Backward Stepwise Selection Research Articles

Applying Andersen's healthcare utilization model to assess factors influencing patients' expectations for diagnostic tests at emergency department visits during the COVID-19 pandemic.

The uncertainties surrounding the COVID-19 pandemic led to a surge in non-urgent emergency department (ED) attendance among people presenting with upper respiratory tract infection (URTI) symptoms. These non-urgent visits, often manageable in primary care, exacerbated ED overcrowding, which could compromise the quality of ED services. Understanding patients' expectations and the reasons for these ED visits is imperative to mitigate the problem of ED overcrowding. Hence, we assessed the factors influencing patients' expectations for diagnostic tests during their ED visits for uncomplicated URTI during different phases of the pandemic. We conducted a cross-sectional study on adults with URTI symptoms seeking care at four public EDs in Singapore between March 2021 and March 2022. We segmented the study period into three COVID-19 pandemic phases-containment, transition, and mitigation. The outcome variables are whether patients expected (1) a COVID-19-specific diagnostic test, (2) a non-COVID-19-specific diagnostic test, (3) both COVID-19-specific and non-COVID-19-specific diagnostic tests, or (4) no diagnostic test. We built a multinomial regression model with backward stepwise selection and classified the findings according to Andersen's healthcare utilization model. The mean age of participants was 34.5 (12.7) years. Factors (adjusted odds ratio [95% confidence interval]) influencing expectations for a COVID-19-specific diagnostic test in the ED include younger age {21-40 years: (2.98 [1.04-8.55])}, no prior clinical consultation (2.10 [1.13-3.89]), adherence to employer's health policy (3.70 [1.79-7.67]), perceived non-severity of illness (2.50 [1.39-4.55]), being worried about contracting COVID-19 (2.29 [1.11-4.69]), and during the transition phase of the pandemic (2.29 [1.15-4.56]). Being non-employed influenced the expectation for non-COVID-19-specific diagnostic tests (3.83 [1.26-11.66]). Factors influencing expectations for both COVID-19-specific and non-COVID-19-specific tests include younger age {21-40 years: (3.61 [1.26-10.38]); 41-60 years: (4.49 [1.43-14.13])}, adherence to employer's health policy (2.94 [1.41-6.14]), being worried about contracting COVID-19 (2.95 [1.45- 5.99]), and during the transition (2.03 [1.02-4.06]) and mitigation (2.02 [1.03-3.97]) phases of the pandemic. Patients' expectations for diagnostic tests during ED visits for uncomplicated URTI were dynamic across the COVID-19 pandemic phases. Expectations for COVID-19-specific diagnostic tests for ED visits for uncomplicated URTI were higher among younger individuals and those worried about contracting COVID-19 during the COVID-19 pandemic. Future studies are required to enhance public communications on the availability of diagnostic services in primary care and public education on self-management of emerging infectious diseases such as COVID-19.

Development and Validation of a Risk Prediction Model for In-Hospital Mortality in Patients With Acute Upper Gastrointestinal Bleeding.

Acute upper gastrointestinal bleeding (UGIB) is a common life-threatening clinical emergency with a poor prognosis. The aim of this study was to develop a risk prediction model for in-hospital mortality in patients with UGIB. We performed a post hoc analysis of a publicly available retrospective clinical data. A total of 360 patients with UGIB were included in this study. The least absolute shrinkage and selection operator regression was used to screen predictors and a restricted cubic spline function was used to investigate the assumption of linear relationships between continuous predictors and the risk of in-hospital mortality. Backward stepwise selection with the Akaike information criterion was used to identify variables for the best prediction model. A nomogram was developed based on the results of the best prediction model. The receiver operating characteristic curve, GiViTI calibration plot, and decision curve analysis were used to evaluate the performance of the nomogram. The optimal prediction model consisting of 4 predictors: red cell distribution width (odds ratio [OR] = 8.44; 95% confidence interval [CI]: 1.77-89.10), platelet count (OR = 0.99; 95% CI: 0.99-1.00), pulse rate (OR = 1.03; 95% CI: 1.01-1.05), and SpO2 (OR = 0.92; 95% CI: 0.86-0.96). The nomogram model had good discrimination (area under the curve = 0.86, 95% CI: 0.78-0.95), calibration, and clinical usefulness. In this study, we developed a nomogram model for predicting death during hospitalization in patients with UGIB based on blood biomarkers and baseline vital signs at the time of admission. The model has good performance, allowing rapid risk stratification of patients with UGIB.

The Quality of Life of Seniors with Eye Diseases during COVID-19.

To assess health-related quality of life (HRQoL), vision-related quality of life (VRQoL), depression and anxiety symptoms, and social support and community integration of seniors with eye diseases and to identify important predictor variables of the outcomes. A cross-sectional survey was performed in seniors with eye diseases (n = 90). Demographic characteristics and questionnaire scores were summarized. Linear regression analysis with backward stepwise selection was used to predict the value of the outcomes of eye disease. Preference-based HRQoL of the study patients with eye diseases during the pandemic was likely good with a mean utility value of 0.88. VRQoL and sleep quality appeared to be good as well. Depression and anxiety symptoms appeared to be low, while community integration and social support were moderate. Furthermore, the presence of retinal disease, number of nonocular comorbidities, and education appeared to have significant negative effects on social support and community integration. The presence of retinal disease and the number of nonocular comorbidities both appeared to negatively impact VRQoL. The use of a mobility aid appeared to negatively affect depressive symptoms and sleep quality. Overall quality of life and wellness among seniors with eye diseases appeared to be good during the COVID-19 pandemic. However, the presence of retinal disease and the number of nonocular comorbidities both appeared to negatively impact VRQoL and social support and community integration. Education appeared to impact social support and community integration negatively. The use of a mobility aid appeared to negatively affect depressive symptoms and sleep quality.

When to Perform Fusion Short of the Pelvis in Patients with Cerebral Palsy?: Indications and Outcomes.

Using 2 prospective databases, we analyzed 87 pediatric patients who underwent PSF short of the pelvis from 2008 to 2015 to treat CP-related scoliosis and who had ≥2 years of follow-up. Preoperative radiographic and clinical variables were analyzed for associations with unsatisfactory correction (defined as pelvic obliquity of ≥10°, distal implant dislodgement, and/or reoperation for increasing deformity at 2- or 5-year follow-up). Continuous variables were dichotomized using the Youden index, and a multivariable model of predictors of unsatisfactory correction was created using backward stepwise selection. Finally, radiographic, health-related quality-of-life, and clinical outcomes of patients with fusion short of the pelvis who had neither of the 2 factors associated with unsatisfactory outcomes were compared with those of 2 matched-control groups. Deformity correction was unsatisfactory in 29 of 87 patients with fusion short of the pelvis. The final model included preoperative pelvic obliquity of ≥17° (odds ratio [OR], 6.8; 95% confidence interval [CI], 2.3 to 19.7; p < 0.01) and dependent sitting status (OR, 3.2; 95% CI, 1.1 to 9.9; p = 0.04) as predictors of unsatisfactory correction. The predicted probability of unsatisfactory correction increased from 10% when neither of these factors was present to a predicated probability of 27% to 44% when 1 was present and to 72% when both were present. Among matched patients with these factors who had fusion to the pelvis, there was no association with unsatisfactory correction. Patients with independent sitting status and pelvic obliquity of <17° who had fusion short of the pelvis had significantly lower blood loss and hospital length of stay, and better 2-year health-related quality-of-life scores compared with matched controls with fusion to the pelvis. In patients with scoliosis secondary to CP, pelvic obliquity of <17° and independent sitting status are associated with a low risk of unsatisfactory correction and better 2-year outcomes when fusion short of the pelvis is performed. These may be used as preoperative criteria to guide the decision of whether to perform fusion short of the pelvis in patients with CP. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Grapevine Yield Big-data for Soil and Climate Zoning. A case study in Languedoc-Roussillon, France

New winegrower and resource datasets appear to be a great opportunity to understand which are the environmental factors involved in grapevine yield spatially. Such analysis can help regional label managers and winegrowers for the conception of local adaptation strategies to climate change, reducing yield gaps. In the present study, we aggregated yield a big dataset obtained from Pays d’Oc winegrowers (n = 96677) between 2010 and 2018 at the municipality level (n = 606), located in the Languedoc-Roussillon region, in the South of France. We used a backward stepwise model selection process using linear mixed-effect models to discriminate and select significant indicators capable of estimating grapevine yield at the municipality level, these include: Soil Available Water Capacity (SAWC), soil pH, Huglin Index, the Climate Dryness Index, the number of Very Hot Days and Days of Frost. We then determined spatial zones by creating clusters of municipalities with similar soil and climate characteristics. The seven zones presented two marked yield levels. Yet, all zones had municipalities with both high yield and high yield gaps. On each zone, grapevine yield was found to be driven by a combination of climate and soil factors, rather than just by a single environmental factor. Environmental factors at this scale largely explained yield variability across the municipalities, but they were not performant in terms of annual yield prediction. Further research is required on the interactions between environmental factors, plant material and farming practices.

Derivation and validation of pragmatic clinical models to predict hospital length of stay after cardiac surgery in Ontario, Canada: a population-based cohort study.

Cardiac surgery is resource intensive and often requires multidisciplinary involvement to facilitate discharge. To facilitate evidence-based resource planning, we derived and validated clinical models to predict postoperative hospital length of stay (LOS). We used linked, population-level databases with information on all Ontario residents and included patients aged 18 years or older who underwent coronary artery bypass grafting, valvular or thoracic aorta surgeries between October 2008 and September 2019. The primary outcome was hospital LOS. The models were derived by using patients who had surgery before Sept. 30, 2016, and validated after that date. To address the rightward skew in LOS data and to identify top-tier resource users, we used logistic regression to derive a model to predict the likelihood of LOS being more than the 98th percentile (> 30 d), and γ regression in the remainder to predict continuous LOS in days. We used backward stepwise variable selection for both models. Among 105 193 patients, 2422 (2.3%) had an LOS of more than 30 days. Factors predicting prolonged LOS included age, female sex, procedure type and urgency, comorbidities including frailty, high-risk acute coronary syndrome, heart failure, reduced left ventricular ejection fraction and psychiatric and pulmonary circulatory disease. The C statistic was 0.92 for the prolonged LOS model and the mean absolute error was 2.4 days for the continuous LOS model. We derived and validated clinical models to identify top-tier resource users and predict continuous LOS with excellent accuracy. Our models could be used to benchmark clinical performance based on expected LOS, rationally allocate resources and support patient-centred operative decision-making.

Export of high-tech goods in the context of innovation transfer for social-economic development: factor analysis

The purpose of the study is to determine factors that have the greatest influence on the growth of export of high-tech goods in the context of innovation transfer for social-economic development. Factor analysis tools, including principal component analysis and the Varimax rotation (orthogonal transformation) method in Statgraphics software, are used to identify the most significant indicators of the impact on export of high-tech goods, as a key determinant characterizing the quality of scientific and educational potential, and to determine the latent signs of their interaction. A modified logistic function is used to normalize input data for 11 investigated factors in a sample of 28 countries. Ten linear combinations of variables are obtained, which explain most of the data variability. The first four components have eigenvalues greater than or equal to 1.0. Together, they account for 88.520% of the variability of the original data. After orthogonal transformation by the Varimax method, the factor load matrix is obtained. The econometric models, which describes the influence of independent indicators on the export of high-tech goods, are represented. Next, the four most influential indicators from the 11 investigated factors are revealed, namely: the country’s research and development expenditure, GDP in current prices, research staff and researchers in the sector of business enterprises, the percentage of ICT staff from total employment. They are taken to develop multiple linear regression models, which describes the influence of independent indicators on the effective export of high-tech goods. The quality results of the factor analysis are confirmed using the Kaiser-Meier-Olkin test and the Bartlett test. Regression analysis with strict screening of non-significant variables using the Backward Stepwise Selection tool confirms the significance of the indicator of scientific research personnel and researchers in the sector of business enterprises, which has the greatest impact on the export of high-tech goods. A pair regression model is obtained, and it is confirmed that increase of research staff and researchers in the sector of business enterprises by 1% causes increase of export of high-tech goods in average by 0,73%.

Effect of Education on Ease of Doing Business in Conditions of Innovation Development: Factor Analysis and Multiple Regression

Education accelerates changes and transformations in social life, as well as one of the main factors of progress and development in general and business. The article substantiates and formalises the relationships between indicators of the level of education (taking into account its innovative development) and the ease of doing business based on evidence from 28 world countries and the data from the World Bank, the United Nations, Tufts University and Standard &amp; Poor’s Ratings Services. The article aims to find indicators of innovative education that most significantly contribute to the ease of doing business. For this, cognitive analysis of statistical data is carried out, and with the help of descriptive analysis tools, a statistically significant characteristic space of indicators is formed. Checking the density and direction of the relationship is carried out by calculating the values of the Pearson correlation coefficients. The multidimensionality of input feature space is reduced to the four most significant indicators from nine investigated ones (digital development indicator; human development index; digital trust, financial literacy index) using the procedure of principal component analysis and orthogonal transformation using the Varimax method in the Statgraphics Centurion 19 software. The quality of the factorisation is confirmed by Kaiser-Meier-Olkin testing and Bartlett’s sphericity testing. As a result of the developed multiple econometric models, which describe the dependence of ease of doing business and the above indicators, and the Backward Stepwise Selection hard screening procedure in Statgraphics 19, a statistically significant model of the effect of digital development on ease of doing business is built. It shows that with the value increase of digital development by 1%, ease of doing business will also increase by 0.79%. The obtained results can be useful to scientists for further research, as well as to change-makers in education and business and all stakeholders in the direction of «business–education» competition.

Association between the Mediterranean Diet Index and self-reported Gingival Health Status Indicators in a population of Chilean adults: a cross-sectional study.

Despite the recognized impact of diet on non-communicable diseases, the association between the Mediterranean diet and periodontal diseases is still uncertain. This study aimed to determine the association between adherence to the Mediterranean Diet Index (MDI) and self-reported gingival health status in Chilean adults, exploring the feasibility of using validated web-based survey questionnaires. Cross-sectional data were collected from a representative sample of a population of Chilean adults (18-60 years old) using a low-cost and time-saving methodology. By the PsyToolkit platform, anonymous survey data were downloaded and analyzed in bivariate (crude) and backward stepwise selection multivariate logistic regression models adjusted for sociodemographic determinants, smoking, and dental attendance using STATA 17. Odds ratios (OR) [95% confidence intervals] were estimated. In total, 351 complete statistical data were mostly obtained from female university students who had never smoked and reported having visited a dentist in the previous year. Multivariate regression models showed an association between MDI and very good/good gingival health status (OR 1.18 [95% CI 1.04-1.34], p=0.013), absence of bleeding on toothbrushing (OR 1.12 [95% CI 1.01-1.25], p=0.035), and absence of clinical signs of gingival inflammation (OR 1.24 [95% CI 1.10-1.40], p<0.001), after controlling for age, sex, educational level, smoking, and dental attendance. We associated adherence to the Mediterranean diet with better self-reported gingival health status in a population of Chilean adults in an entirely web-based research environment. Longitudinal studies with random sampling are required to establish the effect of diet on gingival and periodontal health. Nevertheless, this evidence could contribute to the design of low-cost surveillance programs to reduce the burden of periodontal disease and related "common risk factors".

Development and validation of a predictive model for treatment outcome after emergency extracorporeal shockwave lithotripsy in patients with symptomatic ureteral stones during the COVID-19 pandemic: in a large prospective cohort.

The predictors of treatment outcome after emergency extracorporeal shockwave lithotripsy (SWL) are not well characterized. Therefore, based on a large prospective cohort, we aimed to develop and validate a nomogram for predicting treatment outcome after emergency SWL in patients with symptomatic ureteral stones. The development cohort included 358 patients with symptomatic ureteral stones who underwent emergency SWL between June 2020 and August 2021 in our hospital. One hundred and twenty-nine patients with symptomatic ureteral stones participated in the validation cohort from September 2021 to April 2022. The data were prospectively recorded. The backward stepwise selection was applied using the likelihood ratio test with Akaike's information criterion as the stopping rule. The efficacy of this predictive model was assessed concerning its clinical usefulness, calibration, and discrimination. Finally, 15.6% (56/358) of patients in the development cohort and 14.0% (18/129) of those in the validation cohort suffered from stone-free failure after emergency SWL. We identified four predictors for stone-free failure: stone size, stone density, skin to stone distance (SSD), and degree of hydronephrosis. This model showed good discrimination with an area under the receiver operating characteristic (AUROC) curves of 0.935 (0.899-0.971) and good calibration (P = 0.059). The decision curve analysis showed that the model was clinically valuable. In this large prospective cohort, we found that stone size, stone density, SSD, and degree of hydronephrosis were predictors of treatment outcome after emergency SWL. This nomogram will be helpful in preoperative risk stratification to provide individualized treatment recommendations for each patient. Furthermore, early identification and appropriate management of patients may increase the success rate of emergency SWL during the COVID-19 pandemic.

Nomogram for Early Prediction of Outcome in Coma Patients with Severe Traumatic Brain Injury Receiving Right Median Nerve Electrical Stimulation Treatment.

Accurate outcome prediction can serve to approach, quantify and categorize severe traumatic brain injury (TBI) coma patients for right median electrical stimulation (RMNS) treatment, which can support rehabilitation plans. As a proof of concept for individual risk prediction, we created a novel nomogram model combining amplitude-integrated electroencephalography (AEEG) and clinically relevant parameters. This study retrospective collected and analyzed a total of 228 coma patients after severe TBI in two medical centers. According to the extended Glasgow Outcome Scale (GOSE), patients were divided into a good outcome (GOSE 3-8) or a poor outcome (GOSE 1-2) group. Their clinical and biochemical indicators, together with EEG features, were explored retrospectively. The risk factors connected to the outcome of coma patients receiving RMNS treatment were identified using Cox proportional hazards regression. The discriminative capability and calibration of the model to forecast outcome were assessed by C statistics, calibration plots, and Kaplan-Meier curves on a personalized nomogram forecasting model. The study included 228 patients who received RMNS treatment for long-term coma after a severe TBI. The median age was 40 years, and 57.8% (132 of 228) of the patients were male. 67.0% (77 of 115) of coma patients in the high-risk group experienced a poor outcome after one year and the comparative data merely was 30.1% (34 of 113) in low-risk group patients. The following variables were integrated into the forecasting of outcome using the backward stepwise selection of Akaike information criterion: age, Glasgow Coma Scale (GCS) at admission, EEG reactivity (normal, absence, or the stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs)), and AEEG background pattern (A mode, B mode, or C mode). The C statistics revealed that the nomograms' discriminative potential and calibration demonstrated good predictive ability (0.71). Our findings show that the nomogram model using AEEG parameters has the potential to predict outcomes in severe TBI coma patients receiving RMNS treatment. The model could classify patients into prognostic groups and worked well in internal validation.

ARDS after Pneumonectomy: How to Prevent It? Development of a Nomogram to Predict the Risk of ARDS after Pneumonectomy for Lung Cancer.

(1) Background: The cause of ARDS after pneumonectomy is still unclear, and the study of risk factors is a subject of debate. (2) Methods: We reviewed a large panel of pre-, peri- and postoperative data of 211 patients who underwent pneumonectomy during the period 2014-2021. Univariable and multivariable logistic regression was used to quantify the association between preoperative parameters and the risk of developing ARDS, in addition to odds ratios and their respective 95% confidence intervals. A backward stepwise selection approach was used to limit the number of variables in the final multivariable model to significant independent predictors of ARDS. A nomogram was constructed based on the results of the final multivariable model, making it possible to estimate the probability of developing ARDS. Statistical significance was defined by a two-tailed p-value &lt; 0.05. (3) Results: Out of 211 patients (13.3%), 28 developed ARDS. In the univariate analysis, increasing age, Charlson Comorbidity Index and ASA scores, DLCO &lt; 75% predicted, preoperative C-reactive protein (CRP), lung perfusion and duration of surgery were associated with ARDS; a significant increase in ARDS was also observed with decreasing VO2max level. Multivariable analysis confirmed the role of ASA score, DLCO &lt; 75% predicted, preoperative C-reactive protein and lung perfusion. Using the nomogram, we classified patients into four classes with rates of ARDS ranking from 2.0% to 34.0%. (4) Conclusions: Classification in four classes of growing risk allows a correct preoperative stratification of these patients in order to quantify the postoperative risk of ARDS and facilitate their global management.

Development of a Prediction Model for Acute Kidney Injury after Colistin Treatment for Multidrug-resistant Acinetobacter baumanii Ventilator-Associated Pneumonia: A Pilot Study

Objective: Multidrug-resistant Acinetobacter baumanii (MDR-AB) ventilator-associated pneumonia (VAP) is the major complication following hospital admission in Thailand, increasing morbidity and prolonging hospital stay duration. Treatment of MDR-AB VAP usually requires colistin which has highly nephrotoxic properties. Therefore, we aimed to develop a pilot prediction model for acute kidney injury (AKI) after colistin treatment.Material and Methods: We conducted a retrospective cohort study. All MDR-AB VAP patients who received colistin for at least 72 hours in Suratthani Hospital from January to July 2017 were eligible for inclusion. The primary outcome was the overall presence of AKI on days 3, 5 or 7 after colistin administration. Multivariable logistic regression analysis was used to develop the final prediction model.Results: Of 85 MDR-AB VAP patients, 51 (61%) developed AKI after colistin treatment. Nine factors, female, intensive care unit (ICU) admission, diabetes mellitus, systolic blood pressure (SBP), diastolic blood pressure (DBP), serum sodium, creatinine, blood urea nitrogen (BUN) and glomerular filtration rate were potential predictors of AKI. Multivariable logistic regression with backward stepwise selection revealed the best prediction model had four predictors: female, ICU admission, BUN, and DBP. The area under the receiver operating characteristic curve for the model in predicting AKI among MDR-AB VAP patients was 0.801 (95% confidence interval: 0.703, 0.898).Conclusion: The prediction model containing four predictors, female gender, ICU admission, BUN level, and DBP had fair to good performance in predicting AKI after colistin treatment among MDR-AB VAP patients.

Predicting perineal trauma during childbirth using data from a general obstetric population

Background: Perineal trauma is a common complication of childbirth and can have serious impacts on long-term health. Few studies have examined the combined effect of multiple risk factors. We developed and internally validated a risk prediction model to predict third and fourth degree perineal tears using data from a general obstetric population. Methods: Risk prediction model using data from all singleton vaginal deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019 and 2020. Third/fourth degree tears were diagnosed by an obstetrician or midwife at time of birth and defined as tears that extended into the anal sphincter complex or involved both the anal sphincter complex and anorectal mucosa. We used univariable and multivariable logistic regression with backward stepwise selection to develop the models. Candidate predictors included infant sex, maternal age, maternal body mass index, parity, mode of delivery, birthweight, post-term delivery, induction of labour and public/private antenatal care. We used the receiver operating characteristic (ROC) curve C-statistic to assess discrimination, and bootstrapping techniques were used to assess internal validation. Results: Of 8,403 singleton vaginal deliveries, 8,367 (99.54%) had complete data on predictors for model development. A total of 128 women (1.53%) had a third/fourth degree tear. Three variables remained in the final model: nulliparity, mode of delivery (specifically forceps delivery or ventouse delivery) and increasing birthweight (per 100 gram increase) (C-statistic: 0.75, 95% CI: 0.71, 0.79). We developed a nomogram to calculate individualised risk of third/fourth degree tears using these predictors. Bootstrapping indicated good internal performance. Conclusions: Use of our nomogram can provide an individualised risk assessment of third/fourth degree tears and potentially aid counselling of women on their potential risk.

Abstract B005: Polycyclic aromatic hydrocarbons and pancreatic cancer: An analysis of blood biomarker PheT as an exposure identifier

Abstract Exposure to polycyclic aromatic hydrocarbons (PAHs), a byproduct of incomplete combustion, and its effects on the development of cancer needs further evaluation. Recent studies have analyzed the relationship between PAHs and tobacco or dietary intake in the form of processed foods and smoked/well-done meats. This study aims to assess the association of a blood biomarker and metabolite of PAHs, r-1-,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene (PheT), selected metabolism SNPs, and pancreatic cancer. Demographics, food-frequency data, SNPs, treatment history and levels of PheT in plasma were collected from 400 participants (202 cases and 198 control) and evaluated based on pancreatic adenocarcinoma diagnosis. A two-stage analysis was completed to identify 1) SNPs that were significantly associated with pancreatic cancer and 2) Dietary intake and PheT variables significantly associated with pancreatic cancer. A univariate analysis for variables with OR≠1 and p-value &amp;lt; 0.05 suggests significant relationship with pancreatic cancer and were carried forward to a multiple regression model for either food items or SNPs. Backward stepwise selection was used to eliminate non-significant variables. A collection of 5 SNPs were observed to form several of the most significant (adjusted p-value &amp;lt;0.05) interaction pairs across different SNP models. These were rs566979 (gene: CAT), rs28540420 (gene: PTGER4), rs12029406 (gene: none), rs730368 (gene: PTGER4), and rs3181077 (gene: CCR1). A final multiple regression model combined the significant food items and SNPs. Final significant factors associated with pancreatic cancer after backward selection included: Type 2 Diabetes [7.66 (3.46, 17.76)], corrected percent recovered version of the PAH (cPheT) [1.03 (1.02, 1.05)], very well done red meat [0.90 (0.84, 0.96)], recessive(rs12203582) [3.58 (1.50, 8.83)], recessive(rs56679) [0.25 (0.06, 0.80)], overdominant(rs3784605) [3.42 (1.81, 6.66)], overdominant(rs721430) [0.42 (0.21, 0.83)]. Of note by design, the level of smoking did not differ between our cases and controls. This study provides evidence that a metabolite PheT could be a biomarker of pancreatic cancer independent of dietary intake, smoking, and select metabolism SNPs. Citation Format: Sierra Nguyen, Heather Carlson, Andrea Yoder, William R. Bamlet, Ann L. Oberg, Gloria M. Peterson, Steven Hecht, Rick Jansen. Polycyclic aromatic hydrocarbons and pancreatic cancer: An analysis of blood biomarker PheT as an exposure identifier [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B005.

Development of a Conditional Event-Free Survival Tool in Diffuse Large B-Cell Lymphoma

Background: The International Prognostic Index (IPI) and related prognostic models for diffuse large B-cell lymphoma (DLBCL) use baseline clinical factors to predict outcomes at the time of initial therapy. These models are static and do not update over the disease course. Although most patients respond to initial therapy, patients and their treating physicians need better tools to understand recurrence risk, which decreases over time. For example, a young woman who completed treatment two years ago and is now contemplating pregnancy may wish to know about her recurrence risk in the next year. Similarly, physicians and patients alike may want to design a surveillance strategy that is commensurate with recurrence risk over time. Conditional event-free survival (cEFS) accounts for both patient risk factors plus the declining probability of event recurrence the longer a patient has been in remission. We report the development and validation of a dynamic risk prediction tool for DLBCL patients following completion of front-line immunochemotherapy (IC). Methods: Data from the LEO/MER cohort study and several prospective NCTN clinical trials (Alliance/CALGB 50303 [NCT00118209], S1001 [NCT01359592], E1412 [NCT01856192], and E4494 [NCT00003150]) were used to construct a dynamic prognostic model. Patients who had completed initial therapy and achieved a complete response (CR) (when response data were available; i.e., trial data) or did not experience an event within 12 weeks of end of therapy (when response data were not available; i.e., LEO/MER cohorts) were selected. Baseline variables included age, sex, stage, Eastern Cooperative Oncology Group (ECOG) performance status, pathology, and labs. Cox proportional-hazards regression modeling with backwards stepwise selection was utilized for model development. Model performance was assessed at fixed timepoints, using time-dependent areas under the receiver operating characteristic curve (tAUC of ROC) and Brier scores, and over the entire span of time in the data, using C-statistics. Model performance was compared to the performance of the IPI. Internal validation was performed using bootstrapping. External validation was separately performed using the Danish National Lymphoma Registry (LYFO). To develop the dynamic prediction tool, a landmark method was used, in which time since completion of therapy was also included as a key model input. An R Shiny application was developed to implement this dynamic prediction and facilitate point-of-care utilization, with the output being probability of remaining event-free during a user-specified time frame. Results: 2897 patients were included in model development, with the final model consisting of age (transformed to account for the non-linear association with outcome), sex, ECOG performance status, stage, and lactate dehydrogenase (LDH). Model parameters are shown in the Table. The final model outperformed a model containing only IPI in terms of C-statistic, tAUC, and Brier scores. The C-statistic was 0.66 (95% CI: 0.64, 0.68). Brier scores varied from 0.07 (0.06, 0.08) at 12 months to 0.19 (0.17, 0.20) at 84 months. The tAUCs varied from 0.65 (0.62, 0.69) at 12 months to 0.71 (0.68, 0.74) at 84 months. In contrast, in the IPI-only model, the C-statistic was 0.63, while the Brier scores ranged from 0.07 to 0.21 and tAUCs ranged from 0.64 to 0.65 between the same time intervals. After applying the model to 3250 patients from LYFO, the Brier scores and tAUCs were similar, ranging from 0.11 to 0.22 and 0.69 to 0.71, respectively, also between the same time intervals. In the Figure, we show that the 2-year cEFS of a 38-year-old female with stage III disease, a performance score of 1, and a LDH ratio of 4 having survived one year post-treatment is 0.908, compared to 0.921 for a female with the same model parameters but having survived 2 years. Conclusions: We present a dynamic risk prediction tool for patients with DLBCL having achieved a CR following front-line IC that allows for personalized prognostication of EFS both at the time of therapy completion and during follow-up. We are actively exploring prospective clinical trials of rational post-treatment surveillance based on individual cEFS, as a step towards evidence-based follow-up strategies. Support: U10CA180821 (CALGB 50303), https://acknowledgments.alliancefound.org, U10CA180820, UG1CA233339, and UG1CA232760 (ECOG-ACRIN), and U10CA180888 (SWOG). Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Racial, Ethnic and Socioeconomic Disparity in Outcomes of Patients with Chronic Graft-Versus-Host Disease: A CIBMTR Analysis

Introduction: Prior studies have demonstrated that racial/ethnic minority groups and patients with low socioeconomic status (SES) have worse overall survival (OS) after allogeneic hematopoietic stem cell transplant (allo-HCT). Whether the racial and SES disparity in outcomes persists and possibly may even be exacerbated in allo-HCT recipients with chronic graft-versus-host disease (cGVHD) is unknown. Certain aspects of cGVHD such as the need for long term care, prolonged immunosuppressive treatment, and close follow up for complications may exacerbate racial and SES disparities. In the current study, we analyzed the association of race/ethnicity and SES with the incidence of cGVHD and compared the long-term outcomes of allo-HCT survivors with cGVHD by race/ethnicity and SES. Methods: The study population comprised 14,825 adults with acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL), myelodysplastic syndrome (MDS), or myeloproliferative neoplasm (MPN) who received first allo-HCT between 2008-2018 in the United States. Residential ZIP code at the time of allo-HCT was used to estimate annual household income as the measure of SES. Patients with relapse of the primary disease prior to diagnosis of cGVHD were excluded. The incidence of cGVHD was calculated using the cumulative incidence estimator. Cox regression models were used to determine whether race/ethnicity and SES were associated with overall survival (OS), treatment related mortality (TRM) and relapse among patients with cGVHD. A stepwise backward variable selection was used to develop models for each outcome with a threshold of 0.05 for retention in the model. The main variable was then forced into the models. Interactions between the main variable and the adjusted covariates were tested at the significance level of 0.01. Results: Patients were reported to be Non-Hispanic White (W) (n=11,796), Non-Hispanic Black (B) (n=1,009), Hispanic (H) (n=1,325), and Asian (A) (n=695). A total of 5,600 relapse-free allo-HCT recipients developed cGVHD. There was no significant difference in cumulative incidence of cGVHD based on race/ethnicity (p=0.49, Table 1) or SES (p=0.44). In the multivariable analysis, we observed no significant differences in OS (p=0.15), TRM (p=0.11) and relapse (p=0.03) in patients with cGVHD based on race and ethnicity adjusted for patient-, disease- and transplant-related covariates. SES (household income, with or without adjustment for health insurance status) was significantly associated with OS (p<0.0001) and TRM (p=0.0006). More specifically, the highest quartile of household income (≥ $80,000) was associated with improved OS (HR 0.78, CI 0.71-0.86, p< 0.0001) and reduced TRM (HR 0.75, CI 0.66-0.86, p< 0.0001) compared with the lowest household income quartile (< $48,000) (Table 2). There was no association between the SES and relapse among patients with cGVHD (p=0.29). Conclusions: Our study highlights that low SES, regardless of race/ethnicity, has a negative impact on outcomes after diagnosis of cGVHD. Disparities in outcomes solely based on race/ethnicity were not evident among patients with cGVHD. Future studies are needed to determine if people with low SES and cGVHD have disproportionately worse outcomes than those with low SES without cGVHD. If they do, then optimizing the health care resources available to cGVHD patients with low SES may improve long-term outcomes. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Determinants of life expectancy at birth: a longitudinal study on OECD countries

This paper analyses the influence of several determinants on life expectancy at birth in 36 OECD countries over the 1999–2018 period. We utilized a cross-country fixed-effects multiple regression analysis with year and country dummies and used dynamic models, backward stepwise selection, and Arellano–Bond estimators to treat potential endogeneity issues. The results show the influence of per capita health-care expenditure, incidence of out-of-pocket expenditure, physician density, hospital bed density, social spending, GDP level, participation ratio to labour, prevalence of chronic respiratory diseases, temperature, and total size of the population on life expectancy at birth. In line with previous studies, this analysis confirms the relevance of both health care expenditure and health care system (physicians and hospital beds in our analysis) in influencing a country’s population life expectancy. It also outlines the importance of other factors related to population behaviour and social spending jointly considered on this outcome. Policy makers should carefully consider these mutual influences when allocating public funds, particularly after the COVID-19 pandemic period.

Role of abiotic drivers on crab burrow distribution in a saltmarsh wetland

Crab burrows play an important role in saltmarsh wetlands and are a useful indicator of wetland condition. The spatiotemporal distribution of crab burrows varies considerably in tidal wetlands. However, the reasons for these variations are poorly understood, in part, due to the limited availability of comprehensive field data. Based on a two-year continuous observation at a tidal wetland in the northern Jiangsu Coast, China, this study explored the relationship between crab burrow density and environmental variables, including median grain size, water content, organic matter content, soil salinity, and elevation. Our results show that the distribution of crab burrows was unimodal across the shore in winter and spring (Nov-Apr) when air temperature was relatively low, while bimodal in summer and autumn (May-Oct) when temperature was relatively high. The density of crab burrows was larger at areas with higher water content, higher organic matter content, and lower soil salinity, while it was lower with stronger hydrodynamics and lower suspended sediment concentration. Crab burrows were more abundant in vegetated areas than in un-vegetated areas. A backward stepwise model selection was performed based on R-square and Akaike information criterion (AIC) to distinguish the main driving factors that determine crab burrow distribution. Results suggested that the principal driving factors were organic matter content and soil salinity in all the seasons, with the addition of water content in warm seasons. Overall, this study provides a comprehensive field dataset for a more in-depth understanding of crab burrow distribution and a scientific basis for sustainable management of tidal wetlands.

Development of a prediction model to estimate the 5-year risk of cardiovascular events and all-cause mortality in haemodialysis patients: a retrospective study.

Cardiovascular disease (CVD) is a major cause of mortality in patients on haemodialysis. The development of a prediction model for CVD risk is necessary to help make clinical decisions for haemodialysis patients. This retrospective study aimed to develop a prediction model for the 5-year risk of CV events and all-cause mortality in haemodialysis patients in China. We retrospectively enrolled 398 haemodialysis patients who underwent dialysis at the dialysis facility of the General Hospital of Northern Theater Command in June 2016 and were followed up for 5 years. The composite outcome was defined as CV events and/or all-cause death. Multivariable logistic regression with backwards stepwise selection was used to develop our new prediction model. Seven predictors were included in the final model: age, male sex, diabetes, history of CV events, no arteriovenous fistula at dialysis initiation, a monocyte/lymphocyte ratio greater than 0.43 and a serum uric acid level less than 436 mmol/L. Discrimination and calibration were satisfactory, with a C-statistic above 0.80. The predictors lay nearly on the 45-degree line for agreement with the outcome in the calibration plot. A simple clinical score was constructed to provide the probability of 5-year CV events or all-cause mortality. Bootstrapping validation showed that the new model also has similar discrimination and calibration. Compared with the Framingham risk score (FRS) and a similar model, our model showed better performance. This prognostic model can be used to predict the long-term risk of CV events and all-cause mortality in haemodialysis patients. An MLR greater than 0.43 is an important prognostic factor.