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Background: Harmful alcohol use has detrimental effects for higher education students. Students may fail to fulfil their role obligations on an educational and social level, and become involved in risky practices such as driving under the influence of alcohol, exposure to violence and relationship problems. Aims: The aim of this integrative review was to examine the risk factors for harmful alcohol use among students to illuminate factors potentially predictive of alcohol related harm Methods: An integrative literature search was conducted to search databases PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), EBSCOhost and Google Scholar. Articles were screened to include qualitative, quantitative or mixed methods research, using a population of higher education students, published between 2010 and 2021, written in English. Findings: Results from 35 publications were grouped according to themes to describe factors that increase the risk of alcohol-related harm among students. Physical, behavioral, psychological, and social risk factors were identified. Conclusions: Understanding predisposing factors may help to reduce the harm caused by alcohol use among students. Theoretical integration in the discussion highlight potential interventions to target physical, behavioral, psychological and social domains in specific student populations. [ABSTRACT FROM AUTHOR]

Background: Given the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy. Methods: From December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital. Results: A multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for overall PLGC (95% confidence interval (CI) 0.71–0.76, p < 0.05) and 0.82 for high-risk PLGC (95% CI 0.82–0.83, p < 0.05), which were significantly and robustly better than those of the independent use of either tongue images or inquiry information alone. In addition, AITonguequiry has superior performance compared to existing PLGC screening methodologies, with the AUC value enhancing 45% in terms of PLGC screening (0.74 vs. 0.51, p < 0.05) and 52% in terms of high-risk PLGC screening (0.82 vs. 0.54, p < 0.05). In the independent external verification, the AUC values were 0.69 for PLGC and 0.76 for high-risk PLGC. Conclusion: Our AITonguequiry artificial intelligence model, for the first time, incorporates inquiry information and tongue images, leading to a higher precision and finer-grained pre-endoscopic screening of PLGC. This enhances patient screening efficiency and alleviates patient burden. [ABSTRACT FROM AUTHOR]

Objective: To compare how different imputation methods affect the estimates and performance of a prediction model for premature mortality. Study Design and Setting: Sex-specific Weibull accelerated failure time survival models were run on four separate datasets using complete case, mode, single and multiple imputation to impute missing values. Six performance measures were compared to access predictive accuracy (Nagelkerke R2, integrated brier score), discrimination (Harrell's c-index, discrimination slope) and calibration (calibration in the large, calibration slope). Results: The highest proportion of missingness for a single variable was 10.86% for the female model and 8.24% for the male model. Comparing the performance measures for complete case, mode, single and multiple imputation: the Nagelkerke R2 values for the female model was 0.1084, 0.1116, 0.1120 and 0.111–0.1120 with the male model exhibited similar variation of 0.1050, 0.1078, 0.1078 and 0.1078–0.1081. Harrell's c-index also demonstrated small variation with values of 0.8666, 0.8719, 0.8719 and 0.8711–0.8719 for the female model and 0.8549, 0.8548, 0.8550 and 0.8550–0.8553 for the male model. Conclusion: In the scenarios examined in this study, mode imputation performed well when using a population health survey compared to single and multiple imputation when predictive performance measures is the main model goal. To generate unbiased hazard ratios, multiple imputation methods were superior. This study shows the need to consider the best imputation approach for a predictive model development given the conditions of missing data and the goals of the analysis. [ABSTRACT FROM AUTHOR]

Simple Summary: Incidental intraductal papilloma without atypia (IPA), lobular hyperplasia (LCIS or ALH), flat epithelial atypia (FEA) and complex sclerosing lesion (CSL) are high-risk lesions of the breast. These lesions are not routinely excised when diagnosed in isolation because they have low rates of upgrade to invasive cancer. When identified concurrently with invasive breast cancer, however, the upgrade rate is not well characterized. We sought to both characterize the upgrade rate for these lesions when diagnosed concurrently with invasive breast cancer, and to identify features of the high-risk lesions predictive of upgrade to cancer. In our cohort of 65 patients who were concurrently diagnosed with a high-risk breast lesion and an invasive cancer, 5 patients (7.7%) had an upgrade of their high-risk lesion to carcinoma. The rate of upgrade was higher for high-risk lesions that were ipsilateral to malignancy and within 5 cm of it, and lower for other lesions. High-risk breast lesions including incidental intraductal papilloma without atypia (IPA), lobular hyperplasia (LCIS or ALH), flat epithelial atypia (FEA) and complex sclerosing lesion (CSL) are not routinely excised due to low upgrade rates to carcinoma. We aim to identify features of these lesions predictive of upgrade when identified concurrently with invasive disease. Methods: A single-center retrospective cohort study was performed for patients who underwent multi-site lumpectomies with invasive disease at one site and a high-risk lesion at another site between 2006 and 2021. A multinomial logistic regression was performed. Results: Sixty-five patients met the inclusion criteria. Four patients (6.2%) had an upgrade to in situ disease (DCIS) and one (1.5%) to invasive carcinoma. Three upgraded high-risk lesions were ipsilateral to the concurrent carcinoma and two were contralateral. In the multivariate model, a high-risk lesion within 5 cm of an ipsilateral malignancy was associated with increased risk of upgrade. The 3.8% upgrade rate for high-risk lesions located greater than 5 cm from ipsilateral malignancy or in the contralateral breast suggests that omission of excisional biopsy may be considered. Excisional biopsy of lesions within 5 cm of ipsilateral malignancy is recommended given the 25% upgrade risk in our series. [ABSTRACT FROM AUTHOR]

Simple Summary: We conducted a study at the Melanoma Unit of the Hospital Clinic of Barcelona to investigate brain metastases in patients with cutaneous melanoma. We collected data from patients diagnosed between January 1998 and September 2023. Patients with melanoma in situ or those with prior lung or breast cancer were excluded. Our aim was to identify factors associated with the development and survival outcomes of brain metastases. We analyzed patient demographics, tumor characteristics, and survival data. The diagnosis of brain metastases was confirmed using imaging techniques, and biopsies were performed when feasible. Our study followed strict guidelines for reporting observational studies. We found that younger age and larger primary tumor thickness increased the risk of developing brain metastases. Additionally, the presence of ulceration and microscopic satellitosis in the primary tumor were associated with a higher risk. Melanomas located on the trunk had a higher risk compared to those on the extremities. Patients with brain metastases had a median survival of around six months. Neurological symptoms and leptomeningeal involvement were associated with poorer survival outcomes. Higher number of brain lesions and elevated levels of lactate dehydrogenase (LDH) also predicted worse survival. Our findings highlight the importance of early detection and monitoring of melanoma patients, especially those at higher risk of brain metastases. Understanding these factors can aid in personalized treatment approaches and improving patient outcomes. Background: Melanoma is the cancer with the highest risk of dissemination to the central nervous system (CNS), one of the leading causes of mortality from this cancer. Objective: To identify patients at higher risk of developing CNS metastases and to evaluate associated prognostic factors. Methods: A cohort study (1998–2023) assessed patients who developed CNS melanoma metastases. Multivariate logistic regression was used to identify predictive factors at melanoma diagnosis for CNS metastasis. Cox regression analysis evaluated the CNS-independent metastasis-related variables impacting survival. Results: Out of 4718 patients, 380 (8.05%) developed CNS metastases. Multivariate logistic regression showed that a higher Breslow index, mitotic rate ≥ 1 mm2, ulceration, and microscopic satellitosis were significant risk factors for CNS metastasis development. Higher patient age and the location of the primary tumor in the upper or lower extremities were protective factors. In survival analysis, post-CNS metastasis, symptomatic disease, prior non-CNS metastases, CNS debut with multiple metastases, elevated LDH levels, and leptomeningeal involvement correlated with poorer survival. Conclusion: Predictive factors in the primary tumor independently associated with brain metastases include microscopic satellitosis, ulceration, higher Breslow index, and trunk location. Prognostic factors for lower survival in CNS disease include symptomatic disease, multiple CNS metastases, and previous metastases from different sites. [ABSTRACT FROM AUTHOR]

Simple Summary: Detecting lung cancer early and initiating treatment promptly can greatly enhance patient outcomes. While low-dose computed tomography (LDCT) screening aids in identifying lung cancer at an early stage, there is a risk of diagnostic delays as patients await follow-up scans. To mitigate this challenge, we developed a deep predictive model leveraging generative AI methods to forecast nodule growth patterns in follow-up LDCT scans based on baseline LDCT scans. Our findings illustrated that utilizing the predicted follow-up nodule images generated by our model during baseline screening improved diagnostic accuracy compared to using baseline nodules alone and achieved comparable performance with using real follow-up nodules. This demonstrated the potential of employing deep generative models to forecast nodule appearance in follow-up imaging from baseline LDCT scans, thereby enhancing risk assessment during initial screening. Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam. [ABSTRACT FROM AUTHOR]

Objective: To compare the predictive efficacy of the PADUA and Caprini models for pulmonary embolism (PE) in gynecological inpatients, analyze the risk factors for PE, and validate whether both models can effectively predict mortality rates. Methods: A total of 355 gynecological inpatients who underwent computed tomography pulmonary angiography (CTPA) were included in the retrospective analysis. The comparative assessment of the predictive capabilities for PE between the PADUA and Caprini was carried out using receiver operating characteristic (ROC) curves. Logistic regression analysis was used to identify risk factors associated with PE. Additionally, Kaplan–Meier survival analysis plots were generated to validate the predictive efficacy for mortality rates. Results: Among 355 patients, the PADUA and Caprini demonstrated the area under the curve (AUC) values of 0.757 and 0.756, respectively. There was no statistically significant difference in the AUC between the two models (P = 0.9542). Multivariate logistic analysis revealed immobility (P < 0.001), history of venous thromboembolism (VTE) (P = 0.002), thrombophilia (P < 0.001), hormonal treatment (P = 0.022), and obesity (P = 0.019) as independent risk factors for PE. Kaplan–Meier survival analysis demonstrated the reliable predictive efficacy of both the Caprini (P = 0.00051) and PADUA (P = 0.00031) for mortality. ROC for the three- and six-month follow-ups suggested that the Caprini model exhibited superior predictive efficacy for mortality. Conclusions: The PADUA model can serve as a simple and effective tool for stratifying high-risk gynecological inpatients before undergoing CTPA. The Caprini model demonstrated superior predictive efficacy for mortality rates. [ABSTRACT FROM AUTHOR]

Research algorithms are seldom externally validated or integrated into clinical practice, leaving unknown challenges in deployment. In such efforts, one needs to address challenges related to data harmonization, the performance of an algorithm in unforeseen missingness, automation and monitoring of predictions, and legal frameworks. We here describe the deployment of a high-dimensional data-driven decision support model into an EHR and derive practical guidelines informed by this deployment that includes the necessary processes, stakeholders and design requirements for a successful deployment. For this, we describe our deployment of the chronic lymphocytic leukemia (CLL) treatment infection model (CLL-TIM) as a stand-alone platform adjoined to an EPIC-based Danish Electronic Health Record (EHR), with the presentation of personalized predictions in a clinical context. CLL-TIM is an 84-variable data-driven prognostic model utilizing 7-year medical patient records and predicts the 2-year risk composite outcome of infection and/or treatment post-CLL diagnosis. As an independent validation cohort for this deployment, we used a retrospective population-based cohort of patients diagnosed with CLL from 2018 onwards (n = 1480). Unexpectedly high levels of missingness for key CLL-TIM variables were exhibited upon deployment. High dimensionality, with the handling of missingness, and predictive confidence were critical design elements that enabled trustworthy predictions and thus serves as a priority for prognostic models seeking deployment in new EHRs. Our setup for deployment, including automation and monitoring into EHR that meets Medical Device Regulations, may be used as step-by-step guidelines for others aiming at designing and deploying research algorithms into clinical practice. [ABSTRACT FROM AUTHOR]

Aim: As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X‐rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose sarcopenia, and methods using machine learning are gradually increasing. This study aimed to create a model that can predict sarcopenia using physical characteristics and activity‐related variables without medical diagnostic equipment, such as imaging equipment, for the elderly aged 60 years or older. Methods: A sarcopenia prediction model was constructed using public data obtained from the Korea National Health and Nutrition Examination Survey. Models were built using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi‐layer Perceptron Neural Network (MLP) algorithms, and the feature importance of the models trained with the algorithms, except for SVM and MLP, was analyzed. Results: The sarcopenia prediction model built with the LightGBM algorithm achieved the highest test accuracy, of 0.848. In constructing the LightGBM model, physical characteristic variables such as body mass index, weight, and waist circumference showed high importance, and activity‐related variables were also used in constructing the model. Conclusions: The sarcopenia prediction model, which consisted of only physical characteristics and activity‐related factors, showed excellent performance. This model has the potential to assist in the early detection of sarcopenia in the elderly, especially in communities with limited access to medical resources or facilities. Geriatr Gerontol Int 2024; 24: 595–602. [ABSTRACT FROM AUTHOR]

Depression is recognized as a significant public health issue in the United States. The National Survey on Drug Use and Health reports that 21.0 million adults aged 18 or older had major depressive disorder in 2020, including 14.8 million experiencing a major depressive episode with severe impairment. The aim is to predict the positivity of Patient Health Questionnaire-2 (PHQ-2) outcomes among patients in primary care settings by analyzing a range of variables, including socioeconomic status, demographic characteristics, and health behaviors, thereby identifying those at increased risk for depression. Employing a machine learning approach, the study utilizes retrospective data from electronic health records across 15 primary care clinics in South Florida to explore the relationship between social determinants of health (SDoH), including area of deprivation index (ADI) and PHQ-2 positivity. The study encompasses 15 primary care clinics located in South Florida, where a diverse patient population receives care. Analysis included 94,572 patient visits; 74,636 records were included in the study. If a zip+4 was not available or an ADI score did not exist, the visit was not included in the final analysis. Screening involved the PHQ-2, assessing depressed mood and anhedonia, with a cutoff >2 indicating positive screening. ADI was used to assess SDoH by matching patients' residential postal codes to ADI national percentiles. Demographics, sexual history, tobacco use, caffeine intake, and community involvement were also evaluated in the study. Over 40 machine learning algorithms were explored for their accuracy in predicting PHQ-2 outcomes, using software tools including Scikit-learn and stats models in Python. Variables were normalized, scored, and then subjected to predictive regression models, with Random Forest showing outstanding performance. Feature engineering and correlation analysis identified ADI, age, education, visit type, coffee intake, and marital status as significant predictors of PHQ-2 positivity. The area under the curve and model accuracies varied across clinics, with specific clinics showing higher predictive accuracy and others (p >.05). The study concludes that the ADI, as a proxy for SDoH, alongside other individual factors, can predict PHQ-2 positivity. Health organizations can use this information to anticipate health needs and resource allocation. [ABSTRACT FROM AUTHOR]

Objective: Work‐related violence remains a significant problem in healthcare settings, including EDs. Violence risk assessment tools have been developed to improve risk mitigation in this setting; however, incorporation of these tools into standard hospital processes remains scarce. This research aimed to explore nurses' perspectives on the Bröset Violence Checklist used in routine violence risk assessment and their recommendations for additional items. Methods: Thirty nursing staff who used the Bröset Violence Checklist (BVC) as standard practice for 5 years participated in two focus groups where 23 violence risk factors were presented. Using multiple methods, participants were asked to select and elaborate from a pre‐determined list what they considered most useful in violence risk assessment in respect to descriptors and terminology. Results: Quantitative data showed most risk factors presented to the group were considered to be predictive of violence. Ten were regarded as associated with risk, and overt behaviours received the highest votes. The terms 'shouting and demanding' was preferred over 'boisterous', and 'cognitive impairment' over 'confusion'. Patient clinical characteristics and staff perceptions of harm, inability to observe subtle behaviour, imposed restrictions and interventions and environmental conditions and impact were also important considerations. Conclusions: We recommend that violence risk assessment include: history of violence, cognitive impairment, psychotic symptoms, drug and alcohol influence, shouting and demanding, verbal abuse/hostility, impulsivity, agitation, irritability and imposed restrictions and interventions. These violence risk factors fit within the four categories of historical, clinical, behavioural and situational. [ABSTRACT FROM AUTHOR]

Background: Delayed extubation and transfer to the intensive care unit (ICU) in children undergoing major scoliosis surgery may increase postoperative complications, prolong hospital stay, and increase medical expenses; however, whether a child will require delayed extubation or transfer to the ICU after scoliosis orthopedic surgery is not fully understood. In this study, we reviewed the risk factors for delayed extubation and transfer to the ICU after scoliosis orthopedic surgery in children. Method: The electronic medical records of pediatric patients (≤ 18 years) who underwent posterior spinal fusion surgery between January 2018 and November 2021 were reviewed and analyzed. Patient characteristics (age, sex, body mass index, American Society of Anesthesiologists, ASA, grade, preoperative lung function, and congenital heart disease), preoperative Cobb angle, scoliosis type, correction rate, vertebral fusion segments, pedicle screws, surgical osteotomy, intraoperative bleeding, intraoperative allogeneic transfusion, intraoperative hemoglobin changes, intraoperative mean arterial pressure changes, intraoperative tidal volume (ml/kg predicted body weight), surgical time, postoperative extubation, and transfer to the ICU were collected. The primary outcomes were delayed extubation and transfer to the ICU. Multivariate logistic regression models were used to determine the risk factors for delayed extubation and ICU transfer. Results: A total of 246 children who satisfied the inclusion criteria were enrolled in this study, of whom 23 (9.3%) had delayed extubation and 81 (32.9%) were transferred to the ICU after surgery. High ASA grade (odds ratio [OR] 5.42; 95% confidence interval [CI] 1.49–19.78; p = 0.010), high Cobb angle (OR 1.04; 95% CI 1.02–1.07; p < 0.001), moderate to severe pulmonary dysfunction (OR 10.9; 95% CI 2.00–59.08; p = 0.006) and prolonged surgical time (OR 1.01; 95% CI 1.00–1.03; p = 0.040) were risk factors for delayed extubation. A high Cobb angle (OR 1.02; 95% CI 1.01–1.04; p = 0.004), high intraoperative bleeding volume (OR 1.06; 95% CI 1.03–1.10; p = 0.001), allogeneic transfusion (OR 3.30; 95% CI 1.24–8.83; p = 0.017) and neuromuscular scoliosis (OR 5.38; 95% CI 1.59–18.25; p = 0.007) were risk factors for transfer to the ICU. A high Cobb angle was a risk factor for both delayed extubation and ICU transfer. Age, sex, body mass index, number of vertebral fusion segments, correction rate, and intraoperative tidal volume were not associated with delayed postoperative extubation and ICU transfer. Conclusion: The most common risk factor for delayed extubation and ICU transfer in pediatric patients who underwent posterior spinal fusion was a high Cobb angle. Determining risk factors for a poor prognosis may help optimize perioperative respiratory management strategies and planning of postoperative care for children undergoing complicated spinal surgery. [ABSTRACT FROM AUTHOR]

BACKGROUND Patients with diabetes mellitus are at a higher risk for peripheral artery disease (PAD) and diabetic foot disease (DFD), which can ultimately result in leg amputation. This study aimed to develop a novel scoring system to predict the risk of leg amputation using widely available modalities, including demographic characteristics and various laboratory data. METHODS This study was conducted at Dr. Soetomo General Hospital and analyzed risk factors for leg amputation in 99 patients with DFD who underwent leg amputation compared with 80 control patients. This study presented a predictive model for leg amputation in patients with DFD, using odds ratio (OR) analysis and logistic regression approach with confidence intervals (CIs) set at 95%. The data analysis was conducted using SPSS software version 25.0. RESULTS PAD (OR 6.52, 95% CI = 2.19--19.37, p = 0.001), high low-density lipoprotein cholesterol (OR 5.97, 95% CI = 2.31--15.43, p<0.001), type of DFD (OR 4.58, 95% CI = 2.14--9.79, p<0.001), poor glycemic control (OR 4.48, 95% CI = 1.78--11.28, p = 0.001), and neutrophil-to-lymphocyte ratio level (OR 1.04, 95% CI = 1.00--1.07, p = 0.025) were the independent predictors for leg amputation. The predictive model, developed using multivariate analysis with an area under the curve of 89%, optimum threshold of 0.5 (score 6), sensitivity of 84.8%, and specificity of 78.8%, indicating a promising approach for predicting leg amputation outcomes. CONCLUSIONS The newly developed diabetic foot score may assist in making decisions regarding therapeutic options for patients with DFD. [ABSTRACT FROM AUTHOR]

This editorial comments on the article by Herasevich et al. [ABSTRACT FROM AUTHOR]

Background: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR‐based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. Methods: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. Results: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83–0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98–12.87). Conclusions: This simple EHR‐based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well‐being of older adults at risk of critical illness and its consequences. See related article by Haimovich and Deardorff. [ABSTRACT FROM AUTHOR]

The 2015 Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement was published to improve reporting transparency for prediction modeling studies. The objective of this review is to highlight methodologic challenges that aging‐focused researchers will encounter when designing and reporting studies involving prediction models for older adults and provide guidance for addressing these challenges. In following the 22‐item TRIPOD checklist, researchers must consider the representativeness of cohorts used (e.g., whether older adults with frailty, cognitive impairment, and social isolation were included), strategies for incorporating common geriatric predictors (e.g., age, comorbidities, functional status, and frailty), methods for handling missing data and competing risk of death, and assessment of model performance heterogeneity across important subgroups (e.g., age, sex, race, and ethnicity). We provide guidance to help aging‐focused researchers develop, validate, and report models that can inform and improve patient care, which we label "TRIPOD‐65." [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore whether fibrinogen (Fib) can be used as a predictor of postpartum hemorrhage (PPH) in parturients with vaginal delivery, and the value of combining Fib with other indexes to predict postpartum hemorrhage in vaginal delivery. A total of 207 parturients who delivered via vagina were divided into PPH group (n=102) and non-PPH group (n=105). The PPH group was further divided into mild PPH group and severe PPH group. The differences of Fib, platelet (PLT), mean platelet volume (MPV), platelet distribution width (PDW), D-dimer (D-D), hemoglobin (HGB) and neonatal weight (Nw) between the two groups were compared to explore the significance of these indexes in predicting PPH. Fib, PLT and PDW in PPH group were significantly lower than those in non-PPH group, while D-D and Nw in PPH group were significantly higher than those in non-PPH group. In the binary logistic regression model, we found that Fib, D-D and Nw were independently related to PPH. The risk of PPH increased by 9.87 times for every 1 g/L decrease in Fib. The cut-off value of Fib is 4.395 (sensitivity 0.705, specificity 0.922). The AUC value of PPH predicted by Fib combined with D-D and Nw was significantly higher than that of PPH predicted by Fib (p<0.05, 95 % CI 0.00313–0.0587). Fib, D-D and Nw have good predictive value for PPH of vaginal delivery, among which Fib is the best. The combination of three indexes of Fib, D-D and Nw can predict PPH more systematically and comprehensively, and provide a basis for clinical prevention and treatment of PPH. [ABSTRACT FROM AUTHOR]

Background: This study aims to analyze the influencing factors of postoperative Low Anterior Resection Syndrome (LARS) in patients with middle and low rectal cancer who underwent robotic surgery. It also seeks to predict the probability of LARS through a visual, quantitative, and graphical nomogram. This approach is expected to lower the risk of postoperative LARS in these patients and improve their quality of life through effective prevention and early intervention. Patients and methods: This research involved patients with middle and low rectal cancer who underwent robotic surgery in the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Nanchang University from January 2015 to October 2022. A series of intestinal dysfunction symptoms arising from postoperative rectal cancer were diagnosed and graded using LARS scoring criteria. After the initial screening of all variables related to LARS with Lasso regression, they were included in logistic regression for further univariate and multivariate analysis to identify independent risk factors for LARS. A prediction model was then constructed. Results: The study included 358 patients. The parameters identified by Lasso regression included obstruction, BMI, tumor localization, maximum tumor diameter, AJCC stage, stoma, neoadjuvant therapy (NAT), and postoperative adjuvant therapy (AT). Univariate and multivariate analyses indicated that a higher BMI, lower tumor localization, higher AJCC stage, neoadjuvant therapy, and postoperative adjuvant therapy were independent risk factors for total LARS. The AUC of the prediction nomogram was 0.834, with a sensitivity of 0.825 and specificity of 0.741. The calibration curve demonstrated excellent concordance with the nomogram, indicating the prediction curve fit the diagonal well. Conclusion: Higher BMI, lower tumor localization, higher AJCC stage, neoadjuvant therapy, and adjuvant therapy were identified as independent risk factors for total LARS. A new predictive nomogram for postoperative LARS in patients with middle and low rectal cancer undergoing robotic surgery was developed, proving to be stable and reliable. This tool will assist clinicians in managing the postoperative treatment of these patients, facilitating better clinical decision-making and maximizing patient benefits. [ABSTRACT FROM AUTHOR]

Objectives Prediction models based on traditional risk factors underestimate cardiovascular (CV) risk in systemic lupus erythematosus (SLE). In a large sample of unselected SLE patients, we investigated cross-sectional associations of NT-proBNP with cardiovascular damage (CVD). Methods Serum NT-proBNP was measured in SLE patients enrolled in the MUHC Lupus Clinic registry. Serum was collected between March 2022 and April 2023 at annual research visits. The primary outcome was CVD identified on the SLICC Damage Index. Factors associated with CVD and NT-proBNP levels were determined. Results Overall, 270 SLE patients [female 91%, median age 50.7 (first quartile to third quartile: 39.6–62.1) years] were analysed for the primary outcome. Among them, 33 (12%) had CVD. The ROC curve for NT - proBNP demonstrated strong associations with CVD (AUC 0.78, 95% CI 0.69–0.87) with a threshold of 133 pg/ml providing the best discrimination for those with/without CVD. Hypertension (OR 3.3, 95% CI 1.2–9.0), dyslipidaemia (OR 3.6, 95% CI 1.3–9.6) and NT-proBNP >133 pg/ml (OR 7.0, 95% CI, 2.6–19.1) were associated with CVD in the multivariable logistic regression model. Increased NT-proBNP levels were associated with age (OR 4.2, 95% CI 2.2–8.3), ever smoking (OR 1.9, 95% CI 1.0–3.5), reduced eGFR (4.1, 95% CI 1.3–13.1), prior pericarditis/pleuritis (OR 2.5, 95% CI 1.4–4.5) and aPL antibodies (OR 2.6, 95% CI 1.4–4.9). Conclusion NT-proBNP is a biomarker for CV damage in SLE. The novel associations of NT-proBNP levels with prior pericarditis/pleuritis and aPL antibodies suggest new avenues for research to better understand what drives CV risk in SLE. [ABSTRACT FROM AUTHOR]

Objectives To assess the ability of two risk prediction models in interstitial lung disease (ILD) to predict death or lung transplantation in a cohort of patients with interstitial pneumonia with autoimmune features (IPAF). Methods We performed a retrospective cohort study of adults with IPAF at an academic medical centre. The primary outcome was a composite of lung transplantation or death. We applied the patient data to the previously described Gender–Age–Physiology (GAP) and ILD-GAP models to determine the ability of these models to predict the composite outcome. Model discrimination was assessed using the c-index, and model calibration was determined by comparing the incidence ratios of observed vs expected deaths. Results Ninety-four patients with IPAF were included. Mean (s. d.) age was 58 (13.5) years and the majority were female (62%). The majority met serologic and morphologic criteria for IPAF (94% and 91%, respectively). The GAP model had a c-index of 0.664 (95% CI 0.547–0.781), while the ILD-GAP model had a c-index of 0.569 (95% CI 0.440–0.697). In those with GAP stage 1 or GAP stage 2 disease, calibration of the GAP model was satisfactory at 2 and 3 years for the cumulative end point of lung transplantation or death. Conclusion In patients with IPAF, the GAP model performed well as a predictor of lung transplantation or death at 2 years and 3 years from ILD diagnosis in patients with GAP stage 1 and GAP stage 2 disease. [ABSTRACT FROM AUTHOR]

Objective: The Interpersonal Theory of Suicide (ITS) could help identify differences in groups of suicidal adolescents and inform treatment. Method: Latent Profile Analysis (LPA) using thwarted belongingness (TB), perceived burdensomeness (PB), hopelessness, and capability was conducted on data from an at‐risk clinical sample (N = 500). The ITS prediction that changes in TB and PB are associated with changes in suicidal ideation was tested using admission and discharge data. Results: Latent Profile Analysis identified three profiles with increasing complexity and severity on ITS factors. The profiles were labelled low‐severity (7.6% of participants), moderate‐severity (45.2%), and high‐severity (47.2%). ITS predictions were partially supported for the full sample and only for the high‐severity and moderate‐severity subgroups, whereby changes in TB were significantly associated with changes in suicidal ideation over the course of treatment. However, changes in PB were only significant in the moderate‐severity subgroup, and none of the ITS predictions were supported in the low‐severity subgroup. Additionally, effect sizes for changes in TB and PB were modest in all analyses. Conclusions: Our findings highlight the importance reducing low belongingness in youth, which is a component of all supported interventions of youth suicide prevention. However, given the modest association of changes in ITS variables had with changes in suicidal ideation, it may be fruitful to elaborate on the relative importance on types of low belongingness or include other non‐ITS variables. [ABSTRACT FROM AUTHOR]

Simple Summary: This study included 227 patients, among whom 27.7% (63/227) were diagnosed with tumors spread through air spaces (STASs), which have been shown to be associated with shorter recurrence-free survival and poor prognosis. A prediction model was developed to forecast tumor STAS in early-stage lung adenocarcinoma pathology images. The radiomics prediction model demonstrated good performance, with an AUC value of 0.83. This prediction model can assist pathologists in the diagnostic processes of clinical practice. The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate pre-operative surgical risk predictions form the foundation of pre-operative counseling and informed consent. There are currently no validated risk calculators that are able to accurately predict post-operative complications for women undergoing gynecological cancer surgery in both high- and low-middle-income healthcare settings. Using the dataset from the international GO SOAR database, we present a novel artificial intelligence surgical risk calculator capable of accurately predicting the risk of complications associated with gynecological cancer surgery. The GO SOAR surgical risk calculator uses readily available pre-operative data available across all-income healthcare settings, ensuring benefits to women globally. The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien–Dindo I-II), major morbidity (Clavien–Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities. [ABSTRACT FROM AUTHOR]

Background: Pitch counts are only one measure of the true workload of baseball pitchers. Newer research indicates that workload measurement and prevention of injury must include additional factors. Thus, current monitoring systems gauging pitcher workload may be considered inadequate. Purpose/Hypothesis: The purpose of this study was to develop a novel method to determine workload in baseball pitchers and improve processes for prevention of throwing-related injuries. It was hypothesized that our pitching workload model would better predict throwing-related injuries occurring throughout the baseball season than a standard pitch count model. Study Design: Cohort study; Level of evidence, 2. Methods: This prospective observational study was conducted at an academic medical center and community baseball fields during the 2019 to 2023 seasons. Pitchers aged 13 to 18 years were monitored for pitching-related injuries and workload (which included pitching velocity; intensity, using preseason and in-season velocity as a marker of effort; and pitch counts). Results: A total of 71 pitchers had 313 recorded pitcher outings, 11 pitching-related injuries, and 24,228 pitches thrown. Gameday pitch counts for all pitchers ranged from 19 to 219 (mean, 77.5 ± 41.0). Velocity ranged from 46.8 to 85.7 mph (mean, 71.3 ± 5.8 mph). Intensity ranged from 0.7 to 1.3 (mean, 1.0 ± 0.08). The mean workload was 74.7 ± 40.1 for all pitchers. Risk factors significant for injury included throwing at a higher velocity in game (P =.001), increased intensity (eg, an increase in mean velocity thrown from preseason to in-season; P <.001), and being an older pitcher (P =.014). No differences were found for workload between injured and noninjured pitchers because the analysis was underpowered. Conclusion: Our workload model indicated that throwing at a higher velocity, throwing at a higher intensity, and older age were risk factors for injury. Thus, this novel workload model should be considered as a means to identify pitchers who may be at greater risk for injury. [ABSTRACT FROM AUTHOR]

Background: We investigated (a) whether polygenic risk for schizophrenia predicts different trajectories of social development among those who have not developed psychoses and (b) whether possible associations are PRSSCZ-specific or evident also for any polygenic risk for mental disorders, e.g. for major depression. Methods: Participants came from the population-based Young Finns Study (n = 2377). We calculated a polygenic risk score for schizophrenia (PRSSCZ) and for major depression (PRSDEP). Diagnoses of psychotic disorders were derived from the hospital care register. Social development from adolescence to middle age was measured by (a) perceived social support from friends, family, and a close other, (b) perceived sociability, and (c) family structure (partnership status, number of children, age of first-time parenthood). Results: Among those without manifest psychoses, high PRSSCZ predicted lower experienced support from friends (B = −0.04, p = 0.009–0.035) and family (B = −0.04, p = 0.009–0.035) especially after early adulthood, and also lower perceived sociability (B = −0.05, p = 0.010–0.026). PRSSCZ was not related to family structure. PRSDEP did not predict any domain of social development. Conclusions: Individuals at high PRSSCZ (not converted to psychosis) seem to experience a lower preference to be with others over being alone. Individuals with high (v. low) PRSSCZ seem to have a similar family structure in terms of partnership status or number of children but, nevertheless, they experience less support from their family. Among those not converted to psychosis in a typical age period, high PRSSCZ may predict a 'later risk phase' and reduced functional resilience when approaching middle age. [ABSTRACT FROM AUTHOR]

Fear of relapse into psychosis is an independent risk factor for future relapse events, indicating its importance as a novel intervention target. Twenty-five participants responded to daily ecological momentary assessment prompts assessing common early warning signs of relapse and self-reported positive experiences like feeling supported by others. We conducted multilevel vector auto-regression using common symptoms assessed in early warning signs monitoring relapse prevention while controlling for positive self-reported experiences like feeling supported by others to estimate three networks (to explore concurrent, temporal and overall relationships). Reporting fear of relapse was positively associated (within the same cross-sectional time window) with hearing voices, alongside anxiety, negative affect and sleep change. Fear of relapse appeared to predict anxiety, negative affect and greater fear of relapse on the next consecutive day. However, none of the typical early warning signs predicted fear of relapse within the temporal window, and the observed relationships were small. Early warning signs appeared to be poor predictors of experiencing fear of relapse in this study. Fear of relapse predicts later anxiety and negative affect and may be a valuable intervention target within the daily life of people diagnosed with schizophrenia. [ABSTRACT FROM AUTHOR]

Hyperactivity of coagulation is common in exertional heatstroke (EHS). Disseminated intravascular coagulation (DIC) is the most severe form of coagulation dysfunction and associated with poor outcome. DIC, temperature and Glasgow coma scale score were identified as independent risk factors for in-hospital mortality by multivariate logistic regression analysis, and we developed a nomogram for predicting in-hospital mortality in a 13-year EHS patient cohort. The nomogram was assessed by calibration curves and bootstrap with 1,000 resamples. The receiver operating characteristic curve was constructed, and the area under the curve (AUC) was compared. Two hundred and ten patients were included. The in-hospital mortality was 9.0%, and the incidence of DIC was 17.6%. The AUC of the nomogram was 0.897 (95% CI 0.848–0.935, p <.0001) and was non-inferior to SOFA and APACHE II scores but superior to SIRS score, which were widely-used score systems of disease severity. The nomogram contributed to the adverse outcome prediction of EHS. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes exposes the body to a state of high blood sugar for a long time and causes varying degrees of hardening of the arteries, making it more prone to cardiovascular emergencies. Objective: The aim of the study was to explore the risk factors of early arterial stiffness in patients with type 2 diabetes mellitus (T2DM). Methods: A retrospective study was conducted on 316 T2DM patients without macroangiopathy in The First Hospital of Qinhuangdao. Early arterial stiffness was evaluated by brachial-ankle pulse wave velocity (baPWV). Results: Ninety patients (28.5%) had baPWV≥1800cm/s. baPWV showed positive correlation with systolic blood pressure (r=0.456, p<0.001), diastolic blood pressure (r=0.133, p=0.018), urine albumin-creatinine ratio (UACR) (r=0.232, p<0.001), neutrophil lymphocyte ratio (NLR) (r=0.185, p=0.001), and visceral fat area (r=0.139, p=0.014). In multiple linear regression analysis, systolic blood pressure (β=6.240, p<0.001), UACR (β=3.805, p=0.019), NLR (β=43.722, p=0.013), and visceral fat area (β=0.778, p=0.030) were significant independent predictors for baPWV. Conclusion: The decline of arterial elasticity was common in T2DM patients without macroangiopathy. Elevated blood pressure, microangiopathy, chronic inflammation, and visceral fat accumulation were the risk factors of early arterial stiffness in patients with T2DM. [ABSTRACT FROM AUTHOR]

Background: Young mortality is prevalent among patients with cardiovascular disease (CVD). To develop prediction models for CVD mortality in young adults, it is crucial to assess CVD risks. Early detection of cardiac disorders using machine learning algorithms, a branch of artificial intelligence (AI) is crucial for preventing more damage to coronary arteries and saving lives. Aims: To predict mortality versus a life outcome among young adults (18-45 years) with CVD using AI technique known as Chi-squared automatic interaction detector (CHAID) algorithms. Methods: A large-scale dataset was extracted from the electronic health records of 809 young adult patients diagnosed with CVD using a retrospective design. Information was retrieved regarding young adults from Jordan who were admitted to public health institutions between 2015 and the end of 2021. Results: CHAID algorithms were chosen among seven prediction models based on accuracy and area under curve to predict mortality vs life in young individuals (18-45 years old) with CVD. The mortality prediction algorithms started with pulse pressure, then diastolic blood pressure, then ischemic heart disease, and last geographical location. Conclusions: CHAID model used in our study indicated how the death rate was classified and distributed among a variety of parameters. As a result, we may argue that AI model could provide additional information on how many aspects are articulated in connection to CVD patient fatality situations. [ABSTRACT FROM AUTHOR]

BACKGROUND: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. OBJECTIVES: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. METHODS: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688–141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. RESULTS: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5-km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to e300,000. The directions of associations were less consistent for walkability and share of single residents. DISCUSSION: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. [ABSTRACT FROM AUTHOR]

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not (least absolute shrinkage and selection operator regression [LASSO]) model interactions. Cases made a nonfatal suicide attempt (n  = 6032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n  = 11 963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver-operating characteristic curve (AUC) values were higher in LASSO models (in men, 0.85, 95% CI, 0.84-0.86; in women, 0.89, 95% C, 0.88-0.90) than random forests (in men, 0.76, 95% CI, 0.74-0.78; in women, 0.79, 95% CI = 0.78-0.81). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

Prescribed opioids are a mainstay pain treatment after traumatic injury, but a subgroup of patients may be at risk for continued opioid use. We evaluated the predictive utility of a traditional screening tool, the Opioid Risk Tool (ORT), and two other measures: average in-hospital milligram morphine equivalents (MME) per day and an assessment of opioid demand in predicting pain outcomes. Assessments of pain-related outcomes (pain intensity, interference, injury-related stress, and need for additional pain treatment) were administered at 2 weeks and 12 months post-discharge in a sample of 34 patients hospitalized for traumatic injury. Bayesian linear models were used to evaluate changes in responses over time as a function of predictors. High-risk ORT, higher MME per day, and greater opioid demand predicted less change in outcomes over time. This report provides first evidence that malleable factors of opioid and opioid demand have utility in predicting pain outcomes following traumatic injury. [ABSTRACT FROM AUTHOR]

The cornerstone of obesity treatment is behavioural weight management, resulting in significant improvements in cardio-metabolic and psychosocial health. However, there is ongoing concern that dietary interventions used for weight management may precipitate the development of eating disorders. Systematic reviews demonstrate that, while for most participants medically supervised obesity treatment improves risk scores related to eating disorders, a subset of people who undergo obesity treatment may have poor outcomes for eating disorders. This review summarises the background and rationale for the formation of the Eating Disorders In weight-related Therapy (EDIT) Collaboration. The EDIT Collaboration will explore the complex risk factor interactions that precede changes to eating disorder risk following weight management. In this review, we also outline the programme of work and design of studies for the EDIT Collaboration, including expected knowledge gains. The EDIT studies explore risk factors and the interactions between them using individual-level data from international weight management trials. Combining all available data on eating disorder risk from weight management trials will allow sufficient sample size to interrogate our hypothesis: that individuals undertaking weight management interventions will vary in their eating disorder risk profile, on the basis of personal characteristics and intervention strategies available to them. The collaboration includes the integration of health consumers in project development and translation. An important knowledge gain from this project is a comprehensive understanding of the impact of weight management interventions on eating disorder risk. [ABSTRACT FROM AUTHOR]

To evaluate the component patterns and risk stratification in patients with mixed malignant ovarian germ cell tumors (mMOGCT). A retrospective study of 70 mMOGCT patients treated in our hospital between 2000 and 2022 was conducted. The recurrence-free survival (RFS), disease-specific survival (DSS), and risk stratification systems based on scoring the identified prognostic factors were assessed. Yolk sac tumor component was the most common type (80%), followed by dysgerminoma (50%), immature teratoma (40%), embryonic carcinoma (27.1%), and chorionic carcinoma (15.7%). The 5-year RFS and DSS rates were 77.9% and 87.9%, respectively. International federation of gynecology and obstetrics (FIGO) stage III-IV (RR 3.253, P = 0.029) and normalization of tumor marker (TM) ≤ 3 cycles of chemotherapy (RR 6.249, P = 0.017) were risk factors for RFS and DSS, respectively. Significant DSS (RR 8.268, P = 0.006) was also noted between patients who had normalized TM ≤ 4 and ≥5 cycles of chemotherapy. FIGO stages I–II and stages III–IV were scored as 0 and 2, respectively. AFP normalization ≤3, 4, and ≥5 cycles of chemotherapy were scored as 0, 1, and 4, respectively. A total score of 0, 1–2, and ≥3 stratified patients into low-risk (43 patients), intermediate-risk (13 patients), and high-risk groups (14 patients), respectively. Patients in three risk stratifications manifested significant differences in DSS (P = 0.010) but not in RFS (P > 0.05). Distinct different component patterns existed among mMOGCT patients, and predicting survival outcomes in a universal model was challenging. • The mMOGCT patients manifested complex component combination modes, of which YST, DG, and IT were the most frequented types. • The 5-year RFS and 5-year DSS was 77.9% and 87.9%, respectively, and FIGO stage and the interval between treatment intervention and normalization of TM were the prognostic predictors. • Distinct different component patterns existed among mMOGCT patients, and predicting survival outcomes in a universal model was challenging. [ABSTRACT FROM AUTHOR]

Introduction: It is appealing to accurately predict postoperative delirium (POD) before surgeries. In this study, it was hypothesized that a novel electroencephalogram-derived index, the delirium index (DELi), could extract latent information regarding the predisposing factors of POD preoperatively. This study was aimed at developing a concise model that incorporated this DELi score to predict the early POD of elderly patients after hip surgeries. Materials and Methods: Elderly patients scheduled for elective hip fracture surgeries were prospectively enrolled in a tertiary care hospital from November 2020 to June 2022. DELi scores and patient characteristics (age, sex, types of fracture and surgery, the time interval between fracture and surgery, cognitive function assessed using the Montreal Cognitive Assessment (MoCA), and frailty status assessed using the FRAIL scale) were collected preoperatively as candidate predictors. POD diagnosed using the confusion assessment method (CAM) was the outcome. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select predictors. Then, these predictors were entered into a backward logistical regression analysis to develop a prediction model. Discrimination, calibration and clinical utility were validated using the bootstrapping method. Results: All data (144 qualified patients of 170) were used for development. POD was observed in 71 patients (49.3%). Preoperative DELi scores predicted early POD (the area under the curve (AUC) =.786, 95% confidence interval (CI):.712,.860, in internal validation). A nomogram with MoCA, FRAIL scale and DELi score was constructed with excellent discrimination (AUC =.920, 95% CI:.876,.963, in internal validation), accredited calibration (P =.733, Hosmer‒Lemeshow test), and a wide range of threshold probabilities (5% to 95%). Conclusions: Preoperative DELi scores predicted the early POD of elderly patients after hip surgeries. A concise prediction model was developed and demonstrated excellent discrimination. [ABSTRACT FROM AUTHOR]

This study was to describe the cognitive function status in patients with depressive disorder and to construct a nomogram model to predict the risk factors of cognitive impairment in these patients. From October 2019 to February 2021, a total of 141 patients with depressive disorder completed the survey in two hospitals. The Montreal cognitive assessment (MoCA) was used with a cutoff score of 26 to differentiate cognitive impairment. Univariable and multivariable logistic regression analyses were conducted to identify independent risk factors. A nomogram was then constructed based on the results of the multivariable logistic regression analysis. The patients had an average MoCA score of 23.99 ± 3.02. The multivariable logistic regression analysis revealed that age (OR: 1.096, 95% CI: 1.042–1.153, p < 0.001), education (OR: 0.065, 95% CI: 0.016–0.263, p < 0.001), depression severity (OR: 1.878, 95% CI: 1.021–3.456, p = 0.043), and sleep quality (OR: 2.454, 95% CI: 1.400–4.301, p = 0.002) were independent risk factors for cognitive impairment in patients with depressive disorder. The area under receiver operating characteristic (ROC) curves was 0.868 (95% CI: 0.807–0.929), indicating good discriminability of the model. The calibration curve of the model and the Hosmer–Lemeshow test (p = 0.571) demonstrated a well‐fitted model with high calibration. Age, education, depression severity, and sleep quality were found to be significant predictors of cognitive function. A nomogram model was developed to predict cognitive impairment in patients with depressive disorder, providing a solid foundation for clinical interventions. [ABSTRACT FROM AUTHOR]

Introduction: Cardiovascular disease (CVD) is one of the most important health problems and the leading cause of mortality worldwide. This study aimed to estimate the risk of CVD using the World Health Organization/International Society of Hypertension (WHO/ISH) risk prediction charts. Methods: The demographic characteristics of all participants of this study aged 40-70 years who did not have a prior coronary event were collected. The 10-year CVD risk was estimated using the laboratory version of the WHO/ISH risk score charts. The risk scores for 11678 participants of the Azar cohort population were calculated. Participants were classified as low risk, moderate risk, or high risk. Results: According to the WHO/ISH charts, only 0.1 % of the population was classified as high-risk (> 40%), and 96.8% had a 10-year CVD risk of < 10%. Also, participants with overweight (P = 0.002), obesity, and abdominal obesity had higher CVD risk(P < 0.001). Conclusion: There was a low burden of 10-year CVD risk among the Azar cohort population without prior coronary events. It appears the percentage of people in the high-risk group is underestimated in the WHO/ISH risk prediction charts, leading to delays in receiving appropriate management in the population concerned. Therefore, using other charts alongside the WHO/ISH risk prediction charts is advisable. [ABSTRACT FROM AUTHOR]

In this research note, we describe the results of the first validation study of the U.S. Census Bureau's new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. By employing administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations such as race or age, as well as other vulnerability measures including the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) and the Federal Emergency Management Agency's National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability such as poverty and unemployment, as well as SVI and NRI. We find that a 10-percentage-point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared with alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic. [ABSTRACT FROM AUTHOR]

Adolescent behavior now occurs offline and online. Frequently studied and treated independently, the relationship between offline problem behaviors and online risk taking is not well understood. This study asked whether there are any problematic behaviors predictive of online risk taking by high school students. Using a 2009 dataset of 2,077 high school students grades 9–12, five areas of offline problematic behaviors were examined: Academic problems, anxiety, behavioral wrongdoing, bullying, and social-emotional. Nine binary results were classified as online risk: Sexting, online harassment (perpetrating and experiencing), visiting sex sites, talking about sex, receiving sexual pictures, meeting offline, anything sexual happened, feeling nervous or uncomfortable. Behavioral wrongdoing (fighting, school suspension, trouble with police, theft), emerged as a significant predictor appearing in all nine models, followed by bullying experience (bully or victim) in six models. Identifying common problem behaviors that predict online risk taking are key components in developing strategies to promote adolescent health and well-being. [ABSTRACT FROM AUTHOR]

Adolescent idiopathic scoliosis (AIS) is a common form of scoliosis. As the name suggests, etiopathogenesis is not clearly defined, so treatment is still anchored in the musculoskeletal theory and correction/prevention of high Cobb angle values. This study aimed to determine whether there is any connection between developing scoliotic curvature and a positive history of motion sickness as a symptom of a peripheral vestibular dysfunction/deficit, and if vestibular rehabilitation exercises could be integrated into the treatment plan. The study was conducted over 12 months on a selected population of 159 patients to evaluate or treat scoliotic curvatures in a private clinic. The collected data were analyzed using IBM SPSS Statistics 25 and illustrated using Microsoft Office Excel/Word 2021. Patients with peripheral vestibular dysfunction had significantly higher Cobb angle values when compared to patients with a negative result in an instrumental test for peripheral vestibular dysfunction. Motion sickness was considerably more associated with peripheral vestibular dysfunction, and a positive Fukuda stepping test was associated with a positive history of motion sickness. Adolescent idiopathic scoliosis with higher Cobb angles is related to positive motion sickness history as part of peripheral vestibular dysfunction. Conservative treatment for scoliosis could incorporate sensory integration techniques, and a positive history of motion sickness could be an indicator of a higher risk of progression in adolescent idiopathic scoliosis. [ABSTRACT FROM AUTHOR]

Developing prognostic tools specifically for patients themselves represents an important step in empowering patients to engage in shared decision-making. Incorporating patient-reported outcomes may improve the accuracy of these prognostic tools. We conducted a retrospective population-based study of transplant-ineligible (TIE) patients with multiple myeloma (MM) diagnosed between January 2007 and December 2018. A multivariable Cox regression model was developed to predict the risk of death within 1-year period from the index date. We identified 2356 patients with TIE MM. The following factors were associated with an increased risk of death within 1 year: age > 80 (HR 1.11), history of heart failure (HR 1.52), "CRAB" at diagnosis (HR 1.61), distance to cancer center (HR 1.25), prior radiation (HR 1.48), no proteosome inhibitor/immunomodulatory therapy usage (HR 1.36), recent emergency department (HR 1.55) or hospitalization (HR 2.13), poor performance status (ECOG 3-4 HR 1.76), and increasing number of severe symptoms (HR 1.56). Model discrimination was high with C-statistic of 0.74, and calibration was very good. To our knowledge, this represents one of the first prognostic models developed in MM incorporating patient-reported outcomes. This survival prognostic tool may improve communication regarding prognosis and shared decision-making among older adults with MM and their health care providers. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is associated with increased morbidity and mortality. The systemic immune-inflammation index (SII) has been correlated to a variety of disorders. The present study conducted a systematic review and meta-analysis to investigate the relationship between SII and sarcopenia. Methods: A literature search was performed in Web of Science, PubMed, Embase, Cochrane Library, CINAHL, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Wanfang Database, and VIP Chinese Science and Technology Database, from inception to March 2024. Then, the literature quality was assessed. After the heterogeneity test, a random effects or fixed effects model was applied to establish the forest plot, and investigate the relationship between SII and sarcopenia. Then, the sensitivity analysis and publication bias were examined. Results: Nine articles, which included 18,634 adults, were analyzed. Sarcopenic adults had higher SII levels, when compared to non-sarcopenic adults (standardized mean difference [SMD] = 0.66, 95% confidence interval [CI] = 0.22 − 0.19, p = 0.003). The high SII level was associated to the increased risk of sarcopenia (odds ratio = 1.52, 95% CI = 1.09–2.13, p = 0.01). In addition, the subgroup analysis revealed that the SII levels were higher in the sarcopenic group, when compared to the non-sarcopenic group, in elderly adults, as well as in adults with or without gastrointestinal disorders. The analysis was robust with a low risk of publication bias. Conclusions: SII is closely associated to sarcopenia. Sarcopenic adults had elevated SII levels. The high SII level increased the risk of sarcopenia. Large scale multi-center prospective studies are required to validate these study findings. [ABSTRACT FROM AUTHOR]

We have developed a digital twin-based CKD identification and prediction model that leverages generalized metabolic fluxes (GMF) for patients with Type 2 Diabetes Mellitus (T2DM). GMF digital twins utilized basic clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n = 7072): a Singaporean cohort (EVAS, n = 289) and a North American cohort (NHANES, n = 1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n = 2119 and SDR, n = 3627) for 3-year CKD prediction and risk stratification. We subsequently conducted a comprehensive study utilizing a single dataset to evaluate the clinical utility of GMF for CKD prediction. The GMF-based identification model performed strongly, achieving an AUC between 0.80 and 0.82. In prediction, the GMF generated with complete parameters attained high performance with an AUC of 0.86, while with incomplete parameters, it achieved an AUC of 0.75. The GMF-based prediction model utilizing complete inputs is the standard implementation of our algorithm: HealthVector Diabetes®. We have established the GMF digital twin-based model as a robust clinical tool capable of predicting and stratifying the risk of future CKD within a 3-year time horizon. We report the correlation of GMF with basic input parameters, their ability to differentiate between future health states and medication status at baseline, and their capability to quantify CKD progression rates. This holistic methodology provides insights into patients' health states and CKD progression rates based on GMF metabolic profile differences, enabling personalized care plans. [ABSTRACT FROM AUTHOR]

Background: Postoperative complications remain a significant challenge, especially in settings where healthcare access and infrastructure disparities exacerbate. This systematic review and meta-analysis aimed to determine the pooled incidence and risk factors of postoperative complications among patients undergoing essential surgery in Sub-Saharan Africa (SSA). Method: PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from January 2010 to November 2022 for completed studies reporting the incidence and risk factors associated with postoperative complications among patients undergoing essential surgery in SSA. Severity of postoperative complications was ranked based on the Clavien-Dindo classification system, while risk factors were classified into three groups based on the Donabedian structure-process-outcome quality evaluation framework. Studies quality was appraised using the JBI Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI), and data were analyzed using Comprehensive Meta-Analysis (CMA) software. The study protocol adhered to the PRISMA guidelines and was registered in PROSPERO (CRD42023414342). Results: The meta-analysis included 19 studies (10 cohort and 9 cross-sectional) comprising a total of 24,136 patients. The pooled incidence of postoperative complications in SSA was 20.2% (95% CI: 18.7%-21.8%), with a substantial heterogeneity of incidence observed. The incidence varied from 14.6% to 27.5% based on the Clavien-Dindo classification. The random-effects model indicated significant heterogeneity among the studies (Q=54.202, I = 66.791%, p < 0.001). Contributing factors to postoperative complications were: structure-related factors, which included the availability and accessibility of resources, as well as the quality of both the surgical facility and the hospital.; process-related factors, which encompassed surgical skills, adherence to protocols, evidence-based practices, and the quality of postoperative care; and patient outcome-related factors such as age, comorbidities, alcohol use, and overall patient health status. Conclusion: The meta-analysis reveals a high frequency of postoperative complications in SSA, with noticeable discrepancies among the studies. The analysis highlights a range of factors, encompassing structural, procedural, and patient outcome-related aspects, that contribute to these complications. The findings underscore the necessity for targeted interventions aimed at reducing complications and improving the overall quality of surgical care in the region. [ABSTRACT FROM AUTHOR]

Background: Failed extubation increases the intensive care unit (ICU) length of stay, hospital length of stay, and financial costs and it reduces the patient's functional ability. Avoiding failed extubation is of utmost importance, therefore predictors for successful extubation are paramount. Objective: To determine if successful extubation in mechanically ventilated patients can be predicted by physiotherapists using upper limb muscle strength and exercise endurance. Methods: Fifty-seven patients from the medical and trauma ICUs of a large academic hospital were eligible for testing. Muscle strength was evaluated using the Oxford grading scale, Medical Research Council score (MRC score), handgrip dynamometer, and maximum inspiratory pressure (MIP). Exercise endurance was tested while the patient was actively riding the MOTOmed® letto2 cycle ergometer for six minutes with the upper limbs. Results: Exercise endurance (time the patient rode actively) (P = 0.005), MRC-score (P = 0.007), and number of days ventilated (P = 0.005) were associated with successful extubation. The handgrip strength (P = 0.061), MIP (P = 0.095), and muscle strength of the sternocleidomastoid (P = 0.053) and trapezius muscles (P = 0.075) were marginally associated with successful extubation. Due to multicollinearity when developing the prediction equation, the final multivariable logistic regression prediction model included only exercise endurance and the number of days ventilated. The newly developed prediction equation conferred a sensitivity of 81.82% and a specificity of 77.14% to predict successful extubation. Conclusion: Successful extubation of mechanically ventilated patients can be predicted by physiotherapists using the newly developed prediction equation consisting of exercise endurance and number of days ventilated. [ABSTRACT FROM AUTHOR]

Background. Children and adolescents with type 1 diabetes mellitus (T1DM) are frequently hospitalised for severe hypoglycaemia, hyperglycaemia, and diabetic ketoacidosis (DKA). While several risk factors have been recognised, clinically identifying these children at high risk of acute decompensation remains challenging. Objective. To develop a risk prediction model to accurately estimate the risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA in children and adolescents with T1DM. Materials and Methods. Using a retrospective dataset, baseline demographic and clinical data were collected from patients (<18 years) seen at a regional paediatric diabetes clinic from 1 January 2018 to 1 January 2020. The outcome was the number of emergency department presentations or hospital admissions for severe hypoglycaemia, hyperglycaemia, and DKA across the study period. Variables that were significant in univariate analysis were entered into a multivariable model. Receiver operator characteristic (ROC) curves assessed the model's discrimination and generated cut-offs for risk group stratification (low, medium, and high). Kaplan–Meier survival analysis measured time to acute healthcare utilisation across the risk groups. Results. Our multivariable risk prediction model consisted of five predictors (continuous glucose monitoring device, previous acute healthcare utilisation, missed appointments, and child welfare services involvement and socioeconomic status). The model exhibited good discrimination (area under the ROC = 0.81), accurately stratified children into low-, medium-, and high-risk groups, and demonstrated significant differences between median time to healthcare utilisation. Conclusion. Our model identified patients at an increased risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA. [ABSTRACT FROM AUTHOR]

Introduction: This article mainly studies the risk factors for postoperative acute myocardial infarction (AMI) in elderly hip fracture patients combined with coronary heart disease (CHD), constructs a prediction model, and evaluates the prognosis of all the patients. Methods: This article retrospectively collected elderly patients with hip fracture and CHD who underwent hip fracture surgery at the Third Hospital of Hebei Medical University from January 2019 to December 2021. Demographic data, laboratory indicators, and imaging examination results were collected from the medical case system. The risk factors of postoperative AMI were determined by univariate and multivariate logistic regression, and a nomogram prediction model was established. The ROC curve, calibration curve and DCA decision curve were plotted by R language software. The patients in the training set were followed up for 2 years to evaluate their survival situation. Results: 1094 eligible patients were divided into a training set (n = 824 from January 1, 2019 to September 31, 2021) and a validation set (n = 270 from October 1, 2021 to December 31, 2022). In the training set, women accounted for 58.6%; The average age of the patients was 79.45 years old; The main type of fracture was intertrochanteric fracture. There were 64.7% patients taken B receptor blockers; A total of 166 (20.1%) patients underwent percutaneous coronary intervention (PCI); Hypertension accounted for 55.5%; 520 (63.1%) patients had a preoperative waiting time greater than 3 days; The average hemoglobin value upon admission was 101.36 g/L; The average intraoperative bleeding volume was 212.42 ml; The average surgical time was 2.5 ± 0.3 h; Reginal anesthesia accounted for 29.7%; 63 (68.5%) AMI patients had no obvious clinical symptoms; 68 (73.9%) AMI patients did not show ST-segment elevation in ECG; The risk factors of postoperative AMI were age, hemoglobin at admission, diabetes, chronic kidney disease, intraoperative bleeding, and reginal anesthesia. The AUC of the nomogram prediction model was 0.729. The AUC in the validation set was 0.783. Survival analysis showed a significant statistical difference in 2-year mortality between patients with AMI and without AMI, among all the patients with AMI, patients with ECG ST-segment elevation has higher mortality than patients without ECG ST-segment elevation. Conclusion: Our research results found that the incidence of postoperative AMI in elderly patients with hip fractures and CHD was 11.1%. Age, diabetes, hemoglobin at admission, regional anesthesia, chronic kidney disease, and intraoperative bleeding are risk factors. The AUC of the nomogram in training set is 0.729. The 2-year mortality rate of the patients with AMI is higher than that of patients without AMI. [ABSTRACT FROM AUTHOR]

Background: Jugulo-omohyoid lymph nodes (JOHLN) metastasis has proven to be associated with lateral lymph node metastasis (LLNM). This study aimed to reveal the clinical features and evaluate the predictive value of JOHLN in PTC to guide the extent of surgery. Methods: A total of 550 patients pathologically diagnosed with PTC between October 2015 and January 2020, all of whom underwent thyroidectomy and lateral lymph node dissection, were included in this study. Results: Thyroiditis, tumor location, tumor size, extra-thyroidal extension, extra-nodal extension, central lymph node metastasis (CLNM), and LLMM were associated with JOHLN. Male, upper lobe tumor, multifocality, extra-nodal extension, CLNM, and JOHLN metastasis were independent risk factors from LLNM. A nomogram based on predictors performed well. Nerve invasion contributed the most to the prediction model, followed by JOHLN metastasis. The area under the curve (AUC) was 0.855, and the p-value of the Hosmer-Lemeshow goodness of fit test was 0.18. Decision curve analysis showed that the nomogram was clinically helpful. Conclusion: JOLHN metastasis could be a clinically sensitive predictor of further LLM. A high-performance nomogram was established, which can provide an individual risk assessment of LNM and guide treatment decisions for patients. [ABSTRACT FROM AUTHOR]

Background: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. Methods: Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. Results: In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856–0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. Conclusion: Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care. [ABSTRACT FROM AUTHOR]

Background: To examine the risk factors associated with mortality in individuals suffering from acute diquat poisoning and to develop an effective prediction model using clinical data. Methods: A retrospective review was conducted on the clinical records of 107 individuals who were hospitalized for acute diquat poisoning at a tertiary hospital in Sichuan Province between January 2017 and September 30, 2023, and further categorized into survivor and nonsurvivor groups based on their mortality status within 30 days of poisoning. The patient's demographic information, symptoms within 24 h of admission, and details of the initial clinical ancillary examination, as well as the APACHE II score, were documented. The model was developed using backward stepwise logistic regression, and its performance was assessed using receiver operating characteristic curves, calibration curves, Brier scores, decision curve analysis curves, and bootstrap replicates for internal validation. Results: Multifactorial logistic regression analysis revealed that blood pressure (hypertension, OR 19.73, 95% CI 5.71–68.16; hypotension, OR 61.38, 95% CI 7.40–509.51), white blood count (OR 1.35, 95% CI 1.20–1.52), red cell distribution width-standard deviation (OR 1.22, 95% CI 1.08–1.38), and glomerular filtration rate (OR 0.96, 95% CI 0.94–0.97) were identified as independent risk factors for mortality in patients with diquat. Subsequently, a nomogram with an area under the curve of 0.97 (95% CI: 0.93–1) was developed. Internal bootstrap resampling (1000 repetitions) confirmed the model's adequate discriminatory power, with an area under the curve of 0.97. Decision curve analysis demonstrated greater net gains for the nomogram, while the clinical impact curves indicated greater predictive validity. Conclusion: The nomogram model developed in this study using available clinical data enhances the prediction of risk for DQ patients and has the potential to provide valuable clinical insights to guide patient treatment decisions. [ABSTRACT FROM AUTHOR]