A measure of explained risk in the proportional hazards model

Degree of joint risk factor control and incident chronic kidney disease among individuals with obesity

AIM

To investigate the extent to which joint risk factor control might attenuate the excess risk of chronic kidney disease (CKD) in participants with obesity.

PATIENTS AND METHODS

We included a total of 97 538 participants who were obese at baseline and matched 97 538 normal weight control participants from the UK Biobank in the analysis. The degree of joint risk factor control was assessed based on six major CKD risk factors, including blood pressure, glycated haemoglobin, low-density lipoprotein cholesterol, albuminuria, smoking and physical activity. The Cox proportional hazards models were used to estimate associations between the degree of risk factor control and risk of CKD, following participants from their baseline assessment until the occurrence of CKD, death, or the end of the follow-up period.

RESULTS

Among participants with obesity, joint risk factor control showed an association with a stepwise reduction of incident CKD risk. Each additional risk factor control corresponded to an 11% (hazard ratio: 0.89; 95% confidence interval: 0.86-0.91) reduced risk of CKD among participants with obesity, with the optimal controlling of all six risk factors associated with a 49% (hazard ratio: 0.51; 95% confidence interval: 0.43-0.61) decrease in risk of CKD. Furthermore, in individuals with obesity who jointly controlled all six risk factors, the excess risk of CKD associated with obesity was effectively neutralized compared with normal weight control subjects. Notably, the protective correlations between the degree of joint risk factor control and the incidence of CKD were more pronounced in men compared with women, in individuals with a lower healthy food score versus a higher score, and among diabetes medication users as opposed to non-users (pinteraction = 0.017, 0.033 and 0.014, respectively).

CONCLUSION

The joint risk factor control is associated with an inverse association of CKD risk in an accumulative manner among individuals with obesity. Achieving ideal control over risk factors may effectively counterbalance the excessive risk of CKD typically associated with obesity.

Joint effects of physical frailty and traditional cardiovascular risk factor control on cardiovascular disease in patients with diabetes

OBJECTIVES

Physical frailty has been found to increase the risk of multiple adverse outcomes including cardiovascular disease (CVD) in diabetic patients, but whether this could be modified by traditional risk factor control remains unknown. We aimed to explore the joint and interaction effects of frailty and traditional risk factor control on the risk of CVD.

DESIGN

A population-based cohort study.

SETTING AND PARTICIPANTS

We included 15,753 participants with type 2 diabetes at baseline from UK Biobank.

MEASUREMENTS

Physical frailty was assessed by Fried criteria's frailty phenotype. The degree of risk factor control was determined by the numbers of the following factors controlled within the target range, including glycated hemoglobin, blood pressure, low-density lipoprotein cholesterol, smoking, and kidney condition. Incident CVD included coronary heart disease, stroke, or heart failure. Cox proportional hazard models were used to assess the individual and joint effects of frailty and risk factor control on the risk of CVD.

RESULTS

After a median follow-up of 13.5 years, 1129 incident CVD events were observed. Compared with non-frailty, both prefrailty and frailty were significantly associated with increased risk of CVD (HR 1.22, 95% CI [1.13, 1.31] for pre-frailty and 1.70 [1.53, 1.90] for frailty). For the joint effects, participants with frailty and a low degree of risk factor control (control of 0-1 risk factors) had the highest risk of CVD (2.92 [2.04, 4.17]) compared to those with non-frailty and optimal risk factor control (control of 4-5 risk factors). Moreover, a significant additive interaction between frailty and risk factor control was observed, with around 3.8% of CVD risk attributed to the interactive effects.

CONCLUSIONS

Both prefrailty and frailty were associated with a higher risk of CVD in participants with type 2 diabetes. Moreover, physical frailty could interact with the degree of risk factor control in an additive manner to increase the CVD risk.

Phosphodiesterase 5 Inhibitor Treatment Is Associated With Improved Survival in Pulmonary Hypertension Associated With COPD in the Pulmonary Vascular Research Institute GoDeep Meta-Registry

BACKGROUND

Patients with COPD frequently demonstrate pulmonary hypertension (PH). Severe PH in patients with COPD, identified by pulmonary vascular resistance (PVR) of > 5 Wood units (WU), is closely linked to impaired transplant-free survival. The impact of PH-targeting pharmacotherapy in this context remains unclear.

RESEARCH QUESTION

Is PH-targeted therapy associated with improved transplant-free survival in patients with COPD and PH?

STUDY DESIGN AND METHODS

This study included Pulmonary Vascular Research Institute GoDeep meta-registry patients with COPD and PH and available right heart catheterization at diagnosis. We investigated PH-targeted therapy prevalence and its association with transplant-free survival using diverse statistical methods, including Cox regression and subgroup analyses based on PH severity, comorbidities, and pulmonary function test results. Immortal time bias was addressed through a landmark approach.

RESULTS

As of December 2023, the GoDeep meta-registry included 26,981 patients (28% in PH group 1, 13% in PH group 2, 12% in PH group 3, 10% in PH group 4, 2% in PH group 5, 26% undefined, and 9% control participants). Of these, 836 patients had a diagnosis of COPD with PH and were included in this analysis, with median age of 66 years (59-73 years), FEV1 of 51% predicted (34%-69% predicted), mPAP of 35 mm Hg (28-44 mm Hg), PVR of 5 WU (4-8 WU), cardiac index of 2.5 L/min/m2 (2.0-2.9 L/min/m2), and mostly World Health Organization functional class III were included. Five-year transplant-free survival was 42%, significantly worse than in group 1 PH. A multivariable Cox proportional hazards model identified PVR, but not FEV1, as a major predictor of outcome. Four hundred eighteen patients (50%) received phosphodiesterase 5 inhibitor (PDE5i) therapy, which was associated with significantly reduced mortality: hazard ratio of 0.65 (0.57-0.75) for the entire cohort of patients with COPD and PH and of 0.83 (0.74-0.94) when performing landmark analysis. This PDE5i effect was reproduced robustly when performing subgroup analyses for patients with moderate to severe PH, various comorbidities, and supplemental oxygen requirement and when assessing the impact of unobserved confounders.

INTERPRETATION

Patients with COPD and PH exhibit poor transplant-free survival, with PVR being a predictor of mortality. In this meta-registry, PDE5i therapy was associated with a significant reduction in mortality across all tested models.

Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.

The role of different physical function tests for the prediction of fracture risk in older women

BACKGROUND

Physical function is an important risk factor for fracture. Previous studies found that different physical tests (e.g., one-leg standing [OLS] and timed up and go [TUG]) predict fracture risk. This study aimed to determine which physical function test is the most optimal independent predictor of fracture risk, together with clinical risk factors (CRFs) used in fracture risk assessment (FRAX) and bone mineral density (BMD).

METHODS

In total, 2321 women out of the included 3028 older women, aged 77.7 ± 1.6 (mean ± SD), in the Sahlgrenska University Hospital Prospective Evaluation of Risk of Bone Fractures study had complete data on all physical function tests and were included in the analysis. At baseline, hand grip strength, OLS, TUG, walking speed and chair stand tests were performed. All incident fractures were confirmed by X-ray or review of medical records and subsequently categorized as major osteoporotic fractures (MOFs), hip fractures and any fracture. Multivariate Cox regression (hazard ratios [HRs] and 95% confidence intervals [CIs]) analyses were performed with adjustments for age, body mass index (BMI), FRAX CRFs, femoral neck BMD and all physical function tests as predictors both individually and simultaneously. Receiver operating characteristic (ROC) analyses and Fine and Gray analyses were also performed to investigate associations between physical function and incident fractures.

RESULTS

OLS was the only physical function test to be significantly and independently associated with increased risk of any fracture (HR 1.13 [1.04-1.23]), MOF (HR 1.15 [1.04-1.26]) and hip fracture (HR 1.34 [1.11-1.62]). Adjusting for age, BMI, CRFs and femoral neck BMD did not materially alter these associations. ROC analysis for OLS, together with age, BMI, femoral neck BMD and CRFs, yielded area under the curve values of 0.642, 0.647 and 0.732 for any fracture, MOF and hip fracture, respectively. In analyses considering the competing risk of death, OLS was the only physical function test consistently associated with fracture outcomes (subhazard ratio [SHR] 1.10 [1.01-1.19] for any fracture, SHR 1.11 [1.00-1.22] for MOF and SHR 1.25 [1.03-1.50] for hip fracture). Walking speed was only independently associated with the risk of hip fracture in all Cox regression models and in the Fine and Gray analyses.

CONCLUSIONS

Among the five physical function tests, OLS was independently associated with all fracture outcomes, even after considering the competing risk of death, indicating that OLS is the most reliable physical function test for predicting fracture risk in older women.

Microvascular disease, modifiable risk factor profiles and incident arrhythmias in type 2 diabetes

BACKGROUND

To assess the roles of diabetic microvascular disease and modifiable risk factors and their combination in the development of arrhythmias.

METHODS

We included participants with type 2 diabetes (T2D) who were free of arrhythmias during recruitment in the UK Biobank study. The associations of microvascular disease states (defined by the presence of retinopathy, peripheral neuropathy or chronic kidney disease), four modifiable arrhythmic risk factors (body mass index, smoking, systolic blood pressure and glycosylated haemoglobin) and their joint associations with incident arrhythmias were examined.

RESULTS

Among the 25 632 participants with T2D, 1705 (20.1%) of the 8482 with microvascular disease and 2017 (11.8%) of the 17 150 without microvascular disease developed arrhythmias during a median follow-up of 12.3 years. Having any of the three microvascular diseases was associated with a 48% increase in the hazard of developing arrhythmias. Incorporating microvascular disease states into a model alongside 11 traditional risk factors significantly enhanced arrhythmia prediction. Furthermore, individuals with microvascular disease who had optimal levels of zero to one, two, three or four arrhythmic risk factors showed an HR of 2.05 (95% CI 1.85, 2.27), 1.67 (95% CI 1.53, 1.83), 1.35 (95% CI 1.22, 1.50) and 0.91 (95% CI 0.73, 1.13), respectively, compared with those without microvascular disease.

CONCLUSIONS

Although microvascular disease, a non-traditional risk factor, was associated with incident arrhythmias in individuals with T2D, having optimal levels of risk factors may mitigate this risk.

Risk factor control and incident cardiovascular disease in patients with diabetes: Sex-specific relations

AIM

Women with diabetes are at higher risk of cardiovascular diseases (CVD) than men with diabetes; however, the sex difference in the association between the degree of risk factor control and the risk of CVD in patients with diabetes is unclear.

METHODS

In total, 17 260 participants diagnosed with diabetes from the UK Biobank were included and matched with 86 300 non-diabetes controls based on age, sex and assessment centre. The main exposure was the number of risk factors within the target range, including glycated haemoglobin level <53 mol/mol (7%), blood pressure <140/90 mm/Hg, low-density lipoprotein cholesterol <100 mg/dl, non-current smoking and absence of microalbuminuria.

RESULTS

During a median follow-up of 13.3 years, a total of 3338 incident CVD cases, including 2807 ischaemic heart disease and 793 strokes, were documented. A more stringent control of risk factors was significantly associated with a lower risk of incident CVD, and such an association was significantly stronger in women than men. Compared with non-diabetes participants, the diabetes-related risk of CVD appeared to be eliminated if more than three risk factors were well controlled among women and men with diabetes. Moreover, clinical biomarkers (e.g. glycated haemoglobin and blood pressure) showed greater relative importance than other factors in women, whereas socio-economic and psychological factors (e.g. education and depression) exhibited similar relative importance to clinical biomarkers in men with diabetes.

CONCLUSION

Our findings highlighted the importance of raising awareness of sex differences in the management of CVD risk factors among patients with diabetes.

MASH: MEDIATION ANALYSIS OF SURVIVAL OUTCOME AND HIGH-DIMENSIONAL OMICS MEDIATORS WITH APPLICATION TO COMPLEX DISEASES

Environmental exposures such as cigarette smoking influence health outcomes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for investigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. However, little work has been done on mediation analysis when the mediators are high-dimensional and the outcome is a survival endpoint, and none of it has provided a robust measure of total mediation effect. To this end, we propose an estimation procedure for Mediation Analysis of Survival outcome and High-dimensional omics mediators (MASH) based on sure independence screening for putative mediator variable selection and a second-moment-based measure of total mediation effect for survival data analogous to the R 2 measure in a linear model. Extensive simulations showed good performance of MASH in estimating the total mediation effect and identifying true mediators. By applying MASH to the metabolomics data of 1919 subjects in the Framingham Heart Study, we identified five metabolites as mediators of the effect of cigarette smoking on coronary heart disease risk (total mediation effect, 51.1%) and two metabolites as mediators between smoking and risk of cancer (total mediation effect, 50.7%). Application of MASH to a diffuse large B-cell lymphoma genomics data set identified copy-number variations for eight genes as mediators between the baseline International Prognostic Index score and overall survival.

Degree of joint risk factor control and hazard of mortality in diabetes patients: a matched cohort study in UK Biobank

BACKGROUND

Diabetes patients are at higher risk for mortality than the general population; however, little is known about whether the excess mortality risk associated with diabetes could be mitigated or nullified via controlling for risk factors.

METHODS

We included 18,535 diabetes patients and 91,745 matched individuals without diabetes without baseline cancer or cardiovascular disease (CVD), followed up from 2006 to 2021. The main exposure was the number of optimized risk factors including glycated hemoglobin < 53 mmol/mole, systolic blood pressure < 140 mmHg and diastolic blood pressure < 90 mmHg, no albuminuria, non-current smoking and low-density lipoprotein cholesterol (LDL-C) < 2.5 mmol/L. We used Cox proportional hazards models to explore the association of the degree of risk factor control with all-cause mortality, cancer mortality, CVD mortality and other mortality.

RESULTS

Each additional risk factor control was associated with a 16, 10, 21 and 15% lower risk of all-cause mortality, cancer mortality, CVD mortality and other mortality, respectively. Optimal risk factors control (controlling 5 risk factors) was associated with a 50% (HR 0.50, 95% CI 0.41-0.62), 74% (HR 0.26, 95% CI 0.16-0.43) and 38% (HR 0.62, 95% CI 0.44-0.87) lower risk of all-cause mortality, CVD mortality and other mortality, respectively. Diabetes patients with 4, 3 and 5 or more controlled risk factors, respectively, showed no excess risk of all-cause mortality, cancer mortality and CVD mortality compared to matched non-diabetes patients.

CONCLUSIONS

The results from this study indicate that optimal risk factor control may eliminate diabetes-related excess risk of all-cause mortality, CVD mortality and other mortality.

Improvement of Social Isolation and Loneliness and Excess Mortality Risk in People With Obesity

Importance

Individuals with obesity experience markedly higher levels of social isolation and loneliness than those without obesity, but little is known about whether improvement of social isolation or loneliness might attenuate obesity-related excess risk of mortality.

Objective

To investigate whether improvement of social isolation or loneliness is associated with lower obesity-related excess risk of mortality.

Design, Setting, and Participants

This cohort study included individuals without cancer or cardiovascular disease (CVD) at baseline from the UK Biobank with follow-up beginning in March 2006 and ending in November 2021.

Main Outcomes and Measures

All-cause, cancer-related, and CVD-related mortality were estimated.

Results

A total of 398 972 participants were included in this study (mean [SD] age, 55.85 [8.08] years; 220 469 [55.26%] women; 13 734 [3.44%] Asian, 14 179 [3.55%] multiracial, and 363 685 [91.16%] White participants). Overall, 93 357 (23.40%) had obesity, and 305 615 (76.60%) did not. During a median (IQR) follow-up of 12.73 (12.01-13.43) years, a total of 22 872 incident deaths were recorded. Compared with participants with obesity with an index of 2 or greater for social isolation, the multivariable adjusted hazard ratios (HRs) for all-cause mortality were 0.85 (95% CI, 0.79-0.91) and 0.74 (95% CI, 0.69-0.80) for participants with obesity and a social isolation index of 1 and 0, respectively (P for trend < .001); compared with participants with obesity and an index of 2 for loneliness, the HRs and 0.97 (95% CI, 0.89-1.06) and 0.86 (95% CI, 0.79-0.94) for participants with obesity and a loneliness index of 1 and 0, respectively (P for trend < .001). As the index of social isolation and loneliness went from highest to lowest, the HR for all-cause mortality decreased by 36% and 9%, respectively, in people with obesity compared with people without obesity using the multivariable model. Social isolation was ranked higher than loneliness, depression, anxiety, and lifestyle-related risk factors including alcohol, physical activity, and healthy diet for estimating the risks of all-cause mortality, cancer-related mortality, and CVD-related mortality.

Conclusions and Relevance

In this cohort study of UK Biobank participants, a lower index of social isolation or loneliness was associated with a decreased risk of all-cause mortality among people with obesity, and improvement of social isolation and loneliness attenuated obesity-related excess risk of all-cause mortality.

Accelerated and Interpretable Oblique Random Survival Forests

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Trees in the oblique RSF are grown using linear combinations of predictors, whereas in the standard RSF, a single predictor is used. Oblique RSF ensembles have high prediction accuracy, but assessing many linear combinations of predictors induces high computational overhead. In addition, few methods have been developed for estimation of variable importance (VI) with oblique RSFs. We introduce a method to increase computational efficiency of the oblique RSF and a method to estimate VI with the oblique RSF. Our computational approach uses Newton-Raphson scoring in each non-leaf node, We estimate VI by negating each coefficient used for a given predictor in linear combinations, and then computing the reduction in out-of-bag accuracy. In benchmarking experiments, we find our implementation of the oblique RSF is hundreds of times faster, with equivalent prediction accuracy, compared to existing software for oblique RSFs. We find in simulation studies that "negation VI" discriminates between relevant and irrelevant numeric predictors more accurately than permutation VI, Shapley VI, and a technique to measure VI using analysis of variance. All oblique RSF methods in the current study are available in the aorsf R package, and additional supplemental materials are available online.

Comparative Effectiveness of PET and SPECT MPI for Predicting Cardiovascular Events After Kidney Transplant

BACKGROUND

Advanced chronic kidney disease is associated with high cardiovascular risk, even after kidney transplant. Pretransplant cardiac testing may identify patients who require additional assessment before transplant or would benefit from risk optimization. The objective of the current study was to determine the relative prognostic utility of pretransplant positron emission tomography (PET) and single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) for posttransplant major adverse cardiovascular events (MACEs).

METHODS

We retrospectively followed patients who underwent MPI before kidney transplant for the occurrence of MACE after transplant including myocardial infarction, stroke, heart failure, and cardiac death. An abnormal MPI result was defined as a total perfusion deficit >5% of the myocardium. To determine associations of MPI results with MACE, we utilized Cox hazard regression with propensity weighting for PET versus SPECT with model factors, including demographics and cardiovascular risk factors.

RESULTS

A total of 393 patients underwent MPI (208 PET and 185 SPECT) and were followed for a median of 5.9 years post-transplant. Most were male (58%), median age was 58 years, and there was a high burden of hypertension (88%) and diabetes (33%). A minority had abnormal MPI (n=58, 15%). In propensity-weighted hazard regression, abnormal PET result was associated with posttransplant MACE (hazard ratio, 3.02 [95% CI, 1.78-5.11]; P<0.001), while there was insufficient evidence of an association of abnormal SPECT result with MACE (1.39 [95% CI, 0.72-2.66]; P=0.33). The explained relative risk of the PET result was higher than the SPECT result (R2 0.086 versus 0.007). Normal PET was associated with the lowest risk of MACE (2.2%/year versus 3.6%/year for normal SPECT; P<0.001).

CONCLUSIONS

Kidney transplant recipients are at high cardiovascular risk, despite a minority having obstructive coronary artery disease on MPI. PET MPI findings predict posttransplant MACE. Normal PET may better discriminate lower risk patients compared with normal SPECT, which should be confirmed in a larger prospective study.

Genetic, sociodemographic, lifestyle, and clinical risk factors of recurrent coronary artery disease events: a population-based cohort study

AIMS

Complications of coronary artery disease (CAD) represent the leading cause of death among adults globally. This study examined the associations and clinical utilities of genetic, sociodemographic, lifestyle, and clinical risk factors on CAD recurrence.

METHODS AND RESULTS

Data were from 7024 UK Biobank middle-aged adults with established CAD at enrolment. Cox proportional hazards regressions modelled associations of age at enrolment, age at first CAD diagnosis, sex, cigarette smoking, physical activity, diet, sleep, Townsend Deprivation Index, body mass index, blood pressure, blood lipids, glucose, lipoprotein(a), C reactive protein, estimated glomerular filtration rate (eGFR), statin prescription, and CAD polygenic risk score (PRS) with first post-enrolment CAD recurrence. Over a median [interquartile range] follow-up of 11.6 [7.2-12.7] years, 2003 (28.5%) recurrent CAD events occurred. The hazard ratio (95% confidence interval [CI]) for CAD recurrence was the most pronounced with current smoking (1.35, 1.13-1.61) and per standard deviation increase in age at first CAD (0.74, 0.67-0.82). Additionally, age at enrolment, CAD PRS, C-reactive protein, lipoprotein(a), glucose, low-density lipoprotein cholesterol, deprivation, sleep quality, eGFR, and high-density lipoprotein (HDL) cholesterol also significantly associated with recurrence risk. Based on C indices (95% CI), the strongest predictors were CAD PRS (0.58, 0.57-0.59), HDL cholesterol (0.57, 0.57-0.58), and age at initial CAD event (0.57, 0.56-0.57). In addition to traditional risk factors, a comprehensive model improved the C index from 0.644 (0.632-0.654) to 0.676 (0.667-0.686).

CONCLUSION

Sociodemographic, clinical, and laboratory factors are each associated with CAD recurrence with genetic risk, age at first CAD event, and HDL cholesterol concentration explaining the most.

Joint association of loneliness and traditional risk factor control and incident cardiovascular disease in diabetes patients

AIMS

To investigate the prospective associations of the loneliness and social isolation scales with cardiovascular disease (CVD) risk in diabetes patients and compare the relative importance of loneliness and social isolation with traditional risk factors. Also, the interactions of loneliness or isolation with the degree of risk factor control in relation to CVD risk were evaluated.

METHODS AND RESULTS

A total of 18 509 participants diagnosed with diabetes from the UK Biobank were included. A two-item scale and a three-item scale were used to assess loneliness and isolation levels, respectively. The degree of risk factor control was defined as numbers of glycated hemoglobin (HbA1c), blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), smoking, and kidney condition controlled within the target range. During a mean follow-up of 10.7 years, 3247 total CVD incidents were documented, including 2771 coronary heart disease and 701 strokes. In the fully adjusted model, compared with participants with the lowest loneliness score (zero), hazard ratios (95% confidence interval) for CVD were 1.11 (1.02 and 1.20) and 1.26 (1.11 and 1.42) for participants with a loneliness scale of 1 and 2, respectively (P-trend < 0.001). No significant associations were observed for social isolation. Loneliness ranked higher in relative strength for predicting CVD than the lifestyle risk factors in diabetes patients. A significant additive interaction between loneliness and the degree of risk factor control on the risk of CVD was observed (P for additive interaction = 0.005).

CONCLUSION

Among diabetes patients, loneliness, but not social isolation scale, is associated with a higher risk of CVD and shows an additive interaction with the degree of risk factor control.

Estimated glucose disposal rate is associated with retinopathy and kidney disease in young people with type 1 diabetes: a nationwide observational study

AIMS

The aim of this study was to investigate the association between estimated glucose disposal rate (eGDR), a proxy for insulin resistance, and retinopathy or kidney disease, i.e. micro-, or macroalbuminuria, in young individuals with type 1 diabetes (T1D).

MATERIAL AND METHODS

Using data from the Swedish pediatric registry for diabetes (SweDiabKids) and the registry for adults (NDR), all individuals with T1D with a duration of diabetes of less than 10 years between 1998 and 2017 were included. We calculated the crude incidence rates with 95% confidence intervals (CIs) and used multivariable Cox regression to estimate crude and adjusted hazard ratios (HRs) for two cohorts: retinopathy cohort or kidney disease cohort, stratified by eGDR categories: < 4, 4 to 5.99, 6 to 7.99, and ≥ 8 mg/kg/min (reference).

RESULTS

A total of 22 146 (10 289 retinopathy cohort, and 11 857 kidney disease cohort with an overlapping of 9575) children and adults with T1D (median age 21 years, female 42% and diabetes duration of 6 and 7 years, respectively for the cohorts) were studied. During a median follow-up of 4.8 years (IQR 2.6-7.7) there were 5040 (24.7%), 1909 (48.1%), 504 (52.3%) and 179 (57.6%) events for retinopathy in individuals with an eGDR ≥ 8, 7.99 to 6, 5.99 to 4, and < 4 mg/kg/min, respectively. Corresponding numbers for kidney disease was 1321 (6.5%), 526 (13.3%), 255 (26.8%) and 145 (46.6%). After multiple adjustments for different covariates, individuals with an eGDR 7.99 to 6, 5.99 to 4 and < 4 mg/kg/min, had an increased risk of retinopathy compared to those with an eGDR ≥ 8 mg/kg/min (adjusted HRs, 95% CIs) 1.29 (1.20 to 1.40); 1.50 (1.31 to 1.71) and 1.74 (1.41 to 2.14). Corresponding numbers for kidney disease was (adjusted HRs, 95% CIs) 1.30 (1.11 to 1.52); 1.58 (1.25 to 1.99) and 1.33 (0.95 to 1.86), respectively.

CONCLUSIONS

eGDR, a proxy for insulin resistance, is associated with retinopathy and kidney disease in young adults with T1D. The risk of retinopathy increased with lower eGDR. The risk of kidney disease also increased with lower eGDR; however results show no association between the lowest eGDR and kidney disease. eGDR can be helpful to identify young T1D individuals at risk.

Platelet Reactivity and Cardiovascular Mortality Risk in the LURIC Study

BACKGROUND

The clinical and prognostic implications of platelet reactivity (PR) testing in a P2Y₁₂-inhibitor naïve population are poorly understood.

OBJECTIVES

This explorative study aims to assess the role of PR and explore factors that may modify elevated mortality risk in patients with altered PR.

METHODS

Platelet ADP-induced CD62P and CD63 expression were measured by flow-cytometry in 1520 patients who were referred for coronary angiography in the Ludwigshafen Risk and Cardiovascular Health Study (LURIC).

RESULTS

High- and Low-platelet reactivity to ADP were strong predictors of cardiovascular and all-cause mortality and risk equivalent to the presence of coronary artery disease. (High platelet reactivity 1.4 [95% CI 1.1-1.9]; Low platelet reactivity: 1.4 [95% CI 1.0-2.0]). Relative weight analysis indicated glucose control (HbA1c), renal function ([eGFR]), inflammation (high-sensitive C-reactive protein [hsCRP]) and antiplatelet therapy by Aspirin as consistent mortality risk modifiers in patients with Low- and High-platelet reactivity. Pre-specified stratification of patients by risk modifiers HbA1c (<7.0%), eGFR (>60 mL/min/1.73 m²) and CRP (<3 mg/L) was associated with a lower mortality risk, however irrespective of platelet reactivity. Aspirin treatment was associated with reduced mortality in patients with high platelet reactivity only (p for interaction: 0.02 for CV-death [<0.01 for all-cause mortality].

CONCLUSIONS

Cardiovascular mortality risk in patients with High- and Low platelet reactivity is equivalent to the presence of coronary artery disease. Targeted glucose control, improved kidney function and lower inflammation are associated with reduced mortality risk, however independent of platelet reactivity. In contrast, only in patients with High-platelet reactivity was Aspirin treatment associated with lower mortality.

The importance of targeting multiple risk markers in patients with type 2 diabetes: A post-hoc study from the CANVAS programme

AIMS

To investigate the extent to which improvements in multiple cardiovascular risk markers are associated with a lower risk of cardiovascular and kidney outcomes in patients with type 2 diabetes and high cardiovascular risk participating in the CANVAS programme.

MATERIALS AND METHODS

Clinically relevant improvements in cardiovascular risk factors were defined as a reduction in glycated haemoglobin ≥1.0%, systolic blood pressure ≥10 mmHg, body weight ≥3 kg, urinary-albumin-creatinine ratio ≥30%, uric acid ≥0.5 mg/dl, and an increase in haemoglobin of ≥1.0 g/dl from baseline to week 26. Participants were categorized according to the number of improvements in cardiovascular risk markers: zero, one, two, three, or four or more risk marker improvements. The Cox proportional hazard regression adjusted for treatment assignment, demographic variables and laboratory measurements was performed to determine the association between the number of risk marker improvements and risk of a composite cardiovascular, heart failure or kidney outcomes.

RESULTS

We included 9487 (93.5%) participants with available data at baseline and week 26. After week 26, 566 composite cardiovascular, 370 heart failure/cardiovascular death and 153 composite kidney outcomes occurred. The multivariable adjusted hazard ratios associated with four or more improvements in risk markers versus no risk marker improvement were 0.67 (95% CI 0.48, 0.92), 0.58 (95% CI 0.39, 0.87) and 0.49 (95% CI 0.25, 0.96) for the three outcomes respectively. We observed a trend of decreased hazard ratios across subgroups of increasing number of risk marker improvements (p for trend = .008, .02 and .047, respectively).

CONCLUSIONS

In patients with type 2 diabetes, improvements in multiple risk markers were associated with a reduced risk of cardiovascular and kidney outcomes as compared with no risk marker improvement.

Landmark mediation survival analysis using longitudinal surrogate

Clinical cancer trials are designed to collect radiographic measurements of each patient's baseline and residual tumor burden at regular intervals over the course of study. For solid tumors, the extent of reduction in tumor size following treatment is used as a measure of a drug's antitumor activity. Statistical estimation of treatment efficacy routinely reduce the longitudinal assessment of tumor burden to a binary outcome describing the presence versus absence of an objective tumor response as defined by RECIST criteria. The objective response rate (ORR) is the predominate method for evaluating an experimental therapy in a single-arm trial. Additionally, ORR is routinely compared against a control therapy in phase III randomized controlled trials. The longitudinal assessments of tumor burden are seldom integrated into a formal statistical model, nor integrated into mediation analysis to characterize the relationships among treatment, residual tumor burden, and survival. This article presents a frameworkfor landmark mediation survival analyses devised to incorporate longitudinal assessment of tumor burden. R ² effect-size measures are developed to quantify the survival treatment mediation effects using longitudinal predictors. Analyses are demonstrated with applications to two colorectal cancer trials. Survival prediction is compared in the presence versus absence of longitudinal analysis. Simulation studies elucidate settings wherein patterns of tumor burden dynamics require longitudinal analysis.

Prognostic Utility of Tumor Stage versus American Thyroid Association Risk Class in Thyroid Cancer

OBJECTIVE

To evaluate the prognostic strengths of American Joint Committee on Cancer (AJCC) staging and American Thyroid Association (ATA) risk classification in well-differentiated thyroid cancer (DTC), and their implications in guiding medical decision-making and epidemiological study designs.

METHODS

The 2004-2017 National Cancer Database was queried for DTC patients. Cox proportional hazards (CPH) and Kaplan-Meier analyses modeled patient mortality and overall survival, respectively. Each CPH model was evaluated by its concordance index, measure of explained randomness (MER), Akaike information criterion (AIC), and area under receiver operating characteristic curve (AUC).

RESULTS

Overall, 134,226 patients were analyzed, with an average age of 48.1 ± 15.1 years (76.9% female). Univariate CPH models using AJCC staging demonstrated higher concordance indices, MERs, and AUCs than those using ATA risk classification (all p < 0.001). Multivariable CPH models using AJCC staging demonstrated higher concordance indices (p = 0.049), MERs (p = 0.046), and AUCs (p = 0.002) than those using ATA risk classification. The AICs of multivariable AJCC staging and ATA risk models were 7.564 × 104 and 7.603 × 104 , respectively. AJCC stage I tumors were associated with greater overall survival than those classified as ATA low risk, whereas AJCC stages II-III and stage IV tumors demonstrated worse survival than ATA intermediate- and high-risk tumors, respectively (all p < 0.001).

CONCLUSION

AJCC staging may be a more predictive system for patient survival than ATA risk. The prognostic utility of these two systems converges when additional demographic and clinical factors are considered. AJCC staging was found to classify patients across a wider range of survival patterns than the ATA risk stratification system.

LEVEL OF EVIDENCE

4 Laryngoscope, 133:205-211, 2023.

Relative importance of potential risk factors for dementia in patients with hypertension

Patients with hypertension are at higher risk for dementia than the general population. We sought to understand the relative importance of various risk factors in the development of dementia among patients with hypertension. This population-based cohort study used data from the Korean National Insurance Service database. Using the Cox proportional hazard model, R2 values for each potential risk factor were calculated to test the relative importance of risk factors for the development of dementia. Eligible individuals were adults 40 to 79 years of age with hypertension and without a history of stroke and dementia between 2007 and 2009. A total of 650,476 individuals (mean age, 60 ± 11 years) with hypertension were included in the analyses. During a mean follow-up of 9.5 years (±2.8 years), 57,112 cases of dementia were observed. The three strongest predictors of dementia were age, comorbidity burden (assessed using the Charlson Comorbidity Index), and female sex (R2 values, 0.0504, 0.0023, and 0.0022, respectively). The next strongest risk factors were physical inactivity, smoking, alcohol consumption, and obesity (R2 values, 0.00070, 0.00024, 0.00021, and 0.00020, respectively). Across all age groups, physical inactivity was an important risk factor for dementia occurrence. In summary, controlling and preventing comorbidities are of utmost importance to prevent dementia in patients with hypertension. More efforts should be taken to encourage physical activity among patients with hypertension across all age groups. Furthermore, smoking cessation, avoiding and limiting alcohol consumption, and maintaining an appropriate body weight are urged to prevent dementia.

Risk of fracture in adults with type 2 diabetes in Sweden: A national cohort study

BACKGROUND

Type 2 diabetes mellitus (T2DM) is considered a risk factor for fracture but the evidence regarding the impact of T2DM on fracture risk is conflicting. The objective of the study was to determine if patients with T2DM have increased fracture risk and if T2DM-related risk factors could be identified.

METHODS AND FINDINGS

In this national cohort study in Sweden, we investigated the risk of fracture in 580,127 T2DM patients, identified through the national diabetes register including from both primary care and hospitals, and an equal number of population-based controls without diabetes matched for age, sex, and county from 2007 to 2017. The mean age at entry was 66.7 years and 43.6% were women. During a median follow-up time of 6.6 (interquartile range (IQR) 3.1 to 9.8) years, patients with T2DM had a marginally but significantly increased risk of major osteoporotic fracture (MOF) (hazard ratio (HR) 1.01 (95% confidence interval [CI] 1.00 to 1.03)) and hip fracture (HR 1.06 (95% CI 1.04 to 1.08)) compared to controls, associations that were only minimally affected (HR 1.05 (95% CI 1.03 to 1.06) and HR 1.11 (95% CI 1.09 to 1.14), respectively) by multivariable adjustment (age, sex, marital status, and an additional 20 variables related to general morbidity, cardiovascular status, risk of falls, and fracture). In a multivariable-adjusted Cox model, the proportion of the risk for all fracture outcomes (Heller's R2) explained by T2DM was below 0.1%. Among the T2DM patients, important risk factors for fracture were a low BMI (<25 kg/m2), long diabetes duration (≥15 years), insulin treatment, and low physical activity. In total, 55% of the T2DM patients had none of these risk factors and a significantly lower fracture risk than their respective controls. The relatively short mean duration of T2DM and lack of bone density data, constitute limitations of the analysis.

CONCLUSION

In this study, we observed only a marginally increased fracture risk in T2DM, a condition that explained less than 0.1% of the fracture risk. Consideration of the herein identified T2DM-related risk factors could be used to stratify T2DM patients according to fracture risk.

Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study

Background

There is an increasing trend of Metabolic syndrome (MetS) prevalence, which has been considered as an important contributor for cardiovascular disease (CVD), cancers and diabetes. However, there is often a long asymptomatic phase of MetS, resulting in not diagnosed and intervened so timely as needed. It would be very helpful to explore tools to predict the probability of suffering from MetS in daily life or routinely clinical practice.

Objective

To develop models that predict individuals' probability of suffering from MetS timely with high efficacy in general population.

Methods

The present study enrolled 8964 individuals aged 40-75 years without severe diseases, which was a part of the REACTION study from October 2011 to February 2012. We developed three prediction models for different scenarios in hospital (Model 1, 2) or at home (Model 3) based on LightGBM (LGBM) technique and corresponding logistic regression (LR) models were also constructed for comparison. Model 1 included variables of laboratory tests, lifestyles and anthropometric measurements while model 2 was built with components of MetS excluded based on model 1, and model 3 was constructed with blood biochemical indexes removed based on model 2. Additionally, we also investigated the strength of association between the predictive factors and MetS, as well as that between the predictors and each component of MetS.

Results

In this study, 2714 (30.3%) participants suffer from MetS accordingly. The performances of the LGBM models in predicting the probability of suffering from MetS produced good results and were presented as follows: model 1 had an area under the curve (AUC) value of 0.993 while model 2 indicated an AUC value of 0.885. Model 3 had an AUC value of 0.859, which is close to that of model 2. The AUC values of LR model 1 and 2 for the scenario in hospital and model 3 at home were 0.938, 0.839 and 0.820 respectively, which seemed lower than that of their corresponding machine learning models, respectively. In both LGBM and logistic models, gender, height and resting pulse rate (RPR) were predictors for MetS. Women had higher risk of MetS than men (OR 8.84, CI: 6.70-11.66), and each 1-cm increase in height indicated 3.8% higher risk of suffering from MetS in people over 58 years, whereas each 1- Beat Per Minute (bpm) increase in RPR showed 1.0% higher risk in individuals younger than 62 years.

Conclusion

The present study showed that the prediction models developed by machine learning demonstrated effective in evaluating the probability of suffering from MetS, and presented prominent predicting efficacies and accuracies. Additionally, we found that women showed a higher risk of MetS than men, and height in individuals over 58 years was important factor in predicting the probability of suffering from MetS while RPR was of vital importance in people aged 40-62 years.

Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24

BACKGROUND

We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.

MATERIALS AND METHODS

Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.

RESULTS

CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.

CONCLUSION

We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

The importance of addressing multiple risk markers in type 2 diabetes: Results from the LEADER and SUSTAIN 6 trials

AIMS

To investigate to what extent multiple risk marker improvements confer lower risk of cardiovascular and kidney complications in a contemporary type 2 diabetes population.

MATERIALS AND METHODS

Post-hoc analysis of the LEADER (n = 8638; median follow-up 3.8 years) and SUSTAIN 6 (n = 3040; median follow-up 2.1 years) cardiovascular outcome trials. Participants were those with baseline and year-1 assessment of at least one of the parameters of interest; we pooled the liraglutide-/semaglutide- and placebo-treated groups and categorized them by number of risk markers with clinically relevant improvements after 1 year of study participation. We investigated risk of major adverse cardiovascular events (MACE), expanded MACE, cardiovascular death and nephropathy. Predefined clinically relevant changes: body weight loss ≥5%; reductions in: glycated haemoglobin ≥1%, systolic blood pressure ≥5 mmHg and low-density lipoprotein cholesterol ≥0.5 mmol/L; estimated glomerular filtration rate change ≥0 ml/min/1.73 m² and urinary albumin-to-creatinine ratio change ≥30% of baseline value. Cox regression analysed risk of outcomes adjusted for baseline risk marker levels and treatment group and stratified by trial.

RESULTS

Participants with two, three, or four or more improved risk markers versus participants with no risk marker improvement had reduced risk of expanded MACE [hazard ratio (95% confidence interval) 0.80 (0.67-0.96); 0.80 (0.66-0.97); 0.82 (0.66-1.02)], cardiovascular death [0.66 (0.45-0.96), 0.67 (0.45-0.99), 0.60 (0.38-0.94)] and nephropathy [0.71 (0.52-0.97), 0.48 (0.34-0.68), 0.43 (0.29-0.65)].

CONCLUSIONS

In persons with type 2 diabetes, improvements in ≥2 risk markers conferred cardiovascular risk reduction versus none or one improved risk marker. The nephropathy risk decreased with improvement in more risk markers. These findings stress the importance of multifactorial interventions targeting all risk markers.

Risk Factors for Hospital Readmission and Death After Discharge of Older Adults from Acute Geriatric Units: Taking the Rank of Admission into Account

Objective

To analyze the impact of the number of hospital readmissions on the risks of further hospital readmission and death after adjustment for a range of risk factors.

Methods

We performed a multicentre prospective study of the DAMAGE cohort in the Hauts-de-France region of France. Patients aged 75 and over hospitalized initially in an acute geriatric unit (AGU) were included and followed up for 12 months. The risk of hospital readmission was analyzed using a Cox model, and its extension for recurrent events and the risk of death were analyzed using a Cox model for time-dependent variables.

Results

A total of 3081 patients were included (mean (SD) age: 86.4 (5.5)). In the multivariate analysis, the relative risk (95% confidence interval [CI]) of hospital readmission rose progressively to 2.66 (1.44; 5.14), and the risk of death rose to 2.01 (1.23; 3.32) after five hospital admissions, relative to a patient with no hospital readmissions. The number of hospital readmissions during the follow-up period was the primary risk factor and the best predictor of the risk of hospital readmission and the risk of death.

Conclusion

Hospital readmission is the primary risk factor for further hospital readmissions and for death in older subjects discharged from an AGU.

Fracture Incidence and the Relevance of Dietary and Lifestyle Factors Differ in the United Kingdom and Hong Kong: An International Comparison of Longitudinal Cohort Study Data

Geographic variation in fracture risk may be due to divergent profiles of dietary, lifestyle, and other risk factors between populations. We investigated differences in fracture rates between two older-population cohorts: the European Prospective Investigation into Cancer and Nutrition (EPIC) Norfolk cohort (n = 7732) in the United Kingdom (UK), and the Mr and Ms Os cohort (n = 3956) in Hong Kong (HK). Data were collected by questionnaires, laboratory assessments, and hospital records. Incidence of hip, spine, and wrist fractures in the two cohorts was calculated and multivariable regression was used to explore variables important to fracture risk. Total hip, spine, and wrist fracture incidence was higher in the UK vs HK for women (13.70 vs 8.76 per 1000 person-years; p < 0.001), but not men (5.95 vs 5.37 per 1000 person-years; p = 0.337), and the proportions of different fractures also varied between cohorts (p < 0.001). Hip fracture was the most common UK fracture (accounting for 56.8% fractures in men and 52.6% in women), while wrist fracture was most common in HK (42.9% in men and 57.9% in women). The major contributor to total fracture risk in multivariable regression models of both cohorts and sexes, was age; with BMI also an important contributor to fracture risk HK men and UK women. The distribution of factors relevant to fracture risk, and the rates of different fractures, varied significantly between UK and HK cohorts. However, the importance of each factor in contributing to fracture risk was similar between the cohorts. The differences in fracture rates suggest targeted approaches may be required when developing interventions and public health recommendations to reduce the burden of osteoporosis in these two countries.

Estimated glucose disposal rate and risk of stroke and mortality in type 2 diabetes: a nationwide cohort study

Background and aims

Insulin resistance contributes to the development of type 2 diabetes (T2D) and is also a cardiovascular risk factor. The aim of this study was to investigate the potential association between insulin resistance measured by estimated glucose disposal rate (eGDR) and risk of stroke and mortality thereof in people with T2D.

Materials and methods

Nationwide population based observational cohort study that included all T2D patients from the Swedish national diabetes registry between 2004 and 2016 with full data on eGDR and categorised as following: < 4, 4–6, 6–8, and ≥ 8 mg/kg/min. We calculated crude incidence rates and 95% confidence intervals (CIs) and used multiple Cox regression to estimate hazard ratios (HRs) to assess the association between the risk of stroke and death, according to the eGDR categories in which the lowest category < 4 (i.e., highest grade of insulin resistance), served as a reference. The relative importance attributed of each factor in the eGDR formula was measured by the R² (± SE) values calculating the explainable log-likelihoods in the Cox regression.

Results

A total of 104 697 T2D individuals, 44.5% women, mean age of 63 years, were included. During a median follow up-time of 5.6 years, 4201 strokes occurred (4.0%). After multivariate adjustment the HRs (95% CI) for stroke in patients with eGDR categories between 4–6, 6–8 and > 8 were: 0.77 (0.69–0.87), 0.68 (0.58–0.80) and 0.60 (0.48–0.76), compared to the reference < 4. Corresponding numbers for the risk of death were: 0.82 (0.70–0.94), 0.75 (0.64–0.88) and 0.68 (0.53–0.89). The attributed relative risk R² (± SE) for each variable in the eGDR formula and stroke was for: hypertension (0.045 ± 0.0024), HbA1c (0.013 ± 0.0014), and waist (0.006 ± 0.0009), respectively.

Conclusion

A low eGDR (a measure of insulin resistance) is associated with an increased risk of stroke and death in individuals with T2D. The relative attributed risk was most important for hypertension.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01394-4.

Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer

BACKGROUND

Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).

MATERIALS AND METHOD

180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.

RESULTS

166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.

CONCLUSIONS

Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Severe COVID-19 in people with type 1 and type 2 diabetes in Sweden: A nationwide retrospective cohort study

BACKGROUND

Whether infection with SARS-CoV-2 leads to excess risk of requiring hospitalization or intensive care in persons with diabetes has not been reported, nor have risk factors in diabetes associated with increased risk for these outcomes.

METHODS

We included 44,639 and 411,976 adult patients with type 1 and type 2 diabetes alive on Jan 1, 2020, and compared them to controls matched for age, sex, and county of residence (n=204,919 and 1,948,900). Age- and sex-standardized rates for COVID-19 related hospitalizations, admissions to intensive care and death, were estimated and hazard ratios were calculated using Cox regression analyses.

FINDINGS

There were 10,486 hospitalizations and 1,416 admissions into intensive care. A total of 1,175 patients with diabetes and 1,820 matched controls died from COVID-19, of these 53•2% had been hospitalized and 10•7% had been in intensive care. Patients with type 2 diabetes, compared to controls, displayed an age- and sex-adjusted hazard ratio (HR) of 2•22, 95%CI 2•13-2•32) of being hospitalized for COVID-19, which decreased to HR 1•40, 95%CI 1•34-1•47) after further adjustment for sociodemographic factors, pharmacological treatment and comorbidities, had higher risk for admission to ICU due to COVID-19 (age- and sex-adjusted HR 2•49, 95%CI 2•22-2•79, decreasing to 1•42, 95%CI 1•25-1•62 after adjustment, and increased risk for death due to COVID-19 (age- and sex-adjusted HR 2•19, 95%CI 2•03-2•36, complete adjustment 1•50, 95%CI 1•39-1•63). Age- and sex-adjusted HR for COVID-19 hospitalization for type 1 diabetes was 2•10, 95%CI 1•72-2•57), decreasing to 1•25, 95%CI 0•3097-1•62) after adjustment• Patients with diabetes type 1 were twice as likely to require intensive care for COVID-19, however, not after adjustment (HR 1•49, 95%CI 0•75-2•92), and more likely to die (HR 2•90, 95% CI 1•6554-5•47) from COVID-19, but not independently of other factors (HR 1•38, 95% CI 0•64-2•99). Among patients with diabetes, elevated glycated hemoglobin levels were associated with higher risk for most outcomes.

INTERPRETATION

In this nationwide study, type 2 diabetes was independently associated with increased risk of hospitalization, admission to intensive care and death for COVID-19. There were few admissions into intensive care and deaths in type 1 diabetes, and although hazards were significantly raised for all three outcomes, there was no independent risk persisting after adjustment for confounding factors.

Impact of facility type and volume in low-grade glioma outcomes

OBJECTIVE

The object of this study was to investigate the impact of facility type (academic center [AC] vs non-AC) and facility volume (high-volume facility [HVF] vs low-volume facility [LVF]) on low-grade glioma (LGG) outcomes.

METHODS

This retrospective cohort study included 5539 LGG patients (2004-2014) from the National Cancer Database. Patients were categorized by facility type and volume (non-AC vs AC, HVF vs LVF). An HVF was defined as the top 1% of facilities according to the number of annual cases. Outcomes included overall survival, treatment receipt, and postoperative outcomes. Kaplan-Meier and Cox proportional-hazards models were applied. The Heller explained relative risk was computed to assess the relative importance of each survival predictor.

RESULTS

Significant survival advantages were observed at HVFs (HR 0.67, 95% CI 0.55-0.82, p < 0.001) and ACs (HR 0.84, 95% CI 0.73-0.97, p = 0.015), both prior to and after adjusting for all covariates. Tumor resection was 41% and 26% more likely to be performed at HVFs vs LVFs and ACs vs non-ACs, respectively. Chemotherapy was 40% and 88% more frequently to be utilized at HVFs vs LVFs and ACs vs non-ACs, respectively. Prolonged length of stay (LOS) was decreased by 42% and 24% at HVFs and ACs, respectively. After tumor histology, tumor pattern, and codeletion of 1p19q, facility type and surgical procedure were the most important contributors to survival variance. The main findings remained consistent using propensity score matching and multiple imputation.

CONCLUSIONS

This study provides evidence of survival benefits among LGG patients treated at HVFs and ACs. An increased likelihood of undergoing resections, receiving adjuvant therapies, having shorter LOSs, and the multidisciplinary environment typically found at ACs and HVFs are important contributors to the authors' finding.

A national observation study of cancer incidence and mortality risks in type 2 diabetes compared to the background population over time

We examined changing patterns in cancer incidence and deaths in diabetes compared to the background population. A total of 457,473 patients with type 2 diabetes, included between 1998 and 2014, were matched on age, sex, and county to five controls from the population. Incidence, trends in incidence and post-cancer mortality for cancer were estimated with Cox regression and standardised incidence rates. Causes of death were estimated using logistic regression. Relative importance of risk factors was estimated using Heller’s relative importance model. Type 2 diabetes had a higher risk for all cancer, HR 1.10 (95% CI 1.09–1.12), with highest HRs for liver (3.31), pancreas (2.19) and uterine cancer (1.78). There were lesser increases in risk for breast (1.05) and colorectal cancers (1.20). Type 2 diabetes patients experienced a higher HR 1.23 (1.21–1.25) of overall post-cancer mortality and mortality from prostate, breast, and colorectal cancers. By the year 2030 cancer could become the most common cause of death in type 2 diabetes. Persons with type 2 diabetes are at greater risk of developing cancer and lower chance of surviving it. Notably, hazards for specific cancers (e.g. liver, pancreas) in type 2 patients cannot be explained by obesity alone.

Frailty Screening Using the Electronic Health Record Within a Medicare Accountable Care Organization

BACKGROUND

The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines for frailty management. However, there have been limited applications of EHR-based approaches in the United States.

METHODS

We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls.

RESULTS

The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI.

CONCLUSIONS

Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.

The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach

BACKGROUND

Prognostic modeling in health care has been predominantly statistical, despite a rapid growth of literature on machine-learning approaches in biological data analysis. We aim to assess the relative importance of variables in predicting overall survival among patients with non-small cell lung cancer using a Variable Importance (VIMP) approach in a machine-learning Random Survival Forest (RSF) model for posttreatment planning and follow-up.

METHODS

A total of 935 non-small cell lung cancer patients were randomly and equally divided into 2 training and testing cohorts in an RFS model. The prognostic variables included age, sex, race, the TNM Classification of Malignant Tumors (TNM) stage, smoking history, Eastern Cooperative Oncology Group performance status, histologic type, treatment category, maximum standard uptake value of whole-body tumor (SUVmaxWB), whole-body metabolic tumor volume (MTVwb), and Charlson Comorbidity Index. The VIMP was calculated using a permutation method in the RSF model. We further compared the VIMP of the RSF model to that of the standard Cox survival model. We examined the order of VIMP with the differential functional forms of the variables.

RESULTS

In both the RSF and the standard Cox models, the most important variables are treatment category, TNM stage, and MTVwb. The order of VIMP is more robust in RSF model than in Cox model regarding the differential functional forms of the variables.

CONCLUSIONS

The RSF VIMP approach can be applied alongside with the Cox model to further advance the understanding of the roles of prognostic factors, and improve prognostic precision and care efficiency.

Relative Prognostic Importance and Optimal Levels of Risk Factors for Mortality and Cardiovascular Outcomes in Type 1 Diabetes Mellitus

BACKGROUND

The strength of association and optimal levels for risk factors related to excess risk of death and cardiovascular outcomes in type 1 diabetes mellitus have been sparsely studied.

METHODS

In a national observational cohort study from the Swedish National Diabetes Register from 1998 to 2014, we assessed relative prognostic importance of 17 risk factors for death and cardiovascular outcomes in individuals with type 1 diabetes mellitus. We used Cox regression and machine learning analyses. In addition, we examined optimal cut point levels for glycohemoglobin, systolic blood pressure, and low-density lipoprotein cholesterol. Patients with type 1 diabetes mellitus were followed up until death or study end on December 31, 2013. The primary outcomes were death resulting from all causes, fatal/nonfatal acute myocardial infarction, fatal/nonfatal stroke, and hospitalization for heart failure.

RESULTS

Of 32 611 patients with type 1 diabetes mellitus, 1809 (5.5%) died during follow-up over 10.4 years. The strongest predictors for death and cardiovascular outcomes were glycohemoglobin, albuminuria, duration of diabetes mellitus, systolic blood pressure, and low-density lipoprotein cholesterol. Glycohemoglobin displayed ≈2% higher risk for each 1-mmol/mol increase (equating to ≈22% per 1% glycohemoglobin difference), whereas low-density lipoprotein cholesterol was associated with 35% to 50% greater risk for each 1-mmol/L increase. Microalbuminuria or macroalbuminuria was associated with 2 to 4 times greater risk for cardiovascular complications and death. Glycohemoglobin <53 mmol/mol (7.0%), systolic blood pressure <140 mm Hg, and low-density lipoprotein cholesterol <2.5 mmol/L were associated with significantly lower risk for outcomes observed.

CONCLUSIONS

Glycohemoglobin, albuminuria, duration of diabetes mellitus, systolic blood pressure, and low-density lipoprotein cholesterol appear to be the most important predictors for mortality and cardiovascular outcomes in patients with type 1 diabetes mellitus. Lower levels for glycohemoglobin, systolic blood pressure, and low-density lipoprotein cholesterol than contemporary guideline target levels appear to be associated with significantly lower risk for outcomes.

Regional Variation of Mortality in Heart Failure With Reduced and Preserved Ejection Fraction Across Asia: Outcomes in the ASIAN-HF Registry

Background

Data comparing outcomes in heart failure (HF) across Asia are limited. We examined regional variation in mortality among patients with HF enrolled in the ASIAN‐HF (Asian Sudden Cardiac Death in Heart Failure) registry with separate analyses for those with reduced ejection fraction (EF; <40%) versus preserved EF (≥50%).

Methods and Results

The ASIAN‐HF registry is a prospective longitudinal study. Participants with symptomatic HF were recruited from 46 secondary care centers in 3 Asian regions: South Asia (India), Southeast Asia (Thailand, Malaysia, Philippines, Indonesia, Singapore), and Northeast Asia (South Korea, Japan, Taiwan, Hong Kong, China). Overall, 6480 patients aged >18 years with symptomatic HF were recruited (mean age: 61.6±13.3 years; 27% women; 81% with HF and reduced rEF). The primary outcome was 1‐year all‐cause mortality. Striking regional variations in baseline characteristics and outcomes were observed. Regardless of HF type, Southeast Asians had the highest burden of comorbidities, particularly diabetes mellitus and chronic kidney disease, despite being younger than Northeast Asian participants. One‐year, crude, all‐cause mortality for the whole population was 9.6%, higher in patients with HF and reduced EF (10.6%) than in those with HF and preserved EF (5.4%). One‐year, all‐cause mortality was significantly higher in Southeast Asian patients (13.0%), compared with South Asian (7.5%) and Northeast Asian patients (7.4%; P<0.001). Well‐known predictors of death accounted for only 44.2% of the variation in risk of mortality.

Conclusions

This first multinational prospective study shows that the outcomes in Asian patients with both HF and reduced or preserved EF are poor overall and worst in Southeast Asian patients. Region‐specific risk factors and gaps in guideline‐directed therapy should be addressed to potentially improve outcomes.

Clinical Trial Registration

URL: https://www.clinicaltrials.gov/. Unique identifier: NCT01633398.

Risk Factors for Severe Liver Disease in Patients With Type 2 Diabetes

BACKGROUND & AIMS

Type 2 diabetes is a risk factor for development of cirrhosis and hepatocellular carcinoma. However, risk factors that identify persons with the highest risk for these outcomes are missing from unselected, population-based cohorts.

METHODS

The National Diabetes Register contains data on about 90% of persons in Sweden with type 2 diabetes. In this cohort study, persons with type 2 diabetes listed in the National Diabetes Register were compared with 5 individuals from the general population (controls), matched for age, sex, and county. In total, 406 770 persons with type 2 diabetes and 2 033 850 controls were included and followed for 21 596 934 person-years. We used population-based registers to determine the incidence of severe liver disease, defined as a diagnosis of hepatocellular carcinoma, cirrhosis, decompensation, liver failure and/or death due to liver disease during follow up. Cox regression was performed to estimate the risk of severe liver disease and to examine risk factors in persons with type 2 diabetes.

RESULTS

Risk for severe liver disease was increased in patients with type 2 diabetes compared to controls (hazard ratio, 2.28; 95% CI, 2.21-2.36). Risk factors associated with severe liver disease in persons with type 2 diabetes were higher age, male sex, hypertension, higher body mass index, lower glomerular filtration rate, microalbuminuria, and smoking. Statins were associated with a decreased risk of severe liver disease.

CONCLUSIONS

Persons with type 2 diabetes have an increased risk for severe liver disease. Knowledge of risk factors can be helpful in identifying persons with type 2 diabetes who have a high risk for severe liver disease.

Models estimating risk of hepatocellular carcinoma in patients with alcohol or NAFLD-related cirrhosis for risk stratification

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) risk varies dramatically in patients with cirrhosis according to well-described, readily available predictors. We aimed to develop simple models estimating HCC risk in patients with alcohol-related liver disease (ALD)-cirrhosis or non-alcoholic fatty liver disease (NAFLD)-cirrhosis and calculate the net benefit that would be derived by implementing HCC surveillance strategies based on HCC risk as predicted by our models.

METHODS

We identified 7,068 patients with NAFLD-cirrhosis and 16,175 with ALD-cirrhosis who received care in the Veterans Affairs (VA) healthcare system in 2012. We retrospectively followed them for the development of incident HCC until January 2018. We used Cox proportional hazards regression to develop and internally validate models predicting HCC risk using baseline characteristics at entry into the cohort in 2012. We plotted decision curves of net benefit against HCC screening thresholds.

RESULTS

We identified 1,278 incident cases of HCC during a mean follow-up period of 3.7 years. Mean annualized HCC incidence was 1.56% in NAFLD-cirrhosis and 1.44% in ALD-cirrhosis. The final models estimating HCC were developed separately for NAFLD-cirrhosis and ALD-cirrhosis and included 7 predictors: age, gender, diabetes, body mass index, platelet count, serum albumin and aspartate aminotransferase to √alanine aminotransferase ratio. The models exhibited very good measures of discrimination and calibration and an area under the receiver operating characteristic curve of 0.75 for NAFLD-cirrhosis and 0.76 for ALD-cirrhosis. Decision curves showed higher standardized net benefit of risk-based screening using our prediction models compared to the screen-all approach.

CONCLUSIONS

We developed simple models estimating HCC risk in patients with NAFLD-cirrhosis or ALD-cirrhosis, which are available as web-based tools (www.hccrisk.com). Risk stratification can be used to inform risk-based HCC surveillance strategies in individual patients or healthcare systems or to identify high-risk patients for clinical trials.

LAY SUMMARY

Patients with cirrhosis of the liver are at risk of getting hepatocellular carcinoma (HCC or liver cancer) and therefore it is recommended that they undergo surveillance for HCC. However, the risk of HCC varies dramatically in patients with cirrhosis, which has implications on if and how patients get surveillance, how providers counsel patients about the need for surveillance, and how healthcare systems approach and prioritize surveillance. We used readily available predictors to develop models estimating HCC risk in patients with cirrhosis, which are available as web-based tools at www.hccrisk.com.

Development of models estimating the risk of hepatocellular carcinoma after antiviral treatment for hepatitis C

BACKGROUND & AIMS

Most patients with hepatitis C virus (HCV) infection will undergo antiviral treatment with direct-acting antivirals (DAAs) and achieve sustained virologic response (SVR). We aimed to develop models estimating hepatocellular carcinoma (HCC) risk after antiviral treatment.

METHODS

We identified 45,810 patients who initiated antiviral treatment in the Veterans Affairs (VA) national healthcare system from 1/1/2009 to 12/31/2015, including 29,309 (64%) DAA-only regimens and 16,501 (36%) interferon ± DAA regimens. We retrospectively followed patients until 6/15/2017 to identify incident cases of HCC. We used Cox proportional hazards regression to develop and internally validate models predicting HCC risk using baseline characteristics at the time of antiviral treatment.

RESULTS

We identified 1,412 incident cases of HCC diagnosed at least 180 days after initiation of antiviral treatment during a mean follow-up of 2.5 years (range 1.0-7.5 years). Models predicting HCC risk after antiviral treatment were developed and validated separately for four subgroups of patients: cirrhosis/SVR, cirrhosis/no SVR, no cirrhosis/SVR, no cirrhosis/no SVR. Four predictors (age, platelet count, serum aspartate aminotransferase/√alanine aminotransferase ratio and albumin) accounted for most of the models' predictive value, with smaller contributions from sex, race-ethnicity, HCV genotype, body mass index, hemoglobin and serum alpha-fetoprotein. Fitted models were well-calibrated with very good measures of discrimination. Decision curves demonstrated higher net benefit of using model-based HCC risk estimates to determine whether to recommend screening or not compared to the screen-all or screen-none strategies.

CONCLUSIONS

We developed and internally validated models that estimate HCC risk following antiviral treatment. These models are available as web-based tools that can be used to inform risk-based HCC surveillance strategies in individual patients.

LAY SUMMARY

Most patients with hepatitis C virus have been treated or will be treated with direct-acting antivirals. It is important that we can model the risk of hepatocellular carcinoma in these patients, so that we develop the optimum screening strategy that avoids unnecessary screening, while adequately screening those at increased risk. Herein, we have developed and validated models that are available as web-based tools that can be used to guide screening strategies.

Risk Factors, Mortality, and Cardiovascular Outcomes in Patients with Type 2 Diabetes

BACKGROUND

Patients with diabetes are at higher risk for death and cardiovascular outcomes than the general population. We investigated whether the excess risk of death and cardiovascular events among patients with type 2 diabetes could be reduced or eliminated.

METHODS

In a cohort study, we included 271,174 patients with type 2 diabetes who were registered in the Swedish National Diabetes Register and matched them with 1,355,870 controls on the basis of age, sex, and county. We assessed patients with diabetes according to age categories and according to the presence of five risk factors (elevated glycated hemoglobin level, elevated low-density lipoprotein cholesterol level, albuminuria, smoking, and elevated blood pressure). Cox regression was used to study the excess risk of outcomes (death, acute myocardial infarction, stroke, and hospitalization for heart failure) associated with smoking and the number of variables outside target ranges. We also examined the relationship between various risk factors and cardiovascular outcomes.

RESULTS

The median follow-up among all the study participants was 5.7 years, during which 175,345 deaths occurred. Among patients with type 2 diabetes, the excess risk of outcomes decreased stepwise for each risk-factor variable within the target range. Among patients with diabetes who had all five variables within target ranges, the hazard ratio for death from any cause, as compared with controls, was 1.06 (95% confidence interval [CI], 1.00 to 1.12), the hazard ratio for acute myocardial infarction was 0.84 (95% CI, 0.75 to 0.93), and the hazard ratio for stroke was 0.95 (95% CI, 0.84 to 1.07). The risk of hospitalization for heart failure was consistently higher among patients with diabetes than among controls (hazard ratio, 1.45; 95% CI, 1.34 to 1.57). In patients with type 2 diabetes, a glycated hemoglobin level outside the target range was the strongest predictor of stroke and acute myocardial infarction; smoking was the strongest predictor of death.

CONCLUSIONS

Patients with type 2 diabetes who had five risk-factor variables within the target ranges appeared to have little or no excess risk of death, myocardial infarction, or stroke, as compared with the general population. (Funded by the Swedish Association of Local Authorities and Regions and others.).

A Study of R2 Measure under the Accelerated Failure Time Models

For right-censored data the accelerated failure time (AFT) model is an alternative to the commonly used proportional hazards regression model. It is a linear model for the (log-transformed) outcome of interest, and is particularly useful for censored outcomes that are not time-to-event, such as laboratory measurements. We provide a general and easily computable definition of the R² measure of explained variation under the AFT model for right-censored data. We study its behavior under different censoring scenarios and under different error distributions; in particular, we also study its robustness when the parametric error distribution is misspecified. Based on Monte Carlo investigation results, we recommend the log-normal distribution as a robust error distribution to be used in practice for the parametric AFT model when the R² measure is of interest. We apply our methodology to an alcohol consumption during pregnancy data set from Ukraine.

Prediction of new brain metastases after radiosurgery: validation and analysis of performance of a multi-institutional nomogram

Stereotactic radiosurgery (SRS) without whole brain radiotherapy (WBRT) for brain metastases can avoid WBRT toxicities, but with risk of subsequent distant brain failure (DBF). Sole use of number of metastases to triage patients may be an unrefined method. Data on 1354 patients treated with SRS monotherapy from 2000 to 2013 for new brain metastases was collected across eight academic centers. The cohort was divided into training and validation datasets and a prognostic model was developed for time to DBF. We then evaluated the discrimination and calibration of the model within the validation dataset, and confirmed its performance with an independent contemporary cohort. Number of metastases (≥8, HR 3.53 p = 0.0001), minimum margin dose (HR 1.07 p = 0.0033), and melanoma histology (HR 1.45, p = 0.0187) were associated with DBF. A prognostic index derived from the training dataset exhibited ability to discriminate patients' DBF risk within the validation dataset (c-index = 0.631) and Heller's explained relative risk (HERR) = 0.173 (SE = 0.048). Absolute number of metastases was evaluated for its ability to predict DBF in the derivation and validation datasets, and was inferior to the nomogram. A nomogram high-risk threshold yielding a 2.1-fold increased need for early WBRT was identified. Nomogram values also correlated to number of brain metastases at time of failure (r = 0.38, p < 0.0001). We present a multi-institutionally validated prognostic model and nomogram to predict risk of DBF and guide risk-stratification of patients who are appropriate candidates for radiosurgery versus upfront WBRT.

Three measures of explained variation for correlated survival data under the proportional hazards mixed-effects model

Measures of explained variation are useful in scientific research, as they quantify the amount of variation in an outcome variable of interest that is explained by one or more other variables. We develop such measures for correlated survival data, under the proportional hazards mixed-effects model. Because different approaches have been studied in the literature outside the classical linear regression model, we investigate three measures R(2) , Rres2, and ρ(2) that quantify three different population coefficients. We show that although the three population measures are not the same, they reflect similar amounts of variation explained by the predictors. Among the three measures, we show that R(2) , which is the simplest to compute, is also consistent for the first population measure under the usual asymptotic scenario when the number of clusters tends to infinity. The other two measures, on the other hand, all require that in addition the cluster sizes be large. We study the properties of the measures both analytically and through simulation studies. We illustrate their different usage on a multi-center clinical trial and a recurrent events data set. Copyright © 2016 John Wiley & Sons, Ltd.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

A Gene Selection Method for Survival Prediction in Diffuse Large B-Cell Lymphomas Patients using 1D Discrete Wavelet Transform

BACKGROUND

An important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. To deal with the high dimensionality of this data, use of a dimension reduction procedure along with the survival prediction model is necessary. This study aimed to present a new method based on wavelet transform for survival relevant gene selection.

METHODS

The data included 2042 gene expression measurements from 40 patients with Diffuse Large B-Cell Lymphomas (DLBCL). The pre-processing gene expression data is decomposed using third level of the 1D discrete wavelet transform. The detail coefficients at levels 1 and 2 are filtered out and expression data reconstructed using the approximation and detailed coefficients at the third level. All the genes are then scored based on the t score. Then genes with the highest scores are selected. By using forward selection method in Cox regression model, significant genes were identified.

RESULTS

The results showed wavelet-based gene selection method presents acceptable survival prediction. Using this method, six significant genes were selected. It was indicated the expression of GENE3359X and GENE3968X decreased the survival time, whereas the expression of GENE967X, GENE3980X, GENE3405X and GENE1813X increased the survival time.

CONCLUSION

Wavelet-based gene selection method is a potentially useful tool for the gene selection from microarray data in the context of survival analysis.

Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease

OBJECTIVE

Genome-wide association studies have identified several genetic variants associated with coronary heart disease (CHD). The aim of this study was to evaluate the genetic risk discrimination and reclassification and apply the results for a 2-stage population risk screening strategy for CHD.

APPROACH AND RESULTS

We genotyped 28 genetic variants in 24 124 participants in 4 Finnish population-based, prospective cohorts (recruitment years 1992-2002). We constructed a multilocus genetic risk score and evaluated its association with incident cardiovascular disease events. During the median follow-up time of 12 years (interquartile range 8.75-15.25 years), we observed 1093 CHD, 1552 cardiovascular disease, and 731 acute coronary syndrome events. Adding genetic information to conventional risk factors and family history improved risk discrimination of CHD (C-index 0.856 versus 0.851; P=0.0002) and other end points (cardiovascular disease: C-index 0.840 versus 0.837, P=0.0004; acute coronary syndrome: C-index 0.859 versus 0.855, P=0.001). In a standard population of 100 000 individuals, additional genetic screening of subjects at intermediate risk for CHD would reclassify 2144 subjects (12%) into high-risk category. Statin allocation for these subjects is estimated to prevent 135 CHD cases over 14 years. Similar results were obtained by external validation, where the effects were estimated from a training data set and applied for a test data set.

CONCLUSIONS

Genetic risk score improves risk prediction of CHD and helps to identify individuals at high risk for the first CHD event. Genetic screening for individuals at intermediate cardiovascular risk could help to prevent future cases through better targeting of statins.