Optimizing the frequency of physician encounters in follow - up care for patients with type 2 diabetes mellitus: a systematic review

BACKGROUND

Decisions on the frequency of physician encounters for patients with type 2 diabetes mellitus (T2DM) have significant impacts on both patients' health outcomes and burden on health systems, whereas definitive intervals for physician encounters are still lacking in most clinical guidelines. This study systematically reviewed the existing evidence evaluating different frequencies of physician encounters among T2DM patients.

METHODS

Systematic search of studies evaluating different visit frequencies for follow - up care in T2DM patients was performed in MEDLINE Ovid, Embase Ovid, and Cochrane library from database inception to 25 March 2022. Studies on the follow - up encounters driven by non - physicians and those on the episodic visits in the acute care settings were excluded in the screening. Citation searching was conducted via Google Scholar on the identified papers after screening. The risk of bias was assessed using Cochrane RoB2 tool for randomized controlled trials and Newcastle - Ottawa Scale for cohort studies. Findings were summarized narratively.

RESULTS

Among 6363 records from the database search and 231 references from the citation search, 12 articles were eligible for in - depth review. The results showed that for patients who had not achieved cardiometabolic control, intensifying encounter frequency could enhance medication adherence, shorten the time to achieve the treatment target, and improve the patients' quality of life. However, for the patients who had already achieved the treatment targets, less frequent encounters were equivalent to intensive encounters in maintaining their cardiometabolic control, and could save considerable healthcare costs without substantially lowering the quality of care and patients' satisfaction.

CONCLUSION

Existing evidence suggested that the optimal frequency of physician encounters for patients with T2DM should be individualized, which can be stratified by patients' risk levels based on the cardiometabolic control to guide the differential scheduling of physician encounters in the follow - up. More research is needed to determine how to optimize the frequency of physician encounters for this large and heterogeneous population.

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department

BACKGROUND

Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.

METHODS

The study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISKINDEX, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISKINDEX was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISKINDEX to the attending physician in order to assess the extent to which clinical treatment is influenced by the results.

DISCUSSION

This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830 .

An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy

BACKGROUND AND AIMS

The diagnosis and stratification of gastric atrophy (GA) predict patients' gastric cancer progression risk and determine endoscopy surveillance interval. We aimed to construct an artificial intelligence (AI) system for GA endoscopic identification and risk stratification based on the Kimura-Takemoto classification.

METHODS

We constructed the system using two trained models and verified its performance. First, we retrospectively collected 869 images and 119 videos to compare its performance with that of endoscopists in identifying GA. Then, we included original image cases of 102 patients to validate the system for stratifying GA and comparing it with endoscopists with different experiences.

RESULTS

The sensitivity of model 1 was higher than that of endoscopists (92.72% vs. 76.85 %) at image level and also higher than that of experts (94.87% vs. 85.90 %) at video level. The system outperformed experts in stratifying GA (overall accuracy: 81.37 %, 73.04 %, p = 0.045). The accuracy of this system in classifying non-GA, mild GA, moderate GA, and severe GA was 80.00 %, 77.42 %, 83.33 %, and 85.71 %, comparable to that of experts and better than that of seniors and novices.

CONCLUSIONS

We established an expert-level system for GA endoscopic identification and risk stratification. It has great potential for endoscopic assessment and surveillance determinations.

Validation of the Keele STarT MSK Tool for Patients With Musculoskeletal Pain in United States-based Outpatient Physical Therapy Settings

The STarT MSK tool was developed to enable risk stratification of patients with common musculoskeletal (MSK) pain conditions and help identify individuals who may require more targeted interventions or closer monitoring in primary care settings, however, its validity in U.S.-based outpatient physical therapy settings has not been investigated. The 10-item Keele STarT MSK risk stratification tool was tested for construct (convergent and discriminant) and predictive validity using a multicenter, prospective cohort study design. Participants (n = 141) receiving physical therapy for MSK pain of the back, neck, shoulder, hip, knee, or multisite regions completed intake questionnaires including the Keele STarT MSK tool, Functional Comorbidity Index (FCI), Optimal Screening for Prediction of Referral and Outcome Review-of-Systems and Optimal Screening for Prediction of Referral and Outcome Yellow Flag tools. Pain intensity, pain interference, and health-related quality of life (Medical Outcomes Study 8-item Short-Form Health Survey (SF-8) physical [PCS] and mental [MCS] component summary scores) were measured at 2- and 6-month follow-up. Participants were classified as STarT MSK tool low (44%), medium (39%), and high (17%) risk. Follow-up rates were 70.2% (2 months) and 49.6% (6 months). For convergent validity, fair relationships were observed between the STarT MSK tool and FCI and SF-8 MCS (r = .35-.37) while moderate-to-good relationships (r = .51-.72) were observed for 7 other clinical measures. For discriminant validity, STarT MSK tool risk-dependent relationships were observed for Optimal Screening for Prediction of Referral and Outcome Review-of-Systems, Optimal Screening for Prediction of Referral and Outcome Yellow Flag, pain interference, and SF-8 PCS (low < medium < high; P < .01) and FCI, pain intensity, and SF-8 MCS (low  Collapse

Key Words

• Risk stratification
• musculoskeletal pain
• physical therapy
• prognosis
• screening

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Prediction of Hepatocellular Carcinoma Development in Korean Patients after Hepatitis C Cure with Direct-Acting Antivirals

Background/Aims

With the wide application of direct-acting antivirals (DAAs) for hepatitis C virus infection, the number of patients achieving a sustained virologic response (SVR) will continue to increase. However, no consensus has been achieved on exempting SVR-achieving patients from hepatocellular carcinoma (HCC) surveillance.

Methods

Between 2013 and 2021, 873 Korean patients who achieved SVR following DAA treatment were analyzed. We evaluated the predictive performance of seven noninvasive scores (PAGE-B, modified PAGE-B, Toronto HCC risk index, fibrosis-4, aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin, and age male albumin-bilirubin platelet [aMAP]) at baseline and after SVR.

Results

The mean age of the 873 patients (39.3% males) was 59.1 years, and 224 patients (25.7%) had cirrhosis. During 3,542 person-years of follow-up, 44 patients developed HCC, with an annual incidence of 1.24/100 person-years. Male sex (adjusted hazard ratio [AHR], 2.21), cirrhosis (AHR, 7.93), and older age (AHR, 1.05) were associated with a significantly higher HCC risk in multivariate analysis. The performance of all scores at the time of SVR were numerically better than those at baseline as determined by the integrated area under the curve. Time-dependent area under the curves for predicting the 3-, 5-, and 7-year risk of HCC after SVR were higher in mPAGE-B (0.778, 0.746, and 0.812, respectively) and aMAP (0.776, 0.747, and 0.790, respectively) systems than others. No patients predicted as low-risk by the aMAP or mPAGE-B systems developed HCC.

Conclusions

aMAP and mPAGE-B scores demonstrated the highest predictive performance for de novo HCC in DAA-treated, SVR-achieving patients. Hence, these two systems may be used to identify low-risk patients that can be exempted from HCC surveillance.

The challenge of risk stratification in hypertrophic cardiomyopathy: Clinical, genetic and imaging insights from a quaternary referral centre

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is the commonest genetic cardiomyopathy and may result in sudden cardiac death (SCD). Clinical risk stratification scores are utilised to estimate SCD risk and determine potential utility of a primary prevention implantable cardioverter defibrillator (ICD).

METHODS

Patients with a confirmed diagnosis of HCM from a quaternary HCM service were defined according to clinical characteristics, genetic profiles and cardiac imaging results. European Risk-SCD score and American Heart Association / American College of Cardiology (AHA/ACC) Score were calculated. The primary outcome was cardiac arrest.

RESULTS

380 patients with HCM were followed up for a median of 6.4 years. 18 patients (4.7%) experienced cardiac arrest, with predictive factors being younger age (37.2 vs 54.4 years, p = 0.0041), unexplained syncope (33.3% vs 9.4%, p = 0.007), non-sustained ventricular tachycardia (50.0% vs 12.7%, p < 0.0001), increased septal thickness (21.5 vs 17.5 mm, p = 0.0003), and presence of a sarcomeric gene mutation (100.0% vs 65.8%, p = 0.038). The Risk-SCD and AHA/ACC scores had poor agreement (kappa coefficient 0.38). Risk-SCD score had poor sensitivity (44.4%), classifying 55.6% of patients with cardiac arrest as low-risk but was highly specific (93.7%). AHA/ACC risk score did not discriminate between groups significantly. 20 patients (5.3%) died, with most >60-year-olds having a non-cardiac cause of death (p = 0.0223).

CONCLUSION

This study highlights limited (38%) agreement between the Risk-SCD and AHA/ACC scores. Most cardiac arrests occurred in ostensibly low or medium-risk patients under both scores. Appropriate ICD selection remains challenging. Incorporating newer risk markers such as HCM genotyping and myocardial fibrosis quantification by cardiac MRI may assist future risk refinement.

A gene-based score for the risk stratification of stage IA lung adenocarcinoma

OBJECTIVE

We aim to molecularly stratify stage IA lung adenocarcinoma (LUAD) for precision medicine.

METHODS

Twelve multi-institution datasets (837 cases of IA) were used to classify the high- and low-risk types (based on survival status within 5 years), and the biological differences were compared. Then, a gene-based classifying score (IA score) was trained, tested and validated by several machine learning methods. Furthermore, we estimated the significance of the IA score in the prognostic assessment, chemotherapy prediction and risk stratification of stage IA LUAD. We also developed an R package for the clinical application. The SEER database (15708 IA samples) and TCGA Pan-Cancer (1881 stage I samples) database were used to verify clinical significance.

RESULTS

Compared with the low-risk group, the high-risk group of stage IA LUAD has obvious enrichment of the malignant pathway and more driver mutations and copy number variations. The effect of the IA score on the classification of high- and low-risk stage IA LUAD was much better than that of classical clinicopathological factors (training set: AUC = 0.9, validation set: AUC = 0.7). The IA score can significantly predict the prognosis of stage IA LUAD and has a prognostic effect for stage I pancancer. The IA score can effectively predict chemotherapy sensitivity and occult metastasis or invasion in stage IA LUAD. The R package IAExpSuv has a good risk probability prediction effect for both groups and single stages of IA LUAD.

CONCLUSIONS

The IA score can effectively stratify the risk of stage IA LUAD, offering good assistance in precision medicine.

Disease Knowledge and Behavior Regarding Diabetic Foot Among Persons at Different Risks of Foot Ulceration According to the International Working Group Guidelines

BACKGROUND

This article aims to analyze levels of knowledge and behavior about diabetic foot care and prevention in persons with diabetes according to International Working Group (IWGDF) risk stratification system.

METHODS

A descriptive study in 83 persons with diabetes at different level of risk for foot ulceration (IWGDF risk 0-3). A previously validated questionnaire, the PIN Questionnaire, was used to analyze their levels of understanding of foot complications. Participants were responded on a 5-point Likert scale.

RESULTS

IWGDF-3 risk patients knew that good circulation and absence of polyneuropathy in their feet were related to healthy feet relative to the other groups (19.6 ± 2.7, p<.001 and 14.2 ± 0.7, p<.001 respectively). Additionally, they knew that a foot ulcer (DFU) on their feet will not be painful relative to other groups (6.6 ± 2.8, p<.001). High-risk patients knew which physical causes could affect the development of a DFU (18 ± 1.4, p<.001) and that foot self-care and medical control could prevent DFU appearance (23.4 ± 2.15, p<.001 and 13.9 ± 0.9, p<.001 respectively).

CONCLUSION

IWGDF-3 patients knew the natural progression of diabetes foot complications and how to prevent them. Clinicians should focus their efforts and educate diabetes at lower risk of foot ulcer.

Competing risk nomogram and risk classification system for evaluating overall and cancer-specific survival in neuroendocrine carcinoma of the cervix: a population-based retrospective study

OBJECTIVE

Neuroendocrine carcinoma of the cervix (NECC) is a rare malignancy with poor clinical prognosis due to limited therapeutic options. This study aimed to establish a risk-stratification score and nomogram models to predict prognosis in NECC patients.

METHODS

Data on individuals diagnosed with NECC between 2000 and 2019 were retrieved from the Surveillance Epidemiology and End Results (SEER) database and then randomly classified into training and validation cohorts (7:3). Univariate and multivariate Cox regression analyses evaluated independent indicators of prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis further assisted in confirming candidate variables. Based on these factors, cancer-specific survival (CSS) and overall survival (OS) nomograms that predict survival over 1, 3, and 5 years were constructed. The receiver operating characteristic (ROC) curve, the concordance index (C-index), and the calibration curve estimated the precision and discriminability of the competing risk nomogram for both cohorts. Finally, we assessed the clinical value of the nomograms using decision curve analysis (DCA).

RESULTS

Data from 2348 patients were obtained from the SEER database. Age, tumor stage, T stage, N stage, chemotherapy, radiotherapy, and surgery predicted OS. Additionally, histological type was another standalone indicator of CSS prognosis. For predicting CSS, the C-index was 0.751 (95% CI 0.731 ~ 0.770) and 0.740 (95% CI 0.710 ~ 0.770) for the training and validation cohorts, respectively. Furthermore, the C-index in OS prediction was 0.757 (95% CI 0.738 ~ 0.776) and 0.747 (95% CI 0.718 ~ 0.776) for both cohorts. The proposed model had an excellent discriminative ability. Good accuracy and discriminability were also demonstrated using the AUC and calibration curves. Additionally, DCA demonstrated the high clinical potential of the nomograms for CSS and OS prediction. We constructed a corresponding risk classification system using nomogram scores. For the whole cohort, the median CSS times for the low-, moderate-, and high-risk groups were 59.3, 19.5, and 7.4 months, respectively.

CONCLUSION

New competing risk nomograms and a risk classification system were successfully developed to predict the 1-, 3-, and 5-year CSS and OS of NECC patients. The models are internally accurate and reliable and may guide clinicians toward better clinical decisions and the development of personalized treatment plans.

Identifying optimal candidates for autologous peripheral blood stem cell therapy in patients with decompensated liver cirrhosis: a prognostic scoring system

BACKGROUND

Stem cell transplantation shows great potential to improve the long-term survival of cirrhosis patients. However, therapeutic effects may not be homogeneous across the whole study population. This study constructed an easy-to-use nomogram to improve prognostic prediction and aid in treatment decision making for cirrhotic patients.

METHODS

From August 2005 to April 2019, 315 patients with decompensated cirrhosis receiving autologous peripheral blood stem cell (PBSC) transplantation were enrolled in this study. They were randomly classified into training (2/3) and validation (1/3) groups. A predictive model was developed using Cox proportional hazard models and subsequently validated. The predictive performance of the model was evaluated and also compared with other prognostic models.

RESULTS

Age, creatinine, neutrophil-to-lymphocyte ratio, and Child-Turcotte-Pugh class were included in the nomogram as prognostic variables. The nomogram showed high discrimination power concerning the area under receiver operating characteristic curves (3/5-year AUC: 0.742/0.698) and good consistency suggested by calibration plots. Patients could be accurately stratified into poor- and good-outcome groups regarding liver-transplantation free survival after receiving PBSC therapy (P < 0.001). Compared with poor-outcome group, the liver function of patients listed for liver transplantation in the good-outcome group was significantly improved (P < 0.001). Besides, our nomogram achieved a higher C-index (0.685, 95% CI 0.633-0.738) and better clinical utility compared with other conventional prognostic models.

CONCLUSIONS

The proposed nomogram facilitated an accurate prognostic prediction for patients with decompensated cirrhosis receiving PBSC transplantation. Moreover, it also held the promise to stratify patients in clinical trials or practice to implement optimal treatment regimens for individuals.

Development and validation of a promising 5-gene prognostic model for pediatric acute myeloid leukemia

Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan-Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

Sex differences in abdominal visceral fat composition and metabolism as predictors of DLBCL prognosis: A retrospective cohort study

PURPOSE

We aimed to determine if sex differences in abdominal visceral fat composition and metabolism can help predict the prognosis of diffuse large B-cell lymphoma (DLBCL) patients.

METHODS

This retrospective cohort study included 117 DLBCL patients. The area and metabolic activity of subcutaneous adipose tissue and visceral adipose tissue were measured using CT and PET imaging. Kaplan-Meier survival analysis was employed to evaluate the effect of these parameters on progression-free survival. Multivariate Cox proportional hazard regression models were used to determine the effects of relative visceral fat area (rVFA) on sex-specific survival.

RESULTS

Females with an rVFA greater than the optimal threshold of 35 % and a visceral-to-subcutaneous adipose tissue ratio (V/S) >3.24 had worse progression-free survival (p = 0.01, 0.001, respectively). No rVFA or V/S were identified in significantly stratified males with DLBCL (p = 0.249 and 0.895, respectively). Combining the changes in rVFA and V/S identified a subgroup of females with high rVFA and V/S values and exceptionally poor outcomes. The rVFA was a significant predictor of DLBCL progression in females alone.

CONCLUSION

Once female DLBCL patients accumulate fat over the tolerable range in the visceral area, they might be at an increased risk of progression (hazard ratio, 3.87; 95 % CI, 1.81-12.69, p = 0.02). Sex differences in visceral fat composition and metabolism may provide a new risk stratification system for patients with DLBCL.

Risk stratification model for incidentally detected gallbladder polyps: A multicentre study

PURPOSE

We aimed to develop a 4-level risk stratification model using a scoring system based on conventional ultrasound to improve the diagnosis of gallbladder polyp.

METHOD

Patients with histopathologically confirmed gallbladder polyps were consecutively recruited from three medical centres. Conventional ultrasound findings and clinical characteristics were acquired prior to cholecystectomy. Risk factors for neoplastic and malignant polyps were used to build a risk stratification system via interobserver agreement and multivariate logistic regression analysis. The model was retrospectively trained using 264 pre-surgical samples and prospectively validated using 106 pre-surgical samples. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and malignant polyp rate.

RESULTS

In total, 370 patients (mean age, 51.68 ± 14.41 years, 156 men) were enrolled in this study. Size (≥12 mm), shape (oblate or round), single, vascularity, gallbladder stone or sludge were considered risk factors for neoplastic polyps. Size (≥14 mm), shape (oblate), single, disrupted gallbladder wall, and gallbladder stone or sludge were risk factors for malignant polyps (all p < 0.05). In the scoring system, the sensitivity, specificity, and AUC of score ≥ 9 in diagnosing neoplastic polyps were 0.766, 0.788, and 0.876 respectively; and the sensitivity, specificity, and AUC of score ≥ 15 in diagnosing malignant polyps were 0.844, 0.926, and 0.949 respectively. In our model, the malignancy rates at the four levels were 0 % (0/24), 1.28 % (2/156), 9.26 % (5/54), and 70.37 % (38/54), respectively.

CONCLUSIONS

The 4-level risk stratification model based on conventional ultrasound imaging showed excellent performance in classifying gallbladder polyps.

MRI characteristics and oncological follow-up of patients with ISUP grade group 4 or 5 prostate cancer

OBJECTIVES

To analyze multiparametric MRI (mpMRI) characteristics of patients with International Society of Urological Pathology (ISUP) grade group (GG) 4 or 5 prostate cancer (PC) and to correlate MRI parameters with the occurrence of biochemical recurrence (BCR) after radical prostatectomy (RPE).

METHODS

In this single-center cohort study consecutive patients with mpMRI and ISUP GG 4 or 5 PC were retrospectively analyzed. Clinical, MR-guided biopsy, and diagnostic mpMRI parameter were assessed. A subcohort of patients with RPE and follow-up was analyzed separately. A univariate and multivariate analyses were performed to determine parameters that are associated to patients with BCR after RPE.

RESULTS

145 patients (mean age 70y, median PSA 10.9 ng/ml) were analyzed. 99% had a PI-RADS classification of 4 or 5, 48% revealed MRI T3 stage, and median diameter of the MRI index lesion (IL) was 15 mm. IL showed a median ADC value of 668 ×10-6 mm2/s and exhibited contrast enhancement in 94% of the cases. For patients with follow-up after RPE (n = 82; mean follow-up time 68 ± 27 m), MRI parameters were significantly different for contact length of the IL to the pseudocapsule (LCC), MRI T3 stage, and IL localization (p < 0.05). Higher PSAD and MRI T3 stage were independent parameters for the risk of BCR when incorporating clinical, biopsy, and MRI parameters.

CONCLUSION

ISUP GG 4 or 5 PC has distinctive characteristics on mpMRI and were detected on MRI in all cases. In addition, higher PSAD and MRI T3 stage were significant predictors for BCR after RPE.

Derivation and validation of a novel functional FFRCT score incorporating the burden of coronary stenosis severity and flow impairment to predict clinical events

BACKGROUND

A score combining the burden of stenosis severity on coronary computed tomography angiography (CCTA) and flow impairment by fractional flow reserve derived from computed tomography (FFRCT) may be a better predictor of clinical events than either parameter alone.

METHODS

The Functional FFRCT Score (FFS) combines CCTA and FFRCT parameters in an allocated point-based system. The feasibility of the FFS was assessed in cohort of 72 stable chest pain patients with matched CCTA and FFRCT datasets. Validation was performed using 2 cohorts: (a) 4468 patients from the ADVANCE Registry to define its association with revascularization and major adverse cardiovascular events (MACE); (b) 212 patients from the FORECAST trial to determine predictors of MACE.

RESULTS

The median calculation time for the FFS was 10 (interquartile range 6-17) seconds, with strong intra-operator and inter-operator agreement (Cohen's Kappa 0.89 (±0.37, p ​< ​0.001) and 0.83 (±0.04, p ​< ​0.001, respectively). The FFS correlated strongly with both the CT-SYNTAX and the Functional CT-SYNTAX scores (rS ​= ​0.808 for both, p ​< ​0.001). In the ADVANCE cohort the FFS had good discriminatory abilities for revascularization with an area under the curve of 0.82, 95 ​% confidence interval (CI) 0.81-0.84, p ​< ​0.001. Patients in the highest FFS tertile had significantly higher rates of revascularization (61 ​% vs 5 ​%, p ​< ​0.001) and MACE (1.9 ​% vs 0.5 ​%, p ​= ​0.001) compared with the lowest FFS tertile. In the FORECAST cohort the FFS was an independent predictor of MACE at 9-month follow-up (hazard ratio 1.04, 95 ​% CI 1.01-1.08, p ​< ​0.01).

CONCLUSION

The FFS is a quick-to-calculate and reproducible score, associated with revascularization and MACE in two distinct populations of stable symptomatic patients.

Longitudinal post-radiotherapy plasma Epstein-Barr virus DNA trends inform on optimal risk stratification in endemic nasopharyngeal carcinoma

OBJECTIVES

To characterize longitudinal changes in Epstein-Barr virus (EBV) DNA post-radiotherapy in nasopharyngeal carcinoma (NPC) patients, and investigate whether an early (0-2 weeks) or delayed (8-12 weeks) EBV DNA result better predicts for disease-free survival (DFS).

MATERIALS AND METHODS

Histologically-confirmed NPC patients with ≥1 EBV DNA test quantified using the harmonized BamHI-W polymerase chain reaction-based assay at 0-2 and 8-12 weeks post-radiotherapy were included.

RESULTS

We identified 302 patients with EBV DNA measured at 0-2 weeks post-radiotherapy; of which, 110 (36.4 %) underwent a repeat test at 8-12 weeks post-treatment. Patients harboring a detectable EBV DNA at 0-2 weeks experienced an inferior DFS (adjusted HR1-264 copies 1.72 [95 %CI: 1.05-2.83], P = 0.031; AHR≥265 copies 4.39 [95 %CI: 1.68-11.44], P = 0.002 relative to 0 copies/mL). At 8-12 weeks, we observed substantial shifts in EBV DNA readings from 0 to 2 weeks; 76/110 (69.1 %) and 34/110 (30.9 %) patients at 0-2 weeks versus 90/110 (81.8 %) and 20/110 (18.2 %) at 8-12 weeks recorded undetectable and detectable EBV DNA, respectively. Positive EBV DNA at 8-12 weeks was strongly associated with relapse (73.3 % [11/15] for 1-264; 80.0 % [4/5] for ≥265 subgroups had relapses versus 15.6 % [14/90] for 0 copies/mL). Area under receiver operating curve values for 2-year relapse rates were 0.817 (95 %CI: 0.725-0.909) for stage + EBV DNA8-12w versus 0.654 (95 %CI: 0.542-0.765) for stage + EBV DNA0-2w. CONCLUSION: EBV DNA is dynamic post-radiotherapy, and delayed EBV DNA testing better enriched for higher-risk NPC patients. This implicates trials investigating adjuvant chemotherapy intensification based on early EBV DNA testing.

Interobserver agreement and prognostic value of image-based scoring systems in patients with primary sclerosing cholangitis

BACKGROUND

Primary sclerosing cholangitis (PSC) is a cholestatic liver disease that progresses to cirrhosis and liver failure. The Anali and Amsterdam scores are based upon imaging features on MRI and ERCP, respectively.

AIMS

We aimed to compare the interobserver variability and performances of these scores.

METHODS

Patients with PSC with at least 1 MRCP were included. Images were independently scored by 2 experts. Agreement and prognostic performance with a primary end point of hepatic decompensation was assessed.

RESULTS

Fifty-nine patients were included (67.8% male, 86.4% IBD). Interobserver agreement for the Anali and Amsterdam scores were moderate (k = 0.49; 95% CI 0.35-0.64 and k = 0.43; 95% CI 0.30-0.56, respectively). Among the Anali components, dysmorphy (caudate/right lobe ratio > 0.9) had fair agreement (k = 0.37; 95% CI 0.14-0.60) and portal hypertension (k = 0.64, 95% CI 0.32-0.89) and intrahepatic dilation (k = 0.70; 95% CI 0.53-0.87) had substantial agreement. The Amsterdam extrahepatic and intrahepatic scores had fair agreement (k = 0.38; 95% CI 0.23-0.52) and moderate agreement (k = 0.50; 95% CI 0.34-0.67), respectively. Anali score (HR 5.90, 95% CI 1.64-21.21), total bilirubin (HR = 3.23; 95% Cl 1.06-9.91), and age (HR = 1.05; 95% CI 1.00-1.11) were independent predictors of hepatic decompensation. Mayo risk score and Anali score had good discriminative ability with c-statistics of 0.78 (CI 0.59-0.96) and 0.76 (CI 0.56-0.91). Anali score remained an independent predictor after adjusting for Mayo risk score.

CONCLUSION

Anali score adds additional predictive value for hepatic decompensation in patients with PSC.

Chest Computed Tomography for Lymphoma Staging: A Wasted Opportunity for Cardiovascular Risk Stratification?

AIM

Patients with a lymphoma diagnosis undergo non-gated chest computed tomography (CT) scans as part of cancer diagnosis or staging. Although coronary artery calcification (CAC) is traditionally evaluated on dedicated cardiac CT, CAC can also be detected on standard chest CT. This exploratory study aimed to determine the prognostic value of CAC detected on non-gated chest CT and to report its use on clinical practice.

METHOD

Consecutive patients with a lymphoma diagnosis who performed non-contrasted non-gated chest CT for cancer diagnosis or staging were included and retrospectively evaluated. Coronary artery calcification was evaluated by quantitative (Agatston score) and qualitative (visual) assessment.

RESULTS

Fifty-seven patients were included in this study (mean age 61±15 years; 58% male). Coronary artery calcification was identified in 22 patients (39%), most of them with multi-vessel involvement. Coronary artery calcification was qualitatively classified as mild, moderate and severe in 11%, 19% and 9% patients, respectively. This study suggested that moderate or severe CAC was an independent predictor of all-cause mortality (odds ratio 3, 95% confidence interval 2-11; p=0.04) after adjusting for cardiovascular risk factors and lymphoma staging. Regarding quantitative evaluation, a higher CAC score was also associated with higher mortality. While significant CAC was identified in 22 patients, it was only reported in four patients.

CONCLUSIONS

The preliminary findings of this hypothesis-generating study support the investigation of CAC identified by chest CT for diagnosis/staging of cancer as a risk modifier in the global risk assessment of patients with lymphoma. The unrecognition and underreporting of this finding may represent a wasted opportunity to detect subclinical coronary atherosclerosis in these patients and may help in guiding preventive cardiology care.

A comprehensive predictive model for radiation-induced brain injury in risk stratification and personalized radiotherapy of nasopharyngeal carcinoma

BACKGROUND AND PURPOSE

Radiation-induced brain injury (RBI) is a severe radiotoxicity for nasopharyngeal carcinoma (NPC) patients, greatly affecting their long-term life quality and survival. We aim to establish a comprehensive predictive model including clinical factors and newly developed genetic variants to improve the precision of RBI risk stratification.

MATERIALS AND METHODS

By performing a large registry-based retrospective study with magnetic resonance imaging follow-up on RBI development, we conducted a genome-wide association study and developed a polygenic risk score (PRS) for RBI in 1189 NPC patients who underwent intensity-modulated radiotherapy. We proposed a tolerance dose scheme for temporal lobe radiation based on the risk predicted by PRS. Additionally, we established a nomogram by combining PRS and clinical factors for RBI risk prediction.

RESULTS

The 38-SNP PRS could effectively identify high-risk individuals of RBI (P = 1.42 × 10-34). Based on genetic risk calculation, the recommended tolerance doses of temporal lobes should be 57.6 Gy for individuals in the top 10 % PRS subgroup and 68.1 Gy for individuals in the bottom 50 % PRS. Notably, individuals with high genetic risk (PRS > P50) and receiving high radiation dose in the temporal lobes (D0.5CC > 65 Gy) had an approximate 50-fold risk over individuals with low PRS and receiving low radiation dose (HR = 50.09, 95 %CI = 24.27-103.35), showing an additive joint effect (Pinteraction < 0.001). By combining PRS with clinical factors including age, tumor stage, and radiation dose of temporal lobes, the predictive accuracy was significantly improved with C-index increased from 0.78 to 0.85 (P = 1.63 × 10-2).

CONCLUSIONS

The PRS, together with clinical factors, could improve RBI risk stratification and implies personalized radiotherapy.

Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study

PURPOSE

To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whether DLRN could be applied for risk stratification.

METHODS

Two hundred and twenty five pathologically confirmed early-stage cervical cancers were enrolled and made up the training cohort and internal validation cohort, and 40 patients from another center were enrolled into the external validation cohort. On the basis of region of interest (ROI) of intratumoral and different peritumoral regions, two sets of features representing deep learning and handcrafted radiomics features were created using combined images of T2-weighted MRI (T2WI) and diffusion-weighted imaging (DWI). The signature subset with the best discriminant features was chosen, and deep learning and handcrafted signatures were created using logistic regression. Integrated with independent clinical factors, a DLRN was built. The discrimination and calibration of DLNR were applied to assess its therapeutic utility.

RESULTS

The DLRN demonstrated satisfactory performance for predicting recurrence risk factors, with AUCs of 0.944 (95% confidence interval 0.896-0.992) and 0.885 (95% confidence interval 0.834-0.937) in the internal and external validation cohorts. Furthermore, decision curve analysis revealed that the DLRN outperformed the clinical model, deep learning signature, and radiomics signature in terms of net benefit.

CONCLUSION

A DLRN based on intratumoral and peritumoral regions had the potential to predict and stratify recurrence risk factors for early-stage cervical cancers and enhance the value of individualized precision treatment.

Novel hormone therapy and coordination of care in high-risk biochemically recurrent prostate cancer

Biochemical recurrence (BCR) occurs in 20-50% of patients with prostate cancer (PCa) undergoing primary definitive treatment. Patients with high-risk BCR have an increased risk of metastatic progression and subsequent PCa-specific mortality, and thus could benefit from treatment intensification. Given the increasing complexity of diagnostic and therapeutic modalities, multidisciplinary care (MDC) can play a crucial role in the individualized management of this patient population. This review explores the role for MDC when evaluating the clinical evidence for the evolving definition of high-risk BCR and the emerging therapeutic strategies, especially with novel hormone therapies (NHTs), for patients with either high-risk BCR or oligometastatic PCa. Clinical studies have used different characteristics to define high-risk BCR and there is no consensus regarding the definition of high-risk BCR nor for management strategies. Next-generation imaging and multigene panels offer potential enhanced patient identification and precision-based decision-making, respectively. Treatment intensification with NHTs, either alone or combined with radiotherapy or metastasis-directed therapy, has been promising in clinical trials in patients with high-risk BCR or oligometastases. As novel risk-stratification and treatment options as well as evidence-based literature evolve, it is important to involve a multidisciplinary team to identify patients with high-risk features at an earlier stage, and make informed decisions on the treatments that could optimize their care and long-term outcomes. Nevertheless, MDC data are scarce in the BCR or oligometastatic setting. Efforts to integrate MDC into the standard management of this patient population are needed, and will likely improve outcomes across this heterogeneous PCa patient population.

Prediction of atherosclerotic cardiovascular risk in early childhood

Atherosclerotic lesions are present even in very young individuals and therefore, risk stratification for cardiovascular (CV) disease should be implemented in childhood to promote early prevention strategies. In this review we critically appraise clinical, biochemical and genetic biomarkers available for pediatric clinical practice, which might be integrated to build effective algorithms to identify children at risk of future CV events. The first critical issue is to characterize in children aged 2-5 years, those CV risk factors/clinical conditions associated with dramatically accelerated atherosclerosis. Presence of clinical conditions such as obesity, diabetes mellitus, Kawasaki disease, etc., or positive family history for premature CV disease should be evaluated. Subsequently, a complete lipid profile and Lipoprotein(a) determination are recommended for children with increased baseline CV risk. Individuals with altered lipid profile could then undergo genetic testing for monogenic dyslipidemias to identify children with high CV genetic risk, who will be directed to appropriate therapeutic options. In perspective, calculation of a polygenic risk score, based on the analysis of several common single-nucleotide polymorphisms, could be integrated for better risk assessment. We here emphasize the importance of a "holistic" strategy integrating biochemical, anamnestic and genetic data to stratify CV risk in early childhood.

[The role of cardiac biomarkers in perioperative risk evaluation of noncardiac surgery patients-A summary of the ESAIC guidelines 2023]

BACKGROUND

The recently published ESAIC guidelines highlight the clinical value of cardiac troponins (cTn) and B‑type natriuretic peptides (BNP) for risk assessment in patients undergoing noncardiac surgery.

OBJECTIVE

Summary of the ESAIC guideline recommendations.

MATERIAL AND METHODS

The evidence for the recommendations was extracted from studies that investigated the perioperative role of cTn and BNP as prognostic factors, for risk prediction and for therapeutic guidance. To collate this evidence 12 relevant endpoints as well as risk benefit analyses of systematic screening were considered to issue the strength of the recommendations.

RESULTS

The body of evidence for these guidelines was based on 115 studies. The evidence varied significantly across the 12 predefined endpoints. Additionally, there was a gradient in evidence for the use of cTn and BNP as prognostic factors, for risk prediction and for therapeutic guidance. The guidelines issue a weak recommendation for the use of preoperative, postoperative and combined measurement of cTn as well as for preoperative BNP measurement to assess the prognosis. For risk prediction a weak recommendation was formulated for combined and postoperative cTn and preoperative BNP measurements. No recommendation could be given for the evidence on biomarkers as data were very limited.

CONCLUSION

Both cTn and BNP can be used as prognostic factors or to predict the risk for selected endpoints. Therapeutic interventions should not be guided by cardiac biomarker levels.

Validating the predictive ability of the 2MACE score for major adverse cardiovascular events in patients with atrial fibrillation: results from phase II/III of the GLORIA-AF registry

The 2MACE score was specifically developed as a risk-stratification tool in atrial fibrillation (AF) to predict cardiovascular outcomes. We evaluated the predictive ability of the 2MACE score in the GLORIA-AF registry. All eligible patients from phase II/III of the prospective global GLORIA-AF registry were included. Major adverse cardiac events (MACEs) were defined as the composite outcome of stroke, myocardial infarction and cardiovascular death. Cox proportional hazards were used to examine the relationship between the 2MACE score and study outcomes. Predictive capability of the 2MACE score was investigated using receiver-operating characteristic curves. A total of 25,696 patients were included (mean age 71 years, female 44.9%). Over 3 years, 1583 MACEs were recorded. Patients who had MACE were older, with more cardiovascular risk factors and were less likely to be managed using a rhythm-control strategy. The median 2MACE score in the MACE and non-MACE groups were 2 (IQR 1-3) and 1 (IQR 0-2), respectively (p < 0.001). The 2MACE score was positively associated with an increase in the risk of MACE, with a score of ≥ 2 providing the best combination of sensitivity (69.6%) and specificity (51.6%), HR 2.47 (95% CI, 2.21-2.77). The 2MACE score had modest predictive performance for MACE in patients with AF (AUC 0.655 (95% CI, 0.641-0.669)). Our analysis in this prospective global registry demonstrates that the 2MACE score can adequately predict the risk of MACE (defined as myocardial infarction, CV death and stroke) in patients with AF. Clinical trial registration: http://www.clinicaltrials.gov . Unique identifiers: NCT01468701, NCT01671007 and NCT01937377.

Diagnostic accuracy of NI-RADS for prediction of head and neck squamous cell carcinoma: a systematic review and meta-analysis

OBJECTIVES

This study aimed to assess the diagnostic performance of NI-RADS for the prediction of recurrence in patients treated for Head and Neck Squamous Cell Carcinoma (HNSCC).

METHODS

A literature search was conducted using various databases to identify relevant articles published from June 2016 onwards. We included studies reporting the diagnostic accuracy of NI-RADS in distinguishing recurrence in patients undergoing imaging surveillance, with pathologic results and/or follow-up as the reference standard. Summary estimates of diagnostic accuracy in terms of sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR -), and diagnostic odds ratio (DOR) were calculated with the hierarchical summary receiver operating characteristic (HSROC) model. Meta-regression and subgroup analyses were conducted to investigate different clinical settings. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

A total of 12 studies were included in the current meta-analysis. The pooled sensitivity and specificity were 0.69 (95% CI 0.59-0.79) and 0.94 (95% CI 0.89-0.97), respectively. For the primary site, the pooled summary estimates were 0.67 (95% CI 0.53-0.78) and 0.95 (95% CI 0.90-0.97), for the nodal sites were 0.64 (95% CI 0.44-0.80) and 0.99 (95% CI 0.98-0.99), respectively. The recurrence rate for NI-RADS categories 1-3 was 0.03 (95% CI 0.02-0.05), 0.13 (95% CI 0.10-0.15), and 0.77 (95% CI 0.73-0.81). Meta-regression revealed that the type of analysis (per person vs. per site) and number of sites (≤ 200 vs. > 200) were significant factors associated with heterogeneity.

CONCLUSIONS

NI-RADS demonstrated high specificity but moderate sensitivity in patients after treatment for HNSCC. Summary estimates showed a significantly higher malignancy rate for NI-RADS category 3 compared to category 2.

The prognostic impact of right ventricular-pulmonary arterial coupling in heart failure: a systematic review and meta-analysis

The echocardiographic tricuspid annular plane systolic excursion/pulmonary artery systolic pressure (TAPSE/PASP) ratio is a non-invasive surrogate of right ventricular-pulmonary arterial (RV-PA) coupling which corresponds well with the respective invasively derived index. Recently, a wealth of observational data has arisen, outlining its prognostic value in heart failure (HF) patients. To systematically appraise and quantitatively synthesize the evidence of the prognostic value of TAPSE/PASP ratio in left-sided HF regardless of etiology or left ventricular ejection fraction. A systematic literature review was conducted in electronic databases to identify studies reporting the association of TAPSE/PASP ratio with outcomes in patients with HF and, when appropriate, a random-effects meta-analysis was conducted to quantify the unadjusted and adjusted hazard ratios [(a)HRs] for all-cause death and the composite outcome of all-cause death or HF hospitalization. Eighteen studies were deemed eligible encompassing 8,699 HF patients. The applied cut-off value for RV-PA uncoupling varied substantially from 0.27 to 0.58 mm/mmHg, and in most studies values lower than the applied cutoff conveyed dismal prognosis. Eleven studies reported appropriate data for meta-analysis. TAPSE/PASP reduction by 1 mm/mmHg was independently associated with all-cause death (pooled aHR=1.32 [1.06-1.65]; p=0.01; I2=56%) and the composite outcome (pooled aHR=3.48 [1.67-7.25]; p<0.001; I2=0%). When a TAPSE/PASP cutoff value of 0.36 mm/mmHg was applied it yielded independent association with all-cause death (pooled aHR=2.84 [2.22-3.64]; p<0.001; I2=82%). RV-PA coupling assessed by echocardiographic TAPSE/PASP ratio appears to be an independent outcome predictor for HF patients.

Update on Management of Ductal Carcinoma in Situ

Ductal carcinoma in situ (DCIS) represents 18% to 25% of all diagnosed breast cancers, and is a noninvasive, nonobligate precursor lesion to invasive cancer. The diagnosis of DCIS represents a wide range of disease, including lesions with both low and high risk of progression to invasive cancer and recurrence. Over the past decade, research on the topic of DCIS has focused on the possibility of tailoring treatment for patients according to their risk for progression and recurrence, which is based on clinicopathologic, biomolecular and genetic factors. These efforts are ongoing, with recently completed and continuing clinical trials spanning the continuum of cancer care. We conducted a review to identify recent advances on the topic of diagnosis, risk stratification and management of DCIS. While novel imaging techniques have increased the rate of DCIS diagnosis, questions persist regarding the optimal management of lesions that would not be identified with conventional methods. Additionally, among trials investigating the potential for omission of surgery and use of active surveillance, 2 trials have completed accrual and 2 clinical trials are continuing to enroll patients. Identification of novel genetic patterns is expanding our potential for risk stratification and aiding our ability to de-escalate radiation and systemic therapies for DCIS. These advances provide hope for tailoring of DCIS treatment in the near future.

Evaluation of the ACS NSQIP surgical risk calculator in patients undergoing common bile duct exploration

PURPOSE

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator is a risk stratification tool to help predict risks of postoperative complications, which is important for informed decision-making. The purpose of this study was to evaluate the accuracy of the calculator in predicting postoperative complications in patients undergoing common bile duct (CBD) exploration.

METHODS

A retrospective chart review was completed for 305 patients that underwent open and laparoscopic CBD exploration at a single institution from 2010 to 2018. Patient demographics and preoperative risk factors were entered into the calculator, and the predicted complication risks were compared with observed complication rates. Brier score, C-statistic, and Hosmer-Lemeshow regression analysis were used to assess discrimination and calibration.

RESULTS

The observed rate exceeded the predicted rate for any complication (35.1% vs. 21%), return to operating room (5.9% vs. 3.6%), death (3.3% vs. 1%), and sepsis (3% vs. 2.4%). The model performed best in predicting serious complication (Brier 0.087, C-statistic 0.818, Hosmer-Lemeshow 0.695), surgical site infection (Brier 0.068, C-statistic 0.670, Hosmer-Lemeshow 0.292), discharge to rehabilitation facility (Brier 0.041, C-statistic 0.907, Hosmer-Lemeshow 0.638), and death (Brier 0.028, C-statistic 0.898, Hosmer-Lemeshow 0.004). In multivariable analysis, there was no statistically significant predicted complication type that affected the type of surgery.

CONCLUSION

The calculator was accurate in predicting serious complication, surgical site infection, discharge to rehabilitation facility, and death. However, the model displayed poor predictive ability in all other complications that were analyzed.

Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus

OBJECTIVE

The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability.

STUDY DESIGN AND SETTING

The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores.

RESULTS

In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing.

CONCLUSION

We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.

Prognostic models and autoimmune liver diseases

Autoimmune liver diseases (AILDs) are complex diseases with unknown causes and immune-mediated pathophysiology. In primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH) disease modifying drugs are available which improve patient quality and quantity of life. In primary sclerosing cholangitis (PSC) no medical therapy is available and the only accepted treatment is liver transplantation (LT). PBC, PSC and AIH possess features that describe the archetype of patients within each disorder. On the other hand, the classical disorders are not homogeneous, and patients within each diagnosis may present with a range of clinical, biochemical, serological, and histological findings. Singularly, they are considered rare diseases, but together, they account for approximately 20% of LTs in Europe and USA. Management of these patients is complex, as AILDs are relatively uncommon in clinical practice with challenges in developing expertise, disease presentation can be sneaky, clinical phenotypes and disease course are heterogeneous. Prognostic models are key tools for clinicians to assess patients' risk and to provide personalized care to patients. Aim of this review is to discuss challenges of the management of AILDs and how the available prognostic models can help. We will discuss the prognostic models developed in AILDs, with a special focus on the prognostic models that can support the clinical management of patients with AILDs: in PBC models based on ursodeoxycholic acid (UDCA) response and markers of liver fibrosis; in PSC several markers including biochemistry, disease stage and radiological semiquantitative markers; and finally in AIH, markers of disease stage and disease activity.

Predicting acute and long-term mortality in a cohort of pulmonary embolism patients using machine learning

BACKGROUND

Pulmonary embolism (PE) is a severe condition that causes significant mortality and morbidity. Due to its acute nature, scores have been developed to stratify patients at high risk of 30-day mortality. Here we develop a machine-learning based score to predict 30-day, 90-day, and 365-day mortality in PE patients.

METHODS

The Birmingham and Black Country Venous Thromboembolism registry (BBC-VTE) of 2183 venous thromboembolism patients is used. Random forests were trained on a 70% training cohort and tested against 30% held-out set. The outcomes of interest were 30-day, 90-day, and 365-day mortality. These were compared to the pulmonary embolism severity index (PESI) and simplified pulmonary embolism severity index (sPESI). Shapley values were used to determine important predictors. Oral anticoagulation at discharge was also investigated as a predictor of mortality.

RESULTS

The machine learning risk score predicted 30-day mortality with AUC 0.71 [95% CI: 0.63 - 0.78] compared to the sPESI AUC of 0.65 [95% CI: 0.57 - 0.73] and PESI AUC of 0.64 [95% CI: 0.56 - 0.72]. 90-day mortality and 365-day mortality were predicted with an AUC of 0.74 and 0.73 respectively. High counts of neutrophils, white blood cell counts, and c-reactive protein and low counts of haemoglobin were important for 30-day mortality prediction but progressively lost importance with time. Older age was an important predictor of high risk throughout.

CONCLUSION

Machine learning algorithms have improved on standard clinical risk stratification for PE patients. External cohort validation is required before incorporation into clinical workflows.

Cluster analysis of antiphospholipid antibodies-associated adverse pregnancy outcome patients: based on a 13-years cohort study

Antiphospholipid antibodies (aPLs) are the leading causes of adverse pregnancy outcomes (APOs). We conducted cluster analysis to identify distinct phenotypes among aPLs-associated APOs patients. This approach aims to facilitate risk stratification and improve pregnancy outcomes for obstetric APS. This was a retrospective study of persistent aPLs positive women cohort in Peking Union Medical College Hospital. Baseline demographic characteristics, clinical manifestation, previous APOs and antibodies profiles were included for hierarchical cluster analysis. Placentae from portions of patients were collected and performed the histopathologic diagnoses. Four clusters among 209 patients with 477 pregnancies were identified. Cluster 1 comprised patients with triple aPLs positivity and demonstrates a high incidence of gestational hypertension (34.92%, P < 0.05) and preterm delivery (20.63%, P < 0.05). Patients in cluster 2 were characterized by lupus anticoagulant (LA) positivity, with high risk of whole gestational APOs. Cluster 3 included patients with isolated aPLs-IgM isotype combined with early miscarriage (60.92%, P = 0.016). Patients in cluster 4 majorly presented aPLs-IgG isotype combined with placenta insufficiency (22.73%). During the follow-up, the live birth rate in cluster 1 and 2 was only 69.20%. Placenta pathology revealed the most severe impairment within cluster 1, whereas clusters 3 and 4 exhibited relatively milder damage. By cluster analysis, we identified four clinical subtypes of aPLs-associated APOs patients. Patients with triple antibodies or high-risk lupus characteristics were prone to occurred gestational hypertension and premature delivery. Isolated LA or aCL/aβ2GPI positivity were found to be more frequently associated with early-stage fetal loss.

The novel prognostic nomograms for predicting cancer-specific survival and overall survival in mixed medullary and follicular cell carcinoma: A SEER-based study

BACKGROUND

The aim of this study was to evaluate independent predictors of prognosis in patients with mixed medullary and follicular cell carcinoma (MMFCC) and to establish the novel prognostic nomograms in the academic community for 3-, 5-, and 10 year CSS and OS in patients with MMFCC.

METHODS

Demographic information, clinicopathological characteristics, treatment information, and survival status information of 200 patients with MMFCC and 6615 patients with medullary thyroid carcinoma (MTC) from 2000 to 2020 in the SEER database were retrospectively analyzed. Independent predictors of prognosis in MMFCC patients were derived using univariate and multivariate Cox regression analyses after relevant comparisons based on pathologic typing. On this basis, we developed and validated clinical prognostic nomograms and risk-stratified the patient population.

RESULTS

In this study, the clinical information of 200 patients with MMFCC was compared with that of 5947 patients with MTC (NOS) and 668 patients with MTC with amyloid stroma, and there was a significant difference in the relevant variables among the three, with the CSS being 88.5%, 87.5%, and 90.9%, and the OS being 76.5%, 75.4%, and 83.8%. Univariate and multivariate Cox regression analyses yielded that age at diagnosis, presence of distant metastases, thyroidectomy scope, and lymph node dissection status were significantly correlated with the prognosis of patients (P < 0.05), and were independent predictors of CSS and OS for patients with MMFCC, and the Kaplan-Meier survival curves plotted by these factors demonstrated their predictive power for the prognosis of patients with MMFCC. The concordance index of the prognostic nomograms of CSS and OS established on this basis was 0.838 and 0.794, respectively, and the time-dependent area under curve, calibration curve, and decision curve analysis curve showed that the model had good discriminative ability, accuracy, and clinical applicability.

CONCLUSIONS

In this study, we concluded that there are large differences between MMFCC and MTC in terms of demographic information, clinicopathological characteristics, treatment information, and survival status information, and we constructed the novel prognostic nomograms for 3-, 5-, and 10 year CSS and OS for patients with MMFCC with risk stratification, which will help clinicians to develop individualized protocols for their postoperative treatments and follow-ups.

A discrete choice experiment to understand public preferences and priorities for risk-stratified bowel cancer screening programmes in the UK

OBJECTIVE

Public acceptability of bowel cancer screening programmes must be maintained, including if risk stratification is introduced. We aimed to describe and quantify preferences for different attributes of risk-stratified screening programmes amongst the UK population, focussing on who to invite for bowel screening.

METHODS

We conducted a discrete choice experiment (DCE) including the following attributes: risk factors used to estimate bowel cancer risk (age plus/minus sex, lifestyle factors and genetics); personalisation of risk feedback; risk stratification strategy plus resource implications; default screening in the case of no risk information; number of deaths prevented by screening; and number experiencing physical harm from screening. We used the results of conditional logit regression models to estimate the importance of each attribute, willingness to trade-off between the attributes, and preferences for different programmes using contemporary risk scores and models.

RESULTS

1196 respondents completed the survey, generating 21,528 DCE observations. Deaths prevented was the most influential attribute on respondents' decision-making (contributing to 58.8% of the choice), followed by harms experienced (15.9%). For every three additional deaths prevented, respondents were willing to accept an additional screening harm per 100,000 people. Risk factors and risk stratification strategy contributed to just 11.1% and 3.6% of the choice, respectively. Although the influence on decision-making was small, respondents favoured more personalised feedback.

CONCLUSIONS

Bowel cancer screening programmes that improve cancer outcomes, particularly by preventing more deaths amongst those screened, are most preferred by the public. Optimising risk prediction models, developing public communication, and readying infrastructure should be prioritised for implementation.

OOPHORECTOMY: When and Why? A Novel Risk Stratification Tool as an Aid to Decision Making at Gynecological Surgeries

The decision regarding oophorectomy during gynecological surgeries, especially in perimenopausal and postmenopausal women, has historically posed a significant dilemma. Traditionally, it was widely believed that conserving the ovaries held no benefits, leading to a common practice of recommending bilateral salpingo-oophorectomy alongside hysterectomy for benign conditions in women aged 40-45 and above. Given our evolving comprehension of postmenopausal ovarian function and the genetic susceptibility to ovarian epithelial cancers, the decision regarding oophorectomy poses a dilemma. Oophorectomy is recommended for women with a higher risk of ovarian cancer and ovarian conservation is necessary with women with higher risk of co-morbidities. This paper reviews the available literature on these aspects of oophorectomy. Despite a wealth of literature narrating the advantages and disadvantages of oophorectomy, covering various aspects such as ovarian cancer risk, myocardial infarction incidence, and post-oophorectomy peritoneal cancer, there is a notable absence of a comprehensive evaluation system for risk stratification. The objective of the present paper is to address this gap by consolidating existing literature into a risk stratification system. This system will provide treating physicians a tool that facilitates more informed, case-specific decisions in collaboration with patients and their families. While recognizing that the ultimate decision must be tailored to the individual case and agreed upon mutually by the surgeon, patient, and family, the proposed system seeks to streamline risk stratification. This, in turn, should aid in determining the most suitable course of treatment that maximizes benefits for the patient.

Predictive Value of Machine Learning Models in Postoperative Mortality of Older Adults Patients with Hip Fracture: A Systematic Review and Meta-analysis

BACKGROUND

Some researchers have used machine learning to predict mortality in old patients with hip fracture, but its application value lacks an evidence-based basis. Hence, we conducted this meta-analysis to explore the predictive accuracy of machine learning for mortality in old patients with hip fracture.

METHODS

We systematically retrieved PubMed, Cochrane, Embase, and Web of Science for relevant studies published before July 15, 2022. The PROBAST assessment tool was used to assess the risk of bias in the included studies. A random-effects model was used for the meta-analysis of C-index, whereas a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. The meta-analysis was performed on R and Stata.

RESULTS

Eighteen studies were included, involving 8 machine learning models and 398,422 old patients undergoing hip joint surgery, of whom 60,457 died. According to the meta-analysis, the pooled C-index for machine learning models was 0.762 (95% CI: 0.691 ∼ 0.833) in the training set and 0.838 (95% CI: 0.783 ∼ 0.892) in the validation set, which is better than the C-index of the main clinical scale (Nottingham Hip Fracture Score), that is, 0.702 (95% CI: 0.681 ∼ 0.723). Among different machine learning models, ANN and Bayesian belief network had the best predictive performance.

CONCLUSION

Machine learning models are more accurate in predicting mortality in old patients after hip joint surgery than current mainstream clinical scoring systems. Subsequent research could focus on updating clinical scoring systems and improving their predictive performance by relying on machine learning models.

Muscle quality, not quantity, is associated with outcome after colorectal cancer surgery

INTRODUCTION

Emerging evidence suggests that deconditioned patients benefit most from prehabilitation before colorectal cancer surgery. So far, selecting patients with poor muscle status and high perioperative risk remains challenging. Therefore, this study evaluates the potential of the CT-derived Skeletal Muscle Index (SMI), representing muscle mass, and of the Muscle Radiation Attenuation (MRA), a measure of muscle quality, for risk stratification in colorectal cancer patients.

METHODS

In this retrospective, single-center observational study, 207 patients with resection of colorectal adenocarcinoma between January 2016 and December 2020 were included. The Charlson comorbidity index (CCI), postoperative complications, length of hospital stay, and survival were recorded. Data were analyzed using multivariable linear, logistic, and Cox proportional hazards regression models adjusted for age, sex, BMI, CCI, neoadjuvant therapy, tumor stage, and surgery type.

RESULTS

An increase of the MRA was associated with fewer postoperative complications (anastomotic leakage and pneumonia) and lesser severity according to the Clavien-Dindo classification, shorter hospital stays, and prolonged survival (Hazard ratio: 0.63 [95%CI: 0.49-0.81], p < 0.001). No relevant associations were found between the SMI and postoperative complications, length of hospital stay, or survival.

CONCLUSION

The easy-to-raise MRA serves as a more reliable tool than the SMI for identifying high-risk patients with poor muscle status before colorectal surgery. Those patients may benefit most from prehabilitation, which has to be proven in future interventional trials.

Development and validation of a novel model to predict liver-related mortality in patients with idiosyncratic drug-induced liver injury

BACKGROUND

Early identification of patients with high mortality risk is critical for optimizing the clinical management of drug-induced liver injury (DILI). We aimed to develop and validate a new prognostic model to predict death within 6 months in DILI patients.

METHODS

This multicenter study retrospectively reviewed the medical records of DILI patients admitted to three hospitals. A DILI mortality predictive score was developed using multivariate logistic regression and was validated with area under the receiver operating characteristic curve (AUC). A high-mortality-risk subgroup was identified according to the score.

RESULTS

Three independent DILI cohorts, including one derivation cohort (n = 741) and two validation cohorts (n = 650, n = 617) were recruited. The DILI mortality predictive (DMP) score was calculated using parameters at disease onset as follows: 1.913 × international normalized ratio + 0.060 × total bilirubin (mg/dL) + 0.439 × aspartate aminotransferase/alanine aminotransferase - 1.579 × albumin (g/dL) - 0.006 × platelet count (109/L) + 9.662. The predictive performance for 6-month mortality of DMP score was desirable, with an AUC of 0.941 (95% CI: 0.922-0.957), 0.931 (0.908-0.949) and 0.960 (0.942-0.974) in the derivation, validation cohorts 1 and 2, respectively. DILI patients with a DMP score ≥ 8.5 were stratified into high-risk group, whose mortality rates were 23-, 36-, and 45-fold higher than those of other patients in the three cohorts.

CONCLUSIONS

The novel model based on common laboratory findings can accurately predict mortality within 6 months in DILI patients, which should serve as an effective guidance for management of DILI in clinical practice.

Incidence, risk assessment and prevention of sudden cardiac death in cardiomyopathies

Cardiomyopathies are a significant contributor to cardiovascular morbidity and mortality, mainly due to the development of heart failure and increased risk of sudden cardiac death (SCD). Despite improvement in survival with contemporary treatment, SCD remains an important cause of mortality in cardiomyopathies. It occurs at a rate ranging between 0.15% and 0.7% per year (depending on the cardiomyopathy), which significantly surpasses SCD incidence in the age- and sex-matched general population. The risk of SCD is affected by multiple factors including the aetiology, genetic basis, age, sex, physical exertion, the extent of myocardial disease severity, conduction system abnormalities, and electrical instability, as measured by various metrics. Over the past decades, the knowledge on the mechanisms and risk factors for SCD has substantially improved, allowing for a better-informed risk stratification. However, unresolved issues still challenge the guidance of SCD prevention in patients with cardiomyopathies. In this review, we aim to provide an in-depth discussion of the contemporary concepts pertinent to understanding the burden, risk assessment and prevention of SCD in cardiomyopathies (dilated, non-dilated left ventricular, hypertrophic, arrhythmogenic right ventricular, and restrictive). The review first focuses on SCD incidence in cardiomyopathies and then summarizes established and emerging risk factors for life-threatening arrhythmias/SCD. Finally, it discusses validated approaches to the risk assessment and evidence-based measures for SCD prevention in cardiomyopathies, pointing to the gaps in evidence and areas of uncertainties that merit future clarification.

Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study

OBJECTIVE

This study aimed to evaluate the diagnostic performance of machine learning (ML)-based ultrasound (US) radiomics models for risk stratification of gallbladder (GB) masses.

METHODS

We prospectively examined 640 pathologically confirmed GB masses obtained from 640 patients between August 2019 and October 2022 at four institutions. Radiomics features were extracted from grayscale US images and germane features were selected. Subsequently, 11 ML algorithms were separately used with the selected features to construct optimum US radiomics models for risk stratification of the GB masses. Furthermore, we compared the diagnostic performance of these models with the conventional US and contrast-enhanced US (CEUS) models.

RESULTS

The optimal XGBoost-based US radiomics model for discriminating neoplastic from non-neoplastic GB lesions showed higher diagnostic performance in terms of areas under the curves (AUCs) than the conventional US model (0.822-0.853 vs. 0.642-0.706, p < 0.05) and potentially decreased unnecessary cholecystectomy rate in a speculative comparison with performing cholecystectomy for lesions sized over 10 mm (2.7-13.8% vs. 53.6-64.9%, p < 0.05) in the validation and test sets. The AUCs of the XGBoost-based US radiomics model for discriminating carcinomas from benign GB lesions were higher than the conventional US model (0.904-0.979 vs. 0.706-0.766, p < 0.05). The XGBoost-US radiomics model performed better than the CEUS model in discriminating GB carcinomas (AUC: 0.995 vs. 0.902, p = 0.011).

CONCLUSIONS

The proposed ML-based US radiomics models possess the potential capacity for risk stratification of GB masses and may reduce the unnecessary cholecystectomy rate and use of CEUS.

CLINICAL RELEVANCE STATEMENT

The machine learning-based ultrasound radiomics models have potential for risk stratification of gallbladder masses and may potentially reduce unnecessary cholecystectomies.

KEY POINTS

• The XGBoost-based US radiomics models are useful for the risk stratification of GB masses. • The XGBoost-based US radiomics model is superior to the conventional US model for discriminating neoplastic from non-neoplastic GB lesions and may potentially decrease unnecessary cholecystectomy rate for lesions sized over 10 mm in comparison with the current consensus guideline. • The XGBoost-based US radiomics model could overmatch CEUS model in discriminating GB carcinomas from benign GB lesions.

Development and validation of a nomogram based on geriatric nutritional risk index for predicting prognosis and postoperative complications in surgical patients with upper urinary tract urothelial carcinoma

BACKGROUND

This study aimed to evaluate the potential role of the Geriatric Nutritional Risk Index (GNRI) in predicting oncological outcomes and postoperative complications in UTUC patients undergoing radical nephroureterectomy (RNU) and to develop a nomogram incorporating GNRI to predict outcomes.

METHODS

A retrospective analysis was performed on 458 consecutive patients who underwent RNU in our center. According to nutritional scores, patients were divided into the following groups: low GNRI (GNRI ≤ 98) and high GNRI (GNRI > 98). Univariable and multivariable logistic regression were performed to investigate the role of GNRI in predicting the perioperative complications. The survival was compared with Kaplan - Meier curve, and test by log-rank tests. Risk factors associated with cancer-specific survival (CSS) and overall survival (OS) were evaluated using Cox proportional hazards regression model and were integrated into a nomogram for individualized risk prediction. The calibration and discrimination ability of the model were evaluated by concordance index (C-index) and risk group stratification.

RESULTS

When compared with high GNRI, low GNRI had significantly lower survival (CSS, p < 0.001; OS, p < 0.001). Across all patients, multivariable analyses revealed that low GNRI was an independent prognostic factor (CSS, p = 0.007; OS, p = 0.005). Nomograms for 1-, 3-, and 5 years of CSS and OS had good performance. Patients can be stratified into different groups based on the nomogram, with significant differences in OS and CSS. Further, GNRI was also found to be an independent risk factor for postoperative complications. The complication - prediction nomogram based on GNRI was also internally validated and showed good performance.

CONCLUSIONS

The GNRI score is an independent predictor for the prognosis and postoperative complications of UTUC following RNU. This study presented a nomogram incorporating preoperative GNRI that might be used as a convenient tool to facilitate the preoperative individualized prediction of short- and long-term outcomes for patients with UTUC.

Risk stratification of hemodynamically significant patent ductus arteriosus by clinical and genetic factors

BACKGROUND

Hemodynamically significant patent ductus arteriosus (hsPDA) is associated with increased comorbidities in neonates. Early evaluation of hsPDA risk is critical to implement individualized intervention. The aim of the study was to provide a powerful reference for the early identification of high-risk hsPDA population and early treatment decisions.

METHODS

We enrolled infants who were diagnosed with PDA and performed exome sequencing. The collapsing analyses were used to find the risk gene set (RGS) of hsPDA for model construction. The credibility of RGS was proven by RNA sequencing. Multivariate logistic regression was performed to establish models combining clinical and genetic features. The models were evaluated by area under the receiver operating curve (AUC) and decision curve analysis (DCA).

RESULTS

In this retrospective cohort study of 2199 PDA patients, 549 (25.0%) infants were diagnosed with hsPDA. The model [all clinical characteristics selected by least absolute shrinkage and selection operator regression (all CCs)] based on six clinical variables was acquired within three days of life, including gestational age (GA), respiratory distress syndrome (RDS), the lowest platelet count, invasive mechanical ventilation, and positive inotropic and vasoactive drugs. It has an AUC of 0.790 [95% confidence interval (CI) = 0.749-0.832], while the simplified model (basic clinical characteristic model) including GA and RDS has an AUC of 0.753 (95% CI = 0.706-0.799). There was a certain consistency between RGS and differentially expressed genes of the ductus arteriosus in mice. The AUC of the models was improved by RGS, and the improvement was significant (all CCs vs. all CCs + RGS: 0.790 vs. 0.817, P < 0.001). DCA demonstrated that all models were clinically useful.

CONCLUSIONS

Models based on clinical factors were developed to accurately stratify the risk of hsPDA in the first three days of life. Genetic features might further improve the model performance. Video Abstract (MP4 86834 kb).

The new European Society of Cardiology guideline for the management of cardiomyopathies: key messages for cardiac electrophysiologists

Electrocardiographic findings and arrhythmias are common in cardiomyopathies. Both may be an early indication of a specific diagnosis or may occur due to myocardial fibrosis and/or reduced contractility. Brady- and tachyarrhythmias significantly contribute to increased morbidity and mortality in patients with cardiomyopathies. Antiarrhythmic therapy including risk stratification is often challenging and plays a major role for these patients. Thus, an "electrophysiological" perspective on guidelines on cardiomyopathies may be warranted. As the European Society of Cardiology (ESC) has recently published a new guideline for the management of cardiomyopathies, this overview aims to present key messages of these guidelines. Innovations include a new phenotype-based classification system with emphasis on a multimodal imaging approach for diagnosis and risk stratification. The guideline includes detailed chapters on dilated and hypertrophic cardiomyopathy and their phenocopies, arrhythmogenic right ventricular cardiomyopathy, and restrictive cardiomyopathy as well as syndromic and metabolic cardiomyopathies. Patient pathways guide clinicians from the initial presentation to diagnosis. The role of cardiovascular magnetic resonance imaging and genetic testing during diagnostic work-up is stressed. Concepts of rhythm and rate control for atrial fibrillation have led to new recommendations, and the role of defibrillator therapy in primary prevention is discussed in detail. Whilst providing general guidelines for management, the primary objective of the guideline is to ascertain the disease etiology and disease-specific, individualized management.

Prediction of Sudden Death Risk in Patients with Congenital Heart Diseases

Risk stratification for sudden death should be discussed with patients with congenital heart disease at each stage of personal and cardiac development. For most patients, risk is low through teenage years and the critical factors to consider are anatomy, ventricular function, and symptoms. By adulthood, these are supplemented by screening for atrial arrhythmias, ventricular arrhythmias, and pulmonary hypertension. Therapies include medication, ablation, and defibrillator placement.

A large-scale study integrating nutritional indicators and clinicopathological parameters to evaluate prognosis, follow-up, and postoperative chemotherapy decisions in rectal cancer patients

OBJECTIVE

The aim of this study was to evaluate the role of nutritional indicators and clinicopathological parameters in predicting the progression and prognosis for pathological stage II-III rectal cancer (RC) patients without neoadjuvant radiotherapy. In addition, we sought to explore the high-risk population who may require postoperative chemotherapy.

METHODS

A total of 894 consecutive RC patients were enrolled in this study. Univariate and multivariate Cox analysis were performed to identify the independent risk factors for PFS and OS. The nomogram and calibration curves were conducted according to multivariable analysis result. Kaplan-Meier survival curves and log-rank tests were performed for different groups. Finally, random survival forest (RSF) model was developed to predict the probability of progression.

RESULTS

Our results revealed that CEA level, pathological stage, tumor deposit, and PNI were independently associated with PFS in RC patients. Similarly, the results indicated that CEA level, pathological stage, tumor deposit, PNI, and NRI were independently associated with OS. RSF model revealed that group 1 had the highest risk of progression at the 12th month of follow-up, group 2 had the highest risk of progression at the 15th month of follow-up, while group 3 had the highest risk of progression at the 9th month of follow-up. Besides, subgroup analysis suggested that the high-risk group needs postoperative adjuvant chemotherapy, while patients in the low- and moderate-risk groups may not need postoperative adjuvant chemotherapy. Finally, we validated our results with the SEER database.

CONCLUSIONS

In conclusion, we demonstrated that preoperative nutritional indicator and clinicopathological parameters could act as auxiliary prognostication tools for RC patients without neoadjuvant radiotherapy. We also established follow-up strategies for different groups of patients. Collectively, incorporating nutritional assessment into risk stratification for RC resection is crucial and should be an integral part of preoperative planning.

Prevalence and Impact of Frailty in Patients ≥70 Years Old with Acute Coronary Syndrome Referred for Coronary Angiography

INTRODUCTION

Elderly patients with acute coronary syndrome (ACS) have a higher risk of adverse cardiovascular events and may be frail but are underrepresented in clinical trials. Previous studies have proposed that frailty assessment is a better tool than chronological age, in assessing older patients' biological age, and may exceed conventional risk scores in predicting the prognosis. Therefore, we wanted to investigate the prevalence and impact on 12-month outcomes of frailty in patients ≥70 years with ACS referred for coronary angiography (CAG).

METHODS

Patients ≥70 years with ACS referred for CAG underwent frailty scoring with the clinical frailty scale (CFS). Patients were divided into three groups depending on their CFS: robust (1-3), vulnerable (4), and frail (5-9) and followed for 12 months.

RESULTS

Of 455 patients, 69 (15%) patients were frail, 79 (17%) were vulnerable, and 307 (68%) were robust. Frail patients were older (frail: 80.9 ± 5.7 years, vulnerable: 78.5 ± 5.5 years, and robust: 76.6 ± 4.9 years, p < 0.001) and less often treated with percutaneous coronary intervention (frail: 56.5%, vulnerable: 53.2%, and robust: 68.6%, p = 0.014). 12-month mortality was higher among frail patients (frail: 24.6%, vulnerable: 21.8%, and robust: 6.2%, p < 0.001). Frailty was associated with a higher mortality after adjustment for age, sex, comorbidities, the Global Registry of Acute Coronary Events (GRACE) score, and revascularisation (HR 2.67, 95% CI 1.30-5.50, p = 0.008). There was no difference between GRACE and CFS in predicting 12-month mortality (p = 0.893).

CONCLUSIONS

Fifteen percent of patients ≥70 years old with ACS referred for CAG are frail. Frail patients have significantly higher 12-month mortality. GRACE and CFS are similar in predicting 12-month mortality.

Arrhythmic risk stratification in arrhythmogenic right ventricular cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiomyopathy characterized by a predominantly arrhythmic presentation. It represents the leading cause of sudden cardiac death (SCD) among athletes and poses a significant morbidity threat in the general population. As a causative treatment for ARVC is still not available, the placement of an implantable cardioverter defibrillator represents the current cornerstone for SCD prevention in this setting. Thanks to international ARVC-dedicated efforts, significant steps have been achieved in recent years towards an individualized, patient-centred risk stratification approach. A novel risk calculator algorithm estimating the 5-year risk of arrhythmias of patients with ARVC has been introduced in clinical practice and subsequently validated. The purpose of this article is to summarize the body of evidence that has allowed the development of this tool and to discuss the best way to implement its use in the care of an individual patient.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer

BACKGROUND

Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease.

OBJECTIVE

To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management.

DESIGN, SETTING, AND PARTICIPANTS

Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models.

RESULTS AND LIMITATIONS

The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study.

CONCLUSIONS

The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP.

PATIENT SUMMARY

We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

Epidemiology, oncologic results and risk stratification model for metachronous peritoneal metastases after surgery for pT4 colon cancers: results from an observational retrospective multicentre long-term follow-up study

PURPOSE

Metachronous peritoneal metastases (MPM) following a curative surgery procedure for pT4 colon cancer is a challenging condition. Current epidemiological studies on this topic are scarce.

METHODS

A retrospective multicentre trial was designed. All consecutive patients who underwent operations to treat pT4 cancers between 2015 and 2017 were reviewed. Demographic, clinical, operative, pathological and oncological follow-up variables were included. MPM were described as any oncological disease at the peritoneum, clearly different from a local recurrence. Univariate and multivariate Cox regression models were constructed. A risk stratification model was created on a cumulative factor basis. According to the calculated hazard ratio (HR), a scoring system was designed (HR < 3, 1 point; HR > 3, 2 points) and a scale from 0 to 6 was calculated for peritoneal disease-free rate (PDF-R). A risk stratification model was also created on the basis of these calculations.

RESULTS

Fifty different hospitals were involved, which included a total of 1356 patients. Incidence of MPM was 13.6% at 50 months median follow-up. The strongest independent risk factors for MPM were positive pN stage [HR 3.72 (95% CI 2.56-5.41; p < 0.01) for stage III disease], tumour perforation [HR 1.91 (95% CI 1.26-2.87; p < 0.01)], mucinous or signet ring cell histology [HR 1.68 (95% CI 1.1-2.58; p = 0.02)], poorly differentiated tumours [HR 1.54 (95% CI 1.1-2.2; p = 0.02)] and emergency surgery [HR 1.42 (95% CI 1.01-2.01; p = 0.049)]. In the absence of additional risk factors, pT4 tumours showed 98% and 96% PDF-R in 1-year and 5-year periods based on Kaplan-Meier curves.

CONCLUSIONS

Cumulative MPM incidence was 13.6% at 5-year follow-up. The sole presence of a pT4 tumour resulted in high rates of PDF-R at 1-year and 5-year follow-up (98% and 96% respectively). Five additional risk factors different from pT4 status itself were identified as possible MPM indicators during follow-up.