Objective To explore the application methods and values of using health failure mode and effect analysis (HFMEA) to prevent surgical site infection (SSI) in patients undergoing bone fracture and craniotomy surgery with class Ⅰ incision. Methods Patients undergoing bone fracture and craniotomy surgery with class Ⅰ incision at the Chengdu Pidu District People’s Hospital between January 2020 to December 2021 were selected. Based on whether receiving HFMEA-based risk management or not, the patients were divided into conventional group and intervention group. The compliance rates with infection control measures, changes in risk priority numbers (RPN) at various stages (1 month and 10 months after intervention) of HFMEA implementation, and the incidence of SSI between the conventional group and the intervention group were compared. Results A total of 884 surgeries were included. Among them, there were 399 cases in the conventional group and 485 cases in the intervention group; 16 cases SSI occurred. A total of 7 SSI prevention and control measures had been formulated. Except for proper surgical attire (P＞0.05), there were statistically significant differences in the compliance rate of the other prevention and control measures between the two groups of patients (P＜0.05). In the intervention group, the RPN values of pre-operative, intra-operative, and post-operative risk factors at the 10th month after intervention were all lower than those at the 1st month after intervention (P<0.05). Except for the incidence of SSI during craniotomy surgery (6.1% vs. 1.8%, P=0.375), there were statistically significant differences in the total SSI incidence (3.3% vs. 0.6%) and bone fracture surgery SSI incidence (2.7% vs. 0.5%) between the conventional group and the intervention group (P>0.05). Conclusion Applying HFMEA-based risk management techniques to prospectively identify, assess, analyze, manage and track the risk of SSI in bone fracture and craniotomy surgery with class Ⅰ incision can effectively enhance the adherence of preventive measures and reduce the incidence rate of SSI.
ObjectiveTo investigate the association between cardiometabolic indicators and the risk of incident renal insufficiency using a longitudinal physical examination cohort. MethodsBased on a large-scale, multi-center, repeated-measurement physical examination cohort in China, this study evaluated the impact of baseline cardiovascular metabolic indicators on incident renal insufficiency using logistic regression, Cox proportional hazards models, Poisson regression, and generalized linear mixed-effects models. Furthermore, latent class mixed models (LCMM) were employed to identify the longitudinal trajectories of these indicators, and the associations between distinct trajectory patterns and the risk of renal insufficiency were analyzed using Cox and logistic regression models. ResultsDerived from a massive longitudinal cohort of 680 000 individuals across a multi-center healthcare network, a total of 264 379 participants were ultimately included, among whom 82 849 developed renal insufficiency during the follow-up period. Baseline analyses revealed that elevated levels of triglycerides (TG) and fasting glucose (FG) consistently increased the risk of renal insufficiency across multiple statistical models (all PFDR<0.001). Trajectory analyses demonstrated that high-level longitudinal trajectories of FG (HR=1.274, 95%CI 1.230 to 1.320) and diastolic blood pressure (DBP) (HR=1.042, 95%CI 1.023 to 1.061) were significant independent risk factors for the disease. Notably, the evolutionary trajectory of systolic blood pressure (SBP) exhibited opposite association directions between the Cox and logistic models, whereas the TG trajectory lost its statistical significance after fully adjusting for smoking and alcohol consumption. ConclusionBoth baseline characteristics and longitudinal variations of cardiovascular metabolic indicators are significantly associated with the incidence of renal insufficiency. Abnormal levels and high-level evolutionary trajectories of fasting glucose and triglycerides serve as robust risk factors for the disease. Regular monitoring and longitudinal tracking of these metabolic profiles are of profound clinical value for the prevention and early identification of renal insufficiency.
ObjectiveTo investigate the association between kidney function indicators in routine health examinations and the incidence of urinary stone disease using longitudinal repeated-measurement data. MethodsThis study was based on the large-scale longitudinal data in the WHALE study. Cox proportional hazards models were first applied to examine the associations between baseline kidney function indicators and incident urolithiasis. Subsequently, Poisson regression and mixed-effects models were used to analyze repeated-measurement data to further explore the relationships between kidney function indicators and urolithiasis. Latent class trajectory modeling was then employed to characterize longitudinal patterns of kidney function indicators. Based on the optimal trajectory models, logistic regression analyses and Cox regression analyses were performed to assess the associations between trajectory classes and the risk of urolithiasis. ResultsAmong 313 280 health examination participants included in the study, 8 864 incident cases of urolithiasis were identified during follow-up. In the baseline Cox analyses, cystatin C showed the strongest association with urolithiasis (HR=1.230, 95%CI 1.083 to 1.396, FDR=0.001). In analyses incorporating repeated measurements, serum uric acid demonstrated a strong association in both Poisson regression and mixed-effects models (RR=1.123, 95%CI 1.094 to 1.152, FDR<0.001; RR=1.106, 95%CI 1.075 to 1.137, FDR<0.001). In trajectory analyses, U-shaped trajectories of blood urea (HR=0.548, 95%CI 0.438 to 0.685, FDR<0.001) and uric acid (HR=0.910, 95%CI 0.857 to 0.685, FDR=0.005) were associated with a lower risk of urolithiasis. ConclusionBoth baseline levels and longitudinal changes in kidney function indicators measured during routine health examinations are significantly associated with the incidence of urolithiasis. Regular assessment and longitudinal monitoring of kidney function may contribute to the prevention and early identification of urolithiasis development and progression.