Study Design and Participants
An ambispective cohort study was conducted among participants who underwent annual community physical examination in Guangzhou, Guangdong between January 2015 and December 2020. We retrospectively collected the data of the previous examination reports of participants from the primary medical institution, and we prospectively conducted the study from January 2020. Subjects aged ≥ 65 years or aged ≥ 35 years complicating diabetes or hypertension were enrolled in the analysis. The exclusion criteria included: 1) pre-existing end-stage kidney disease (estimated glomerular filtration rate < 15 mL/min/1.73m2) , and 2) missing follow-up creatinine data. The Ethics Committee of the Nanfang Hospital has approved this study. And the written informed consents were obtained from participants enrolled prospectively. All the participants were randomly assigned to a development cohort and a validation cohort in a 2:1 ratio (Figure 1).
The Modification of Diet in Renal Disease (MDRD) equation was used to calculate estimated glomerular filtration rate(9). Serum creatinine was measured by enzyme method. Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg, diastolic blood pressure (DBP) ≥90 mmHg, or use of antihypertensive medications. Diabetes mellitus was defined as random blood glucose level ≥11.1mmol/L or fasting plasma glucose level ≥7.0mmol/L or hemoglobin A1c (HbA1c) ≥6.5%. Body mass index (BMI) values calculated by the body weight and height of each participant as the follow equation: BMI = weight (kg)/[height (m)]2. Smoking was classified as ever smoking vs never smoking. Exercise was divided into four categories according the frequency: never, once a week, few times a week and daily.
The endpoint of this study was rapid kidney function decline, defined as the reduction of estimated glomerular filtration rate ≥40% during follow-up period. And participants without RKFD during the whole follow-up period were defined as event-free. After enrollment, laboratory measures and medical history were annually performed and collected during the follow-up period.
Patient and Public Involvement
The study was an ambispective cohort study. The retrospective data range from 2015 to 2019 was obtained from the electronic health records in primary medical institution. In 2020 year, we conducted face-to-face follow-up in the communities. And the results of our study had been send to participants with their examination reports. And we thanks all participants for their valuable contribution.
Sample size calculation and statistical analysis
Determination of minimum sample size
Use pmsampsize package of R(10-11) to calculate the minimum sample size required for developing a multivariable prediction model with a survival outcome using 5 candidate predictors. We hypothesis an AUC value of 0.72, so we selected the related predicted value of R2D was 0.294 from table 1 in the work of Jinks et al(12) according to this AUC. The mean follow-up of the entire cohort was 3.72 years, and overall event rate was 0.1196. We select a timepoint of interest for prediction using the newly developed model of 5 years. Therefore, the events per candidate predictor parameter (EPP) was 11.3. In the development cohort, the minimum sample size required 945 participants. And because of the proportion of development cohort and validation group is 2:1, so the validation cohort need at least 473 participants.
The unpaired, 2-tailed t test was used to analyse quantitative variables that were normally distributed and homoscedastic, this type of variables were summarized by mean ± SD. The Wilcoxon rank-sum test was used to analyse quantitative variables that were non-normally distributed or not homoscedastic, and this type of variables were summarized by median (interquartile range). Qualitative variables such as gender, comorbidities, life style, and medication situation were compared using the χ2 test or Fisher’s exact test and are expressed as percentages.
The multivariable Cox regression analysis was used to determine the risk factors of rapid eGFR decline. Variables with no more than 15% missing values and were imbalanced between groups in the development cohort or that are clinically important were included in the univariable Cox regression analysis. Variables with significance in the univariate analysis were preliminarily screened out and to be included in the multivariable Cox regression analysis. For the determination of significance variables, p<0.05 was the threshold for identification. And we manually investigated the contribution of the remaining variables to determine the final predictors. Then, the risk prediction nomogram was formulated based on the results and by using the rms package of R. To form the nomogram, each regression coefficient in the multivariable Cox regression was proportionally converted into a 0- to 100-point scale. The variable with the highest β coefficient (absolute value) was assigned 100 points. The points are added across each variable to calculate the total points, which are finally converted to predicted probabilities.
The nomogram was evaluated in both the training and validation cohorts. Discriminative ability was assessed using the concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC). Calibration was assessed using a bootstrap approach with 1000 resamples to compare the predicted event rate with the observed one in the study. The entire cohort was also divided into low-risk group (≤ 150 points) and high-risk group (>150 points), and the event rate was also compared between groups. Missing data were not imputed. In all analyses, P < 0.05 was considered statistically significant. All analyses were conducted with R software (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria) and SPSS (version 26.0).