Objective:To investigate the impact of body composition and inflammatory nutritional indicators on postoperative complications in patients with obstructive colorectal cancer,and to develop and validate a nomogram model.Methods:This is a retrospective case series study. The clinical data of 293 patients with obstructive colorectal cancer who were treated at the Department of Gastrointestinal Surgery,the Affiliated Hospital of Qingdao University,between January 2016 and January 2024,were retrospectively collected. The cohort included 182 males and 111 females,aged (65.0±12.1) years (range: 18 to 80 years). The dataset was randomly divided into a training group ( n=196) and a validation group ( n=97) with a 7∶3 ratio. Independent sample t test and multivariate logistic regression analysis were employed to identify independent risk factors associated with postoperative complications in patients with obstructive colorectal cancer. A preoperative nomogram model was subsequently developed for predicting postoperative complications,which was further validated using a validation cohort. Results:The training group comprised 119 males and 77 females,with 68 cases experiencing postoperative complications and 128 cases without complications. The validation group included 63 males and 34 females,with 30 cases experiencing postoperative complications and 67 cases without complications.Univariate analysis and multivariate analysis revealed that low skeletal muscle index ( OR=0.867,95% CI: 0.795 to 0.947),high visceral fat index ( OR=1.058,95% CI: 1.028 to 1.089),high systemic immune inflammation index ( OR=1.002, 95% CI: 1.000 to 1.003), low prognostic nutritional index ( OR=0.847,95% CI: 0.782 to 0.917),and preoperative anemia ( OR=2.714,95% CI: 1.161 to 6.344) were independent risk factors for postoperative complications (all P<0.05). A nomogram prediction model based on these five indicators was established. The area under the receiver operating characteristic (ROC) curve for the prediction model was 0.878 (95% CI: 0.829 to 0.928) in the training group and 0.849 (95% CI:0.767 to 0.930) in the validation group. Conclusions:The preoperative nomogram model,which incorporates inflammatory and nutritional indicators,demonstrates a good accuracy in predicting postoperative complications for patients with obstructive colorectal cancer. This model can effectively assist in guiding treatment decisions.

OBJECTIVE: To investigate the application value of pediatric sepsis-induced coagulation (pSIC) score and mean platelet volume/platelet count (MPV/PLT) ratio in the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis. METHODS: A retrospective cohort study was conducted, selecting 112 children with sepsis (sepsis group) admitted to pediatric intensive care unit (PICU) of Liaocheng Second People's Hospital from January 2020 to December 2023 as the study objects, and 50 children without sepsis admitted to the pediatric surgery department of our hospital during the same period for elective surgery due to inguinal hernia as the control (control group). The children with sepsis were divided into two groups according to the pediatric critical case score (PCIS). The children with PCIS score of ≤ 80 were classified as critically ill group, and those with PCIS score of > 80 was classified as non-critically ill group. pSIC score, coagulation indicators [prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and fibrinogen (FIB)], and platelet related indicators (PLT, MPV, and MPV/PLT ratio) were collected. Pearson correlation method was used to analyze the correlation between pSIC score and MPV/PLT ratio as well as their correlation with coagulation indicators. Multivariate Logistic regression analysis was used to screen the independent risk factors for pediatric sepsis and critical pediatric sepsis. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the application value of the above independent risk factors on the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis. RESULTS: 112 children with sepsis and 50 children without sepsis were enrolled in the final analysis. pSIC score, PT, INR, APTT, FIB, MPV, and MPV/PLT ratio in the sepsis group were significantly higher than those in the control group [pSIC score: 0.93±0.10 vs. 0.06±0.03, PT (s): 14.76±0.38 vs. 12.23±0.15, INR: 1.26±0.03 vs. 1.06±0.01, APTT (s): 40.08±0.94 vs. 32.47±0.54, FIB (g/L): 3.51±0.11 vs. 2.31±0.06, MPV (fL): 8.86±0.14 vs. 7.62±0.11, MPV/PLT ratio: 0.037±0.003 vs. 0.022±0.001, all P < 0.01], and PLT was slightly lower than that in the control group (×10⁹/L: 306.00±11.01 vs. 345.90±10.57, P > 0.05). Among 112 children with sepsis, 46 were critically ill and 66 were non-critically ill. pSIC score, PT, INR, APTT, MPV, and MPV/PLT ratio in the critically ill group were significantly higher than those in the non-critically ill group [pSIC score: 1.74±0.17 vs. 0.36±0.07, PT (s): 16.55±0.80 vs. 13.52±0.23, INR: 1.39±0.07 vs. 1.17±0.02, APTT (s): 43.83±1.72 vs. 37.77±0.95, MPV (fL): 9.31±0.23 vs. 8.55±0.16, MPV/PLT ratio: 0.051±0.006 vs. 0.027±0.001, all P < 0.05], PLT was significantly lower than that in the non-critically ill group (×10⁹/L: 260.50±18.89 vs. 337.70±11.90, P < 0.01), and FIB was slightly lower than that in the non-critically ill group (g/L: 3.28±0.19 vs. 3.67±0.14, P > 0.05). Correlation analysis showed that pSIC score was significantly positively correlated with MPV/PLT ratio and coagulation indicators including PT, APTT and INR in pediatric sepsis (r value was 0.583, 0.571, 0.296 and 0.518, respectively, all P < 0.01), and MPV/PLT ratio was also significantly positively correlated with PT, APTT and INR (r value was 0.300, 0.203 and 0.307, respectively, all P < 0.05). Multivariate Logistic regression analysis showed that pSIC score and MPV/PLT ratio were independent risk factors for pediatric sepsis and critical pediatric sepsis [pediatric sepsis: odds ratio (OR) and 95% confidence interval (95%CI) for pSIC score was 14.117 (4.190-47.555), and the OR value and 95%CI for MPV/PLT ratio was 1.128 (1.059-1.202), both P < 0.01; critical pediatric sepsis: the OR value and 95%CI for pSIC score was 8.142 (3.672-18.050), and the OR value and 95%CI for MPV/PLT ratio was 1.068 (1.028-1.109), all P < 0.01]. ROC curve analysis showed that pSIC score and MPV/PLT ratio had certain application value in the diagnosis of pediatric sepsis [area under the ROC curve (AUC) and 95%CI was 0.754 (0.700-0.808) and 0.720 (0.643-0.798), respectively] and the determination of critical pediatric sepsis [AUC and 95%CI was 0.849 (0.778-0.919) and 0.731 (0.632-0.830)], and the combined AUC of the two indictors was 0.815 (95%CI was 0.751-0.879) and 0.872 (95%CI was 0.806-0.938), respectively. CONCLUSIONS pSIC score and MPV/PLT ratio have potential application value in the diagnosis of pediatric sepsis and the determination of critical pediatric sepsis, and the combined application of both is more valuable.
Humans ; Sepsis/complications* ; Platelet Count ; Mean Platelet Volume ; Retrospective Studies ; Child ; Blood Coagulation Disorders/diagnosis* ; Intensive Care Units, Pediatric ; Male ; Female ; Partial Thromboplastin Time ; Child, Preschool ; Blood Coagulation ; International Normalized Ratio ; Infant

Objective To explore the role of the brief visuospatial memory test-revised(BVMT-R)in predicting the clinical conversion to psychosis in subjects with clinical high-risk for psychosis(CHR-P).Methods A total of 217 CHR subjects were recruited and assessed using BVMT-R at baseline.Participants were followed up for three years to determine whether they converted to psychosis.The relationship between BVMT-R total score and CHR-P conversion probability was analyzed using generalized additive model,and the cutoff values of BVMT-R total score for predicting CHR-P conversion were calculated by maximally selected rank statistics.Then,the total BVMT-R score was stratified into different intervals based on the cutoff values obtained as previously described.Finally,the positive likelihood ratios and the conversion rates at different time points were calculated for each interval.Results A total of 168 subjects with CHR-P completed the 3-year follow-up assessment.According to the results of the generalized additive model,the relationship between the BVMT-R total score and CHR-P conversion probability exhibited the characteristics of a piecewise function model.The cutoff values identified using the maximally selected rank statistics method were 18 and 29,which divided the BVMT-R total scores into three intervals:0-18,19-29,and 30-36.The positive likelihood ratios of the three intervals for predicting CHR-P psychosis conversion were significantly different(all P<0.01).All three intervals had significantly different rates of psychosis conversion at different follow-up time points(all P<0.01).Conclusion The total BVMT-R score can be divided into three intervals,each associated with a distinct positive likelihood ratio for predicting psychosis conversion in CHR-P individuals.Accordingly,the BVMT-R total score may serve as a preliminary indicator for estimating the probability of psychosis conversion in the CHR-P population.

Objective:To investigate the risk factors of pediatric sepsis-induced coagulopathy（pSIC），and to construct a nomogram prediction model for early prediction of pSIC.Methods:Using a cross-sectional retrospective cohort design，children with sepsis who were hospitalized in PICU of the Second People's Hospital of Liaocheng Subsidiary to Shandong First Medical University from January 2017 to December 2023 were selected as the study objects，and the diagnosis of sepsis met the diagnostic criteria for childhood sepsis of the 2015 edition.According to the diagnostic criteria of pSIC，the children with sepsis were divided into common sepsis group and pSIC group.The clinical data of both groups were compared，such as general condition，inflammatory indicators，coagulation indicators，sequential organ failure assessment（pSOFA），pSIC score，PICU duration，etc.The risk factors of pSIC were initially screened by Lasso regression analysis，and the independent risk factors were screened by multivariate Logistic regression analysis.R software was used to construct the risk prediction nomogram and evaluate the model.Results:A total of 150 children with sepsis were included in the study，including 121 in the common sepsis group and 29 in the pSIC group.Lasso regression and multivariate Logistic regression analysis showed that pSOFA，prothrombin time（PT），alanine aminotransferase（ALT），blood urea nitrogen（BUN），mean platelet volume/platelet（MPV/PLT）and pediatric critical illness score(PCIS) were independent risk factors for pSIC（all P<0.05）.Since the sources of the pSIC score overlaped with those of pSOFA and PT, only four indicators including ALT，BUN，MPV/PLT and PCIS were used to construct a nomogram model for predicting pSIC.The consistency index of the nomogram model was 0.98，and the area under the receiver operating characteristic curve was 0.975（95% CI 0.952-0.999）.The calibration curve was shown as a straight line with slope close to 1，indicating that the nomogram model had good accuracy in predicting pSIC.The clinical decision curve indicated that the nomogram model had good clinical applicability. Conclusion:pSOFA，PT，ALT，BUN，MPV/PLT and PCIS were all independent risk factors for pSIC.The risk prediction nomogram model of pSIC based on ALT，BUN，MPV/PLT and PCIS can predict the occurrence of pSIC，and provide reference for early clinical recognition and intervention.

Objective To explore the role of the brief visuospatial memory test-revised(BVMT-R)in predicting the clinical conversion to psychosis in subjects with clinical high-risk for psychosis(CHR-P).Methods A total of 217 CHR subjects were recruited and assessed using BVMT-R at baseline.Participants were followed up for three years to determine whether they converted to psychosis.The relationship between BVMT-R total score and CHR-P conversion probability was analyzed using generalized additive model,and the cutoff values of BVMT-R total score for predicting CHR-P conversion were calculated by maximally selected rank statistics.Then,the total BVMT-R score was stratified into different intervals based on the cutoff values obtained as previously described.Finally,the positive likelihood ratios and the conversion rates at different time points were calculated for each interval.Results A total of 168 subjects with CHR-P completed the 3-year follow-up assessment.According to the results of the generalized additive model,the relationship between the BVMT-R total score and CHR-P conversion probability exhibited the characteristics of a piecewise function model.The cutoff values identified using the maximally selected rank statistics method were 18 and 29,which divided the BVMT-R total scores into three intervals:0-18,19-29,and 30-36.The positive likelihood ratios of the three intervals for predicting CHR-P psychosis conversion were significantly different(all P<0.01).All three intervals had significantly different rates of psychosis conversion at different follow-up time points(all P<0.01).Conclusion The total BVMT-R score can be divided into three intervals,each associated with a distinct positive likelihood ratio for predicting psychosis conversion in CHR-P individuals.Accordingly,the BVMT-R total score may serve as a preliminary indicator for estimating the probability of psychosis conversion in the CHR-P population.

Objective:To explore latent categories of death literacy among oncology nurses and analyze their influencing factors to inform the development of targeted interventions.Methods:Convenience sampling was used to select 560 oncology nurses from three ClassⅢ Grade A hospitals in Zhengzhou City, Henan Province, from June to July 2024 for the study. General Information Questionnaire, Death Literacy Index, Self-Competence in Death Work Scale, and Hospice Care Environment Scale were used to conduct the survey. Oncology nurses' death literacy categories were explored using latent profile analysis, and factors influencing each category were explored using unordered multicategorical Logistic regression analysis.Results:The 560 oncology nurses' death literacy were categorized into three profiles of low-level death literacy group (31.8%), medium-level death literacy group (50.7%), and high-level death literacy group (17.5%). Unordered multicategorical Logistic regression analysis showed that receiving death education and training since work, self-assessment of psychological status, self-competence in death work, and evaluation of the hospice care environment were influencing factors in the latent category of death literacy among oncology nurses ( P<0.05) . Conclusions:There is group heterogeneity in death literacy among oncology nurses, which is influenced by a variety of factors. Nursing managers can provide targeted interventions for oncology nurses based on different latent categories to improve their death literacy.

Objective:To investigate the risk factors of pediatric sepsis-induced coagulopathy（pSIC），and to construct a nomogram prediction model for early prediction of pSIC.Methods:Using a cross-sectional retrospective cohort design，children with sepsis who were hospitalized in PICU of the Second People's Hospital of Liaocheng Subsidiary to Shandong First Medical University from January 2017 to December 2023 were selected as the study objects，and the diagnosis of sepsis met the diagnostic criteria for childhood sepsis of the 2015 edition.According to the diagnostic criteria of pSIC，the children with sepsis were divided into common sepsis group and pSIC group.The clinical data of both groups were compared，such as general condition，inflammatory indicators，coagulation indicators，sequential organ failure assessment（pSOFA），pSIC score，PICU duration，etc.The risk factors of pSIC were initially screened by Lasso regression analysis，and the independent risk factors were screened by multivariate Logistic regression analysis.R software was used to construct the risk prediction nomogram and evaluate the model.Results:A total of 150 children with sepsis were included in the study，including 121 in the common sepsis group and 29 in the pSIC group.Lasso regression and multivariate Logistic regression analysis showed that pSOFA，prothrombin time（PT），alanine aminotransferase（ALT），blood urea nitrogen（BUN），mean platelet volume/platelet（MPV/PLT）and pediatric critical illness score(PCIS) were independent risk factors for pSIC（all P<0.05）.Since the sources of the pSIC score overlaped with those of pSOFA and PT, only four indicators including ALT，BUN，MPV/PLT and PCIS were used to construct a nomogram model for predicting pSIC.The consistency index of the nomogram model was 0.98，and the area under the receiver operating characteristic curve was 0.975（95% CI 0.952-0.999）.The calibration curve was shown as a straight line with slope close to 1，indicating that the nomogram model had good accuracy in predicting pSIC.The clinical decision curve indicated that the nomogram model had good clinical applicability. Conclusion:pSOFA，PT，ALT，BUN，MPV/PLT and PCIS were all independent risk factors for pSIC.The risk prediction nomogram model of pSIC based on ALT，BUN，MPV/PLT and PCIS can predict the occurrence of pSIC，and provide reference for early clinical recognition and intervention.

Objective:To explore latent categories of death literacy among oncology nurses and analyze their influencing factors to inform the development of targeted interventions.Methods:Convenience sampling was used to select 560 oncology nurses from three ClassⅢ Grade A hospitals in Zhengzhou City, Henan Province, from June to July 2024 for the study. General Information Questionnaire, Death Literacy Index, Self-Competence in Death Work Scale, and Hospice Care Environment Scale were used to conduct the survey. Oncology nurses' death literacy categories were explored using latent profile analysis, and factors influencing each category were explored using unordered multicategorical Logistic regression analysis.Results:The 560 oncology nurses' death literacy were categorized into three profiles of low-level death literacy group (31.8%), medium-level death literacy group (50.7%), and high-level death literacy group (17.5%). Unordered multicategorical Logistic regression analysis showed that receiving death education and training since work, self-assessment of psychological status, self-competence in death work, and evaluation of the hospice care environment were influencing factors in the latent category of death literacy among oncology nurses ( P<0.05) . Conclusions:There is group heterogeneity in death literacy among oncology nurses, which is influenced by a variety of factors. Nursing managers can provide targeted interventions for oncology nurses based on different latent categories to improve their death literacy.

Objective:To investigate the impact of body composition and inflammatory nutritional indicators on postoperative complications in patients with obstructive colorectal cancer,and to develop and validate a nomogram model.Methods:This is a retrospective case series study. The clinical data of 293 patients with obstructive colorectal cancer who were treated at the Department of Gastrointestinal Surgery,the Affiliated Hospital of Qingdao University,between January 2016 and January 2024,were retrospectively collected. The cohort included 182 males and 111 females,aged (65.0±12.1) years (range: 18 to 80 years). The dataset was randomly divided into a training group ( n=196) and a validation group ( n=97) with a 7∶3 ratio. Independent sample t test and multivariate logistic regression analysis were employed to identify independent risk factors associated with postoperative complications in patients with obstructive colorectal cancer. A preoperative nomogram model was subsequently developed for predicting postoperative complications,which was further validated using a validation cohort. Results:The training group comprised 119 males and 77 females,with 68 cases experiencing postoperative complications and 128 cases without complications. The validation group included 63 males and 34 females,with 30 cases experiencing postoperative complications and 67 cases without complications.Univariate analysis and multivariate analysis revealed that low skeletal muscle index ( OR=0.867,95% CI: 0.795 to 0.947),high visceral fat index ( OR=1.058,95% CI: 1.028 to 1.089),high systemic immune inflammation index ( OR=1.002, 95% CI: 1.000 to 1.003), low prognostic nutritional index ( OR=0.847,95% CI: 0.782 to 0.917),and preoperative anemia ( OR=2.714,95% CI: 1.161 to 6.344) were independent risk factors for postoperative complications (all P<0.05). A nomogram prediction model based on these five indicators was established. The area under the receiver operating characteristic (ROC) curve for the prediction model was 0.878 (95% CI: 0.829 to 0.928) in the training group and 0.849 (95% CI:0.767 to 0.930) in the validation group. Conclusions:The preoperative nomogram model,which incorporates inflammatory and nutritional indicators,demonstrates a good accuracy in predicting postoperative complications for patients with obstructive colorectal cancer. This model can effectively assist in guiding treatment decisions.

This article reviews the concept and significance of death literacy, assessment tools, current status domestically and internationally, influencing factors, and intervention measures. The aim is to provide insights for effective strategies to enhance residents' death literacy, thereby offering a new perspective for improving palliative care practices and research in China and ensuring quality of death.