Background Health-related quality of life, evaluated by a patient-reported outcomes measure, is an important prognostic marker in patients with chronic heart failure. This study aimed to use PROM to establish an effective readmission nomogram for chronic heart failure.Methods Using patient-reported outcomes measure as a measurement tool, we conducted a readmission nomogram for chronic heart failure on a prospective observational study comprising 454 patients with chronic heart failure hospitalized between May 2017 to January 2020. Concordance index and calibration curve were used to evaluate the discriminative ability and predictive accuracy of the nomogram. A bootstrap resampling method was used for internal validation of results. ResultsThe median follow-up period in the study was 197 days. After a final COX regression analysis, the computed gender, income, health care, appetite-sleep, anxiety, depression, paranoia, support, and independence were identified and included in the nomogram. The nomogram showed moderate discrimination, with concordance index of 0.737 (95% CI 0.673-0.800). The calibration curves for the probability of readmission for patients with chronic heart failure showed high consistency between the probability, as predicted, and the actual probability.Conclusions This model offers a platform to assess the risk of readmission for different populations with CHF and can assist clinicians with personalized treatment recommendations.

Long non-coding RNAs (lncRNAs) play significant roles in the disease process. Understanding the pathological mechanisms of lncRNAs during the course of various diseases will help clinicians prevent and treat diseases. With the emergence of high-throughput techniques, many biological experiments have been developed to study lncRNA-disease associations. Because experimental methods are costly, slow, and laborious, a growing number of computational models have emerged. Here, we present a new approach using network consistency projection and bi-random walk (NCP-BiRW) to infer hidden lncRNA-disease associations. First, integrated similarity networks for lncRNAs and diseases were constructed by merging similarity information. Subsequently, network consistency projection was applied to calculate space projection scores for lncRNAs and diseases, which were then introduced into a bi-random walk method for association prediction. To test model performance, we employed 5- and 10-fold cross-validation, with the area under the receiver operating characteristic curve as the evaluation indicator. The computational results showed that our method outperformed the other five advanced algorithms. In addition, the novel method was applied to another dataset in the Mammalian ncRNA-Disease Repository (MNDR) database and showed excellent performance. Finally, case studies were carried out on atherosclerosis and leukemia to confirm the effectiveness of our method in practice. In conclusion, we could infer lncRNA-disease associations using the NCP-BiRW model, which may benefit biomedical studies in the future.

Background A significant proportion of women with preeclampsia (PE) exhibit persistent postpartum hypertension (PHTN) at 3 months postpartum associated with cardiovascular morbidity. This study aimed to screen patients with PE to identify the high-risk population with persistent PHTN. Methods This retrospective cohort study enrolled 1,000 PE patients with complete parturient and postpartum blood pressure (BP) profiles at 3 months postpartum. The enrolled patients exhibited new-onset hypertension after 20 weeks of pregnancy, while those with PE superimposed upon chronic hypertension were excluded. Latent class cluster analysis (LCCA), a method of unsupervised learning in machine learning, was performed to ascertain maternal exposure clusters from eight variables and 35 subordinate risk factors. Logistic regression was applied to calculate odds ratios (OR) indicating the association between clusters and PHTN. Results The 1,000 participants were classified into three exposure clusters (subpopulations with similar characteristics) according to persistent PHTN development: high-risk cluster (31.2%), medium-risk cluster (36.8%), and low-risk cluster (32.0%). Among the 1,000 PE patients, a total of 134 (13.4%) were diagnosed with persistent PHTN, while the percentages of persistent PHTN were24.68%, 10.05%, and 6.25% in the high-, medium-, and low-risk clusters, respectively. Persistent PHTN in the high-risk cluster was nearly five times higher (OR, 4.915; 95% confidence interval (CI), 2.92–8.27) and three times (OR, 2.931; 95% CI, 1.91–4.49) than in the low- and medium-risk clusters, respectively. Persistent PHTN did not differ between the medium- and low-risk clusters. Subjects in the high-risk cluster were older and showed higher BP, poorer prenatal organ function, more adverse pregnancy events, and greater medication requirement than the other two groups. Conclusion Patients with PE can be classified into high-, medium-, and low-risk clusters according to persistent PHTN severity; each cluster has cognizable clinical features. This study’s findings stress the importance of controlling persistent PHTN to prevent future cardiovascular disease.

Background Health-related quality of life, evaluated by a patient-reported outcomes measure, is an important prognostic marker in patients with chronic heart failure. This study aimed to use PROM to establish an effective readmission nomogram for chronic heart failure. Methods Using patient-reported outcomes measure as a measurement tool, we conducted a readmission nomogram for chronic heart failure on a prospective observational study comprising 454 patients with chronic heart failure hospitalized between May 2017 to January 2020. Concordance index and calibration curve were used to evaluate the discriminative ability and predictive accuracy of the nomogram. A bootstrap resampling method was used for internal validation of results. Results The median follow-up period in the study was 372 days. After a final COX regression analysis, the gender, income, health care, appetite-sleep, anxiety, depression, paranoia, support, and independence were identified and included in the nomogram. The nomogram showed moderate discrimination, with concordance index of 0.737 (95% CI 0.673-0.800). The calibration curves for the probability of readmission for patients with chronic heart failure showed high consistency between the probability, as predicted, and the actual probability. Conclusions This model offers a platform to assess the risk of readmission for different populations with CHF and can assist clinicians with personalized treatment recommendations.

Background Health-related quality of life, evaluated by a patient-reported outcomes measure, is an important prognostic marker in patients with chronic heart failure. This study aimed to use PROM to establish an effective readmission nomogram for chronic heart failure. Methods Using patient-reported outcomes measure as a measurement tool, we conducted a readmission nomogram for chronic heart failure on a prospective observational study comprising 454 patients with chronic heart failure hospitalized between May 2017 to January 2020. Concordance index and calibration curve were used to evaluate the discriminative ability and predictive accuracy of the nomogram. A bootstrap resampling method was used for internal validation of results. Results The median follow-up period in the study was 372 days. After a final COX regression analysis, the gender, income, health care, appetite-sleep, anxiety, depression, paranoia, support, and independence were identified and included in the nomogram. The nomogram showed moderate discrimination, with concordance index of 0.737 (95% CI 0.673-0.800). The calibration curves for the probability of readmission for patients with chronic heart failure showed high consistency between the probability, as predicted, and the actual probability. Conclusions This model offers a platform to assess the risk of readmission for different populations with CHF and can assist clinicians with personalized treatment recommendations.