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PURPOSE: Patient-reported outcome measures (PROMs) such as the Neurological Disorders Depression Inventory in Epilepsy (NDDI-E), a 6-item epilepsy-specific PROM, is used to screen for major depressive disorder symptoms for patients with epilepsy (PWE). The validity and interpretation of PROMs can be affected by differential item functioning (DIF), which occurs when subgroups of patients with the same underlying health status respond to and interpret questions about their health status differently. This study aims to determine whether NDDI-E items exhibit DIF and to identify subgroups of PWE that exhibit DIF in NDDI-E items. METHODS: Data were from the Calgary Comprehensive Epilepsy Program database, a clinical registry of adult PWE in Calgary, Canada. A tree-based partial credit model based on recursive partitioning (PCTree) was used to identify subgroups that exhibit DIF on NDDI-E items using patients' characteristics as covariates. Differences in the identified subgroups were characterized using multinomial logistic regression. RESULTS: Of the 1,576 patients in this cohort, 806 (51.1%) were female, and the median age was 38.0 years. PCTree identified four patient subgroups defined by employment status, age, and sex. Subgroup 1 were unemployed patients ≤ 26 years old, subgroup 2 were unemployed patients > 26 years, subgroup 3 were employed females, while subgroup 4 were employed male patients. The subgroups exhibited significant differences on education level, comorbidity index scores, marital status, type of epilepsy, and driving status. CONCLUSION: PWE differed in their interpretation and responses to questions about their depression symptoms, and these differences were a function of sociodemographic and clinical characteristics.

Patient-reported depression screening measures are prone to differential item functioning (DIF), which occurs when patients with the same levels of depression respond to and interpret the questions on the measures differently, as a result of different socio-demographic characteristics. Heterogeneity in how epilepsy patients interpret and respond to depression measures, if not identified, could lead to measurement biases that threaten the validity of inferences from patient-reported outcome measure scores to inform clinical and healthcare decisions for epilepsy management. Using data from a clinical registry of patients with epilepsy, the tree-based item response theory model was used to examine the presence of DIF in the items comprising the patient-reported Neurological Disorders Depression Inventory in Epilepsy (NDDI-E) scale. Four subgroups of epilepsy patients were noted to exhibit DIF on NDDI-E items. These groups were defined by interactions among employment status, age and sex. These characteristics affect the interpretation of NDDI-E item scores. It is recommended that clinicians carefully scrutinize responses to individual items alongside the overall NDDI-E score in different patient groups to inform clinical decisions for epilepsy management.

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BACKGROUND: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). RESULTS: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUC: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians. CONCLUSIONS: This study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

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The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional in vivo and in vitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

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This study demonstrated the correlation of molecular structures of Peroxisome proliferator-activated receptor gamma (PPARγ) modulators and their biological activities. Bayesian classification, and recursive partitioning (RP) studies have been applied to a dataset of 323 PPARγ modulators with diverse scaffolds. The results provide a deep insight into the important sub-structural features modulating PPARγ. The molecular docking analysis again confirmed the significance of the identified sub-structural features in the modulation of PPARγ activity. Molecular dynamics simulations further underscored the stability of the complexes formed by investigated modulators with PPARγ. Overall, the integration of many computational approaches unveiled key structural motifs essential for PPARγ modulatory activity that will shed light on the development of effective modulators in the future.

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BACKGROUND: Glioma is the most common primary brain tumor with high mortality and disability rates. Recent studies have highlighted the significant prognostic consequences of subtyping molecular pathological markers using tumor samples, such as IDH, 1p/19q, and TERT. However, the relative importance of individual markers or marker combinations in affecting patient survival remains unclear. Moreover, the high cost and reliance on postoperative tumor samples hinder the widespread use of these molecular markers in clinical practice, particularly during the preoperative period. We aim to identify the most prominent molecular biomarker combination that affects patient survival and develop a preoperative MRI-based predictive model and clinical scoring system for this combination. METHODS: A cohort dataset of 2,879 patients was compiled for survival risk stratification. In a subset of 238 patients, recursive partitioning analysis (RPA) was applied to create a survival subgroup framework based on molecular markers. We then collected MRI data and applied Visually Accessible Rembrandt Images (VASARI) features to construct predictive models and clinical scoring systems. RESULTS: The RPA delineated four survival groups primarily defined by the status of IDH and TERT mutations. Predictive models incorporating VASARI features and clinical data achieved AUC values of 0.85 for IDH and 0.82 for TERT mutations. Nomogram-based scoring systems were also formulated to facilitate clinical application. CONCLUSIONS: The combination of IDH-TERT mutation status alone can identify the most distinct survival differences in glioma patients. The predictive model based on preoperative MRI features, supported by clinical assessments, offers a reliable method for early molecular mutation prediction and constitutes a valuable scoring tool for clinicians in guiding treatment strategies.

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Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case. In simulated and real-world data, we assess performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate compared to structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.

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OBJECTIVE: Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years. PARTICIPANTS: We recruited college students (N = 5494) from ten universities across eight states. METHOD: Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive partitioning to predict the presence, absence, and severity, of suicidal ideation. RESULTS: Recursive partitioning models varied in their accuracy and performance. The best-performing model consisted of predictors and outcomes measured by the DSM-5 Level 1 Cross-Cutting Symptom Measure. Sexual orientation was also an important predictor in most models. CONCLUSIONS: A single measure of DSM-5 symptom severity may help universities understand suicide severity to promote targeted interventions. Though further work is needed, as similar scaling amongst predictors could have influenced the model.

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Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.

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BACKGROUND: Non-anatomical factors significantly affect treatment guidance and prognostic prediction in nasopharyngeal carcinoma (NPC) patients. Here, we developed a novel survival model by combining conventional TNM staging and serological indicators. METHODS: We retrospectively enrolled 10,914 eligible patients with nonmetastatic NPC over 2009-2017 and randomly divided them into training (n = 7672) and validation (n = 3242) cohorts. The new staging system was constructed based on T category, N category, and pretreatment serological markers by using recursive partitioning analysis (RPA). RESULTS: In multivariate Cox analysis, pretreatment cell-free Epstein-Barr virus (cfEBV) DNA levels of >2000 copies/mL [HROS (95 % CI) = 1.78 (1.57-2.02)], elevated lactate dehydrogenase (LDH) levels [HROS (95 % CI) = 1.64 (1.41-1.92)], and C-reactive protein-to-albumin ratio (CAR) of >0.04 [HROS (95 % CI) = 1.20 (1.07-1.34)] were associated with negative prognosis (all P < 0.05). Through RPA, we stratified patients into four risk groups: RPA I (n = 3209), RPA II (n = 2063), RPA III (n = 1263), and RPA IV (n = 1137), with 5-year overall survival (OS) rates of 93.2 %, 86.0 %, 80.6 %, and 71.9 % (all P < 0.001), respectively. Compared with the TNM staging system (eighth edition), RPA risk grouping demonstrated higher prognostic prediction efficacy in the training [area under the curve (AUC) = 0.661 vs. 0.631, P < 0.001] and validation (AUC = 0.687 vs. 0.654, P = 0.001) cohorts. Furthermore, our model could distinguish sensitive patients suitable for induction chemotherapy well. CONCLUSION: Our novel RPA staging model outperformed the current TNM staging system in prognostic prediction and clinical decision-making. We recommend incorporating cfEBV DNA, LDH, and CAR into the TNM staging system.

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BACKGROUND: Histone deacetylase 9 (HDAC9) is an important member of the class IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer, lung cancer, lymphoblastic leukaemia, etc. The important role of HDAC9 is also recognized in the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to find out the important structural attributes of HDAC9 inhibitors for developing selective HDAC9 inhibitors with higher potency. METHODS: The classification QSAR-based methods namely Bayesian classification and recursive partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural features strongly suggested that sulphur-containing compounds can be a good choice for HDAC9 inhibition. For this reason, these models were applied further to screen some natural compounds from Allium sativum. The screened compounds were further accessed for the ADME properties and docked in the homology-modelled structure of HDAC9 in order to find important amino acids for the interaction. The best-docked compound was considered for molecular dynamics (MD) simulation study. RESULTS: The classification models have identified good and bad fingerprints for HDAC9 inhibition. The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide had been identified as compounds having potent HDAC9 inhibitory activity. The results from ADME and molecular docking study of these compounds show the binding interaction inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory results in terms of different validation parameters of MD simulation study. CONCLUSION: This in-silico modelling study has identified the natural potential lead (s) from Allium sativum. Specifically, the ajoene with the best in-silico features can be considered for further in-vitro and in-vivo investigation to establish as potential HDAC9 inhibitors.

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Human meprin ß is a Zn2+-containing multidomain metalloprotease enzyme that belongs to the astacin family of the metzincin endopeptidase superfamily. Meprin ß, with its diverse tissue expression pattern and wide substrate specificity, plays a significant role in various biological processes, including regulation of IL-6R pathways, lung fibrosis, collagen deposition, cellular migration, neurotoxic amyloid ß levels, and inflammation. Again, meprin ß is involved in Alzheimer's disease, hyperkeratosis, glomerulonephritis, diabetic kidney injury, inflammatory bowel disease, and cancer. Despite a crucial role in diverse disease processes, no such promising inhibitors of meprin ß are marketed to date. Thus, it is an unmet requirement to find novel promising meprin ß inhibitors that hold promise as potential therapeutics. In this study, a series of arylsulfonamide and tertiary amine-based hydroxamate derivatives as meprin ß inhibitors has been analyzed through ligand-based and structure-based in silico approaches to pinpoint their structural and physiochemical requirements crucial for exerting higher inhibitory potential. This study identified different crucial structural features such as arylcarboxylic acid, sulfonamide, and arylsulfonamide moieties, as well as hydrogen bond donor and hydrophobicity, inevitable for exerting higher meprin ß inhibition, providing valuable insight for their further future development.Communicated by Ramaswamy H. Sarma.

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The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

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PURPOSE: Whole-brain radiotherapy (WBRT) may not be beneficial for patients with brain metastases (BMs). The Glasgow Prognostic Score (GPS) is a suggested prognostic factor for malignancies. However, GPS has never been assessed in patients with BMs who have undergone WBRT. The purpose of this study was to determine whether GPS can be used to identify subgroups of patients with BMs who have a poor prognosis, such as recursive partitioning analysis (RPA) Class 2 and Class 3, and who will not receive clinical prognostic benefits from WBRT. MATERIALS AND METHODS: A total of 180 Japanese patients with BMs were treated with WBRT between May 2008 and October 2015. We examined GPS, age, Karnofsky Performance Status (KPS), RPA, graded prognostic assessment (GPA), number of lesions, tumor size, history of brain surgery, presence of clinical symptoms, and radiation doses. RESULTS: The overall median survival time (MST) was 6.1 months. seventeen patients (9.4%) were alive more than 2 years after WBRT. In univariate analysis, KPS ≤ 70 (p = 0.0066), GPA class 0-2 (p = 0.0008), > 3 BMs (p = 0.012), > 4 BMs (p = 0.02), patients who received ≥ 3 Gy per fraction (p = 0.0068), GPS ≥ 1 (p = 0.0003), and GPS ≥ 2 (p = 0.0009) were found to significantly decrease the MST. Patients who had brain surgery before WBRT (p = 0.036) had a longer survival. On multivariate analysis, GPS ≥ 1 (p = 0.008) was found to significantly decrease MST. CONCLUSION: Our results suggest that GPS ≥ 1 indicates a poor prognosis in patients undergoing WBRT for intermediate and poor prognosis BMs.

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BACKGROUND: Sepsis-associated acute kidney injury (SA-AKI) is a critical condition with significant clinical implications, yet there is a need for a predictive model that can reliably assess the risk of its development. This study is undertaken to bridge a gap in healthcare by creating a predictive model for SA-AKI with the goal of empowering healthcare providers with a tool that can revolutionize patient care and ultimately lead to improved outcomes. METHODS: A cohort of 615 patients afflicted with sepsis, who were admitted to the intensive care unit, underwent random stratification into 2 groups: a training set (n = 435) and a validation set (n = 180). Subsequently, a multivariate logistic regression model, imbued with nonzero coefficients via LASSO regression, was meticulously devised for the prognostication of SA-AKI. This model was thoughtfully rendered in the form of a nomogram. The salience of individual risk factors was assessed and ranked employing Shapley Additive Interpretation (SHAP). Recursive partition analysis was performed to stratify the risk of patients with sepsis. RESULTS: Among the panoply of clinical variables examined, hypertension, diabetes mellitus, C-reactive protein, procalcitonin (PCT), activated partial thromboplastin time, and platelet count emerged as robust and independent determinants of SA-AKI. The receiver operating characteristic curve analysis for SA-AKI risk discrimination in both the training set and validation set yielded an area under the curve estimates of 0.843 (95% CI: 0.805 to 0.882) and 0.834 (95% CI: 0.775 to 0.893), respectively. Notably, PCT exhibited the most conspicuous influence on the model's predictive capacity. Furthermore, statistically significant disparities were observed in the incidence of SA-AKI and the 28-day survival rate across high-risk, medium-risk, and low-risk cohorts (P < .05). CONCLUSION: The composite predictive model, amalgamating the quintet of SA-AKI predictors, holds significant promise in facilitating the identification of high-risk patient subsets.

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Matrix metalloproteinases (MMPs) are belonging to the Zn2+-dependent metalloenzymes. These can degenerate the extracellular matrix (ECM) that is entailed with various biological processes. Among the MMP family members, MMP-9 is associated with several pathophysiological circumstances. Apart from wound healing, remodeling of bone, inflammatory mechanisms, and rheumatoid arthritis, MMP-9 has also significant roles in tumor invasion and metastasis. Therefore, MMP-9 has been in the spotlight of anticancer drug discovery programs for more than a decade. In this present study, classification-based QSAR techniques along with fragment-based data mining have been carried out on divergent MMP-9 inhibitors to point out the important structural attributes. This current study may be able to elucidate the importance of several pivotal molecular fragments such as sulfonamide, hydroxamate, i-butyl, and ethoxy functions for imparting potential MMP-9 inhibition. These observations are in correlation with the ligand-bound co-crystal structures of MMP-9. Therefore, these findings are beneficial for the design and discovery of effective MMP-9 inhibitors in the future.

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BACKGROUND: Typhoid fever, or enteric fever, is a highly fatal infectious disease that affects over 9 million people worldwide each year, resulting in more than 110,000 deaths. Reduction in the burden of typhoid in low-income countries is crucial for public health and requires the implementation of feasible water, sanitation, and hygiene (WASH) interventions, especially in densely populated urban slums. OBJECTIVE: In this study, conducted in Mirpur, Bangladesh, we aimed to assess the association between household WASH status and typhoid risk in a training subpopulation of a large prospective cohort (n=98,087), and to evaluate the performance of a machine learning algorithm in creating a composite WASH variable. Further, we investigated the protection associated with living in households with improved WASH facilities and in clusters with increasing prevalence of such facilities during a 2-year follow-up period. METHODS: We used a machine learning algorithm to create a dichotomous composite variable ("Better" and "Not Better") based on 3 WASH variables: private toilet facility, safe drinking water source, and presence of water filter. The algorithm was trained using data from the training subpopulation and then validated in a distinct subpopulation (n=65,286) to assess its sensitivity and specificity. Cox regression models were used to evaluate the protective effect of living in "Better" WASH households and in clusters with increasing levels of "Better" WASH prevalence. RESULTS: We found that residence in households with improved WASH facilities was associated with a 38% reduction in typhoid risk (adjusted hazard ratio=0.62, 95% CI 0.49-0.78; P<.001). This reduction was particularly pronounced in individuals younger than 10 years at the first census participation, with an adjusted hazard ratio of 0.49 (95% CI 0.36-0.66; P<.001). Furthermore, we observed an inverse relationship between the prevalence of "Better" WASH facilities in clusters and the incidence of typhoid, although this association was not statistically significant in the multivariable model. Specifically, the adjusted hazard of typhoid decreased by 0.996 (95% CI 0.986-1.006) for each percent increase in the prevalence of "Better" WASH in the cluster (P=.39). CONCLUSIONS: Our findings demonstrate that existing variations in household WASH are associated with differences in the risk of typhoid in densely populated urban slums. This suggests that attainable improvements in WASH facilities can contribute to enhanced typhoid control, especially in settings where major infrastructural improvements are challenging. These findings underscore the importance of implementing and promoting comprehensive WASH interventions in low-income countries as a means to reduce the burden of typhoid and improve public health outcomes in vulnerable populations.

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BACKGROUND: Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO insertion. METHODS: This is a multicenter study to develop and validate an easy-to-use prognostic model to predict in-hospital mortality of VA-ECMO therapy, using unbiased recursive partitioning with conditional inference trees. We compared two sets with different numbers of variables (small and comprehensive), all of which were available just before ECMO initiation. The area under the curve (AUC), the cross-validated Brier score, and the error rate were applied to assess model performance. Data were collected retrospectively between 2007 and 2019. RESULTS: 837 patients were eligible for this study; 679 patients in the derivation cohort (median (IQR) age 60 (49 to 69) years; 187 (28%) female patients) and a total of 158 patients in two external validation cohorts (median (IQR) age 57 (49 to 65) and 70 (63 to 76) years). For the small data set, the model showed a cross-validated error rate of 35.79% and an AUC of 0.70 (95% confidence interval from 0.66 to 0.74). In the comprehensive data set, the error rate was the same with a value of 35.35%, with an AUC of 0.71 (95% confidence interval from 0.67 to 0.75). The mean Brier scores of the two models were 0.210 (small data set) and 0.211 (comprehensive data set). External validation showed an error rate of 43% and AUC of 0.60 (95% confidence interval from 0.52 to 0.69) using the small tree and an error rate of 35% with an AUC of 0.63 (95% confidence interval from 0.54 to 0.72) using the comprehensive tree. There were large differences between the two validation sets. CONCLUSIONS: Conditional inference trees are able to augment prognostic clinical decision making for patients undergoing ECMO treatment. They may provide a degree of accuracy in mortality prediction and prognostic stratification using readily available variables.

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Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations. The resulting algorithm can discover subpopulations with potentially varying magnitude and causal direction of effects under mild parameter inequality assumptions. Feasibility conditions are described and results from synthetic data experiments are presented suggesting that large effects and large sample sizes are beneficial for detecting causally competing subgroups with acceptable statistical performance. In a real-world data example, the extraction of meaningful subgroups that differ in the causal mechanism underlying the development of numerical cognition is illustrated. Potential extensions and recommendations for best practice applications are discussed.

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BACKGROUND: Several researches have shown that pan-immune-inflammation value (PIV) is related to cancer prognosis in recent years. In esophageal squamous cell carcinoma (ESCC), nevertheless, the prognostic impact of PIV remains unclear. The present study sought to investigate the prognostic impact of preoperative PIV in ESCC with radical resection. METHODS: The data of 294 ESCC patients who received radical resection were retrospectively analyzed. Based on analyzing the non-linear relationship between PIV and cancer-specific survival (CSS), the optimal cutoff value for PIV was calculated by the restricted cubic spline (RCS) model. Cox proportional hazards regression was carried out to identify the prognostic factors. A risk stratification model was established by recursive partitioning analysis (RPA). The performance of the RPA-based model was assessed by the decision curve analysis (DCA) and receiver operating characteristic (ROC). RESULTS: The RCS visualized the non-linear relationship between PIV and CSS (P < 0.0001). Then patients were then divided into high and low groups based on the optimal threshold of 308.2. The 5-year CSS (17.7 % vs. 48.3 %, P < 0.001) was significantly worse in patients with high PIV than those in the low group. Subgroup analyses confirmed that patients with low PIV also achieved better 5-year survival at different pathological tumor node metastasis (pTNM) stages (pTNM I: P = 0.022; pTNM II: P = 0.001; pTNM III: P = 0.011). PIV served as an independent prognostic factor of CSS (hazard ratio = 1.983, P < 0.001). A new staging involving three risk groups with significantly different CSS was developed using RPA algorithms based on pTNM and PIV. Compared with the pTNM classification, the RPA-based model exhibited significantly superior performance for prognostication. CONCLUSION: The present study confirmed the prognostic impact of PIV in ESCC who treated with radical resection. PIV was associated with the tumor stage and prognosis, which might be useful in the preoperative assessment of ESCC.

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INTRODUCTION: Treatment persistence is a proxy for efficacy, safety and patient satisfaction, and a switch in treatment or treatment discontinuation has been associated with increased indirect and direct costs in inflammatory arthritis (IA). Hence, there are both clinical and economic incentives for the identification of factors associated with treatment persistence. Until now, studies have mainly leveraged traditional regression analysis, but it has been suggested that novel approaches, such as statistical learning techniques, may improve our understanding of factors related to treatment persistence. Therefore, we set up a study using nationwide Swedish high-coverage administrative register data with the objective to identify patient groups with distinct persistence of subcutaneous tumor necrosis factor inhibitor (SC-TNFi) treatment in IA, using recursive partitioning, a statistical learning algorithm. METHODS: IA was defined as a diagnosis of rheumatic arthritis (RA), ankylosing spondylitis/unspecified spondyloarthritis (AS/uSpA) or psoriatic arthritis (PsA). Adult swedish biologic-naïve patients with IA initiating biologic treatment with a SC-TNFi (adalimumab, etanercept, certolizumab or golimumab) between May 6, 2010, and December 31, 2017. Treatment persistence of SC-TNFi was derived based on prescription data and a defined standard daily dose. Patient characteristics, including age, sex, number of health care contacts, comorbidities and treatment, were collected at treatment initiation and 12 months before treatment initiation. Based on these characteristics, we used recursive partitioning in a conditional inference framework to identify patient groups with distinct SC-TNFi treatment persistence by IA diagnosis. RESULTS: A total of 13,913 patients were included. Approximately 50% had RA, while 27% and 23% had AS/uSpA and PsA, respectively. The recursive partitioning algorithm identified sex and treatment as factors associated with SC-TNFi treatment persistence in PsA and AS/uSpA. Time on treatment in the groups with the lowest treatment persistence was similar across all three indications (9.5-11.3 months), whereas there was more variation in time on treatment across the groups with the highest treatment persistence (18.4-48.9 months). CONCLUSIONS: Women have low SC-TNFi treatment persistence in PsA and AS/uSpA whereas male sex and golimumab are associated with high treatment persistence in these indications. The factors associated with treatment persistence in RA were less distinct but may comprise disease activity and concurrent conventional systemic disease-modifying anti-rheumatic drug (DMARD) treatment.

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