Emerging evidence demonstrates that frailty measures can predict adverse outcomes after cardiac procedures. Our objectives were to examine whether the inclusion of frailty measures adds incremental predictive value to existing surgical risk prediction models in patients undergoing cardiac surgery and to evaluate the reporting and methods of studies that investigated the prediction of frailty measures in cardiology. The inclusion of frailty measures adds incremental predictive value on existing perioperative risk-scoring systems. We systematically searched the EMBASE, MEDLINE, and Web of Science databases for relevant studies. Studies were included according to predefined inclusion criteria. The quality of included studies was appraised using the QUADAS-2 tool. Data were extracted and synthesized according to predefined methods. Twelve studies were included in the analysis. Included studies demonstrated the incremental predictive value of frailty measures on existing surgical risk models for mortality, but the predictive value of frailty measures alone was not consistent across literature. Few studies that investigated the predictive ability of frailty measures reported all important model performance measures. When comparing the predictive value of frailty measures with existing models, few studies reported if the frailty measurement was separately performed from the existing perioperative risk assessment. The addition of frailty measures to the existing perioperative risk models improved the prediction performance for mortality, but the incorporation of frailty assessment into perioperative risk assessment requires further evidence before making health policy recommendations.

Background: Cardiovascular disease (CVD) and stroke are leading causes of death. It has several risk factors, including stress and pressure. Stock volatility can cause acute stress for stockholders so that it can cause CVD events. Recently, the spread of new coronaviruses worldwide has affected economic development greatly, leading to more severe stock market fluctuations, so we systematically quantify the short-term effect of stock volatility and CVD events.Methods: Time-series analysis on the effect of stock volatility and cardiovascular events were concluded.We conducted a systematic literature search for studies published in PubMed, Embase, and Cochrane Data up to the date February 9, 2020. We assessed publication bias using Egger's test. Overall analysis and sensitivity analysis were conducted separately.Results: Four studies were finally included. Every 100-point increase in the stock market will bring about 1.01% increases in cardiovascular mortality [95% confidence intervals (CI), −0.18% to 2.21%]. The metaanalysis showed no statistical significance for cardiovascular mortality. Every 100-point increase in the stock market brought 1.01% increases in the cardiovascular mortality [95% CI, −0.18% to 2.21%]. In terms of stroke events, the estimated effect was 2.999% (95% CI, 0.325% to 5.673%). Different lag patterns also have effects on cardiovascular mortality. Every 100-point increase brought about 4.026% (95% CI, 1.516% to 6.536%) and 4.424% (95% CI, 1.145% to 7.703%) for lag 01 and 04 separately.Conclusions: Though our study has a number of limitations due to the limited studies included, it suggested that stock volatility had a lagging effect on CVD mortality, which may last for several days. Also, it might increase the incidence of stroke.

Background: Sepsis is a major cause of neonatal morbidity and mortality in developing countries, and early-onset sepsis has poor outcomes. The causative bacteria vary depending on the geographical location of the hospital. This study aimed to determine the changing trends of causative bacteria and antibiotic susceptibility in the past decade.Methods: This study retrospectively analyzed the blood culture of positive cases of early-onset sepsis admitted to the neonatal intensive care unit of our hospital between 2009 and 2018. The cases were divided into two phases, i.e., phase I (2009 to 2013) and phase II (2014II ( to 2018. Changing trends in the bacteriological profiles and antibiotic susceptibility were recorded and analyzed.Results: A total of 1,479 causative bacteria were detected. Gram-positive bacteria were isolated in 74.92% of the cases, and coagulase-negative Staphylococci (CoNS) (63.22%) was identified as the common isolate.Klebsiella pneumoniae (10.01%) followed by Escherichia coli (8.72%) were the dominant Gram-negative bacteria. Comparative analysis showed a significant reduction in CoNS. Among Gram-negative bacteria, K. pneumoniae was initially predominant but was replaced by E. coli in phase II. Gram-positive bacteria showed relatively high susceptibility to aminoglycosides and quinolones. K. pneumoniae exhibited higher resistance to cephalosporin compared with E. coli. Reduced sensitivity against the first-and secondgeneration antibiotics was observed in phase II. Conclusions:The etiological profile of neonatal sepsis (NS) has undergone a significant change in the last decade. Antibiotic resistance has increased, and continuous surveillance for antibiotic susceptibility is required to ensure efficient therapeutic outcomes.

Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs who met the criteria for inclusion was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to the training (269 cases) and validation sets (270 cases). Prognostic factors in patients with PHNETs were screened using the Cox proportional regression model and Fine–Gray competing risk model. Based on the training set analysis using the Fine–Gray competing risk model, a nomogram was constructed to predict cumulative probabilities for PHNET-specific death. The performance of the nomogram was measured by using receiver operating characteristic curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). No differences in clinical baseline characteristics between the training and validation sets were observed, and the Fine–Gray analysis showed that surgery and more than one primary malignancy were associated with a low cumulative probability of PHNET-specific death. The training set nomograms were well-calibrated and had good discriminative ability, and good agreement between predicted and observed survival was observed. Patients with PHNETs with a high-risk score had a significantly increased risk of PHNET-specific death and non-PHNET death. Surgical treatment and the number of primary malignancies were found to be independent protective factors for PHNETs. The competing risk nomogram has high accuracy in predicting disease-specific survival (DSS) for patients with PHNETs, which may help clinicians to develop individualized treatment strategies.