BACKGROUND: Early identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy.
METHODS: We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike\'s information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model\'s performance.
RESULTS: The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9.
CONCLUSIONS: Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

The main objective of this study was to analyze dietary patterns using data-driven approaches and to explore preventive or risk dietary factors for kidney stone disease (KSD). A case-control matching study was conducted in adults (n = 6396) from a suburb of Shanghai. A food frequency questionnaire was used to assess the consumption of various types of food, and B-ultrasound was used to identify kidney stones. Principal component analysis and regression were used to generate dietary patterns and further explore the relationship between dietary patterns and KSD. LASSO regression and post-selection inference were used to identify food groups most associated with KSD. Among males, the \"balanced but no-sugary-beverages pattern\" (OR = 0.78, p < 0.05) and the \"nuts and pickles pattern\" (OR = 0.84, p < 0.05) were protective dietary patterns. Among females, \"high vegetables and low-sugary-beverages pattern\" (OR = 0.83, p < 0.05) and \"high-crustaceans and low-vegetables pattern\" (OR = 0.79, p < 0.05) were protective dietary patterns, while the \"comprehensive pattern with a preference for meat\" (OR = 1.06, p < 0.05) and \"sugary beverages pattern\" (OR = 1.16, p < 0.05) were risk dietary patterns. We further inferred that sugary beverages (p < 0.05) were risk factors and pickles (p < 0.05) and crustaceans (p < 0.05) were protective factors.

UNASSIGNED: Developmental dyslexia (DD) has been generally recognized as a multifactorial psychological disorder in recent decades. However, studies on reading and learning environment, social and demographic factors affecting Chinese developmental dyslexia (DD) are still scarce in China. This study aims to explore multidimensional home influencing factors associated with DD before and after birth.
UNASSIGNED: A total of 60 dyslexic and 252 normal elementary school students graded 2-5 were recruited in Shantou, China. The Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used for the social and demographic variables screening. Odds ratios (ORs) with 95 % confidence intervals (CIs) for associations between DD and related factors were estimated by multivariate logistic regression models.
UNASSIGNED: Through LASSO regression, we ultimately identified 13 key variables, including maternal education level and family monthly income, among others. The logistic regression analyses showed that the risk of DD was higher in children with lower maternal education levels. Divergent parenting styles may be a risk factor for developing DD as opposed to consistent parenting styles (OR = 4.93, 95%CI: 1.11-21.91). Children whose mothers suffered from malnutrition during pregnancy were more likely to develop DD (OR = 10.31, 95%CI: 1.84-37.86), as well as exposure to second-hand smoking at home every day (OR = 5.33, 95%CI: 1.52-18.66). Interestingly, children\'s active reading (OR = 0.26, 95%CI: 0.08-0.84; OR = 0.17, 95%CI: 0.04-0.76 for \"sometimes\" and \"often\" compared to none, respectively), children having extracurricular reading fairy tale books (OR = 0.37, 95%CI: 0.15-0.90), and children having extracurricular reading composition books (OR = 0.25, 95%CI: 0.09-0.69) were significant protective factors for DD.
UNASSIGNED: Home reading environment, several educational, sociometric and demographic factors may influence the development of dyslexia. We should pay attention to these factors on the development of dyslexia, so as to provide the well social and familial environment to ensure the healthy development of children.

UNASSIGNED: Although a wide range of risk factors for microtia were identified, the limitation of these studies, however, is that risk factors were not estimated in comparison with one another or from different domains. Our study aimed to uncover which factors should be prioritized for the prevention and intervention of non-syndromic microtia via tranditonal and meachine-learning statistical methods.
UNASSIGNED: 293 pairs of 1:1 matched non-syndromic microtia cases and controls who visited Shanghai Ninth People\'s Hospital were enrolled in the current study during 2017-2019. Thirty-nine risk factors across four domains were measured (i.e., parental sociodemographic characteristics, maternal pregnancy history, parental health conditions and lifestyles, and parental environmental and occupational exposures). Lasso regression model and multivariate conditional logistic regression model were performed to identify the leading predictors of microtia across the four domains. The area under the curve (AUC) was used to calculate the predictive probabilities.
UNASSIGNED: Eight predictors were identified by the lasso regression, including abnormal pregnancy history, genital system infection, teratogenic drugs usage, folic acid supplementation, paternal chronic conditions history, parental exposure to indoor decoration, paternal occupational exposure to noise and maternal acute respiratory infection. The additional predictors identified by the multivariate conditional logistic regression model were maternal age and maternal occupational exposure to heavy metal. Predictors selected from the conditional logistic regression and lasso regression both yielded AUCs (95% CIs) of 0.83 (0.79-0.86).
UNASSIGNED: The findings from this study suggest some factors across multiple domains are key drivers of non-syndromic microtia regardless of the applied statistical methods. These factors could be used to generate hypotheses for further observational and clinical studies on microtia and guide the prevention and intervention strategies for microtia.

OBJECTIVE: This study investigates predictors of weight loss among individuals with serious mental illness participating in an 18-month behavioral weight loss intervention, using Lasso regression to select the most powerful predictors.
METHODS: Data were analyzed from the intervention group of the ACHIEVE trial, an 18-month behavioral weight loss intervention in adults with serious mental illness. Lasso regression was employed to identify predictors of at least five-pound weight loss across the intervention time span. Once predictors were identified, classification trees were created to show examples of how to classify participants into having likely outcomes based on characteristics at baseline and during the intervention.
RESULTS: The analyzed sample contained 137 participants. Seventy-one (51.8%) individuals had a net weight loss of at least five pounds from baseline to 18 months. The Lasso regression selected weight loss from baseline to 6 months as a primary predictor of at least five pound 18-month weight loss, with a standardized coefficient of 0.51 (95% CI: -0.37, 1.40). Three other variables were also selected in the regression but added minimal predictive ability.
CONCLUSIONS: The analyses in this paper demonstrate the importance of tracking weight loss incrementally during an intervention as an indicator for overall weight loss, as well as the challenges in predicting long-term weight loss with other variables commonly available in clinical trials. The methods used in this paper also exemplify how to effectively analyze a clinical trial dataset containing many variables and identify factors related to desired outcomes.