Backward Stepwise Regression Research Articles

#2271 Cardiovascular event prediction in kidney transplant recipients: a new risk score derivation and internal validation

Abstract Background and Aims Kidney transplant recipients (KTRs) have a cardiovascular (CV) risk 3 to 5-fold higher than that of the general population.CV disease represents the main cause of death in KTRs, both due to the presence of traditional risk factors and factors strictly related to transplant. The main CV risk calculators proved to be poorly predictive in the KTR population. We, therefore, aimed to compare the performance of CV risk scores already in use and to develop and validate a CV risk score in our KTRs population. Method Our analysis included 371 adult KTRs in follow-up at the outpatient clinic from 1 January 2015 to 31 December 2016. The composite outcome was the occurrence of MACE. We compared the performance of three main CV risk calculators (Framingham Risk Score-FRS-, INDANA score and PORT score). Thereafter, we built a predictive model selecting study variables by backward stepwise Cox regression. The risk score for each variable was weighted according to the HR of the final multivariable model. Internal and external validation of the prediction score and its discriminative capacity was assessed via area under the curve (AUC) curve, and the calibration via the Hosmer–Lemeshow test. Results After a mean follow-up of 68 months, CV events occurred in 71 (19%) KTRs. FRS, INDANA and PORT were demonstrated to have low predictive power in our population (Fig. 1). The accuracy did not improve after adjustment for immunological and transplant-related variables. Therefore, we derived a model including significative variables at multivariate analysis: age, dialysis vintage, systolic blood pressure, eGFR and 24-hour proteinuria (Akaike information criteria 713). The model discrimination was good (Harrel's c: 0.73). According to the hazard ratio, 3 points have been attributed to age higher than 60 years and 24 h proteinuria higher than 1 g/d, 2 points to dialysis vintage longer than 5 years, systolic blood pressure higher than 140 mmHg and eGFR less than 30 ml/min/1.73 m2. The new score demonstrated good discrimination (AUC 0.70, Fig. 2) and calibration (Hosmer-Lemeshow test 11.34, P = 0.12). The new score was internally validated by 10-fold cross-validation (mean AUC 0.70, 95% CI 0.60-0.77). For each point of the new score, the risk of the event increased by 40% and a score higher than three was associated with a 4-fold increased risk of composite endpoint. Conclusion We confirmed the poor predictive power of the known CV risk assessment tools in our real-world KTRs population. Moreover, their discriminatory power was not improved by adjustment for the occurrence of biopsy-proven rejection, HLA compatibility and donor-specific antibody. A new score for CV event prediction including traditional risk factors, such as age and systolic blood pressure, and variables related to renal function and the effects of previous chronic kidney disease, was developed and internally validated.

Comparison of in vitro membrane permeabilities of diverse environmental chemicals with in silico predictions

The parallel artificial membrane permeability assay (PAMPA) is widely used for estimating biomembrane permeabilities of experimental drugs in pharmaceutical research. However, there are few reports of studies using PAMPA to measure membrane permeabilities of chemicals of environmental concern (CECs) outside the pharmaceutical domain, many of which differ substantially from drugs in their physicochemical properties. We applied PAMPA methods simulating gastrointestinal (PAMPA-GIT) and blood-brain barrier (PAMPA-BBB) membranes under consistent conditions to 51 CECs, including some pharmaceuticals. A backward stepwise multivariate linear regression was implemented to explore the correlation between the differences of measured permeabilities from PAMPA-GIT and PAMPA-BBB and Abraham solute descriptors. In addition, a previously reported in silico model was evaluated by comparing predicted and measured permeability results. PAMPA-GIT and PAMPA-BBB experimental permeability results agreed relatively well. The backward stepwise multivariate linear regression identified excess molar refraction and polarizability to be significant at the 0.10 level in predicting the differences between PAMPA-GIT and PAMPA-BBB. The in silico model performed well – with predicted permeability of most compounds within two-fold of experimentally measured values. We found that CECs pose experimental challenges to the PAMPA method in terms of having lower solubility and lower stability compared to most drugs.

Sleep Quality and Interoception Are Associated with Generalized Anxiety in Baccalaureate Nursing Students: A Cross-Sectional Study.

Baccalaureate nursing students are at increased risk for anxiety and related mood disorders. We conducted a cross-sectional study to explore the relationships among anxiety symptoms measured by the Generalized Anxiety Disorder (GAD-7) questionnaire and lifestyle behaviors including habitual diet, sleep quality (Pittsburg Sleep Quality Index [PSQI]), and physical activity. Descriptive statistics were obtained for sample characteristics, and Pearson correlations and backward stepwise linear regression explored relationships between the GAD-7 scores, the Multidimensional Assessment of Interoceptive Awareness, version 2 (MAIA-2) subscales, and other variables. Sixty-eight students completed the survey, with 38% having moderate-to-severe anxiety. On average, respondents had moderate diet quality (Healthy Eating Index median 60/100 [range 51-75]), had high sleep quality (PSQI median 7/21 [range 4-10]), and were highly active, with a median of 43 (range 24-78) weekly metabolic equivalent (MET) hours. Sixty-seven out of 68 respondents indicated a willingness to change lifestyle behaviors; the most prevalent time-related factors were school and social commitments, with stress and financial constraints being reported among half or more of respondents. Regression analysis determined that PSQI (β = 0.446) and the MAIA-2 Not-Worrying subscale (β = -0.366) were significant (p < 0.001 for both) predictors of anxiety severity. These results indicate that mindfulness and sleep hygiene may be the most actionable foci for interventions to reduce anxiety in baccalaureate nursing students. This study was not registered as a clinical trial.

Neural Diversity and Decisions

PurposeDifferences in choices provide a window into the variety of human preferences and behaviors. Most non-trivial decisions recruit multiple regions of the brain with activity that shows substantial variation across individuals and also depends on personality traits and attitudes.MethodsThe present study measured nine physiologic factors and collected 15 types of psychological data while participants made decisions in standard tasks measuring risk aversion, patience, altruism, cooperation, generosity, trust, and trustworthiness. Robust predictors were defined as those that were statistically significant in both forward and backward stepwise regressions using all the collected independent variables.ResultsThe analysis showed that multiple measures of physiologic stress decrease cooperative behaviors while testosterone consistently increased antisocial behaviors. In addition, cognitive abilities robustly increased patience but decreased cooperation, while those more satisfied with their lives were more trustworthy. Participants scoring high on the personality trait of imagination were shown to be less altruistic and generous, while neuroticism increased prosociality.ConclusionThis study identified key drivers of behaviors that should be measured in decision-making experiments in order to capture the multiple factors that affect choices. We conclude with suggestions on how to establish causal relationships between the identified factors and decisions.

Abstract PO2-10-11: Impact of COVID-19 Pandemic on the Disease Volume, Severity and Clinical Complications at Diagnosis and During First Year of Advanced Breast Cancer: COVID19-ABC Quasi-Experimental Study

Abstract Background: The COVID-19 pandemic affected healthcare systems, tumbling cancer screening programs and increasing fear of infection among medical facilities users. We hypothesized that the pandemic negatively impacted the clinical outcomes of patients (pts) with advanced breast cancer (ABC). The COVID19-ABC study aims to assess the influence of the pandemic on disease volume, severity, and clinical complications at diagnosis and during 1st year of ABC. Methods: Single-center, retrospective, quasi-experimental study with historical control. We included women treated for unresectable/metastatic recurrent or stage IV de novo ABC in a Belgian cancer center. Pts were eligible if ABC was diagnosed between 1/Jun/2020-30/Nov/2021 - pandemic exposure (Exp) cohort - or between 1/Jun/2017-30/Nov/2018 - historical (Hist) cohort. We collected data from medical records for baseline characteristics and study outcomes: proportion of oligometastatic disease (OMD) at ABC diagnosis, and proportion of visceral crisis and disease-related serious clinical events during the 1st month and between 1-12 months after ABC diagnosis. We compared baseline variables between the two cohorts using χ2 test for frequencies and Wilcoxon rank-sum test for continuous variables. We performed a pre-planned subgroup analysis by year of pandemic, immunohistochemistry (IHC) subtype, and recurrent vs de novo ABC. Univariate and multivariate backward stepwise logistic regressions were used to determine the odds ratio (OR) and the 95% confidence interval (CI) of the association of exposure to the pandemic with study outcomes, controlling for confounders. Results: We included 150 pts, 74 in Exp and 76 in Hist cohort. Age and proportion of de novo ABC and triple-negative breast cancer (TNBC) were numerically higher, while comorbidities and Ki67 index were significantly higher among patients in the Exp cohort (Table 1). Pts in the Exp cohort had a significantly higher risk of visceral crisis during the 1st month of ABC (28.4% vs 15.8%; OR 4.24, 95% CI, 1.45-12.39, adjusted for histology grade and recurrent vs de novo), with similar trend between 1-12 months of ABC (19.4% vs 12.7%, OR 1.66, 95% CI, 0.67-4.13). Visceral crisis tended to be more common in patients diagnosed in the 2nd vs 1st year of the pandemic (35.7% vs 26.7%, p=0.50). We observed higher proportions of visceral crisis in the Exp cohort for pts with recurrent ABC (36.5% vs 17.7%, p=0.02), HER2+ (66.7% vs 10.0%, p=0.01), and TNBC (40.0% vs 18.2%, p=0.21) IHC subtypes. The most common indicators of visceral crisis in the Exp cohort were symptomatic brain metastases (33.3%), malignant hypercalcemia (28.6%), and liver metastases with hepatic tests abnormalities (23.8%). OMD at presentation (35.1% vs 36.8%, p=0.83) and disease-related serious clinical events during 1st month (31.1% vs 37.3%, p=0.42) and between 1-12 months (31.9% vs 34.2%, p=0.77) did not differ between cohorts. Conclusion: Our study reports a more clinically severe presentation of ABC during the pandemic compared to historical control. This emerged particularly in recurrences and in more aggressive disease subtypes such as HER2+ and TNBC, suggesting a later diagnosis of ABC during the pandemic. These findings require validation in a larger sample, and longer follow-up is needed to assess survival impact. Table 1. Baseline clinicopathologic characteristics at diagnosis of advanced breast cancer (missing values: 1 for menopausal status, 21 for grade). Citation Format: Diogo Martins-Branco, Véronique Debien, Michel Moreau, Giovanna Pinna, Ottavia Amato, François Cherifi, Guilherme Nader-Marta, Marianne Paesmans, Evandro de Azambuja. Impact of COVID-19 Pandemic on the Disease Volume, Severity and Clinical Complications at Diagnosis and During First Year of Advanced Breast Cancer: COVID19-ABC Quasi-Experimental Study [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 PO2-10-11.

Predictors of post-operative left atrioventricular valve regurgitation in pediatric patients with complete atrioventricular canal defects.

In infants with complete atrioventricular canal (CAVC) defects, post-operative left atrioventricular valve regurgitation (LAVVR) is a known major cause of morbidity and mortality and a common indication for re-operation. However, there is scarce data to identify risk factors for poor outcomes. Our study aims to find echocardiographic characteristics that predict post-operative LAVVR at discharge and 1-year follow-up. Retrospective cohort study of patients with initial CAVC repair at our hospital who were followed for 1 year between 2013 and 2022. Patients with major co-morbid conditions were excluded. Serial echocardiograms were reviewed. Anatomic details, quantitative and qualitative measure of LAVVR including the number of regurgitant jets, regurgitant jet length and vena contracta width, and ventricular function were collected. The time points measured include pre-operative transthoracic echocardiogram (TTE), post-operative transesophageal echocardiogram (PO-TEE), routine protocol based post-operative day 1 (POD1) TTE, discharge TTE and 1-year post-operative (1yPO) TTE. Paired t-tests, chi-square analysis, and linear regression analysis were performed comparing measured variables to LAVVR outcomes. Fifty-two patients were included; 92% had Trisomy 21. The majority were classified as Rastelli A (71%), others Rastelli C (29%). Only two patients had moderate or greater LAVVR pre-operatively. The mean age at repair was 125 ± 44 days. Pre-operative LAVVR was the only significant predictor of LAVVR severity at 1 year after backward stepwise regression. Of those with<moderate LAVVR on PO-TEE, 20% had worsening to≥moderate at discharge, but only 9% remained that way at 1 year. Of those with≥moderate LAVVR on PO-TEE, 40% improved to<moderate by 1 year. Two patients who worsened at 1 year, both secondary to likely cleft suture dehiscence. Only one patient required reoperation in the immediate post-operative period secondary to severe LAVVR due to suture dehiscence. Routine protocol-based POD1 echo did not have any association with altered outcomes. Pre-operative LAVVR was the only significant predictor of LAVVR severity at 1 year. A significant percentage (40%) of patient with≥moderate LAVVR on PO-TEE improved to<moderate by 1 year. Furthermore, routine protocol-based POD1 echo did not have any association with altered outcomes.

Prediction of Live Weight and Carcass Characteristics from Linear Body Measurements of Yearling Male Local Sheep

Measurements of the body structure in sheep are worthy of judging the quantitative features of meat and useful in developing appropriate selection requirements. The current study was aimed to predict live weight and hot carcass weight from linear body measurements of yearling male local sheep. 84 days feeding period fortnightly taken data on 24 local sheep for body weight, body length, heart girth, wither height, sub-sternal height, tail length, tail width, scrotal circumference, and scrotal length were analyzed to study the relationship between linear body measurements and body weight. At the end of the trial all sheep were slaughtered to measure the relationship between body measurements, and hot carcass weight. Microsoft Excel 2010 was used for data analysis. The relationships between the various body measurements were calculated using pearson's correlation coefficient. The backward stepwise multiple regression procedure was used for the determination of the most suitable model for the prediction of the live weight and hot carcass weight. Hot carcass weight was highly correlated (P&lt;0.01) with body weight and scrotal circumference. Besides, it was significantly (P&lt;0.05) correlated with tail width. Body weight was significantly (P&lt;0.05) correlated with all body measurements except tail length and scrotal length. It is concluded that the body weight of the local sheep can be predicted with heart girth, sub-sternal height and tail width; the equation is LW= -97.2 + 0.36HG + 2.1SBSH + 0.57TW with a better coefficient of determination; R2 = 0.55 and the hot carcass weight can be predicted with sub-sternal height and tail width; the equation is HCW= -75.66 + 1.75SBSH + 0.85TW with a coefficient of determination; R2 = 0.33. But, hot carcass can be predicted with body weight, the equation is HCW= -9.39+0.85BWT when weighing scales are affordable with a better coefficient of determination; R2= 0.557.

Exploring pathways to successful aging among older black lesbians and sexual minority women: A focus on life satisfaction and intersectional factors

Purpose Satisfaction with life is a core aspect of successful aging, which is influenced by a broad range of factors, including health, socioeconomic status, and social relationships. Black lesbians experience various social challenges, including racism, sexism, ageism, and heterosexualism, that may present as deterrents to aging successfully. To develop better policies and programs in support of successful aging among all adults, an understanding of the unique components associated with their intersecting identities must be explored. This study was carried out to gain a better understanding of how Black lesbians and other Black sexual minority women (BSMW) view successful aging within the context of life satisfaction. Methods Inferential statistics were used to determine relationships between satisfaction with life (SWLS-3) and selected independent variables. Backward stepwise regression was conducted to ascertain significant relationships of the study outcome variable. Results The respondents were 118 Black lesbians and other BSMW aged 50 and above. Independent t-tests showed that those living with a partner had a significantly higher SWLS-3. Backwards stepwise regression indicated the predictor variables for higher rates of SWLS-3 to be harmony in life, annual household income, and provider sensitivity/cultural competence to their patient being Black. Conclusion This study provides insight on factors increasing and diminishing satisfaction with life for aging Black lesbians and other BSMW. Future research should replicate this study with a more diverse and nationally representative sample, especially given the study limitations of having recruited a highly educated sample during a global pandemic.

Machine learning-based survival prediction nomogram for postoperative parotid mucoepidermoid carcinoma

Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I–IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model’s clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.

Risk factors related to the SARS-CoV-2 vaccine additional doses hesitancy among pregnant and non-pregnant people of reproductive age and partners: A Brazilian cross-sectional study.

The aim of this study was to assess the predictors of acceptance and hesitancy of additional doses of any SARS-CoV-2 (COVID-19) vaccine among pregnant or recently pregnant and non-pregnant people of reproductive age and partners in Brazil. We conducted an online cross-sectional study from June 2022 to April 2023 and invited women and partners between 18 and 49 years old to participate. We employed a snowball strategy to reach all potential eligible participants. Our primary outcome was the acceptance rate of the COVID-19 booster vaccine. We estimated the frequency and percentage for the three groups and compared categorical variables using the Chi-square test. Moreover, bivariate, backward stepwise regression, and subgroup analyses were performed to evaluate risk factors and predictors of COVID-19 vaccine booster hesitancy. We reported the effect size as OR with a 95% CI. We included 1487 participants, and among them, 334 (22.5%) were pregnant or recently pregnant people, 905 (60.8%) were non-pregnant people, and 247 (16.6%) were male partners. Pregnant and recently pregnant people showed greater hesitancy for the COVID-19 vaccine booster than non-pregnant people (28% vs 15%, P < 0.001) and male partners (28% vs 16%, P < 0.001). Non-pregnant women accepted the COVID-19 vaccine more often than pregnant or recently pregnant people (OR 1.75; 95% CI: 1.13-2.70). The associated factors to the reduced COVID-19 vaccine booster acceptance were family income between US$ 566-945.00 (54%), evangelic religion (65%), concern about vaccine safety (80%) and perceived common vaccine importance (93%). Pregnant people were more hesitant than non-pregnant people to accept the COVID-19 booster vaccine. Family income, religious beliefs, vaccine safety concerns, and perceived common vaccine importance were significant barriers to accepting COVID-19 booster vaccines. The impact of these factors was more evident among pregnant or recently pregnant people, emphasizing the harmful effect of misinformation among this vulnerable population.

Electrocardiographic predictors of response to sacubitril/valsartan therapy in heart failure with reduced ejection fraction

BackgroundSacubitril/valsartan (SV) is currently recommended as a first-line therapy in patients with heart failure and reduced ejection fraction (HFrEF) due to its significant clinical and prognostic benefit; however, not all patients respond to therapy and predictors of clinical response to SV remain under-studied. AimsTo identify electrocardiographic (ECG) predictors of response to SV therapy in HFrEF patients. MethodsA retrospective analysis of a hospital heart failure registry was undertaken. Consecutive HFrEF patients (New York Heart Association class II–III) on maximal-dose SV were studied. Response to SV was defined as ≥10% relative improvement in left ventricular ejection fraction (LVEF) at 3-months post-maximal-dose therapy. Pre-therapy ECGs were retrospectively analyzed for axes and standard wave and interval durations. Logistic regression was used to estimate odds ratios and 95% confidence intervals for associations between predictors and therapeutic response. Backward stepwise regression was employed to develop a parsimonious model. ResultsP-wave duration (PWD) 100–120 ms, PWD >120 ms, and QTc >460 ms were associated with response to SV on univariate analysis: OR 18.00 (4.45–122.90), 5.00 (1.47–20.42), and 3.10 (1.18–9.22), respectively. The preferred model that included the former two predictors in combination with pre-therapy creatinine, mineralocorticoid receptor antagonist use, and LVEF was highly selective (area under the ROC curve = 0.868). ConclusionsProlongation of both PWD and QTc interval on baseline ECG in HFrEF patients is predictive of therapeutic response to maximal-dose SV therapy and may indicate early cardiac remodeling that is highly amenable to reversal.

Development of hypertension models for lung cancer screening cohorts using clinical and thoracic aorta imaging factors

This study aims to develop and validate nomogram models utilizing clinical and thoracic aorta imaging factors to assess the risk of hypertension for lung cancer screening cohorts. We included 804 patients and collected baseline clinical data, biochemical indicators, coexisting conditions, and thoracic aorta factors. Patients were randomly divided into a training set (70%) and a validation set (30%). In the training set, variance, t-test/Mann–Whitney U-test and standard least absolute shrinkage and selection operator were used to select thoracic aorta imaging features for constructing the AIScore. Multivariate logistic backward stepwise regression was utilized to analyze the influencing factors of hypertension. Five prediction models (named AIMeasure model, BasicClinical model, TotalClinical model, AIBasicClinical model, AITotalClinical model) were constructed for practical clinical use, tailored to different data scenarios. Additionally, the performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analyses (DCA). The areas under the ROC curve for the five models were 0.73, 0.77, 0.83, 0.78, 0.84 in the training set, and 0.77, 0.78, 0.81, 0.78, 0.82 in the validation set, respectively. Furthermore, the calibration curves and DCAs of both sets performed well on accuracy and clinical practicality. The nomogram models for hypertension risk prediction demonstrate good predictive capability and clinical utility. These models can serve as effective tools for assessing hypertension risk, enabling timely non-pharmacological interventions to preempt or delay the future onset of hypertension.

Regression model and pathways of halonitromethane formation from amino acids in the presence of bromide ion under UV/monochloramine disinfection

Halonitromethanes (HNMs) including chlorinated and brominated HNMs, a category of nitrogenous disinfection by-products with high toxicity, were formed from amino acids (AAs) in the presence of bromide ion (Br-) under UV/monochloramine (UV/NH2Cl) disinfection. This study developed a regression model using water characteristics (pH, HCO3– and Br-) and disinfection conditions (disinfection time and NH2Cl doses) to predict HNM formation from AAs (tyrosine (Tyr), tryptophan (Trp) and aspartic acid (Asp)) in the presence of Br- under UV/NH2Cl disinfection. Main contributors to the abatement of Tyr and Trp were UV (6.1%-18.7%), HO (18.5%-62.3%), Cl2− (0.3%-17.9%), Br (2.4%-41.3%, kBr•,AA=∼109-1010 M−1·s−1), Br2− (1.3%-11.0%, kBr2•-,AA=∼108-109 M−1·s−1), NO (2.6%-43.0%) and CO3− (0%-43.2%) based on the first-principle kinetic model of UV/NH2Cl/Br- process, while the removal ratio of Asp was low. It was responsible for higher HNM concentrations from Tyr and Trp than those from Asp. Due to the decline in HNM concentrations with prolongation of reaction time, HNM concentrations were considered as the quadratic function of disinfection time with coefficients related to water characteristics and NH2Cl doses. The regression model with backward stepwise regression described HNM formation well. After introducing specific UV absorbance at 254 nm (SUVA254) and interaction terms associated with SUVA254, HNM formation from three AAs was successfully predicted by one regression equation (R2 = 0.91, mean absolute error = 0.46, mean relative error = 37%, root-mean-square error = 0.81). Using the optimal regression model, increasing NH2Cl doses was found to advance the peak time for HNM formation. Besides, obtained heatmaps of HNM formation revealed that a high NH2Cl dose with a high UV dose or low NH2Cl dose helped to limit HNM formation. Lastly, the possible formation pathways of HNMs were presented.

Use of Residuals and Rank Product in Detection of Outlier in Survival Analysis with Crimean-Congo Hemorrhagic Fever Data

Purpose: Survival analysis is a statistical method used in many fields, especially in the field of health. It involves modeling the relationship between the survival time of individuals after a treatment or procedure and the event called response. The presence of outliers in the data may cause biased parameter estimations of the established models. Also, this situation causes the proportional hazards assumption to be violated especially in Cox regression analysis. Outlier(s) are identified with the help of residuals, Bootstrap Hypothesis test and Rank product test. Method: In R.4.0.3 software, outlier(s) are determined on a clinical dataset by the Schoenfeld residual, Martingale residual, Deviance residual method and Bootstrap Hypothesis test (BHT) based on Concordance index, and Rank product test. Results: After the cox regression established by the backward stepwise and robust cox regression, it was observed that the established models did not fit. So, the outlier(s) determined by the methods mentioned. Conclusion: It was decided that only one observation could be excluded from the study. As in the survival data, in many data types, outliers can be detected and further analyzes can be applied by using the methods mentioned.

Non-binary gender, vulnerable populations and mental health during the COVID-19 pandemic: Data from the COVID-19 MEntal health inTernational for the general population (COMET-G) study

BackgroundThe COVID-19 pandemic has brought significant mental health challenges, particularly for vulnerable populations, including non-binary gender individuals. The COMET international study aimed to investigate specific risk factors for clinical depression or distress during the pandemic, also in these special populations. MethodsChi-square tests were used for initial screening to select only those variables which would show an initial significance. Risk Ratios (RR) were calculated, and a Multiple Backward Stepwise Linear Regression Analysis (MBSLRA) was followed with those variables given significant results at screening and with the presence of distress or depression or the lack of both of them. ResultsThe most important risk factors for depression were female (RR = 1.59–5.49) and non-binary gender (RR = 1.56–7.41), unemployment (RR = 1.41–6.57), not working during lockdowns (RR = 1.43–5.79), bad general health (RR = 2.74–9.98), chronic somatic disorder (RR = 1.22–5.57), history of mental disorders (depression RR = 2.31–9.47; suicide attempt RR = 2.33–9.75; psychosis RR = 2.14–10.08; Bipolar disorder RR = 2.75–12.86), smoking status (RR = 1.15–5.31) and substance use (RR = 1.77–8.01). The risk factors for distress or depression that survived MBSLRA were younger age, being widowed, living alone, bad general health, being a carer, chronic somatic disorder, not working during lockdowns, being single, self-reported history of depression, bipolar disorder, self-harm, suicide attempts and of other mental disorders, smoking, alcohol, and substance use. ConclusionsTargeted preventive interventions are crucial to safeguard the mental health of vulnerable groups, emphasizing the importance of diverse samples in future research. LimitationsOnline data collection may have resulted in the underrepresentation of certain population groups.

A comparative analysis of the clinical pregnancy and perinatal outcomes between oocyte vitrification and embryo vitrification based on the propensity score matching method.

This study investigated the safety and effectiveness of oocyte vitrification by comparing the clinical pregnancy and perinatal outcomes between transfer cycles of vitrified oocytes and those of vitrified embryos. A retrospective cohort study was conducted to analyze the clinical data of patients who underwent cleavage-stage embryo transfer at the Department of Reproductive Medicine between January 2011 and June 2021. Seventy-seven transfer cycles of fresh cleavage-stage embryos developed from vitrified-thawed oocytes (oocyte vitrification group) and 2170 transfer cycles of vitrified-thawed cleavage-stage embryos developed from fresh oocytes (embryo vitrification group) were included. Further, 293 cases were selected from the embryo vitrification group after applying propensity score matching at 1:4. The primary outcomes were miscarriage rate, live birth rate, and neonatal birth weight. No statistically significant differences were observed in the baseline data, pregnancy, perinatal outcomes, or neonatal outcomes for either singleton or twin births between the two groups after matching. Backwards stepwise regression was used to analyze the length of gestation. The age of female participants (β = - 0.410, 95% CI = - 1.339 ~ - 0.620, P < 0.001) had a statistically significant effect. Oocyte vitrification results in similar clinical pregnancy and perinatal outcomes as does embryo vitrification; hence, it is a relatively safe assisted reproductive technique.

A Machine Learning–Based Readability Model for Gujarati Texts

This study aims to develop a machine learning–based model to predict the readability of Gujarati texts. The dataset was 50 prose passages from Gujarati literature. Fourteen lexical and syntactic readability text features were extracted from the dataset using a machine learning algorithm of the unigram parts of speech tagger and three Python programming scripts. Two samples of native Gujarati speaking secondary and higher education students rated the Gujarati texts for readability judgment on a 10-point scale of “easy” to “difficult” with the interrater agreement. After dimensionality reduction, seven text features as the independent variables and the mean readability rating as the dependent variable were used to train the readability model. As the students' level of education and gender were related to their readability rating, four readability models for school students, university students, male students, and female students were trained with a backward stepwise multiple linear regression algorithm of supervised machine learning. The trained model is comparable across the raters’ groups. The best model is the university students’ readability rating model. The model is cross-validated. It explains 91% and 88% of the variance in readability ratings at training and cross-validation, respectively, and its effect size and power are large and high.

Prognostic evaluation of a multi-target magnetic bead-enriched circulating tumor cell-enriched identification system for colorectal cancer.

Colorectal cancer (CRC) is the third most prevalent cancer in the world. Traditional tissue biopsy cannot provide dynamic monitoring of patients' tumors or reflect the characteristics of tumors in real time because the sampling process is invasive and accompanied by risks. Circulating tumor cells (CTCs) are considered a major cause of tumor metastasis, and investigating CTCs helps to understand the biology and vulnerability of malignant tumors during hematogenous metastasis. We sequentially used epithelial cell adhesion molecule (EpCAM)-coated immunoliposomal magnetic beads (Ep-IMBs) and vimentin-coated immunoliposomal magnetic beads (Vi-IMBs) to capture and characterize CTCs from 110 CRC patients. We further constructed a Cox risk regression model, optimized the model composition using backward stepwise regression, and finally applied nomograms to show the effect of each variable on survival risk. The specificity of the CTCs enrichment and identification system was 100% and the sensitivity was 79.0%. Multivariate analysis indicated total CTC number was an important predictor for bad survival, whereas American Joint Committee on Cancer (AJCC) stage, lymph node metastasis, and carcinoembryonic antigen (CEA) level were associated with prognosis, and the risk of mortality was associated with the AJCC stage of the CRC. The CTC enrichment and identification system constructed in this research demonstrated superior accuracy. In addition, CTCs can be used as an important predictor for prognosis of patients with CRC, and the combination of other clinical predictive factors can help clinicians to better design individualized treatment regimens, which is of great clinical application value.

Abstract WP94: Forecasting Outcomes After Mechanical Thrombectomy: Predictive Modeling of Neurological Improvement in the SELECT Database

Introduction: Outcome prediction and prognostication are at the heart of determining next steps in stroke care. We aim to evaluate two variable selection strategies to provide optimal prediction of functional independence among post-EVT patients at admission, day 1, and time of discharge using data from the SELECT prospective cohort study. Methods: Prediction strategies were created using baseline demographics, clinical presentation and imaging characteristics for the admission model, procedure characteristics and outcomes, clinical status, and baseline factors for the day 1 model, and follow-up infarct volume, cerebral edema, clinical status, and previously mentioned factors for the discharge model. Model selection used backwards stepwise regression and least absolute shrinkage and selection operator (LASSO) strategy. Patients with unavailable data were excluded. Calibration and internal validation was performed using bootstrapping for 50 repetitions. Results: 149 EVT patients with all available data points were included. Functional independence (mRS 0-2) was observed in 89 (60%) patients. LASSO and stepwise selection methods selected different variables at baseline, with LASSO providing reasonably better model performance over stepwise model selection. Both stepwise and LASSO regression methods selected baseline ASPECTS and NIHSS at day 1 for day 1 model, and baseline ASPECTS, NIHSS at day 1 and NIHSS at day 7 for discharge model. Day 1 and discharge models demonstrated significantly better predictive performance in bootstrap validation over baseline models [Table]. Conclusions: Clinical symptoms in the post-acute phase were the strongest predictors of functional independence at 90-day follow-up and were selected over procedural factors or cerebral edema. Prediction of functional independence at baseline involved several clinical and imaging variables with lower predictive performance than post-acute phase models.

Comparative effects of epidural analgesia and intramuscular morphine on maternal and neonatal outcomes: a retrospective cohort study

BackgroundThe global practice of pain management during labor involves the use of Epidural Analgesia (EA) or intramuscular (IM) morphine. However, the impact of these methods on maternal and neonatal short-term outcomes remains unclear. ObjectiveThe study evaluates labor exposure to EA and IM morphine on NICU admission rates and explores other associated maternal and neonatal factors like sepsis, respiratory distress, instrumental delivery, birth traumas, low APGAR scores, and chorioamnionitis. Study DesignA study at the Women's Wellness and Research Center in Qatar analyzed 7,721 low-risk normal vaginal deliveries from January 2017 to April 2018. Results were analyzed using descriptive and backward stepwise multinomial regression analysis, categorizing outcomes based on pain management during active labor. ResultsOf the sample, 2606 women received EA, 1338 received IM morphine, 286 received both, and 3304 received neither. Multinomial regression analysis revealed no differences in NICU admissions in the EA group as well as the IM morphine group as compared to the nongroup. However, the analysis showed a significant association between the combined use of EA and IM morphine and NICU admission due to respiratory depression (aOR= 8.63, 95% CI 1.07-69.46, p-value = 0.04). Moreover, there was a significant association between prolonged duration of the second stage of labor and receiving EA alone (aOR=1.02, 95% CI 1.01-1.02, p-value < 0.001) or both EA and IM morphine (aOR=1.02, 95% CI 1.01-1.03, p-value < 0.001). In addition, the combined use of EA and IM morphine was associated with gestational age (aOR= 1.86, 95% CI 1.19-2.90, p-value =0.01) and baby gender (aOR= 3.72, 95% CI 1.54-9.01, p-value =0.003). IM morphine alone was only linked to low Apgar scores at 1 minute (aOR=6.29, 95%CI 1.33-29.83, p-value=0.02). ConclusionThe study found no significant association between EA or IM morphine use during labor and NICU admissions. However, other specific outcomes were associated with the use of EA, IM morphine, or both. Further research is warranted to enhance understanding and optimize pain management protocols.