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Background: Left ventricular hypertrophy (LVH) is common in hypertension patients. Hypertension is a recognized risk factor of acute aortic dissection. This study aimed to explore the prognostic value of LVH in predicting postoperative outcomes in type A acute aortic dissection (ATAAD) patients.Methods: This was a single-central retrospectively designed study. One hundred and ninety-three ATAAD patients who underwent surgical repair at Renmin Hospital of Wuhan University from January 2018 to November 2021 were enrolled. Patients were divided based on their left ventricular mass index (LVMI).We compared their baseline characteristics, perioperative data, and in-hospital outcome. Then nomogram models were developed based on logistic regression to predict the postoperative outcomes.Results: LVH presented in 28.5% (55 in 193) patients. LVH group had a higher proportion of female patients compared with the non-LVH group (32.7% vs. 17.4%, P=0.03). Decreased left ventricular ejection fraction and cardiac tamponade were more prevalent in patients with LVH. LVH group had a higher risk of postoperative composite major outcomes (CMO) and operative mortality. Based on multivariable logistic regression, LVH/LVMI, Penn classification, hyperlipidemia, emergency surgery and cardiopulmonary bypass duration were applied to develop nomogram models for predicting postoperative CMO. The area under curve was 0.825 (95% CI: 0.749-0.900) for Model LVH and 0.841 (95% CI: 0.776-0.905) for Model LVMI. Nomogram models for predicting postoperative cardiac were developed based on LVH/LVMI and cardiopulmonary bypass duration. The area under curves for the models involving LVH or LVMI were 0.782 (95% CI: 0.640-0.923) and 0.795 (95% CI: 0.643-0.947), respectively.Conclusions: LVH and increased LVMI was associated with increased risk of postoperative CMO and cardiac events in ATAAD patients. The nomogram models based on LVH or LVMI might help predict postoperative CMO. Future research would be necessary to investigate prognostic value of LVH for longterm outcomes in ATAAD patients.

BackgroundThis study aimed to develop reliable nomogram-based predictive models that could guide prognostic stratification and individualized treatments in patients with multiple myeloma (MM).MethodsClinical information of 560 patients was extracted from the MM dataset of the MicroArray Quality Control (MAQC)-II project. The patients were divided into a development cohort (n = 350) and an internal validation cohort (n = 210) according to the therapeutic regimens received. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for nomogram construction. Nomogram performance was assessed using concordance indices, the area under the curve, calibration curves, and decision curve analysis. The nomograms were also validated in an external cohort of 56 patients newly diagnosed with MM at Nanjing Drum Tower Hospital from May 2016 to June 2019.ResultsLactate dehydrogenase (LDH), albumin, and cytogenetic abnormalities were incorporated into the nomogram to predict overall survival (OS), whereas LDH, β2-microglobulin, and cytogenetic abnormalities were incorporated into the nomogram to predict event-free survival (EFS). The nomograms showed good predictive performances in the development, internal validation, and external validation cohorts. Additionally, we observed a superior prognostic predictive ability in nomograms compared to that of the International Staging System. According to the prognostic nomograms, risk stratification was applied to divide the patients into two risk groups. The OS and EFS rates of low-risk patients were significantly better than those of high-risk patients, suggesting a greater function of the nomogram models for risk stratification.ConclusionTwo simple-to-use prognostic models were established and validated. The proposed nomograms have potential clinical applications in predicting OS and EFS for patients with MM.

N6-methyladenosine (m6A) regulators play an important role in tumorigenesis; however, their role in multiple myeloma (MM) remains unknown. This study aimed to create an m6A RNA regulators prognostic signature for MM patients. We integrated data from the Multiple Myeloma Research Foundation CoMMpass Study and the Genotype-Tissue Expression database to analyze gene expression profiles of 21 m6A regulators. Consistent clustering analysis was used to identify the clusters of patients with MM having different clinical outcomes. Gene distribution was analyzed using principal component analysis. Next, we generated an mRNA gene signature of m6A regulators using a multivariate logistic regression model with least absolute shrinkage and selection operator. The expressions of m6A regulators, except FMR1, were significantly different in MM samples compared with those in normal samples. The KIAA1429, HNRNPC, FTO, and WTAP expression levels were dramatically downregulated in tumor samples, whereas those of other signatures were remarkably upregulated. Three clusters of patients with MM were identified, and significant differences were found in terms of overall survival (p = .024). A prognostic two-gene signature (KIAA1429 and HNRNPA2B1) was constructed, which had a good prognostic significance using the ROC method (AUC = 0.792). Moreover, the risk score correlated with the infiltration immune cells. In addition, KEGG pathway analysis showed that 16 pathways were dramatically enriched. The m6A signature might be a novel biomarker for predicting the prognosis of patients with MM (p = .002). Our study is the first to explore the potential application value of m6A in MM. These findings may enhance the understanding of the functional organization of m6A in MM and provide new insights into the treatment of MM patients.