables influencing HDI are expected years of schooling (X1) and the open unemployment rate (X2). Given the relatively low HDI in Indonesia, the East Java Government should focus on improving these key areas to enhance HDI across districts and cities in the region.

Application of Geographically Weighted Logistic Regression in Modeling the Human Development Index in East Java

Main Objectives: pinpoint the factors influencing HDI, taking into consideration location and spatial factors. Problems: The Human Development Index (HDI) in East Java often fails to reflect actual conditions accurately, as disparities exist among districts and cities, with some falling below government expectations. Novelty: GWLR extends logistics regression by incorporating spatial factors, allowing for the identification of regional differences and influential factors affecting HDI based on actual data. Methods: To address this issue, the Geographically Weighted Logistic Regression (GWLR) method is employed. The independent variables used are Expected Years of Schooling (X1), Open Unemployment Rate (X2), and Morbidity Rate ( X3) in 2021, while dependent variable is the Human Development Index (Y). Finding/Results: The study reveals that GWLR provides a superior model compared to Ordinary Logistic Regression, indicated by a lower Akaike Information Criterion (AIC) of 28.72. Additionally, the GWLR model with Fixed Gaussian Kernel weights outperforms other weighting methods. At 90% confidence level, the significant variables influencing HDI are expected years of schooling (X1) and the open unemployment rate (X2). Given the relatively low HDI in Indonesia, the East Java Government should focus on improving these key areas to enhance HDI across districts and cities in the region.

Modeling Cost-Estimation Factors for Public Building Projects with Hybrid Approach in Addis Ababa

Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes.

#1486 Looking for a new role of known players: the additional value of plasmatic C3 and C4 in predicting IgA Nephropathy prognosis, an observational study

Abstract Background and Aims IgA Nephropathy (IgAN) is the most common glomerular disease worldwide [1]. Complement system activation through the alternative and lectin pathways is crucial in its pathogenesis and leads to worse outcome, eliciting the need for (new) biomarkers to assess risk of disease progression and potential response to new targeted treatments [2, 3]. Only few works have correlated serum C3 and C4 with disease activity and prognosis [4, 5]. Our work explores the potential advantages of incorporating serum C3 and C4 levels into a standardized prognostic model to assess kidney outcome in IgAN. Method This retrospective study, conducted at Sant'Orsola University Hospital in Bologna, investigated the prognostic value of serum complement C3 and C4 in patients with IgAN. The primary outcome was defined as a 40% decline in eGFR or the onset of kidney failure. The study aimed to evaluate whether the addition of serum C3 and C4 to an established predictive model, incorporating variables from the International IgA Prediction Tool (IntIgAPT), enhances the accuracy of outcome prediction. We computed the measures of goodness of fit for each model: Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), Nagelkerke R2 test (NR2) that depicts the% explained variation of the outcome based on the set of variables included, and Likelihood Ratio Test (LRT); discrimination with the c-index. Furthermore, we aimed to evaluate the characteristics of patients stratified by baseline serum C3 levels (Low &amp;lt;90 mg/dl, Medium 90-140 mg/dl, High &amp;gt;140 mg/dl). Results 101 patients were included in the cohort. 90% were Caucasian and 65.3% were male with a mean age of 42.6 years. As reported in Table 1, mean eGFR and median proteinuria levels at baseline were 70.4 ml/min/1.73 m² and 1.04 g/24 h respectively. Mean serum C3 was 114.9 mg/dL. The Low group had lower values of eGFR at baseline (47.0 vs 75.67 and 68.64 ml/min/1.73 m², p = 0.015), while the Medium group had lower values of uProt (0.75 vs 1.81 and 1.46 g/24 h, p = 0.004). During a median 54.28 months follow-up, the Low group exhibited significantly higher primary outcome incidence (16.3 events vs 2.9 and 1.7 events x 100 pts/year, p = 0.003). We computed a first model (M1) based upon IntIgAPT variables and then a second model (M2) as the same of M1 plus serum C3 and C4 at baseline (Table 2). C3 showed a negative association with outcome (OR 0.88, CI 0.78 to 0.99, p = 0.05) whereas C4 had a positive one (OR 1.21, CI 1.02 to 1.48, p = 0.05). M2 had an improved prediction accuracy than M1, as evidenced by lower AIC (49.1 vs 53.8), higher c-index (0.74 vs 0.65), and NR2 (0.76 vs 0.65). Likelihood ratio test was significant between the two (p = 0.008). Conclusion The inclusion of serum C3 and C4 in a validated prognostic model enhances prediction accuracy and improves the percentage of events explained, highlighting complement's inherent informativeness. In our population lower C3 and higher C4 levels were associated with poorer prognosis in IgAN, characterizing a more ‘Complement-Pathic’ subset of patients. This result expands the current evidence, for the first time to our knowledge, in a Caucasian-prevalent study population.

SQEAIR: an Improved Infectious Disease Dynamics Model

The Covid-19 pandemic poses a significant threat to human health and life. Timely and accurate prediction of the epidemic’s trajectory is crucial for devising effective prevention and control strategies. Traditional infectious disease models may not capture the complexity of modern epidemics, especially when governments implement diverse policies. Drawing from China’s epidemic prevention strategies and Covid-19 transmission characteristics, this study introduces two distinct categories quarantined cases and asymptomatic cases to enhance the traditional SEIR model in depicting disease dynamics. To address the intricate nature of prevention and control efforts, the quarantined cases are further segmented into three subgroups: exposed quarantined, asymptomatic quarantined, and infected quarantined cases. Consequently, a novel SQEAIR model is proposed to model the dynamics of Covid-19. Evaluation metrics such as the Akaike information criterion (AIC) and Absolute Percentage Error (MAE) are employed to assess the efficacy and accuracy of both the newly proposed and traditional models. By fitting the models to the number of infected cases in Shanghai (March to May 2022) and Guangzhou (November 2022), it was observed that the SQEAIR model exhibited a lower AIC value compared to the SEIR model, indicating superior fitting accuracy for Covid-19 infections. Moreover, the high accuracy of the SQEAIR model enabled precise predictions of confirmed cases in Guangzhou. Leveraging the SQEAIR model, various parameters were tested to simulate the impact of different influencing factors, enabling the evaluation of defense strategies. These findings underscore the effectiveness of key epidemic control measures, such as quarantining exposed cases, in enhancing public health and promoting awareness of personal protection.

Abstract PO4-13-02: Molecular Expression Assays Improve the Prediction of Local and Invasive Local Recurrence after Breast Conserving Surgery for DCIS

Abstract Introduction Ductal carcinoma in situ (DCIS) is routinely treated with adjuvant radiotherapy (RT) after breast conserving surgery (BCS) in order to reduce the risk of local recurrence (LR) and invasive LR. Nomograms based on clinicopathological features (CPF) and molecular expression assays have been developed in an effort to provide individualized risk estimates and personalize decision-making. However, molecular assays are costly and it remains unclear if they provide more accurate recurrence risk estimates compared to algorithms based on CPF alone. We examined the impact of the 12-Gene DCIS Score (DS) and the 21-Gene Recurrence Score (RS) molecular expression assays, in addition to CPF, on the accuracy of predicting 10-year LR and invasive LR risk compared to predicted estimates based on CPF alone. In addition, we examined if a model including the 21-Gene RS improves the 10-year predicted risks of invasive LR after BCS for DCIS compared to estimates based on the DS+CPF or CPF alone. Methods We used a population-based cohort diagnosed with pure DCIS treated with BCS +/- RT from 1994-2003. All cases had expert pathology review providing contemporary assessment of diagnosis, margin status, margin width, multifocality, presence and extent of comedo necrosis, subtype, nuclear grade, and tumor size. For each case, a representative tissue block or unstained slide was sent to measure the 12-Gene DS and 21-Gene RS. Predictive models were developed using multivariable Cox regression analyses with backward selection and included all CPF, treatment with RT, and interactions. The performance of each model was evaluated based on c-statistic, -2log likelihood estimate (-2LLE), and Akaike information criterion (AIC). Calibration was performed using bootstrap resamples, with replacement. We compared the performance of the best model derived from CPF alone, the 12-Gene DS with CPF, and the 21-Gene RS with CPF on their ability to predict the 10 year risks of LR and invasive LR measured against outcomes observed in the cohort. Results The population-based cohort includes 1226 women, 514 were treated with BCS alone and 712 were treated with BCS + RT. Median age was 56 years. Median follow-up was 10 years. Fifty-two percent of tumors were between 1 and 2.5 cm, 35% were ≤1cm, and 13% were &amp;gt;2.5 cm. Comedo necrosis was present in 68%, and nuclear grade was low, moderate, and high in 7%, 54%, and 39%, respectively. Margins were negative in 90.5% of cases (N=1109). The 12-Gene DS was low, intermediate, and high in 53.5%, 20.9%, and 25.6% and the 21-Gene RS was &amp;gt;25 in 30% of patients. 194 women (15.8%) experienced ipsilateral LR as a first event; 112 were invasive LR. Models including either the DS or RS expression assays performed better in predicting the 10-year risk of LR after BCS compared to the model based on CPFs alone, demonstrating higher c-statistics (0.705, 0.699, and 0.662, respectively), lower AIC and lower -2LLE. The two molecular-based predictive models also performed better in predicting the risk of invasive LR compared to CPF model, although with smaller differences in c-statistics (0.684, 0.683, and 0.667, respectively), AIC or -2LLE. The predictive model based on the 21-Gene RS with CPF did not perform better in the prediction of the 10 year risk of invasive LR compared the 12-Gene DS + CPF model. All models were well calibrated. Conclusion The predictive model based on the 12-Gene DS with CPF more accurately predicted the 10-year risk of LR and invasive LR after BCS compared to model based on CPF alone. Inclusion of the 21-Gene RS with CPF did not improve the prediction of the 10-year risk of LR or invasive LR. This suggests that nomograms that include the 12-Gene assay with CPF provide more accurate individualized estimates of recurrence risk after BCS and can help improve personalized decision-making in the management of DCIS. Citation Format: Ezra Hahn, Rinku Sutradhar, Lawrence Paszat, Lena Nguyen, Danielle Rodin, Sharon Nofech-Mozes, Sabina Trebinjac, Cindy Fong, Eileen Rakovitch. Molecular Expression Assays Improve the Prediction of Local and Invasive Local Recurrence after Breast Conserving Surgery for DCIS [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-02.

Abstract PO5-11-03: Associations among BMI and patient-reported body image dissatisfaction after breast reconstruction

Abstract INTRODUCTION Breast reconstruction surgery helps women restore their physical appearance and contributes to overall well-being. We assessed body image concerns in the context of breast reconstruction using patient-reported outcome measures: Body Image Scale (BIS), Brief Symptom Inventory (BSI), Appearance Schemas Inventory (ASI-R), and BREAST-Q. Since obesity is known to be associated with body image dissatisfaction1, body size may influence a patient’s adjustment to bodily changes arising from breast cancer and its treatment. Thus, we investigated the associations among BMI and patient-reported body image dissatisfaction after immediate autologous breast reconstruction. MATERIAL AND METHODS Patients undergoing breast reconstruction surgery were enrolled in an IRB approved study from 2011 to 2014 at The University of Texas MD Anderson Cancer Center. Participants (N=44) included patients who underwent immediate autologous (DIEP/TRAM) reconstruction and completed the BIS, BSI, ASI-R, and BREAST-Q, both preoperatively and postoperatively at 12+ months. Descriptive statistics including means, standard deviations, and medians were used to summarize continuous variables, while frequencies and percentages were used to summarize the categorical clinical and demographic characteristics. Univariate analyses were conducted to identify potential factors among preoperative variables for subsequent multivariable regression analysis: age, race, ethnicity, BMI, laterality, BIS, BSI, ASI-R, BREAST-Q psychosocial wellbeing score (PWBS), and BREAST-Q satisfaction with breast score (SWBS). Factors with a t test p-value≤0.2 were considered potential predictive variables for the postoperative dependent variables BIS, BREAST-Q PWBS, and BREAST-Q SWBS. Multivariable linear regression models were used to assess the relationship between the dependent and the selected predictive variables. A multivariable linear regression model was fitted using stepwise selection procedure, and the model with the highest adjusted R-squared value and the lowest Akaike information criterion (AIC) was chosen. All tests were two-sided with p-value&amp;lt; 0.05 considered statistically significant, and for each significant predictor, we report the unstandardized coefficient β. Analyses were performed using SPSS 29.0.0 and SAS 9.4. RESULTS Results from the selected models are summarized in Table 1. Age and BMI were significant predictors for postoperative BIS score. Older patients and those with BMI categorized as overweight and obese tend to feel less dissatisfied with their body image after immediate autologous breast reconstruction compared to patients with healthy BMI. Preoperative BSI anxiety was identified as significant predictor for postoperative BREAST-Q PWBS, i.e., patients with higher preoperative anxiety scores tend to have lower postoperative BREAST-Q PWBS. Preoperative BSI anxiety and laterality were significant predictors for postoperative BREAST-Q SWBS. Patients with higher preoperative anxiety scores tend to be less satisfied with their breasts after immediate autologous reconstruction and patients who underwent unilateral reconstruction tend to be less satisfied with their breasts postoperatively compared to patients with bilateral reconstruction. CONCLUSION Women with higher BMI may experience improved body image after undergoing immediate autologous breast reconstruction, as compared to women with healthy BMI. The use of BIS may provide more insight to plastic surgeons compared to relying solely on the body image correlates provided by the BREAST-Q. Moreover, body image counseling can be beneficial for individuals across all BMI categories, including those with healthy BMI. REFERENCES 1. Weinberger, N.A., et al., Body Dissatisfaction in Individuals with Obesity Compared to Normal-Weight Individuals: A Systematic Review and Meta-Analysis. Obes Facts, 2016. 9(6): p. 424-441. Table 1: Table for reporting selected multivariable linear regression models; (a) Postoperative BIS with explanatory variables age and BMI category, (b) Postoperative BREAST-Q PWBS with explanatory variable Preoperative BSI anxiety score, (c) Postoperative BREAST-Q SWBS with explanatory variables Preoperative BSI anxiety score and laterality. Citation Format: Sara Bouhali, Nassima Noufail, Tzuan Chen, Mary Catherine Bordes, Greg Reece, Mia Markey, Fatima Merchant, Deepti Chopra. Associations among BMI and patient-reported body image dissatisfaction after breast reconstruction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-11-03.

Impact of climate change on dengue incidence in Singapore: time-series seasonal analysis

ABSTRACT This study aimed to identify the meteorological factors that contribute to dengue epidemics. The monthly incidence of dengue was used as the outcome variable, while maximum temperature, humidity, precipitation, and sunshine hours were used as independent variables. The results showed a consistent increase in monthly dengue cases from 2013 to 2021, with seasonal patterns observed in stationary time-series data. The ARIMA (2, 1, 3) × seasonal (0, 1, 2)12 model was used based on its lowest Akaike Information Criterion (AIC) values. The analysis revealed that a 1-unit increase in rainfall was positively correlated with a small 0.062-unit increase in dengue cases, whereas a 1-unit increase in humidity was negatively associated, leading to a substantial reduction of approximately 16.34 cases. This study highlights the importance of incorporating weather data into national dengue prevention programs to enhance public awareness and to promote recommended safety measures.

An Accident Prediction Model Based on ARIMA in Kuala Lumpur, Malaysia, Using Time Series of Actual Accidents and Related Data

Recently, there has been an emerging trend to analyse time series data and utilise sophisticated tools for optimally fitting time series models. To date, Malaysian industrial accident data is underutilised and lacks informative records. Thus, this paper aims to investigate the Malaysian accident database and further evaluate the optimal forecasting models in accident prediction. The model’s input was based on available data from the Department of Occupational Safety and Health, Malaysia (DOSH), from 2018 until 2021, with 80% of the dataset to train the models and the remaining 20% for validation. The negative binomial and Poisson distribution prediction showed a mean absolute percentage error (MAPE) of 33% and 51%, respectively. It indicated that the negative binomial performed better than the Poisson distribution in accident frequency prediction. The available time series accident data were gathered for four years, and stationarity was checked in R Studio software for the Augmented Dickey-Fuller test. The lowest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other error values were used to justify the best model, which was the ARIMA(2,0,2)(2,0,0)(12) model. The ARIMA models were considered after the data showed autocorrelation. The MAPE for both ARIMA in R and manual time series were 40% and 49%, respectively. Therefore, the accident prediction by using R Studio would outperform the manually negative binomial and Poisson distribution. Based on the findings, industrial safety practitioners should report accidents to DOSH truthfully in the era of digitalisation. It could enable future data-driven accident predictions to be carried out.

Abstract P229: Cross-Sectional Relationship Between Posttraumatic Stress Disorder and Atherosclerotic Disease Assessed by Coronary Calcium Score: A Counterfactual Model of Older Male Twins

Background: Posttraumatic stress disorder (PTSD) is a risk factor for cardiovascular disease. However, the causal relationship between PTSD and atherosclerotic burden is controversial, leaving uncertain whether atherosclerosis is implicated in the association between PTSD and cardiovascular events. Objective: To test the cross-sectional relationship between lifetime PTSD and coronary atherosclerotic burden assessed by calcium score by comparing PTSD-discordant twin pairs, a design that inherently controls for familial and early environmental factors. Methods: We examined 218 male twins from the Vietnam Era Twin Registry, who underwent coronary calcium score assessment with computed tomography as part of the Emory Twin Study Follow-Up study. Lifetime history of PTSD was obtained with the Structured Clinical Interview and PTSD symptom severity with the Clinician-Administered PSTD Scale (CAPS) for DSM-IV diagnostic criteria. Coronary calcium scoring was performed by the Agatston Method as a measure of atherosclerotic burden. The relationship between PTSD and calcium score was tested across individuals and within PTSD-discordant twin pairs as a counterfactual model for genetic and early environmental factors. Initially, 2 linear mixed models were built according to the exposure variable (PTSD clinical diagnosis or symptoms scale), with log-transformed coronary calcium score as the dependent variable. We defined a priori that the model with the lowest Akaike Information Criteria (AIC) would be the primary analysis to drive the conclusion. Based on a priori direct acyclic graph decision process, we defined the need to adjust for income and education as potential confounders and not to adjust for traditional risk factors, considering their potential roles as mediators. Results: Of the 218 twins analyzed, the median age was 68 (IQR 67-70) years, and the calcium score had a median of 114 (IQR 5.2-347). There were 88 twin pairs (30 monozygotic), 26 discordant for the binary diagnosis of PTSD, and 40 discordant for the ordinal PTSD symptom score (within-pair differences in scale points ranged from zero to 48.5). The model with the best AIC was the one with the PTSD score, and showed no increment of log-calcium score as the PTSD symptoms increased, both between individuals (Beta coefficient = - 0.005; 95% CI = -0.02 to + 0.03) and within twin pairs (Beta coefficient = - 0.010; 95% CI = -0.03 to + 0.01). Conclusion: The present analysis does not support a relationship between PTSD and coronary atherosclerotic burden. The link between PTSD and cardiovascular events may occur through other pathways, such as microvascular disease.

Relationship Between Tenofovir Diphosphate Concentrations in Dried Blood Spots and Virological Outcomes After Initiating Tenofovir-Lamivudine-Dolutegravir as First-Line or Second-Line Antiretroviral Therapy.

Tenofovir diphosphate (TFV-DP) concentration in dried blood spots is a marker of long-term adherence. We investigated the relationship between TFV-DP concentrations and virological outcomes in participants initiating tenofovir-lamivudine-dolutegravir (TLD) as first-line or second-line antiretroviral therapy. Three primary care clinics in Khayelitsha, Cape Town, South Africa. We conducted a post hoc analysis of 2 randomized controlled trials of participants initiating TLD. TFV-DP concentrations and viral loads were measured at 12, 24, and 48 weeks. Multivariable logistic regression was performed to assess the association with virological suppression (<50 copies/mL) per natural logarithm increase in TFV-DP concentration. Generalized estimating equations with logit link were used to assess associations with virological rebound. The Akaike Information Criterion and Quasi-likelihood Information Criteria were used to compare models built on continuous TFV-DP data to 4 previously defined concentration categories. We included 294 participants in the analysis, 188 (64%) of whom initiated TLD as second-line therapy. Adjusted odds ratios (95% CIs) of virological suppression were 2.12 (1.23, 3.75), 3.11 (1.84, 5.65), and 4.69 (2.81, 8.68) per natural logarithm increase in TFV-DP concentration at weeks 12, 24, and 48, respectively. In participants with virological suppression at week 12, the adjusted odds ratio for remaining virologically suppressed was 3.63 (95% CI: 2.21 to 5.69) per natural logarithm increase in TFV-DP concentration. Models using continuous TFV-DP data had lower Akaike Information Criterion and Quasi-likelihood Information Criteria values than those using categorical data for predicting virological outcomes. TFV-DP concentrations in dried blood spots exhibit a dose-response relationship with viral load. Analyzing TFV-DP concentrations as continuous variables rather than conventional categorization may be appropriate.

LABS score– a prognostic tool for FOLFOX4-treated advanced hepatocellular carcinoma and real-world efficacy: a single-center retrospective study

BackgroundNo widely used prognostic tool exists to demonstrate the benefit of oxaliplatin plus 5-fluorouracil/leucovorin (FOLFOX4) in patients with advanced hepatocellular carcinoma (HCC). We aimed to establish a prognostic score and demonstrate the real-world efficacy of FOLFOX4 chemotherapy in Thai patients.MethodsBetween August 2017 and December 2021, we identified 58 FOLFOX4-treated patients with HCC. Overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) were assessed. The prognostic score was constructed by stepwise Cox proportional hazards regression analysis to select variables for the best model with the lowest Akaike information criterion from all potential variables.ResultsForty-four patients (76%) received FOLFOX4 as first-line therapy. The ORR in the entire cohort was 8.6%, and the disease control rate was 29.3%. The PFS and OS were 3.7 and 4.8 months, respectively. Four clinically relevant variables were included in the new prognostic score to predict 6-month OS: L, the presence of lung metastasis; A, alcoholic cirrhosis; B, elevated total bilirubin level; and S, sorafenib-naïve status. Using the LABS score, patients were classified into low-, intermediate-, and high-risk groups, demonstrating OS values of 9.3, 4.2, and 2.1 months, respectively (p < 0.0001). The C-index and area under the receiver-operating characteristic curve of the score were 0.71 and 0.73, respectively.ConclusionsThe proposed LABS score could discriminate patients who would derive benefit from FOLFOX4 chemotherapy. FOLFOX4 chemotherapy is an option for patients who cannot receive immunotherapy and targeted therapy, particularly those with a low-risk score. However, further validation of this model via larger cohorts is warranted.

MASH Resolution Index: development and validation of a non-invasive score to detect histological resolution of MASH

BackgroundDynamic changes in non-invasive tests, such as changes in alanine aminotransferase (ALT) and MRI proton-density-fat-fraction (MRI-PDFF), may help to detect metabolic dysfunction-associated steatohepatitis (MASH) resolution, but a combination of non-invasive...

Systematic development and validation of a predictive model for major postoperative complications in the Peri-operative Quality Improvement Project (PQIP) dataset.

Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre-operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. Complete patient records from the UK Peri-operative Quality Improvement Programme dataset were analysed. Major complications were defined as Clavien-Dindo grade ≥ 2 for novel models. In a 75% train:25% test split cohort, we developed a pipeline of increasingly complex models, prioritising pre-operative predictors using Least Absolute Shrinkage and Selection Operators (LASSO). We defined the best model in the training cohort by the lowest Akaike's information criterion, balancing accuracy and simplicity. Of the 24,983 included cases, 6389 (25.6%) patients developed major complications. Potentially modifiable risk factors (pain, reduced mobility and smoking) were retained. The best-performing model was highly complex, specifying individual hospital complication rates and 11 patient covariates. This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re-prioritised predictor variables and identified hospital-level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best-performing model does, however, highlight the need for a step-change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems.