Personalized Prechemotherapy Education Reduces Peri-Chemotherapy Anxiety in Colorectal Cancer Patients

How education level affects postoperative rehabilitation and follow-up: a single-center experience

BACKGROUND

Radical prostatectomy remains the fundamental treatment for prostate cancer, and improving patients' compliance with postoperative follow-ups is essential for improving patients' quality of life. This study investigates the effect of education levels on patients' recovery and follow-up after radical prostatectomy.

METHODS

Data from 1,112 patients undergoing radical prostatectomy between 2011 and 2020 were collected using medical records, and "pc-follow" systems were used to collect patients' baseline information, education level, pathological information, number of outpatient visits, the time interval between each visit, and PSA test data.

RESULTS

Regarding postoperative outpatient data, there was no difference in the number of outpatient visits among the different education level groups in Shanghai (P = 0.063). A significant difference was found in the interval between outpatient visits among the groups (P < 0.001). Furthermore, significant differences were detected in the number and duration of outpatient clinic visits among the education level groups in all patients (P = 0.016, P = 0.0027). By contrast, no significant difference was found in the recovery time of urinary continence between all patients and those in Shanghai, grouped according to education level (P = 0.082, P = 0.68). For all patients and patients in the Shanghai area, the number of PSA follow-ups increased gradually with an increasing level of education (P < 0.001, P = 0.0029).

CONCLUSIONS

Education level affected the number of postoperative clinic visits, compliance, and the number of PSA tests. However, no significant effect on the recovery of urinary continence was found. Further, clinicians must increase their focus on patients with low education levels to achieve equitable access to health services for all patients.

AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine

The development of high-throughput molecular testing techniques has enabled the large-scale exploration of the underlying molecular causes of diseases and the development of targeted treatment for specific genetic alterations. However, knowledge to interpret the impact of genetic variants on disease or treatment is distributed in different databases, scientific literature studies and clinical guidelines. AIMedGraph was designed to comprehensively collect and interrogate standardized information about genes, genetic alterations and their therapeutic and diagnostic relevance and build a multi-relational, evidence-based knowledge graph. Graph database Neo4j was used to represent precision medicine knowledge as nodes and edges in AIMedGraph. Entities in the current release include 30 340 diseases/phenotypes, 26 140 genes, 187 541 genetic variants, 2821 drugs, 15 125 clinical trials and 797 911 supporting literature studies. Edges in this release cover 621 731 drug interactions, 9279 drug susceptibility impacts, 6330 pharmacogenomics effects, 30 339 variant pathogenicity and 1485 drug adverse reactions. The knowledge graph technique enables hidden knowledge inference and provides insight into potential disease or drug molecular mechanisms. Database URL: http://aimedgraph.tongshugene.net:8201.

Targeted Perioperative Nursing Combined with Propofol and Fentanyl for Gynecological Laparoscopic Surgery

Objective

The aim of this study is to investigate the clinical effects of targeted perioperative nursing combined with propofol and fentanyl in gynecological laparoscopic surgery.

Methods

Patients who were admitted to our hospital for gynecological laparoscopic surgeries from October 1, 2019 to November 30, 2021 were included in this retrospective study. Patients in group A received routine propofol and fentanyl. Patients in group B received targeted perioperative nursing on the basis of interventions in group A. The anesthetic effects, clinical indicators, mental health status, and adverse reactions were compared between the two groups.

Results

A total of 84 qualified patients were retrieved. The total effective anesthesia rate, extubation time, operation time, consciousness recovery time, intraoperative blood loss, hospital stay, SAS score, SDS score, health status indicators, and adverse events in group B were all significantly better than those in group A (P < 0.05 for all comparisons).

Conclusion

Combined intervention (propofol + fentanyl + targeted perioperative care) for gynecological laparoscopic surgery patients has a significant anesthesia effect, which can effectively improve the patient's clinical indicators and mental health status and can also reduce the occurrence of adverse events. It has good safety and can be widely used in clinical practice.

Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer

Objective

To identify and validate effective clinical predictors for the long-term prognosis of patients with cervical cancer.

Methods

Cervical cancer patients were retrieved from the TCGA database, and patients' clinical data were collected and analyzed for the predictive value of long-term prognosis. In the other branch of the study, patients with cervical cancer and admitted to our hospital between January 1, 2016, and December 31, 2016, were retrieved and followed up for prognosis analysis.

Results

In the database patient cohort of our study, 607 cases with cervical cancer were analyzed. Aneuploidy score (p = 0.012), Buffa hypoxia score (p = 0.013), histologic grade (p = 0.01), fraction genome altered >0.4 (p < 0.001), weight > 60 kg (p < 0.001), height > 160 cm (p = 0.047), BMI <18.5 (p = 0.023), Winter hypoxia score (p = 0.002), and adjuvant postoperative radiotherapy were good predictors for disease-free survival (DFS), while aneuploidy score (p = 0.001), MSI sensor score > 0.5 (p = 0.035), person neoplasm status (p < 0.001), race (p = 0.006), Ragnum hypoxia score (p = 0.012), weight (p < 0.001), height (p < 0.001), and BMI < 18.5 (p = 0.04) were good predictors for overall survival (OS). In the admitted patient cohort, age over 60 years old at the time of diagnosis was the only clinical factor influencing the long-term DFS (p = 0.004). TNM stage above III (p = 0.004), body weight > 70 kg (p < 0.001), and complicated with other cancer (p < 0.001) were clinical factor influencing the long-term OS.

Conclusions

Clinical factors, especially common to both cohorts, could be used to show the long-term prognosis of cervical cancer.

Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer

Objective

To determine the efficacy of clinical characteristics in the prediction of prognosis in patients with ovarian cancer.

Methods

Clinical data were collected from 3 datasets from TCGA database, including 1680 cases of ovarian serous cystadenocarcinoma, and were analyzed. Patients with ovarian cancer admitted to our hospital in 2016 were retrieved and followed up for prognosis analysis.

Results

From the datasets, for patients > 75 years old at the time of diagnosis, histologic grade and mutation count were good predictors for disease-free survival, while for patients > 50 years old at the time of diagnosis, histologic grade, race, fraction genome altered, and mutation count were good predictors for overall survival. In the patients (n = 38) retrieved from our hospital, the longest dimension of lesion (cm) and body weight at admission were good predictors for overall survival.

Conclusions

Those clinical factors, together with the two predictive equations, could be used to comprehensively predict the long-term prognosis of patients with ovarian cancer.