A transformer model for cause-specific hazard prediction.

BACKGROUD: Modelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand. RESULTS: Using synthetic datasets of 2000-50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model's prediction using the integrated gradients method. CONCLUSIONS: Our model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.

BACKGROUND: Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. METHODS: We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. RESULTS: The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. CONCLUSIONS: This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments.

BACKGROUND: Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients. METHODS: Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients. RESULTS: The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker. CONCLUSION: The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Predictors of recovery from severe acute malnutrition among 6-59 months children admitted to a hospital.

Background and aim: Severe acute malnutrition is a threat to child survival as mortality rates in children with severe malnutrition are nine times higher. Globally, about 19 million children are severely malnourished. This study looked at children aged 6-59 months admitted to hospital to see how quickly they recovered from severe acute malnutrition as well as what factors predicted their recovery. Methods: The study included 543 systematically chosen children with severe acute malnutrition who were admitted to the stabilization center of a hospital. Data from the patient registry were gathered using a retrospective follow-up study design. In order to find predictors of recovery, the Cox proportional hazard model was applied. Results: From 543 children, 425 (78.27%) were recovered. The median survival time was 8 days. Having grade II edema, grade III edema, and pneumonia were negatively associated with recovery. Similarly, taking ceftriaxone, cloxacillin, and being on a nasogastric tube were associated with poor recovery. Conversely, better recovery rates were linked to exclusive breastfeeding and vitamin A supplementation. Conclusion: Both the recovery rate and the median survival time fell within acceptable bounds. To boost the recovery rate, efforts are needed to lessen comorbidities.

TYG Index as a Novel Predictor of Clinical Outcomes in Advanced Chronic Heart Failure with Renal Dysfunction Patients.

Background: The triglyceride-glucose (TYG) index is a novel and reliable marker reflecting insulin resistance. Its predictive ability for cardiovascular disease onset and prognosis has been confirmed. However, for advanced chronic heart failure (acHF) patients, the prognostic value of TYG is challenged due to the often accompanying renal dysfunction (RD). Therefore, this study focuses on patients with aHF accompanied by RD to investigate the predictive value of the TYG index for their prognosis. Methods and Results: 717 acHF with RD patients were included. The acHF diagnosis was based on the 2021 ESC criteria for acHF. RD was defined as the eGFR < 90 mL/(min/1.73 m2). Patients were divided into two groups based on their TYG index values. The primary endpoint was major adverse cardiovascular events (MACEs), and the secondary endpoints is all-cause mortality (ACM). The follow-up duration was 21.58 (17.98-25.39) months. The optimal cutoff values for predicting MACEs and ACM were determined using ROC curves. Hazard factors for MACEs and ACM were revealed through univariate and multivariate COX regression analyses. According to the univariate COX regression analysis, high TyG index was identified as a risk factor for MACEs (hazard ratio = 5.198; 95% confidence interval [CI], 3.702-7.298; P < 0.001) and ACM (hazard ratio = 4.461; 95% CI, 2.962-6.718; P < 0.001). The multivariate COX regression analysis showed that patients in the high TyG group experienced 440.2% MACEs risk increase (95% CI, 3.771-7.739; P < 0.001) and 406.2% ACM risk increase (95% CI, 3.268-7.839; P < 0.001). Kaplan-Meier survival analysis revealed that patients with high TyG index levels had an elevated risk of experiencing MACEs and ACM within 30 months. Conclusion: This study found that patients with high TYG index had an increased risk of MACEs and ACM, and the TYG index can serve as an independent predictor for prognosis.

Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors.

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56-0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48-0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53-0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42-0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23-0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73-0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50-0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke events during postinfection follow-up.

External validation of a multi-biomarker-based score for predicting risk of cardiovascular disease in patients with rheumatoid arthritis.

BACKGROUND: A multi-biomarker disease activity (MBDA)-based cardiovascular disease (CVD) risk score was developed and internally validated in a Medicare cohort to predict 3-year risk for myocardial infarction (MI), stroke or CVD death in patients with rheumatoid arthritis (RA). It combines the MBDA score, leptin, MMP-3, TNF-R1, age and four clinical variables. We are now externally validating it in a younger RA cohort. METHODS: Claims data from a private aggregator were linked to MBDA test data to create a cohort of RA patients ≥18 years old. A univariable Cox proportional hazards regression model was fit using the MBDA-based CVD risk score as sole predictor of time-to-a-CVD event (hospitalized MI or stroke). Hazard ratio (HR) estimate was determined for all patients and for clinically relevant subgroups. A multivariable Cox model evaluated whether the MBDA-based CVD risk score adds predictive information to clinical data. RESULTS: 49,028 RA patients (340 CVD events) were studied. Mean age was 52.3 years; 18.3% were male. HR for predicting 3-year risk of a CVD event by the MBDA-based CVD risk score in the full cohort was 3.99 (95% CI: 3.51-4.49, p = 5.0×10-95). HR were also significant for subgroups based on age, comorbidities, disease activity, and drug use. In a multivariable model, the MBDA-based CVD risk score added significant information to hypertension, diabetes, tobacco use, history of CVD, age, sex and CRP (HR = 2.27, p = 1.7×10-7). CONCLUSION: The MBDA-based CVD risk score has been externally validated in an RA cohort that is younger than and independent of the Medicare cohort that was used for development and internal validation.

Impact of Asian and Black Donor and Recipient Ethnicity on the Outcomes After Deceased Donor Kidney Transplantation in the United Kingdom.

Patients of Asian and black ethnicity face disadvantage on the renal transplant waiting list in the UK, because of lack of human leucocyte antigen and blood group matched donors from an overwhelmingly white deceased donor pool. This study evaluates outcomes of renal allografts from Asian and black donors. The UK Transplant Registry was analysed for adult deceased donor kidney only transplants performed between 2001 and 2015. Asian and black ethnicity patients constituted 12.4% and 6.7% of all deceased donor recipients but only 1.6% and 1.2% of all deceased donors, respectively. Unadjusted survival analysis demonstrated significantly inferior long-term allograft outcomes associated with Asian and black donors, compared to white donors. On Cox-regression analysis, Asian donor and black recipient ethnicities were associated with poorer outcomes than white counterparts, and on ethnicity matching, compared with the white donor-white recipient baseline group and adjusting for other donor and recipient factors, 5-year graft outcomes were significantly poorer for black donor-black recipient, Asian donor-white recipient, and white donor-black recipient combinations in decreasing order of worse unadjusted 5-year graft survival. Increased deceased donation among ethnic minorities could benefit the recipient pool by increasing available organs. However, it may require a refined approach to enhance outcomes.

[Association, differences, and applications of three commonly used statistical indicators: risk ratio, hazard ratio, and odds ratio].

Relative Risk (RR), Hazard Ratio (HR), and Odds Ratio (OR) are commonly used statistical measures in the field of public health to assess the magnitude of the effect of exposure factors on outcomes. These indicators have different calculation principles and implications in public health. However, a few researchers misused or misinterpreted RR, HR, and OR values when interpreting study results. Therefore, this article explores the relationships and differences among these measures, as well as the correct selection and application of RR, HR, and OR in both cohort study and case-control study.

Use of antiviral drugs and incidence of Parkinson's disease in Taiwan.

Patients infected with herpes zoster might be at risk for Parkinson's disease (PD). However, antiviral drugs may impede viral deoxyribonucleic acid (DNA) synthesis. This study aimed to determine whether the currently observed association between herpes zoster and PD is consistent with previous findings, and whether antiviral drug use is associated with PD. This retrospective cohort study used the Longitudinal Generation Tracking Database. We included patients aged 40 years and above and applied propensity score matching at 1:1 ratio for study comparability. PD risk was evaluated using Cox proportional hazards regression methods. A total of 234,730 people were analyzed. The adjusted hazard ratio (aHR) for PD in patients with herpes zoster was 1.05. Furthermore, the overall incidence of PD was lower in those treated with antiviral drugs than in the untreated ones (3.17 vs. 3.76 per 1,000 person-years); the aHR was 0.84. After stratifying for sex or age, a similar result was observed. In conclusion, herpes zoster may increase the risk of PD, particularly among females, but receiving antiviral treatment reduces the risk by 16%. Therefore, using antiviral drugs may help prevent PD. However, additional research is required to determine the underlying mechanism(s).

Time to Death and Its Predictors among Neonates Hospitalized with Sepsis in Eastern Ethiopia.

Background: Although neonatal sepsis is a major public health problem contributing to 30-50% of neonatal deaths in low- and middle-income countries, data on predictors of time to death are limited in Eastern Ethiopia. This study is aimed at determining predictors of time to death among neonates with sepsis admitted in public hospitals in Eastern Ethiopia. Methods: An institutional-based retrospective cohort study was conducted among 415 neonates admitted to referral hospitals in Eastern Ethiopia with sepsis from January 1, 2021, to December 31, 2021. Data were collected from medical records by using structured checklist and entered using EpiData 3.1 and analyzed using Stata 17. The Kaplan-Meier curves and log-rank tests were used to describe survival experience among different categories. The proportional hazard assumption and goodness of fit for the Cox regression model were checked. The Cox regression model was used to identify the significant predictors. Hazard ratios (HRs) with 95% confidence intervals (CI) were calculated. Finally, statistical significance was set at a p value < 0.05 in the Cox regression analysis. Results: Of the 415 neonates with neonatal sepsis, 71 (17.1%) (95% CI: 13.60-21.08) died at discharge, with a median time to death of 14 days. The overall incidence rate of mortality was 36.5 per 1000 neonate days. Low birthweight (AHR = 2.50; 95% CI: 1.15-5.44), maternal age ≥ 35 years (AHR = 3.17; 95% CI: 1.11, 9.04), low fifth-minute Apgar score (AHR: 2.32; 95% CI: 1.30-4.14), and late initiation of breastfeeding (AHR = 4.82; 95% CI: 1.40-16.65) were independent predictors of mortality among neonates with sepsis. Conclusions: Almost one in five neonates with sepsis died at discharge. Low birthweight, maternal age ≥ 35 years, low fifth-minute Apgar score, and late initiation of breastfeeding were predictors of mortality.

Impact of tumor size on overall survival and cancer-specific survival of early-onset colon and rectal cancer: a retrospective cohort study.

PURPOSE: This study aimed to investigate the impact of tumor size on survival in early-onset colon and rectal cancer. METHODS: Early-onset colon and rectal cancer patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Tumor size was analyzed as both continuous and categorical variables. Several statistical techniques, including restricted cubic spline (RCS), Cox proportional hazard model, subgroup analysis, propensity score matching (PSM), and Kaplan-Meier survival analysis, were employed to demonstrate the association between tumor size and overall survival (OS) and cancer-specific survival (CSS) of early-onset colon and rectal cancer. RESULTS: Seventeen thousand five hundred fifty-one (76.7%) early-onset colon and 5323 (23.3%) rectal cancer patients were included. RCS analysis confirmed a linear association between tumor size and survival. Patients with a tumor size > 5 cm had worse OS and CSS, compared to those with a tumor size ≤ 5 cm for both early-onset colon and rectal cancer. Notably, subgroup analysis showed that a smaller tumor size (≤ 50 mm) was associated with worse survival in stage II early-onset colon cancer, although not statistically significant. After PSM, Kaplan-Meier survival curves showed that the survival of patients with tumor size ≤ 50 mm was better than that of patients with tumor size > 50 mm. CONCLUSION: Patients with tumors larger than 5 cm were associated with worse survival in early-onset colon and rectal cancer. However, smaller tumor size may indicate a more biologically aggressive phenotype, correlating with poorer survival in stage II early-onset colon cancer.

Living well with diabetes in Alaska.

Many people with diabetes mellitus experience minimal or no complications. Our objective was to determine the proportion of Alaska Native people who experienced four major complications or mortality and to identify factors that may be associated with these outcomes. We used records in a diabetes registry and clinical and demographic variables in our analyses. We used logistic regression and Cox Proportional Hazards models to evaluate associations of these parameters with death and complications that occurred prior to 2013. The study included 591 Alaska Native people with non-type 1 diabetes mellitus, diagnosed between 1986 and 1992. Over 60% of people in this study remained free of four major diabetes-related complications for the remainder of life or throughout the approximately 20-year study period. Lower BMI, higher age at diagnosis of diabetes, and use of at least one diabetes medication were associated with death and a composite of four complications. A majority of Alaska Native people with DM had none of four major complications over a 20-year period. Lower BMI and use of diabetes medications were associated with higher hazard for some deleterious outcomes. This suggests that goals in care of elders should be carefully individualised. In addition, we discuss several programme factors that we believe contributed to favourable outcomes.

Urinary non-albumin protein-creatinine ratio is an independent predictor of mortality in patients with type 2 diabetes: a retrospective cohort study.

Albuminuria is a well-known predictor of chronic kidney disease in patients with type 2 diabetes mellitus (DM). However, proteinuria is associated with chronic complications in patients without albuminuria. In this retrospective cohort study, we explored whether non-albumin proteinuria is associated with all-cause mortality and compared the effects of non-albumin proteinuria on all-cause mortality between patients with and without albuminuria. We retrospectively collected data from patients with type 2 DM for whom we had obtained measurements of both urinary albumin-to-creatinine ratio (UACR) and urinary protein-to-creatinine ratio (UPCR) from the same spot urine specimen. Urinary non-albumin protein-creatinine ratio (UNAPCR) was defined as UPCR-UACR. Of the 1809 enrolled subjects, 695 (38.4%) patients died over a median follow-up of 6.4 years. The cohort was separated into four subgroups according to UACR (30 mg/g) and UNAPCR (120 mg/g) to examine whether these indices are associated with all-cause mortality. Compared with the low UACR and low UNAPCR subgroup as the reference group, multivariable Cox regression analyses indicated no significant difference in mortality in the high UACR and low UNAPCR subgroup (hazard ratio [HR] 1.189, 95% confidence interval [CI] 0.889-1.589, P = 0.243), but mortality risks were significantly higher in the low UACR and high UNAPCR subgroup (HR 2.204, 95% CI 1.448-3.356, P < 0.001) and in the high UACR with high UNAPCR subgroup (HR 1.796, 95% CI 1.451-2.221, P < 0.001). In the multivariable Cox regression model with inclusion of both UACR and UNAPCR, UNAPCR ≥ 120 mg/g was significantly associated with an increased mortality risk (HR 1.655, 95% CI 1.324-2.070, P < 0.001), but UACR ≥ 30 mg/g was not significantly associated with mortality risk (HR 1.046, 95% CI 0.820-1.334, P = 0.717). In conclusion, UNAPCR is an independent predictor of all-cause mortality in patients with type 2 DM.

Long-term exposure to PM2.5 and mortality in a national cohort in South Korea: effect modification by community deprivation, medical infrastructure, and greenness.

BACKGROUND: Long-term exposure to PM2.5 has been linked to increased mortality risk. However, limited studies have examined the potential modifying effect of community-level characteristics on this association, particularly in Asian contexts. This study aimed to estimate the effects of long-term exposure to PM2.5 on mortality in South Korea and to examine whether community-level deprivation, medical infrastructure, and greenness modify these associations. METHODS: We conducted a nationwide cohort study using the National Health Insurance Service-National Sample Cohort. A total of 394,701 participants aged 30 years or older in 2006 were followed until 2019. Based on modelled PM2.5 concentrations, 1 to 3-year and 5-year moving averages of PM2.5 concentrations were assigned to each participant at the district level. Time-varying Cox proportional-hazards models were used to estimate the association between PM2.5 and non-accidental, circulatory, and respiratory mortality. We further conducted stratified analysis by community-level deprivation index, medical index, and normalized difference vegetation index to represent greenness. RESULTS: PM2.5 exposure, based on 5-year moving averages, was positively associated with non-accidental (Hazard ratio, HR: 1.10, 95% Confidence Interval, CI: 1.01, 1.20, per 10 µg/m3 increase) and circulatory mortality (HR: 1.22, 95% CI: 1.01, 1.47). The 1-year moving average of PM2.5 was associated with respiratory mortality (HR: 1.33, 95% CI: 1.05, 1.67). We observed higher associations between PM2.5 and mortality in communities with higher deprivation and limited medical infrastructure. Communities with higher greenness showed lower risk for circulatory mortality but higher risk for respiratory mortality in association with PM2.5. CONCLUSIONS: Our study found mortality effects of long-term PM2.5 exposure and underlined the role of community-level factors in modifying these association. These findings highlight the importance of considering socio-environmental contexts in the design of air quality policies to reduce health disparities and enhance overall public health outcomes.

Association of diabetic retinopathy on all-cause and cause-specific mortality in older adults with diabetes: National Health and Nutrition Examination Survey, 2005-2008.

To evaluate the effect of diabetic retinopathy (DR) status or severity on all-cause and cause-specific mortality among diabetic older adults in the United States using the most recent National Health and Nutrition Examination Survey (NHANES) follow-up mortality data. The severity of DR was graded according to the Early Treatment Diabetic Retinopathy Study (ETDRS) grading scale. Multiple covariate-adjusted Cox proportional hazards regression models, Fine and Gray competing risk regression models, and propensity score matching (PSM) methods were used to assess the risk of all-cause and cause-specific mortality in individuals with diabetes. All analyses adopted the weighted data and complex stratified design approach proposed by the NHANES guidelines. Time to death was calculated based on the time between baseline and date of death or December 31, 2019, whichever came first. Ultimately 1077 participants, representing 3,025,316 US non-hospitalized individuals with diabetes, were included in the final analysis. After a median follow-up of 12.24 years (IQR, 11.16-13.49), 379 participants were considered deceased from all-causes, with 43.90% suffering from DR, including mild DR (41.50%), moderate to severe DR (46.77%), and proliferative DR (PDR) (67.21%). DR was associated with increased all-cause, cardiovascular disease (CVD) and diabetes mellitus (DM)-specific mortality, which remained consistent after propensity score matching (PSM). Results of DR grading assessment suggested that the presence of mild, moderate to severe NPDR was significantly associated with increased risk of all-cause and CVD-specific mortality, while the presence and severity of any DR was associated with increased DM-specific mortality, with a positive trend. The presence of DR in elderly individuals with diabetes is significantly associated with the elevated all-cause and CVD mortality. The grading or severity of DR may reflect the severity of cardiovascular disease status and overall mortality risk in patients with diabetes.

Importance of resection margin after resection of colorectal liver metastases in the era of modern chemotherapy: population-based cohort study.

BACKGROUND: Resection margin has been associated with overall survival following liver resection for colorectal liver metastasis. The aim of this study was to examine how resection margins of 0.0 mm, 0.1-0.9 mm and ≥1 mm influence overall survival in patients resected for colorectal liver metastasis in a time of modern perioperative chemotherapy and surgery. METHODS: Using data from the national registries Swedish Colorectal Cancer Registry and Swedish National Quality Registry for Liver, Bile Duct and Gallbladder Cancer, patients that had liver resections for colorectal liver metastasis between 2009 and 2013 were included. In patients with a narrow or unknown surgical margin the original pathological reports were re-reviewed. Factors influencing overall survival were analysed using a Cox proportional hazard model. RESULTS: A total of 754 patients had a known margin status, of which 133 (17.6%) patients had a resection margin <1 mm. The overall survival in patients with a margin of 0 mm or 0.1-0.9 mm was 42 (95% c.i. 31 to 53) and 48 (95% c.i. 35 to 62) months respectively, compared with 75 (95% c.i. 65 to 85) for patients with ≥1 mm margin, P < 0.001. Margins of 0 mm or 0.1-0.9 mm were associated with poor overall survival in the multivariable analysis, HR 1.413 (95% c.i. 1.030 to 1.939), P = 0.032, and 1.399 (95% c.i. 1.025 to 1.910), P = 0.034, respectively. CONCLUSIONS: Despite modern chemotherapy the resection margin is still an important factor for the survival of patients resected for colorectal liver metastasis, and a margin of ≥1 mm is needed to achieve the best possible outcome.

Does improved nurse staffing impact patient outcomes in cancer? Association between chronic diseases and mortality among older adult patients with lung cancer in Korea.

BACKGROUND: Evidence regarding the impact of nurse staffing on the health outcomes of older adult patients with cancer is scarce. Therefore, this study aimed to evaluate the impact of nurse staffing on long-term and short-term mortality in elderly lung cancer patients. METHODS: This study analyzed data from 5,832 patients with lung cancer in Korea from 2008 to 2018. Nursing grade was considered to assess the effect of nursing staff on mortality in older adult patients with lung cancer. The Cox proportional hazards model was used to evaluate the effect of the initial treatment hospital's nursing grade on one- and five-year mortality. Additionally, economic status and treatment type of patients were analyzed. RESULTS: Approximately 31% of older adult patients with lung cancer died within one year post-diagnosis. Patients in hospitals with superior nursing grades (lower nurse-to-bed ratios) exhibited lower mortality rates. Hospitals with nursing grades 2 and 3 exhibited approximately 1.242-1.289 times higher mortality than grade 1 hospitals. Further, the lower the nursing grade (higher nurse-to-bed ratio), the higher the five-year mortality rate. CONCLUSION: Both short- and long-term mortality rates for older adult patients with lung cancer increased at inferior nursing grades. Treatment in hospitals having inferior nursing grades, upon initial hospitalization, may yield better outcomes. This study provides valuable insight into the quality of adequate staffing to improve the quality of care for elderly cancer patients.

Surgery for carpal tunnel syndrome in patients with and without diabetes-Is there a difference in the frequency of surgical procedures?

Carpal tunnel syndrome (CTS) occurs more often among individuals with diabetes. The aim of this retrospective observational registry study was to examine whether individuals with diabetes and CTS are treated surgically to the same extent as individuals with CTS but without diabetes. Data on CTS diagnosis and surgery were collected from the Skåne Healthcare Register (SHR). A total of 35,105 individuals (age ≥ 18 years) diagnosed with CTS from 2004-2019 were included. Data were matched to the Swedish National Diabetes Register (NDR. Cox regression models were used to calculate the risk of the use of surgical treatment. Of the 35,105 included individuals with a CTS diagnosis, 17,662 (50%) were treated surgically, and 4,966 (14%) had diabetes. A higher number of individuals with diabetes were treated surgically (2,935/4,966, 59%) than individuals without diabetes (14,727/30,139, 49%). In the Cox regression model, diabetes remained a significant risk factor for surgical treatment (PR 1.14 (95% CI 1.11-1.17)). Individuals with type 1 diabetes were more frequently treated surgically (490/757, 65%) than individuals with type 2 diabetes (2,445/4,209, 58%). There was no difference between the sexes and their treatment. The duration of diabetes was also a risk factor for surgical treatment in diabetes type 2, but high HbA1c levels were not. Individuals with diabetes are more likely to be treated surgically for CTS than individuals without diabetes. Individuals with type 1 diabetes are more likely to be treated surgically for CTS than individuals with type 2 diabetes.

Current dialyzer classification in Japan and mortality risk in patients undergoing hemodialysis.

Dialyzers are classified into five types based on their ß2-microglobulin clearance rate and albumin sieving coefficient: Ia, Ib, IIa, and IIb. In addition, a new classification system introduced a type S dialyzer. However, limited information is available regarding the impact of dialyzer type on patient outcomes. A cohort study was conducted using data from the Japanese Society for Dialysis Therapy Renal Data Registry database. Total 181,804 patients on hemodialysis (HD) were included in the study, categorized into four groups (type Ia, IIa, IIb, and S). The associations between each group and two-year all-cause mortality were assessed using Cox proportional hazard models. Furthermore, propensity score-matching analysis was performed. By the end of 2019, 34,185 patients on dialysis had died. After adjusting for all confounders, the risk for all-cause mortality was significantly lower in the type IIa, and S groups than in the type Ia group. These significant findings were consistent after propensity score matching. In conclusion, our findings suggest that super high-flux dialyzers, with a ß2-microglobulin clearance of ≥ 70 mL/min, may be beneficial for patients on HD, regardless of their albumin sieving coefficient. In addition, type S dialyzers may be beneficial for elderly and malnourished patients on dialysis.Trial registration number: UMIN000018641.