BACKGROUND:Trauma-induced coagulopathy(TIC)due to serious injuries significantly leads to increased mortality and morbidity among elderly patients.However,the risk factors of TIC are not well elucidated.This study aimed to explore the risk factors of TIC in elderly patients who have major trauma. METHODS:In this retrospective study,the risk factors for TIC in elderly trauma patients at a single trauma center were investigated between January 2015 and September 2020.The demographic information including gender,age,trauma parts,injury severity,use of blood products,use of vasopressors,need of emergency surgery,duration of mechanical ventilation,length of stay in the intensive care unit(ICU)and hospital,and clinical outcomes were extracted from electric medical records.Multivariate logistic regression analysis was performed to differentiate risk factors,and the performance of the model was evaluated using receiver operating characteristics(ROC)curves. RESULTS:Among the 371 elderly trauma patients,248(66.8%)were male,with the age of 72.5±6.8 years,median injury severity score(ISS)of 24(IQR:17-29),and Glasgow coma score(GCS)of 14(IQR:7-15).Of these patients,129(34.8%)were diagnosed with TIC,whereas 242(65.2%)were diagnosed with non-TIC.The severity scores such as ISS(25[20-34]vs.21[16-29],P<0.001)and shock index(SI),(0.90±0.66 vs.0.58±0.18,P<0.001)was significantly higher in the TIC group than in the non-TIC group.Serum calcium levels(1.97±0.19 mmol/L vs.2.15±0.16 mmol/L,P<0.001),fibrinogen levels(1.7±0.8 g/L vs.2.8±0.9 g/L,P<0.001),and base excess(BE,-4.9±4.6 mmol/L vs.-1.2±3.1 mmol/L,P<0.001)were significantly lower in the TIC group than in the non-TIC group.Multivariate logistic regression analysis revealed that ISS>16(OR:3.404,95%CI:1.471-7.880;P=0.004),SI>1(OR:5.641,95%CI:1.700-18.719;P=0.005),low BE(OR:0.868,95%CI:0.760-0.991;P=0.037),hypocalcemia(OR:0.060,95%CI:0.009-0.392;P=0.003),and hypofibrinogenemia(OR:0.266,95%CI:0.168-0.419;P<0.001)were independent risk factors for TIC in elderly trauma patients.The AUC of the prediction model included all these risk factors was 0.887(95%CI:0.851-0.923)with a sensitivity and specificity of 83.6%and 82.6%,respectively. CONCLUSION:Higher ISS(more than 16),higher SI(more than 1),acidosis,hypocalcemia,and hypofibrinogenemia emerged as independent risk factors for TIC in elderly trauma patients.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Objective:To evaluate the value of 18F-FDG PET metabolic parameters in predicting histopathological grade of soft tissue sarcoma (STS). Methods:From December 2012 to December 2021, 51 patients (26 males, 25 females, age range: 32-84 years) who underwent 18F-FDG PET/CT imaging before treatment and confirmed STS pathologically in the First Affiliated Hospital of Dalian Medical University were retrospectively collected. 18F-FDG PET metabolic parameters SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and intertumoral FDG uptake heterogeneity (IFH) were measured. Kruskal-Wallis rank sum test was used to analyze the differences in metabolic parameters among different groups and Spearman rank correlation analysis was used to analyze the correlation of each metabolic parameter and histological grade. Logistic regression was used to screen and construct the prediction model for high-grade STS. ROC curve was plotted and Delong test was used to analyze the differences among AUCs. Results:The metabolic parameters SUV max, MTV, TLG and IFH were significantly different among French Federation of Cancer Centers Sarcoma Group (FNCLCC)Ⅰ( n=8), Ⅱ( n=10) and Ⅲ ( n=33) grade groups ( H values: 16.24, 10.52, 19.29 and 16.99, all P<0.05), and each metabolic parameter was positively correlated with histological grade ( rs values: 0.58, 0.45, 0.52, and 0.62, all P<0.05). Multivariate logistic regression analysis showed that SUV max(odds ratio ( OR)=1.27, 95% CI: 1.06-1.51, P=0.009) and IFH ( OR=6.83, 95% CI: 1.44-32.27, P=0.015) were independent risk indicators for high-grade STS. The prediction model constructed by combining SUV max and IFH had better diagnostic efficacy for differentiating high-grade STS with the AUC of 0.93, and the sensitivity of 93.9%(31/33) and the specificity of 16/18, respectively. The AUC of prediction model was significant different from SUV max, MTV, TLG and IFH (AUCs: 0.81, 0.78, 0.86 and 0.85; z values: 2.69, 2.53, 1.94 and 1.97, all P<0.05). Conclusions:The metabolic parameters SUV max, MTV, TLG and IFH are valuable predictors for histological grade of STS. The combination of SUV max and IFH may be a more meaningful method than using each of the above metabolic parameters alone.

Objective:To observe the recurrence condition of hepatitis B in different risk groups after liver transplantation in an attempt to provide useful information on whether to discontinue hepatitis B immunoglobulin (HBIG) in the future at an early stage.Methods:The patient population was divided into high, low-risk, and special groups [especially primary hepatocellular carcinoma (HCC)] according to the guidelines for the prevention and treatment of hepatitis B recurrence after liver transplantation. The recurrence condition and risk factors in this population were observed for hepatitis B. Measurement data were analyzed using a t-test and a rank-sum test. Count data were compared using a χ2 test between groups. Results:This study finally included 532 hepatitis B-related liver transplant cases. A total of 35 cases had HBV recurrence after liver transplantation, including 34 cases that were HBsAg positive, one case that was HBsAg negative, and 10 cases that were hepatitis B virus (HBV) DNA positive. The overall HBV recurrence rate was 6.6%. The recurrence rate of HBV was 9.2% and 4.8% in the high- and low-risk HBV DNA positive and negative groups before surgery ( P = 0.057). Among the 293 cases diagnosed with HCC before liver transplantation, 30 had hepatitis B recurrence after surgery, with a recurrence rate of 10.2%. The independent related factors for the recurrence of hepatitis B in patients with HCC after liver transplantation were HCC recurrence ( HR =181.92, 95% CI 15.99~2 069.96, P < 0.001), a high postoperative dose of mycophenolate mofetil dispersible tablets (MMF) ( HR =5.190, 95% CI 1.289~20.889, P = 0.020), and a high dosage of HBIG ( HR = 1.012, 95% CI 1.001~1.023, P = 0.035). Among the 239 cases who were non-HCC before liver transplantation, five cases (recurrence rate of 2.1%) arouse postoperative hepatitis B recurrence. Lamivudine was used in all cases, combined with on-demand HBIG prophylaxis after surgery. There was no hepatitis B recurrence in non-HCC patients who treated with entecavir combined with HBIG after surgery. Conclusion:High-barrier-to-resistance nucleotide analogues combined with long-term HBIG have a good effect on preventing the recurrence of hepatitis B after liver transplantation. The discontinuation of HBIG may be considered at an early stage after administration of a high-barrier-to-resistance nucleotide analogue in low-risk patients. Domestically, the HBV infection rate is high, so further research is still required to explore the timing of HBIG discontinuation for high-risk patients, especially those with HCC.

BACKGROUND:Trauma-induced coagulopathy(TIC)due to serious injuries significantly leads to increased mortality and morbidity among elderly patients.However,the risk factors of TIC are not well elucidated.This study aimed to explore the risk factors of TIC in elderly patients who have major trauma. METHODS:In this retrospective study,the risk factors for TIC in elderly trauma patients at a single trauma center were investigated between January 2015 and September 2020.The demographic information including gender,age,trauma parts,injury severity,use of blood products,use of vasopressors,need of emergency surgery,duration of mechanical ventilation,length of stay in the intensive care unit(ICU)and hospital,and clinical outcomes were extracted from electric medical records.Multivariate logistic regression analysis was performed to differentiate risk factors,and the performance of the model was evaluated using receiver operating characteristics(ROC)curves. RESULTS:Among the 371 elderly trauma patients,248(66.8%)were male,with the age of 72.5±6.8 years,median injury severity score(ISS)of 24(IQR:17-29),and Glasgow coma score(GCS)of 14(IQR:7-15).Of these patients,129(34.8%)were diagnosed with TIC,whereas 242(65.2%)were diagnosed with non-TIC.The severity scores such as ISS(25[20-34]vs.21[16-29],P<0.001)and shock index(SI),(0.90±0.66 vs.0.58±0.18,P<0.001)was significantly higher in the TIC group than in the non-TIC group.Serum calcium levels(1.97±0.19 mmol/L vs.2.15±0.16 mmol/L,P<0.001),fibrinogen levels(1.7±0.8 g/L vs.2.8±0.9 g/L,P<0.001),and base excess(BE,-4.9±4.6 mmol/L vs.-1.2±3.1 mmol/L,P<0.001)were significantly lower in the TIC group than in the non-TIC group.Multivariate logistic regression analysis revealed that ISS>16(OR:3.404,95%CI:1.471-7.880;P=0.004),SI>1(OR:5.641,95%CI:1.700-18.719;P=0.005),low BE(OR:0.868,95%CI:0.760-0.991;P=0.037),hypocalcemia(OR:0.060,95%CI:0.009-0.392;P=0.003),and hypofibrinogenemia(OR:0.266,95%CI:0.168-0.419;P<0.001)were independent risk factors for TIC in elderly trauma patients.The AUC of the prediction model included all these risk factors was 0.887(95%CI:0.851-0.923)with a sensitivity and specificity of 83.6%and 82.6%,respectively. CONCLUSION:Higher ISS(more than 16),higher SI(more than 1),acidosis,hypocalcemia,and hypofibrinogenemia emerged as independent risk factors for TIC in elderly trauma patients.

BACKGROUND:Trauma-induced coagulopathy(TIC)due to serious injuries significantly leads to increased mortality and morbidity among elderly patients.However,the risk factors of TIC are not well elucidated.This study aimed to explore the risk factors of TIC in elderly patients who have major trauma. METHODS:In this retrospective study,the risk factors for TIC in elderly trauma patients at a single trauma center were investigated between January 2015 and September 2020.The demographic information including gender,age,trauma parts,injury severity,use of blood products,use of vasopressors,need of emergency surgery,duration of mechanical ventilation,length of stay in the intensive care unit(ICU)and hospital,and clinical outcomes were extracted from electric medical records.Multivariate logistic regression analysis was performed to differentiate risk factors,and the performance of the model was evaluated using receiver operating characteristics(ROC)curves. RESULTS:Among the 371 elderly trauma patients,248(66.8%)were male,with the age of 72.5±6.8 years,median injury severity score(ISS)of 24(IQR:17-29),and Glasgow coma score(GCS)of 14(IQR:7-15).Of these patients,129(34.8%)were diagnosed with TIC,whereas 242(65.2%)were diagnosed with non-TIC.The severity scores such as ISS(25[20-34]vs.21[16-29],P<0.001)and shock index(SI),(0.90±0.66 vs.0.58±0.18,P<0.001)was significantly higher in the TIC group than in the non-TIC group.Serum calcium levels(1.97±0.19 mmol/L vs.2.15±0.16 mmol/L,P<0.001),fibrinogen levels(1.7±0.8 g/L vs.2.8±0.9 g/L,P<0.001),and base excess(BE,-4.9±4.6 mmol/L vs.-1.2±3.1 mmol/L,P<0.001)were significantly lower in the TIC group than in the non-TIC group.Multivariate logistic regression analysis revealed that ISS>16(OR:3.404,95%CI:1.471-7.880;P=0.004),SI>1(OR:5.641,95%CI:1.700-18.719;P=0.005),low BE(OR:0.868,95%CI:0.760-0.991;P=0.037),hypocalcemia(OR:0.060,95%CI:0.009-0.392;P=0.003),and hypofibrinogenemia(OR:0.266,95%CI:0.168-0.419;P<0.001)were independent risk factors for TIC in elderly trauma patients.The AUC of the prediction model included all these risk factors was 0.887(95%CI:0.851-0.923)with a sensitivity and specificity of 83.6%and 82.6%,respectively. CONCLUSION:Higher ISS(more than 16),higher SI(more than 1),acidosis,hypocalcemia,and hypofibrinogenemia emerged as independent risk factors for TIC in elderly trauma patients.

BACKGROUND:Trauma-induced coagulopathy(TIC)due to serious injuries significantly leads to increased mortality and morbidity among elderly patients.However,the risk factors of TIC are not well elucidated.This study aimed to explore the risk factors of TIC in elderly patients who have major trauma. METHODS:In this retrospective study,the risk factors for TIC in elderly trauma patients at a single trauma center were investigated between January 2015 and September 2020.The demographic information including gender,age,trauma parts,injury severity,use of blood products,use of vasopressors,need of emergency surgery,duration of mechanical ventilation,length of stay in the intensive care unit(ICU)and hospital,and clinical outcomes were extracted from electric medical records.Multivariate logistic regression analysis was performed to differentiate risk factors,and the performance of the model was evaluated using receiver operating characteristics(ROC)curves. RESULTS:Among the 371 elderly trauma patients,248(66.8%)were male,with the age of 72.5±6.8 years,median injury severity score(ISS)of 24(IQR:17-29),and Glasgow coma score(GCS)of 14(IQR:7-15).Of these patients,129(34.8%)were diagnosed with TIC,whereas 242(65.2%)were diagnosed with non-TIC.The severity scores such as ISS(25[20-34]vs.21[16-29],P<0.001)and shock index(SI),(0.90±0.66 vs.0.58±0.18,P<0.001)was significantly higher in the TIC group than in the non-TIC group.Serum calcium levels(1.97±0.19 mmol/L vs.2.15±0.16 mmol/L,P<0.001),fibrinogen levels(1.7±0.8 g/L vs.2.8±0.9 g/L,P<0.001),and base excess(BE,-4.9±4.6 mmol/L vs.-1.2±3.1 mmol/L,P<0.001)were significantly lower in the TIC group than in the non-TIC group.Multivariate logistic regression analysis revealed that ISS>16(OR:3.404,95%CI:1.471-7.880;P=0.004),SI>1(OR:5.641,95%CI:1.700-18.719;P=0.005),low BE(OR:0.868,95%CI:0.760-0.991;P=0.037),hypocalcemia(OR:0.060,95%CI:0.009-0.392;P=0.003),and hypofibrinogenemia(OR:0.266,95%CI:0.168-0.419;P<0.001)were independent risk factors for TIC in elderly trauma patients.The AUC of the prediction model included all these risk factors was 0.887(95%CI:0.851-0.923)with a sensitivity and specificity of 83.6%and 82.6%,respectively. CONCLUSION:Higher ISS(more than 16),higher SI(more than 1),acidosis,hypocalcemia,and hypofibrinogenemia emerged as independent risk factors for TIC in elderly trauma patients.

BACKGROUND:Trauma-induced coagulopathy(TIC)due to serious injuries significantly leads to increased mortality and morbidity among elderly patients.However,the risk factors of TIC are not well elucidated.This study aimed to explore the risk factors of TIC in elderly patients who have major trauma. METHODS:In this retrospective study,the risk factors for TIC in elderly trauma patients at a single trauma center were investigated between January 2015 and September 2020.The demographic information including gender,age,trauma parts,injury severity,use of blood products,use of vasopressors,need of emergency surgery,duration of mechanical ventilation,length of stay in the intensive care unit(ICU)and hospital,and clinical outcomes were extracted from electric medical records.Multivariate logistic regression analysis was performed to differentiate risk factors,and the performance of the model was evaluated using receiver operating characteristics(ROC)curves. RESULTS:Among the 371 elderly trauma patients,248(66.8%)were male,with the age of 72.5±6.8 years,median injury severity score(ISS)of 24(IQR:17-29),and Glasgow coma score(GCS)of 14(IQR:7-15).Of these patients,129(34.8%)were diagnosed with TIC,whereas 242(65.2%)were diagnosed with non-TIC.The severity scores such as ISS(25[20-34]vs.21[16-29],P<0.001)and shock index(SI),(0.90±0.66 vs.0.58±0.18,P<0.001)was significantly higher in the TIC group than in the non-TIC group.Serum calcium levels(1.97±0.19 mmol/L vs.2.15±0.16 mmol/L,P<0.001),fibrinogen levels(1.7±0.8 g/L vs.2.8±0.9 g/L,P<0.001),and base excess(BE,-4.9±4.6 mmol/L vs.-1.2±3.1 mmol/L,P<0.001)were significantly lower in the TIC group than in the non-TIC group.Multivariate logistic regression analysis revealed that ISS>16(OR:3.404,95%CI:1.471-7.880;P=0.004),SI>1(OR:5.641,95%CI:1.700-18.719;P=0.005),low BE(OR:0.868,95%CI:0.760-0.991;P=0.037),hypocalcemia(OR:0.060,95%CI:0.009-0.392;P=0.003),and hypofibrinogenemia(OR:0.266,95%CI:0.168-0.419;P<0.001)were independent risk factors for TIC in elderly trauma patients.The AUC of the prediction model included all these risk factors was 0.887(95%CI:0.851-0.923)with a sensitivity and specificity of 83.6%and 82.6%,respectively. CONCLUSION:Higher ISS(more than 16),higher SI(more than 1),acidosis,hypocalcemia,and hypofibrinogenemia emerged as independent risk factors for TIC in elderly trauma patients.