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This study aimed to investigate distress levels, using the distress thermometer (DT), and the factors associated with distress in postoperative patients with pancreatobiliary cancer. This study retrospectively investigated 155 patients who underwent surgery for pancreatobiliary cancer between December 1, 2019 and September 30, 2021. The DT and problem list were used to measure distress. Descriptive statistics, t-test, and multivariate logistic regression analysis were used to analyze the data. Of the 155 patients, 16.8% (n = 26) and 83.2% (n = 129) were in the mild-distress and moderate-to-severe distress groups, respectively. The average DT score was 6.21; that for the mild-distress and moderate-to-severe distress groups was 2.46 and 6.97, respectively. More patients in the moderate-to-severe distress group reported having problems of “sadness” (χ 2  = 4.538, P < 0.05), “indigestion” (χ 2  = 10.128, P < 0.001), “eating” (χ 2  = 6.147, P < 0.013), and “getting around” (χ 2  = 4.275, P < 0.039) than in the mild-distress group. In addition, occupation status (odds ratio [OR] = 0.342, 95% confidence interval [CI] = 0.133–0.879, P = 0.026) and indigestion (OR = 5.897, 95% CI = 1.647–21.111, P = 0.006) were independent risk factors for the presence of severe distress. Patients with pancreatobiliary cancer demonstrated elevated levels of psychological distress. Healthcare providers should therefore be vigilant when evaluating patients for distress and providing appropriate referrals, particularly those who are unemployed or have indigestion.

Introduction Postoperative length of hospital stay is a widely recognized indicator of surgical care quality and hospital performance. Extended hospital stays increase the risk of hospital-acquired infections and limit bed availability for new patients. Despite its clinical relevance, there is limited evidence on the length of postoperative stay and its associated factors among adult surgical patients in Ethiopia. This study aimed to assess the duration of postoperative hospital stay and its associated factors among adult patients admitted to the surgical ward of Debre Markos Comprehensive Specialized Hospital, Ethiopia. Methods An institution-based cross-sectional study was conducted on 515 adult postoperative patients from October 30, 2024, to January 30, 2025, selected via a systematic random sampling method. Prolonged postoperative hospital stay was defined as ≥ 75th percentile of length of stay within each surgical procedure group. Data were collected through face-to-face interviews and chart reviews and analyzed using logistic regression, with p  < 0.05 considered statistically significant. Results This study found that 29.2% of patients experienced prolonged postoperative hospital stays (95% CI: 25.2–33.1), with a median length of stay of 5 days (IQR: 3–8). The median surgery duration was 80 min (IQR: 45–115). Factors associated with prolonged postoperative stay were preoperative anemia (AOR: 3.81, 95% CI: 2.39–6.06), delayed ambulation (AOR: 2.57, 95% CI: 1.66–3.99), hospital-acquired pneumonia (AOR: 3.53, 95% CI: 1.75–7.11), and surgical site infection (AOR: 2.74, 95% CI: 1.38–5.43). Conclusion Almost one-third of the participants experienced an extended postoperative hospital stay. Key contributing factors were preoperative anemia, delayed ambulation, hospital-acquired pneumonia, and surgical site infections. Interventions such as enhanced infection control, early mobilization, and preoperative anemia screening and management are recommended to reduce length of stay.

The analgesic effects between fentanyl, sufentanil, and butorphanol combined with flurbiprofen axetil on postoperative patient-controlled intravenous analgesia (PCIA) after cesarean delivery has never been evaluated. To evaluate the postoperative analgesic efficacy of selected PCIA formulae. This is a retrospective study. Department of Anesthesiology, Shenzhen Second People's Hospital, a medical center in Shenzhen City, Guangdong Province, People's Republic of China. From January 2022 through October 2023, the records of 463 patients who underwent a cesarean delivery were reviewed at Shenzhen Second People's hospital. All used a postoperative PCIA formula combined with flurbiprofen axetil and an antiemetic (ondansetron or tropisetron). The patients were placed into one of 3 groups: the Fentanyl Group (fentanyl plus flurbiprofen axetil plus ondansetron or tropisetron, 178 patients); the Sufentanil Group (sufentanil plus flurbiprofen axetil plus ondansetron or tropisetron, 159 patients); or the Butorphanol Group (butorphanol plus flurbiprofen axetil plus ondansetron or tropisetron, 126 patients). The primary data collected were the perioperative use of analgesics, postoperative Visual Analog Scale score, and no differences in adverse reactions were observed, except for the incidence of nausea and vomiting. A significant difference was found between using epidural analgesics (such as morphine) and intravenous analgesics (such as butorphanol, flurbiprofen axetil, tramadol, parecoxib, and dexmedetomidine). There was no difference among the groups in postoperative Visual Analog Scale scores at 24 hours and 48 hours post cesarean delivery. There also was no difference in adverse reactions. Our study was limited by a small sample size and did not differentiate the Visual Analog Scale scores between states of rest and movement. The analgesic effect in patients who underwent cesarean delivery is similar when using different postoperative PCIA formulae. Although butorphanol displayed no analgesic advantage over fentanyl and sufentanil postoperatively, it caused fewer postoperative nausea and vomiting incidences than fentanyl and sufentanil.

Studies were eligible when our primary outcome was documented in the paper, namely: RR measured in adult non-critically ill postoperative patients on a (standard care) surgical ward (appendix A: literature search, inclusion- and exclusion criteria). [...]this systematic review is a first step in establishing an acceptable RR range on the postoperative ward. [...]research is necessary, preferably employing staff-independent objective measurement methods like wireless monitoring.Funding sources None.Declaration of Competing Interest BP is part of an advisory board for Sensium Healthcare UK, all other authors declare to have no conflict of interest regarding the current manuscript.Acknowledgements None.Appendix A Supplementary data Supplementary material 1:

Postoperative bleeding is a serious complication following abdominal tumor surgery, but it is often not clearly diagnosed and documented in clinical practice in China. Previous studies have relied on manual interpretation of medical records to determine the presence of postoperative bleeding in patients, which is time-consuming and laborious. More critically, this manual approach severely hinders the efficient analysis of large volumes of medical data, impeding in-depth research into the incidence patterns and risk factors of postoperative bleeding. It remains unclear whether machine learning can play a role in processing large volumes of medical text to identify postoperative bleeding effectively. This study aimed to develop a machine learning model tool for identifying postoperative patients with major bleeding based on the electronic medical record system. This study used data from the available information in the National Health and Medical Big Data (Eastern) Center in Jiangsu Province of China. We randomly selected the medical records of 2,000 patients who underwent in-hospital tumor resection surgery between January 2018 and December 2021 from the database. Physicians manually classified each note as present or absent for a major bleeding event during the postoperative hospital stay. Feature engineering involved bleeding expressions, high-frequency related expressions, and quantitative logical judgment, resulting in 270 features. Logistic regression (LR), K-nearest neighbor (KNN), and convolutional neural network (CNN) models were developed and trained using the 1600-note training set. The main outcomes were accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each model. Major bleeding was present in 4.31% (69/1600) of the training set and 4.75% (19/400) of the test set. In the test set, the LR method achieved an accuracy of 0.8275, a sensitivity of 0.8947, a specificity of 0.8241, a PPV of 0.2024, an NPV of 0.9937, and an F1-score of 0.3301. The CNN method demonstrated an accuracy of 0.8900, sensitivity of 0.8421, specificity of 0.8924, PPV of 0.2807, NPV of 0.9913, and an F1-score of 0.4211. While the KNN method showed a high specificity of 0.9948 and an accuracy of 0.9575 in the test set, its sensitivity was notably low at 0.2105. The C-statistic for the LR method was 0.9018 and for the CNN method was 0.8830. Both the LR and CNN methods demonstrate good performance in identifying major bleeding in patients with postoperative malignant tumors from electronic medical records, exhibiting high sensitivity and specificity. Given the higher sensitivity of the LR method (89.47%) and the higher specificity of the CNN method (89.24%) in the test set, both models hold promise for practical application, depending on specific clinical priorities.

Postoperative pain management has consistently been a critical topic in the medical field, with patient-controlled intravenous analgesia (PCIA) being one of the most commonly utilized methods for postoperative analgesia. Currently, opioids remain the primary choice for PCIA in clinical practice. However, in recent years, an increasing number of studies have explored analgesic strategies aimed at reducing or eliminating the use of opioids in PCIA to mitigate the associated side effects and dependence. This article systematically reviews the progress of research on opioid-free analgesic strategies in PCIA through a comprehensive analysis of relevant literature.

Background The recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery. Methods We extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan–Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage. Results Seven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675–0.690), 0.689 (95% CI: 0.677–0.701), and 0.823 (95% CI: 0.786–0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients. Conclusion The nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

Background The use of prophylactic cranial irradiation (PCI) in early stage small cell lung cancer (SCLC) patients post-surgery remains controversial. This meta-analysis aimed to evaluate the efficacy of PCI in resected early stage SCLC patients. Methods Relevant literature was reviewed through PubMed, Cochrane, and Embase databases. The pooled hazard ratios (HRs) for overall survival (OS) were analyzed for the overall population, as well as for pathologically node-negative (pN0) and pathologically node-positive (pN+) patients. We also assessed the pooled HRs for brain metastasis-free survival (BMFS) in all patients. Sensitivity analyses were conducted to validate these results. Results A total of 13 retrospective studies were included, encompassing 3,530 postoperative SCLC patients, of whom 880 received PCI treatment. In the overall patient population, PCI significantly improved OS compared to non-PCI group (HR: 0.66, 95% CI 0.58–0.74, p  < 0.001). For pN0 patients, there was no significant OS benefit from PCI (HR: 0.85, 95% CI 0.65–1.10, p  = 0.22). In contrast, pN + patients showed a significant OS improvement with PCI (HR: 0.52, 95% CI 0.41–0.66, p  < 0.001). Furthermore, PCI significantly improved BMFS in all patients (HR: 0.42, 95% CI 0.29–0.60, p  < 0.001). Sensitivity analyses confirmed the stability of these results. Conclusions PCI was associated with a significant improvement in OS and BMFS in resected early stage SCLC patients. The benefits of PCI were particularly pronounced in pN + patients, whereas pN0 patients did not experience a significant OS benefit. These findings supported the selective use of PCI based on nodal status to optimize treatment outcomes in postoperative SCLC patients.

Gastric cancer (GC) is a disease with high prevalence and mortality, but we lack convenient and accurate methods to screen for this disease. Thus, we aimed to search for some salivary biomarkers and explore changes in metabolites in patients' saliva after radical gastrectomy. A total of 152 subjects were divided into three groups (healthy group, GC group, and one-week postoperative group). After simple processing, saliva samples were analyzed by liquid chromatography-mass spectrometry. First, we used total ion chromatography and principal component analysis to determine the metabolite profiles. Next, -test, partial least squares discriminant analysis, support vector machine, and receiver operating characteristics curve analysis were performed to identify biomarkers. Then, Fisher discriminant analysis and hierarchical clustering analysis were performed to determine the discriminating ability of biomarkers. Finally, we established a generalized linear model to predict GC based on biomarkers, and used bootstrapping for internal validation. Between the healthy and GC groups, we identified four biomarkers: lactic acid, kynurenic acid, 3-hydroxystachydrine, and S-(1,2,2-trichlorovinyl)-L-cysteine. We used stepwise regression to select five metabolites and develop a model with areas under the curve equal to 0.973 in the training dataset and 0.924 in the validation dataset. Between the GC and one-week postoperative groups, we found two differential metabolites: 19-hydroxyprostaglandin E and DG (14:0/0:0/18:2n6). Differential metabolites were observed among the three groups. GC could be initially diagnosed on the basis of detection of these biomarkers. Moreover, changes in salivary metabolites in postoperative patients could provide important insights for basic studies.

Background: Patients undergoing surgery often demonstrate coagulopathy. Usually, this derangement in coagulation is assessed by the laboratory based evaluation of blood samples. However, collection of samples, their transportation to the lab, and the analyses can result in several errors and as such these tests may not be representative of the complete coagulation process. In our study, we compared the lab coagulation parameters with the point of care TEG indices and attempted to compare the outcome prediction of our patients based on the TEG indices and the various practiced ICU scores. Methods: A prospective, observational study was conducted between May 2014 and May 2015. Fifty adult patients who had undergone noncardiac surgery and had developed new onset 2 or more than 2 system involvement in the postoperative period were enrolled in the study. They were sampled simultaneously for lab coagulation parameters (PT, APTT, INR, fibrinogen, and platelet count) and TEG on days 1, 3, and 5 post admission. Results: There were significant differences between TEG and lab coagulation parameters on day 1 of the study 1 (P = 0.004) but not on days 3 and 5. On days 1 and 3 of our study, the ICU scores (SOFA and APACHE II) were significantly higher in the group with deranged TEG parameters (P = 0.003, 0.02). The patient subpopulation with deranged TEG parameters had significantly higher mortality at median survival time (P = 0.014). Such a difference was not found in patients with higher ICU scores or deranged lab coagulation times. We constructed a ROC curve and arrived at a cutoff value of the reaction time to predict the median survival day mortality. Conclusions: The agreement between TEG and conventional lab parameters remains poor but the TEG parameters seem to be more deranged in sicker patients. As the relationship between the overall severity of illness and derangement in the hemostatic system has been well explored in medical literature, TEG may be a more appropriate modality in such patients.