Objective To determine the influence of combinative assessment of 64 multi-slice spiral computer tomography (MSCT) and serum amyloid A protein (SAA) on the selection of operative procedures of upper rectal cancer in multi-disciplinary team. Methods Prospectively enrolled 110 patients, who were diagnosed definitely as upper rectal cancer (distance of tumor to the dentate line gt;7 cm) at West China Hospital of Sichuan University from August 2007 to October 2008, randomly assigned into two groups. In one group named MSCT+SAA group, both MSCT and SAA combinative assessment were made for the preoperative evaluation. In another group named MSCT group, only MSCT was made preoperatively. Then, the pooled data were analyzed for the correlative relationship between the choice of surgery strategy and clinicopathologic factors. Furthermore, the preoperative staging and predicted operative procedures were compared with postoperative pathologic staging and practical operative procedures, respectively. Results According to the criteria, 106 patients with upper rectal cancer were randomly assigned into MSCT+SAA group (n=52) and MSCT group (n=54). The baseline characteristics of two groups were statistically identical. When analyzing the proportion of multiple clinicopathologic factors in different operative procedures of upper rectal cancer, there were statistical differences in the preoperative N staging (P=0.003), M staging (P=0.022), TNM staging (P=0.003), serum level of SAA (P=0.005) and general category of tumor (P=0.027). For MSCT+SAA group the accuracies of preoperative staging T, N, M and TNM were 84.6%, 86.5%, 100% and 86.5%, respectively; For MSCT group the corresponding rates were 83.3%, 2.9%, 100% and 64.8%, respectively. There were statistically significant differences accuracies of preoperative N staging and TNM staging (P=0.005, P=0.009, respectively) in two groups. There was a statistically significant difference of the accuracy of prediction to operative procedures in two groups (96.2% vs. 81.5%, P=0.017). Conclusion Combinative assessment of 64 MSCT and SAA could improve the accuracy of preoperative staging, and thus provide higher predictive coincidence rate to operative procedures for surgeon.
ObjectiveThis longitudinal cohort study aimed to identify the association between baseline physiological characteristics and the risk of post-trauma depressive symptoms, characterize the heterogeneous developmental trajectories of these symptoms, and explore differences in baseline biomarkers across these trajectory groups to provide evidence for early clinical intervention. MethodsA total of 4 709 trauma patients were recruited from the China Severe Trauma Cohort (CSTC) between March 2020 and July 2025. 17 baseline biomarkers and 6 composite inflammatory index were measured, and depressive symptoms were assessed using the PHQ-9 at 1, 3, 6, and 12 months post-admission. Multivariate-adjusted Poisson regression was used to evaluate the predictive value of baseline indicators for post-trauma depressive symptoms, and Latent Class Mixed Models (LCMM) were employed to identify depression symptom developmental trajectories. ResultsElevated baseline systemic inflammation (higher leukocytes, NLR, SII, and CAR) and compromised nutritional/metabolic status (lower RBC count and albumin) significantly increased the risk of post-trauma depressive symptoms throughout the follow-up period. Trajectory analysis identified two distinct classes: "Consistently Low" and "Persistently Elevated" symptoms. Individuals in the "Persistently Elevated" group were significantly characterized by baseline profiles of low lymphocytes, albumin, red blood cells, and hematocrit. Additionally, biomarker effects exhibited some heterogeneity across gender. ConclusionSystemic inflammation activation (high white blood cell count, NLR, SII and CAR) and biological disorders driven by impaired red blood cell or nutritional status (low red blood cell count, MCHC and albumin) are significant predictors of the occurrence and persistent evolution of depressive symptoms. Baseline innate immune activation and insufficient nutritional reserves serve as early biological predictors driving the onset and chronicity of post-trauma depressive symptoms. Integrating routine hematological indicators facilitates clinical risk stratification and precision prediction of symptom trajectories.
Objective To determine the role of multimodal preoperative evaluation (MPE) system of transrectal ultrasound (TRUS), 64 multi-slice spiral computer tomography (MSCT) and serum amyloid A protein (SAA) in assessment of preoperative staging and selection of operative procedures of the lower and middle rectal cancer in multi-disciplinary team. Methods Prospectively enrolled 150 patients, who were diagnosed definitely as lower and middle rectal cancer (distance of tumor to the dentate line ≤10 cm) at West China Hospital of Sichuan University from November 2008 to March 2009, randomly assigned into two groups. In one group named MPE group, MPE consisting of TRUS, MSCT and SAA were made for the preoperative evaluation. In another group named MSCT+SAA group, both MSCT and SAA were made preoperatively. Then, the preoperative staging and predicted operative procedures were compared with postoperative pathologic staging and practical operative procedures, respectively. Furthermore, the pooled data were analyzed for the correlative relationship between the choice of surgery strategy and clinicopathological factors. Results According to the criteria, 146 patients with lower and middle rectal cancer were randomly assigned into MPE group (n=74) and MSCT+SAA group (n=72). The baselines characteristics of two groups were statistically identical. For MPE group the accuracy of preoperative staging T, N, M and TNM were 94.6% (70/74), 85.1% (63/74), 100% （74/74） and 82.4% (61/74), respectively; For MSCT+SAA group the corresponding rates were 77.8% (56/72), 84.7% (61/72), 100% （72/72） and 81.9% (59/72), respectively. The analysis showed a statistically difference in the accuracy of preoperative T staging between two groups (P=0.003) while there was no statistically significant difference of the accuracies of preoperative N, M and TNM staging between two groups (Pgt;0.05). There wasn’t a statistically significant increasing of the accuracy of prediction to operative procedures in MPE group compared with MSCT+SAA group 〔95.9% (71/74) vs.88.9% (64/72), P=0.106〕. When analyzing the relationship between multiple clinicopathologic factors and the operative procedures of lower and middle rectal cancer, there were statistical correlations between the pathological T staging (r=0.216， P=0.009), N staging (r=0.264， P=0.001), TNM staging (r=0.281， P=0.001), serum level of SAA before operation (r=0.252， P=0.002) or the distance of tumor to the dentate line (r=－0.261， P=0.001) and the operative procedures. Conclusion MPE system could display the accurate preoperative staging for lower and middle rectal cancer, on which the prediction of operative procedures can rest convincingly.
Objective To integrate multi-dimensional and multimodal data to develop a tool for predicting the risk of chronic kidney disease (CKD). Methods Data from the UK Biobank were utilized, involving 6561 participants recruited between 2006 and 2010, with a follow-up window from April 17, 2007 to November 30, 2022. In the development cohort (n=5248), a multimodal random survival forest (RSF) model was constructed, integrating conventional risk factors [variables from the CKD Prognosis Consortium (CKD-PC) equation], social determinants of health, Life’s Essential 8 data, and retinal optical coherence tomography imaging features. Comparative models included a base model (based solely on estimated glomerular filtration rate and urine albumin-to-creatinine ratio), the CKD-PC equation, a unimodal RSF model (conventional risk factors + social determinants of health + Life’s Essential 8 data), and an extended model (the multimodal RSF model plus a polygenic risk score). The performance of these models was compared in the validation cohort (n=1313). Results After a median follow-up of 12.7 years, 3.57% (234/6561) of the participants developed CKD. In the validation cohort, the 5-year concordance index (C-index) of the multimodal RSF model was 0.73 [95% confidence interval (CI) (0.68, 0.77)], which was significantly higher than that of the base model [C-index=0.67, 95%CI (0.65, 0.71)], the CKD-PC equation [C-index=0.64, 95%CI (0.58, 0.70)], and the unimodal RSF model [C-index=0.69, 95%CI (0.64, 0.73)], and the extended model with the inclusion of the polygenic risk score did not significantly improve predictive performance [C-index=0.72, 95%CI (0.67, 0.76)]. The results of the time-dependent area under the receiver operating characteristic curve analysis were consistent with these findings. Based on the predicted CKD risk derived from the multimodal RSF model for risk stratification, the actual proportions of individuals who developed CKD in the high-, medium-, and low-risk strata were 19.7%, 4.1%, and 1.5%, respectively. In addition to established risk factors, retinal imaging information, healthcare accessibility, and financial status were among the top-ranked predictors for CKD risk. Conclusion Integrating multi-dimensional and multimodal data—including conventional risk factors, social factors, lifestyle factors, and retinal imaging—can improve the performance of CKD risk prediction and stratification.