Impact of Frailty on Heart Failure Prognosis: Is Sex Relevant?

PURPOSE OF REVIEW: Heart failure (HF) is one of the most frequent causes of hospital admission in elderly patients, especially in women, who present a high prevalence of geriatric syndromes like frailty. Studies have suggested that frailty and its impact may also differ between males and females. Understanding how frailty may differently affect HF patients depending on sex is therefore imperative for providing personalized care. The aim of this review is to summarize the role of sex in the prognostic impact of frailty in HF patients. RECENT FINDINGS: Numerous studies have identified frailty as a significant predictor of all-cause mortality and hospital readmissions. A recent study of elderly HF out-patients demonstrated that while women had a higher prevalence of frailty, it was an independent predictor of mortality and readmission only in men. Moreover, another study revealed that physical frailty was associated with time to first clinical event among men but not among women. These results raise the question about why frailty affects differently HF prognosis in men and women. Women with HF present a higher prevalence of frailty, especially when it is considered as physical decline. Nevertheless, frailty affects differently HF prognosis in men and women. Women with HF present lower mortality than men and frailty is related with prognosis only in men. The different severity of HF between men and women and other hormonal, psychosocial, and clinical factors might be involved in this fact.

Prognostic effect of systematic geriatric assessment on patients with acute heart failure.

AIMS: Frailty and dependence are frequent in patients admitted for acute heart failure (AHF), but their prognostic significance is unknown, especially in young adults. We aimed to study in adults admitted for AHF, regardless of age, the effect of frailty and dependence on the incidence of mortality and a combined event of mortality, readmissions for AHF, and visits to the emergency room (ER) for AHF at 1 and 6 months. METHODS AND RESULTS: We designed a prospective cohort study by including all the patients with AHF admitted in our Cardiology Department from July 2020 through May 2021. A multidimensional geriatric assessment was performed during the admission. We clinically followed up the patients 6 months after discharge. We enrolled 202 patients. The mean age was 73 ± 12.32 years, and 100 (49.5%) of the patients were elderly (>75 years). Just 78 patients (38.6%) were women, and 100 (49.5%) had previous HF. Frailty (FRAIL ≥ 3) was observed in 68 (33.7%) patients (mean FRAIL score: 1.88 ± 1.48). Dependence (Barthel < 100) was observed in 65 (32.2%) patients (mean Barthel index: 94.38 ± 11.21). Frailty and dependence showed a significant association with both prognostic events at 1 and 6 months. In the multivariable analysis, frailty was associated with higher mortality at 1 month [hazard ratio (HR) 12.61, 95% confidence interval (CI) 1.57-101.47, P = 0.017] but not at 6 months (HR 2.25, 95% CI 0.61-8.26, P = 0.224) or with the combined endpoint at neither 1 month (HR 1.64, 95% CI 0.54-5.03, P = 0.384) nor 6 months (HR 1.35, 95% CI 0.75-2.46, P = 0.320). Dependence was related to higher mortality at 1 month (HR 13.04, 95% CI 1.62-104.75, P = 0.016) and 6 months (HR 7.18, 95% CI 1.99-25.86, P = 0.003) and to higher incidence of the combined event at 1 month (HR 5.93, 95% CI 1.63-21.50, P = 0.007) and 6 months (HR 2.62, 95% CI 1.49-4.61, P = 0.001). CONCLUSIONS: In AHF patients, frailty and dependence implied a worse prognosis, rising mortality, readmissions, and ER visits for AHF.

Novel deep learning method for coronary artery tortuosity detection through coronary angiography.

Coronary artery tortuosity is usually an undetected condition in patients undergoing coronary angiography. This condition requires a longer examination by the specialist to be detected. Yet, detailed knowledge of the morphology of coronary arteries is essential for planning any interventional treatment, such as stenting. We aimed to analyze coronary artery tortuosity in coronary angiography with artificial intelligence techniques to develop an algorithm capable of automatically detecting this condition in patients. This work uses deep learning techniques, in particular, convolutional neural networks, to classify patients into tortuous or non-tortuous based on their coronary angiography. The developed model was trained both on left (Spider) and right (45°/0°) coronary angiographies following a fivefold cross-validation procedure. A total of 658 coronary angiographies were included. Experimental results demonstrated satisfactory performance of our image-based tortuosity detection system, with a test accuracy of (87 ± 6)%. The deep learning model had a mean area under the curve of 0.96 ± 0.03 over the test sets. The sensitivity, specificity, positive predictive values, and negative predictive values of the model for detecting coronary artery tortuosity were (87 ± 10)%, (88 ± 10)%, (89 ± 8)%, and (88 ± 9)%, respectively. Deep learning convolutional neural networks were found to have comparable sensitivity and specificity with independent experts' radiological visual examination for detecting coronary artery tortuosity for a conservative threshold of 0.5. These findings have promising applications in the field of cardiology and medical imaging.

Fast systematic geriatric assessment in acute heart failure patients admitted in Cardiology.

BACKGROUND: Heart failure prevalence is increasing in elder adults. These patients usually present geriatric syndromes, especially frailty. The effect of frailty on heart failure is under discussion but there are few data about the clinical characterization of frail patients who are admitted for acute heart failure decompensation. OBJECTIVE: The purpose of this study was to study the differences in clinical baseline variables and geriatric scales between frail and non-frail patients admitted to the Cardiology unit via the Emergency Department for acute heart failure. METHODS: We enrolled all patients with acute heart failure who were admitted to the Cardiology unit from the Emergency Department of our hospital from July 2020 through May 2021. A multidimensional and comprehensive geriatric assessment was performed at the moment of admission. We studied differences in baseline variables and geriatric scales according to the frailty status determined by the FRAIL scale. RESULTS: A total of 202 patients were included. In the whole population, 68 (33.7%) patients presented frailty defined by a FRAIL score ≥ 3. The frail patients were older (80±9 vs. 69±12 years; p<0.001), and had a worse quality of life (58.31±12.18 vs.39.26±13.71 points; p<0.001) according to the Minnesota scale, presented high comorbidity (47 (69.1%) vs. 67 (50.4%) patients; p = 0.011) defined as ≥3 points according to the Charlson scale and were more dependent (40 (58.8%) vs. 25 (18.8%) patients; p<0.001) according to the Barthel scale. The frail patients presented higher MAGGIC risk scores (24.09±4.99 vs. 18.89±6.26; p<0.001). Despite this adverse profile, the treatments prescribed during the admission and at the hospital discharge were similar. CONCLUSIONS: The prevalence of geriatric syndromes, especially frailty, is very high in patients admitted for acute heart failure. Frail patients with acute heart failure had an adverse clinical profile with more prevalence of concomitant geriatric syndromes. Therefore, we consider that a geriatric assessment should be performed during the admission of acute heart failure patients to improve care and attention.

A deep learning model for prognosis prediction after intracranial hemorrhage.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty. While several prediction models for ICH detection have already been published, here we present a deep learning predictive model for ICH prognosis. METHODS: We included patients with ICH (n = 262), and we trained a custom model for the classification of patients into poor prognosis and good prognosis, using a hybrid input consisting of brain CT images and other clinical variables. We compared it with two other models, one trained with images only (I-model) and the other with tabular data only (D-model). RESULTS: Our hybrid model achieved an area under the receiver operating characteristic curve (AUC) of .924 (95% confidence interval [CI]: .831-.986), and an accuracy of .861 (95% CI: .760-.960). The I- and D-models achieved an AUC of .763 (95% CI: .622-.902) and .746 (95% CI: .598-.876), respectively. CONCLUSIONS: The proposed hybrid model was able to accurately classify patients into good and poor prognosis. To the best of our knowledge, this is the first ICH prognosis prediction deep learning model. We concluded that deep learning can be applied for prognosis prediction in ICH that could have a great impact on clinical decision-making. Further, hybrid inputs could be a promising technique for deep learning in medical imaging.

Artificial intelligence to estimate wine volume from single-view images.

In this paper, we present a method to determine the volume of wine in different types of glass liquid containers from a single-view image. The proposed model predicts red wine volume from a photograph of the glass containing the wine. Experimental results demonstrated satisfactory performance of our image-based wine measurement system, with a Mean Absolute Error lower than 10 mL . To train and evaluate our system, we introduced the WineGut_BrainUp dataset, a new dataset of glasses of wine that contains 24305 laboratory images, including a wide range of containers, volumes of wine, backgrounds, object distances, angles and lightning, with or without calibration object. The proposed methodology is a suitable analytical tool for automate measurement of red wine volume. Indeed, it has potential real life applications in diet monitoring and wine consumption studies.

SEOM—GECOD clinical guideline for unknown primary cancer (2021)

Cancer of unknown primary site (CUP) is defined as a heterogeneous group of tumors that appear as metastases, and of which standard diagnostic work-up fails to identify the origin. It is considered a separate entity with a specific biology, and nowadays molecular characteristics and the determination of actionable mutations may be important in a significant group of patients. In this guide, we summarize the diagnostic, therapeutic, and possible new developments in molecular medicine that may help us in the management of this unique disease entity.

SEOM-GECOD clinical guideline for unknown primary cancer (2021).

Cancer of unknown primary site (CUP) is defined as a heterogeneous group of tumors that appear as metastases, and of which standard diagnostic work-up fails to identify the origin. It is considered a separate entity with a specific biology, and nowadays molecular characteristics and the determination of actionable mutations may be important in a significant group of patients. In this guide, we summarize the diagnostic, therapeutic, and possible new developments in molecular medicine that may help us in the management of this unique disease entity.

Differences According to Age in the Diagnostic Performance of Cardiac Biomarkers to Predict Frailty in Patients with Acute Heart Failure.

Frailty has traditionally been studied in the elderly population but scarcely in younger individuals. The objective of the present study is to analyze differences according to age in the diagnostic performance of cardiac biomarkers to predict frailty in patients admitted to the hospital for acute heart failure (AHF). A frailty assessment was performed with the SPPB and FRAIL scales (score > 3). We included 201 patients who were divided according to age: those older and younger than 75 years. In the younger group, no biomarker was related to the presence of frailty. This was mainly determined by age and comorbidities. In the elderly group, NT-proBNP was significantly related to the presence of frailty, but none of the baseline characteristics were. The best cut-off point in the elderly group for NT-proBNP was 4000 pg/mL. The area under the curve (AUC) for proBNP for frailty detection was 0.62 in the elderly. Another similar frailty scale, the SPPB, also showed a similar AUC in this group; however, adding the NT-proBNP (one point if NT-proBNP < 4000 pg/mL), it showed a slightly higher yield (AUC 0.65). The addition of biomarkers could improve frailty detection in members of the elderly population who are admitted to the hospital for AHF.

Ibero-American Expert Consensus on Squamous Cell Carcinoma of the Head and Neck Treatment in Patients Unable to Receive Cisplatin: Recommendations for Clinical Practice.

Cisplatin is the standard of treatment for squamous cell carcinoma of the head and neck (SCCHN) that has demonstrated efficacy, either in locally advanced disease when combined with radiotherapy at high doses, or in metastatic/recurrent disease when combined with other agents. However, the usual toxicities related to cisplatin, such as neurotoxicity, nephrotoxicity, ototoxicity, and hematologic toxicities, especially when high doses have been administered, have important implications in the patients' quality of life. The decision to administer cisplatin depends on several patient factors, such as age, performance status, weight loss, comorbidities, previous toxicities, chronic viral infection, or even the current SARS-CoV-2 pandemic. In order to establish recommendations for the management of patients with SCCHN, a group of experts in medical and radiation oncology from Spain and Latin-American discussed how to identify patients who are not candidates for cisplatin to offer them the most suitable therapeutic alternative.

A primer on deep learning and convolutional neural networks for clinicians.

Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

Oncogenic driver mutations predict outcome in a cohort of head and neck squamous cell carcinoma (HNSCC) patients within a clinical trial.

234 diagnostic formalin-fixed paraffin-embedded (FFPE) blocks from homogeneously treated patients with locally advanced head and neck squamous cell carcinoma (HNSCC) within a multicentre phase III clinical trial were characterised. The mutational spectrum was examined by next generation sequencing in the 26 most frequent oncogenic drivers in cancer and correlated with treatment response and survival. Human papillomavirus (HPV) status was measured by p16INK4a immunohistochemistry in oropharyngeal tumours. Clinicopathological features and response to treatment were measured and compared with the sequencing results. The results indicated TP53 as the most mutated gene in locally advanced HNSCC. HPV-positive oropharyngeal tumours were less mutated than HPV-negative tumours in TP53 (p < 0.01). Mutational and HPV status influences patient survival, being mutated or HPV-negative tumours associated with poor overall survival (p < 0.05). No association was found between mutations and clinicopathological features. This study confirmed and expanded previously published genomic characterization data in HNSCC. Survival analysis showed that non-mutated HNSCC tumours associated with better prognosis and lack of mutations can be identified as an important biomarker in HNSCC. Frequent alterations in PI3K pathway in HPV-positive HNSCC could define a promising pathway for pharmacological intervention in this group of tumours.

Blood Predictive Biomarkers for Patients With Non-small-cell Lung Cancer Associated With Clinical Response to Nivolumab.

BACKGROUND: Immunotherapy is a promising cancer treatment, but surrogate biomarkers of clinical efficacy have not been fully validated. The aim of this work was to evaluate several biomarkers as predictors of response to nivolumab monotherapy in patients with non-small-cell lung cancer. PATIENTS AND METHODS: Blood samples was collected at baseline, at 2 months after treatment start, and at disease progression. Lactate dehydrogenase level (LDH), neutrophils, and leukocyte values were obtained from medical record. Interleukin (IL)-8, IL-11, and kynurenine/tryptophan levels were determined by enzyme-linked immunosorbent assay. Total protein was extracted from circulating CD8+ T cells, and BCL-2 interacting mediator of cell death (BIM) protein expression tested by western blotting. RESULTS: Baseline LDH levels were significantly higher in non-responder patients than in those who responded (P = .045). The increase in indoleamine 2,3 dioxygenase activity was related to progression of disease, mainly in patients who did not respond to nivolumab treatment (P = .001). Increased levels of circulating IL-8 were observed in initially responding patients at time of progression, and it was related to lower overall survival (hazard ratio, 7.49; P = .025). A highest expression of BIM in circulating CD8+ T cells could be related to clinical benefit. The Student t test and Mann-Whitney U test were used to compare groups for continuous variables. Time to events was estimated using the Kaplan-Meier method, and compared by the log-rank test. CONCLUSIONS: Changes in plasma LDH and IL-8, indoleamine 2,3 dioxygenase activity, and BIM expression in CD8+ T cells could be used to monitor and predict clinical benefit from nivolumab treatment in these patients.

Blood mRNA expression of REV3L and TYMS as potential predictive biomarkers from platinum-based chemotherapy plus pemetrexed in non-small cell lung cancer patients.

PURPOSE: Therapeutic options for cancer patients have increased in the last years, although drugs resistance problem remains unresolved. Genetic background in individual susceptibility to cancer treatment could influence the therapy responses. The aim of this study was to explore the feasibility of using blood 4 genes (AEG-1, BRCA-1, REV3L and TYMS) expression levels as a predictor of the efficacy of pemetrexed therapy in patients with advanced non-small cell lung cancer. METHODS: Sixteen patients from the Medical Oncology Department at "12 de Octubre" Hospital, were included in the study. Total mRNA was isolated from blood samples, and gene expression was analyzed by RT-qPCR. A panel of lung tumor cell lines were used in cell proliferation tests and siRNA-mediated silencing assays. RESULTS: Similarity between blood gene expression levels and protein expression in matched tumor tissue was observed in 54.54% (REV3L) and 81.81% (TYMS) of cases. Gene expression of REV3L and TYMS in blood correlated directly and inversely, respectively, with progression-free survival and overall survival in the patients from our cohort. In tumor cell lines, the knockdown of REV3L conferred resistance to pemetrexed treatment, and the TYMS silencing increased the pemetrexed sensitivity of tumor cells. CONCLUSIONS: The use of peripheral blood samples for expression quantification of interest genes is an affordable method with promising results in the evaluation of response to pemetrexed treatment. Therefore, expression levels of REV3L and TYMS genes might be used as predictive biomarkers in advanced NSCLC patients.

Consenso 2018 de la Sociedad Española de Anatomía Patológica y la Sociedad Española de Oncología Médica sobre el diagnóstico y tratamiento del cáncer de origen desconocido / 2018 Consensus statement by the Spanish Society of Pathology and the Spanish Society of Medical Oncology on the diagnosis and treatment of cancer of unknown primary

El cáncer de origen desconocido se define como un grupo heterogéneo de tumores que se manifiestan con metástasis y para los que no se ha conseguido identificar su localización original. Sus características biológicas y forma de diseminación difieren del resto de tumores primarios, lo que hace que puedan considerarse como una entidad independiente. Aunque se han planteado varias hipótesis sobre su origen, la explicación más plausible sobre su agresividad y quimiorresistencia parece estar relacionada con la inestabilidad cromosómica. Dependiendo del tipo de estudio llevado a cabo, el cáncer de origen desconocido puede llegar a suponer entre el 2-9% de todos los pacientes con cáncer, principalmente entre los 60-75 años. En este artículo se revisan los principales estudios clínicos, patológicos y moleculares llevados a cabo para el análisis y determinación del origen del cáncer de origen desconocido, así como las principales estrategias terapéuticas y de manejo del paciente, tanto a nivel clínico como de anatomía patológica

Cancer of unknown primary is defined as a heterogeneous group of tumours that present with metastasis, and in which attempts to identify the original site have failed. They differ from other primary tumours in their biological features and how they spread, which means they can be considered a separate entity. There are several hypotheses regarding their origin, but the most plausible explanation for their aggressiveness and chemoresistance seems to involve chromosomal instability. Depending on the type of study done, cancer of unknown primary can account for 2-9% of all cancer patients, mostly 60-75 years old. This article reviews the main clinical, pathological and molecular studies conducted to analyse and determine the origin of cancer of unknown primary. The main strategies for patient management and treatment, by both clinicians and pathologists, are also addressed

[2018 Consensus statement by the Spanish Society of Pathology and the Spanish Society of Medical Oncology on the diagnosis and treatment of cancer of unknown primary]. / Consenso 2018 de la Sociedad Española de Anatomía Patológica y la Sociedad Española de Oncología Médica sobre el diagnóstico y tratamiento del cáncer de origen desconocido.

Cancer of unknown primary is defined as a heterogeneous group of tumours that present with metastasis, and in which attempts to identify the original site have failed. They differ from other primary tumours in their biological features and how they spread, which means they can be considered a separate entity. There are several hypotheses regarding their origin, but the most plausible explanation for their aggressiveness and chemoresistance seems to involve chromosomal instability. Depending on the type of study done, cancer of unknown primary can account for 2-9% of all cancer patients, mostly 60-75 years old. This article reviews the main clinical, pathological and molecular studies conducted to analyse and determine the origin of cancer of unknown primary. The main strategies for patient management and treatment, by both clinicians and pathologists, are also addressed.

Prospective Clinical Integration of an Amplicon-Based Next-Generation Sequencing Method to Select Advanced Non-Small-Cell Lung Cancer Patients for Genotype-Tailored Treatments.

INTRODUCTION: A substantial fraction of non-small-cell lung cancers (NSCLCs) harbor targetable genetic alterations. In this study, we analyzed the feasibility and clinical utility of integrating a next-generation sequencing (NGS) panel into our routine lung cancer molecular subtyping algorithm. PATIENTS AND METHODS: After routine pathologic and molecular subtyping, we implemented an amplicon-based gene panel for DNA analysis covering mutational hot spots in 22 cancer genes in consecutive advanced-stage NSCLCs. RESULTS: We analyzed 109 tumors using NGS between December 2014 and January 2016. Fifty-six patients (51%) were treatment-naive and 82 (75%) had lung adenocarcinomas. In 89 cases (82%), we used samples derived from lung cancer diagnostic procedures. We obtained successful sequencing results in 95 cases (87%). As part of our routine lung cancer molecular subtyping protocol, single-gene testing for EGFR, ALK, and ROS1 was attempted in nonsquamous and 3 squamous-cell cancers (n = 92). Sixty-nine of 92 samples (75%) had sufficient tissue to complete ALK and ROS1 immunohistochemistry (IHC) and NGS. With the integration of the gene panel, 40 NSCLCs (37%) in the entire cohort and 30 NSCLCs (40%) fully tested for ALK and ROS1 IHC and NGS had actionable mutations. KRAS (24%) and EGFR (10%) were the most frequently mutated actionable genes. Ten patients (9%) received matched targeted therapies, 6 (5%) in clinical trials. CONCLUSION: The combination of IHC tests for ALK and ROS1 and amplicon-based NGS is applicable in routine clinical practice, enabling patient selection for genotype-tailored treatments.

Second-line Treatment of Non-Small Cell Lung Cancer: Focus on the Clinical Development of Dacomitinib.

Dacomitinib is a second-generation, irreversible, covalent pan-HER tyrosine-kinase inhibitor (TKI). It showed potent EGFR signaling inhibition in experimental models, including first-generation TKI-resistant non-small cell lung cancer (NSCLC) cell lines. This preclinical efficacy did not translate into clinically meaningful treatment benefits for advanced, pretreated, molecularly unselected NSCLC patients enrolled in two parallel phase III trials. Dacomitinib and erlotinib showed overlapping efficacy data in chemotherapy-pretreated EGFR wild-type (WT) patients in the ARCHER 1009 trial. Similarly, it failed to demonstrate any survival benefits as compared to placebo in EGFR WT subsets progressing on chemotherapy and at least one previous first-generation TKI (erlotinib or gefitinib) in the BR.26 trial. In the case of EGFR-mutant NSCLCs, a pooled analysis of the ARCHER 1009 and ARCHER 1028 trials comparing the efficacy of dacomitinib vs. erlotinib in chemotherapy-pretreated, EGFR TKI-naïve patients showed a trend to a longer progression-free survival (PFS) and overall survival in favor of dacomitinib that did not reach statistical significance, with a higher rate of treatment related adverse events (mainly skin rash, paronychia, and gastrointestinal toxicities). On the other hand, the clinical activity in patients with EGFR-mutant NSCLCs with acquired TKI resistance that were included in phase II/III trials was equally poor (response rate <10%; PFS 3-4 months). Therefore, with the results of the ARCHER 1050 trial (NCT01774721) still pending, the current clinical development of dacomitinib is largely focused on EGFR-mutant, TKI-naïve patients. Here, we review the most relevant clinical data of dacomitinib in advanced NSCLC. We discuss the potential role of dacomitinib in pretreated EGFR WT and EGFR-mutant (TKI-naïve and TKI-resistant) patients. Finally, we briefly comment the available clinical data of dacomitinib in HER2-mutant NSCLC patients.