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Aims Frailty is associated with prognosis of cardiovascular diseases. However, the significance of frailty in patients with heart failure with preserved ejection fraction (HFpEF) remains to be elucidated. The purpose of this study was to examine the prognostic significance of the Clinical Frailty Scale (CFS) in real-world patients with HFpEF using data from a prospective multicentre observational study of patients with HFpEF (PURSUIT-HFpEF study). Method and ResultsWe classified 842 patients with HFpEF enrolled in the PURSUIT-HFpEF study into two groups using CFS. The registry enrolled patients hospitalized with a diagnosis of decompensated heart failure. Median age was 82 [interquartile range: 77, 87], and 45% of the patients were male. Of 842 patients, 406 were classified as high CFS (CFS ≥ 4, 48%) and 436 as low CFS (CFS ≤ 3, 52%). The primary endpoint was the composite of all-cause mortality and heart failure admission. Secondary endpoints were all-cause mortality and heart failure admission. Patients with high CFS were older (85 vs. 79 years, P < 0.001), predominantly female (65% vs. 46%, P < 0.001) and more likely to have New York Heart Association (NYHA) ≥ 2 (75% vs. 53%, P < 0.001) and a higher level of NT-proBNP (1360 vs 838 pg/mL, P < 0.001) than those with low CFS. Patients with high CFS had a significantly greater risk of composite endpoint (Kaplan-Meier estimated 1-year event rate 39% vs. 23%, log-rank P < 0.001), all-cause mortality (Kaplan-Meier estimated 1-year event rate 17% vs. 7%, log-rank P < 0.001) and heart failure admission (Kaplan-Meier estimated 1-year event rate 28% vs. 19%, log-rank P = 0.002) than those with low CFS. Multivariable Cox regression analysis revealed that high CFS was significantly associated with composite endpoint (adjusted HR 1.92, 95% CI 1.35-2.73, P < 0.001), all-cause mortality (adjusted HR 2.54, 95% CI 1.39-4.66, P = 0.003) and heart failure admission (adjusted HR 1.55, 95% CI 1.03-2.32, P = 0.035) even after adjustment for covariates. Moreover, change in CFS grade was also significantly associated with composite endpoint (adjusted HR 1.23, 95% CI 1.11-1.36, P < 0.001), all-cause mortality (adjusted HR 1.32, 95% CI 1.13-1.55, P = 0.001) and heart failure admission (adjusted HR 1.15, 95% CI 1.02-1.30, P = 0.021). Conclusions Frailty assessed by the CFS was associated with poor prognosis in patients with HFpEF.

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To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system. Ninety patients (50 men, 40 women; mean age, 66 years; range, 40–88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared. No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists ( P >.11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P = .98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity ( P = .0005), but significantly superior specificity ( P = .02). Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists.

To investigate the conjunctival microbiota and the association between the development of conjunctival mucosa-associated lymphoid tissue (MALT) lymphoma and dysbiosis, DNA samples were collected from 25 conjunctival MALT lymphoma patients and 25 healthy controls. To compare the microbiota, samples were collected from the following four body locations: conjunctiva, meibomian gland, periocular skin and hand. Extracted DNA was analyzed by 16S rRNA sequences, and libraries were sequenced on an Illumina MiSeq sequencer. The differences in bacteria were characterized by using principal coordinate analysis of metagenomics data, and the differences in bacterial compositions were evaluated by linear discriminant analysis effect size. The conjunctival microbiota of MALT lymphoma patients was compositionally different from that of healthy controls. For the conjunctival MALT lymphoma patients, alterations in the microbial composition were detected, and a remarkable change was detected at the conjunctiva. Detailed analysis showed that a specific population of the microbiota, the genus Delftia , was significantly more abundant in conjunctival MALT lymphoma patients, and the genera Bacteroides and Clostridium were less abundant in the MALT lymphoma patients. A specific microbiota on the ocular surface in conjunctival MALT lymphoma patients was detected, and dysbiosis may play an important role in the pathophysiology of conjunctival MALT lymphoma.