Fit Of Logistic Regression Model Research Articles

Association between fibrinogen levels and stroke-associated pneumonia in acute ischemic stroke patients

PurposePrior research had indicated a relationship between fibrinogen and stroke-associated pneumonia (SAP), yet the nature of this relationship had not been thoroughly investigated. Therefore, this study was designed to elucidate the prognostic value of fibrinogen levels in forecasting the occurrence of SAP among patients with acute ischemic stroke (AIS).Patients and methodsIn this retrospective cross-sectional analysis, we included 1092 patients who had experienced AIS and were admitted to our facility within 72 h of the onset of their symptoms. Based on the SAP diagnostic criteria, patients were classified into two groups: SAP and non-SAP. The correlation between serum fibrinogen concentration and SAP was examined using univariate analysis. Curve fitting and multivariable logistic regression model were utilized for statistical evaluation.ResultsOut of the ischemic stroke patients included in the study, SAP was identified in 112 (10.26%) patients. A direct correlation was observed between fibrinogen levels and the incidence of SAP. An increase in fibrinogen levels corresponded with a heightened incidence of SAP. Multivariable logistic regression revealed a significant positive association between fibrinogen levels and SAP incidence (OR = 1.53, 95% confidence interval [CI]: 1.18, 1.99)).ConclusionA linear relationship between serum fibrinogen levels and the incidence of SAP in ischemic stroke patients was shown. The serum fibrinogen levels were positively and linearly correlated to SAP risk.

PSI-3 Effect of feed and water consumption patterns during the lactation period on wean-to-estrus interval in weaned sows

Abstract There is sufficient evidence that adequate feed and water consumption during the lactation period positively influences sow body condition at weaning. Optimum body condition score after the lactation period decreases sow wean-to-estrus interval and non-productive sow days and promotes sow reproductive performance in the following reproductive cycle, in general. However, feed and water intake patterns have not been extensively studied in commercial sow research; thus, the objective of this study was to determine the effect of feed and water consumption patterns in the lactation period on wean-to-estrus interval in weaned sows. This trial was conducted in a commercial sow farm located in southwestern Minnesota in March 2023. Each lactation feeder was outfitted with a MealMeter 2.0 device (PigEasy, LLC, Templeton, IA), which uses a rotating feed dispenser to release feed ad libitum at the request of each sow. In addition, the MealMeter 2.0 device utilizes sensors to track the number of times feed and water were accessed at 15-min time intervals across the entire lactation period. Average daily feed (ADF) and water accessions (ADW) were summarized for each sow during the lactation period (n = 84 sows). Root mean squared error (RMSEF and RMSEW for feed and water accessions, respectively) was calculated from the residuals of the regression between days post-farrowing and ADF and ADW for each sow and was considered a measure of variation in feed and water accessions during the lactation period. Moreover, the number of days from weaning to the first estrus was recorded and defined as the wean-to-estrus interval (WTE). Due to the distribution of WTE, sows with a WTE ≤ 5 d and WTE > 5 d were separated into a binary classifier (0 = WTE ≤ 5 d and 1 = WTE > 5 d). The relationship between each feed and water measurement and WTE class was estimated using a logistic regression model fit with R Statistical Software. Average daily feed and water accessions were negatively associated with WTE class (P- = 0.07 and 0.04, respectively; Table 1), indicating that as feed and water accessions increase, the probability of observing a WTE greater than 5 d decreases. In addition, RMSEF and RMSEW were also negatively associated with WTE class, which suggests that as the variation in each measure across the lactation period increases, the probability of observing a WTE greater than 5 d is reduced. This result was not expected and, thus, requires further investigation. Nonetheless, results from this study suggest that the mean and variation of daily feed and water accessions are associated with WTE and sufficient consumption of nutrients during the lactation period is critical to reducing the number of days to first estrus in weaned sows.

Understanding the Relation between Short Birth Spacing and Child Maltreatment: Are Associations Due to Parental History of Childhood Abuse and Neglect?

AbstractExisting literature documents connections between short birth spacing (birth‐to‐conception interval of under 18 months) and the risk of child maltreatment, but explanations for this relation are understudied. We hypothesise that short birth spacing predicts child maltreatment, and it mediates the association between maternal history of childhood maltreatment and their child's risk of abuse and neglect. We use data for second and higher parity children from the Prospective Study of Infant Development (n = 335) and fit logistic regression models with relevant covariates to examine the association between maternal history of childhood maltreatment, her offspring's short birth spacing and her offspring's child maltreatment (CM) as measured by Child Protective Services (CPS) assessments. Findings demonstrate that children of mothers who reported experiencing any abuse or neglect during childhood have a higher likelihood of being born shortly spaced and a higher likelihood of CM. Additionally, short birth spacing is associated with increased likelihood of CM. No evidence for mediation from maternal history of maltreatment to CM operating through short birth spacing was detected. We conclude that maternal history of childhood maltreatment and short birth spacing are independent, additive factors for CM risk.

Endometrial cancer with positive sentinel lymph nodes: pathologic characteristics of metastases as predictors of extent of lymphatic dissemination and prognosis

ObjectivesTo assess predictors of extensive lymph node dissemination and non-vaginal recurrence in patients with endometrial cancer with positive sentinel lymph nodes (SLNs).MethodsPatients with endometrial cancer who underwent primary surgery with...

Experiences of menstrual restrictions: Freedom lost and never regained

Among numerous issues that make experiences during menstruation an impediment to gender equity, restrictions on daily activities is a concern about limiting individual freedom. Studies of menstrual restrictions have identified the arrays of “dos-and-don'ts”, but what remained missing was a detailed exploration into its origin, nature, correlates and the reasons behind their uncanny consistency over time. This paper, using primary survey of 1200 married young adults in two eastern states in India, adds to the existing literature by identifying the nature of restrictions, the changes in its contour with marriage and their internalization. Classifying restrictions in three broad categories, (restrictions on self, restrictions over household activities and restrictions over worship), the paper finds that nearly all woman are barred from performing rituals. While marriage increases the restrictions on women's agency to eat, go out, sleep in usual home or have sexual activities, it reduces the restrictions on household chores (cooking, serving). Strikingly, restrictions on herself do not alter with education or menstrual literacy. However, fitting logistic regression models over subsamples of hygienic menstrual product-users, finds restrictions are higher for backward classes and those who incorrectly define menstruation. Astonishingly, women do not find menstrual restrictions to be subduing and is reluctant in aspiring for changing community's perception, letting this thrive through generations. They only aspire for better menstrual products. This study shows that a holistic policy should target ‘changing perceptions’ rather than ‘changing protections’, to usher in a menstrual experience with freedom. Educating girls and their elderly care-givers (mothers, grandmothers or mothers-in-law) about this physiological process, giving them a space to discuss and fight the concept of impurity of their bodies can actually gift them a better living experience for sure.

886. Impact of Race on the Early Diagnosis and Treatment of Lyme Disease

Abstract Background Previous studies using single-state and Medicare surveillance data found that Black patients with Lyme Disease (LD) are more likely to present outside of the typical summer months and have higher rates of disseminated disease at diagnosis. We further explored differences in clinical presentation and early treatment of Lyme Disease by self-reported race among a large cohort of patients at an academic research clinic. Methods We abstracted data from all patients with untreated or post-treatment LD seen at the Johns Hopkins Lyme Disease Research Center (LDRC)—either for clinical care or research participation. Patients self-reported their demographics, and clinical information regarding their initial Lyme presentation was either recorded at the time of diagnosis or was abstracted from prior, external medical records. We examined the distribution of race across initial LD clinical presentations—categorized as 1) erythema migrans (EM) rash only, 2) disseminated disease, or 3) non-specific symptoms only—using chi-squared or Fisher’s exact test and fit logistic regression models. We compared racial differences in time to appropriate treatment using a one-sided Mann-Whitney test. Results Of 1363 individuals seen at the LDRC, 93% were white, 3% were Black, 2% were Asian, and 3% self-identified as either ‘other’ or more than one race. Compared to white patients, Black patients were less likely to present with erythema migrans (EM) rash (p < 0.0001) and more likely to present with disseminated disease or non-specific symptoms (p=0.009, p=0.019, respectively). Black patients were more likely to have received a course of inappropriate antibiotic therapy ( 16.7% vs 8.0 %, p= 0.058). Also, Black patients with EM rash experienced longer time to appropriate treatment (16.3 days vs 4.0, p=0.062). Number of asterisks indicates degree of statistical significance. N/S means "non-significant" Conclusion We identified several racial disparities in the early detection and treatment of Lyme Disease, highlighting the need for increased awareness of Lyme and its early manifestations among patients of color and the front-line clinicians who care for them. Because darker skin tones are generally under-represented in medical textbooks and patient education materials, we include several examples of EM rashes from Black patients seen at the LDRC (Figure 2). Disclosures John Aucott, MD, Pfizer, Inc.: Advisor/Consultant

23 The Utility of Global versus Domain-specific Neuropsychological Test Score Dispersion as Markers of Cognitive Decline

Objective:Higher baseline dispersion (intra-individual variability) across neuropsychological test scores at a single time-point has been associated with more rapid cognitive decline, onset of Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), faster rates of hippocampal and entorhinal atrophy, and increased AD neuropathology. Comparison between predictions made from test score dispersion within a cognitive domain versus global, cross-domain dispersion is understudied. Global dispersion may be influenced by ability-and test-specific characteristics. This study examined the performance of global versus domain-specific dispersion metrics to identify which is most predictive of cognitive decline over time.Participants and Methods:Data for baseline and five follow-up visits of 308 participants with normal cognition (Mage=73.90, SD=8.12) were selected from the National Alzheimer’s Coordinating Center (NACC) Dataset. Participants were required to have no focal neurological deficits, or history of depression, stroke, or heart attack. Diagnoses and progression to MCI and/or dementia were determined at each visit through consensus conferences. Raw neuropsychological scores were standardized using NACC norms. Global baseline dispersion was defined as the intraindividual standard deviation (ISD) across the 10 scores in the NACC battery. Domain-specific dispersions were calculated by constructing composites and ISD was computed across tests sampling their respective domains (executive functioning/attention/processing speed [EFAS], language, and memory; see Table 1 for details on these tests). Higher values on each of these metrics reflect greater dispersion. Multinomial logistic regression model fit statistics and parameter estimates were compared across four different models (global, EFAS, Language, and Memory dispersion) covarying for age, years of education, sex, race, ethnicity, and ApoE4 status. Models were compared using the Likelihood Ratio Test (LRT) and the Akaike Information Criteria (AIC) of Models statistics.Results:Of the 308 participants, 70 (22.7%) progressed to MCI, and 82 (26.6%) progressed to dementia. Tables 1 and 2 show the results of the logistic regressions for the four models. All models fit the data well, with statistically significant predictions of conversion. Model 1 (global dispersion) showed a better fit than domain-specific models of dispersion per LRT and AIC values. Consistent with the results from mean differences between groups, parameter estimates showed that only global dispersion and EFAS dispersion significantly predicted conversion to dementia (when included with other covariates in models), with higher dispersion reflecting a greater risk of conversion.Conclusions:In this sample, baseline global and EFAS dispersion measures significantly predicted conversion to dementia. Although global dispersion was a stronger predictor of dementia progression, findings suggest that executive functioning performance may be driving this relationship. A single index of global variability, from the calculation of standard deviation across test scores, may be supplementary for clinicians when distinguishing individuals at risk for dementia progression. None of the models were predictive of conversion to MCI. Further research is required to examine cognitive variability differences among patients who progress to MCI and patient-specific factors that may relate to test score dispersion and its utility in predicting the progression of symptoms.

Low-dose aspirin and racial disparities in spontaneous preterm delivery in low-risk individuals

Preterm birth is a leading cause of perinatal morbidity and mortality. There are significant racial disparities in the rates of preterm delivery in the United States, with Black individuals at disproportionately higher risk than their White counterparts. Although low-dose aspirin is currently under investigation for reducing the rates of preterm delivery, limited data are available on how the use of low-dose aspirin might affect racial and ethnic disparities in the rates of preterm delivery. Our group and others have shown that low-dose aspirin decreases spontaneous preterm delivery in low-risk parturients. This study aimed to examine whether the relationship between low-dose aspirin and the risk of spontaneous preterm delivery is modified by race and ethnicity. This was a secondary analysis of a randomized clinical trial examining low-dose aspirin for preeclampsia prevention in low-risk nulliparous individuals. The parent trial defined low risk as the absence of preexisting hypertension or other medical comorbidities. Participants received 60-mg aspirin or placebo between 13 and 25 weeks of gestation. Here, multiple pregnancies, fetal anomalies, terminations or abortions at <20 weeks of gestation, and participants with previous miscarriages were excluded. Our exposure, race and ethnicity, was self-reported in the parent trial and categorized as non-Hispanic White, Hispanic, non-Hispanic Black, and other. The primary outcome was spontaneous preterm delivery at <34 weeks of gestation; the secondary outcomes included spontaneous preterm delivery at <37 weeks of gestation and all preterm deliveries at <34 and <37 weeks of gestation. Fit logistic regression models were used to examine how the use of low-dose aspirin modified the relationship between race and ethnicity and preterm delivery, adjusting for confounders. Furthermore, sensitivity analyses were performed to compare the rates of preterm delivery by race and ethnicity. Of note, 2528 of 3171 parent study participants were included in this analysis. Of the participants, 425 (16.8%) were White, 819 (32.4%) were Hispanic, 1265 (50%) were Black, and 19 (0.8%) were other. The baseline characteristics differed among racial and ethnic groups, including maternal age, body mass index, education level, marital status, tobacco and alcohol use, and pregnancy loss. The rate of spontaneous preterm delivery at <34 weeks of gestation was significantly higher in Black participants (2.8%) than in White (1.2%) and Hispanic (1.2%) participants (P=.04). Logistical regression analysis showed that Black race was no longer an independent risk factor for spontaneous preterm delivery at <34 weeks of gestation when controlling for low-dose aspirin (adjusted odds ratio, 1.71; 95% confidence interval, 0.67-4.40). A similar pattern was found for spontaneous preterm delivery at <37 weeks of gestation and preterm delivery at <34 and <37 weeks of gestation. In our sensitivity analyses, spontaneous preterm delivery at <34 weeks of gestation differed by race and ethnicity in the placebo group (P=.01) but did not differ in the low-dose aspirin group (P=.90). The use of low-dose aspirin mitigated racial disparities in spontaneous preterm delivery at <34 weeks of gestation. Additional investigation is warranted to assess the reproducibility of our findings.

Impact of oral/dental disease burden on postoperative infective complications: a prospective cohort study

ObjectivesThis prospective cohort study aimed to assess the association between dental disease burden and postoperative infective complications (POICs) in patients undergoing major surgical procedures under general anaesthesia.MethodsPre-surgical dental assessment was undertaken on patients planned for major surgery. Demographic and surgical variables including putative risk factors for POICs and POIC status were documented. The univariable association between POIC status and each factor was examined. Those variables associated at P value ≤ 0.2 were candidates for inclusion in multiple logistic regression models. Backward stepwise variable selection was used to identify the independent predictors for POIC in the best fitting logistic regression model. The area under the receiver operating curve (AUC) was used to quantify the model’s global classification performance.ResultsAmong the 285 patients, 49 patients (17.2%) had POICs. The independent predictors for POIC were expected length of hospital stay (4–6 days; odds ratio [OR] = 4.80, 95% confidence internal [CI]: 1.30–17.70, P = 0.018, 7–9 days; OR = 5.42, 95% CI: 1.51–19.41, P = 0.009, ≥ 10 days; OR = 28.80, 95% CI: 4.12–201.18, P < 0.001), four or more decayed teeth (OR = 6.03, 95% CI: 2.28–15.94, P < 0.001) and visible tongue plaque (OR = 3.21, 95% CI: 1.54–6.70, P = 0.002). The AUC was 0.78 (95% CI: 0.71–0.85) indicating good discrimination. A simple screening tool for POIC was developed.Conclusions/Clinical relevanceIn addition to systemic/surgical factors, this study identified clinically detected decayed teeth and visible tongue plaque as independent predictors for POICs. Preoperative dental assessment/care might be beneficial to assess risk for POICs and improve postoperative outcomes.

Application of the class-balancing strategies with bootstrapping for fitting logistic regression models for post-fire tree mortality in South Korea

We aimed to tackle a common problem in post-fire tree mortality where the number of trees that survived surpasses the number of dead trees. Here, we investigated the factors that affect Korean red pine (Pinus densiflora Siebold & Zucc.) tree mortality following fires and assessed the statistical effects of class-balancing methods when fitting logistic regression models for predicting tree mortality using empirical bootstrapping (B = 100,000). We found that Slope, Aspect, Height, and Crown Ratio potentially impacted tree mortality, whereas the bark scorch index (BSI) and diameter at breast height (DBH) significantly affected tree mortality when fitting a logistic regression with the original dataset. The same variables included in the fitted logistic regression model were observed using the class-balancing regimes. Unlike the imbalanced scenario, lower variabilities of the estimated parameters in the logistic models were found in balanced data. In addition, class-balancing scenarios increased the prediction capabilities, showing reduced root mean squared error (RMSE) and improved model accuracy. However, we observed various levels of effectiveness of the class-balancing scenarios on our post-fire tree mortality data. We still suggest a thorough investigation of the minority class, but class-balancing scenarios, especially oversampling strategies, are appropriate for developing parsimonious models to predict tree mortality following fires.

Neutrophil-to-lymphocyte ratio as a predictor of surgical outcomes in head and neck cancer.

Neutrophil-to-lymphocyte ratio (NLR) is a biomarker of systemic inflammation that is associated with adverse oncologic and surgical outcomes. We investigated the use of NLR as a prognostic indicator of complications of head and neck cancer (HNC) surgeries. We conducted a retrospective study of 11 187 Veterans who underwent HNC surgery between 2000 and 2020. We calculated preoperative NLR values and fit logistic regression models adjusting for potential confounding factors, comparing high-NLR patients to low-NLR patients. The cohort had a median age of 63 and was 98% men. High-NLR patients had increased odds of 30-day mortality (p < 0.001), having 1+ perioperative complications (p < 0.001), sepsis (p = 0.03), failure to wean from mechanical ventilation (p = 0.04), pneumonia (p < 0.001), and pulmonary embolism (p = 0.02) compared with low-NLR patients. NLR was a robust, independent predictor of 30-day mortality, having 1+ surgical complications, sepsis, failure to wean from mechanical ventilation, pneumonia, and pulmonary embolism.

MP47-15 THE AI-R.E.N.A.L. + SCORE SURPASSES THE HUMAN EXPERT-GENERATED R.E.N.A.L. SCORE IN PREDICTING ONCOLOGIC OUTCOMES OF RENAL TUMORS

You have accessJournal of UrologyCME1 Apr 2023MP47-15 THE AI-R.E.N.A.L. + SCORE SURPASSES THE HUMAN EXPERT-GENERATED R.E.N.A.L. SCORE IN PREDICTING ONCOLOGIC OUTCOMES OF RENAL TUMORS Nour Abdallah, Tarik Benidir, Nicholas Heller, Andrew Wood, Fabian Isensee, Resha Tejpaul, Dillon Corrigan, Chalairat Suk-Ouichai, Onuralp Ergun, Alex You, Erick Remer, Samuel Haywood, Venkatesh Kirshnamurthi, Steven Campbell, Nikolaos Papanikolopoulos, and Christopher Weight Nour AbdallahNour Abdallah More articles by this author , Tarik BenidirTarik Benidir More articles by this author , Nicholas HellerNicholas Heller More articles by this author , Andrew WoodAndrew Wood More articles by this author , Fabian IsenseeFabian Isensee More articles by this author , Resha TejpaulResha Tejpaul More articles by this author , Dillon CorriganDillon Corrigan More articles by this author , Chalairat Suk-OuichaiChalairat Suk-Ouichai More articles by this author , Onuralp ErgunOnuralp Ergun More articles by this author , Alex YouAlex You More articles by this author , Erick RemerErick Remer More articles by this author , Samuel HaywoodSamuel Haywood More articles by this author , Venkatesh KirshnamurthiVenkatesh Kirshnamurthi More articles by this author , Steven CampbellSteven Campbell More articles by this author , Nikolaos PapanikolopoulosNikolaos Papanikolopoulos More articles by this author , and Christopher WeightChristopher Weight More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003293.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The R.E.N.A.L. nephrometry score is a widely accepted surgical aid for extirpation of a renal mass. Excluding the tumor's location (anterior/posterior), the score categorizes variables that are otherwise continuous in nature to facilitate it. It has not yet been universally adopted at the point of care due to its ambiguity, time consumption, and interobserver variability. We previously showed that the artificial intelligence (AI)-generated R.E.N.A.L. score was non-inferior to the human-generated score in predicting perioperative and oncologic outcomes. We hypothesize that we can surpass the predictive ability of human-generated R.E.N.A.L. scores by creating an AI-generated R.E.N.A.L. score+ (AI+ score) with continuous variables rather than ordinal. METHODS: We had 300 patients with preoperative computed tomography scan showing suspected renal cancer at a single institution. Human score was tabulated by 3 trained medical personnel blinded to AI-generated scores. Deep neural network approach was used to automatically segment kidneys into normal parenchyma and tumor, and geometric algorithms were used to estimate the score's components as continuous variables rather than ordinal. Operating characteristic curves (ROC) were created from logistic regression models, and areas under the curve (AUC) were compiled to evaluate the discriminatory ability of the AI+ score vs ordinally-derived AI and human-generated scores for the oncologic outcomes. We assessed the relative importance of the AI+ score components with respect to the odds of outcome through fit logistic regression models using standardized values. RESULTS: The median age was 60 years (IQR 51-68), and 40% were female. The median tumor size was 4.2 cm (2.6-6.12), and 92% were malignant, including 27%, 37%, and 23% with high-stage, high-grade, and necrosis, respectively. The AI+ score had a superior discriminatory ability for each oncologic outcome, including the prediction of malignancy, high stage, high grade, and tumor necrosis (Figure 1). The “R” component had the highest predictive odds ratio for oncologic outcomes. CONCLUSIONS: The AI+ score was superior in predicting meaningful oncologic outcomes compared to ordinal AI-generated and human-generated scores in a non-ambiguous time-efficient manner. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e649 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nour Abdallah More articles by this author Tarik Benidir More articles by this author Nicholas Heller More articles by this author Andrew Wood More articles by this author Fabian Isensee More articles by this author Resha Tejpaul More articles by this author Dillon Corrigan More articles by this author Chalairat Suk-Ouichai More articles by this author Onuralp Ergun More articles by this author Alex You More articles by this author Erick Remer More articles by this author Samuel Haywood More articles by this author Venkatesh Kirshnamurthi More articles by this author Steven Campbell More articles by this author Nikolaos Papanikolopoulos More articles by this author Christopher Weight More articles by this author Expand All Advertisement PDF downloadLoading ...

The Value of Peripheral Blood Cell Ratios in Primary Membranous Nephropathy: A Single Center Retrospective Study.

Primary membranous nephropathy (PMN) is a common cause of nephrotic syndrome in adults. Forty percent of the patients continue to progress and eventually develop into chronic renal failure. Although phospholipase A2 receptor (PLA2R) is the major antigen of PMN, the clinical features do not often parallel with the antibody titers. Therefore, it is significant to find relative credible markers to predict the treatment response. One hundred and eighteen PMN patients were recruited. The response to treatment was defined as ALB≥30g/L at 6 months and complete remission (CR) or not at the end of the follow-up. Renal outcome endpoint was defined as 50% or more Cr increase at the end. The patients with poor treatment effects had numerically higher platelet-lymphocytes ratio (PLR). For patients with CR or not, the difference was near to statistic significant (P=0.095). When analyzing CR or not, the fitting of the binary logistic regression model including both PLA2R Ab titer and PLR (Hosmer-Lemeshow test: χ 2=8.328, P = 0.402; OR (PLA2R Ab titer) = 1.002 (95% CI 1.000-1.004, P = 0.042); OR (PLR) = 1.006 (95% CI 0.999-1.013, P = 0.098)) was markedly better than that with only PLA2R Ab titer (Hosmer-Lemeshow test: χ 2=13.885, P = 0.016). The patients with renal function deterioration showed significantly higher monocyte-lymphocyte ratio (MLR) (0.26 (0.22-0.31) vs 0.18 (0.13-0.22), P = 0.012). PMN patients with poor treatment response tended to have higher PLR at the time of renal biopsy, and a higher MLR was associated with poor renal outcomes. Our findings suggested that PLR and MLR might be used to predict treatment efficacy and prognosis for PMN patients, respectively.

MRI-derived radiomics to guide post-operative management of glioblastoma: Implication for personalized radiation treatment volume delineation.

The glioblastoma's bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation. We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity ± post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model. Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68-0.88). This is the first hypothesis-generating study which applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GB patients in order to achieve a margin reduction strategy.

Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study.

Smartphone apps that capture surveys and sensors are increasingly being leveraged to collect data on clinical conditions. In mental health, this data could be used to personalize psychiatric support offered by apps so that they are more effective and engaging. Yet today, few mental health apps offer this type of support, often because of challenges associated with accurately predicting users' actual future mental health. In this protocol, we present a study design to explore engagement with mental health apps in college students, using the Technology Acceptance Model as a theoretical framework, and assess the accuracy of predicting mental health changes using digital phenotyping data. There are two main goals of this study. First, we present a logistic regression model fit on data from a prior study on college students and prospectively test this model on a new student cohort to assess its accuracy. Second, we will provide users with data-driven activity suggestions every 4 days to determine whether this type of personalization will increase engagement or attitudes toward the app compared to those receiving no personalized recommendations. The study was completed in the spring of 2022, and the manuscript is currently in review at JMIR Publications. This is one of the first digital phenotyping algorithms to be prospectively validated. Overall, our results will inform the potential of digital phenotyping data to serve as tailoring data in adaptive interventions and to increase rates of engagement. PRR1-10.2196/37954.

P02.11.B An hypothesis generating study of MRI-Derived Radiomics on tumor and microenvironment tissue heterogeneity to guide post-operative management of glioblastoma: toward personalized radiation treatment volume delineation

Abstract Background The glioblastoma’s bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification.The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigated the role of radiomic analysis in GBM management, to verify if radiomic features in the tissue around the resection cavity which may guide the radiation target volume delineation. Material and Methods We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction.The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologist reviewed by a neuroradiologist, both with at least 10 years of experience. The Region of interest (ROI) considered for the analysis were: the surgical cavity +/- post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modelling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using the 226 features extracted from the Ring to perform a binary classification and predict the PFS at 6 months (statistical, morphological and textural features). A 3-fold cross-validation repeated five times was implemented for internal validation of the model. Results Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (p-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68 - 0.88). Conclusion This is the first hypothesis-generating study who applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GBM patients in order to achieve a margin reduction strategy.

An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes

BackgroundIn binary logistic regression data are ‘separable’ if there exists a linear combination of explanatory variables which perfectly predicts the observed outcome, leading to non-existence of some of the maximum likelihood coefficient estimates. A popular solution to obtain finite estimates even with separable data is Firth’s logistic regression (FL), which was originally proposed to reduce the bias in coefficient estimates. The question of convergence becomes more involved when analyzing clustered data as frequently encountered in clinical research, e.g. data collected in several study centers or when individuals contribute multiple observations, using marginal logistic regression models fitted by generalized estimating equations (GEE). From our experience we suspect that separable data are a sufficient, but not a necessary condition for non-convergence of GEE. Thus, we expect that generalizations of approaches that can handle separable uncorrelated data may reduce but not fully remove the non-convergence issues of GEE.MethodsWe investigate one recently proposed and two new extensions of FL to GEE. With ‘penalized GEE’ the GEE are treated as score equations, i.e. as derivatives of a log-likelihood set to zero, which are then modified as in FL. We introduce two approaches motivated by the equivalence of FL and maximum likelihood estimation with iteratively augmented data. Specifically, we consider fully iterated and single-step versions of this ‘augmented GEE’ approach. We compare the three approaches with respect to convergence behavior, practical applicability and performance using simulated data and a real data example.ResultsOur simulations indicate that all three extensions of FL to GEE substantially improve convergence compared to ordinary GEE, while showing a similar or even better performance in terms of accuracy of coefficient estimates and predictions. Penalized GEE often slightly outperforms the augmented GEE approaches, but this comes at the cost of a higher burden of implementation.ConclusionsWhen fitting marginal logistic regression models using GEE on sparse data we recommend to apply penalized GEE if one has access to a suitable software implementation and single-step augmented GEE otherwise.

Time of Liver Function Abnormal Identification on Prediction of the Risk of Anti-tuberculosis-induced Liver Injury.

Anti-tuberculosis (anti-TB) drug-induced liver injury (AT-DILI) is the most common side effect in patients who received anti-TB therapy. AT-DILI management includes monitoring liver function until symptoms arise in patients without high-risk factors for liver damage. The present study aimed to investigate the effect of liver function test (LFT) abnormal identification on the risk of DILI, including liver failure and anti-TB drug resistance in patients without high-risk factors. A total of 399 patients without high-risk factors for liver damage at baseline and who experienced LFT abnormal during the 6 months of first-line anti-TB treatment were enrolled. The Roussel Uclaf Causal Relationship Assessment Method (RUCAM, 2016) was applied in suspected DILI. The correlations between the time of LFT abnormal identification and DILI, liver failure, and anti-TB drug resistance were analyzed by smooth curve fitting and multivariable logistic regression models. Among all study patients, 131 met the criteria for DILI with a mean RUCAM causality score of 8.86±0.63. 26/131 and 105/131 were in the probable grading and highly probable grading, respectively. The time of abnormal LFT identification was an independent predictor of DILI, liver failure, and anti-TB drug resistance in the crude model and after adjusting for other risk patient factors. The time of abnormal LFT identification was positively correlated with DILI, liver failure, and anti-TB drug resistance. The late identification group (>8 weeks) had the highest risk of DILI, followed by liver failure compared with the other two groups. The time to identification of LFT was positively correlated with DILI, liver failure, and anti-TB drug resistance. The risk of DILI and liver failure was significantly increased in the late identification group with abnormal LFT identified after 8 weeks compared with 4 and 8 weeks. Early monitoring of LFT is recommended for patients without the high-risk factor of DILI after anti-TB treatment is initiated.

Risk factors for concomitant positive midstream urine culture in patients presenting with symptomatic ureterolithiasis

In patients with symptomatic ureterolithiasis, immediate treatment of concomitant urinary tract infection (UTI) may prevent sepsis. However, urine cultures require at least 24 h to confirm or exclude UTI, and therefore, clinical variables may help to identify patients who require immediate empirical broad-spectrum antibiotics and surgical intervention. Therefore, we divided a consecutive cohort of 705 patients diagnosed with symptomatic ureterolithiasis at a single institution between 2011 and 2017 into a training (80%) and a testing cohort (20%). A machine-learning-based variable selection approach was used for the fitting of a multivariable prognostic logistic regression model. The discriminatory ability of the model was quantified by the area under the curve (AUC) of receiver-operating curves (ROC). After validation and calibration of the model, a nomogram was created, and decision curve analysis (DCA) was used to evaluate the clinical net-benefit. UTI was observed in 40 patients (6%). LASSO regression selected the variables elevated serum CRP, positive nitrite, and positive leukocyte esterase for fitting of the model with the highest discriminatory ability. In the testing cohort, model performance evaluation for prediction of UTI showed an AUC of 82 (95% CI 71.5–95.7%). Model calibration plots showed excellent calibration. DCA showed a clinically meaningful net-benefit between a threshold probability of 0 and 80% for the novel model, which was superior to the net-benefit provided by either one of its singular components. In conclusion, we developed and internally validated a logistic regression model and a corresponding highly accurate nomogram for prediction of concomitant positive midstream urine culture in patients presenting with symptomatic ureterolithiasis.

Organ-Space Surgical Site Infections: Consequences and Prediction Using ACS-NSQIP.

The incidence of organ-space surgical site infections (OSIs) across the US has not decreased to the same extent as superficial and deep surgical site infections (SSIs). This study aimed to highlight the consequences and create a predictive model of OSIs using 2019 NSQIP data. The primary data source was the ACS NSQIP 2019 Participant Use Targeted File (PUF). Chi-squared and independent t-tests evaluated the association of OSIs and other postoperative complications. Variables of interest for our predictive model included 10 surgery types, 10 relevant preoperative laboratory values, and 26 other variables including demographics and comorbidities. A stepwise logistic regression model fit with OSIs as the outcome was used to calculate the predicted ROC and c-index. A total of 1,076,441 cases submitted from 719 NSQIP-participating sites were included in our analysis, comprised of 16,751 (1.6%) OSIs with a median of 10 days (IQR: 11 days) between surgery and the development of an OSI. Forty-two variables were included in our final multivariable analysis. Esophageal and pancreatic surgeries yielded an approximate 4- and 6-fold higher increase in the odds of OSI, respectively (P < .001). ROC analysis yielded a c-index statistic of 0.846 (Figure 1). Associated consequences of OSIs included hospital readmission (OR = 20.74, 95% CI: 20.094-21.412, p < .001), sepsis (OR = 35.084; 95%CI: 33.75-36.47, p < .001) and higher probabilities of mortality (34% vs. 0.8%, p < .001) and morbidity (16% vs. 5.8%, p < .001). OSIs have extensive consequences and may be predictable. These types of infections are multifactorial and require quality surveillance distinct from other SSIs.