Cut-off Value For Risk Stratification Research Articles

Development and validation of the SPEAR scoring model and predicting overall survival in unresectable hepatocellular carcinoma patients treated with TACE, molecular targeted therapies, and immune checkpoint inhibitors.

533 Background: To develop and validate a predictive model to identify hepatocellular carcinoma (HCC) patients likely to benefit from combined treatment of transarterial chemoembolization (TACE), molecular targeted therapies (MTTs), and immune checkpoint inhibitors (ICIs). Methods: Data from 500 patients with unresectable HCC treated with TACE, MTTs, and ICIs (2019-2023) were analyzed. Patients were divided into a training set (241 from Nanfang Hospital) and a validation set (259 from various hospitals). Key prognostic variables were identified using random forest survival analysis, and the top five were used to construct a Cox proportional hazards model. Model performance was assessed using time-dependent AUC values, calibration curves, clinical decision curves (DCA), and net reclassification improvement (NRI) indices. Risk scores and optimal cut-off values for risk stratification were determined and validated. Results: Five key survival variables were identified: serum alpha-fetoprotein (AFP), red cell distribution width coefficient of variation (RDW-CV), aspartate aminotransferase (AST), platelet-to-lymphocyte ratio (PLR), and extrahepatic metastasis (EHM). The Cox model had a C-index of 0.694 in the training and 0.691 in the validation set. The training set's 1-year, 2-year, 2.5-year, and 3-year AUC values were 0.77, 0.73, 0.73, and 0.72, respectively, and 0.74, 0.74, 0.76, and 0.66 in the validation set. The ARAPE model demonstrated a more significant net benefit than existing models. Median overall survival (mOS) was 33.9 months for low-risk, 20.4 months for intermediate-risk, and 14.2 months for high-risk groups in the training set; in the validation set, mOS was 30.8, 18.7, and 10.5 months, respectively. Conclusions: The SPEAR model (Serum AFP, PLR, EHM, AST, and RDW-CV) model effectively stratifies risk for HCC patients receiving TACE combined with MTTs and ICIs, aiding in personalized treatment decisions.

Identifying key circulating tumor DNA parameters for predicting clinical outcomes in metastatic non-squamous non-small cell lung cancer after first-line chemoimmunotherapy

Circulating tumor DNA (ctDNA) provides valuable tumor-related information without invasive biopsies, yet consensus is lacking on optimal parameters for predicting clinical outcomes. Utilizing longitudinal ctDNA data from the large phase 3 IMpower150 study (NCT02366143) of atezolizumab in combination with chemotherapy with or without bevacizumab in patients with stage IV non-squamous Non-Small Cell Lung Cancer (NSCLC), here we report that post-treatment ctDNA response correlates significantly with radiographic response. However, only modest concordance is identified, revealing that ctDNA response is likely not a surrogate for radiographic response; both provide distinct information. Various ctDNA metrics, especially early ctDNA nadirs, emerge as primary predictors for progression-free survival and overall survival, potentially better assessing long-term benefits for chemoimmunotherapy in NSCLC. Integrating radiographic and ctDNA assessments enhances prediction of survival outcomes. We also identify optimal cutoff values for risk stratification and key assessment timepoints, notably Weeks 6–9, for insights into clinical outcomes. Overall, our identified optimal ctDNA parameters can enhance the prediction of clinical outcomes, refine trial designs, and inform therapeutic decisions for first-line NSCLC patients.

International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation.

Total metabolic tumor volume (TMTV) is prognostic in lymphoma. However, cutoff values for risk stratification vary markedly, according to the tumor delineation method used. We aimed to create a standardized TMTV benchmark datasetallowing TMTV to be tested and applied as a reproducible biomarker. Methods: Sixty baseline 18F-FDG PET/CT scans were identified with a range of disease distributions (20 follicular, 20 Hodgkin, and 20 diffuse large B-cell lymphoma). TMTV was measured by 12 nuclear medicine experts, each analyzing 20 cases split across subtypes, with each case processed by 3-4 readers. LIFEx or ACCURATE software was chosen according to reader preference. Analysis was performed stepwise: TMTV1 with automated preselection of lesions using an SUV of at least 4 and a volume of at least 3 cm3 with single-click removal of physiologic uptake; TMTV2 with additional removal of reactive bone marrow and spleen with single clicks; TMTV3 with manual editing to remove other physiologic uptake, if required; and TMTV4 with optional addition of lesions using mouse clicks with an SUV of at least 4 (no volume threshold). Results: The final TMTV (TMTV4) ranged from 8 to 2,288 cm3, showing excellent agreement among all readers in 87% of cases (52/60) with a difference of less than 10% or less than 10 cm3 In 70% of the cases, TMTV4 equaled TMTV1, requiring no additional reader interaction. Differences in the TMTV4 were exclusively related to reader interpretation of lesion inclusion or physiologic high-uptake region removal, not to the choice of software. For 5 cases, large TMTV differences (>25%) were due to disagreement about inclusion of diffuse splenic uptake. Conclusion: The proposed segmentation method enabled highly reproducible TMTV measurements, with minimal reader interaction in 70% of the patients. The inclusion or exclusion of diffuse splenic uptake requires definition of specific criteria according to lymphoma subtype. The publicly available proposed benchmark allows comparison of study results and could serve as a reference to test improvements using other segmentation approaches.

Effect of Elevated Neutrophil-to-Lymphocyte Ratio on Adverse Outcomes in Patients With Myocardial Infarction: A Systematic Review and Meta-Analysis.

Myocardial infarction (MI), a leading cause of morbidity and mortality globally, is characterized by an underlying inflammatory process driven by atherosclerosis. The neutrophil-to-lymphocyte ratio (NLR), a readily available and cost-effective marker of systemic inflammation, has emerged as a potential predictor of adverse outcomes in patients with MI. This meta-analysis aimed to evaluate the association between elevated NLR and the risk of major adverse cardiovascular events (MACE) and all-cause mortality in patients with MI. A comprehensive literature search was conducted across multiple databases, including Embase, Web of Science, PubMed, and OVID Medicine, to identify relevant studies published from January 1, 2011, onward. Studies reporting the effect of NLR values on MACE and mortality in adult patients with MI, including both ST-elevation (STEMI) and non-ST-elevation (NSTEMI) subtypes, were included. Data extraction and quality assessment were performed independently by multiple authors. The meta-analysis included 37 studies, comprising a total of 18 studies evaluating the risk of MACE and 30 studies assessing all-cause mortality. The pooled analysis revealed a significantly increased risk of MACE (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.53-2.28, P < 0.01) and all-cause mortality (OR2.29, 95% CI1.94-2.70, P < 0.01) in patients with elevated NLR compared to those without elevated NLR. Subgroup analyses stratified by follow-up duration and study design further supported the consistent association between elevated NLR and adverse outcomes. In conclusion, this meta-analysis demonstrates a significant association between elevated NLR and an increased risk of MACE and all-cause mortality in patients with MI. These findings highlight the potential clinical utility of NLR as a prognostic marker and underscore the importance of further research to validate its predictive value and establish optimal cutoff values for risk stratification in this patient population.

Prospective cohort study of Torque Teno Virus (TTV) viral load kinetics and the association with graft rejection in renal transplant patients.

Graft survival is mainly determined by rejections and infectious complications in transplant recipients. Torque Teno Virus (TTV), a nonpathogenic and ubiquitous single-stranded DNA virus, has been proposed as a biomarker of the immune status in transplant patients. This study aimed to determine the correlation between a Home-Brew TTV PCR and R-GENE®PCR; the TTV viral load kinetics in renal transplant recipients and the association with graft rejection. Prospective cohort study on 107 adult renal transplant recipients. TTV viral load was determined in 746 plasma samples collected before and after renal transplantation by a Home-Brew PCR and a commercial PCR (R-GENE®PCR). Associations of TTV viral load with graft rejections were analyzed. Agreement of both PCR assays was 93.2% and Pearson correlation coefficient was r: 0.902 (95%CI: 0.8881-0.9149, p<0.0001). TTV viral load kinetics showed an initial gradual increase reaching a peak at 3 months. This highest value was followed by a slight decrease, reaching a plateau significantly higher than the initial baseline at 6 months (p<0.0001). Between (181-270) days post-transplantation, TTV median viral load in patients with graft rejection was significantly lower, 3.59 Log10 copies/mL (by Home-Brew PCR) and 3.10 Log10 copies/mL (by R-GENE®PCR) compared to patients without graft rejection (6.14 and 5.96 Log10 copies/mL, respectively). Significantly lower TTV viral load was observed in patients with renal rejection occurring at a median of 243 days post-transplantation. Given the dynamic behavior of TTV viral load post-transplantation, cut-off values for risk stratification to predict rejection might be determined in relation to the post-transplant period.

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules.

This study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms. A retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC). The malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost. Combining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

Validating and Comparing C-TIRADS, K-TIRADS and ACR-TIRADS in Stratifying the Malignancy Risk of Thyroid Nodules.

The thyroid imaging reporting and data system (TIRADS) was proposed by experts for optimal ultrasound evaluation of malignancy risk of thyroid focal lesions. There are several versions of TIRADS, some of them have been validated sufficiently, and the others have not been well assessed. In this study, a recently launched Chinese version of TIRADS (C-TIRADS) for malignancy risk stratification of thyroid nodules was validated, and the performance was compared to that of the Korean TIRADS (K-TIRADS) and American College of Radiology(ACR) TIRADS (ACR-TIRADS). Archives of 2177 patients who had undergone thyroid ultrasound examination, coarse needle tissue biopsy and/or surgery were reviewed, and 1978 patients with 1982 thyroid nodules were assessed according to the three TIRADSs. The histopathology was taken as the golden standard. The results showed the 1982 thyroid nodules were consisted of 1306 benign nodules and 676 malignant nodules. The malignancy risk accounted for 1.09%, 2.14%, 10.34%, 49.28%, 88.19% and 85.29% of the total nodules that were categorised as C-TIRADS 2, 3, 4A, 4B, 4C and 5, respectively; 0.00%, 1.64%, 2.87%,18.71% and 82.22% of the total nodules that were categorised as ACR-TIRADS 1, 2, 3, 4 and 5, respectively; 0.85%, 3.27%, 24.27% and 80.96% of the total nodules that were categorised as K-TIRADS 2, 3, 4 and 5, respectively. The correlation between the category of TIRADS and percentile of malignancy was 0.94 in the C-TIRADS, 1.00 in the ACR-TIRADS, and 1.00 in the K-TIRADS, respectively. The highest values of accuracy(AUC) of ROC curves of C-TIRADS 4B, K-TIRADS 5 and ACR-TIRADS 5 were taken as the cut-off values for risk stratification, respectively. The sensitivity, specificity, positive and negative predictive values and AUC by C-TIRADS 4B, K-TIRADS 5 and ACR-TIRADS 5 for malignancy risk stratification of thyroid nodules were 90.83%, 84.23%, 74.88% and 94.66% and 0.88, respectively; 83.58%, 89.82%, 80.95%, 91.36% and 0.87, respectively; and 85.50%, 90.35%, 82.10%, 92.33% and 0.88, respectively (P>0.05 for all). We concluded that the C-TIRADS has excellent performance in the malignancy risk stratification of thyroid nodules by the optimized cut-off value, which is comparable to that in K-TIRADS and ACR-TIRADS.

Risk stratification in patients with anaplastic thyroid carcinoma: role of age.

Thyroid carcinoma is the only cancer that regards age as an important predictor of thyroid cancer-specific survival (CSS). While the 8th American Joint Committee on Cancer (AJCC) staging system raised the age cutoff from 45 to 55 years for differentiated thyroid carcinoma (DTC) to more accurately predict the prognosis, there is no new information regarding the role of age in the prognosis of anaplastic thyroid carcinoma (ATC). The aim of this study was to determine the optimal age cutoff values for mortality risk stratification in ATC patients. Furthermore, a nomogram to predict ATC CSS was developed in each age group. Patients diagnosed with ATC between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. After applying inclusion and exclusion criteria, a total of 1140 patients were enrolled as cohort 1 to describe the characteristics of ATC, while a total of 556 patients were included as cohort 2 to determine age cutoff values for risk stratification by X-tile program. Training set and testing set were randomly generated to develop and validate a predictive nomogram of CSS in each age group. The 6-month, 1-year, and 2-year survival was 27.6%, 15.1%, and 6.2%, respectively, in cohort 1. X-tile program results showed that the optimal age cutoff values for mortality risk stratification were 65 and 85 years old (p < 0.001). Distant metastasis was independently associated with CSS in patients younger than 85 years old, and these patients benefited more from complete resection of the tumor and radiotherapy/chemotherapy. However, no predictors of CSS were identified in patients over 85 years old, and interventions (surgery, radiotherapy, and chemotherapy) targeting ATC had little role in disease control in these patients. The nomogram was developed and validated based on the independent CSS predictors in each age group. The C-index values of the training set and testing set were 0.735 [95% CI, 0.696-0.774] and 0.733 [95% CI, 0.663-0.804] for CSS in patients of ≤64 years old, while the values were 0.767 [95% CI, 0.730-0.804] and 0.783 [95% CI, 0.718-0.848] in patients of 65-84 years old. All of the C-index values were larger than 0.7, which showed acceptable prediction performance of the nomograms. Age can be used as an auxiliary stratification factor of prognosis in ATC patients. The survival may be improved in patients younger than 85 years old if combination therapy (surgery, radiotherapy, and chemotherapy) was indicated and applicable, while no optimal therapeutic strategy was determined in patients older than 85 years old. The established nomograms can provide good prediction of CSS according to age group.

Integrated Immunologic Monitoring in Solid Organ Transplantation: The Road Toward Torque Teno Virus-guided Immunosuppression.

Potent immunosuppressive drugs have been introduced into clinical care for solid organ transplant recipients. It is now time to guide these drugs on an individual level to optimize their efficacy. An ideal tool simultaneously detects overimmunosuppression and underimmunosuppression, is highly standardized, and is straightforward to implement into routine. Randomized controlled interventional trials are crucial to demonstrate clinical value. To date, proposed assays have mainly focused on the prediction of rejection and were based on the assessment of few immune compartments. Recently, novel tools have been introduced based on a more integrated approach to characterize the immune function and cover a broader spectrum of the immune system. In this respect, the quantification of the plasma load of a highly prevalent and apathogenic virus that might reflect the immune function of its host has been proposed: the torque teno virus (TTV). Although TTV control is driven by T cells, other major immune compartments might contribute to the hosts’ response. A standardized in-house polymerase chain reaction and a conformité européenne-certified commercially available polymerase chain reaction are available for TTV quantification. TTV load is associated with rejection and infection in solid organ transplant recipients, and cutoff values for risk stratification of such events have been proposed for lung and kidney transplantation. Test performance of TTV load does not allow for the diagnosis of rejection and infection but is able to define at-risk patients. Hitherto TTV load has not been used in interventional settings, but two interventional randomized controlled trials are currently testing the safety and efficacy of TTV-guided immunosuppression.

NT-proBNP Cut-off Values for Risk Stratification in Acute MI and Comparison with Other Risk Assessment Scores

Background: N-terminal pro-brain natriuretic peptide (NT-proBNP) provides prognostic information regarding the risk of death, acute heart failure and the development of AF in patients with acute coronary syndrome. While there are established cut-off values for the association between clinical risk assessment scores and in-hospital mortality, there is no clear cut-off value for NT-proBNP to guide risk stratification in patients with acute MI (AMI). Our study sought to evaluate the cut-off values of NT-proBNP in all-cause mortality post AMI and to compare with other available risk assessment scores. Methods: We conducted a multi-centre, prospective, observational study involving 411 patients admitted for AMI. Plasma NT-proBNP was assessed within 24 hours of admission. Results: One-year all-cause mortality occurred in 31 (7.6%) of 411 patients. NT-proBNP ≥404 pg/ml had an area under the curve of 0.66 (95% CI [0.54–0.77]; p=0.004) to predict all-cause mortality at 1 year (sensitivity: 80.6%; specificity: 36.9%; positive predictive value: 9.47%; negative predictive value: 95.89%). Using the Youden index, an NT-proBNP level ≥1,995 pg/ml was an independent predictor of all-cause mortality at 1 year (adjusted HR 2.6; 95% CI [1.3–5.5]; p=0.010), regardless of cardiovascular disease risk factors or revascularisation status. There were no significant differences among the predictive values of NT-proBNP, Thrombolysis in MI risk score, Global Registry of Acute Coronary Events risk score and left ventricular ejection fraction in predicting all-cause mortality at 1 year (p&gt;0.05). Conclusion: NT-proBNP level ≥1,995 pg/ml measured within 24 hours of admission for AMI was associated with higher all-cause mortality at 1 year. Randomised controlled trials are needed to further validate the usefulness of NT-proBNP for risk stratification in patients with AMI.

Red Cell Distribution Width Is Independently Associated with Mortality in Sepsis

Background: Mortality in sepsis remains high. Studies on small cohorts have shown that red cell distribution width (RDW) is associated with mortality. The aim of this study was to validate these findings in a large multicenter cohort. Methods: We conducted this retrospective analysis of the multicenter eICU Collaborative Research Database in 16,423 septic patients. We split the cohort in patients with low (≤15%; n = 7,129) and high (>15%; n = 9,294) RDW. Univariable and multivariable multilevel logistic regressions were used to fit regression models for the binary primary outcome of hospital mortality and the secondary outcome intensive care unit (ICU) mortality with hospital unit as random effect. Optimal cutoffs were calculated using the Youden index. Results: Patients with high RDW were more often older than 65 years (57% vs. 50%; p < 0.001) and had higher Acute Physiology and Chronic Health Evaluation (APACHE) IV scores (69 vs. 60 pts.; p < 0.001). Both hospital (adjusted odds ratios [aOR] 1.18; 95% CI: 1.16–1.20; p < 0.001) and ICU mortality (aOR 1.16; 95% CI: 1.14–1.18; p < 0.001) were associated with RDW as a continuous variable. Patients with high RDW had a higher hospital mortality (20 vs. 9%; aOR 2.63; 95% CI: 2.38–2.90; p < 0.001). This finding persisted after multivariable adjustment (aOR 2.14; 95% CI: 1.93–2.37; p < 0.001) in a multilevel logistic regression analysis. The optimal RDW cutoff for the prediction of hospital mortality was 16%. Conclusion: We found an association of RDW with mortality in septic patients and propose an optimal cutoff value for risk stratification. In a combined model with lactate, RDW shows equivalent diagnostic performance to Sequential Organ Failure Assessment (SOFA) score and APACHE IV score.

Web-based calculator for biliary atresia screening in neonates and infants with cholestasis.

BackgroundDistinguishing biliary atresia from non-biliary atresia in patients with cholestasis is challenging, as these conditions have a similar clinical presentation. We developed and externally validated a screening model for biliary atresia and devised a web-based calculator for use in clinical settings.MethodsA screening model was developed based on data from 227 cholestatic infants (82 and 145 with and without biliary atresia, respectively) and validated in 234 infants (90 and 144 with and without biliary atresia, respectively) admitted to three hospitals. Variables were selected from routine examination results using the least absolute shrinkage and selection operator method and entered into a logistic regression model to construct a biliary-atresia-risk–predicting equation. Cutoff values for risk stratification were estimated using model sensitivity, derived from the receiver-operating characteristic curves.ResultsThe final screening model included seven variables (i.e., weight at admission, clay-colored stools, γ-glutamyl transpeptidase and albumin levels at admission, abnormal gallbladder, triangular cord sign, and change in total bilirubin levels). The model generated an area under the curve of 0.94 with a sensitivity of 91.46 and specificity of 86.62 in the derivation cohort. This was confirmed in the validation cohort, as we found an area under the curve of 0.93 with a sensitivity of 93.1 and specificity of 80.15. Patients were stratified into three risk groups (low-, moderate-, and high-risk groups). Biliary atresia was excluded in the low-risk group. The high-risk group showed a higher detection rate of biliary atresia compared to the stool color screening method alone. This model was integrated into a user-friendly web-based system.ConclusionsThe screening tool had a high predictive value and may help in decision-making by physicians at tertiary and community hospitals.

Identification of the optimal hsTnI cut-off value for risk stratification of normotensive patients with pulmonary embolism

Abstract Background/Introduction While numerous studies confirmed the prognostic role of high-sensitivity troponin T (hsTnT) in pulmonary embolism (PE), the prognostic relevance of high-sensitivity troponin I (hsTnI) is inappropriately studied and disease specific cut-off values remain undefined. Purpose To investigate the prognostic relevance of hsTnI in normotensive PE patients, establish the optimal cut-off value for risk stratification and compare the prognostic performances of hsTnI and hsTnT. Methods Consecutive PE patients enrolled in a prospective single-centre registry between 09/2008 and 04/2018 were studied. Using receiver operating curve analysis, an optimised hsTnI cut-off concentration was identified and the prognostic value for the prediction of in-hospital adverse outcomes (PE-related death, cardiopulmonary resuscitation or vasopressor treatment) and all-cause mortality analysed. Results We analysed data from 459 PE patients (age 69 [interquartile range (IQR) 57–77] years, 52% female). Patients who suffered an in-hospital adverse outcome (4.8%) had higher median hsTnI concentrations compared to those with a favorable clinical course (57 [IQR 22–197] vs. 15 [IQR 10–86] pg/ml, p=0.03). A hsTnI cut-off value of 16 ng/ml provided the best prognostic performance and predicted an in-hospital adverse outcome (Odds ratio [OR] 6.5, 95% confidence interval [CI] 1.9–22.4) and all-cause mortality (OR 3.7, 95% CI 1.0–13.3). Between female and male patients, no relevant differences in hsTnI concentrations (17 [IQR 10–97] vs. 17 [IQR 10–92] pg/ml, p=0.79) or optimized cut-off values (17 pg/ml and 19 pg/ml, respectively) were observed. Stratification of patients to risk classes according to the 2019 European Society of Cardiology (ESC) algorithm revealed no differences if calculated based on either hsTnI or hsTnT (Table). Conclusions Our findings confirm the prognostic relevance of hsTnI in normotensive PE. An optimal hsTnI cut-off value of 16 pg/ml predicted in-hospital adverse outcome and all-cause mortality. The use of sex specific cut-off values does not appear necessary. Importantly, our results suggest that hsTnI and hsTnT can be used interchangeably for risk stratification. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): This study was supported by the German Federal Ministry of Education and Research (BMBF 01EO1503). BRAHMS GmbH, part of Thermo Fisher Scientific, Hennigsdorf/Berlin, Germany provided financial support for biomarker measurements. The sponsor was neither involved in biomarker measurements, statistical analyses, writing of the abstract nor had any influence on the scientific contents.

High-sensitivity troponin I for risk stratification in normotensive pulmonary embolism.

While numerous studies have confirmed the prognostic role of high-sensitivity troponin T (hsTnT) in pulmonary embolism (PE), high-sensitivity troponin I (hsTnI) is inappropriately studied. This study aimed to investigate the prognostic relevance of hsTnI in normotensive PE, establish the optimal cut-off value for risk stratification and to compare the prognostic performances of hsTnI and hsTnT.Based on data from 459 consecutive PE patients enrolled in a single-centre registry, receiver operating characteristic analysis was used to identify an optimal hsTnI cut-off value for prediction of in-hospital adverse outcomes (PE-related death, cardiopulmonary resuscitation or vasopressor treatment) and all-cause mortality.Patients who suffered an in-hospital adverse outcome (4.8%) had higher hsTnI concentrations compared with those with a favourable clinical course (57 (interquartile range (IQR) 22–197) versus 15 (IQR 10–86) pg·mL−1, p=0.03). A hsTnI cut-off value of 16 ng·mL−1 provided optimal prognostic performance and predicted in-hospital adverse outcomes (OR 6.5, 95% CI 1.9–22.4) and all-cause mortality (OR 3.7, 95% CI 1.0–13.3). Between female and male patients, no relevant differences in hsTnI concentrations (17 (IQR 10–97) versus 17 (IQR 10–92) pg·mL−1, p=0.79) or optimised cut-off values were observed. Risk stratification according to the 2019 European Society of Cardiology algorithm revealed no differences if calculated based on either hsTnI or hsTnT (p=0.68).Our findings confirm the prognostic role of hsTnI in normotensive PE. HsTnI concentrations >16 pg·mL−1 predicted in-hospital adverse outcome and all-cause mortality; sex-specific cut-off values do not seem necessary. Importantly, our results suggest that hsTnI and hsTnT can be used interchangeably for risk stratification.

The Performance of a Calcaneal Quantitative Ultrasound Device, CM-200, in Stratifying Osteoporosis Risk among Malaysian Population Aged 40 Years and Above.

Background: Calcaneal quantitative ultrasound (QUS) is widely used in osteoporosis screening, but the cut-off values for risk stratification remain unclear. This study validates the performance of a calcaneal QUS device (CM-200) using dual-energy X-ray absorptiometry (DXA) as the reference and establishes a new set of cut-off values for CM-200 in identifying subjects with osteoporosis. Methods: The bone health status of Malaysians aged ≥40 years was assessed using CM-200 and DXA. Sensitivity, specificity, area under the curve (AUC) and the optimal cut-off values for risk stratification of CM-200 were determined using receiver operating characteristic (ROC) curves and Youden’s index (J). Results: From the data of 786 subjects, CM-200 (QUS T-score <−1) showed a sensitivity of 82.1% (95% CI: 77.9–85.7%), specificity of 51.5% (95% CI: 46.5–56.6%) and AUC of 0.668 (95% CI: 0.630–0.706) in identifying subjects with suboptimal bone health (DXA T-score <−1) (p < 0.001). At QUS T-score ≤−2.5, CM-200 was ineffective in identifying subjects with osteoporosis (DXA T-score ≤−2.5) (sensitivity 14.4% (95% CI: 8.1–23.0%); specificity 96.1% (95% CI: 94.4–97.4%); AUC 0.553 (95% CI: 0.488–0.617); p > 0.05). Modified cut-off values for the QUS T-score improved the performance of CM-200 in identifying subjects with osteopenia (sensitivity 67.7% (95% CI: 62.8–72.3%); specificity 72.8% (95% CI: 68.1–77.2%); J = 0.405; AUC 0.702 (95% CI: 0.666–0.739); p < 0.001) and osteoporosis (sensitivity 79.4% (95% CI: 70.0–86.9%); specificity 61.8% (95% CI: 58.1–65.5%); J = 0.412; AUC 0.706 (95% CI: 0.654–0.758); p < 0.001). Conclusion: The modified cut-off values significantly improved the performance of CM-200 in identifying individuals with osteoporosis. Since these values are device-specific, optimization is necessary for accurate detection of individuals at risk for osteoporosis using QUS.

Prognostic significance of semi-quantitative FDG-PET parameters in stage I non-small cell lung cancer treated with carbon-ion radiotherapy

PurposePrognostic significance of volumetric 18F-fluorodeoxyglucose (FDG) positron emission tomography/computer tomography (PET/CT) parameters in carbon-ion radiotherapy (C-ion RT) treated stage I non-small cell lung cancer, and need of histology-wise separate cut-off values for risk stratification were assessed.MethodsThirty-nine patients (29 men and 10 women, 71.9 ± 8.3 years) who underwent FDG PET/CT examinations before C-ion RT were retrospectively evaluated. FDG-PET parameters: standardized uptake values (SUVmax, SUVpeak, and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), and clinicopathological variables were assessed for prognosis using Cox proportional hazards regression analysis. Mann-Whitney test compared medians of significant parameters between adenocarcinoma (AC) and squamous cell carcinoma (SCC), and Kaplan-Meier curves were plotted for median-based low- and high-risk groups.ResultsMedian follow-up period was 44.8 months. 1/2/3-year overall survival (OS), progression-free survival (PFS) and local control (LC) rates were 94.9/84.3/70.8, 82.1/69.2/58.4 and 97.3/85.7/82.3%. Multivariate analysis revealed age (hazard ratio, HR: 1.09; 95% confidence interval, CI: 1.0–1.19, p < 0.05) and MTV (HR 4.83, 95% CI 1.21–19.27, p < 0.03) predicted OS, and only MTV predicted PFS (HR 5.3, CI 1.32–21.35, p < 0.02) independently. Compared with AC, SCC had higher MTV (median, 6.625cm3 vs 0.2 cm3, p < 0.01). Single MTV cut-off based on overall cohort was insignificant in SCC for PFS (p > 0.02); separate cut-offs of MTV, 0.2 cm3 for AC (p < 0.03) and 6.625 cm3 for SCC (p < 0.05) were relevant.ConclusionAmong all FDG PET/CT parameters, only MTV beared prognostic ability for stage I NSCLC treated with C-ion RT, and its histological variation may need consideration for risk-adapted therapeutic management.

Prognostic role of FDG PET-derived parameters in preoperative staging of endometrial cancer.

To investigate the preoperative prognostic role of 18F-FDG PET/CT in patients with endometrial carcinoma (EC). 18F-FDG PET/CT was performed in 57 patients for EC preoperative staging. Maximum and mean standardized uptake values (SUVmax, mean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors, at different thresholds of 40%, 50%, 60% (40-50-60), were evaluated and compared with anatomopathological features. The diagnostic performance of PET-parameters (categorized by ROC analysis) in discriminating low-intermediate and high-risk disease and the prognostic role on survival (overall survival -OS; disease free survival - DFS) was evaluated. The categorized TLG40-50-60 were the only parameters related to FIGO stage I versus II-III-IV (p = 0.0035 for all). The cut-off values for risk stratification were 83.69, 61.81 and 41.32, respectively (sensitivity: 60.00%; specificity; 71.43% for all parameters). Pathological stage 1 (pT1) of the primary tumor was predicted by MTV60 and TLG40-50 (p = 0.0328, 0.0240, 0.0147, respectively). The optimal thresholds were 7.795, 99.55 and 77.58, respectively (sensitivity: 38.46%, 53.85% and 53.85%, respectively; specificity: 88.64%, 79.55% and 81.82%, respectively). SUVmax and SUVmean40-50-60 were the only parameters discriminating endometrioid from non-endometrioid subtype. The corresponding sensitivity was 64.86% and 62.16% for SUVmax and SUVmean 50-60 and 62.16% for SUVmean40; specificity was 70.00% for all parameters. The mean (SD) OS was 79.77% (3.34%) and the mean DFS was 77.89% (3.73%). The tumor type was the only variable significantly associated with OS (p = 0.0486). TLG50 > 77.58 cm3 was the only variable associated with a higher risk of relapse (p = 0.0472). TLG40-50-60 and MTV60 of primary EC have prognostic value in discriminating FIGO and pathological staging. These results suggest a possible role of these parameters in predicting EC aggressiveness, thus improving the preoperative characterization of endometrial cancer.

Prognostic role of FDG PET-derived parameters in preoperative staging of endometrial cancer

PurposeTo investigate the preoperative prognostic role of 18F-FDG PET/CT in patients with endometrial carcinoma (EC). Methods18F-FDG PET/CT was performed in 57 patients for EC preoperative staging. Maximum and mean standardized uptake values (SUVmax, mean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors, at different thresholds of 40%, 50%, 60% (40–50–60), were evaluated and compared with anatomopathological features. The diagnostic performance of PET-parameters (categorized by ROC analysis) in discriminating low-intermediate and high-risk disease and the prognostic role on survival (overall survival –OS; disease free survival – DFS) was evaluated. ResultsThe categorized TLG40–50–60 were the only parameters related to FIGO stage I versus II-III-IV (p=0.0035 for all). The cut-off values for risk stratification were 83.69, 61.81 and 41.32, respectively (sensitivity: 60.00%; specificity; 71.43% for all parameters). Pathological stage 1 (pT1) of the primary tumor was predicted by MTV60 and TLG40-50 (p=0.0328, 0.0240, 0.0147, respectively). The optimal thresholds were 7.795, 99.55 and 77.58, respectively (sensitivity: 38.46%, 53.85% and 53.85%, respectively; specificity: 88.64%, 79.55% and 81.82%, respectively). SUVmax and SUVmean40–50–60 were the only parameters discriminating endometrioid from non-endometrioid subtype. The corresponding sensitivity was 64.86% and 62.16% for SUVmax and SUVmean 50–60 and 62.16% for SUVmean40; specificity was 70.00% for all parameters. The mean (SD) OS was 79.77% (3.34%) and the mean DFS was 77.89% (3.73%). The tumor type was the only variable significantly associated with OS (p=0.0486). TLG50>77.58cm3 was the only variable associated with a higher risk of relapse (p=0.0472). ConclusionTLG40–50–60 and MTV60 of primary EC have prognostic value in discriminating FIGO and pathological staging. These results suggest a possible role of these parameters in predicting EC aggressiveness, thus improving the preoperative characterization of endometrial cancer.

The clinical utility of shock index to predict the need for blood transfusion and outcomes in trauma

BackgroundWe aimed to evaluate the clinical utility of shock index (SI) to assess the need for blood transfusion and predict the outcomes in trauma. Materials and methodsWe conducted a retrospective analysis for trauma patients between 2012 and 2016 in a level-1 trauma center. Data included patient demographics, vital signs, mechanism of injury, Injury Severity Score (ISS), New Injury Severity Score (NISS), Trauma and Injury Severity Score (TRISS), blood transfusion, hospital length of stay (HLOS), and mortality. Patients were classified into group I (SI < 0.8) and group II (SI ≥ 0.8). ResultsOut of 8710 admitted patients, 1535 (22%) had SI ≥ 0.8 and 976 (12.5%) received blood transfusion (89 received massive transfusion, following massive blood transfusion protocol [MTP]). In comparison to lower SI, patients with SI ≥ 0.8 were mostly female patients, 8 y younger (43 ± 22 versus 51 ± 23), had greater ISS (15 ± 12 versus 10.5 ± 8), higher NISS (19 ± 15 versus 14 ± 11), lower pulse pressure (43 ± 14 versus 62 ± 18), lower TRISS (0.892 ± 0.20 versus 0.953 ± 0.11), and received more blood transfusion (28.6% versus 9.0%) or MTP (17.7% versus 3%), P = 0.001. Also, they had mostly exploratory laparotomy (13.3% versus 6.6%, P = 0.001), longer HLOS (11.3 versus 7.0 d, P = 0.001), and higher mortality (7.0% versus 3.1%, P = 0.001). SI was correlated with age (r = −0.188), pulse pressure (r = −0.51), HLOS (r = 0.168), ISS (r = 0.251), NISS (r = 0.211), amount of blood transfused (r = 0.27), Glasgow Coma Scale (r = −0.96), and TRISS (r = −0.230). After adjusting for age and sex, ISS, and Glasgow Coma Scale in two multivariable analyses, high SI was found to be an independent predictor for mortality (odd ratio, 2.553; 95% confidence intervals: 1.604-4.062) and blood transfusion (odd ratio, 3.57; 95% confidence intervals: 3.012-4.239). The cutoff point of SI for predicting MTP is 0.81 (sensitivity, 85%; specificity, 64%; positive predictive value, 16%; and negative predictive value, 98%). ConclusionsThe SI after injury can be used early to predict the need for MTP and laparotomy and mortality. It correlates with other physiological and anatomical variables. However, its cutoff values for risk stratification and prognostication need further evaluation.

Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population.

Objective:Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic’s Food and Drug Administration–approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories.Methods:Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR’s BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density–based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities.Results:The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%.Conclusions:Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase.