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Objective: To evaluate the effect of the time from morphological confirmation of the diagnosis to the start of specialized treatment (time to treatment start: TTS) on cancer-specific survival (CSS) of patients with cancer of the female reproductive system (CFRS) using the data from the regional cancer registry of the Arkhangelsk region (ARCR) over the period 2000 to 2022. Material and methods. depersonalized data on all cases of breast (BC), cervical (CC), corpus uteri (CUC) and ovarian (OC) cancers were obtained from the ARCR for the period 2000–2022. We compared survival with TTS of 0–7 days (recommended by the state guarantee program), 2–4, 5–8, 9–12 weeks, and 3–6, 7–12 months. Cancer-specific survival was calculated using the life tables and Kaplan-Meier methods. The Cox proportional hazard model was used to correct for other prognostic factors. Results. A total of 19321 cases were analyzed. The ministerial recommended TTS of ≤7 days was observed in 61.9 %, 28.5 %, 27.3 % and 55.9 % of patients with BC, CC, CUC and OC, respectively. the five-year CSS for BC, CC, CUC and OC was 73.9 %, 66.5 %, 73.0 % and 52.1 %, respectively. The TTS of ≤7 days compared to longer TTS was associated with the highest 5-year CSS rate in breast cancer (74.3 vs 45–4-67.0 %) and CC (80.2 vs 63.1–75.0 %); no significant differences were found in CUC and OC. The risk of death from CFRS was the same for TTS of ≤2 months in breast cancer and ≤12 months in other CFRS. Conclusion. Clinical practice shows that the time before the start of special treatment in accordance to state guarantees may be insufficient. early treatment initiation is often possible in the early stages of CFRS. The time to treatment start of < 8 weeks after morphological verification does not worsen survival. The identification of individual groups of patients who need earlier treatment is warranted.

Abstract Background Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. Methods A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. Results In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. Conclusions Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.

Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. Methods: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. Results: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. Conclusions: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer (BC) is the most prevalent malignant tumor worldwide among women, with the highest incidence rate. The mechanisms underlying nucleotide metabolism on biological functions in BC remain incompletely elucidated. Materials and Methods: We harnessed differentially expressed nucleotide metabolism-related genes from The Cancer Genome Atlas-BRCA, constructing a prognostic risk model through univariate Cox regression and LASSO regression analyses. A validation set and the GSE7390 dataset were used to validate the risk model. Clinical relevance, survival and prognosis, immune infiltration, functional enrichment, and drug sensitivity analyses were conducted. Results: Our findings identified four signature genes (DCTPP1, IFNG, SLC27A2, and MYH3) as nucleotide metabolism-related prognostic genes. Subsequently, patients were stratified into high- and low-risk groups, revealing the risk model's independence as a prognostic factor. Nomogram calibration underscored superior prediction accuracy. Gene Set Variation Analysis (GSVA) uncovered activated pathways in low-risk cohorts and mobilized pathways in high-risk cohorts. Distinctions in immune cells were noted between risk cohorts. Subsequent experiments validated that reducing SLC27A2 expression in BC cell lines or using the SLC27A2 inhibitor, Lipofermata, effectively inhibited tumor growth. Conclusions: We pinpointed four nucleotide metabolism-related prognostic genes, demonstrating promising accuracy as a risk prediction tool for patients with BC. SLC27A2 appears to be a potential therapeutic target for BC among these genes. [ABSTRACT FROM AUTHOR]

Purpose: Higher dietary intake or higher circulating levels of the trace element boron have been associated with beneficial effects on human health. However, the relationship between plasma boron levels and survival in the general population is not known. Therefore, we aimed to assess the association between plasma boron concentrations and all-cause mortality in a population-based cohort from northern Germany (n = 863 individuals; median age 62.3 years, 42.8% women). Methods: Plasma boron concentrations (median 31.9 µg/L [22.9; 43.5]) were measured by inductively coupled plasma-mass spectrometry. Cox proportional hazards regression models adjusted for relevant confounders were used to associate plasma boron concentrations with all-cause mortality. Results: After a median follow-up time of 11 years, n = 99 individuals had died. In the overall sample, plasma boron concentrations were associated with all-cause mortality in the crude model (HR: 1.07 [95% CI 1.03–1.11] per 5-unit-increment). However, multivariable adjustment rendered the association non-significant (HR: 1.03 [95% CI 0.99–1.07]). Sex-stratified analyses revealed slightly higher mortality hazards with increasing plasma boron concentrations in women (HR: 1.11 [95% CI 1.03–1.18], pInteraction = 0.034), but not in men (HR: 1.00 [95% CI 0.95–1.06]). Conclusion: We conclude that in a moderate-sized sample from the general population, higher plasma boron concentrations were associated with a higher risk of all-cause mortality in women, but not in men. Due to the low number of events in the female subsample (n = 27), this observation has to be interpreted with caution. [ABSTRACT FROM AUTHOR]

Hip fractures are a significant cause of mortality among older adults. However, predictive markers for an unfavorable prognosis are still lacking. Serum calcium is an essential element in several biochemical reactions in the body. This study investigated the role of the preoperative serum calcium level as a prognostic parameter for postoperative mortality in older adult patients with hip fractures. We conducted a prospective cohort study at the trauma center in our hospital, from January 1, 2015, to September 30, 2019. A total of 2333 older patients with hip fractures were recruited. This prospective cohort study was conducted in accordance with the criteria of STROCSS. Older adult patients with hip fractures were screened between January 2015 and September 2019 at the trauma center of our hospital. Demographic and clinical characteristics of the patients were collected. Linear and nonlinear multivariate Cox regression models were used to identify the association between preoperative serum calcium level and all-cause mortality. All analyses were performed using EmpowerStats and the R software. A total of 2333 older adult patients with hip fractures were included in the study. The mean follow-up was 37.5 months. Overall, 770 patients (33%) died of all causes. The preoperative serum calcium concentrations were 2.18 ± 0.13 mmol/L. Linear multivariate Cox regression models showed that preoperative serum calcium levels were associated with mortality [hazard ratio (HR) 0.37, 95% CI 0.21-0.67; P =.0009] after adjusting for confounders. However, the linear association was unstable, and nonlinearity was identified. A preoperative serum calcium level of 2.3 mmol/L was an inflection point for prediction. When the preoperative serum calcium concentration was below 2.3 mmol/L, serum calcium concentration increased by 1 mmol/L, and mortality risk decreased by 77% (HR 0.23, 95% CI 0.13-0.43, P <.0001). In contrast, when the preoperative serum calcium concentration was more significant than 2.3 mmol/L, the mortality risk increased with serum calcium concentration (HR 6.27, 95% CI 1.65-23.74, P =.0069). The preoperative serum calcium level is nonlinearly associated with mortality in older adults with hip fractures, with a U-shaped association, and could be used as a potential predictor of prognosis. [ABSTRACT FROM AUTHOR]

Concerns about whether highly educated daughters of non-Western immigrants will make use of their education after family formation or will instead follow a gender-based division of labour and childcare have been raised in Western Europe. Drawing on theoretical arguments from human capital theory, socio-cultural approaches and mechanisms of the 'immigrant drive,' this study investigates work re-entry following childbirth among highly educated descendants of immigrants from Vietnam and Pakistan. By exploiting unique population-based data from Norwegian public registers, Cox proportional hazards regressions show that descendants of Pakistani immigrants are less likely to return to work following childbirth. However, the difference between daughters of Pakistani immigrants and their native peers is no longer statistically significant when looking at return to full-time work. The chance of returning to work per se among descendants of Vietnamese immigrants does not differ from native majority women. Consistent with the literature, this study demonstrates that education level is probably the most important determinator for return to work following childbirth—irrespective of immigrant background. Overall, the results indicate that the highly educated descendants of immigrants are similar to their highly educated majority peers in organising their working life and childcare in the gender-egalitarian Norwegian welfare state. [ABSTRACT FROM AUTHOR]

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Background: Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. Methods: A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. Results: In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. Conclusions: Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development. [ABSTRACT FROM AUTHOR]

Purpose: Dietary pattern scores reflecting a high intake of beneficial food groups were associated with reduced mortality risk. Data on associations of such dietary pattern scores in population-based samples from northern Germany are lacking. Therefore, we examined the association of three dietary pattern scores with all-cause mortality in a moderate-sized prospective sample from northern Germany. Methods: The study sample comprised 836 participants (43.8% females, median age 62.4 years). Based on a validated, self-administered Food Frequency Questionnaire, the dietary scores Dietary Approaches to Stop Hypertension (DASH), Modified Mediterranean Diet Score (MMDS), and Healthy Nordic Food Index (HNFI) were calculated. Cox proportional hazard regression models, adjusted for age, sex, body mass index, waist to hip ratio, education, smoking status, total energy intake, and physical activity, were used to separately relate DASH, MMDS, and HNFI to all-cause mortality. Results: During a median follow-up period of 11 years, 93 individuals died. While DASH and MMDS scores were not associated with all-cause mortality, greater adherence to HNFI was associated with lower mortality hazards (HR: 0.47 [95% CI 0.25–0.89] when comparing the highest score quartile to the lowest; HR: 0.79 [95% CI 0.64–0.98] for HNFI modeled as a 1-Standard Deviation increment). Among different HNFI components, higher intake of oats and cereals displayed the most conclusive association with all-cause mortality (HR: 0.59 [95% CI 0.38–0.91] when comparing high and low intake). Conclusion: In an elderly general population sample from northern Germany, we observed greater adherence to HNFI to be associated with lower all-cause mortality. [ABSTRACT FROM AUTHOR]

Background and Objectives Cardiovascular diseases are the leading cause of death and the most important cause of disability in most countries. This study aimed to investigate short-term survival (28 days) in patients with acute myocardial infarction (AMI) and to determine some predictive factors of short-term survival in these patients. Subjects and Methods This study was a retrospective cohort study conducted on 260 patients with AMI in Ganjavian Hospital, a referral hospital in Dezful, Iran, during 2018-2019. Life table and Kaplan Meier analyses were used for the assessment of short-term survival rate. Log rank, Wilcoxon tests, and Cox multivariate regression were used to compare survival functions and determine the predictive factors of short-term survival. Results The mean age of the patients was 57.91 ± 10.76 years, and 193 (74.23%) were male. The survival rate was 90% on the 28th day. In Cox regression, the risk of death in women was higher than that in men (HR=1.82, 95% CI: 1.08-3.47). Also, the risk of death had a statistically significant association with diabetes status (HR=2.87,95% CI: 1.21-6.78), history of ACS (HR=3.03, CI95%: 1.32-6.92), EF<30 (HR=5.24, CI95%: 1.68-9.30), thrombolytic drugs (HR=1.84, CI95%: 1.10-3.57), arrhythmia (HR=6.98,95% CI: 2.31-21.08) and anterior MI (HR=1.67, CI95%: 1.27-2.79). Conclusion In the AMI patients, the 28-day survival rate was about 90%, which was the highest risk of death in the first 8 days after an AMI. The factors of sex, diabetes status, history of ACS, EF, thrombolytic drugs, arrhythmia, and location of AMI were associated with the survival of patients. Therefore, timely and appropriate follow-up of treatment, especially in high-risk groups, plays an important role in increasing survival in patient with AMI. [ABSTRACT FROM AUTHOR]

The Cox regression analysis is used to determine the relationship between a dependent variable and covariates in survival analysis involving censored data. The proportional hazards assumption is one of the most important assumptions of Cox regression. Outliers may have a strong influence on the Cox regression model's parameter estimates and lead to violation of the proportional hazard assumption. Therefore, having outliers in the data set is a problem for researchers. In this case, robust estimations are commonly used to infer the parameters in a more robust way. However, we explore a new approach consisting of considering an outlier as missing data and replacing it by the multiple imputation method. The aim of this study is to compare these two methods through simulation. Furthermore, an analysis of a lung cancer data set is considered for illustration. According to the results of the study carried out based on simulated data sets and a real data set, the multiple imputation method, which is a missing data analysis method, solves the problem of outliers better than the robust estimation method, as the outcome is closer to the results obtained through original data. [ABSTRACT FROM AUTHOR]

ObjectiveAlthough the impact of the variants of COVID-19 on the general population is diminishing, there is still a certain mortality rate for severe and critically ill patients, especially for the elderly with comorbidities. The present study investigated whether the D-dimer to albumin ratio (DAR) can predict the severity of illness and mortality in COVID-19 patients.MethodsA total of 1,993 patients with COVID-19 were retrospectively reviewed and the association of DAR with severe or critical illness or death during hospitalization was analyzed. The area under the ROC curve was used to screen the best indicators, Chi-square test, rank sum test, and univariate and multivariate binary logistic regression analysis were used to calculate the mean value of difference and adjusted odds ratio (aORs) with their 95% CI, and finally, survival was analyzed using Kaplan–Meier (KM) curves.ResultsAmong 1,993 patients with COVID-19, 13.4% were severely ill, and the mortality rate was 2.3%. The area under the curve (AUC) using DAR to predict severe and critically ill patients was higher than that using other parameters. The best cut-off value of DAR was 21 in the ROC with a sensitivity of 83.1% and a specificity of 68.7%. After adjusting age, gender, comorbidities, and treatment, the binary logistic regression analysis showed that elevated DAR was an independent risk factor for severely ill and mortality of COVID-19 patients. The KM curve suggested that patients with a higher DAR was associated with worse survival. The negative predictive value of DAR (21) for adverse prognosis and death was 95.98 and 99.84%, respectively, with a sensitivity of 80.9 and 95.65%, respectively.ConclusionThe DAR may be an important predictor for severe illness and mortality in COVID-19 patients.

Technological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever. We review these advances from a statistical computing perspective. Cloud computing makes access to supercomputers affordable. Deep learning software libraries make programming statistical algorithms easy and enable users to write code once and run it anywhere-from a laptop to a workstation with multiple graphics processing units (GPUs) or a supercomputer in a cloud. Highlighting how these developments benefit statisticians, we review recent optimization algorithms that are useful for high-dimensional models and can harness the power of HPC. Code snippets are provided to demonstrate the ease of programming. We also provide an easy-to-use distributed matrix data structure suitable for HPC. Employing this data structure, we illustrate various statistical applications including large-scale positron emission tomography and ℓ1-regularized Cox regression. Our examples easily scale up to an 8-GPU workstation and a 720-CPU-core cluster in a cloud. As a case in point, we analyze the onset of type-2 diabetes from the UK Biobank with 200,000 subjects and about 500,000 single nucleotide polymorphisms using the HPC ℓ1-regularized Cox regression. Fitting this half-million-variate model takes less than 45 minutes and reconfirms known associations. To our knowledge, this is the first demonstration of the feasibility of penalized regression of survival outcomes at this scale.

Abstract Background Despite substantial advancements in gastric cancer treatment in recent years, our understanding of the disease’s pathophysiology and progression processes remains limited, and the prognosis for gastric cancer patients remains poor. This study investigated potential prognostic indicators based on m7G-associated long non-coding RNA (lncRNA) and its relationship with gastric cancer (STAD). Methods The researchers used RNA-seq and prognostic data from TCGA, employing Cox regression, co-expression network analysis, and multivariate Cox regression to identify relevant lncRNAs. We compiled four m7G-related lncRNAs into a single signature. Results We found it may be used as a prognostic indicator for gastric cancer. The m7G-related lncRNA profile had an area under the curve of 0.710, significantly more diagnostic than clinicopathological markers. The study also found that the TMB and tumor microenvironment were associated with gastric cancer risk, highlighting their signature’s potential utility for personalized treatment and disease monitoring. Conclusions This study provides a novel signature of m7G-related lncRNAs that can be used as a prognostic indicator for gastric cancer and may help guide the development of targeted immunotherapy for the condition.

Abstract Background Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies. Methods This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients. Results Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p 4.12, p

Abstract Background Certified cancer centers aim to ensure high-quality care by establishing structural and procedural standards according to evidence-based guidelines. Despite the high clinical and health policy relevance, evidence from a nation-wide study for the effectiveness of care for colorectal cancer in certified centers vs. other hospitals in Germany is still missing. Methods In a retrospective cohort study covering the years 2009–2017, we analyzed patient data using demographic information, diagnoses, and treatments from a nationwide statutory health insurance enriched with information on certification. We investigated whether patients with incident colon or rectal cancer did benefit from primary therapy in a certified cancer center. We used relative survival analysis taking into account mortality data of the German population and adjustment for patient and hospital characteristics via Cox regression with shared frailty for patients in hospitals with and without certification. Results The cohorts for colon and rectal cancer consisted of 109,518 and 51,417 patients, respectively, treated in a total of 1052 hospitals. 37.2% of patients with colon and 42.9% of patients with rectal cancer were treated in a certified center. Patient age, sex, comorbidities, secondary malignoma, and distant metastases were similar across groups (certified/non-certified) for both colon and rectal cancer. Relative survival analysis showed significantly better survival of patients treated in a certified center, with 68.3% (non-certified hospitals 65.8%) 5-year survival for treatment of colon cancer in certified (p

Background: The aim of this study was to investigate the activation of the C-X-C chemokine receptor type 4 (CXCR4)/Akt signaling pathway in the peripheral blood of individuals diagnosed with brain glioma and assess its potential as a predictive marker.

Background: Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies. Methods: This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients. Results: Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months. Conclusion: The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation. [ABSTRACT FROM AUTHOR]

Background: Despite substantial advancements in gastric cancer treatment in recent years, our understanding of the disease's pathophysiology and progression processes remains limited, and the prognosis for gastric cancer patients remains poor. This study investigated potential prognostic indicators based on m7G-associated long non-coding RNA (lncRNA) and its relationship with gastric cancer (STAD). Methods: The researchers used RNA-seq and prognostic data from TCGA, employing Cox regression, co-expression network analysis, and multivariate Cox regression to identify relevant lncRNAs. We compiled four m7G-related lncRNAs into a single signature. Results: We found it may be used as a prognostic indicator for gastric cancer. The m7G-related lncRNA profile had an area under the curve of 0.710, significantly more diagnostic than clinicopathological markers. The study also found that the TMB and tumor microenvironment were associated with gastric cancer risk, highlighting their signature's potential utility for personalized treatment and disease monitoring. Conclusions: This study provides a novel signature of m7G-related lncRNAs that can be used as a prognostic indicator for gastric cancer and may help guide the development of targeted immunotherapy for the condition. [ABSTRACT FROM AUTHOR]

Background: Patients with head and neck squamous cell carcinoma (HNSCC) enter the palliative phase when cure is no longer possible or when they refuse curative treatment. The mean survival is five months, with a range of days until years. Realistic prognostic counseling enables patients to make well-considered end-of-life choices. However, physicians tend to overestimate survival. The aim of this study was to develop a prognostic model that calculates the overall survival (OS) probability of palliative HNSCC patients. Methods: Patients diagnosed with incurable HNSCC or patients who refused curative treatment for HNSCC between January 1st 2006 and June 3rd 2019 were included (n = 659). Three patients were lost to follow-up. Patients were considered to have incurable HNSCC due to tumor factors (e.g. inoperability with no other curative treatment options, distant metastasis) or patient factors (e.g. the presence of severe comorbidity and/or poor performance status).Tumor and patients factors accounted for 574 patients. An additional 82 patients refused curative treatment and were also considered palliative. The effect of 17 candidate predictors was estimated in the univariable cox proportional hazard regression model. Using backwards selection with a cut-off P-value < 0.10 resulted in a final multivariable prediction model. The C-statistic was calculated to determine the discriminative performance of the model. The final model was internally validated using bootstrapping techniques. Results: A total of 647 patients (98.6%) died during follow-up. Median OS time was 15.0 weeks (95% CI: 13.5;16.6). Of the 17 candidate predictors, seven were included in the final model: the reason for entering the palliative phase, the number of previous HNSCC, cT, cN, cM, weight loss in the 6 months before diagnosis, and the WHO performance status. The internally validated C-statistic was 0.66 indicating moderate discriminative ability. The model showed some optimism, with a shrinkage factor of 0.89. Conclusion: This study enabled the development and internal validation of a prognostic model that predicts the OS probability in HNSCC patients in the palliative phase. This model facilitates personalized prognostic counseling in the palliative phase. External validation and qualitative research are necessary before widespread use in patient counseling and end-of-life care. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is the most common cancer and the leading cause of cancer death in women. Immune features play an important role in improving the prognosis prediction of BC. However, while previous immune signatures consisted mainly of immune genes, immune cell-based signatures have been rarely reported. Methods: In this study, we report that a novel immune cell signature is effective in improving prognostic prediction by combining M2 macrophages. We identified 17 differentially infiltrating immune cells between cancer and normal groups. Prognostic features of the four immune cells identified by LASSO COX analysis showed good performance for survival risk stratification in both the training and validation datasets. Independent prognostic analysis showed that M2 macrophages were significantly associated with survival in BC patients and both the cells and the risk score were the main prognostic factors independent of survival in BC patients. Results: Therefore, we combined M2 macrophages and risk scores to create a nomogram with good prognostic predictive power. Finally, we attempted to study the effect of M2 on M2 macrophage progression in BC in vitro. BC cells cultured with M2 macrophage-conditioned medium exhibited distinct malignant features, including migration and invasion. Conclusion: The findings suggest that M2 macrophages are associated with poor prognosis in breast cancer patients possibly by promoting tumor invasion and migration. This work may provide a new strategy for prognostic prediction and immunotherapy in BC. [ABSTRACT FROM AUTHOR]

Background and Purpose: Survival rate of patients with lung cancer has increased by over 60% in the recent two decades. With longer survival, the identification of genes associated with survival has emerged as an issue of utmost importance to uncover the most promising biomarkers and therapeutic targets. Experimental Approach: An integrated database was set up by combining multiple independent datasets with clinical data and transcriptome‐level gene expression measurements. Univariate and multivariate survival analyses were performed to identify genes with higher expression levels linked to shorter survival. The strongest genes were filtered to include only those with known druggability. Key Results: The entire database includes 2852 tumour specimens from 17 independent cohorts. Of these, 2227 have overall survival data and 1256 samples have progression‐free survival time. The most significant genes associated with survival were MIF, UBC and B2M in lung adenocarcinoma and ANXA2, CSNK2A2 and KRT18 in squamous cell carcinoma. We also aimed to reveal the best druggable targets in non‐smokers lung cancer. The three most promising hits in this cohort were MDK, THY1 and PADI2. The established lung cancer cohort was added to the Kaplan–Meier plotter (https://www.kmplot.com) enabling the validation of future gene expression‐based biomarkers in both the present and yet unexamined subgroups of patients. Conclusions and Implications: In this study, we established a comprehensive database of transcriptome‐level data for lung cancer. The database can be utilized to identify and rank the most promising biomarkers and therapeutic targets for different subtypes of lung cancer. [ABSTRACT FROM AUTHOR]

As one of the most common messenger ribonucleic acid modifications in eukaryotic organisms, N6‐methyladenosine (m6A) is involved in a wide variety of biological functions. The imbalance of m6A RNA modification may be linked to cancer and other disorders, according to a growing body of studies. Its effects on clear cell renal cell carcinoma (KIRC) have not been well discussed, though. Here, we acquired the expression patterns of 23 important regulators of m6A RNA modification and assess how they might fare in KIRC. We observed that 17 major m6A RNA modification regulatory factors had a substantial predictive influence on KIRC. Using the "ConsensusCluster" program, we defined two groupings (Cluster 1 and Cluster 2) depending on the expression of the aforementioned 17 key m6A RNA methylation regulators. The Cluster 2 has a less favorable outcome and is strongly related with a lesser immune microenvironment, according to the findings. We also developed a strong risk profile for three m6A RNA modifiers (METTL14, YTHDF1, and LRPPRC) using multivariate Cox regression analysis. According to further research, the aforementioned risk profile could serve as an independent predicting factor for KIRC, and the chemotherapy response sensitivity was analyzed between two risk groups. Moreover, to effectively forecast the future outlook of KIRC clients, we established a novel prognostic approach according to gender, age, histopathological level, clinical stage, and risk score. Finally, the function of hub gene METTL14 was validated by cell proliferation and subcutaneous graft tumor in mice. In conclusion, we discovered that m6A RNA modifiers play an important role in controlling KIRC and created a viable risk profile as a marker of prediction for KIRC clients. [ABSTRACT FROM AUTHOR]

To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine−learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single−factor and multi−factor comparative analyses, as well as for model validation, and the goodness−of−fit was assessed using the Akaike information criterion (AIC). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6‰, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9‰, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors (p < 0.05), the AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun−dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun−dried green tea under abiotic stress. [ABSTRACT FROM AUTHOR]

Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database. However, linkage errors are usually unavoidable, regardless of record linkage methods, and ignoring these errors may lead to biased estimates. While different methods have been developed to deal with the linkage errors in the generalized linear model, there is not much interest on Cox regression model, although this is one of the most important statistical models in clinical and epidemiological research. In this work, we propose an adjusted estimating equation for secondary Cox regression analysis, where linked data have been prepared by a third‐party operator, and no information on matching variables is available to the analyst. Through a Monte Carlo simulation study, the proposed method is shown to lead to substantial bias reductions in the estimation of the parameters of the Cox model caused by false links. An asymptotically unbiased variance estimator for the adjusted estimators of Cox regression coefficients is also proposed. Finally, the proposed method is applied to a linked database from the Brest stroke registry in France. [ABSTRACT FROM AUTHOR]

Background: Certified cancer centers aim to ensure high-quality care by establishing structural and procedural standards according to evidence-based guidelines. Despite the high clinical and health policy relevance, evidence from a nation-wide study for the effectiveness of care for colorectal cancer in certified centers vs. other hospitals in Germany is still missing. Methods: In a retrospective cohort study covering the years 2009–2017, we analyzed patient data using demographic information, diagnoses, and treatments from a nationwide statutory health insurance enriched with information on certification. We investigated whether patients with incident colon or rectal cancer did benefit from primary therapy in a certified cancer center. We used relative survival analysis taking into account mortality data of the German population and adjustment for patient and hospital characteristics via Cox regression with shared frailty for patients in hospitals with and without certification. Results: The cohorts for colon and rectal cancer consisted of 109,518 and 51,417 patients, respectively, treated in a total of 1052 hospitals. 37.2% of patients with colon and 42.9% of patients with rectal cancer were treated in a certified center. Patient age, sex, comorbidities, secondary malignoma, and distant metastases were similar across groups (certified/non-certified) for both colon and rectal cancer. Relative survival analysis showed significantly better survival of patients treated in a certified center, with 68.3% (non-certified hospitals 65.8%) 5-year survival for treatment of colon cancer in certified (p < 0.001) and 65.0% (58.8%) 5-year survival in case of rectal cancer (p < 0.001), respectively. Cox regression with adjustment for relevant covariates yielded a lower hazard of death for patients treated in certified centers for both colon (HR = 0.92, 95% CI = 0.89–0.95) and rectal cancer (HR = 0.92, 95% CI = 0.88–0.95). The results remained robust in a series of sensitivity analyses. Conclusions: This large cohort study yields new important evidence that patients with colorectal cancer have a better chance of survival if treated in a certified cancer center. Certification thus provides one powerful means to improve the quality of care for colorectal cancer. To decrease the burden of disease, more patients should thus receive cancer care in a certified center. [ABSTRACT FROM AUTHOR]

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan–Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles. [ABSTRACT FROM AUTHOR]

Introduction: Objectively measured physical activity (PA) data were collected in the accelerometry substudy of the UK Biobank. UK Biobank also contains information about multiple sclerosis (MS) diagnosis at the time of and after PA collection. This study aimed to 1) quantify the difference in PA between prevalentMS cases and matched healthy controls, and 2) evaluate the predictive performance of objective PA measures for incident MS cases. Methods: The first analysis compared eight accelerometer-derived PA summaries between MS patients (N = 316) and matched controls (30 controls for each MS case). The second analysis focused on predicting time to MS diagnosis among participants who were not diagnosed with MS. A total of 19 predictors including eight measures of objective PA were compared using Cox proportional hazards models (number of events = 47; 585,900 person-years of follow-up). Results: In the prevalent MS study, the difference between MS cases and matched controls was statistically significant for all PA summaries (P < 0.001). In the incidentMS study, the most predictive variable of progression toMS in univariate Cox regression models was lower age (C = 0.604), and the most predictive PA variable was lower relative amplitude (RA, C = 0.594). A two-stage forward selection using Cox regression resulted in a model with concordance C = 0.693 and four predictors: age (P = 0.015), stroke (P = 0.009), Townsend deprivation index (P = 0.874), and RA (P = 0.004). A model including age, stroke, and RA had a concordance of C = 0.691. Conclusions: Objective PAsummarieswere significantly different and consistent with lower activity among study participants who had MS at the time of the accelerometry study. Among individuals who did not have MS, younger age, stroke history, and lower RA were significantly associated with a higher risk of a future MS diagnosis. [ABSTRACT FROM AUTHOR]

Sample size and power determination are crucial design considerations for biomedical studies intending to formally test the effects of key variables on an outcome. Other known prognostic factors may exist, necessitating the use of techniques for covariate adjustment when conducting this evaluation. Moreover, the main interest often includes assessing the impact of more than one variable on an outcome, such as multiple treatments or risk factors. Regression models are frequently employed for these purposes, formalizing this assessment as a test of multiple regression parameters. But, the presence of multiple variables of primary interest and correlation between covariates can complicate sample size/power calculation. Given the paucity of available sample size formulas for this context, these calculations are often performed via simulation, which can be both time‐consuming as well as demanding extensive probability modeling. We propose a simpler, general approach to sample size and power determination that may be applied when testing multiple parameters in commonly used regression models, including generalized linear models as well as ordinary and stratified versions of the Cox and Fine–Gray models. Through both rigorous simulations and theoretical derivations, we demonstrate the formulas' accuracy in producing sample sizes that will meet the type I error rate and power specifications of the study design. [ABSTRACT FROM AUTHOR]

Missing data is common in survival analysis. It is either removed or imputed using various methods. Expectation-maximization (EM) imputation is a popular method in Cox regression studies. This paper investigated the effect of different regression methods on Cox regression modeling within the framework of EM. A stratified Cox regression model was derived from a dataset of categorical and numerical variables. Missing data were imputed using the EM framework with five machine learning algorithms and then were compared to the full model. The results show that the recursive partition and regression tree (RPART) method performed better than others. However, all regression methods performed poorly in categorical covariate imputation. R code is available online. [ABSTRACT FROM AUTHOR]

Aim: Prediabetes is a strong predictor for type 2 diabetes mellitus (T2DM). However, the possible effects of other factors on the above association are unknown. This study aimed to determine factors associated with the progression of prediabetes to T2DM. Materials and methods: In the present prospective cohort study, 757 individuals with prediabetes included in the Fasa cohort study were identified and followed for 5 years in order to diagnose T2DM. The baseline demographic, medical, and clinical records of participants were surveyed. Latent class analysis (LCA) and Cox regression model were applied to evaluate potential factors affecting the rate of progression of prediabetes to T2DM. p values less than 0.05 were considered significant. Results: The annual cumulative incidence rate of diabetes was 124.1 per 1000 population. A total of 3 different latent classes were among participants including fatty liver & DII class (FLDc), with 18.58%, metabolic syndrome class (MSc), with 31.44%, and high-risk class (HRc), with 49.98% of the participants. Based on the results of Cox multivariate regression analysis, the risk of progression to T2DM was significantly raised with the presence of FLDc (HR = 2.37, 95% CI: 1.19, 4.74). Conclusion: Our findings show that the cumulative diabetes incidence was alarmingly high in Iran. Also, the cooccurrence of factors such as high fatty liver index, high DII score, metabolic syndrome, and waist-to-hip ratio increased the risk of diabetes in prediabetic individuals. These results point out the importance of applying effective preventive interventional programs for prediabetic individuals. [ABSTRACT FROM AUTHOR]

Prior studies have shown an association between malnutrition and mortality. However, it is uncertain whether malnutrition assessed with the Mini Nutritional Assessment (MNA) instrument is suitable for providing long-term prognostic information regarding older adults admitted to hospital. The aim of the present study was to examine if MNA-assessed malnutrition was associated with long-term mortality in older adults admitted to hospital and for how long the association persisted. 1768 older adults (≥65 years old) admitted to a Swedish hospital were assessed with the 18-item MNA during 2008–2009 and followed-up after 10 years. All-cause mortality (ACM) was analyzed separately for the five follow-up periods 0 to ≤2 years, >2 to ≤4 years, >4 to ≤6 years, >6 to ≤8 years, and >8 to ≤10 years using Cox regression models adjusted for important demographic, nutritional, and clinical confounders. The participants were on average 78.1 years old at baseline, with 56.0% being females. At 10 years follow-up, 174 (94.1%) malnourished patients, 757 (75.9%) patients at risk of malnutrition, and 297 (50.7%) well-nourished patients had died. For all follow-up periods, malnourished patients and patients at risk of malnutrition had significantly higher risks of early death in the adjusted regression analyses when compared with well-nourished patients (all P < 0.05), with the highest risk observed for malnourished patients. For patients still alive at 8 years, the risk of death during the following two years was 2.7 times higher for patients being malnourished at baseline (P = 0.013) and 1.9 times higher for patients being at risk of malnutrition at baseline (P = 0.001), compared with patients being well-nourished at baseline. MNA-assessed malnutrition is an important independent predictor of long-term mortality in older adults admitted to hospital and the association is consistent over 10 years of follow-up. In clinical practice, MNA may provide long-term prognostic information to rule out those at low risk of mortality and therefore in less need of further assessment and intervention, such that the resources can focus on those in actual need of nutritional support. [ABSTRACT FROM AUTHOR]

Most of the economic losses in the Turkish sheep industry are due to lamb deaths between birth and weaning period. Survival analysis is an essential step in the understanding of the lamb’s survival function and the hazards that have an impact on mortality. The lamb records were accessed from 2013 to 2019, with the information of 11,523 lambs from birth to weaning at 90 days of age. The Kaplan-Meier method was used for the estimation of the survival function of the lambs. The Cox proportional hazards survival analysis was performed to evaluate the factors influencing Awassi lamb mortality in the central Anatolian region of Türkiye. A multivariable Cox regression was fitted to the data after checking the assumptions of the regression. Year of birth, lambing season, lamb sex, method of birth, and birth weight groups were used as explanatory variables. The overall survival of the lambs to weaning was 75.7%, and the Kaplan-Meier estimate of the average survival time to weaning was 62.25 days. Cox proportional hazards modeling showed that the hazard rate of death before weaning was higher in male lambs (1.205 and 95% CI, 1.119–1.300) compared to female lambs. It was also noted that the hazard rate of death (HR = 0.956 and 95% CI, 0.937–0.974) had significant years. The single-born lambs had a lower risk of death among the lambs. The summer and autumn seasons had a very highly advanced effect on the survival rate of the lambs. The hazard rate for the categorical birth weight group was 0.703 (95% CI, 0.667–0.741). This indicates that the risk of pre-weaning mortality decreased with increasing birth weight > 3.5 kg. The results of this study showed that lamb survival can be increased by paying more attention to winter born lambs, male sex, twin births, and lambs about birth weight (< 3.5 kg). [ABSTRACT FROM AUTHOR]

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Introduction: Colorectal cancer (CRC), also known as colorectal cancer, is a significant disease marked by high fatality rates, ranking as the third leading cause of global mortality. The main objective of this study was to assess the accuracy of predictive models in predicting both mortality events and the probability of disease recurrence. Method: A retrospective analysis was conducted on a cohort of 284 individuals diagnosed with colorectal cancer between 2001 and 2017. Demographic and clinical data, including gender, disease stage, age at diagnosis, recurrence status, and treatment details, were meticulously recorded. We rigorously evaluated various predictive models, including Decision Trees, Random Forests, Random Survival Forests (RSF), Gradient Boosting, mboost, Deep Learning Neural Network (DLNN), and Cox regression. Performance metrics, such as sensitivity, positive predictive value (PPV), specificity, area under the receiver operating characteristic curve (ROC area), and overall accuracy, were calculated for each model to predict mortality and disease recurrence. The analysis was performed using R version 4.1.3 software and the Python programming language. Results: For mortality prediction, the mboost model demonstrated the highest sensitivity at 96.9% (95% CI: 0.83–0.99) and an ROC area of 0.88. It also exhibited high specificity at 80% (95% CI: 0.59–0.93), a positive predictive value of 86.1% (95% CI: 0.70–0.95), and an overall accuracy of 89% (95% CI: 0.78–0.96). Random Forests showed perfect sensitivity of 100% (95% CI: 0.85–1) but had low specificity at 0% (95% CI: 0–0.52) and poor overall accuracy (50%). On the other hand, DLNN had the lowest performance metrics for mortality prediction, with a sensitivity of 24% (95% CI: 0.222–0.268), specificity of 75% (95% CI: 0.73–0.77), and a lower positive predictive value of 42% (95% CI: 0.38–0.45). The Gradient Boosting model showed the best performance in predicting recurrence, achieving perfect sensitivity of 100% (95% CI: 0.87–1) and high specificity at 92.9% (95% CI: 0.76–0.99). It also had a high positive predictive value of 93.3% (95% CI: 0.77–0.99). Gradient Boosting, with an ROC area of 96.4%, and mboost, with an ROC area of 75%, demonstrated remarkable performance. DLNN had the lowest performance metrics for recurrence prediction, with sensitivity at 1.75% (95% CI: 0.01–0.02), specificity at 98% (95% CI: 0.97–0.98), and a lower positive predictive value at 52.6% (95% CI: 0.39–0.65). Conclusion: In summary, the mboost model demonstrated outstanding performance in predicting mortality, achieving exceptional results across various evaluation metrics. Random Forests exhibited perfect sensitivity but showed poor specificity and overall accuracy. The DLNN model displayed the lowest performance metrics for mortality prediction. In terms of recurrence prediction, the Gradient Boosting model outperformed other models with perfect sensitivity, high specificity, and positive predictive value. The DLNN model had the lowest performance metrics for recurrence prediction. Overall, the results emphasize the effectiveness of the mboost and Gradient Boosting models in predicting mortality and recurrence in colorectal cancer patients.

Objective To highlight the impact of behavioral and non-behavioral factors on cardiovascular health, disease prevention and management. Methods Cox regression and forest plots were used to assess and demonstrate the relationship between their behavioral and non-behavioral factors and cardiovascular disease (CVD) risk in a study population of 20 745 people at high cardiovascular risk from Zhejing province. Apriori algorithm was used to analyze the correlation between behavioral factors and non-behavioral factors for people with at least one of the greatest risk behavioral factors. Results The incidence of cardiovascular risk in Zhejiang province was positively related to age and inversely related to education level of target population. The higher risk was identified in in male and population in urban citities as compared to female and the people in rural areas; The risk was positively related to smoking intensity and showed a U-shaped association with alcohol intake; The incidence was lowest in those who were mainly standing during routine work; The risk was lower in those who had a clear distinction between busy and idle farming activity in farmers; Men who were engaged in general physical labor and never smoked nor drank high dose of alcohol; Women who never smoked and drank, but did not work and at home were important factors related to the higher cardiovascular risk rate. Conclusions There are a wide range of factors affecting the high-risk population of cardiovascular disease, and there is a certain correlation among these factors.

Abstract Objective To evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU). Methods In the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare. Results A total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients’ 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively. Conclusions RSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.

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.

Xiyuan Li,1,2 Xiaomeng Wang,1 Liangshan Wang,1 Chenglong Li,1 Xing Hao,1 Zhongtao Du,1 Haixiu Xie,1 Feng Yang,1 Hong Wang,1 Xiaotong Hou1 1Center for Cardiac Intensive Care, Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, People’s Republic of China; 2Department of Intensive Care Unit, Aviation General Hospital of China Medical University, Beijing, 100012, People’s Republic of ChinaCorrespondence: Xiaotong Hou; Hong Wang, Center for Cardiac Intensive Care, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing, 100029, People’s Republic of China, Tel +86 10 64456631 ; +86 15010516438, Email xt.hou@ccmu.edu.cn; 914286855@qq.comObjective: There was no consensus on the impact of nosocomial infection on In-hospital mortality rate in patients receiving ECMO. This study aimed to investigate the impact of nosocomial infection (NI) on In-hospital mortality rate in adult patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO) after cardiac surgery.Materials and Methods: This retrospective study included 503 adult patients who underwent VA-ECMO after cardiac surgery. The impact of time-dependent NIs on In-hospital mortality rate within 28 days of ECMO initiation was investigated using a Cox regression model. The cumulative incidence function for death was compared between patients with NIs and those without NIs using a competing risk model.Results: Within 28 days after ECMO initiation, 206 (41.0%) patients developed NIs, and 220 (43.7%) patients died. The prevalence rates of NIs were 27.8% and 20.3% during and after ECMO therapy, respectively. The incidence rates of NIs during and after ECMO therapy were 49‰ and 25‰, respectively. Time-dependent NI was an independent risk factor for predicting death (hazard ratio = 1.05, 95% confidence interval = 1.00– 1.11). The cumulative incidence of death in patients with NI was significantly higher than that in patients without NI at each time point within 28 days of ECMO initiation. (Z = 5.816, P = 0.0159).Conclusion: NI was a common complication in adult patients who received VA-ECMO after cardiac surgery, and time-dependent NI was an independent risk factor for predicting mortality in these patients. Using a competing risk model, we confirmed that NIs increased the risk of In-hospital mortality rate in these patients.Keywords: venoarterial extracorporeal membrane oxygenation, nosocomial infection, in-hospital mortality rate, Cox regression, competing risk model

Summary Background To compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC). Methods Patients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models. Results For binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model. Conclusions Cox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution.

Abstract Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the Cancer Genome Atlas (TCGA) cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 3.1% when cumulative incidence is of 10% to 29.1% when the cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of adjuvant trials in HCC, we find that the number of patients screened for eligibility can be divided by 2.4 when broadening eligibility criteria. Last, we find that the Cox-Snell $${R}_{\mathsf{C}\mathsf{S}}^{2}$$ R C S 2 is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers. Code and results are available at https://github.com/owkin/CovadjustSim .

Objectives: The Puumala virus (PUUV) is a hantavirus that causes hemorrhagic fever with renal syndrome. Studies showing an increased risk of lymphoid malignancies after hantavirus infection, together with the observation that PUUV infects B cells, motivated us to study the risk of lymphoid malignancies after PUUV infection. Methods: We linked data from the Finnish Cancer Registry and National Infectious Diseases Register for 2009-2019. We used a time-dependent Cox regression model to evaluate the hazard of the lymphoid malignancies grouped according to the HAEMACARE classification. Results: We identified 68 cases of lymphoid malignancies after PUUV infection among 16,075 PUUV-infected individuals during 61,114,826 person-years of observation. A total of 10 cases occurred within 3-

BackgroundThe relationship between baseline fasting blood glucose (bFBG) and mortality in peritoneal dialysis (PD) patients has been the subject of debate, with limited exploration of the non-linear relationship between bFBG and death in these patients.MethodsThis retrospective study categorized patients into four groups based on their bFBG using quartiles. Baseline clinical data at the initiation of dialysis were compared. Survival curves were plotted, and subgroup analyses were stratified by relevant covariates. To address the non-linear relationship, curve fitting and a threshold effect analysis were performed.ResultsThe study included 379 PD patients with a median follow-up of 41.8 (22.6, 60.1) months. The COX proportional hazards model showed an association between bFBG and the risk of death after adjusting for confounding factors [hazard ratio (HR): 1.22, 95% CI: 1.05−1.41, P = 0.009]. Stratified analyses indicated a stable correlation between bFBG and mortality. The Kaplan-Meier curve analysis revealed significant differences in survival rates among different groups based on bFBG levels (P < 0.01). The curve fitting analysis revealed a U-shaped relationship between bFBG and mortality, with an inflection point at approximately 5.1 mmol/L.ConclusionOur study has demonstrated a non-linear relationship between bFBG and mortality in PD patients. Additionally, we have found that the optimal bFBG value associated with the lowest risk of mortality is approximately 5.1 mmol/L.

Background: Cystatin C, a low-molecular-weight protein, belongs to cysteine protease inhibitors produced primarily by nucleated cells. Its serum concentration, independent of sex, age, or muscle mass, is a good predictor of renal dysfunction in older adults. This study evaluated the association between all-cause mortality and preoperative cystatin C levels in hip fractures. Materials and methods: Data describing the demographic and clinical characteristics of the patients were gathered specifically from older individuals who had suffered hip fractures. The study used linear and non-linear multivariate Cox regression models to investigate the association between preoperative cystatin C levels and mortality. The analyses were conducted using the R and EmpowerStats software. Results: In total, two thousand three hundred and ninety-four patients were included in this study. A total of 790 patients (33 %) died of all causes. The mean follow-up was 37.62 months. The preoperative cystatin C was 0.91 ± 0.41 mg/L. Linear multivariate Cox regression analysis revealed a significant association between preoperative cystatin C level and death, with a hazard ratio (HR) of 2.19 (95 % confidence interval [CI]: 1.72–2.79, P

PurposeCompeting-risk analysis was used to accurately assess prognostic factors for cancer-specific death in patients with adenocarcinoma of transverse colon (ATC), and the results were compared with those from a conventional Cox regression analysis.Materials and MethodsPatients diagnosed with ATC between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results database. The crude mortality rates of patients with ATC were calculated and their differences were tested using the Gray’s test, respectively. In performing multivariate analysis, the Cox regression model and the subdistribution hazard function (SD) in competing risk analysis were utilized, respectively.ResultsThis study included 21,477 eligible patients. The SD model indicated that age, etc. are actual independent prognostic factors. In contrast to previous recognition, the results of the Cox regression showed false-positives for sex and Carcinoembryonic antigen, and underestimated point-estimates in the stage and American Joint Committee on Cancer stage due to competing events. A detailed comparison of treatment revealed that the larger surgical scopes were prognostic risk factors compared with the smaller scope of local tumor excision, partial colectomy, or segmental resection. Patients treated with external proton beam radiotherapy had an increased risk compared with those with no radiotherapy and internal radiotherapy.ConclusionsAfter comparing the results of the two methods and mitigating the significant bias introduced by Cox regression, we found independent factors that really affect the prognosis of ATC. On the other hand, in terms of ATC, a larger surgical scope and external proton beam radiotherapy may not improve the long-term survival of patients. Therefore, when faced with ATC patients, these differences should be noted and treated differently from common colorectal cancer patients. Thus, clinicians are able to give more targeted treatment plans and prognostic assessments.

Introduction: The present study presents the development and validation of a clinical prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to predict the probability of pelvic inflammatory disease (PID) progressing to sepsis. Methods: A retrospective cohort study was conducted, gathering clinical data of patients diagnosed with PID between 2008 and 2019 from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who met the Sepsis 3.0 diagnostic criteria were selected, with sepsis as the outcome. Univariate Cox regression and stepwise Cox regression were used to screen variables for constructing a nomogram. Moreover, an RSF model was created using machine learning algorithms. To verify the model's performance, a calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve were utilized. Furthermore, the capabilities of the two models for estimating the incidence of sepsis in PID patients within 3 and 7 days were compared. Results: A total of 1064 PID patients were included, of whom 54 had progressed to sepsis. The established nomogram highlighted dialysis, reduced platelet (PLT) counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts as significant predictive factors. The areas under the curve (AUCs) of the nomogram for prediction of PID progression to sepsis at 3-day and 7-day (3-/7-day) in the training set and the validation set were 0.886/0.863 and 0.824/0.726, respectively, and the C-index of the model was 0.8905. The RSF displayed excellent performance, with AUCs of 0.939/0.919 and 0.712/0.571 for 3-/7-day risk prediction in the training set and validation set, respectively. Conclusion: The nomogram accurately predicted the incidence of sepsis in PID patients, and relevant risk factors were identified. While the RSF model outperformed the Cox regression models in predicting sepsis incidence, its performance exhibited some instability. On the other hand, the Cox regression-based nomogram displayed stable performance and improved interpretability, thereby supporting clinical decision-making in PID treatment.