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We aim to clarify the usefulness of different measures of economic resilience in the context of global shocks. In relation to the Great Recession, Oil& European migrant crises, and COVID-19, we compute and validate from a statistical point of view, then characterize five metrics on 317 NUTS2 regions and 21 years. ROC curves and Cox regression compare them by the capacity to predict the post-shock state. Heterogenous behavior characterizes resistance, recovery, and loss. Furthermore, the non-linear resistance performs best in future state prediction, while the composite index has the lowest efficiency as a predictor of the regions' recovery. [ABSTRACT FROM AUTHOR]

Background: Police officers are at a high risk of noise-induced hearing loss (NIHL) owing to the nature of their work. Therefore, this study aimed to compare the risk of NIHL in police officers and controls. Methods: This study used the National Health Insurance claims data of workers aged 25–65 years obtained from 2005 to 2015. The case group comprised police officers, while the control group comprised general workers and public officers. The study followed a three-phase cohort design. The standardized incidence ratio (SIR) was calculated using an indirect standardization method based on age. Propensity score matching was performed using the greedy matching method, with a police officer-to-control group ratio of 1:3. Cox regression analysis was performed for each matched control group. Statistical significance was determined by a lower limit of greater than 1, based on the 95% confidence interval (CI). Results: The SIR values for police officers were 1.62 (95% CI: 1.44–1.82) compared with general workers and 1.78 (95% CI: 1.66–1.73) compared with public officers. Police officers exhibited an increased risk of NIHL compared with general workers (hazard ratio (HR): 1.71, 95% CI: 1.49–1.98) and public officers (HR: 2.19, 95% CI: 1.88–2.56). Conclusions: It is necessary to prevent NIHL by reducing occupational noise exposure through measures such as wearing earplugs, improving shooting training methods, and improving the shift work system. [ABSTRACT FROM AUTHOR]

Background: Early detection of Alzheimer's disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling. Methods: We developed a prognostic model using a large-scale Alzheimer's Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from the Open Access Series of Imaging Studies. We considered individuals who were initially CN or had MCI but progressed to AD and those with MCI with no progression to AD during the five-year follow-up period. As the exact conversion time to AD is unknown, we inferred this information using imputation approaches. We generated cortex-wide principal FC gradients using manifold learning techniques and computed subcortical-weighted manifold degrees from baseline functional magnetic resonance imaging data. A penalized Cox regression model with an elastic net penalty was adopted to define a risk score predicting the risk of conversion to AD, using FC gradients and clinical factors as regressors. Results: Our prognostic model predicted the conversion risk and confirmed the role of imaging-derived manifolds in the conversion risk. The brain regions that largely contributed to predicting AD conversion were the heteromodal association and visual cortices, as well as the caudate and hippocampus. Our risk score based on Cox regression was consistent with the expected disease trajectories and correlated with positron emission tomography tracer uptake and symptom severity, reinforcing its clinical usefulness. Our findings were validated using an independent dataset. The cross-sectional application of our model showed a higher risk for AD than that for MCI, which correlated with symptom severity scores in the validation dataset. Conclusion: We proposed a prognostic model predicting the risk of conversion to AD. The associated risk score may provide insights for early intervention in individuals at risk of AD conversion. [ABSTRACT FROM AUTHOR]

Background: Biologics are among the most effective therapies for psoriasis. However, many patients are only introduced to them at advanced stages of the disease course. Objectives: Our aim was to identify predictors of initiating biologic therapy in patients with psoriasis and compare patients initiating biologics early versus late in their disease course. Methods: Kaplan–Meier curves visualized time to biologic initiation, while Cox regression models further explored variables as predictors of biologic initiation. Mann–Whitney U and chi‐squared tests compared patients who started biologics early with those who began biologics later in the disease course. Results: Our primary analysis included 233 psoriasis patients. Cox regression showed that age at diagnosis (P = 0.007), general physical well‐being (P = 0.02), and nail psoriasis severity (P = 0.02) were significantly associated with time to biologic initiation. Our secondary analysis, the comparisons between patients starting biologics early versus later in the disease course, included a total of 378 patients. The median (interquartile range [IQR]) age at diagnosis was 34.5 (25.0–51.2) years for patients initiating biologics within 5 years, compared to 22.0 (15.0–32.8) years for patients initiating biologics later (P < 0.0001). The median (IQR) age at initiation was 37.0 (27.0–53.2) and 45.0 (36.0–55.0) years for patients initiating biologics earlier versus later than 5 years (P = 0.04). Conclusions: Age at diagnosis, general well‐being, and severity of nail psoriasis significantly predicted future initiation of biologic treatment. Patients initiating biologics early in their disease course were generally older at diagnosis but younger at the time of biologic initiation compared to patients initiating biologics later in their disease course. [ABSTRACT FROM AUTHOR]

Background: Rising health care costs and consequent increases inMedicare reimbursements have led tomany payment reforms over the years. Implementation of the prospective payment system (PPS) for hospitals in 1983 incentivized hospitals to either purchase skilled nursing facilities (SNFs) or utilize their excess capacity to establish one within the hospital. With PPS reimbursement being applied to SNFs in 1998, prior monetary incentives for hospitals to own an SNF disappeared. However, despite the reduction in numbers, many hospitals continued to operate their hospitalbased skilled nursing facilities (HBSNFs). Purpose: This study examines the organizational andmarket-level factors associated with the survival of HBSNFs using the population ecology of organizations framework. Methodology: Using American Hospital Association survey data, event histories of all U.S. acute care hospitals with an open HBSNF in 1998 were plotted to examine if a hospital closed its HBSNF during a 22-year period (1998-2020). The primary independent variables included hospital size, ownership, totalmargin, market competition, andMedicare Advantage penetration. The independent and control variableswere lagged by 1 year. Cox regressionswere conducted to estimate the hazard ratios capturing the risk of HBSNF closure. Results: The results showed that HBSNFs located in large, not-for-profit hospitals and those operating in less competitive markets had greater odds of surviving. Practice Implications: The HBSNF administrators of small, for-profit hospitals and those operating in highly competitive markets could utilize the findings of this study to judiciously allocate slack resources to theirHBSNFs to keep those open given the current emphasis on continuity of care by regulatory bodies. [ABSTRACT FROM AUTHOR]

Purpose: The leading cause of cancer-related deaths in Iraqi women is breast cancer, followed by other malignancies. The purpose of this research is to investigate the association between covariates (pathological and demographic characteristics) and time to death in women with breast cancer. Methods: Data were collected from the cancer archive in the city of Sulaimani regarding 305 women who received breast cancer diagnoses between 2011 and 2020. Using R and SPSS Programs, Cox regression was used to investigate the relationship between explanatory variables (sociodemographic, pathological factors, and treatment) and time to death as a response variable. Results: The study's findings showed that occupation was statistically the greatest risk factor (−2.361), followed by weight and treatment (0.789 and 1.605, respectively), while age, place of habitation, and tumor size had no statistically significant effect on survival time in breast cancer patients. Conclusion: This study discovered that patients who receive therapy, such as chemotherapy, radiotherapy, and hormonal therapy, alone or in combination live longer than patients who do not receive therapy. In addition, it emphasizes that not exercising and staying at home, not eating nutritious food, and eating red meat and fried meals are the most common factors associated with shorter survival time in breast cancer patients. [ABSTRACT FROM AUTHOR]

Background: The age‐standardised incidence ratio of gastrointestinal cancers in type 1 diabetes (T1D) patients has been reported to be higher than that in the general population. After adjusting for shared risk factors, we aimed to explore the association between T1D and gastrointestinal cancer and examine how this relationship varies by age and sex. Materials and Methods: This retrospective cohort study included 268,179 participants from the Korean National Health Insurance Service‐National Sample Cohort. The primary outcome is the incident of gastrointestinal cancers, based on diagnostic codes. Multivariate Cox regression analyses were performed to assess the association between T1D and gastrointestinal cancers. Results: Of the 268,179 participants, 2681 had T1D at baseline and were followed for 12.98 (± 2.92) years. Compared with non‐T1D, T1D patients had a significantly increased risk of all gastrointestinal cancer (adjusted hazard ratio [aHR]: 1.403, 95% confidence interval [CI]: 1.253–1.573). T1D patients increased risks of oesophageal cancer (aHR: 1.864, 95% CI: 1.038–3.349), gastric cancer (aHR: 1.313, 95% CI: 1.066–1.616), colon cancer (aHR: 1.365, 95% CI: 1.110–1.678), liver cancer (aHR: 1.388, 95% CI: 1.115–1.727), and pancreatic cancer (aHR: 1.716, 95% CI: 1.182–2.492). The consistency of this association persisted among both male and female, with its strength increasing with older age. Conclusions: The risk of gastrointestinal cancer was significantly increased in T1D patients. Older male T1D patients exhibit a higher risk, suggesting the need for targeted attention in their care. [ABSTRACT FROM AUTHOR]

In this work, a method to regularize Cox frailty models is proposed that accommodates time‐varying covariates and time‐varying coefficients and is based on the full likelihood instead of the partial likelihood. A particular advantage of this framework is that the baseline hazard can be explicitly modeled in a smooth, semiparametric way, for example, via P‐splines. Regularization for variable selection is performed via a lasso penalty and via group lasso for categorical variables while a second penalty regularizes wiggliness of smooth estimates of time‐varying coefficients and the baseline hazard. Additionally, adaptive weights are included to stabilize the estimation. The method is implemented in the R function coxlasso, which is now integrated into the package PenCoxFrail, and will be compared to other packages for regularized Cox regression. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Whether hypoglycaemia increases the risk of other adverse outcomes in diabetes remains controversial, especially for hypoglycaemia episodes not requiring assistance from another person. An objective of the Hypoglycaemia REdefining SOLutions for better liVEs (Hypo-RESOLVE) project was to create and use a dataset of pooled clinical trials in people with type 1 or type 2 diabetes to examine the association of exposure to all hypoglycaemia episodes across the range of severity with incident event outcomes: death, CVD, neuropathy, kidney disease, retinal disorders and depression. We also examined the change in continuous outcomes that occurred following a hypoglycaemia episode: change in eGFR, HbA1c, blood glucose, blood glucose variability and weight. Methods: Data from 84 trials with 39,373 participants were pooled. For event outcomes, time-updated Cox regression models adjusted for age, sex, diabetes duration and HbA1c were fitted to assess association between: (1) outcome and cumulative exposure to hypoglycaemia episodes; and (2) outcomes where an acute effect might be expected (i.e. death, acute CVD, retinal disorders) and any hypoglycaemia exposure within the last 10 days. Exposures to any hypoglycaemia episode and to episodes of given severity (levels 1, 2 and 3) were examined. Further adjustment was then made for a wider set of potential confounders. The within-person change in continuous outcomes was also summarised (median of 40.4 weeks for type 1 diabetes and 26 weeks for type 2 diabetes). Analyses were conducted separately by type of diabetes. Results: The maximally adjusted association analysis for type 1 diabetes found that cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of neuropathy, kidney disease, retinal disorders and depression, with risk ratios ranging from 1.55 (p=0.002) to 2.81 (p=0.002). Associations of a similar direction were found when level 1 episodes were examined separately but were significant for depression only. For type 2 diabetes cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of death, acute CVD, kidney disease, retinal disorders and depression, with risk ratios ranging from 2.35 (p<0.0001) to 3.00 (p<0.0001). These associations remained significant when level 1 episodes were examined separately. There was evidence of an association between hypoglycaemia episodes of any kind in the previous 10 days and death, acute CVD and retinal disorders in both type 1 and type 2 diabetes, with rate ratios ranging from 1.32 (p=0.017) to 2.68 (p<0.0001). These associations varied in magnitude and significance when examined separately by hypoglycaemia level. Within the range of hypoglycaemia defined by levels 1, 2 and 3, we could not find any evidence of a threshold at which risk of these consequences suddenly became pronounced. Conclusions/interpretation: These data are consistent with hypoglycaemia being associated with an increased risk of adverse events across several body systems in diabetes. These associations are not confined to severe hypoglycaemia requiring assistance. [ABSTRACT FROM AUTHOR]

Background: Impulse control disorders (ICDs) are an increasingly recognized complication in Parkinson disease (PD). The pathogenesis of ICDs is currently unclear. Few genetic studies have been conducted in this area. Objective: We aimed to ascertain the correlation between APOE and ICDs, and identify clinical predictors of ICDs in PD. Methods: This study included 287 PD patients from the Parkinson's Progression Markers Initiative. They were followed up to investigate the progression of ICDs over a period of 5 years. The cumulative incidence of ICDs and potential risk factors were evaluated using Kaplan-Meier and Cox regression analyses. Results: 44.3% (31/70) patients with APOE ɛ4 and 32.3% (70/217) patients without APOE ɛ4 developed ICDs during the five-year follow up period. There were significant differences between the PD with and without ICDs development group in age, MSEADLG score, ESS score, GDS score, and STAI score at baseline. In multivariable Cox regression analysis, APOE ε4 (HR = 1.450, p = 0.048) and STAI score (HR = 1.017, p = 0.001) were predictors of the development of ICDs. Patients with APOE ɛ4 group showed significantly lower CSF Aβ42 and CSF α-syn level than patients without APOE ɛ4 group at baseline. In patients with APOE ɛ4 group, the "low α-syn level" group and the "low ptau/tau ratio" group had a significantly higher incidence of ICDs, respectively. Conclusions: This study provides important insights into the potential role of the APOE gene in the development of ICDs in PD. Further studies are needed to confirm our findings and to investigate the underlying mechanisms in more detail. [ABSTRACT FROM AUTHOR]

Background: The exploration of optimizing cardiopulmonary function and athletic performance through high-intensity metabolic exercises (HIMEs) is paramount in sports science. Despite the acknowledged efficacy of HIMEs in enhancing cardiopulmonary endurance, the high metabolic stress imposed on the cardiopulmonary system, especially for amateurs, necessitates a scaled approach to training. Objective: The aim of this study is to ascertain whether adjustments in the initiation posture and the adoption of an appropriate breathing strategy can effectively mitigate the cardiopulmonary stress induced by HIMEs without compromising training efficacy. Methods: Twenty-two subjects were recruited into this study. The post-exercise heart rate (PHR) and post-exercise oxygen consumption rate (POCR) were collected within 30 min after exercise. A two-way ANOVA, multi-variable Cox regression, and random survival forest machine learning algorithm were used to conduct the statistical analysis. Results: Under free breathing, only the maximum POCR differed significantly between standing and prone positions, with prone positions showing higher stress (mean difference = 3.15, p < 0.001). In contrast, the regulated breathing rhythm enhanced performance outcomes compared to free breathing regardless of the starting position. Specifically, exercises initiated from prone positions under regulated breathing recorded a significantly higher maximum and average PHR than those from standing positions (maximum PHR: mean difference = 13.40, p < 0.001; average PHR: mean difference = 6.45, p < 0.001). The multi-variable Cox regression highlighted the starting position as a critical factor influencing the PHR and breathing rhythm as a significant factor for the POCR, with respective variable importances confirmed by the random survival forest analysis. These results underscore the importance of controlled breathing and starting positions in optimizing HIME outcomes. Conclusions: Regulated breathing in high-intensity exercises enhances performance and physiological functions, emphasizing the importance of breathing rhythm over starting position. Effective training should balance exercise volume and technique to optimize performance and minimize stress, reducing overtraining and injury risks. [ABSTRACT FROM AUTHOR]

Aims: To assess the survival function of cementless total trapezium metacarpal prostheses (TTMPs) at 20 years, to compare survival functions by trapezium size, and to evaluate the association between the instantaneous risk of TTMP failure and small trapezium size using a multivariate Cox regression model. Methods: This observational cohort study included 221 consecutive patients with a mean follow-up after TTMP of 137.3 months (maximum of 246 months). Kaplan-Meier and actuarial life-table methods were used to evaluate the survival function of thecohort. Kaplan-Meier survival curves were compared by trapezium size. Multivariate Cox regression analysis was used to determine the effect of potential confounders on the association between small trapezium and the instantaneous risk of TTMP failure. Results: At the end of follow-up, there was a 89.01% chance of the TTMP surviving for 246 months or more. There was an association between TTMP survival time and trapezium size showing a significant trend such that the survival curves weresignificantly higher with larger trapezium size (Mantel-Cox test, p = 0.0001; WilcoxonBreslow test, p = 0.0002; Tarone-Ware test, p = 0.0001).The unadjusted Cox regression model showed a significant association between small trapezium size (smaller than 9 mm) and the instantaneous risk of TTPM failure (HR: 7.37, 95% CI: 2.46-22.07). In the multivariate Cox analysis, "age", "trapezium morphology", and "complications" were confounders in the association between small trapezium size and the hazard ratio of prosthetic failure (HR = 3.76; 95% CI 0.96 to 13.82). Conclusion: These results confirm the long-term functional survival of TTMP prostheses and reveal a significant increase in trend of the survival curve with larger trapezium size. Patient age, trapezium morphology, and the presence of post-surgical complications are confounders in the association between small trapezium size and the hazard ratio of TTMP failure. [ABSTRACT FROM AUTHOR]

The primary goal of this article is to extend the reciprocal LASSO for applications to binary and survival outcomes. We consider the least squares approximation (LSA) as a solver for the reciprocal LASSO problem. The LSA is a general theoretical framework that includes generalized linear models, Cox regression, and many others as special cases. By applying LSA to reciprocal LASSO regularization, we transfer the original reciprocal LASSO problem into an asymptotically equivalent least squares problem. While the existing literature on reciprocal LASSO has mostly focused on linear models, our algorithm can be easily implemented for general likelihoods, providing a flexible framework for variable selection using reciprocal penalties. To handle the computational burden of implementing the resulting procedure, we employ a scalable stochastic search method called Simplified Shotgun Stochastic Search with Screening (S5), which is easy to implement, without requiring any sophisticated optimization package other than a linear equation solver. We examine the effectiveness of our procedure through Monte Carlo simulations and real data analyses. [ABSTRACT FROM AUTHOR]

This study aimed to pinpoint independent predictors influencing overall survival (OS) and cancer-specific survival (CSS) in elderly patients with small cell lung cancer (SCLC) brain metastasis (BM), and to create and validate nomograms for OS and CSS prediction. Data from elderly SCLC BM patients were extracted out of the Surveillance, Epidemiology, and End Results database, including 1200 patients identified from 2010 and 2015 who were randomly allocated into a training set and an internal validation set at a proportion of 7:3, and 666 patients diagnosed between 2018 and 2020 as a temporal external validation set. Independent predictors for OS and CSS were determined through univariate Cox analysis, least absolute shrinkage and selection operator analysis, and multivariate Cox analysis sequentially. Nomograms for OS and CSS were constructed, and validated by the internal and temporal external validation sets. Age, N stage, chemotherapy, and liver metastasis were determined as independent predictors of OS and CSS, while radiotherapy and surgery were not. Nomograms were constructed based on these independent predictors. The results of the receiver operator characteristic curves, the areas under the curve and calibration curve demonstrated that the nomograms exhibited commendable discriminative ability and calibration. Moreover, decision curve analysis, net reclassification improvement, and integrated discrimination improvement also suggested that the nomograms possessed superior clinical usefulness and predictive capability relative to the TNM system. Prognostic nomograms for elderly patients with SCLC BM have been developed, demonstrating good performance in terms of accuracy, reliability, and practicality. [ABSTRACT FROM AUTHOR]

While it is well known that privacy-preserving cox regression generally consists of a semi-honest cloud service provider (CSP) who performs curious-but-honest computations on ciphertexts to train the cox model. No one can verify the behaviors of CSP when he performs computations dishonestly in reality. Focusing on this problem, we propose a verifiable privacy-preserving cox regression algorithm tailored with the semi-malicious CSP, where all his behaviors are recorded on a witness tape fulfilling the requirement of transparency. To be specific, a multi-key fully homomorphic encryption (FHE) is used to protect the information of different data owners. The verifiability of our proposed multi-key homomorphic message authenticator (HMAC) ensures CSP sends correct results back to data owners. Furthermore, the compactness of FHE and succinctness of HMAC both under multi keys make the cox regression scheme more feasible. The efficiency of our proposed cox regression scheme is also proved by both theoretical analyses and experimental evaluations. After 21 iterations, it costs no more than 10 min to evaluate our cox regression scheme. [ABSTRACT FROM AUTHOR]

Abstract India’s high tuberculosis (TB) burden is exacerbated by concurrent silicosis, which increases TB susceptibility and worsens treatment outcomes. Limited studies on TB patients with silicosis highlight the need to address this vulnerable group’s specific challenges, particularly to improve diagnosis and management. This retrospective cohort study analyzed survival data from 137 silico-tuberculosis and 2,605 TB-only patients in Khambhat, India, using Kaplan-Meier curves, log-rank tests, and comparisons between Cox proportional hazards and accelerated failure time (AFT) models. The lognormal AFT model, selected for its lowest Akaike Information Criterion (AIC), estimated survival times based on age, gender, HIV status, and prior TB treatment. Among the 2,742 patients, 309 (11%) died within 27 months. Median time from diagnosis to outcome was shorter for deceased patients (1.7 months) than for censored patients (5.6 months, p

Furui Zhai, Shanshan Mu, Yinghui Song, Min Zhang, Cui Zhang, Ze Lv Gynecological Clinic, Cangzhou Central Hospital, Cangzhou, Hebei, People’s Republic of ChinaCorrespondence: Furui Zhai, Gynecological Clinic, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou City, Hebei Province, People’s Republic of China, Tel +86-0317-2075783, Email zfr860708@126.comPurpose: Loop electrosurgical excision procedure (LEEP) for high-grade cervical intraepithelial neoplasia (CIN) carries significant risks of recurrence and persistence. This study compares the efficacy of a random survival forest (RSF) model with that of a conventional Cox regression model for predicting residual and recurrent high-grade CIN in premenopausal women after LEEP.Methods: Data from 458 premenopausal women treated for CIN2/3 at our hospital between 2016 and 2020 were analyzed. The RSF model incorporated demographic, pathological, and treatment-related variables. Feature selection utilizing LASSO and three other algorithms was performed to enhance the RSF model, which was further compared to a Cox regression model. Model performance was assessed using area under the curve (AUC), out-of-bag (OOB) error rates, and SHAP values to interpret predictor importance.Results: The RSF model showed superior performance compared to the Cox regression model, with AUC values of 0.767– 0.901 and peak predictive performance at 36 months post-LEEP. In contrast, the highest AUC achieved by Cox regression was 0.880. The RSF model also exhibited relatively lower OOB error rates, indicating better generalizability. Moreover, SHAP value analysis identified margin status and CIN severity as the most prominent predictors that directly affected risk predictions. Lastly, an online tool providing real-time predictions in clinical settings was successfully implemented using the RSF model.Conclusion: The RSF model outperformed the traditional Cox regression model in predicting residual and recurrent high-grade CIN risks post-LEEP. This model may be a more accurate clinical tool that facilitates improved personalized care and early interventions in gynecological oncology.Keywords: cervical intraepithelial neoplasia, residual/recurrent, random survival forest, Cox regression, premenopausal women

Xuechun Li,1 Dennis Steenhuis,1 Maarten J Bijlsma,1,2 Stijn de Vos,1 Sumaira Mubarik,1 Jens HJ Bos,1 Catharina CM Schuiling-Veninga,1 Eelko Hak1 1Pharmacotherapy, Epidemiology and Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, the Netherlands; 2Laboratory of Population Health, Max Planck Institute for Demographic Research, Rostock, GermanyCorrespondence: Xuechun Li, PhD Research Fellow, Pharmacotherapy, Epidemiology and Economics, Groningen Research Institute of Pharmacy, University of Groningen, 9713 AV Groningen, the Netherlands, Tel +31 649019602, Email xuechen.li@rug.nlPurpose: Anti-hyperlipidemic drug treatments are effective in reducing the risk of cardiovascular disease. In a long-term retrospective inception cohort study, we aimed to assess the real-world comparative effectiveness of anti-hyperlipidemic monotherapies for primary prevention of cardiovascular events.Patients and Methods: Patients aged 18 years and older, who initiated primary prevention with anti-hyperlipidemic monotherapy, were selected from the University of Groningen IADB.nl dispensing database. In intention-to-treat (ITT) analysis we included all patients, whereas in per-protocol (PP) analysis we included both all patients independent of adherence (PPIA) and adherent patients (PPA). Study outcome was the time to first prescription of acute cardiac drug therapy measured by valid drug proxies to identify a first major cardiovascular event. We applied inverse probability of treatment-weighted (IPTW) analysis using Cox regression and time-varying Cox regression with simvastatin as the reference category to estimate the average treatment effect hazard ratios (HR) and their corresponding 95% confidence intervals (CI).Results: Atorvastatin users had significantly higher hazards compared to simvastatin users (HR range: 1.27 to 1.47, 95% CI: 1.15 to 1.69). Similarly, Pravastatin users also exhibited increased hazards compared to simvastatin users (HR range: 1.41 to 1.56, 95% CI: 1.14 to 2.04). Similar patterns were observed in patients with diabetes, rheumatoid arthritis, and asthma/COPD. No differences were found in the hazards of rosuvastatin, fluvastatin, fibrates, and simvastatin.Conclusion: Atorvastatin and pravastatin users had higher long-term rates of cardiovascular events compared to simvastatin monotherapy in primary prevention, the difference may be attributed to the confounding by severity, but also possibly due to differences in drug mechanisms or patient response. These findings could influence current guideline recommendations, suggesting a potential preference for simvastatin in primary prevention, underscoring the need for further research to explore long-term impacts and underlying mechanisms, especially in diverse populations. Keywords: acute cardiac drug therapy, time-varying confounding, inverse probability treatment weighting, Cox regression

Abstract Background Police officers are at a high risk of noise-induced hearing loss (NIHL) owing to the nature of their work. Therefore, this study aimed to compare the risk of NIHL in police officers and controls. Methods This study used the National Health Insurance claims data of workers aged 25–65 years obtained from 2005 to 2015. The case group comprised police officers, while the control group comprised general workers and public officers. The study followed a three-phase cohort design. The standardized incidence ratio (SIR) was calculated using an indirect standardization method based on age. Propensity score matching was performed using the greedy matching method, with a police officer-to-control group ratio of 1:3. Cox regression analysis was performed for each matched control group. Statistical significance was determined by a lower limit of greater than 1, based on the 95% confidence interval (CI). Results The SIR values for police officers were 1.62 (95% CI: 1.44–1.82) compared with general workers and 1.78 (95% CI: 1.66–1.73) compared with public officers. Police officers exhibited an increased risk of NIHL compared with general workers (hazard ratio (HR): 1.71, 95% CI: 1.49–1.98) and public officers (HR: 2.19, 95% CI: 1.88–2.56). Conclusions It is necessary to prevent NIHL by reducing occupational noise exposure through measures such as wearing earplugs, improving shooting training methods, and improving the shift work system.

Abstract Background Early detection of Alzheimer’s disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling. Methods We developed a prognostic model using a large-scale Alzheimer’s Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from the Open Access Series of Imaging Studies. We considered individuals who were initially CN or had MCI but progressed to AD and those with MCI with no progression to AD during the five-year follow-up period. As the exact conversion time to AD is unknown, we inferred this information using imputation approaches. We generated cortex-wide principal FC gradients using manifold learning techniques and computed subcortical-weighted manifold degrees from baseline functional magnetic resonance imaging data. A penalized Cox regression model with an elastic net penalty was adopted to define a risk score predicting the risk of conversion to AD, using FC gradients and clinical factors as regressors. Results Our prognostic model predicted the conversion risk and confirmed the role of imaging-derived manifolds in the conversion risk. The brain regions that largely contributed to predicting AD conversion were the heteromodal association and visual cortices, as well as the caudate and hippocampus. Our risk score based on Cox regression was consistent with the expected disease trajectories and correlated with positron emission tomography tracer uptake and symptom severity, reinforcing its clinical usefulness. Our findings were validated using an independent dataset. The cross-sectional application of our model showed a higher risk for AD than that for MCI, which correlated with symptom severity scores in the validation dataset. Conclusion We proposed a prognostic model predicting the risk of conversion to AD. The associated risk score may provide insights for early intervention in individuals at risk of AD conversion.

Abstract Background Impulse control disorders (ICDs) are an increasingly recognized complication in Parkinson disease (PD). The pathogenesis of ICDs is currently unclear. Few genetic studies have been conducted in this area. Objective We aimed to ascertain the correlation between APOE and ICDs, and identify clinical predictors of ICDs in PD. Methods This study included 287 PD patients from the Parkinson’s Progression Markers Initiative. They were followed up to investigate the progression of ICDs over a period of 5 years. The cumulative incidence of ICDs and potential risk factors were evaluated using Kaplan-Meier and Cox regression analyses. Results 44.3% (31/70) patients with APOE ɛ4 and 32.3% (70/217) patients without APOE ɛ4 developed ICDs during the five-year follow up period. There were significant differences between the PD with and without ICDs development group in age, MSEADLG score, ESS score, GDS score, and STAI score at baseline. In multivariable Cox regression analysis, APOE ε4 (HR = 1.450, p = 0.048) and STAI score (HR = 1.017, p = 0.001) were predictors of the development of ICDs. Patients with APOE ɛ4 group showed significantly lower CSF Aβ42 and CSF α-syn level than patients without APOE ɛ4 group at baseline. In patients with APOE ɛ4 group, the “low α-syn level” group and the “low ptau/tau ratio” group had a significantly higher incidence of ICDs, respectively. Conclusions This study provides important insights into the potential role of the APOE gene in the development of ICDs in PD. Further studies are needed to confirm our findings and to investigate the underlying mechanisms in more detail.

This article describes survivorship and explores factors affecting mortality risks in a captive colony of coppery titi monkeys (Plecturocebus cupreus) housed at the California National Primate Research Center (CNPRC), at UC Davis, in Davis, CA. We analyzed data collected on individuals since the colonys creation in the 1960s, with a sample of 600 animals with partially complete information (date of birth, age at death, body mass, parental lineage). We used three methods: (1) Kaplan-Meier regressions followed by a log-rank test to compare survival in male and female titi monkeys, (2) a breakpoint analysis to identify shifts in the survival curves, and (3) Cox regressions to test the effect of body mass change, parental pair tenure, and parental age on mortality risk. We found that males tend to have a longer median lifespan than females (14.9 and 11.4 years; p = 0.094) and that survival decreases earlier in males than in females during adulthood (9.8 and 16.2 years). A body mass loss of 10% from adulthood to the time of death led to a 26% higher risk of dying (p

Background: Pressure injuries (PIs) impose a significant burden on patients in the intensive care unit (ICU) and the healthcare system. Assessing the risk of developing PIs is crucial for prevention. However, it is unclear whether all subscales of the Waterlow scale can be used to assess PIs risk in ICU. Objectives: To assess whether all subscales of the Waterlow scale can predict PIs risk in ICU. Design: Multicentre prospective study. Methods: A total of 18,503 patients from ICUs in 40 tertiary‐level hospitals in Gansu province of China were enrolled from April 2021 to August 2023. The incidence and characteristics of PIs were recorded. Univariate Cox regression analyses were performed for each subscale as a predictor of PIs development, followed by multivariate Cox regression with covariates for each subscale separately. Results: Out of 17,720 patients included, the incidence of PIs was 1.1%. Multivariate analysis revealed skin type (HR: 1.468, 95% CI: 1.229, 1.758), sex (HR: 0.655, 95% CI: 0.472, 0.908), advanced age (HR: 1.263, 95% CI: 1.106, 1.442), continence (HR: 1.245, 95% CI: 1.052, 1.473), tissue malnutrition (HR: 1.070, 95% CI: 1.007, 1.136) and neurological deficit (HR: 1.153, 95% CI: 1.062, 1.251) were independently predictive of PIs development for all participants. Skin type (HR: 2.326, 95% CI: 1.153, 3.010) (HR: 2.217, 95% CI: 1.804, 2.573) independently predicted PIs occurrence for high‐risk and very high‐risk group, respectively, while sex (HR: 0.634, 95% CI: 0.431, 0.931) and age (HR: 1.269, 95% CI: 1.083, 1.487) predicted PIs development for very high‐risk group. Conclusions: This study found that not all subscales of the Waterlow scale are associated with the PIs development in patients in ICU, highlighting the importance of the skin type subscale in predicting PI risk across all patient groups. Implications for Clinical Practice: Nurses need to focus on patient's skin and related (moisture, pain and pressure) conditions and take measures to promote skin health and avoid the occurrence of PI. Patient or Public Contribution: None. [ABSTRACT FROM AUTHOR]

Abstract Background Acute myocardial infarction (AMI) is a leading cause of death worldwide. Mitochondrial dysfunction is a key determinant of cell death post-AMI. Preventing mitochondrial dysfunction is thus a key therapeutic strategy. This study aimed to explore key genes and target compounds related to mitochondrial dysfunction in AMI patients and their association with major adverse cardiovascular events (MACE). Methods Differentially expressed genes in AMI were identified from the Gene Expression Omnibus (GEO) datasets (GSE166780 and GSE24519), and mitochondria-related genes were obtained from MitoCarta3.0 database. By intersection of the two gene groups, mitochondria-related genes in AMI were identified. Next, the identified genes related to mitochondria were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Protein-protein interaction (PPI) network was constructed, and key genes were screened. Then, targeted drug screening and molecular docking were performed. Blood samples from AMI patients and healthy volunteers were analyzed for the key genes expressions using quantitative real time polymerase chain reaction (qRT-PCR). Later, receiver operating characteristic (ROC) curves assessed the diagnostic value of key genes, and univariate and multivariate COX analyses identified risk factors and protective factors for MACE in AMI patients. Results After screening and identification, 138 mitochondria-related genes were identified, mainly enriched in the processes and pathways of cellular respiration, redox, mitochondrial metabolism, apoptosis, amino acid and fatty acid metabolism. According to the PPI network, 5 key mitochondria-related genes in AMI were obtained: translational activator of cytochrome c oxidase I (TACO1), cytochrome c oxidase subunit Va (COX5A), PTEN-induced putative kinase 1 (PINK1), SURF1, and NDUFA11. Molecular docking showed that Cholic Acid, N-Formylmethionine interacted with COX5A, nicotinamide adenine dinucleotide + hydrogen (NADH) and NDUFA11. Subsequent basic experiments revealed that COX5A and NDUFA11 expressions were significantly lower in the blood of patients with AMI than those in the corresponding healthy volunteers; also, AMI patients with MACE had lower COX5A and NDUFA11 expressions in the blood than those without MACE (P 0.85]. In terms of COX results, COX5A, NDUFA11 and left ventricular ejection fraction (LVEF) were protective factors for MACE in AMI, while C-reactive protein (CRP) was a risk factor. Conclusion COX5A and NDUFA11, key mitochondria-related genes in AMI, may be used as biomarkers to diagnose AMI and predict MACE.

Xin Qiu,1,2,&ast; Shuang Shen,1,&ast; Donghong Lu,2 Nizhen Jiang,3 Yifei Feng,3 Jindu Li,1 Chenglei Yang,1 Bangde Xiang1,4,5 1Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, People’s Republic of China; 2Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 3Guangxi Medical University Cancer Hospital, Nanning, People’s Republic of China; 4Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Nanning, People’s Republic of China; 5Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bangde Xiang; Chenglei Yang, Email xiangbangde@gxmu.edu.cn; chenglei2017yang@163.comBackground: Hepatocellular carcinoma (HCC) presents a significant global health challenge due to its poor prognosis and high recurrence rates post-surgery. This study examines the predictive efficacy of the Advanced Lung Cancer Inflammation Index (ALI) in assessing the post-hepatectomy prognosis of patients with HCC.Methods: A cohort comprising 1654 HCC patients who underwent hepatectomy at Guangxi Medical University Cancer Hospital from 2013 to 2019 was enrolled. Patients were stratified into two groups according to the median ALI level, and then subjected to propensity score matching (PSM) in a 1:1 ratio. Kaplan-Meier survival curves, the traditional Cox proportional hazards (CPH) model, and machine learning (ML) models were employed to analyze and evaluate ALI’s prognostic significance. Furthermore, ALI’s prognostic value in digestive system tumors was validated via analysis of the National Health and Nutrition Examination Survey (NHANES) database.Results: After applying PSM, a final cohort of 1284 patients, categorized into high and low ALI groups, revealed a significantly reduced survival time in the low ALI cohort. Univariate and multivariate Cox analyses identified ALI, BCLC stage, CK19, Hepatitis B virus (HBV) DNA, lymph node metastasis, and microvascular invasion (MVI) as independent predictors of prognosis. Both traditional CPH and ML models incorporating ALI demonstrated excellent predictive accuracy, validated through calibration curves, time-dependent ROC curves, and decision curve analysis. Furthermore, the prognostic value of ALI in digestive tumors was confirmed in the NHANES database.Conclusion: The ALI exhibits potential as a prognostic predictor in patients with HCC following hepatectomy, providing valuable insights into postoperative survival.Keywords: advanced lung cancer inflammatory index, ALI, hepatocellular carcinoma, HCC, prognosis, Cox regression, machine learning, ML

Background: Early evaluation of prognosis in cardiogenic shock (CS) is crucial for tailored treatment selection. Both lactate clearance and lactate levels are considered useful prognostic biomarkers in patients with CS. However, there is yet no literature comparing the 6-hour lactate clearance rate (Δ6Lac) with lactate levels measured at admission (L1) and after 6 h (L2) to predict 30-day mortality in CS. Methods: In this observational cohort study, 95 patients with CS were treated at Department of Intensive Care Unit, Yiwu Central Hospital between January 2020 and December 2022. Of these, 88 patients met the eligibility criteria. The lactate levels were measured after admission (L1) as the baseline lactate value, and were measured after 6 h (L2) following admission. The primary endpoint of the study was survival rate at 30 days. A receiver operating characteristic curve was used for data analysis. Univariate and multivariate Cox regression analyses were performed based on Δ6Lac. Kaplan–Meier (KM) survival curves were generated to compare the 30-day survival rates among L1, L2, and Δ6Lac. Results: The Δ6Lac model showed the highest area under the curve value (0.839), followed by the L2 (0.805) and L1 (0.668) models. The Δ6Lac model showed a sensitivity of 84.2% and specificity of 75.4%. The L1 and L2 models had sensitivities of 57.9% each and specificities of 89.9% and 98.6%, respectively. The cut-off values for Δ6Lac, L1, and L2 were 18.2%, 6.7 mmol/L, and 6.1 mmol/L, respectively. Univariate Cox regression analysis revealed a significant association between Δ6Lac and 30-day mortality. After adjusting for five models in multivariate Cox regression, Δ6Lac remained a significant risk factor for 30-day mortality in patients with CS. In our fifth multivariate Cox regression model, Δ6Lac remained a risk factor associated with 30-day mortality (hazard ratio [HR]=5.14, 95% confidence interval [CI]: 1.48 to 17.89, P=0.010) as well as L2 (HR=8.42, 95% CI: 1.26 to 56.22, P=0.028). The KM survival curve analysis revealed that L1 >6.7 mmol/L (HR=8.08, 95% CI: 3.23 to 20.20, P 6.1 mmol/L (HR=25.97, 95% CI: 9.76 to 69.15, P

Problems such as the disruption of the income-expenditure balance and the decrease in active-passive ratio, which emerged at the end of the 1990s in Turkey, brought the need for reforms in the social security system. As a result of these reform efforts, a private pension system, complementary to the existing social security system, was put into practice. To our knowledge, no study has examined the private pension system using the Cox regression model, accelerated failure time models, and machine learning methods together under survival analysis. In this work, we show that machine learning methods provide non parametric alternatives to traditional survival models such as Cox regression. In addition to the statistics obtained, other important results are that socio-economic problems such as gender inequality, education inequality and income inequality can also be seen in private pension systems. [ABSTRACT FROM AUTHOR]

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification. [ABSTRACT FROM AUTHOR]

Background: Acute myocardial infarction (AMI) is a leading cause of death worldwide. Mitochondrial dysfunction is a key determinant of cell death post-AMI. Preventing mitochondrial dysfunction is thus a key therapeutic strategy. This study aimed to explore key genes and target compounds related to mitochondrial dysfunction in AMI patients and their association with major adverse cardiovascular events (MACE). Methods: Differentially expressed genes in AMI were identified from the Gene Expression Omnibus (GEO) datasets (GSE166780 and GSE24519), and mitochondria-related genes were obtained from MitoCarta3.0 database. By intersection of the two gene groups, mitochondria-related genes in AMI were identified. Next, the identified genes related to mitochondria were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Protein-protein interaction (PPI) network was constructed, and key genes were screened. Then, targeted drug screening and molecular docking were performed. Blood samples from AMI patients and healthy volunteers were analyzed for the key genes expressions using quantitative real time polymerase chain reaction (qRT-PCR). Later, receiver operating characteristic (ROC) curves assessed the diagnostic value of key genes, and univariate and multivariate COX analyses identified risk factors and protective factors for MACE in AMI patients. Results: After screening and identification, 138 mitochondria-related genes were identified, mainly enriched in the processes and pathways of cellular respiration, redox, mitochondrial metabolism, apoptosis, amino acid and fatty acid metabolism. According to the PPI network, 5 key mitochondria-related genes in AMI were obtained: translational activator of cytochrome c oxidase I (TACO1), cytochrome c oxidase subunit Va (COX5A), PTEN-induced putative kinase 1 (PINK1), SURF1, and NDUFA11. Molecular docking showed that Cholic Acid, N-Formylmethionine interacted with COX5A, nicotinamide adenine dinucleotide + hydrogen (NADH) and NDUFA11. Subsequent basic experiments revealed that COX5A and NDUFA11 expressions were significantly lower in the blood of patients with AMI than those in the corresponding healthy volunteers; also, AMI patients with MACE had lower COX5A and NDUFA11 expressions in the blood than those without MACE (P < 0.01). ROC analysis also showed high diagnostic value for COX5A and NDUFA11 [area under the curve (AUC) > 0.85]. In terms of COX results, COX5A, NDUFA11 and left ventricular ejection fraction (LVEF) were protective factors for MACE in AMI, while C-reactive protein (CRP) was a risk factor. Conclusion: COX5A and NDUFA11, key mitochondria-related genes in AMI, may be used as biomarkers to diagnose AMI and predict MACE. [ABSTRACT FROM AUTHOR]

Purpose: Traumas cause great casualties, accompanied by heavy economic burdens every year. The study aimed to use ML (machine learning) survival algorithms for predicting the 8-and 24-hour survival of severe traumas. Methods: A retrospective study using data from National Trauma Data Bank (NTDB) was conducted. Four ML survival algorithms including survival tree (ST), random forest for survival (RFS) and gradient boosting machine (GBM), together with a Cox proportional hazard model (Cox), were utilized to develop the survival prediction models. Following this, model performance was determined by the comparison of the C-index, integrated Brier score (IBS) and calibration curves in the test datasets. Results: A total of 191,240 individuals diagnosed with severe trauma between 2015 and 2018 were identified. Glasgow Coma Scale (GCS), trauma type, age, SaO2, respiratory rate (RR), systolic blood pressure (SBP), EMS transport time, EMS on-scene time, pulse, and EMS response time were identified as the main predictors. For predicting the 8-hour survival with the complete cases, the C-indexes in the test sets were 0.853 (0.845, 0.861), 0.823 (0.812, 0.834), 0.871 (0.862, 0.879) and 0.857 (0.849, 0.865) for Cox, ST, RFS and GBM, respectively. Similar results were observed in the 24-hour survival prediction models. The prediction error curves based on IBS also showed a similar pattern for these models. Additionally, a free web-based calculator was developed for potential clinical use. Conclusion: The RFS survival algorithms provide non-parametric alternatives to other regression models to be of clinical use for estimating the survival probability of severe trauma patients. [ABSTRACT FROM AUTHOR]

Researchers often estimate the association between the hazard of a time‐to‐event outcome and the characteristics of individuals and the clusters in which individuals are nested. Lin and Wei's robust variance estimator is often used with a Cox regression model fit to clustered data. Recently, alternative variance estimators have been proposed: the Fay–Graubard estimator, the Kauermann–Carroll estimator, and the Mancl–DeRouen estimator. Using Monte Carlo simulations, we found that, when fitting a marginal Cox regression model with both individual‐level and cluster‐level covariates: (i) in the presence of weak to moderate within‐cluster homogeneity of outcomes, the Lin–Wei variance estimator can result in estimates of the SE with moderate bias when the number of clusters is fewer than 20–30, while in the presence of strong within‐cluster homogeneity, it can result in biased estimation even when the number of clusters is as large as 100; (ii) when the number of clusters was less than approximately 20, the Fay–Graubard variance estimator tended to result in estimates of SE with the lowest bias; (iii) when the number of clusters exceeded approximately 20, the Mancl–DeRouen estimator tended to result in estimated standard errors with the lowest bias; (iv) the Mancl–DeRouen estimator used with a t‐distribution tended to result in 95% confidence that had the best performance of the estimators; (v) when the magnitude of within‐cluster homogeneity in outcomes was strong or very strong, all methods resulted in confidence intervals with lower than advertised coverage rates even when the number of clusters was very large. [ABSTRACT FROM AUTHOR]

Weighting methods are widely used for causal effect estimation in non‐randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings. [ABSTRACT FROM AUTHOR]

Objective: This research endeavors to explore the relationship between serum uric acid (SUA) concentration and all-cause mortality in stroke patients. Methods: We undertook a cross-sectional analysis utilizing data derived from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2016. The concentrations of SUA served as the independent variable, while the dependent variable was defined as all-cause mortality in stroke patients. The quartile method was utilized to classify uric acid levels into four distinct categories. Subsequently, three models were developed, and Cox proportional hazards regression was used to assess the effect of varying uric acid concentrations on the risk of all-cause mortality among stroke patients. Results: The study included a total of 10,805 participants, of whom 395 were stroke patients. Among all populations, the group with elevated levels of uric acid (Q4) exhibited a significant association with the overall mortality risk among stroke patients in all three models (model 1 p < 0.001, model 2 p < 0.001, model 3 p < 0.001). In the male population, there was no significant correlation observed between uric acid levels and the overall mortality risk among stroke patients in model 3 (Q2 p = 0.8, Q3 p = 0.2, Q4 p = 0.2). However, within the female population, individuals with high uric acid levels (Q4) demonstrated a noteworthy association with the overall mortality risk among stroke patients across all three models (model 1 p < 0.001, model 2 p < 0.001, model 3 p < 0.001). Conclusion: This cross-sectional investigation reveals a significant correlation between SUA levels and all-cause mortality in stroke patients, with a noticeable trend observed among females. Consequently, SUA may serve as a promising biomarker for assessing the prognosis of individuals affected by stroke. [ABSTRACT FROM AUTHOR]

Background: This study aimed to assess whether integrating handgrip strength (HGS) into the concept of motoric cognitive risk (MCR) would enhance its predictive validity for incident dementia and all-cause mortality. Methods: A cohort of 5, 899 adults from the Health and Retirement Study underwent assessments of gait speed, subjective cognitive complaints, and HGS were involved. Over a 10-year follow-up, biennial cognitive tests and mortality data were collected. Cox proportional hazard analyses assessed the predictive power of MCR alone and MCR plus HGS for incident dementia and all-cause mortality. Results: Patients with MCR and impaired HGS (MCR-HGS) showed the highest adjusted hazard ratios (AHR) for dementia (2.33; 95% CI, 1.49-3.65) and mortality (1.52; 95% CI, 1.07-2.17). Even patients with MCR and normal HGS (MCR-non-HGS) experienced a 1.77-fold increased risk of incident dementia; however, this association was not significant when adjusted for socioeconomic status, lifestyle factors, and medical conditions. Nevertheless, all MCR groups demonstrated increased risks of all-cause mortality. The inclusion of HGS in the MCR models significantly improved predictive discrimination for both incident dementia and all-cause mortality, as indicated by improvements in the C-statistic, integrated discrimination improvement (IDI) and net reclassification indices (NRI). Conclusion: Our study underscores the incremental predictive value of adding HGS to the MCR concept for estimating risks of adverse health outcomes among older adults. A modified MCR, incorporating HGS, could serve as an effective screening tool during national health examinations for identifying individuals at risk of dementia and mortality. [ABSTRACT FROM AUTHOR]

Background: The prediction of patients' outcomes is a key component in personalized medicine. Oftentimes, a prediction model is developed using a large number of candidate predictors, called high-dimensional data, including genomic data, lab tests, electronic health records, etc. Variable selection, also called dimension reduction, is a critical step in developing a prediction model using high-dimensional data. Methods: In this paper, we compare the variable selection and prediction performance of popular machine learning (ML) methods with our proposed method. LASSO is a popular ML method that selects variables by imposing an L 1 -norm penalty to the likelihood. By this approach, LASSO selects features based on the size of regression estimates, rather than their statistical significance. As a result, LASSO can miss significant features while it is known to over-select features. Elastic net (EN), another popular ML method, tends to select even more features than LASSO since it uses a combination of L 1 - and L 2 -norm penalties that is less strict than an L 1 -norm penalty. Insignificant features included in a fitted prediction model act like white noises, so that the fitted model will lose prediction accuracy. Furthermore, for the future use of a fitted prediction model, we have to collect the data of all the features included in the model, which will cost a lot and possibly lower the accuracy of the data if the number of features is too many. Therefore, we propose an ML method, called repeated sieving, extending the standard regression methods with stepwise variable selection. By selecting features based on their statistical significance, it resolves the over-selection issue with high-dimensional data. Results: Through extensive numerical studies and real data examples, our results show that the repeated sieving method selects far fewer features than LASSO and EN, but has higher prediction accuracy than the existing ML methods. Conclusions: We conclude that our repeated sieving method performs well in both variable selection and prediction, and it saves the cost of future investigation on the selected factors. [ABSTRACT FROM AUTHOR]

Purpose: To examine the associations of age when first substance use and early-onset substance use before age 18 with age at onset (AAO) of hypertension. Methods: This study included 19,270 individuals with AAO of hypertension from the 2015–2019 National Survey on Drug Use and Health. Age when first use of 10 substance use variables included alcohol, daily cigarettes, cigars, smokeless tobacco, marijuana, cocaine, hallucinogens, lysergic acid diethylamide (LSD), inhalants, and methamphetamine use. The outcome was AAO of hypertension and variable cluster analysis was used to classify the exposures and outcome. Substance use status was classified into three categories: early-onset substance use (first used substance before age 18), late-onset substance use (first used substance after age 18), and never used. Results: The mean AAO of hypertension was 42.7 years. Age when first use of 10 substance use variables had significant correlations with AAO of hypertension (all p values < 0.001). Individuals with early-onset alcohol, cigars, smokeless tobacco, marijuana, hallucinogens, inhalants, cocaine, LSD, and methamphetamine use revealed significantly earlier onset of hypertension than those never used. Compared with never used substances, the Cox regression model showed that early-onset alcohol, smokeless tobacco, marijuana, inhalants, and methamphetamine use had an increased risk of AAO of hypertension [hazard ratio (HR) (95%CI) = 1.22 (1.13, 1.31), 1.36 (1.24, 1.49), 1.85 (1.75, 1.95), 1.41 (1.30, 1.52), and 1.27 (1.07,1.50), respectively]. Conclusion: These findings suggest that intervention strategies or programs focusing on preventing early-onset substance use before age 18 may delay the onset of adult hypertension. [ABSTRACT FROM AUTHOR]

Telescopic crown-retained dentures (TCDs) are one of the most common types of prosthetic restorations for partially edentulous patients; however, post and core (PC) treatment shows the worst survival probability if the tooth is used as an abutment for the TCD. Due to extra axial forces, abutment tooth fracture is a common cause of failure for TCDs; thus, PC treatment is often needed to refit the existing telescopic crown (TC). However, there are no clinical survival data on whether the PC treatment was used to refit the TC after abutment tooth fracture (PC2) or the PC was already fitted at the time of TCD treatment (PC1). A total of 246 patients with 399 PC treatments were retrospectively evaluated for follow-ups up to 17.33 years. The files were analysed for PC1 and PC2. Furthermore, the influence of the jaw, type of tooth, luting material, PC material, bone attachment, therapist and cause of failure was recorded. For statistical analysis, Kaplan–Meier and Cox regression analyses were conducted. PC2 showed highly significant lower survival probabilities than PC1 (p < 0.001). Moreover, the bone attachment and the age of the patient at the time of fitting the PC crown had an influence on the survival (p < 0.001). Therefore, PC2 should be carefully discussed with the patient and PC1 should be favoured in endodontically treated abutment teeth for TCDs. [ABSTRACT FROM AUTHOR]

Administrative health records provide a rich source of information pertaining to various exposures, many of which are time-varying in nature. When internal time-varying covariates are included in a Cox regression model, likelihood-based inference procedures are no longer applicable to infer model parameters (Kalbfleisch and Prentice in The Statistical analysis of failure time data, Wiley, New York, 2002). Motivated by the ongoing opioid epidemic, we summarize an individual's opioid agonist treatment (OAT) dispensation history and additional exposures with (i) a model-based summary, or (ii) its functional principal component scores. We show that the OAT dispensation proportion has a non-linear effect on the mortality hazard over time, and a significant interaction with time of birth. Particularly a clear protective effect against mortality for Millennials and Generation Z is revealed. Our approach is easy to implement by virtually any statistical software, and provides a risk assessment tool for utilizing available health records. [ABSTRACT FROM AUTHOR]

Background: A common sense is that lower serum cholesterol levels are better. However, a growing number of researches have questioned this especially for the oldest old. The current study was to assess the association between total cholesterol and all-causemortality in a group of people aged 85 years old and over. Methods: We selected 903 Chinese old participants who aged =85 years from the Chinese Longitudinal Healthy Longevity Survey(CLHLS) at baseline in 2012. The participants were followed up until death or until December 31, 2014. The outcome was all-cause mortality. The univariate and multivariate Cox regression analyses were used to estimate risk levels of all-cause mortality. We stratified the participants into three groups (<3.40, 3.40-4.39, =4.39 mmol/L) based on the restricted cubic splines methods. The survival probability according to total cholesterol category was calculated using the Kaplan-Meier curves, and the log-rank test was performed to analyze differences between the groups. Results: During the follow-up of three years, 282 participants died, 497 survived and 124 lost to follow-up. There was significant relationship between the total cholesterol and lower risk of all-cause mortality in the multivariable Cox regression analysis (HR=0.88, 95% CI: 0.78-1.00). Based on the restricted cubic splinesmethods, the total cholesterol was converted from a continuous variable to a categorical variable. The populations were divided into three groups (<3.40, 3.40-4.39, =4.39 mmol/L) according to the total cholesterol categorized by cutoff values. Compared to the total cholesterol level of <3.40 mmol/L, populations in the total cholesterol level of 3.40-4.39 mmol/L (HR = 0.72, 95% CI: 0.53-0.97) and =4.39 mmol/L (HR = 0.71, 95% CI: 0.52-0.96) groups had lower all-cause mortality in multivariate Cox regression analysis and higher survival probability in survival analysis. When two groups were divided, similar results were found among the populations in the total cholesterol level of =3.40 mmol/L compared to the populations in the total cholesterol level of <3.40 mmol/L groups. Conclusion: In oldest old aged 85 and older, serum total cholesterol levels are inversely associated with all-cause mortality. This study suggested that total cholesterol should be maintained to acceptable levels (= 3.40 mmol/L) in oldest old to achieve longevity. [ABSTRACT FROM AUTHOR]

Background: The 8th AJCC TNM staging for non-metastatic lymph node-positive colon adenocarcinoma patients(NMLP-CA) stages solely by lymph node status, irrespective of the positivity of tumor deposits (TD). This study uses machine learning and Cox regression to predict the prognostic value of tumor deposits in NMLP-CA. Methods: Patient data from the SEER registry (2010–2019) was used to develop CSS nomograms based on prognostic factors identified via multivariate Cox regression. Model performance was evaluated by c-index, dynamic calibration, and Schmid score. Shapley additive explanations (SHAP) were used to explain the selected models. Results: The study included 16,548 NMLP-CA patients, randomized 7:3 into training (n = 11,584) and test (n = 4964) sets. Multivariate Cox analysis identified TD, age, marital status, primary site, grade, pT stage, and pN stage as prognostic for cancer-specific survival (CSS). In the test set, the gradient boosting machine (GBM) model achieved the best C-index (0.733) for CSS prediction, while the Cox model and GAMBoost model optimized dynamic calibration(6.473) and Schmid score (0.285), respectively. TD ranked among the top 3 most important features in the models, with increasing predictive significance over time. Conclusions: Positive tumor deposit status confers worse prognosis in NMLP-CA patients. Tumor deposits may confer higher TNM staging. Furthermore, TD could play a more significant role in the staging system. [ABSTRACT FROM AUTHOR]

Introduction: Antihypertensive drugs are used preventatively to lower the risk of cardiovascular disease events. Comparative effectiveness studies on angiotensinconverting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), beta-blockers (BBs), calcium channel blockers (CCBs), and thiazides have yielded inconsistent results and given little consideration to patient adherence. Using a longitudinal cohort and considering time-varying adherence and confounding factors, we aimed to estimate the real-world effectiveness of five major antihypertensive drug monotherapies in the primary prevention of cardiovascular events. Methods: Eligible patients for a retrospective inception cohort study were selected using information obtained from the University of Groningen IADB.nl pharmacy prescription database. Cohort 1 comprised adherent patients with a follow-up time exceeding 1 year, and cohort 2 comprised all patients independent of adherence. The exposures were ACEIs, ARBs, BBs, CCBs, and thiazides. The primary outcome was the time to the first prescription for an acute cardiac drug therapy (CDT) measured using valid drug proxies to identify the first major cardiovascular event. A per-protocol analytical approach was adopted with inverse probability of treatment weighted (IPTW), time-varying Cox regression analysis to obtain the hazard ratios (HRs) and 95% confidence intervals (CIs). Results: In cohort 1 (n = 22,441), 1,294 patients (5.8%) were prescribed an acute CDT with an average follow-up time of 4.2 ± 2.8 years. Following IPTW, the hazard measures of ARBs and thiazides were lower than those of BBs (HRs: 0.79 and 0.80, respectively; 95% CIs: 0.64-0.97 and 0.69-0.94, respectively). Among drug-treated diabetic patients, the hazard measures were even lower, with HR point estimates of 0.43 (CI: 0.19-0.98) for ARBs and 0.32 (CI: 0.13-0.82) for thiazides. In cohort 2 (n = 33,427) and sensitivity analysis, the comparative effectiveness results for thiazides and BBs were similar to those for cohort 1. Conclusion: The findings of this real-world analysis suggest that the incidence of CDT associated with long-term thiazide or ARB monotherapy is lower than the incidence of CDTwith BBs, notably among high-risk patients. Incidences of CDT associated with ACEIs and CCBs were comparable relative to those associated with BBs. [ABSTRACT FROM AUTHOR]

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)&lowbar;min, chloride&lowbar;max, base excess&lowbar;min, bicarbonate&lowbar;max, anion gap&lowbar; min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR&lowbar;min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis. [ABSTRACT FROM AUTHOR]

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Can the application of the theory of planned behavior (TPB) help predict heterosexual parents' disclosure of donor conception to their children? SUMMARY ANSWER Parents with a stronger will to act in accordance with social norms favoring disclosure were more likely to start the disclosure process within the next 5–9 years. WHAT IS KNOWN ALREADY In contrast to single mothers by choice and same-sex couples, heterosexual couples need to make an active decision to disclose their use of donor conception to their child. While disclosure at an early age is encouraged by international guidelines, many heterosexual-couple parents struggle with this. A previous study has found an association between parental scores of TPB factors and disclosure intention, but so far, no study has applied the TPB to predict parents' disclosure behavior. STUDY DESIGN, SIZE, DURATION The present study is based on the fourth and fifth waves of data collection (T4 and T5) in a nation-wide longitudinal study. Participating parents had conceived through identity-release oocyte donation (n = 68, response rate 65%) and sperm donation (n = 62, response rate 56%) as part of a heterosexual couple. PARTICIPANTS/MATERIALS, SETTING, METHODS The present study is part of the prospective longitudinal Swedish Study on Gamete Donation (SSGD). Consecutive recruitment of couples starting oocyte or sperm donation treatment was conducted at all seven fertility clinics providing gamete donation in Sweden during a 3-year period (2005–2008). Participants were requested to complete postal surveys at five time points. The present study includes heterosexual-couple parents following oocyte or sperm donation who participated at the two latest time points when their children were 7–8 years old (T4), and 13–17 years old (T5). At T4, participants completed the study-specific TPB Disclosure Questionnaire (TPB-DQ) measuring attitudes and intentions to disclose the donor conception to the child, and disclosure behavior was assessed at both T4 and T5. Data from those participants who had not yet disclosed at T4 were analyzed using survival analysis with Cox regressions. MAIN RESULTS AND THE ROLE OF CHANCE Forty participants had not disclosed the donor conception to their children at T4 and, out of these, 13 had still not disclosed at T5. We found a significant association between scores of the TPB factor Subjective norms at T4 and their subsequent disclosure behavior at T5 (HR = 2.019; 95% CI: 1.36–3.01). None of the other factors were significantly associated with disclosure behavior. LIMITATIONS, REASONS FOR CAUTION The present study concerns heterosexual-couple parents with children conceived following treatment with gametes from open-identity donors, which limits the generalizability of our findings to other groups and contexts. Other limitations include the risk of systematic attrition due to the longitudinal study design and decreased statistical power due to few participants. WIDER IMPLICATIONS OF THE FINDINGS Our findings highlight the importance of perceived subjective norms for parents' disclosure behavior and indicate that the co-parent's opinion about disclosure is of particular relevance in this regard. Counselors should focus on supporting prospective parents to initiate and maintain a healthy and open dialogue about concerns around building a family with donor conception. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the Swedish Research Council. The authors have no competing interests to declare. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Purpose: Suicide attempters are at high risk of premature death, both for suicide and for non-suicidal causes. The aim of this study is to investigate risk factors and temporal span for mortality in a cohort of cases admitted to hospital for suicide attempt. Methods: The cohort included 1489 patients resident in Piedmont Region, North West of Italy, who had been admitted to hospital or emergency department for suicide attempt between 2010 and 2020. Cox regression models were used to identify risk factors for death. The final multivariate model included gender, age, area deprivation index, family composition, psychiatric disorders, malignant neoplasms, neurological disorders, diabetes mellitus, cardiovascular diseases, chronic obstructive pulmonary disease, and intracranial injury or skull fracture. Results: During the observation period, 7.3% of patients died. The highest mortality was observed within the first 12 months after suicide attempt, and remained elevated for many years afterwards. Male gender, older age, high deprivation index of the census area, single-parent family, mood disorders, malignant neoplasms, diabetes mellitus and intracranial injuries or skull fracture were independent predictors of death. Risk factors for natural and unnatural causes of death were also identified. Conclusions: The mortality risk of suicide attempters is very high, both in the months immediately following the attempt and afterwards. The identification of high-risk groups can help to plan outpatient care following the hospital discharge. Our findings urge the need to design strategies for the assistance and care of these patients at long term in order to reduce the unfavourable outcomes. [ABSTRACT FROM AUTHOR]

The liver is the most common organ for the formation of colorectal cancer metastasis. Non-invasive prognostication of colorectal cancer liver metastasis (CRLM) may better inform clinicians for decision-making. Contrast-enhanced computed tomography images of 180 CRLM cases were included in the final analyses. Radiomics features, including shape, first-order, wavelet, and texture, were extracted with Pyradiomics, followed by feature engineering by penalized Cox regression. Radiomics signatures were constructed for disease-free survival (DFS) by both elastic net (EN) and random survival forest (RSF) algorithms. The prognostic potential of the radiomics signatures was demonstrated by Kaplan-Meier curves and multivariate Cox regression. 11 radiomics features were selected for prognostic modelling for the EN algorithm, with 835 features for the RSF algorithm. Survival heatmap indicates a negative correlation between EN or RSF risk scores and DFS. Radiomics signature by EN algorithm successfully separates DFS of high-risk and low-risk cases in the training dataset (log-rank test: p < 0.01, hazard ratio: 1.45 (1.07–1.96), p < 0.01) and test dataset (hazard ratio: 1.89 (1.17–3.04), p < 0.05). RSF algorithm shows a better prognostic implication potential for DFS in the training dataset (log-rank test: p < 0.001, hazard ratio: 2.54 (1.80–3.61), p < 0.0001) and test dataset (log-rank test: p < 0.05, hazard ratio: 1.84 (1.15–2.96), p < 0.05). Radiomics features have the potential for the prediction of DFS in CRLM cases.