
[ad_1]
This observational study is carried out within a register-based framework, utilizing data from several national health registries and administrative registries in Sweden. Data linking is facilitated by a personal identification number, a unique identifier assigned to each Swedish citizen. The registers included in this study constitute the Swedish National Diabetes Register (NDR). [17]Total Population Register (TPR) (maintained by Statistics Sweden) [18]National Patient Registry (NPR) [19]Prescription Drug Register (PDR) [20]Swedish cause of death register [21]and the Longitudinal Integrated Database for Health Insurance and Labor Market Research (LISA). [22].
study cohort
NDR has been thoroughly documented in previous literature [4, 10, 17]. In summary, NDR, established in 1996, boasts national coverage and includes almost all of Sweden’s T2D patients. Each year, data covering patient characteristics, risk factors, treatments, and complications associated with diabetes are collected from patient encounters at health care facilities specialized in the management of patients with T2D and reported in the NDR .
Epidemiological definition of T2D (dietary therapy with or without the use of oral hypoglycemic agents; additionally, individuals aged 40 years or older, diagnosed with diabetes, and receiving insulin treatment alone or in combination with other oral hypoglycemic agents) ) In this study, to identify T2D patients from NDR, they are also classified as T2D patients.
The study cohort consisted of all T2D patients enrolled at least once in the NDR between January 1, 2005 and December 31, 2008. At the time of initial enrollment into the NDR (which also served as the index date for the study), each T2D patient was matched by gender, age, and county to her two control participants without T2D. Controls were randomly sampled with replacement from the general population (Total Population Register, Statistics Sweden), and the main aim of the matching process was to secure her T2D-free controls for use as matched comparison subjects. was. Control participants initially did not have T2D at the time of selection, but a small subset of these subjects developed her T2D during the pre-follow-up wash-in period (2005-2008). did. Instead of censoring these cases, we matched them with two new controls, resulting in a non-integer case-to-control ratio. Controls who received a T2D diagnosis in NPR during follow-up were censored at the time of diagnosis. Study participants with apparent PAD or amputation at baseline were excluded from the current analysis.
A subset of T2D patients and controls who experienced the onset of an incident PAD diagnosis during the study period began follow-up at the time of incident PAD diagnosis registered in the NPR. We then established baseline data as her last pre-PAD observation, including gender, age, and all relevant comorbidities and socio-economic factors listed below. Study participants were followed until death or the end of the study period on December 31, 2020, for both all-cause and ASCVD deaths.
Baseline data and PAD exposure during the study period
Baseline comorbidity data and identification of incident PAD during the study period were obtained from the NPR adopting the Swedish revised version. International classification of diagnoses (ICD) version 10 code. Used for diagnosis and classification of medical conditions. NPR, managed by the Swedish National Board of Health and Welfare, serves as a source for this health-related data. [19]. A detailed description and definition of all comorbidities and the corresponding ICD-10 codes considered in this study are provided in Supplementary Table 1, available in the online data supplement. His definition of ASCVD in this study includes a combination of myocardial infarction, ischemic heart disease, and stroke. Socioeconomic factors such as marital status, education level, region of origin, and income level were collected from LISA maintained by Statistics Sweden. [22]. The PDR facilitated the collection of data on filled prescriptions for statins, antihypertensives, antiplatelets, and anticoagulants over the 1-year period prior to the index date. [20]. The methodology for defining PAD, other comorbidities and outcomes using the corresponding ICD-10 codes in NPR, along with the collection of additional baseline and outcome data from various Swedish national health registries and administrative registries. , has been adopted in previous national and international collaborative research. [4, 8, 10, 23, 24].
Risk factors for death in T2D and PAD patients
Clinical characteristics and cardiovascular risk factors of T2D patients were collected from the NDR. The following baseline variables were considered in the analysis investigating the association between risk factors and all-cause and ASCVD mortality in T2D patients after onset of PAD: glycated hemoglobin (HbA1c), blood pressure levels (systolic and diastolic ), total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, body mass index (BMI), smoking status (defined as current smoker at study entry), trace Presence of lipoproteins and cholesterol macroalbuminuria, and estimated glomerular filtration rate (eGFR). eGFR was calculated using the MDRD formula [25]. Microalbuminuria was defined as two separate samples with a urine albumin/creatinine ratio between 3 and 30 mg/mmol (30 and 300 mg/g) within 1 year. Macroalbuminuria, on the other hand, followed a similar definition but with a ratio higher than 30 mg/mmol. Risk factor data for control subjects were not available for this study.
result
The two predefined primary outcomes of this study were incident all-cause mortality and ASCVD mortality (defined by ICD codes I00-I99, R570, R960, and R961). Information on these outcomes was obtained from the Swedish Cause of Death Register maintained by the Swedish National Board of Health and Welfare. This register contains comprehensive details about all deaths, including cause of death and time of death, for individuals registered in Sweden. The underlying cause of death is coded based on his ICD code and reporting to this registry is mandatory. [21]. Outcomes were similar for T2D patients and controls.
statistical analysis
Descriptive statistics are reported using mean values with standard deviation (SD) or median values with interquartile range (IQR) values as appropriate for continuous variables. Categorical variables are displayed as counts (n) with the corresponding percentage (%) of categorical variables. The magnitude of differences at baseline between variables between study groups was calculated using the standardized mean difference (SMD), and values less than 0.1 were considered not significant.
Event rates were calculated as the number of all-cause and ASCVD death events per person-year in T2D patients and controls. Event rates for individuals without PAD are reported for each PAD-free year. At the time of PAD diagnosis, individuals are censored. For patients with PAD, incidence is reported by year of PAD exposure. Stratification was performed based on whether the onset of PAD occurred during the study period and gender. These proportions were expressed with 95% exact Poisson confidence intervals (CI) and visualized using Kaplan-Meier curves and cumulative incidence curves.
Risks of incident all-cause mortality and ASCVD mortality were further assessed using Cox proportional hazards regression. A competing risks approach was applied to his ASCVD mortality using the Fine-Gray method. These models were also stratified on whether the onset of an incident PAD diagnosis occurred in a multistate design. Risk comparisons were made between T2D patients and controls. Specifically, the risk comparison was divided into two sub-analyses. One subanalysis compared risk between T2D patients and controls who experienced the onset of an incident PAD diagnosis during the study period. Another sub-analysis compared risks between T2D patients and controls who experienced the onset of her PAD diagnosis during the study period. T2D patients and controls who did not experience PAD. This approach allowed for a more nuanced assessment of risk differences in these different subgroups. The Cox proportional hazards model underwent a stepwise adjustment process. The final model included age, gender, income level, education level, marital status, region of origin, selected medications (antihypertensives, statins, anticoagulants, antiplatelet therapy), as defined in Supplementary Table 1 Adjustment for comorbidities was included. The relevance is: It is summarized as the hazard ratio (HR) expressed with a 95% confidence interval (CI).
The secondary objective focused on evaluating the association between T2D-related ASCVD risk factors and all-cause and ASCVD mortality in T2D patients after PAD onset, analyzed using Cox proportional hazards test. I did. A gradient boosting model (GBM) with a proportional hazard loss function was employed to assess the relative importance of T2D-related cardiovascular risk factors in models estimating the risk of all-cause and ASCVD mortality. Ta. [26]. The GBM model was fitted with a contraction of 0.05 and an interaction depth of 3 to increase computational efficiency. The number of 1167 optimized trees was determined by 5-fold cross validation. The relative importance of each predictor variable to the model of outcome risk (also known as partial grade effects) is displayed as an estimate. This estimate, called relative importance, indicates the predictive importance of each risk factor to the model of outcome risk and was determined using a machine learning technique, GBM.
Given the registry-based nature of this study, we acknowledged that the NDR lacked data on T2D-related cardiovascular risk factors. Missing at random was assumed, and a multiple imputation model (Multiple Imputation with Chained Equations (MICE)) was applied to handle missing data (mainly clinical biomarkers of NDR). This iterative imputation method used observed data to predict missing values and combined the imputed datasets to create a final dataset for subsequent analyses. Imputed data were used to assess mortality risk, assess the importance of risk factors to the model, and assess associations between risk factors and outcomes. Supplementary Table 2 provides a comprehensive list of variables considered during the imputation process, ensuring transparency of the imputation model.
Evaluation of the Schoenfeld and Martingale residuals showed that the proportional hazards assumption was met. All statistical tests adhered to a two-sided 5% significance level and used 95% confidence intervals (CI). Given the exploratory nature of the study, no correction for multiple comparisons was applied. Statistical analyzes were performed using R version 4.0.3.
[ad_2]
Source link