[Prostate cancer-multiparametric MRI and alternative approaches in intervention and therapy planning]

BACKGROUND

In recent years, multiparametric magnetic resonance imaging (mpMRI) of the prostate has gained importance and plays a crucial role in both personalized diagnostics and increasingly in the treatment planning for patients with prostate cancer.

OBJECTIVE

The aim of this study is to present established and innovative applications of MRI in the diagnosis and treatment of localized prostate cancer, evaluating their strengths and weaknesses. Furthermore, it will explore alternative approaches and compare them in a comprehensive manner.

MATERIALS AND METHODS

A systematic literature review on the application of mpMRI for biopsy and therapy planning was conducted.

RESULTS

The integration of modern imaging techniques, especially mpMRI, into the diagnostic algorithm has revolutionized prostate cancer diagnosis. MRI and MRI-guided biopsy detect more significant prostate cancer, with the potential to reduce unnecessary biopsies and the diagnosis of clinically insignificant carcinomas. In addition, MRI provides crucial information for risk stratification and treatment planning in prostate cancer patients, both before radical prostatectomy and during active surveillance.

CONCLUSION

Multiparametric MRI offers significant added value for the diagnosis and treatment of localized prostate cancer. The advancement of MRI analysis, such as the implementation of artificial intelligence algorithms, holds the potential for further enhancing imaging diagnostics.

Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma in adults. Standard treatment includes chemoimmunotherapy with R-CHOP or similar regimens. Despite treatment advancements, many patients with DLBCL experience refractory disease or relapse. While baseline 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) parameters have shown promise in predicting survival, they may not fully capture lesion heterogeneity. This study aimed to assess the prognostic value of baseline 18F-FDG PET radiomics features in comparison with clinical factors and metabolic parameters for assessing 2-year progression-free survival (PFS) and 5-year overall survival (OS) in patients with DLBCL.

RESULTS

A total of 201 patients with DLBCL were enrolled in this study, and 1328 radiomics features were extracted. The radiomics signatures, clinical factors, and metabolic parameters showed significant prognostic value for individualized prognosis prediction in patients with DLBCL. Radiomics signatures showed the lowest Akaike information criterion (AIC) value and highest Harrell's concordance index (C-index) value in comparison with clinical factors and metabolic parameters for both PFS (AIC: 571.688 vs. 596.040 vs. 576.481; C-index: 0.732 vs. 0.658 vs. 0.702, respectively) and OS (AIC: 339.843 vs. 363.671 vs. 358.412; C-index: 0.759 vs. 0.667 vs. 0.659, respectively). Statistically significant differences were observed in the area under the curve (AUC) values between the radiomics signatures and clinical factors for both PFS (AUC: 0.768 vs. 0.681, P = 0.017) and OS (AUC: 0.767 vs. 0.667, P = 0.023). For OS, the AUC of the radiomics signatures were significantly higher than those of metabolic parameters (AUC: 0.767 vs. 0.688, P = 0.007). However, for PFS, no significant difference was observed between the radiomics signatures and metabolic parameters (AUC: 0.768 vs. 0.756, P = 0.654). The combined model and the best-performing individual model (radiomics signatures) alone showed no significant difference for both PFS (AUC: 0.784 vs. 0.768, P = 0.163) or OS (AUC: 0.772 vs. 0.767, P = 0.403).

CONCLUSIONS

Radiomics signatures derived from PET images showed the high predictive power for progression in patients with DLBCL. The combination of radiomics signatures, clinical factors, and metabolic parameters may not significantly improve predictive value beyond that of radiomics signatures alone.

Improving 10-year cardiovascular risk prediction in apparently healthy people: flexible addition of risk modifiers on top of SCORE2

AIMS

In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics.

METHODS AND RESULTS

Individuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant predictor. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 [95% confidence interval (CI) 0.0115; 0.0281] and hs-Troponin-T with 0.0100 (95% CI 0.0063; 0.0137). External validation was performed in the Clinical Practice Research Datalink (CPRD) cohort (UK, n = 518 015, 12 675 CVD events). Adjustment of SCORE2-predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination [0.740 (95% CI 0.736-0.745) vs. unimproved SCORE2 risk C-index 0.737 (95% CI 0.732-0.741)].

CONCLUSION

The current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers.

N-terminal pro-B-type natriuretic peptide is associated with clinical outcomes after transcatheter aortic valve replacement

BACKGROUND

Limited data on the prognostic value of periprocedural changes of plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) after transcatheter aortic valve replacement (TAVR).

METHODS

Data of plasma NT-proBNP were retrospectively collected in 357 patients before TAVR procedure and at discharge from January 1, 2018 to December 31, 2021 in our single center. Patients were grouped as responders and non-responders according to the NT-proBNP ratio (postprocedural NT-proBNP at discharge/ preprocedural NT-proBNP). Responders were defined as NT-proBNP ratio < 1 and non-responders were defined as NT-proBNP ratio ≥ 1. Outcomes were defined according to the Valve Academy Research Consortium (VARC)-3 criteria.

RESULTS

A total of 234 patients (65.5%) and 123 patients (34.5%) were grouped as the responders and the non-responders, respectively. Responders and non-responders were significantly different in both median preprocedural (2103.5 vs. 421.0 pg/ml, p < 0.001) and postprocedural (707.6 vs. 1009.0, p < 0.001) NT-proBNP levels. Patients in the non-responder group were more inclined to have comorbidities of hypertension (73.2% vs. 51.7%, p < 0.001), hyperlipidaemia (46.3% vs. 34.6%, p = 0.031), peripheral vascular disease (20.3% vs. 8.5%, p = 0.001) and pure aortic insufficiency (15.4% vs. 4.3%, p < 0.001). In the contrast, patients in the responder group had higher prevalence of maximum transvalvular velocity (4.6 vs. 4.2 m/s, p < 0.001), reduced left ventricular ejection fraction (58.0% vs. 63.0%, p < 0.001), heart failure (9.4% vs. 2.4%, p = 0.014), mitral regurgitation ≥ moderate (13.7% vs. 4.9%, p = 0.010), tricuspid regurgitation ≥ moderate (12.0% vs. 2.4%, p = 0.002), and pulmonary hypertension (32.9% vs. 13.0%, p < 0.001). Patients in the non-responder group were moderately longer than the responder group in total hospitalization length (14 vs. 12 days, p < 0.001). The non-responder group were significantly associated with cumulative all-cause mortality (p = 0.009) and cardiac mortality (p < 0.001) during the follow-up period.

CONCLUSIONS

Periprocedural changes of NT-proBNP is clinically useful for the risk stratification of survival in patients after TAVR.

Integrative bioinformatics analysis and experimental validation of key biomarkers for risk stratification in primary biliary cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is an autoimmune liver disease, whose etiology is yet to be fully elucidated. Currently, ursodeoxycholic acid (UDCA) is the only first-line drug. However, 40% of PBC patients respond poorly to it and carry a potential risk of disease progression. So, in this study, we aimed to explore new biomarkers for risk stratification in PBC patients to enhance treatment.

METHODS

We first downloaded the clinical characteristics and microarray datasets of PBC patients from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Hub genes were further validated in multiple public datasets and PBC mouse model. Furthermore, we also verified the expression of the hub genes and developed a predictive model in our clinical specimens.

RESULTS

A total of 166 DEGs were identified in the GSE79850 dataset, including 95 upregulated and 71 downregulated genes. Enrichment analysis indicated that DEGs were significantly enriched in inflammatory or immune-related process. Among these DEGs, 15 risk-related genes were recognized and further validated in the GSE119600 cohort. Then, TXNIP, CD44, ENTPD1, and PDGFRB were identified as candidate hub genes. Finally, we proceeded to the next screening with these four genes in our serum samples and developed a three-gene panel. The gene panel could effectively identify those patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657-0.870).

CONCLUSIONS

In summary, combining bioinformatics analysis and experiment validation, we identified TXNIP, CD44, and ENTPD1 as promising biomarkers for risk stratification in PBC patients.

Prognostic Models in Metastatic Renal Cell Carcinoma

As many new systemic therapy options have recently emerged, the standard of care for patients with metastatic renal cell carcinoma (mRCC) is gradually changing. The increasing complexity of treatment options requires more personalized treatment strategies. This evolution in the systemic therapy landscape comes with a need for validated stratification models that facilitate decision making and patient counseling for clinicians through a risk-adapted approach. This article summarizes the available evidence on risk stratification and prognostic models for mRCC, including the International mRCC Database Consortium and Memorial Sloan Kettering Cancer Center models, as well as their association with clinical outcomes.

Validation of the orthopedic frailty score for measuring frailty in hip fracture patients: a cohort study based on the United States National inpatient sample

BACKGROUND

The Orthopedic Frailty Score (OFS) has been proposed as a tool for measuring frailty in order to predict short-term postoperative mortality in hip fracture patients. This study aims to validate the OFS using a large national patient register to determine its relationship with adverse outcomes as well as length of stay and cost of hospital stay.

METHODS

All adult patients (18 years or older) registered in the 2019 National Inpatient Sample Database who underwent emergency hip fracture surgery following a traumatic fall were eligible for inclusion. The association between the OFS and mortality, complications, and failure-to-rescue (FTR) was determined using Poisson regression models adjusted for potential confounders. The relationship between the OFS and length of stay and cost of hospital stay was instead determined using a quantile regression model.

RESULTS

An estimated 227,850 cases met the study inclusion criteria. There was a stepwise increase in the rate of complications, mortality, and FTR for each additional point on the OFS. After adjusting for potential confounding, OFS 4 was associated with an almost ten-fold increase in the risk of in-hospital mortality [adjusted IRR (95% CI): 10.6 (4.02-27.7), p < 0.001], a 38% increased risk of complications [adjusted IRR (95% CI): 1.38 (1.03-1.85), p = 0.032], and an almost 11-fold increase in the risk of FTR [adjusted IRR (95% CI): 11.6 (4.36-30.9), p < 0.001], compared to OFS 0. Patients with OFS 4 also required a day and a half additional care [change in median length of stay (95% CI): 1.52 (0.97-2.08), p < 0.001] as well as cost approximately $5,200 more to manage [change in median cost of stay (95% CI): 5166 (1921-8411), p = 0.002], compared to those with OFS 0.

CONCLUSION

Patients with an elevated OFS display a substantially increased risk of mortality, complications, and failure-to-rescue as well as a prolonged and more costly hospital stay.

Biomarkers for prediction of acute kidney injury in pediatric patients: a systematic review and meta-analysis of diagnostic test accuracy studies

BACKGROUND

Severity of acute kidney injury (AKI) confers higher odds of mortality. Timely recognition and early initiation of preventive measures may help mitigate the injury further. Novel biomarkers may aid in the early detection of AKI. The utility of these biomarkers across various clinical settings in children has not been evaluated systematically.

OBJECTIVE

To synthesize the currently available evidence on different novel biomarkers for the early diagnosis of AKI in pediatric patients.

DATA SOURCES

We searched four electronic databases (PubMed, Web of Science, Embase, and Cochrane Library) for studies published between 2004 and May 2022.

STUDY ELIGIBILITY CRITERIA

Cohort and cross-sectional studies evaluating the diagnostic performance of biomarkers in predicting AKI in children were included.

PARTICIPANTS AND INTERVENTIONS

Participants in the study included children (aged less than 18 years) at risk of AKI.

STUDY APPRAISAL AND SYNTHESIS METHODS

We used the QUADAS-2 tool for the quality assessment of the included studies. The area under the receiver operating characteristics (AUROC) was meta-analyzed using the random-effect inverse-variance method. Pooled sensitivity and specificity were generated using the hierarchical summary receiver operating characteristic (HSROC) model.

RESULTS

We included 92 studies evaluating 13,097 participants. Urinary NGAL and serum cystatin C were the two most studied biomarkers, with summary AUROC of 0.82 (0.77-0.86) and 0.80 (0.76-0.85), respectively. Among others, urine TIMP-2*IGFBP7, L-FABP, and IL-18 showed fair to good predicting ability for AKI. We observed good diagnostic performance for predicting severe AKI by urine L-FABP, NGAL, and serum cystatin C.

LIMITATIONS

Limitations were significant heterogeneity and lack of well-defined cutoff value for various biomarkers.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

Urine NGAL, L-FABP, TIMP-2*IGFBP7, and cystatin C showed satisfactory diagnostic accuracy in the early prediction of AKI. To further improve the performance of biomarkers, they need to be integrated with other risk stratification models.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO (CRD42021222698). A higher resolution version of the Graphical abstract is available as "Supplementary information".

Consensus on the tertiary prevention of primary liver cancer

To effectively prevent recurrence, improve the prognosis and increase the survival rate of primary liver cancer (PLC) patients with radical cure, the Chinese Society of Hepatology, Chinese Medical Association, invited clinical experts and methodologists to develop the Consensus on the Tertiary Prevention of Primary Liver Cancer, which was based on the clinical and scientific advances on the risk factors, histopathology, imaging finding, clinical manifestation, and prevention of recurrence of PLC. The purpose is to provide a current basis for the prevention, surveillance, early detection and diagnosis, and the effective measures of PLC recurrence.

Impact of age on the prognosis of patients with ventricular tachyarrhythmias and aborted cardiac arrest

BACKGROUND

This study evaluated the prognostic impact of age on patients presenting with ventricular tachyarrhythmias (VTA) and aborted cardiac arrest.

MATERIAL AND METHODS

The present registry-based, monocentric cohort study included all consecutive patients presenting at the University Medical Center Mannheim (UMM) between 2002 and 2016 with ventricular tachycardia (VT), ventricular fibrillation (VF) and aborted cardiac arrest. Middle-aged (40-60 years old) were compared to older patients (> 60 years old). Furthermore, age was analyzed as a continuous variable. The primary endpoint was all-cause mortality at 2.5 years. The secondary endpoints were cardiac death at 24 h, all-cause mortality at index hospitalization, all-cause mortality after index hospitalization and the composite endpoint at 2.5 years of cardiac death at 24 h, recurrent VTA, and appropriate implantable cardioverter defibrillator (ICD) treatment.

RESULTS

A total of 2259 consecutive patients were included (28% middle-aged, 72% older). Older patients were more often associated with all-cause mortality at 2.5 years (27% vs. 50%; hazard ratio, HR = 2.137; 95% confidence interval, CI 1.809-2.523, p = 0.001) and the secondary endpoints. Even patient age as a continuous variable was independently associated with mortality at 2.5 years in all types of VTA. Adverse prognosis in older patients was demonstrated by multivariate Cox regression analyses and propensity score matching. Chronic kidney disease (CKD), systolic left ventricular dysfunction (LVEF) < 35%, cardiopulmonary resuscitation (CPR) and cardiogenic shock worsened the prognosis for both age groups, whereas acute myocardial infarction (STEMI/NSTEMI) and the presence of an ICD improved prognosis.

CONCLUSION

The results of this study suggest that increasing age is associated with increased mortality in VTA patients. Compared to the middle-aged, older patients were associated with higher all-cause mortality at 2.5 years and the secondary endpoints.

Flares in Lupus Nephritis: Risk Factors and Strategies for Their Prevention

PURPOSE OF REVIEW

Discuss the prognostic significance of kidney flares in patients with lupus nephritis, associated risk factors, and possible preventative strategies.

RECENT FINDINGS

Recently performed clinical trials and observational cohort studies underscore the high frequency of relapses of kidney disease, following initial response, in patients with proliferative and/or membranous lupus nephritis. Analysis of hard disease outcomes such as progression to chronic kidney disease or end-stage kidney disease, coupled with histological findings from repeat kidney biopsy studies, have drawn attention to the importance of renal function preservation that should be pursued as early as lupus nephritis is diagnosed. In this respect, non-randomized and randomized evidence have suggested a number of factors associated with reduced risk of renal flares such as attaining a very low level of proteinuria (< 700-800 mg/24 h by 12 months), using mycophenolate over azathioprine, adding belimumab to standard therapy, maintaining immunosuppressive/biological treatment for at least 3 to 5 years, and using hydroxychloroquine. Other factors that warrant further clarification include serological activity and the use of repeat kidney biopsy to guide the intensity and duration of treatment in selected cases. The results from ongoing innovative studies integrating kidney histological and clinical outcomes, together with an expanding spectrum of therapies in lupus nephritis, are expected to facilitate individual medical care and long-term disease and patient prognosis.

The effect of adjuvant transarterial chemoembolization for hepatocellular carcinoma after liver resection based on risk stratification

BACKGROUND

There is currently no standard adjuvant treatment proven to prevent hepatocellular carcinoma (HCC) recurrence. Recent studies suggest that postoperative adjuvant transarterial chemoembolization (PA-TACE) is beneficial for patients at high risk of tumor recurrence. However, it is difficult to select the patients. The present study aimed to develop an easy-to-use score to identify these patients.

METHODS

A total of 4530 patients undergoing liver resection were recruited. Independent risk factors were identified by Cox regression model in the training cohort and the Primary liver cancer big data transarterial chemoembolization (PDTE) scoring system was established.

RESULTS

The scoring system was composed of ten risk factors including alpha-fetoprotein (AFP), albumin-bilirubin (ALBI) grade, operative bleeding loss, resection margin, tumor capsular, satellite nodules, tumor size and number, and microvascular and macrovascular invasion. Using 5 points as risk stratification, the patients with PA-TACE had higher recurrence-free survival (RFS) compared with non-TACE in > 5 points group (P < 0.001), whereas PA-TACE patients had lower RFS compared with non-TACE in ≤ 5 points group (P = 0.013). In the training and validation cohorts, the C-indexes of PDTE scoring system were 0.714 [standard errors (SE) = 0.010] and 0.716 (SE = 0.018), respectively.

CONCLUSIONS

The model is a simple tool to identify PA-TACE for HCC patients after liver resection with a favorable performance. Patients with > 5 points may benefit from PA-TACE.

Development and Validation of Multi-Omics Thymoma Risk Classification Model Based on Transfer Learning

The paper aims to develop prediction model that integrates clinical, radiomics, and deep features using transfer learning to stratifying between high and low risk of thymoma. Our study enrolled 150 patients with thymoma (76 low-risk and 74 high-risk) who underwent surgical resection and pathologically confirmed in Shengjing Hospital of China Medical University from January 2018 to December 2020. The training cohort consisted of 120 patients (80%) and the test cohort consisted of 30 patients (20%). The 2590 radiomics and 192 deep features from non-enhanced, arterial, and venous phase CT images were extracted and ANOVA, Pearson correlation coefficient, PCA, and LASSO were used to select the most significant features. A fusion model that integrated clinical, radiomics, and deep features was developed with SVM classifiers to predict the risk level of thymoma, and accuracy, sensitivity, specificity, ROC curves, and AUC were applied to evaluate the classification model. In both the training and test cohorts, the fusion model demonstrated better performance in stratifying high and low risk of thymoma. It had AUCs of 0.99 and 0.95, and an accuracy of 0.93 and 0.83, respectively. This was compared to the clinical model (AUCs of 0.70 and 0.51, accuracy of 0.68 and 0.47), the radiomics model (AUCs of 0.97 and 0.82, accuracy of 0.93 and 0.80), and the deep model (AUCs of 0.94 and 0.85, accuracy of 0.88 and 0.80). The fusion model integrating clinical, radiomics and deep features based on transfer learning was efficient for noninvasively stratifying high risk and low risk of thymoma. The models could help to determine surgery strategy for thymoma cancer.

The impact of adjuvant EGFR-TKIs and 14-gene molecular assay on stage I non-small cell lung cancer with sensitive EGFR mutations

Background

Currently, the role of EGFR-TKIs as adjuvant therapy for stage I, especially IA NSCLC, after surgical resection remains unclear. We aimed to compare the effect of adjuvant EGFR-TKIs with observation in such patients by incorporating an established 14-gene molecular assay for risk stratification.

Methods

This retrospective cohort study was conducted at the First Affiliated Hospital of Guangzhou Medical University (Study ID: ChNCRCRD-2022-GZ01). From March 2013 to February 2019, completely resected stage I NSCLC (8th TNM staging) patients with sensitive EGFR mutation were included. Patients with eligible samples for molecular risk stratification were subjected to the 14-gene prognostic assay. Inverse probability of treatment weighting (IPTW) was employed to minimize imbalances in baseline characteristics.

Findings

A total of 227 stage I NSCLC patients were enrolled, with 55 in EGFR-TKI group and 172 in the observation group. The median duration of follow-up was 78.4 months. After IPTW, the 5-year DFS (HR = 0.30, 95% CI, 0.14-0.67; P = 0.003) and OS (HR = 0.26, 95% CI, 0.07-0.96; P = 0.044) of the EGFR-TKI group were significantly better than the observation group. For subgroup analyses, adjuvant EGFR-TKIs were associated with favorable 5-year DFS rates in both IA (100.0% vs. 84.5%; P = 0.007), and IB group (98.8% vs. 75.3%; P = 0.008). The 14-gene assay was performed in 180 patients. Among intermediate-high-risk patients, EGFR-TKIs were associated with a significant improvement in 5-year DFS rates compared to observation (96.0% vs. 70.5%; P = 0.012), while no difference was found in low-risk patients (100.0% vs. 94.9%; P = 0.360).

Interpretation

Our study suggested that adjuvant EGFR-TKI might improve DFS and OS of stage IA and IB EGFR-mutated NSCLC, and the 14-gene molecular assay could help patients that would benefit the most from treatment.

Funding

This work was supported by China National Science Foundation (82022048, 82373121).

Ultrasound evaluation of intraluminal magnets in an ex vivo model

PURPOSE

The management of foreign body ingestion proves to be a challenge. Magnets pose a unique set of risks when ingested due to their attractive forces and subsequent risk of adherence, pressure necrosis, and perforation complications. Radiographs only provide a limited snapshot in the setting of multiple magnet ingestion when the risk of complication is highest. We hypothesize that abdominal ultrasound (US) has the potential to supplement radiographs in assessing ingested magnets by determining the presence of bowel loop entrapment and of any extraluminal fluid.

METHODS

We recreated various scenarios of magnet configurations using animal cadaveric bowel models. X-ray and US images were obtained in various bowel-magnet orientations.

RESULTS

We identified several key US features to suggest bowel wall tethering. These include direct visualization of bowel wall entrapment between magnets (what we term the "dangerous V sign"), anti-dependent positions of the magnets, and inability to separate loops of bowel with compression.

CONCLUSION

These findings could potentially provide valuable information when directing the urgency of intervention in foreign body ingestion. Ultrasound may supplement and improve the current guidelines in management of magnet ingestion.

Recommendations for Management of Secondary Antibody Deficiency in Multiple Myeloma

Secondary antibody deficiency (SAD) is a subtype of secondary immunodeficiency characterized by low serum antibody concentrations (hypogammaglobulinemia) or poor antibody function. SAD is common in patients with multiple myeloma (MM) due to underlying disease pathophysiology and treatment-related immune system effects. Patients with SAD are more susceptible to infections and infection-related morbidity and mortality. With therapeutic advancements improving MM disease control and survival, it is increasingly important to recognize and treat the often-overlooked concurrent immunodeficiency present in patients with MM. The aims of this review are to define SAD and its consequences in MM, increase SAD awareness, and provide recommendations for SAD management. Based on expert panel discussions at a standalone meeting and supportive literature, several recommendations were made. Firstly, all patients with MM should be suspected to have SAD regardless of serum antibody concentrations. Patients should be evaluated for immunodeficiency at MM diagnosis and stratified into management categories based on their individualized risk of SAD and infection. Infection-prevention strategy education, early infection reporting, and anti-infective prophylaxis are key. We recommend prophylactic antibiotics or immunoglobulin replacement therapy (IgRT) should be considered in patients with severe hypogammaglobulinemia associated with a recurrent or persistent infection. To ensure an individualized and efficient treatment approach is utilized, patient's immunoglobin G concentration and infection burden should be closely monitored throughout treatment. Patient choice regarding route and IgRT treatment is also key in reducing treatment burden. Together, these recommendations and proposed management algorithms can be used to aid physician decision-making to improve patient outcomes.

Development and validation of colorectal cancer risk prediction tools: A comparison of models

BACKGROUND

Identification of individuals at elevated risk can improve cancer screening programmes by permitting risk-adjusted screening intensities. Previous work introduced a prognostic model using sex, age and two preceding faecal haemoglobin concentrations to predict the risk of colorectal cancer (CRC) in the next screening round. Using data of 3 screening rounds, this model attained an area under the receiver-operating-characteristic curve (AUC) of 0.78 for predicting advanced neoplasia (AN). We validated this existing logistic regression (LR) model and attempted to improve it by applying a more flexible machine-learning approach.

METHODS

We trained an existing LR and a newly developed random forest (RF) model using updated data from 219,257 third-round participants of the Dutch CRC screening programme until 2018. For both models, we performed two separate out-of-sample validations using 1,137,599 third-round participants after 2018 and 192,793 fourth-round participants from 2020 onwards. We evaluated the AUC and relative risks of the predicted high-risk groups for the outcomes AN and CRC.

RESULTS

For third-round participants after 2018, the AUC for predicting AN was 0.77 (95% CI: 0.76-0.77) using LR and 0.77 (95% CI: 0.77-0.77) using RF. For fourth-round participants, the AUCs were 0.73 (95% CI: 0.72-0.74) and 0.73 (95% CI: 0.72-0.74) for the LR and RF models, respectively. For both models, the 5% with the highest predicted risk had a 7-fold risk of AN compared to average, whereas the lowest 80% had a risk below the population average for third-round participants.

CONCLUSION

The LR is a valid risk prediction method in stool-based screening programmes. Although predictive performance declined marginally, the LR model still effectively predicted risk in subsequent screening rounds. An RF did not improve CRC risk prediction compared to an LR, probably due to the limited number of available explanatory variables. The LR remains the preferred prediction tool because of its interpretability.

Identification and validation of a plasma metabolomics-based model for risk stratification of intrahepatic cholangiocarcinoma

BACKGROUND

Liver resection is the mainstay of curative treatment for intrahepatic cholangiocarcinoma (ICC) while the postoperative prognosis varies greatly, with no recognized biomarker. We aimed to identify the plasma metabolomic biomarkers that could be used for preoperative risk stratification of ICC patients.

METHODS

108 eligible ICC patients who underwent radical surgical resection between August 2012 and October 2020 were enrolled. Patients were randomly divided into a discovery cohort (n = 76) and a validation cohort (n = 32) by 7:3. Metabolomics profiling of preoperative plasma was performed and clinical data were collected. The least absolute shrinkage and selection operator (LASSO) regression, Cox regression, and receiver operating characteristic (ROC) analyses were used to screen and validate the survival-related metabolic biomarker panel and construct a LASSO-Cox prediction model.

RESULTS

10 survival-related metabolic biomarkers were used for construction of a LASSO-Cox prediction model. In the discovery and validation cohorts, the LASSO-Cox prediction model achieved an AUC of 0.876 (95%CI: 0.777-0.974) and 0.860 (95%CI: 0.711-1.000) in evaluating 1-year OS of ICC patients, respectively. The OS of ICC patients in the high-risk group was significantly worse than that in the low-risk group (discovery cohort, p < 0.0001; validation cohort: p = 0.041). Also, the LASSO-Cox risk score (HR 2.43, 95%CI: 1.81-3.26, p < 0.0001) was a significant independent risk factor associated with OS.

CONCLUSIONS

The LASSO-Cox prediction model has potential as an important tool in evaluating the OS of ICC patients after surgical resection and can be used as prediction tools to implement the best treatment options that could result in better outcomes.

Application of MFI-5 in severe complications and unfavorable outcomes after radical resection of colorectal cancer

BACKGROUND

Frailty is considered a characteristic manifestation of physiological decline in multiple organ systems, which significantly increases the vulnerability of elderly individuals with colorectal cancer (CRC) and is associated with a poor prognosis. While studies have demonstrated that the 11-factor Modified Frailty Index (mFI-11) can effectively predict adverse outcomes following radical resection of CRC, there is a lack of research on the applicability of the 5-factor Modified Frailty Index (mFI-5) within this patient population.

METHODS

In this retrospective analysis, we examined a cohort of CRC patients aged 65 years and above who had undergone radical resection. For each patient, we calculated their mFI-5 score, considering a score of ≥ 2 as an indication of frailty. We conducted univariate and multivariate analyses to assess the association between the mFI-5 and adverse outcomes as well as postoperative complications.

RESULTS

Patients with an mFI-5 score ≥ 2 exhibited a significantly higher incidence of serious postoperative complications (53% vs. 30%; P = 0.001) and experienced a longer hospital stay [19.00 (15.00-24.50) vs. 17.00 (14.00-20.00); P < 0.05]. Notably, an mFI-5 score greater than 2 emerged as an independent risk factor for severe postoperative complications (odds ratio: 2.297; 95% confidence interval: 1.216 to 4.339; P = 0.01). Furthermore, the mFI-5 score displayed predictive capabilities for severe postoperative complications with an area under the receiver operating characteristic (ROC) curve of 0.629 (95% confidence interval: 0.551 to 0.707; P < 0.05).

CONCLUSION

The mFI-5 demonstrates a high level of sensitivity in predicting serious complications, prolonged hospital stays, and mortality following radical resection of colorectal carcinoma. As a practical clinical assessment tool, the mFI-5 enables the identification of high-risk patients and facilitates preoperative optimization.

A nomogram for predicting breast cancer specific survival in elderly patients with breast cancer: a SEER population-based analysis

BACKGROUND

The number of elderly patients diagnosed with breast cancer is increasing worldwide. However, treatment decisions for these patients are highly variable. Although researchers have identified the effects of surgery, radiotherapy, endocrine therapy, and chemotherapy in elderly patients with breast cancer, clinicians still struggle to make appropriate decisions for these patients.

METHODS

We identified 75,525 female breast cancer patients aged ≥ 70 years in the Surveillance, Epidemiology, and End Results (SEER) database treated between January 1, 2010, and December 31, 2016. The patients were further divided into training and testing cohorts. The cumulative occurrence of breast cancer-specific deaths (BCSDs) and other cause-specific deaths (OCSD) was calculated using the cumulative incidence function. In the univariate analysis, risk factors were screened using the Fine-Gray model. In the multivariate analysis for competing risks, the sub-distribution hazard ratio with a 95% confidence interval for each independent predictor associated with BCSD was calculated for the construction of nomograms. Based on the above analyses, a competing risk nomogram was constructed to predict the probability of BCSD in the 1st, 3rd, and 5th years after treatment. During validation, the concordance index (C-index) was selected to quantify the predictive ability of the competing risk model.

RESULTS

A total of 33,118 patients were included in this study, with 24,838 in the training group and 8,280 in the testing group. Age, race, marital status, cancer grade, tumor stage, node stage, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor--2 status, and treatment including surgery, radiation, and chemotherapy were used to establish a nomogram. The C-index of 0.852 (0.842-0.862) in the training cohort and 0.876 (0.868-0.892) in the testing cohort indicated satisfactory discriminative ability of the nomogram. Calibration plots showed favorable consistency between the nomogram predictions and actual observations in both the training and validation cohorts.

CONCLUSIONS

Our study identified independent predictors of BCSD in elderly patients with breast cancer. A prognostic nomogram was developed and validated to aid clinical decision-making.

International validation of the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score in heart failure

AIMS

Current European heart failure (HF) guidelines suggest the use of risk score: among them, the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score has demonstrated to be one of the most accurate. However, the risk scores are still poorly implemented in clinical practice, also due to the lack of strong evidence regarding their external validation in different populations. Thus, the current study was designed as an external validation test of the MECKI score in an international multicentre setting.

METHODS AND RESULTS

The study cohort consisted of patients diagnosed with HF with reduced ejection fraction (HFrEF) across international centres (not Italian), retrospectively recruited. Collected data included demographics, HF aetiology, laboratory testing, electrocardiogram (ECG), echocardiographic findings, and cardiopulmonary exercise testing (CPET) results as described in the original MECKI score publication. A total of 1042 patients across 8 international centres (7 European and 1 Asian) were included and followed up from 1998 till 2019. Patients were divided according to the calculated MECKI scores into three subgroups: (i) MECKI score <10%, (ii) 10-20%, and (iii) ≥ 20%. Survival analysis comparison among the three MECKI score subgroups showed a worse prognosis in patients with higher MECKI score value: median event-free survival times were 4396 days for MECKI score <10%, 3457 days for 10-20%, and 1022 days for ≥20% (P < 0.0001). Receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) were like those reported in the original internal validation studies.

CONCLUSION

In patients diagnosed with HFrEF, the power of the MECKI score was confirmed in terms of prognosis and risk stratification, supporting its implementation as advised by the HF guidelines.

Risk stratification tools for patients with syncope in emergency medical services and emergency departments: a scoping review

BACKGROUND

Patients with a syncope constitute a challenge for risk stratification in (prehospital) emergency care. Professionals in EMS and ED need to differentiate the high-risk from the low-risk syncope patient, with limited time and resources. Clinical decision rules (CDRs) are designed to support professionals in risk stratification and clinical decision-making. Current CDRs seem unable to meet the standards to be used in the chain of emergency care. However, the need for a structured approach for syncope patients remains. We aimed to generate a broad overview of the available risk stratification tools and identify key elements, scoring systems and measurement properties of these tools.

METHODS

We performed a scoping review with a literature search in MEDLINE, CINAHL, Pubmed, Embase, Cochrane and Web of Science from January 2010 to May 2022. Study selection was done by two researchers independently and was supervised by a third researcher. Data extraction was performed through a data extraction form, and data were summarised through descriptive synthesis. A quality assessment of included studies was performed using a generic quality assessment tool for quantitative research and the AMSTAR-2 for systematic reviews.

RESULTS

The literature search identified 5385 unique studies; 38 were included in the review. We discovered 19 risk stratification tools, one of which was established in EMS patient care. One-third of risk stratification tools have been validated. Two main approaches for the application of the tools were identified. Elements of the tools were categorised in history taking, physical examination, electrocardiogram, additional examinations and other variables. Evaluation of measurement properties showed that negative and positive predictive value was used in half of the studies to assess the accuracy of tools.

CONCLUSION

A total of 19 risk stratification tools for syncope patients were identified. They were primarily established in ED patient care; most are not validated properly. Key elements in the risk stratification related to a potential cardiac problem as cause for the syncope. These insights provide directions for the key elements of a risk stratification tool and for a more advanced process to validate risk stratification tools.

Implementation of risk stratification within bowel cancer screening: a community jury study exploring public acceptability and communication needs

BACKGROUND

Population-based cancer screening programmes are shifting away from age and/or sex-based screening criteria towards a risk-stratified approach. Any such changes must be acceptable to the public and communicated effectively. We aimed to explore the social and ethical considerations of implementing risk stratification at three different stages of the bowel cancer screening programme and to understand public requirements for communication.

METHODS

We conducted two pairs of community juries, addressing risk stratification for screening eligibility or thresholds for referral to colonoscopy and screening interval. Using screening test results (where applicable), and lifestyle and genetic risk scores were suggested as potential stratification strategies. After being informed about the topic through a series of presentations and discussions including screening principles, ethical considerations and how risk stratification could be incorporated, participants deliberated over the research questions. They then reported their final verdicts on the acceptability of risk-stratified screening and what information should be shared about their preferred screening strategy. Transcripts were analysed using codebook thematic analysis.

RESULTS

Risk stratification of bowel cancer screening was acceptable to the informed public. Using data within the current system (age, sex and screening results) was considered an obvious next step and collecting additional data for lifestyle and/or genetic risk assessment was also preferable to age-based screening. Participants acknowledged benefits to individuals and health services, as well as articulating concerns for people with low cancer risk, potential public misconceptions and additional complexity for the system. The need for clear and effective communication about changes to the screening programme and individual risk feedback was highlighted, including making a distinction between information that should be shared with everyone by default and additional details that are available elsewhere.

CONCLUSIONS

From the perspective of public acceptability, risk stratification using current data could be implemented immediately, ahead of more complex strategies. Collecting additional data for lifestyle and/or genetic risk assessment was also considered acceptable but the practicalities of collecting such data and how the programme would be communicated require careful consideration.

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit

PURPOSE

This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying a machine learning algorithm.

METHODS

Adult patients who were diagnosed with sepsis during admission to ICU were extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used for model development and internal validation. The other three databases were used for external validation. Our proposed model was developed based on the Extreme Gradient Boosting (XGBoost) algorithm. The generalizability, discrimination, and validation of our model were evaluated. The Shapley Additive Explanation values were used to interpret our model and analyze the contribution of individual features.

RESULTS

A total of 16,741, 15,532, 22,617, and 1,198 sepsis patients were extracted from the MIMIC-III, MIMIC-IV, eICU, and Zigong databases, respectively. The proposed model had an area under the receiver operating characteristic curve (AUROC) of 0.84 in the internal validation, which outperformed all the traditional scoring systems. In the external validations, the AUROC was 0.87 in the MIMIC-IV database, better than all the traditional scoring systems; the AUROC was 0.83 in the eICU database, higher than the Simplified Acute Physiology Score III and Sequential Organ Failure Assessment (SOFA),equal to 0.83 of the Acute Physiology and Chronic Health Evaluation IV (APACHE-IV), and the AUROC was 0.68 in the Zigong database, higher than those from the systemic inflammatory response syndrome and SOFA. Furthermore, the proposed model showed the best discriminatory and calibrated capabilities and had the best net benefit in each validation.

CONCLUSIONS

The proposed algorithm based on XGBoost and SHAP-value feature selection had high performance in predicting the mortality of sepsis patients within 24 h of ICU admission.

A prognostic model for systemic lupus erythematosus-associated pulmonary arterial hypertension: CSTAR-PAH cohort study

BACKGROUND

Pulmonary arterial hypertension is a major cause of death in systemic lupus erythematosus, but there are no tools specialized for predicting survival in systemic lupus erythematosus-associated pulmonary arterial hypertension.

RESEARCH QUESTION

To develop a practical model for predicting long-term prognosis in patients with systemic lupus erythematosus-associated pulmonary arterial hypertension.

METHODS

A prognostic model was developed from a multicenter, longitudinal national cohort of consecutively evaluated patients with systemic lupus erythematosus-associated pulmonary arterial hypertension. The study was conducted between November 2006 and February 2020. All-cause death was defined as the endpoint. Cox regression and least absolute shrinkage and selection operators were used to fit the model. Internal validation of the model was assessed by discrimination and calibration using bootstrapping.

RESULTS

Of 310 patients included in the study, 81 (26.1%) died within a median follow-up of 5.94 years (interquartile range 4.67-7.46). The final prognostic model included eight variables: modified World Health Organization functional class, 6-min walking distance, pulmonary vascular resistance, estimated glomerular filtration rate, thrombocytopenia, mild interstitial lung disease, N-terminal pro-brain natriuretic peptide/brain natriuretic peptide level, and direct bilirubin level. A 5-year death probability predictive algorithm was established and validated using the C-index (0.77) and a satisfactory calibration curve. Risk stratification was performed based on the predicted probability to improve clinical decision-making.

CONCLUSIONS

This new risk stratification model for systemic lupus erythematosus-associated pulmonary arterial hypertension may provide individualized prognostic probability using readily obtained clinical risk factors. External validation is required to demonstrate the accuracy of this model's predictions in diverse patient populations.

Value of increased soluble suppressor tumorigenicity biomarker 2 (sST2) on admission as an indicator of severity in patients with COVID-19

BACKGROUND

Soluble suppressor of tumorigenicity-2 (sST2) is a biomarker for heart failure and pulmonary injury. We hypothesize that sST2 could help predict severity of SARS-CoV-2 infections.

METHODS

sST2 was analyzed in patients consecutively admitted for SARS-CoV-2 pneumonia. Other prognostic markers were also measured. In-hospital complications were registered, including death, ICU admission, and respiratory support requirements.

RESULTS

495 patients were studied (53% male, age: 57.6±17.6). At admission, median sST2 concentrations was 48.5ng/mL [IQR, 30.6-83.1ng/mL] and correlated with male gender, older age, comorbidities, other severity biomarkers, and respiratory support requirements. sST2 levels were higher in patients who died (n=45, 9.1%) (45.6 [28.0, 75.9]ng/mL vs. 144 [82.6, 319] ng/mL, p<0.001) and those admitted to ICU (n=46, 9.3%) (44.7 [27.5, 71.3] ng/mL vs. 125 [69.0, 262]ng/mL, p<0.001). sST2 levels>210ng/mL were a strong predictor of complicated in-hospital courses, with higher risk of death (OR, 39.3, CI95% 15.9, 103) and death/ICU (OR 38.3, CI95% 16.3-97.5) after adjusting for all other risk factors. The addition of sST2 enhanced the predictive capacity of mortality risk models.

CONCLUSIONS

sST2 represents a robust severity predictor in COVID-19 and could be an important tool for identifying at-risk patients who may benefit from closer follow-up and specific therapies.

Solitary fibrous tumor of head and neck region; A clinicopathological study of 67 cases emphasizing the diversity of histological features and utility of various risk stratification models

BACKGROUND

Head and neck SFT (HNSFT) exhibit diverse histological features and can mimic various neoplasms with different treatment and behavior. While risk stratification systems have been developed for this tumor at various anatomic sites, a specific scheme for head and neck tumors is lacking. Our aim was to describe the histologic patterns present in HNSFT cases as well as assess the utility of risk assessment models in this location.

METHODS

A retrospective review of pathology reports and microscopy glass slides of HNSFT cases diagnosed between January 2010 and August 2022 was performed.STAT6 was additionally performed on selected cases if needed. Follow up was obtained and various risk stratification models were applied.

RESULTS

Sixty seven cases of HNSFT were collected (age range from 11 to 87 years; median 42 years; M:F 1.6:1). Most common tumor sites were orbit (n = 21; 31.3 %), sinonasal tract (n = 18; 26.9 %), and oral cavity (n = 13; 19.4 %). Tumor size ranged from 1 to 16 cm (median 4cm). Apart from common histological features, tumor cells also showed focal epithelioid morphology, clear cell change and nuclear atypia in a subset of cases. Stromal findings included myxoid and lipomatous change, pseudoglandular spaces, pseudovascular spaces and multinucleated stromal giant cells. CD34 and STAT6 were expressed in 57/67 (85.1 %) and 56/56 (100 %) cases, respectively. Recurrence was observed in 4/26 (15.4 %) cases, while none (0/22) of the patients experienced distant metastasis (follow up 1-150 months; median 20.5 months). Clinical outcome was partially concordant with risk-categories of different risk stratification models.

CONCLUSION

Knowledge about histological diversity of HNSFT is essential for establishing correct diagnosis. Current risk stratification models do not perfectly predict outcome, and larger studies are needed to develop more accurate criteria for aggressive behavior.

The presence of additional cytogenetic aberrations in chronic myeloid leukemia cells at the time of diagnosis or their appearance on tyrosine kinase inhibitor therapy predicts the imatinib treatment failure

Currently used treatment of CML dramatically improved the prognosis of disease. However, additional chromosome aberrations (ACA/Ph+) are still one of the adverse prognostic factors.

OBJECTIVES

evaluation of the impact of ACA/Ph+ appearance during disease outcome on the response to treatment. THE STUDY GROUP: consisted of 203 patients. The median time of follow-up was 72 months. ACA/Ph+ was found in 53 patients.

RESULTS

patients were divided into four groups: standard risk, intermediate, high and very high risk. When ACA/Ph+ presence was documented at diagnosis time the optimal response was observed in 41.2%, 25%, and 0% of pts with intermediate, high and very high risk, respectively. If ACA/Ph+ were detected during imatinib treatment the optimal response was in 4.8% of patients. The risk of blastic transformation for patients with standard risk, intermediate, high and very high risk was 2.7%, 18.4%, 20% and 50%, respectively.

CONCLUSIONS

the presence of ACA/Ph+ at diagnosis time or their appearance on therapy seems to be clinically relevant not only in terms of the risk of blastic transformation but also in terms of the treatment failure. Gathering patients with various karyotypes and their responses to treatment would allow to set better guidelines and predictions.

A novel 5-gene prognostic signature to improve risk stratification of cytogenetically normal acute myeloid leukemia

PURPOSE

Prognostic prediction is a challenging task in cytogenetically normal acute myeloid leukemia (CN-AML) patients. In this study, we aimed at developing a novel prognostic signature to predict and stratify the survival of CN-AML patients.

METHODS

Using a training dataset (GSE12417), 5-gene prognostic signature was established to predict survival of CN-AML patients. The prognostic performance of this prognostic signature was further validated in testing dataset (TCGA CN-AML cohort) and validation dataset (GSE6891 CN-AML cohort).

RESULTS

In training, testing and validation datasets, the increased 5-gene risk score was significantly related with inferior overall survival (OS) of patients, and the area under the receiver operating characteristic curve (AUC) demonstrated that our prognostic signature had overall prediction accuracy. The excellent prognostic value of the 5-gene prognostic signature was also supported by the comparison with three previously proposed prognostic models. For the intermediate-risk CN-AML patients and the CN-AML patients with FLT3 or NPM1 mutation, our model could also well dichotomize them into two subgroups with distinct prognosis. Multivariate analysis demonstrated that 5-gene risk score was the only independent risk factor in TCGA CN-AML cohort. Nomogram including the 5-gene risk score performed well in predicting 1-year, 2-year and 3-year OS.

CONCLUSION

In summary, our novel 5-gene prognostic signature facilitated the improvement in risk stratification of CN-AML patients.

A pragmatic digital health informatics based approach for aiding clinical prioritisation and reducing backlog of care: A study in cohort of 4022 people with diabetes

BACKGROUND AND AIMS

The backlog of care in resource stretched healthcare systems requires innovative approaches to aid clinical prioritisation. Our aim was to develop an informatics tool to identify and prioritise people with diabetes who are likely to deteriorate whilst awaiting an appointment to optimise clinical outcomes and resources.

MATERIALS AND METHODS

Using data from electronic health care records we identified 6 risk-factors that could be addressed in 4022 people (52% male, 30% non-Caucasian) with diabetes attending a large university hospital in London. The risk-factors were new clinical events/data occurring since their last routine clinic visit. To validate and compare data-led prioritisation tool to a traditional 'clinical approach' a sample of 450 patients were evaluated.

RESULTS

Of the 4022 people, 549 (13.6%) were identified as having one or more risk events/factors. People with risk were more likely to be non-Caucasian and had greater socio-economic deprivation. Taking clinical prioritisation as the gold standard, informatics tool identified high risk patients with a sensitivity of 83% and lower risk patients with a specificity of 81%. An operational pilot pathway over 3 months using this approach demonstrated in 101 high risk people that 40% received interventions/care optimisation to prevent deterioration in health.

CONCLUSION

A pragmatic data-driven method identifies people with diabetes at highest need for clinical prioritisation within restricted resources. Health informatics systems such as our can enhance care and improve operational efficiency and better healthcare delivery for people with diabetes.

A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer

OBJECTIVE

The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer.

METHOD

From the Surveillance, Epidemiology, and End Results (SEER) database, AYA women with primary breast cancer diagnosed from 2010 to 2018 were included in our study. A deep learning algorithm, referred to as DeepSurv, was used to construct a prognostic predictive model based on 19 variables, including demographic and clinical information. Harrell's C-index, the receiver operating characteristic (ROC) curve, and calibration plots were adopted to comprehensively assess the predictive performance of the prognostic predictive model. Then, a novel clinical risk stratification was constructed based on the total risk score derived from the prognostic predictive model. The Kaplan-Meier method was used to plot survival curves for patients with different death risks, using the log-rank test to compared the survival disparities. Decision curve analyses (DCAs) were adopted to evaluate the clinical utility of the prognostic predictive model.

RESULTS

Among 14,243 AYA women with breast cancer finally included in this study, 10,213 (71.7%) were White and the median (interquartile range, IQR) age was 36 (32-38) years. The prognostic predictive model based on DeepSurv presented high C-indices in both the training cohort [0.831 (95% CI 0.819-0.843)] and the test cohort [0.791 (95% CI 0.764-0.818)]. Similar results were observed in ROC curves. The excellent agreement between the predicted and actual OS at 3 and 5 years were both achieved in the calibration plots. The obvious survival disparities were observed according to the clinical risk stratification based on the total risk score derived from the prognostic predictive model. DCAs also showed that the risk stratification possessed a significant positive net benefit in the practical ranges of threshold probabilities. Lastly, a user-friendly Web-based calculator was generated to visualize the prognostic predictive model.

CONCLUSION

A prognostic predictive model with sufficient prediction accuracy was construct for predicting OS of AYA women with breast cancer. Given its public accessibility and easy-to-use operation, the clinical risk stratification based on the total risk score derived from the prognostic predictive model may help clinicians to make better-individualized management.

Use of the HEAR Score for 30-Day Risk-Stratification in Emergency Department Patients

BACKGROUND

The 2021 American College of Cardiology/American Heart Association chest pain guidelines recommend risk scores such as HEAR (History, Electrocardiogram, Age, Risk factors) for short-term risk stratification, yet limited data exist integrating them with high-sensitivity cardiac troponin T (hs-cTnT).

METHODS

Retrospective, multicenter (n = 2), observational, US cohort study of consecutive emergency department patients without ST-elevation myocardial infarction who had at least one hs-cTnT (limit of quantitation [LoQ] <6 ng/L, and sex-specific 99th percentiles of 10 ng/L for women and 15 ng/L for men) measurement on clinical indications in whom HEAR scores (0-8) were calculated. The composite major adverse cardiovascular event (MACE) outcome was 30-day prognosis.

RESULTS

Among 1979 emergency department patients undergoing hs-cTnT measurement, 1045 (53%) were low risk (0-3), 914 (46%) intermediate risk (4-6), and 20 (1%) high risk (7-8) based on HEAR scores. HEAR scores were not associated with increased risk of 30-day MACE in adjusted analyses. Patients with quantifiable hs-cTnT (LoQ-99th) had an increased risk for 30-day MACE (3.4%) irrespective of HEAR scores. Those with serial hs-cTnT <99th percentile remained at low risk (range 0%-1.2%) across all HEAR score strata. Higher scores were not associated with long-term (2-year) events.

CONCLUSIONS

HEAR scores are of limited value in those with baseline hs-cTnT 99th percentile to define short-term prognosis. In those with baseline quantifiable hs-cTnT within the reference range (<99th percentile), a higher risk (>1%) for 30-day MACE exists even in those with low HEAR scores. With serial hs-cTnT measurements, HEAR scores overestimate risk when hs-cTnT remains <99th percentile.

Sudden Death Risk Assessment in Hypertrophic Cardiomyopathy Across the Lifespan: Reconciling the American and European Approaches

Hypertrophic cardiomyopathy (HCM) is the most prevalent inherited cardiac disease. Since the modern description of HCM more than seven decades ago, great focus has been placed on preventing its most catastrophic complication: sudden cardiac death (SCD). Implantable cardioverter-defibrillators (ICD) have been recognized to provide effective prophylactic therapy. Over the years, two leading societies, the European Society of Cardiology (ESC) and the American Heart Association/American College of Cardiology (AHA/ACC), have proposed risk stratification models to assess SCD in adults. European guidelines rely on a risk calculator, the HCM Risk-SCD, while American guidelines propose a stand-alone risk factor approach. Recently, risk prediction models were also developed in the pediatric population. This article reviews the latest recommendations on the risk stratification of SCD in HCM and summarises current indications for ICD use.

Artificial intelligence and heart failure: A state-of-the-art review

Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.

Fructosamine as a predictor of incident diabetic microvascular disease in a population with high prevalence of red cell disorders: A cohort study

AIMS

Fructosamine can be used to estimate glycaemia in individuals in whom HbA1c may be unreliable. We aimed to establish clinically useful fructosamine treatment targets in a population with a high prevalence of conditions affecting erythrocyte survival, including variant haemoglobin and G6PD deficiency.

METHODS

Fructosamine was measured on a clinical basis in individuals in whom HbA1c was suspected to be unreliable by their primary physician. Study endpoints were incident retinopathy and albuminuria in individuals with Prediabetes (n = 60), Type 1 (n = 161) or Type 2 diabetes (n = 1350) during follow up of 4.4 ± 2.3 years.

RESULTS

Fructosamine ≥ 250 umol/L was significantly associated with incident retinopathy, and fructosamine ≥ 300 umol/L with incident microalbuminuria, in univariate analysis and adjusted for established risk factors. Fructosamine ≥ 250 umol/L was also significantly associated with incident retinopathy in individuals with HbA1c < 7.0% (53 mmol/mol) at inclusion.

CONCLUSIONS

In this patient population, a single measurement of fructosamine significantly and independently predicts incident retinopathy in individuals with HbA1c < 7.0% (53 mmol/mol). Routine measurement of fructosamine on at least one occasion is recommended as part of assessment of prediabetes or diabetes mellitus in populations with a high prevalence of conditions affecting erythrocyte lifespan.

Circulating IGF-1 and IGFBP-2 may be biomarkers for risk stratification in patients with acute coronary syndrome: A prospective cohort study

BACKGROUND AND AIM

The involvement of insulin-like growth factor-1 (IGF-1) and insulin-like growth factor binding protein-2 (IGFBP-2) following acute coronary syndrome (ACS) is rarely studied in clinical practice. Therefore, we sought to evaluate the relationship between IGF-1 and IGFBP-2 concentrations at admission and risk stratification based on the Thrombolysis in Myocardial Infarction (TIMI) risk score in patients with ACS.

METHODS AND RESULTS

In all, 304 patients diagnosed with ACS were included in this study. Plasma IGF-1 and IGFBP-2 were measured using commercially available ELISA kits. The TIMI risk score was calculated and the study population was stratified into high (n = 65), medium (n = 138), and low (n = 101) risk groups. Levels of IGF-1 and IGFBP-2 were analyzed for their predictive ability of risk stratification based on the TIMI risk scores. Correlation analysis showed that IGF-1 levels were negatively correlated with TIMI risk levels (r = -0.144, p = 0.012), while IGFBP-2 levels were significantly and positively correlated with TIMI risk levels (r = 0.309, p < 0.001). In multivariate logistic regression analysis, IGF-1 (odds ratio [OR]: 0.995; 95% confidence interval [CI]: 0.990-1.000; p = 0.043) and IGFBP-2 (OR: 1.002; 95%CI: 1.001-1.003; p < 0.001) were independent predictors of high TIMI risk levels. In receiver operating characteristic curves, the area under the curve values for IGF-1 and IGFBP-2 in the prediction of high TIMI risk levels were 0.605 and 0.723, respectively.

CONCLUSIONS

IGF-1 and IGFBP-2 levels are excellent biomarkers for risk stratification in patients with ACS, which provides further guidance for clinicians to identify patients at high risk and to lower their risk.

Diagnostic and therapeutic dilemmas in a patient with myocarditis, Brugada syndrome and arrhythmic syncope

We present a case of a previously healthy 23-year-old male who presented with chest pain, palpitations and spontaneous type 1 Brugada electrocardiographic (ECG) pattern. Positive family history for sudden cardiac death (SCD) was remarkable. Initially, clinical symptoms in combination with myocardial enzymes elevation, regional myocardial oedema with late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) and inflammatory lymphocytoid-cell infiltrates in the endomyocardial biopsy (EMB) suggested the diagnosis of a myocarditis-induced Brugada phenocopy (BrP). Under immunosuppressive therapy with methylprednisolone and azathioprine, a complete remission of both symptoms and biomarkers was accomplished. However, the Brugada pattern did not resolve. The eventually spontaneous Brugada pattern type 1 established the diagnosis of Brugada syndrome (BrS). Due to his previous history of syncope, the patient was offered an ICD that he declined. After his discharge he experienced a new episode of arrhythmic syncope. He was readmitted and received an ICD.

Assessment of machine learning algorithms in national data to classify the risk of self-harm among young adults in hospital: A retrospective study

BACKGROUND

Self-harm is one of the most common presentations at accident and emergency departments in the UK and is a strong predictor of suicide risk. The UK Government has prioritised identifying risk factors and developing preventative strategies for self-harm. Machine learning offers a potential method to identify complex patterns with predictive value for the risk of self-harm.

METHODS

National data in the UK Mental Health Services Data Set were isolated for patients aged 18-30 years who started a mental health hospital admission between Aug 1, 2020 and Aug 1, 2021, and had been discharged by Jan 1, 2022. Data were obtained on age group, gender, ethnicity, employment status, marital status, accommodation status and source of admission to hospital and used to construct seven machine learning models that were used individually and as an ensemble to predict hospital stays that would be associated with a risk of self-harm.

OUTCOMES

The training dataset included 23 808 items (including 1081 episodes of self-harm) and the testing dataset 5951 items (including 270 episodes of self-harm). The best performing algorithms were the random forest model (AUC-ROC 0.70, 95%CI:0.66-0.74) and the ensemble model (AUC-ROC 0.77 95%CI:0.75-0.79).

INTERPRETATION

Machine learning algorithms could predict hospital stays with a high risk of self-harm based on readily available data that are routinely collected by health providers and recorded in the Mental Health Services Data Set. The findings should be validated externally with other real-world, prospective data.

FUNDING

This study was supported by the Midlands and Lancashire Commissioning Support Unit.

Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas

OBJECTIVE

Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC).

MATERIALS & METHODS

675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified. These clusterings were combined into a 3-level patient stratification and included along with other known clinical features in a Cox model for predicting survival outcomes, and logistic regression for toxicity, using independent subsets for training and validation.

RESULTS

Four groups were identified and combined into a 3-level stratification. The inclusion of patient stratifications in predictive models for 5-yr Overall survival (OS), 5-year recurrence free survival, (RFS) and Radiation-associated dysphagia (RAD) consistently improved model performance measured using the area under the curve (AUC). Test set AUC improvements over models with clinical covariates, was 9 % for predicting OS, and 18 % for predicting RFS, and 7 % for predicting RAD. For models with both clinical and AJCC covariates, AUC improvement was 7 %, 9 %, and 2 % for OS, RFS, and RAD, respectively.

CONCLUSION

Including data-driven patient stratifications considerably improve prognosis for survival and toxicity outcomes over the performance achieved by clinical staging and clinical covariates alone. These stratifications generalize well to across cohorts, and sufficient information for reproducing these clusters is included.

A validation study of the high acuity readmission risk pediatric screen (HARRPS) tool©: Predicting readmission risk within the pediatric population

BACKGROUND

The initial research study of the High Acuity Readmission Risk Pediatric Screen (HARRPS) Tool © focused on using retrospective data to apply weighted values to the questions within the tool, identify overall risk score, and attribute risk categories (low, moderate, high risk) to the overall risk score. This study focused on validating the data from the initial study, as well as cross examining the need to include admission diagnosis within the tool.

METHOD

Study was a single-centered, retrospective chart review study using a different subset of patients from the initial study. Pediatric patients with thirty-day readmissions were compared to pediatric patients without a thirty-day readmission over a twelve-month period. Utilized same statistical software and methodology from initial study to identify if readmission risk probability could be replicated with a different population.

RESULTS

The initial study performed in 2018 demonstrated a c-statistic score/ area under the curve (AUC) of 0.68 [95% CI: 0.67, 0.69]. In addition, the initial study demonstrated as risk score increases, the probability of readmission gradually increased until a patient had a risk score of seven or greater, at which point readmission risk plateaued. This resulted in low, moderate, and high readmission risk categories. The current study performed using data from 2019 demonstrated an improved c-statistic score / AUC of 0.83 [95% CI: 0.80, 0.87] with admission diagnosis included, and a c-statistic score / AUC of 0.80 [95% CI: 0.76, 0.83] without the admission diagnosis included. The analysis of overall risk score demonstrated a substantial difference in how to interpret final readmission risk scores. Both the initial study and validation study were consistent in demonstrating a risk score of three or less was associated with low readmission risk. However, in the validation study, there was no substantial difference between moderate or high risk, leading to updating the tool from 3 risk categories into 2 risk categories of low risk and at risk of readmission.

CONCLUSION

Based on the finding from the validation study, the admission diagnosis was removed from the HARRPS Tool© as the difference in c-statistic score was nominal, and the risk categories were changed from three categories (low, moderate, high risk) to two categories of low risk (score 0-2) and at risk of readmission for a score of 3+. The ability of the HARRPS Tool© to predict readmission risk preforms best with a c-statistic = 0.80, outperforming the following tools: LACE (0.65), LACE -SDH (0.67), LACE + (0.61), Epic's readmission risk model (0.69), and SQLAPE ® (0.71) (Ryan, et al., 2021; Hwang, et al., 2021).

Construction and validation of a SEER-based prognostic nomogram for young and middle-aged males patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common digestive tumor, and we aimed to develop and validate nomogram models, predicting the overall survival (OS) of young and middle-aged male patients with HCC.

METHODS

We extracted eligible data from relevant patients between 2000 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database. In addition, randomly divided all patients into two groups (training and validation = 7:3). The nomogram was established using effective risk factors based on univariate and multivariate analysis. The area under the time-dependent curve, calibration plots, and decision curve analysis (DCA) were used to evaluate the effective performance of the nomogram. The risk stratifications of the nomogram and the AJCC criteria-based tumor stage were compared.

RESULTS

11 variables were selected by univariate and multivariate analysis to establish the nomogram of HCC. The AUC values of 3, 4, and 5 years of the time-ROC curve are 0.858, 0.862 and 0.859 for the training cohort, and 0.858, 0.877 and 0.869 for the validation cohort, respectively, indicating that the nomogram has a good ability of discrimination. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. In addition, the decision curve DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the AJCC criteria-based tumor stage.

CONCLUSION

Prognostic nomogram of young and middle-aged male patients with HCC was developed and validated to help clinicians evaluate the prognosis of patients.

Recommendations for risk stratification of periocular squamous cell carcinoma

Periocular squamous cell carcinoma is a common cutaneous malignancy with generally favorable outcomes; however, the periocular region is intrinsically a high-risk location, and there exist a subset of lesions with a propensity for poor outcomes. Orbital invasion, intracranial perineural spread, nodal and distant metastasis are feared complications. There are several staging systems for eyelid carcinoma and cutaneous squamous cell carcinoma, but the definition of high-risk lesions remains heterogeneous. It is unclear exactly which lesions can be safely deescalated, and which require nodal evaluation and adjuvant multimodal therapy. We seek to answer these questions by summarizing the literature on clinicopathologic variables, molecular markers, and gene profiling tests in periocular squamous cell carcinoma, with the extrapolation of data from the cutaneous squamous cell carcinoma literature. Standardized pathology reports with information on tumor dimensions, histological subtype and grade, perineural invasion, and lymphovascular invasion should become uniform. Integration with gene expression profiling assessments will individualize and improve the predictive accuracy of risk stratification tools to ultimately inform multidisciplinary decision-making.

Development of the national Dutch PEWS: the challenge against heterogeneity and implementation difficulties of PEWS in the Netherlands

BACKGROUND

For the early recognition of deteriorating patients several Pediatric Early Warning Score (PEWS) systems have been developed with the assumption that early detection can prevent further deterioration. Although PEWS are widely being used in hospitals in the Netherlands, there is no national consensus on which score to use and how to embed the score into a PEWS system. This resulted in a substantial heterogeneity of PEWS systems, of which many are unvalidated or self-designed. The primary objective of this study was to develop a pragmatic consensus-based PEWS system that can be utilized in all Dutch hospitals (University Medical Centers, teaching hospitals, and general hospitals).

METHODS

This study is an iterative mixed-methods study. The methods from the Core Outcome Measures in Effectiveness Trials (COMET) initiative were used and consisted of two Delphi rounds, two inventories set out to all Dutch hospitals and a focus group session with parents. The study was guided by five expert meetings with different stakeholders and a final consensus meeting that resulted in a core PEWS set.

RESULTS

The first Delphi round was completed by 292 healthcare professionals, consisting of pediatric nurses and physicians. In the second Delphi round 217 healthcare professionals participated. Eventually, the core PEWS set was been developed comprising of the parameters work of breathing, respiratory rate, oxygen therapy, heart rate and capillary refill time, and AVPU (Alert, Verbal, Pain, and Unresponsive). In addition, risk stratification was added to the core set with standardized risk factors consisting of [1] worried signs from healthcare professionals and parents and [2] high-risk treatment, with the option to add applicable local defined risk factors. Lastly, the three categories of risk stratification were defined (standard, medium, and high risk) in combination with standardized actions of the professionals for each category.

CONCLUSION

This study demonstrates a way to end a country's struggle with PEWS heterogeneity by co-designing a national Dutch PEWS system. Currently, the power of the system is being investigated in a large multi-center study in the Netherlands.

Risk Stratification in Pediatric Wolff-Parkinson-White: Practice Variation Among Pediatric Cardiologists and Electrophysiologists

BACKGROUND

Published guidelines provide recommendations for risk stratification in pediatric Wolff-Parkinson-White (WPW). There are no data on provider concordance with these guidelines. We hypothesized that significant practice variation exists between pediatric cardiologists (PC) and electrophysiologists (EP).

METHOD

The records of all patients, age 8 to 21 years, with a new ECG diagnosis of WPW between 1/1/2013 and 12/31/2018, from a single center, were retrospectively reviewed. Subjects were categorized on the basis of symptoms and resting ECG findings as one of the following: asymptomatic intermittent WPW, asymptomatic persistent WPW, or symptomatic WPW. The performance and results of diagnostic testing, including Holter monitor, event monitor, exercise stress test (EST), and electrophysiology study (EPS), were recorded. The primary outcome was concordance with published guidelines. A secondary outcome was documentation of a discussion of sudden cardiac death (SCD) risk.

RESULTS

615 patient encounters were analyzed in 231 patients with newly diagnosed WPW pattern on ECG (56% male; mean age at diagnosis 13.9 ± 2.5 years). EP were observed to have a significantly higher rate of guideline concordance than PC (95% vs. 71%, p < 0.001). There was significant practice variation between PC and EP in the documentation of a discussion of SCD risk: 96% in EP vs. 39% in PC (p < 0.001).

CONCLUSION

Significant practice variation exists in the non-invasive and invasive risk stratification of pediatric WPW, with lower concordance to published guidelines amongst PC, when compared to EP. This report highlights the need to promote awareness of current WPW guidelines in the pediatric cardiology community at large.

Performance of a multi-sensor implantable defibrillator algorithm for heart failure monitoring in the presence of atrial fibrillation

AIMS

The HeartLogic Index combines data from multiple implantable cardioverter defibrillators (ICDs) sensors and has been shown to accurately stratify patients at risk of heart failure (HF) events. We evaluated and compared the performance of this algorithm during sinus rhythm and during long-lasting atrial fibrillation (AF).

METHODS AND RESULTS

HeartLogic was activated in 568 ICD patients from 26 centres. We found periods of ≥30 consecutive days with an atrial high-rate episode (AHRE) burden <1 h/day and periods with an AHRE burden ≥20 h/day. We then identified patients who met both criteria during the follow-up (AHRE group, n = 53), to allow pairwise comparison of periods. For control purposes, we identified patients with an AHRE burden <1 h throughout their follow-up and implemented 2:1 propensity score matching vs. the AHRE group (matched non-AHRE group, n = 106). In the AHRE group, the rate of alerts was 1.2 [95% confidence interval (CI): 1.0-1.5]/patient-year during periods with an AHRE burden <1 h/day and 2.0 (95% CI: 1.5-2.6)/patient-year during periods with an AHRE-burden ≥20 h/day (P = 0.004). The rate of HF hospitalizations was 0.34 (95% CI: 0.15-0.69)/patient-year during IN-alert periods and 0.06 (95% CI: 0.02-0.14)/patient-year during OUT-of-alert periods (P < 0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95% CI: 1.67-55.31) during periods with an AHRE burden <1 h/day and 2.70 (95% CI: 1.01-28.33) during periods with an AHRE burden ≥20 h/day. In the matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95% CI: 0.12-0.60)/patient-year during IN-alert periods and 0.04 (95% CI: 0.02-0.08)/patient-year during OUT-of-alert periods (P < 0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44).

CONCLUSION

Patients received more alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio in comparison with non-AF periods and non-AF patients.

CLINICAL TRIAL REGISTRATION

http://clinicaltrials.gov/Identifier: NCT02275637.

Prognostic value of circulating Epstein-Barr virus DNA level post-induction chemotherapy for patients with nasopharyngeal carcinoma: A recursive partitioning risk stratification analysis

BACKGROUND

To evaluate the prognostic value of plasma Epstein-Barr virus (EBV) DNA level post-induction chemotherapy (IC) for patients with nasopharyngeal carcinoma (NPC).

METHODS

A total of 893 newly diagnosed NPC patients treated with IC were retrospectively reviewed. The recursive partitioning analysis (RPA) was performed to construct a risk stratification model. The receiver operating characteristic (ROC) analysis was applied to determine the optimal cut-off value of post-IC EBV DNA.

RESULTS

Post-IC EBV DNA levels and overall stage were independent predictors for distant metastasis-free survival (DMFS), overall survival (OS), and progression-free survival (PFS). The RPA model base on post-IC EBV DNA and overall stage categorized the patients into three distinct risk groups: RPA I (low-risk: stage II-III and post-IC EBV DNA < 200 copies/mL), RPA II (median-risk: stage II-III and post-IC EBV DNA ≥ 200 copies/mL, or stage IVA and post-IC EBV DNA < 200 copies/mL), and RPA III (high-risk: stage IVA and post-IC EBV DNA ≥ 200 copies/mL), with 3-year PFS of 91.1%, 82.6%, and 60.2%, respectively (p < 0.001). The DMFS and OS rates in different RPA groups were also distinct. The RPA model showed better risk discrimination than either the overall stage or post-RT EBV DNA alone.

CONCLUSIONS

Plasma EBV DNA level post-IC was a robust prognostic biomarker for NPC. We developed an RPA model that provides improved risk discrimination over the 8th edition of the TNM staging system by integrating the post-IC EBV DNA level and the overall stage.

AIMS65, Glasgow-Blatchford bleeding score and modified Glasgow-Blatchford bleeding score in predicting outcomes of upper gastrointestinal bleeding: An accuracy and calibration study

BACKGROUND

Albumin, international normalized ratio (INR), mental status, systolic blood pressure, age >65 years (AIMS65), Glasgow-Blatchford bleeding score (GBS) and modified GBS (mGBS) are three pre-endoscopy scoring systems used in the risk stratification of upper gastrointestinal bleeding (UGIB). The utility of such scoring systems in a population is estimated by their accuracy and calibration in the population. We aimed at validating and comparing the accuracy of the three scoring systems in predicting clinical outcomes including in-hospital mortality, need for blood transfusion, endoscopic treatment and rebleeding risk.

METHOD

We conducted a single-center, retrospective cohort study on patients with UGIB at a tertiary care center in India over 12 months. Clinical and laboratory data was collected from all patients admitted with UGIB. All patients were risk stratified using AIMS65, GBS and mGBS. The clinical outcome examined were: in-hospital mortality, requirement of blood transfusion, need for endoscopic treatment and rebleeding during hospital stay. The area under receiver-operating curve (AUROC) was calculated to assess the performance and calibration curves (Hosmer-Lemeshow goodness of fit curve) were plotted to examine how accurately the model describes the data of all three scoring systems.

RESULTS

Total 260 patients were included in the study, of which 236 (90.8%) were males. As many as 144 (55.4%) patients required blood transfusion and 64 (30.8%) required endoscopic treatment. While the incidence of rebleeding was 7.7%, in hospital mortality was 15.4%. Of 208 who underwent endoscopy, the most common causes identified were varices (49%) and gastritis (18.2%), followed by ulcer (11%), Mallory-Weiss tear (8.1%), portal hypertensive gastropathy (6.7%), malignancy (4.8%) and esophageal candidiasis (1.9%). The median AIMS65 score was 1, GBS 7 and mGBS 6. The area under curve (AUROC) for AIMS65, GBS and mGBS was (0.77, 0.73,0.70), (0.75, 0.82,0.83), (0.56, 0.58,0.83), (0.81, 0.94,0.53) for in-hospital mortality, blood transfusion requirement, endoscopic treatment and rebleeding prediction, respectively.

CONCLUSION

GBS and mGBS are superior to AIMS65 in predicting the requirement of blood transfusion and rebleeding risk, whereas in-hospital mortality was better predicted by AIMS 65. Both scores performed poorly in predicting the need of endoscopic treatment. An AIMS65 of 0,1 and a GBS of ≤ 1 are not associated with significant adverse events. A poor calibration of the scores in our population points to the lack of generalizability of these scoring systems.