Objective:To explore the prognostic value of a combined model of baseline 18F-FDG PET/CT tumor metabolic parameters and clinical factors for predicting progression-free survival (PFS) in Hodgkin′s lymphoma (HL). Methods:From January 2014 to May 2023, 171 HL patients (102 males, 69 females; median age 40 years) who underwent 18F-FDG PET/CT before treatment at the First Affiliated Hospital of Nanjing Medical University and Nanjing Drum Tower Hospital were retrospectively collected. HL patients from the First Affiliated Hospital of Nanjing Medical University were classified as the training set (101 patients) and HL patients from Nanjing Drum Tower Hospital were classified as the validation set (70 patients). Clinical factors and tumor metabolic parameters associated with PFS were determined by multivariate Cox regression analysis, and then the combined model and the independent model of each factor were constructed respectively. The consistency index (C-index) and AUC were used to evaluate the predictive efficacy of models, and nomogram was constructed based on the optimal model, and calibration curves were used to assess the goodness of fit of the models. The differences in Kaplan-Meier survival curves of the high-risk and low-risk groups were compared using log-rank test. Results:The multivariate Cox regression analysis indicated that the independent prognostic factors associated with PFS were the Lugano staging (hazard ratio ( HR)=3.10, 95% CI: 1.17-8.23, P=0.023), total metabolic tumor volume (TMTV) ( HR=2.65, 95% CI: 1.23-5.74, P=0.014), and maximum distance between tumors ( Dmax) ( HR=2.23, 95% CI: 1.02-4.85, P=0.044). These factors were used to construct the combined model, with the highest prognostic efficacy of the C-index for the training and validation sets of 0.692 and 0.653, and the AUC of 0.732 and 0.697, respectively. The calibration curves demonstrated that the predictions made by the combined model were in high agreement with the actual results in both the training and validation sets. The Kaplan-Meier analysis revealed a significantly lower PFS rate in the high-risk group compared to the low-risk group both in training and validation sets ( χ2 values: 5.88 and 4.52, P values: 0.015 and 0.033). Conclusion:The combined model incorporating tumor metabolic parameters and clinical factors improves prognostic efficacy in predicting PFS in HL patients.

Objective:To explore the value of baseline 18F-FDG PET/CT metabolic parameters and related clinical factors in the prognostic assessment of patients with angioimmunoblastic T-cell lymphoma (AITL). Methods:From July 2013 to December 2023, 70 patients with AITL (44 males, 26 females, age (63.9±9.6) years) from Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University (32 cases) as well as the First Affiliated Hospital of Nanjing Medical University (38 cases) who were diagnosed pathologically and underwent PET/CT imaging prior to treatment were retrospectively analyzed. PET/CT metabolic parameters (calculated using the 41%SUV max threshold method) and related clinical factors were collected. The optimal cut-off values of metabolic parameters were determined by using the ROC curve analysis. Cox proportional risk regression models were used for prognostic analyses, prediction models were constructed and efficacies were assessed by calibration curves and time-dependent ROC curves. Results:With the follow-up of 19.0(10.0, 33.3) months, disease progression or recurrence occurred in 51 patients, and 28 patients died. ROC curves showed that the optimal cut-off values on diagnosing AITL of total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), and SUV max were 767.1cm 3, 2159.6g and 13.0, respectively. TMTV (hazard ratio ( HR)=0.485, 95% CI: 0.252-0.935, P=0.031) and gender ( HR=0.441, 95% CI: 0.236-0.824, P=0.010) were independent risk factors for progression-free survival (PFS); TMTV ( HR=0.422, 95% CI: 0.178-0.997, P=0.049) and treatment regimen ( HR=0.346, 95% CI: 0.154-0.777, P=0.010) were independent risk factors for overall survival (OS). Time-dependent ROC curves indicated that the combined model of TMTV combining gender or treatment regimen had better prognostic results in predicting PFS (AUCs: 0.67-0.82) or OS (AUCs: 0.62-0.80) in patients with AITL. The calibration curve showed the predicted values of the combined models were in good consistency with the actual values. Conclusions:The metabolic parameter TMTV is an independent risk factor for PFS and OS in patients with AITL. The combined model of TMTV combining gender or treatment regimen can effectively improve the prognostic prediction efficacy of PFS or OS in patients with AITL.

Objective:To investigate the value of a cross-combination machine learning approach in constructing a PET radiomics score (RadScore) for predicting early treatment response and prognosis in patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:This retrospective cohort study was conducted on 108 patients (59 males and 49 females, age (55.6±12.1) years) diagnosed with PGI-DLBCL between November 2016 and December 2021 at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University ( n=85) and West China Hospital, Sichuan University ( n=23). Patients were divided into a training set ( n=86) and a validation set ( n=22) with the ratio of 8∶2 using stratified random sampling method. Seven machine learning models were employed to generate 49 feature selection-classification candidates, and the optimal candidate was selected to construct the RadScore, with five-fold cross-validation applied to determine the best-performing model. Logistic regression analysis was performed to identify risk factors for early treatment response, and a radiomics nomogram was developed by integrating RadScore with clinical predictors. Survival results between different groups of RadScore was compared by log-rank test. Results:Nineteen predictive features were selected from 111 radiomic features to construct the RadScore. In the training set, lactate dehydrogenase (LDH) (odds ratio ( OR)=3.53, 95% CI: 1.21-10.31, P=0.021), intestinal involvement ( OR=3.04, 95% CI: 1.04-8.88, P=0.042), total lesion glycolysis (TLG; OR=6.73, 95% CI: 2.23-20.29, P<0.001) and RadScore ( OR=15.11, 95% CI: 3.95-57.80, P<0.001) were identified as independent risk factors for predicting early treatment response. The combined model integrating RadScore, LDH, intestinal involvement, and TLG demonstrated good discriminatory ability for early treatment response (AUC=0.860 in the training set; AUC=0.902 in the validation set). Significant differences were observed in progression-free survival (PFS) and overall survival (OS) between different RadScore groups ( χ2 values: 13.92 and 8.56, both P<0.01). Conclusions:The machine learning-based RadScore may effectively predict survival outcomes in patients with PGI-DLBCL. The combined model integrating RadScore, clinical factors, and metabolic indicators can predict early treatment response in PGI-DLBCL patients.

Objective:To clarify the current status of mechanical ventilation management in critically ill patients and identify prognostic risk factors in Xinjiang Uygur Autonomous Region, thereby providing evidence for targeted training programs and quality improvement initiatives.Methods:A cohort study was conducted across multiple ICUs in Xinjiang Uygur Autonomous Region from January 31 to February 1, 2024. Patients receiving mechanical ventilation during the study period were enrolled, with clinical outcomes followed up until February 28, 2024. Statistical analyses included demographic characteristics, therapeutic interventions, laboratory parameters, and medication regimens.Results:A total of 77 ICUs and 727 patients were screened in the study, and 253 (34.80%) patients who received mechanical ventilation were ultimately included. Among these patients, 177 patients (69.96%) were treated in tertiary hospitals, and 76 patients (30.04%) in secondary hospitals. Significant differences were observed between tertiary and secondary hospitals regarding ventilator mode selection and mechanical ventilation parameter settings (all P<0.05). No significant differences were found in the 28-day mortality rate between tertiary hospitals and secondary hospitals (33.9% vs. 43.4%, P=0.194). Compared with patients in the survival group, death group patients were older and had more severe disease severity. Multivariate Cox regression analysis demonstrated that body temperature ( HR=1.573, 95% CI: 1.173-2.110, P=0.003), white blood cell count ( HR=1.048, 95% CI: 1.012-1.084, P=0.008), pH ( HR=0.019, 95% CI: 0.001-0.349, P=0.007), age > 65 years ( HR=1.817, 95% CI: 1.086-3.041, P=0.023), and fraction of inspired oxygen ≥ 60% ( HR=2.072, 95% CI: 1.143-3.757, P=0.016) were independent influencing factors for 28-day mortality in mechanically ventilated patients. Conclusions:Mechanically ventilated patients are a major component of the ICU population in Xinjiang Uygur Autonomous Region, with the characteristics of high risk of death. The clinical practice of mechanical ventilation in this region is heterogeneous. In the future, it is urgent to strengthen the improvement of medical quality and related training to improve the success rate of patients with mechanical ventilation.

Objective:To conduct a baseline review of the best evidence for perioperative fraility management in elderly patients, construct review indicators, systematically analyze obstacle factors and promoting factors, and formulate corresponding change strategies, so as to provide reference for clinical transformation of evidence.Methods:With the Joanna Briggs Institute evidence-based health care model as the guiding framework, the best evidence of perioperative frailty-management in elderly patients was summarized, and the review indicators and review methods were formulated on the basis of the evidence. From April 2024 to May 2024, a baseline review of the hospital system of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, medical staff and patients were conducted to analyze the obstacles in clinical practice and formulate corresponding change strategies, guided by integrated-promoting action on research implementation in health services framework(i-PARIHS).Results:According to 19 best evidence, 20 review indicators were established, among which 10 review indicators compliance rate was less than 60% and higher than 0, 2 review indicators compliance rate was 0. Based on the results of the i-PARIHS evidence-based conceptual framework analysis, the obstacle factors included the evidence content was not specific and inaccessible; lack of knowledge and skills in healthcare; the workload of medical care increased; the patient was old and had weak receptivity, lack of systematic practice process; lack of multidisciplinary communication mechanisms.Conclusions:There is a big gap between the best evidence of perioperative frailties management in elderly patients and clinical practice. Clinical departments need to deeply analyze the obstacles to clinical practice based on the existing clinical environment and resource allocation, and formulate targeted strategies to promote the better integration of evidence into clinical practice.

Objective:To construct a bedside handover management in surgical ward based on the intervention mapping theory and verify the practical effect, to provide a reference for further standardizing handover behavior and improving the efficiency of handover management.Methods:A quasi-experimental research method was adopted. From January 2022 to January 2023, nurses from the Department of Urology of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology were selected by cluster sampling. From January to June 2022, routine bedside handover management was implemented. In July 2022, a one-month training on the bedside handover management in surgical ward based on the intervention mapping theory was conducted for urology nurses. From August 2022 to January 2023, the bedside handover management in surgical ward based on the intervention mapping theory was implemented. The incidence of handover problems among nurses, the evaluation of handover among nurses, the qualified rate of nurses' disease awareness, and the satisfaction of patients with handover were compared before and after the training.Results:A total of 48 nurses were included. They were the same batch of nurses before and after the training, including 2 males and 46 females, with an age of (32.23 ± 5.50) years. A total of 1 180 inpatients were included before the training, including 921 males and 259 females, with an age of (56.69 ± 17.24) years. After the training, 1 240 inpatients were included, including 946 males and 294 females, with an age of (55.50 ± 18.85) years. The incidence of handover problems among nurses after the training was 3.71% (46/1 240), which was lower than 9.92% (117/1 180) before the training, and the difference was statistically significant ( χ2=37.07, P<0.05). After the training, the total score of handover evaluation for nurses and the qualified rate of nurses' disease awareness were (80.08 ± 3.74) points and 91.67% (44/48) respectively, which were higher than (73.10 ± 3.53) points and 72.92% (35/48) before the training, and the differences were statistically significant ( t=-0.94, χ2=5.79, both P<0.05). The total score of satisfaction with bedside handover of patients after the training was (76.13 ± 4.50) points, which was higher than (67.92 ± 4.64) points before the training, and the difference was statistically significant ( t=-13.99, P<0.05). Conclusions:The bedside handover management plan based on the intervention mapping theory can effectively strengthen the quality of bedside handover, improve the satisfaction with bedside handover of patients, and deepen nursing quality.

Objective:To conduct a baseline review of the best evidence for perioperative fraility management in elderly patients, construct review indicators, systematically analyze obstacle factors and promoting factors, and formulate corresponding change strategies, so as to provide reference for clinical transformation of evidence.Methods:With the Joanna Briggs Institute evidence-based health care model as the guiding framework, the best evidence of perioperative frailty-management in elderly patients was summarized, and the review indicators and review methods were formulated on the basis of the evidence. From April 2024 to May 2024, a baseline review of the hospital system of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, medical staff and patients were conducted to analyze the obstacles in clinical practice and formulate corresponding change strategies, guided by integrated-promoting action on research implementation in health services framework(i-PARIHS).Results:According to 19 best evidence, 20 review indicators were established, among which 10 review indicators compliance rate was less than 60% and higher than 0, 2 review indicators compliance rate was 0. Based on the results of the i-PARIHS evidence-based conceptual framework analysis, the obstacle factors included the evidence content was not specific and inaccessible; lack of knowledge and skills in healthcare; the workload of medical care increased; the patient was old and had weak receptivity, lack of systematic practice process; lack of multidisciplinary communication mechanisms.Conclusions:There is a big gap between the best evidence of perioperative frailties management in elderly patients and clinical practice. Clinical departments need to deeply analyze the obstacles to clinical practice based on the existing clinical environment and resource allocation, and formulate targeted strategies to promote the better integration of evidence into clinical practice.

Objective:To construct a bedside handover management in surgical ward based on the intervention mapping theory and verify the practical effect, to provide a reference for further standardizing handover behavior and improving the efficiency of handover management.Methods:A quasi-experimental research method was adopted. From January 2022 to January 2023, nurses from the Department of Urology of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology were selected by cluster sampling. From January to June 2022, routine bedside handover management was implemented. In July 2022, a one-month training on the bedside handover management in surgical ward based on the intervention mapping theory was conducted for urology nurses. From August 2022 to January 2023, the bedside handover management in surgical ward based on the intervention mapping theory was implemented. The incidence of handover problems among nurses, the evaluation of handover among nurses, the qualified rate of nurses' disease awareness, and the satisfaction of patients with handover were compared before and after the training.Results:A total of 48 nurses were included. They were the same batch of nurses before and after the training, including 2 males and 46 females, with an age of (32.23 ± 5.50) years. A total of 1 180 inpatients were included before the training, including 921 males and 259 females, with an age of (56.69 ± 17.24) years. After the training, 1 240 inpatients were included, including 946 males and 294 females, with an age of (55.50 ± 18.85) years. The incidence of handover problems among nurses after the training was 3.71% (46/1 240), which was lower than 9.92% (117/1 180) before the training, and the difference was statistically significant ( χ2=37.07, P<0.05). After the training, the total score of handover evaluation for nurses and the qualified rate of nurses' disease awareness were (80.08 ± 3.74) points and 91.67% (44/48) respectively, which were higher than (73.10 ± 3.53) points and 72.92% (35/48) before the training, and the differences were statistically significant ( t=-0.94, χ2=5.79, both P<0.05). The total score of satisfaction with bedside handover of patients after the training was (76.13 ± 4.50) points, which was higher than (67.92 ± 4.64) points before the training, and the difference was statistically significant ( t=-13.99, P<0.05). Conclusions:The bedside handover management plan based on the intervention mapping theory can effectively strengthen the quality of bedside handover, improve the satisfaction with bedside handover of patients, and deepen nursing quality.

Objective:To explore the prognostic value of a combined model of baseline 18F-FDG PET/CT tumor metabolic parameters and clinical factors for predicting progression-free survival (PFS) in Hodgkin′s lymphoma (HL). Methods:From January 2014 to May 2023, 171 HL patients (102 males, 69 females; median age 40 years) who underwent 18F-FDG PET/CT before treatment at the First Affiliated Hospital of Nanjing Medical University and Nanjing Drum Tower Hospital were retrospectively collected. HL patients from the First Affiliated Hospital of Nanjing Medical University were classified as the training set (101 patients) and HL patients from Nanjing Drum Tower Hospital were classified as the validation set (70 patients). Clinical factors and tumor metabolic parameters associated with PFS were determined by multivariate Cox regression analysis, and then the combined model and the independent model of each factor were constructed respectively. The consistency index (C-index) and AUC were used to evaluate the predictive efficacy of models, and nomogram was constructed based on the optimal model, and calibration curves were used to assess the goodness of fit of the models. The differences in Kaplan-Meier survival curves of the high-risk and low-risk groups were compared using log-rank test. Results:The multivariate Cox regression analysis indicated that the independent prognostic factors associated with PFS were the Lugano staging (hazard ratio ( HR)=3.10, 95% CI: 1.17-8.23, P=0.023), total metabolic tumor volume (TMTV) ( HR=2.65, 95% CI: 1.23-5.74, P=0.014), and maximum distance between tumors ( Dmax) ( HR=2.23, 95% CI: 1.02-4.85, P=0.044). These factors were used to construct the combined model, with the highest prognostic efficacy of the C-index for the training and validation sets of 0.692 and 0.653, and the AUC of 0.732 and 0.697, respectively. The calibration curves demonstrated that the predictions made by the combined model were in high agreement with the actual results in both the training and validation sets. The Kaplan-Meier analysis revealed a significantly lower PFS rate in the high-risk group compared to the low-risk group both in training and validation sets ( χ2 values: 5.88 and 4.52, P values: 0.015 and 0.033). Conclusion:The combined model incorporating tumor metabolic parameters and clinical factors improves prognostic efficacy in predicting PFS in HL patients.

Objective:To explore the value of baseline 18F-FDG PET/CT metabolic parameters and related clinical factors in the prognostic assessment of patients with angioimmunoblastic T-cell lymphoma (AITL). Methods:From July 2013 to December 2023, 70 patients with AITL (44 males, 26 females, age (63.9±9.6) years) from Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University (32 cases) as well as the First Affiliated Hospital of Nanjing Medical University (38 cases) who were diagnosed pathologically and underwent PET/CT imaging prior to treatment were retrospectively analyzed. PET/CT metabolic parameters (calculated using the 41%SUV max threshold method) and related clinical factors were collected. The optimal cut-off values of metabolic parameters were determined by using the ROC curve analysis. Cox proportional risk regression models were used for prognostic analyses, prediction models were constructed and efficacies were assessed by calibration curves and time-dependent ROC curves. Results:With the follow-up of 19.0(10.0, 33.3) months, disease progression or recurrence occurred in 51 patients, and 28 patients died. ROC curves showed that the optimal cut-off values on diagnosing AITL of total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), and SUV max were 767.1cm 3, 2159.6g and 13.0, respectively. TMTV (hazard ratio ( HR)=0.485, 95% CI: 0.252-0.935, P=0.031) and gender ( HR=0.441, 95% CI: 0.236-0.824, P=0.010) were independent risk factors for progression-free survival (PFS); TMTV ( HR=0.422, 95% CI: 0.178-0.997, P=0.049) and treatment regimen ( HR=0.346, 95% CI: 0.154-0.777, P=0.010) were independent risk factors for overall survival (OS). Time-dependent ROC curves indicated that the combined model of TMTV combining gender or treatment regimen had better prognostic results in predicting PFS (AUCs: 0.67-0.82) or OS (AUCs: 0.62-0.80) in patients with AITL. The calibration curve showed the predicted values of the combined models were in good consistency with the actual values. Conclusions:The metabolic parameter TMTV is an independent risk factor for PFS and OS in patients with AITL. The combined model of TMTV combining gender or treatment regimen can effectively improve the prognostic prediction efficacy of PFS or OS in patients with AITL.