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Journal of Hepatocellular Carcinoma 2024Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer. Early diagnosis is crucial for improving prognosis. Elderly HCC patients often have...
PURPOSE
Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer. Early diagnosis is crucial for improving prognosis. Elderly HCC patients often have underlying liver diseases such as chronic hepatitis and cirrhosis, leading to impaired liver function and suboptimal liver reserve. Radiofrequency ablation (RFA) has rapidly become one of the most important methods for treating early-stage hepatocellular carcinoma (EHCC) due to its advantages, including minimal trauma, short operation time, less intraoperative bleeding, quick postoperative recovery, cost-effectiveness, and few postoperative-complications. However, the prognostic model for early recurrence after local ablation in elderly EHCC patients has not been widely evaluated. We have developed a prognostic model for the recurrence of local RFA in elderly EHCC patients. This is expected to provide a new early warning system for preventing early recurrence in elderly EHCC patients, prolonging patient's life, and improving overall quality of life.
METHODS
In this study, we included 661 EHCC patients who underwent local ablation, dividing them into a Primary cohort and a Validation cohort in a 7:3 ratio. We characterized the cohorts and utilized the primary cohort to develop a prognostic nomogram model for recurrence after local ablation in elderly EHCC patients. Additionally, the validation cohort was used to assess the potential of the nomogram as a non-invasive biomarker for post-ablation recurrence in EHCC.
RESULTS
The user-friendly nomogram incorporates common clinical variables including gender, BCLC stage, tumor number, tumor size, red blood cell (RBC), gamma-glutamyl transferase (GGT), and prothrombin time activity (PTA). The nomogram constructed using the identified seven variables exhibits robust discriminatory capabilities, favorable predictive performance, and noteworthy clinical utility.
CONCLUSION
We developed a user-friendly nomogram based on the BCLC stage classification, which may provide prognostic assessments for elderly EHCC patients at 1, 3, and 5 years post-RFA.
PubMed: 38774590
DOI: 10.2147/JHC.S459250 -
Thrombosis Journal May 2024This study aimed to evaluate the association of antiphospholipid antibodies (aPL) and conventional markers of coagulation with ischemic and bleeding risk in patients...
BACKGROUND
This study aimed to evaluate the association of antiphospholipid antibodies (aPL) and conventional markers of coagulation with ischemic and bleeding risk in patients with atrial fibrillation (AF) undergoing percutaneous coronary intervention (PCI).
METHODS
In this prospective two-center observational cohort study, patients with AF and an indication for oral anticoagulation (OAC) were enrolled after PCI. Blood was drawn on day 1-3 after PCI. Dilute Russell's viper venom time was used to determine lupus anticoagulant (LA) in OAC-free plasma. Anti-cardiolipin (aCL) IgG, IgM, and anti-β2-Glycoprotein 1 (aβ2GP1) IgG were analyzed by enzyme-linked immunosorbent assay (ELISA). Fibrinogen (FIB), d-dimer, and prothrombin fragment 1 and 2 (PF 1 + 2) were measured in citrated plasma. The primary ischemic outcome was time to major adverse cardiovascular events (MACE; death, myocardial infarction, or stroke) assessed at 6 months. Bleeding was defined according to International Society on Thrombosis and Haemostasis.
RESULTS
158 patients were enrolled between May 2020 and May 2021 on day 1-3 after PCI. The median age was 78 years (interquartile range [IQR] 72-82), 111 (70%) were male, and 39 (25%) presented with acute coronary syndrome. D-dimer was elevated in 74 (47%) patients, FIB was increased in 40 (25%) and PF1 + 2 in 68 (43%) patients. 32 (20%) patients had ≥ 1 antiphospholipid antibody elevated (aPL; LA: 19 [12%], aCL: 14 [9%], aβ2GP1: 2 [1%]). The presence of aPL was neither significantly associated with MACE (HR 1.46, 95% CI [0.39-5.49], p = 0.579), nor bleeding (HR 1.07 [0.30-3.84], p = 0.917). Elevated d-dimer was significantly associated with higher risk for MACE (HR 5.06 [1.09-23.41], p = 0.038) and major bleeding (HR 7.04 [1.58-31.47], p = 0.011). Elevated D-dimer increased the predictive capacity of HAS-BLED for major bleedings (HAS-BLED: AUC 0.71 [0.60-0.83] vs. HAS-BLED + d-dimer: AUC 0.79 [0.70-0.88]; p = 0.025). Increased levels of FIB were associated with higher risk for MACE (HR 3.65 [1.11-11.96], p = 0.033).
CONCLUSION
Biomarkers of coagulation might be suitable to assess ischemic and bleeding risk in patients with AF following PCI.
PubMed: 38773510
DOI: 10.1186/s12959-024-00610-x -
Animal Models and Experimental Medicine May 2024Thromboelastography (TEG) is a widely utilized clinical testing method for real-time monitoring of platelet function and the thrombosis process. Lipid metabolism...
BACKGROUND
Thromboelastography (TEG) is a widely utilized clinical testing method for real-time monitoring of platelet function and the thrombosis process. Lipid metabolism disorders are crucial risk factors for thrombosis. The lipid metabolism characteristics of hamsters resemble those of humans more closely than mice and rats, and their relatively large blood volume makes them suitable for studying the mechanisms of thrombosis related to plasma lipid mechanisms. Whole blood samples from golden Syrian hamsters and healthy humans were obtained following standard clinical procedures. TEG was employed to evaluate coagulation factor function, fibrinogen (Fib) function, platelet function, and the fibrinolytic system.
METHODS
The whole blood from hamster or healthy human was isolated following the clinical procedure, and TEG was employed to evaluate the coagulation factor function, Fib function, platelet function, and fibrinolytic system. Coagulation analysis used ACLTOP750 automatic coagulation analysis pipeline. Blood routine testing used XN-2000 automatic blood analyzer.
RESULTS
TEG parameters revealed that hamsters exhibited stronger coagulation factor function than humans (reaction time [R], p = 0.0117), with stronger Fib function (alpha angle, p < 0.0001; K-time [K], p < 0.0001). Platelet function did not differ significantly (maximum amplitude [MA], p = 0.077). Hamsters displayed higher coagulation status than humans (coagulation index [CI], p = 0.0023), and the rate of blood clot dissolution in hamsters differed from that in humans (percentage lysis 30 min after MA, p = 0.02). Coagulation analysis parameters indicated that prothrombin time (PT) and activated partial thromboplastin time (APTT) were faster in hamsters than in humans (PT, p = 0.0014; APTT, p = 0.03), whereas the Fib content was significantly lower in hamsters than in humans (p < 0.0001). No significant difference was observed in thrombin time (p = 0.1949).
CONCLUSIONS
In summary, TEG could be used to evaluate thrombosis and bleeding parameters in whole blood samples from hamsters. The platelet function of hamsters closely resembled that of humans, whereas their coagulation function was significantly stronger.
PubMed: 38769667
DOI: 10.1002/ame2.12403 -
Frontiers in Medicine 2024To establish a mortality risk nomogram for predicting in-hospital mortality of sepsis patients in the Chinese population.
OBJECTIVE
To establish a mortality risk nomogram for predicting in-hospital mortality of sepsis patients in the Chinese population.
METHODS
Data were obtained from the medical records of sepsis patients enrolled at the Affiliated Huadu Hospital, Southern Medical University, between 2019 and 2021. A total of 696 sepsis patients were initially included in our research, and 582 cases were finally enrolled after screening and divided into the survival group ( = 400) and the non-survival group ( = 182) according to the incidence of mortality during hospitalization. Twenty-eight potential sepsis-related risk factors for mortality were identified. Least absolute shrinkage and selection operator (LASSO) regression was used to optimize variable selection by running cyclic coordinate descent with -fold (tenfold in this case) cross-validation. We used binary logistic regression to build a model for predicting mortality from the variables based on LASSO regression selection. Binary logistic regression was used to establish a nomogram based on independent mortality risk factors. To validate the prediction accuracy of the nomogram, receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and restricted cubic spline (RCS) analysis were employed. Eventually, the test and calibration curve were used for nomogram calibration.
RESULTS
LASSO regression identified a total of ten factors, namely, chronic heart disease (CHD), lymphocyte count (LYMP), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), C reactive protein (CRP), Procalcitonin (PCT), lactic acid, prothrombin time (PT), alanine aminotransferase (ALT), total bilirubin (Tbil), interleukin-6 (IL6), that were incorporated into the multivariable analysis. Finally, a nomogram including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6 was established by multivariable logistic regression. The ROC curves of the nomogram in the training and validation sets were 0.9836 and 0.9502, respectively. DCA showed that the nomogram could be applied clinically if the risk threshold was between 29.52 and 99.61% in the training set and between 31.32 and 98.49% in the testing set. RCS showed that when the value of independent risk factors from the predicted model exceeded the median, the mortality hazard ratio increased sharply. The results of the test ( = 0.1901, = 2, = 0.9091) and the calibration curves of the training and validation sets showed good agreement with the actual results, which indicated good stability of the model.
CONCLUSION
Our nomogram, including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6, exhibits good performance for predicting mortality risk in adult sepsis patients.
PubMed: 38765257
DOI: 10.3389/fmed.2024.1360197 -
Heliyon May 2024Influenza and COVID-19 patients share similar features and outcomes amongst adults. However, the difference between these diseases is not explored in paediatric age...
BACKGROUND
Influenza and COVID-19 patients share similar features and outcomes amongst adults. However, the difference between these diseases is not explored in paediatric age group especially in terms of inflammatory markers, coagulation profile and outcomes. Hence, we did this review to compare the inflammatory, coagulation features and outcomes between influenza and COVID-19 infected children.
METHODS
Literature search was done in PubMed Central, Scopus, EMBASE, CINAHL, Cochrane library, Google Scholar & ScienceDirect from November 2019 to May 2022. Risk of bias assessment was done through Newcastle Ottawa scale. Meta-analysis was done using random-effects model and the final pooled estimate was reported as pooled odds ratio (OR) or standardized mean difference (SMD) along with 95 % confidence interval (CI) depending on the type of outcome.
RESULTS
About 16 studies were included with most studies having higher risk of bias. Influenza paediatric patients had significantly higher erythrocyte sedimentation rate (ESR) (pooled SMD = 0.60; 95%CI: 0.30-0.91; I = 0 %), lactate dehydrogenase (LDH) (pooled SMD = 2.01; 95%CI: 0.37-3.66; I = 98.4 %) and prothrombin time (PT) (pooled SMD = 2.12; 95%CI: 0.44-3.80; I = 98.3 %) when compared to paediatric COVID-19 patients. There was no significant difference in terms of features like CRP, procalcitonin, serum albumin, aPTT, mortality and need for mechanical ventilation.
CONCLUSION
Inflammatory markers like ESR, LDH and PT was significantly higher in influenza patients when compared to COVID-19 in children, while rest of the markers and adverse clinical outcomes were similar between both the groups. Identification of these biomarkers has helped in understanding the distinctness of COVID-19 and influenza virus and develop better management strategies.
PubMed: 38765052
DOI: 10.1016/j.heliyon.2024.e30391 -
Frontiers in Medicine 2024To predict mortality in severe patients with COVID-19 at admission to the intensive care unit (ICU) using thromboelastography (TEG).
OBJECTIVE
To predict mortality in severe patients with COVID-19 at admission to the intensive care unit (ICU) using thromboelastography (TEG).
METHODS
This retrospective, two-center, observational study involved 87 patients with PCR-and chest CT-confirmed severe COVID-19 who were admitted to at Wuhan Huoshenshan Hospital and the 908th Hospital of Chinese PLA Logistic Support Force between February 2020 and February 2023. Clinic demographics, laboratory results, and outcomes were compared between those who survived and those who died during hospitalization.
RESULTS
Thromboelastography showed that of the 87 patients, 14 were in a hypercoagulable state, 25 were in a hypocoagulable state, and 48 were normal, based on the time to maximum amplitude (TMA). Patients who died showed significantly lower α angle, but significantly longer R-time, K-time and TMA than patients who survived. Random forest selection showed that K-time, TMA, prothrombin time (PT), international normalized ratio (INR), D-dimer, C-reactive protein (CRP), aspartate aminotransferase (AST), and total bilirubin (Tbil) were significant predictors. Multivariate logistic regression identified that TMA and CRP were independently associated with mortality. TMA had a greater predictive power than CRP levels based on time-dependent AUCs. Patients with TMA ≥ 26.4 min were at significantly higher risk of mortality (hazard ratio 3.99, 95% Confidence Interval, 1.92-8.27, < 0.01).
CONCLUSION
TMA ≥26.4 min at admission to ICU may be an independent predictor of in-hospital mortality for patients with severe COVID-19.
PubMed: 38756947
DOI: 10.3389/fmed.2024.1356283 -
Zhong Nan Da Xue Xue Bao. Yi Xue Ban =... Feb 2024Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk...
OBJECTIVES
Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk.
METHODS
Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model.
RESULTS
A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all <0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% 0.710 to 0.724) and 0.716 (95% 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value.
CONCLUSIONS
The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.
Topics: Humans; Sepsis; Female; Male; Risk Factors; Prognosis; Databases, Factual; Risk Assessment; Intensive Care Units; Middle Aged; Area Under Curve; Aged; Organ Dysfunction Scores; Proportional Hazards Models
PubMed: 38755721
DOI: 10.11817/j.issn.1672-7347.2024.230390 -
World Journal of Urology May 2024To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from...
PURPOSE
To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters.
METHODS
A retrospective analysis of data from patients who underwent bipolar-TURP encompassing patient characteristics, preoperative routine blood test outcomes, and post-surgery uroflowmetry were used to develop and educate machine learning models. Various metrics, such as F1 score, model accuracy, negative predictive value, positive predictive value, sensitivity, specificity, Youden Index, ROC AUC value, and confidence interval for each model, were used to assess the predictive performance of machine learning models for urethral stricture development.
RESULTS
A total of 109 patients' data (55 patients without urethral stricture and 54 patients with urethral stricture) were included in the study after implementing strict inclusion and exclusion criteria. The preoperative Platelet Distribution Width, Mean Platelet Volume, Plateletcrit, Activated Partial Thromboplastin Time, and Prothrombin Time values were statistically meaningful between the two cohorts. After applying the data to the machine learning systems, the accuracy prediction scores for the diverse algorithms were as follows: decision trees (0.82), logistic regression (0.82), random forests (0.91), support vector machines (0.86), K-nearest neighbors (0.82), and naïve Bayes (0.77).
CONCLUSION
Our machine learning models' accuracy in predicting the post-TURP urethral stricture probability has demonstrated significant success. Exploring prospective studies that integrate supplementary variables has the potential to enhance the precision and accuracy of machine learning models, consequently progressing their ability to predict post-TURP urethral stricture risk.
Topics: Humans; Male; Urethral Stricture; Machine Learning; Retrospective Studies; Aged; Transurethral Resection of Prostate; Postoperative Complications; Algorithms; Middle Aged; Predictive Value of Tests
PubMed: 38748256
DOI: 10.1007/s00345-024-05017-x -
Scientific Reports May 2024Intravenous application of tranexamic acid (TXA) in posterior lumbar interbody fusion (PLIF) can effectively reduce blood loss without affecting coagulation function....
Intravenous application of tranexamic acid (TXA) in posterior lumbar interbody fusion (PLIF) can effectively reduce blood loss without affecting coagulation function. However, it has not been reported whether preoperative use of anticoagulants may affect the efficacy of TXA in PLIF. The purpose of this study is to observe the effect of preoperative use of anticoagulants on coagulation indicators and blood loss after PLIF receiving intravenous unit dose TXA. A retrospective analysis was conducted on data from 53 patients with PLIF between 2020.11 and 2022.9, who received intravenous application of a unit dose of TXA (1 g/100 mL) 15 min before the skin incision after general anesthesia. Those who used anticoagulants within one week before surgery were recorded as the observation group, while those who did not use anticoagulants were recorded as the control group. The main observation indicators include surgical time, intraoperative blood loss, postoperative drainage volume, blood transfusion, and red blood cell (RBC), hemoglobin (HB), and hematocrit (HCT) measured on the 1st, 4th, 7th, and last-test postoperative days. Secondary observation indicators included postoperative incision healing, deep vein thrombosis of lower limbs, postoperative hospital stay, and activated partial thrombin time (APTT), prothrombin time (PT), thrombin time (TT), fibrinogen (FIB), and platelets (PLT) on the 1st and 4th days after surgery. The operation was successfully completed in both groups, the incision healed well after operation, and no lower limb deep vein thrombosis occurred. There was no significant difference in surgical time, intraoperative blood loss, postoperative drainage volume, and blood transfusion between the two groups (p > 0.05). There was no significant difference in the RBC, HB, and HCT measured on the 1st, 4th, 7th, and last-test postoperative days between the two groups (p > 0.05). There was no statistically significant difference in APTT, PT, TT, FIB and PLT between the two groups on the 1st and 4th postoperative days (p > 0.05). There was no significant difference in postoperative hospital stay between the two groups (p > 0.05). The use of anticoagulants within one week before surgery does not affect the hemostatic effect of intravenous unit dose TXA in PLIF.
Topics: Humans; Tranexamic Acid; Female; Male; Middle Aged; Retrospective Studies; Case-Control Studies; Anticoagulants; Blood Loss, Surgical; Aged; Administration, Intravenous; Spinal Fusion; Preoperative Care; Antifibrinolytic Agents; Blood Coagulation
PubMed: 38744855
DOI: 10.1038/s41598-024-60440-9 -
Frontiers in Medicine 2024The purpose of this study is to develop and evaluate a nomogram that is capable of predicting poor operative visibility during functional endoscopic sinus surgery.
OBJECTIVE
The purpose of this study is to develop and evaluate a nomogram that is capable of predicting poor operative visibility during functional endoscopic sinus surgery.
METHOD
To identify potential risk factors, patients with chronic rhinosinusitis who underwent functional endoscopic sinus surgery (FESS) between January 2019 and December 2022 were selected from our hospital's electronic medical record system. Data on general patient information, clinical manifestations, clotting-related test indices, Lund-Machay score of sinuses CT scanning, Lund-kennedy score of nasal endoscopies, anesthesia methods, intraoperative blood pressure and heart rate, and Boezaart bleeding score were collected. Minimum absolute convergence and selection operator (LASSO) regression, as well as multivariate logistic regression, were used to determine the risk factors. A nomogram was developed in order to predict poor operating visibility during FESS, and its performance was evaluated utilizing both the training and verification datasets via various measures including receiver operating characteristic (ROC) curve analysis, area under the curve (AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis.
RESULTS
Of the 369 patients who met the inclusion criteria, 88 of them exhibited POV during FESS. By deploying LASSO and multivariate logistic regression analyses, six risk factors were identified and used to construct a nomogram for predicting POV during FESS. These factors include prothrombin time (PT), prothrombin activity (PTA), Lund-Mackay score (LMS), Lund-Kennedy score (LKS), anesthetic method, and intraoperative hypertension. The AUC of the training set was found to be 0.820 while that of the verification set was 0.852. The Hosmer-Lemeshow goodness-of-fit test and calibration curve analysis revealed good consistency between predicted and actual probabilities. Also, the decision curve demonstrated that the nomogram had a high degree of clinical usefulness and net benefit.
CONCLUSION
The constructed nomogram has a strong ability to predict the poor intraoperative field in patients with chronic rhinosinusitis, which can help preoperative judgment of high-risk patients and provide evidence for perioperative management and preoperative plan formulation.
PubMed: 38741764
DOI: 10.3389/fmed.2024.1344661