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BACKGROUND: General anesthesia is commonly used in the surgical management of gastrointestinal tumors; however, it can lead to emergence agitation (EA). EA is a common complication associated with general anesthesia, often characterized by behaviors, such as crying, struggling, and involuntary limb movements in patients. If treatment is delayed, there is a risk of incision cracking and bleeding, which can significantly affect surgical outcomes. Therefore, having a proper understanding of the factors influencing the occurrence of EA and implementing early preventive measures may reduce the incidence of agitation during the recovery phase from general anesthesia, which is beneficial for improving patient prognosis. AIM: To analyze influencing factors and develop a risk prediction model for EA occurrence following general anesthesia for primary liver cancer. METHODS: Retrospective analysis of clinical data from 200 patients who underwent hepatoma resection under general anesthesia at Wenzhou Central Hospital (January 2020 to December 2023) was conducted. Post-surgery, the Richmond Agitation-Sedation Scale was used to evaluate EA presence, noting EA incidence after general anesthesia. Patients were categorized by EA presence postoperatively, and the influencing factors were analyzed using logistic regression. A nomogram-based risk prediction model was constructed and evaluated for differentiation and fit using receiver operating characteristics and calibration curves. RESULTS: EA occurred in 51 (25.5%) patients. Multivariate analysis identified advanced age, American Society of Anesthesiologists (ASA) grade III, indwelling catheter use, and postoperative pain as risk factors for EA (P < 0.05). Conversely, postoperative analgesia was a protective factor against EA (P < 0.05). The area under the curve of the nomogram was 0.972 [95% confidence interval (CI): 0.947-0.997] for the training set and 0.979 (95%CI: 0.951-1.000) for the test set. Hosmer-Lemeshow test showed a good fit (χ 2 = 5.483, P = 0.705), and calibration curves showed agreement between predicted and actual EA incidence. CONCLUSION: Age, ASA grade, catheter use, postoperative pain, and analgesia significantly influence EA occurrence. A nomogram constructed using these factors demonstrates strong predictive accuracy.

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In general, small stream basins, characterized by narrow channels and steep slopes, face heightened vulnerability to climate change-induced flooding, posing challenges for evacuation procedures. With the increasing intensity of floods and typhoons in recent years, urgent measures are necessary to mitigate damage in such areas. This research endeavors to address these challenges by developing a novel small stream flood early warning system (SSFEWS) tailored to small streams and piloting its application. The proposed system integrates real-time hydrodynamic data collection, flood probability forecasting, and proactive warning issuance through an amalgamation of IoT-based sensor networks, statistical models leveraging measurement data, a robust constrained nonlinear optimization algorithm (RCNOA), and four-parameter logistic method (4PL). Moreover, system accuracy and reliability are enhanced by an automated iterative process that continuously refines forecasting model parameters via a user-defined rainfall-discharge nomograph and rating curve using RCNOA and 4PL. The developed SSFEWS is expected to contribute to the safety of the community as well as prevent possible small stream-related casualties by enabling efficient disaster response. © 2024 Elsevier Ltd. All rights reserved.

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Purpose: To investigate the factors influencing hypothermia during pancreaticoduodenectomy and establish and verify a prediction model. Method: The clinical data of patients undergoing pancreaticoduodenectomy in Hunan People's Hospital between January 1, 2022 and October 15, 2022 were analysed. The patients were divided into a hypothermia group (n = 302) and a non-hypothermia group (n = 164) according to whether hypothermia occurred during surgery. A binary logistic regression model was used to analyse the independent risk factors for hypothermia in patients undergoing pancreaticoduodenectomy. A risk prediction model was established, and R software was used to plot a column graph. The predictive value of the model was evaluated using the receiver operating characteristic (ROC) curve. Results: Among the 466 patients undergoing pancreaticoduodenectomy, 302 (64.81 %) had hypothermia, including 154 men and 148 women, with a median age of 58.6 (38-86) years. The binary logistic regression analysis showed that low body mass index (BMI), room temperature at the time of entry, intraoperative flushing fluid volume and peritoneal flushing fluid temperature were independent risk factors for intraoperative hypothermia in patients undergoing pancreaticoduodenal surgery (P < 0.05). A multivariate logistic regression analysis (backward logistic regression) was used to establish the prediction model. The area under the ROC curve was 0.927, P ≤ 0.001, the sensitivity was 0.921 and the specificity was 0.848, indicating good differentiation by the prediction model. Conclusion: The nomogram constructed using four independent risk factors: BMI, room temperature at the time of entry, intraoperative peritoneal flushing fluid volume and intraoperative peritoneal flushing fluid temperature, has good predictive efficacy and good clinical application value for predicting intraoperative hypothermia in patients undergoing pancreaticoduodenectomy.

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BACKGROUND: This study aimed to explore the factors associated with extended length of stay (LOSE) for patients with tuberculosis (TB) in China, and construct a nomogram to predict it. In addition, the impact of extended hospital stay on short-term readmission after discharge was assessed. METHODS: A retrospective observational study was conducted at Changsha Central Hospital, from January 2018 to December 2020. Patients (≥18 years who were first admitted to hospital for TB treatment) with non-multidrug-resistant TB were selected using the World Health Organization's International Classification of Diseases, 10th Revision (ICD-10-CM), and the hospital's electronic medical record system. RESULTS: A multivariate logistic regression analysis was used to evaluate the associations between TB and LOSE. The relationship between length of hospital stay and readmission within 31 days after discharge was assessed using a univariate Cox proportional risk model. A total of 14259 patients were included in this study (13629 patients in the development group and 630 in the validation group). The factors associated with extended hospital stays were age, smear positivity, extrapulmonary involvement, surgery, transfer from other medical structures, smoking, chronic liver disease, and drug-induced hepatitis. There was no statistical significance in the 31-day readmission rate of TB between the LOSE and length of stay≤14 days groups (hazards ratio: 0.92, 95% CI: 0.80-1.06, P=0.229). CONCLUSION: LOSE with TB was influenced by several patient-level factors, which were combined to construct a nomograph. The established nomograph can help hospital administrator and clinicians to identify patients with TB requiring extended hospital stays, and more efficiently plan for treatment programs and resource needs.

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BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common types of cancers worldwide, ranking fifth among men and seventh among women, resulting in more than 7 million deaths annually. With the development of medical technology, the 5-year survival rate of HCC patients can be increased to 70%. However, HCC patients are often at increased risk of cardiovascular disease (CVD) death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients. Moreover, CVD and cancer have become major disease burdens worldwide. Thus, further research is needed to lessen the risk of CVD death in HCC patient survivors. AIM: To determine the independent risk factors for CVD death in HCC patients and predict cardiovascular mortality (CVM) in HCC patients. METHODS: This study was conducted on the basis of the Surveillance, Epidemiology, and End Results database and included HCC patients with a diagnosis period from 2010 to 2015. The independent risk factors were identified using the Fine-Gray model. A nomograph was constructed to predict the CVM in HCC patients. The nomograph performance was measured using Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) value. Moreover, the net benefit was estimated via decision curve analysis (DCA). RESULTS: The study included 21545 HCC patients, of whom 619 died of CVD. Age (< 60) [1.981 (1.573-2.496), P < 0.001], marital status (married) [unmarried: 1.370 (1.076-1.745), P = 0.011], alpha fetoprotein (normal) [0.778 (0.640-0.946), P = 0.012], tumor size (≤ 2 cm) [(2, 5] cm: 1.420 (1.060-1.903), P = 0.019; > 5 cm: 2.090 (1.543-2.830), P < 0.001], surgery (no) [0.376 (0.297-0.476), P < 0.001], and chemotherapy(none/unknown) [0.578 (0.472-0.709), P < 0.001] were independent risk factors for CVD death in HCC patients. The discrimination and calibration of the nomograph were better. The C-index values for the training and validation sets were 0.736 and 0.665, respectively. The AUC values of the ROC curves at 2, 4, and 6 years were 0.702, 0.725, 0.740 in the training set and 0.697, 0.710, 0.744 in the validation set, respectively. The calibration curves showed that the predicted probabilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities. DCA demonstrated that the prediction model has a high net benefit. CONCLUSION: Risk factors for CVD death in HCC patients were investigated for the first time. The nomograph served as an important reference tool for relevant clinical management decisions.

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BACKGROUND/OBJECTIVE: We aimed to develop a comprehensive and effective nomogram for predicting cancer-specific survival (CSS) in patients with pulmonary sarcomatoid carcinoma (PSC). METHODS: Data for patients diagnosed with PSC between 2004 and 2018 from the Surveillance, Epidemiology, and End Results database were retrospectively collected and randomly divided into training and internal validation sets. We then retrospectively recruited patients diagnosed with PSC to construct an external validation cohort from the Southwest Hospital. A prognostic nomogram for CSS was established using independent prognostic factors that were screened from the multivariate Cox regression analysis. The performance of the nomogram was evaluated using area under the receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), calibration diagrams, and decision curve analysis (DCA). The clinical value of the nomogram and tumor, nodes, and metastases (TNM) staging system was compared using the C-index and net reclassification index (NRI). RESULTS: Overall, 1356 patients with PSC were enrolled, including 876, 377, and 103 in the training, internal validation, and external validation sets, respectively. The C-index and ROC curves, calibration, and DCA demonstrated satisfactory nomogram performance for CSS in patients with PSC. In addition, the C-index and NRI of the nomogram suggested a significantly higher nomogram value than that of the TNM staging system. Subsequently, a web-based predictor was developed to help clinicians obtain this model easily. CONCLUSIONS: The prognostic nomogram developed in this study can conveniently and precisely estimate the prognosis of patients with PSC and individualize treatment, thereby assisting clinicians in their shared decision-making with patients.

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Objective To construct a nomogram to predict the prognosis of patients with gallbladder cancer(GBC).Methods The clinicopathological data of GBC patients were extracted from the SEER database,and the independent prognostic factors of GBC patients were analyzed by Cox regression,and a nomogram was constructed.Finally,the column diagrams in the training queue and validation queue are verified.Results Age,T stage,M stage,histological grade,radiotherapy,surgery and tumor size were independent prognostic factors in GBC patients,and the differences were statistically significant(P＜0.05).In the training cohort,the C index was 0.735(95％CI=0.721～0.749),and the AUC values at 1,3 and 5 years were 0.821,0.820 and 0.833,respectively.In the verification group,the C index was 0.733(95％CI=0.711～0.755),and the AUC values for 1,3 and 5 years were 0.816,0.807 and 0.827,respectively.The calibration curve shows that the predicted values of the nomogram are in good agreement with the observed values.The decision curve shows that the nomogram model has better prediction ability than TNM staging system.Conclusion The constructed dynamic prognosis nomogram of GBC patients has high accuracy and reliability.

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OBJECTIVE: To identify the factors related to the severity of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) and establishment of a clinical nomogram assessment model. METHODS: Clinical data of 200 patients with DEACMP admitted to the First Hospital of Yulin from January 2019 to December 2022 were retrospectively analyzed. The patients were classified into severe and non-severe groups according to the severity of the disease. Clinical data was collected from both groups. Logistic regression was applied to analyze the risk factors for disease severity of DEACMP patients. The risk prediction model of the nomogram was constructed by incorporating risk factors, and its effectiveness was verified. Model differentiation performance was evaluated using the Respondent Operating Characteristic (ROC) Curve. Model calibration curve was adopted for fitting the situation of evaluation. The consistency of the model was evaluated by Hosmer-Lemeshow (H-L) analysis. RESULT: Age, coma time out of exposure, creatine kinase (CK), caspase, and red blood cell distribution width (RDW) were the risk factors for the severe DEACMP. A nomogram prediction model was built based on the above indicators. The area under the curve (AUC) of the model in predicting severe DEACMP was 0.961 (95% CI: 0.934-0.988) and 0.929 (95% CI: 0.841-1) in the training and test sets, respectively. The H-L test showed good goodness of fit (χ2 = 4.468, P = 0.813). The calibration curve showed a good agreement between the predicted values of the nomogram and the actual observed values. CONCLUSION: Age, coma time out of exposure, CK, caspase, and RDW were significantly correlated with the severity of DEACMP patients. The nomogram prediction model incorporating the five indicators has certain clinical reference value for predicting the severe DEACMP and could be used as an accurate and rapid clinical assessment tool.

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Objective: To explore the risk factors of anastomotic leakage after minimally invasive esophagectomy (MIE) and to build a prediction model of the probability of postoperative anastomotic leakage. Methods: Clinical data of patients undergoing MIE, admitted in the Fourth Hospital of Hebei Medical University from March 2018 to March 2022, were retrospectively selected, and risk factors of anastomotic leakage after MIE were analyzed by univariate and multivariate logistic regression. A prediction nomogram model was established based on the independent risk factors, and its prediction effect was evaluated. Results: A total of 308 patients were included. Thirty patients had postoperative anastomotic leakage, with an incidence of 9.74%. Logistic regression analysis showed that age, postoperative delirium, pleural adhesion, postoperative pulmonary complications, high postoperative white blood cell count and low lymphocyte count were risk factors for postoperative anastomotic leakage. A nomograph prediction model was constructed based on these risk factors. The predicted probability of occurrence of the nomograph model was consistent with the actual probability of occurrence. The calculated C-index value (Bootstrap method) was 0.9609, indicating that the nomograph prediction model had a good discrimination ability. By drawing the receiver operating characteristic (ROC) curve, we showed that the area under the curve (AUC) of the nomograph prediction model was 0.9609 (95%CI: 0.937-0.985), which indicated a good prediction efficiency of the model. Conclusions: The nomograph prediction model based on the independent risk factors of anastomotic leakage after MIE can accurately predict the probability of postoperative anastomotic leakage.

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Objectives: To construct a prognostic nomogram to predict the ablation zone disappearance for patients with papillary thyroid microcarcinoma (PTMC) after microwave ablation (MWA). Materials and methods: From April 2020 to April 2022, patients with PTMC who underwent MWA treatment were collected retrospectively. Ultrasound (US) or contrast-enhanced ultrasound (CEUS) was performed at 1 day, 1, 3, 6, 12, 18 and 24 months after MWA to observe the curative effect after ablation. The volume, volume reduction rate (VRR) and complete disappearance rate of the ablation zone at each time point were calculated. Univariate and multivariate logistic regression analysis were used to determine the prognostic factors associated with the disappearance of the ablation zone after MWA, and the nomogram was established and validated. Results: 72 patients with PTMCs underwent MWA were enrolled into this study. After MWA, no tumor progression (residual, recurrence or lymph node metastasis) and major postoperative complications occurred. The ablation zone in 28 (38.89%) patients did not completely disappear after MWA in the follow-up period. Three variables, including age (odds ratio [OR]: 1.216), calcification type (OR: 12.283), initial maximum diameter (OR: 2.051) were found to be independent prognostic factors predicting ablation zone status after MWA by multivariate analysis. The above variables and outcomes were visualized by nomogram (C-index=0.847). Conclusions: MWA was a safe and effective treatment for PTMC. Older patients with macrocalcification and larger size PTMCs were more unlikely to obtain complete disappearance of ablation zones. Incomplete disappearance of ablation zone was not related to recurrence.

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BACKGROUND: Arsenic (As) exposure is one of the risk factors for gestational diabetes mellitus (GDM). This study aimed to explore the effect of As-exposure on DNA methylation in GDM and to establish a risk assessment model of GDM in As exposed pregnant women. METHOD: We collected elbow vein blood of pregnant women before delivery to measure As concentration and DNA methylation data. Then compared the DNA methylation data and established a nomogram. RESULT: We identified a total of 10 key differentially methylated CpGs (DMCs) and found 6 corresponding genes. Functions were enriched in Hippo signaling pathway, cell tight junction, prophetic acid metabolism, ketone body metabolic process, and antigen processing and presentation. A nomogram was established that can predict GDM risks (c-index = 0.595, s:p = 0.973). CONCLUSION: We found 6 genes associated with GDM with high As exposure. The prediction of the nomograms has been proven to be effective.

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PURPOSE: A nomograph model of predicting the risk of post-operative central nervous system infection (PCNSI) after craniocerebral surgery was established and validated. METHODS: The clinical medical records of patients after cranial surgery in Renmin Hospital of Wuhan University from January 2020 to September 2022 were collected, of whom 998 patients admitted to Shouyi Hospital District were used as the training set and 866 patients admitted to Guanggu Hospital District were used as the validation set. Lasso regression was applied to screen the independent variables in the training set, and the model was externally validated in the validation set. RESULTS: A total of 1864 patients after craniocerebral surgery were included in this study, of whom 219 (11.75%) had PCNSI. Multivariate logistic regression analysis showed that age > 70 years, a previous history of diabetes, emergency operation, an operation time ≥ 4 h, insertion of a lumbar cistern drainage tube ≥ 72 h, insertion of an intracranial drainage tube ≥ 72 h, intraoperative blood loss ≥ 400 mL, complicated with shock, postoperative albumin ≤ 30 g/L, and an ICU length of stay ≥ 3 days were independent risk factors for PCNSI. The area under the curve (AUC) of the training set was 0.816 (95% confidence interval (95%CI), 0.773-0.859, and the AUC of the validation set was 0.760 (95%CI, 0.715-0.805). The calibration curves of the training set and the validation set showed p-values of 0.439 and 0.561, respectively, with the Hosmer-Lemeshow test. The analysis of the clinical decision curve showed that the nomograph model had high clinical application value. CONCLUSION: The nomograph model constructed in this study to predict the risk of PCNSI after craniocerebral surgery has a good predictive ability.

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This study aimed to identify factors that affect the prognosis of children with pulmonary valve atresia and intact ventricular septum treated with transthoracic balloon dilation of the pulmonary valve. The study included 148 participants who were followed up for 5 years. Of these, 10 died, while 138 survived. Independent sample t-test and χ2 test were used to analyze clinical data of children in the death and survival groups. It was found that height, weight, body surface area, arterial oxygen saturation, degree of tricuspid regurgitation, pulmonary valve cross valve pressure difference, ICU length of stay, length of stay, reoperation intervention, and complications were statistically significant (P<0.05). ROC curve analysis of the measurement indicators with statistically significant differences showed that height, weight, body surface area, arterial oxygen saturation, ICU length of stay, and length of stay had AUCs ranging from 0.723 to 0.870. Logistic regression analysis revealed that the degree of tricuspid regurgitation, pulmonary valve cross valvular pressure difference, ICU length of stay, reoperation intervention, and complications were independent risk factors that affect the prognosis of patients with PA/IVS undergoing transthoracic balloon dilation of pulmonary valve. The study proposed a nomogram prediction model using R language software 4.0 "rms" package, which was validated using calibration curve and decision curve. The model had a C-index of 0.667 (95% CI: 0.643-0.786) and high degree of fit. This study provides clinicians with a prediction model to identify children with poor prognosis after treatment with transpulmonary valve balloon dilatation. .

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OBJECTIVE: To construct a novel nomogram model that predicts the risk of hyperuricemia incidence in IgA nephropathy (IgAN). METHODS: Demographic and clinicopathological characteristics of 1184 IgAN patients in the First Affiliated Hospital of Zhengzhou University Hospital were collected. Univariate analysis and multivariate logistic regression were used to screen out hyperuricemia risk factors. The risk factors were used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using an area under the receiver-operating characteristic curve (AUC), calibration plots, and a decision curve analysis. RESULTS: Independent predictors for hyperuricemia incidence risk included sex, hypoalbuminemia, hypertriglyceridemia, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), 24 h urinary protein (24 h TP), gross hematuria and tubular atrophy/interstitial fibrosis (T). The nomogram model exhibited moderate prediction ability with an AUC of 0.834 (95% CI 0.804-0.864). The AUC from validation reached 0.787 (95% CI 0.736-0.839). The decision curve analysis displayed that the hyperuricemia risk nomogram was clinically applicable. CONCLUSION: Our novel and simple nomogram containing 8 factors may be useful in predicting hyperuricemia incidence risk in IgAN.

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This study aims to explore the role of SKA1 in cancer diagnosis and prognosis and to investigate the mechanism by which SKA1 affects the malignant behaviors of ovarian cancer. Herein, we analyzed the oncogenic role of SKA1 at pan-cancer level by multiple informatics databases and verified the analysis by in vitro experiments. As a result, SKA1 was upregulated across cancers and was related to poor clinical outcome and immune infiltration. Specifically, the constructed nomogram showed superior performance in predicting the prognosis of epithelial ovarian cancer patients. Furthermore, the in vitro experiments revealed that silencing SKA1 significantly inhibited the proliferation, migratory ability and enhanced the cisplatin sensitivity of ovarian cancer cells. Therefore, we explored the oncogenic and potential therapeutic role of SKA1 across cancers through multiple bioinformatic analysis and revealed that SKA1 may promote ovarian cancer progression and chemoresistance to cisplatin by activating the AKT-FOXO3a signaling pathway.

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Objective To investigate the risk factors of lower extremity deep venous thrombosis(DVT)after internal fixation in patients with type C pelvic fracture,and to establish a relevent nomogram model.Methods Atotal of 217 patients with type C pelvic fractures who were admitted to the Orthopedic Center of the hospital from January 2018 to January 2022 were included in the study.All patients underwent internal fixation.According to whether DVT of the lower extremities was formed after operation,they were divided into the DVT group and the non-DVT group(N-DVT).The general clinical data and preoperative in-flammatory factor expression levels of the two groups was compared.Multivariate analysis was used to obtain independent predictors of DVT formation after internal fixation in patients with type C pelvic fractures.The correlation between preoperative inflammatory factors and DVT formation after internal fixation in patients with type C pelvic fractures was analyzed.The relevant nomograph model was constructed,and the Bootstrap method and calibration curve were used to verify the nomograph model internally.The ROC curve and deci-sion curve for predicting DVT formation after internal fixation in patients with type C pelvic fracture were drawn,and the predictive efficiency and net rate of return of independent prediction and combined prediction were an-alyzed.Results Multivariate analysis showed that age,diabetes,preoperative tumor necrosis factor-α(TNF-α),traction and braking,and bed rest time were independent predictors of DVT formation after internal fixa-tion in patients with type C pelvic fractures(P＜0.05).A nomograph model was constructed based on inde-pendent predictors to predict the formation of DVT after internal fixation in patients with type C pelvic frac-ture,and the C index of the distinguishing evaluation index of the nomogram model was 0.834(95％CI:0.812-0.924),the results of goodness of fit(H-L)test showed that the predicted value of DVT formation probability after internal fixation in patients with type C pelvic fracture was in good agreement with the actual observed value(P＞0.05).ROC curve analysis and decision curve analysis showed that age,diabetes,TNF-α,traction and braking,bed rest time and combined prediction model had good predictive performance and net yield in predicting DVT formation after internal fixation in patients with type C pelvic fracture.Conclusion Age,diabetes,TNF-α,traction braking and bed rest time are independent predictors of DVT formation after internal fixation in pa-tients with type C pelvic fracture.The nomogram model based on the above independent predictors has a high value in predicting DVT formation after internal fixation in patients with type C pelvic fracture.

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Objective:To construct a predictive model based on acute skin failure, and to evaluate its predictive value on the 28-day prognosis of patients with sepsis, to provide a basis for medical staff to develop effective intervention measures.Methods:A prospective survey method was adopted, 231 patients with sepsis hospitalized in Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from May 2020 to April 2023 were enrolled as the research subjects, of which 162 patients from May 2020 to March 2022 were allocated into the test group for construct a prediction model, and 69 patients from April 2022 to April 2023 in the validation group for external validation. Univariate and multivariate Logistic regression were implemented to analyze the risk factors of 28-day mortality in sepsis patients, construction of a joint prediction model based on acute skin failure, and drawing of a column chart to verify its accuracy.Results:The 53 of 162 cases in the test group died, with mortality rate of 32.7%. The 19 of 69 cases in the validation group died, with mortality rate of 27.5%, there was no statistically significant difference in mortality rates between the two groups ( χ2 = 0.61, P = 0.437). The results of multivariate analysis in the test group showed that APACHE II score ( OR = 0.674, 95% CI 0.509-0.631), Sequential Organ Failure Assessment ( OR = 0.391, 95% CI 0.242-0.631), lactate ( OR = 2.291, 95% CI 1.306-4.019), skin mottling score ( OR = 2.950, 95% CI 1.586-5.488), skin wet cold ( OR = 3.678, 95% CI 0.910-1.865), capillary filling time>2 s ( OR = 6.070, 95% CI 0.774-1.579), decreased fingertip transcutaneous oxygen saturation ( OR = 2.046, 95% CI 1.312-2.076), and weakened skin sensation ( OR = 3.354, 95% CI 0.796-1.124) were independent risk factors that affecting the 28-day mortality of patients with sepsis. The verification results of combined predictive model for acute skin failure showed that the C-index of test group and validation group were 0.834 and 0.811 respectively; the areas under ROC curve were 0.834 and 0.807, respectively. Conclusions:Acute skin failure-based nomogram model can predict the 28-day mortality of patients with sepsis, and help medical staff to implement personalized intervention measures.

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Objective:To investigate the factors affecting the effect of periprostatic nerve block (PNB), establish a prediction model of pain degree, and verify the prediction efficiency.Methods:The clinical data of 314 patients who underwent transperineal prostate biopsy in our hospital from June 2022 to January 2023 were retrospectively analyzed. The median age was 71 (65, 76) years, the median prostate-specific antigen (PSA) was 14.6 (10.70, 24.65) ng/ml, and the median puncture needle number was 21 (19, 23) needles, median prostate volume 45.86 (31.52, 67.96) ml, median body mass index (BMI)24.02(22.97, 25.33)kg/m 2, including 109 patients with a history of diabetes, 90 patients with a history of surgery, and 57 patients with a history of severe trauma. The patients were divided into mild pain group (1-3 points), moderate pain group (4-6 points) and severe pain group (7-10 points) according to the intraoperative visual analogue scale (VAS). According to the clinical characteristics, the factors affecting the effect of PNB were analyzed by univariate analysis and multiple ordered logistic regression method. R language was used to construct a nomogram model for predicting PNB effect, receiver operating characteristic (ROC) curve and calibration curve were drawn, and Hosmer-Lemeshow test was carried out to verify the prediction efficiency of the model. Results:The results of univariate analysis showed that 171 patients in the mild pain group had a median age of 71 (65, 75) years, a median PSA14.5 (9.6, 24.6) ng/ml, a median number of puncture needles of 20 (18, 22), and a median prostate volume of 34.94 (26.36, 45.12) ml, median BMI24.17(23.14, 25.79)kg/m 2, including 74 patients with a history of diabetes, 51 patients with a history of surgery, and 40 patients with a history of severe trauma; There were 110 patients in the moderate pain group, the median age was 71 (65, 76) years, the median PSA14.8 (11.03, 24.27) ng/ml, the median number of puncture needles was 23 (20, 24) needles, median prostatic volume 63.24 (49.14, 78.72) ml, median BMI23.91(22.58, 24.88)kg/m 2, including 26 patients with a history of diabetes, 29 patients with a history of surgery, and 10 patients with a history of severe trauma; In the severe pain group, 33 patients had a median age of 73 (67, 78) years, a median PSA14.6 (10.85, 34.80) ng/ml, and a median puncture needle number of 23 (22.5, 24) needles, median prostate volume 70.64 (61.50, 104.51) ml, median BMI24.32(23.00, 26.06)kg/m 2, including 9 patients with a history of diabetes, 10 patients with a history of surgery, and 7 patients with a history of severe trauma. The results of univariate analysis showed that the number of puncture needles ( P<0.01), prostate volume ( P<0.01), history of diabetes ( P=0.002) and history of major trauma ( P= 0.009) were the factors affecting the effect of PNB. Multiple logistic regression analysis showed that puncture needle number ( P=0.009), prostate volume ( P<0.01) and diabetes history ( P=0.041) were independent risk factors for PNB effect. The area under ROC curve (AUC) of the moderate and above pain prediction model was 0.872, P<0.01; the area under ROC curve of the severe pain prediction model was 0.817, P<0.01; the result of Hosmer-Lemeshow test of the moderate and above pain prediction model was χ2=5.001, P=0.757. The results of the severe pain prediction model were χ2=4.452 and P=0.814. The calibration curve was established, which showed that the prediction probability of pain degree was in good agreement with the actual risk. Conclusions:The number of puncture needles, prostate volume and history of diabetes are the risk factors affecting the effect of PNB. The prediction model of PNB effect based on this model can be used to predict the pain degree of patients undergoing prostate biopsy after PNB.

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Objective To analysis risk factor and to construct a line graph prediction model for bone cement leakage after percutaneous transluminal vertebroplasty treatment in patients with osteoporotic spinal compression fractures. Methods A total of 236 patients with osteoporotic spinal compression fractures who came to our hospital from December 2019 to December 2021 were selected for the stud)', and they were divided into a leakage group (n = 58) and a non-leakage group (n = 178) according to whether bone cement leakage occurred after percutaneous transluminal vertebroplasty treatment. The clinical data were collected to analyze the factors associated with bone cement leakage; The work receiver operating characteristic

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Objective@#To establish a nomograph model for prediction of cervical central lymph node metastasis (CLNM) among patients with thyroid papillary carcinoma (PTC), so as to provide the evidence for designing personalized treatment plans for PTC.@* Methods @#The data of patients that underwent thyroidectomy and were pathologically diagnosed with PTC post-surgery in the Affiliated Traditional Chinese Medicine Hospital of Xinjiang Medical University from 2018 to 2021 were collected. Patients' data captured from 2018 to 2020 and from 2021 were used as the training set and the validation set, respectively. Predictive factors were screened using a multivariable logistic regression model, and the nomograph model for prediction of CLNM risk was established. The predictive value of the model was evaluated using the receiver operating characteristic (ROC) curve and the adjusted curve.@* Results@#Totally 1 820 PTC cases were included in the training set, including 458 cases with CLNM (25.16%), and 797 cases in the validation set, including 207 cases with CLNM (25.98%). The prediction model is p=ey/(1+ey), y=0.761 + 0.525 × sex + (-0.039) ×age + 0.351 × extrathyroid invasion + 0.368 × neck lymph node enlargement + 1.021×maximum tumor diameter + (-0.009) × TT4 + (-0.001) × anti-TPOAb. The area under the ROC curve was 0.732 for the training set and 0.731 for the validation set, and Hosmer-Lemeshow test showed a good fitting effect (P=0.936, 0.722).@*Conclusion@# The nomograph model constructed in this study has a high predictive value for CLNM among patients with PTC.