Prediction of individual mortality risk among patients with chronic obstructive pulmonary disease: a convenient, online, individualized, predictive mortality risk tool based on a retrospective cohort study.

Background: Chronic obstructive pulmonary disease (COPD) is a serious condition with a poor prognosis. No clinical study has reported an individual-level mortality risk curve for patients with COPD. As such, the present study aimed to construct a prognostic model for predicting individual mortality risk among patients with COPD, and to provide an online predictive tool to more easily predict individual mortality risk in this patient population. Patients and methods: The current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. A prognostic model for predicting mortality risk was constructed using eight risk factors. Results: Cox proportional hazards regression analysis identified eight independent risk factors among COPD patients: B-type natriuretic peptide (hazard ratio [HR] 1.248 [95% confidence interval (CI) 1.155-1.348]); albumin (HR 0.952 [95% CI 0.931-0.974); age (HR 1.033 [95% CI 1.022-1.044]); globulin (HR 1.057 [95% CI 1.038-1.077]); smoking years (HR 1.011 [95% CI 1.006-1.015]); partial pressure of arterial carbon dioxide (HR 1.012 [95% CI 1.007-1.017]); granulocyte ratio (HR 1.018 [95% CI 1.010-1.026]); and blood urea nitrogen (HR 1.041 [95% CI 1.017-1.066]). A prognostic model for predicting risk for death was constructed using these eight risk factors. The areas under the time-dependent receiver operating characteristic curves for 1, 3, and 5 years were 0.784, 0.801, and 0.806 in the model cohort, respectively. Furthermore, an online predictive tool, the "Survival Curve Prediction System for COPD patients", was developed, providing an individual mortality risk predictive curve, and predicted mortality rate and 95% CI at a specific time. Conclusion: The current study constructed a prognostic model for predicting an individual mortality risk curve for COPD patients after discharge and provides a convenient online predictive tool for this patient population. This predictive tool may provide valuable prognostic information for clinical treatment decision making during hospitalization and health management after discharge (https://zhangzhiqiao15.shinyapps.io/Smart_survival_predictive_system_for_COPD/).

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools.

BACKGROUND: Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients. METHODS: Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model. RESULTS: There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744-0.828]), 0.736 (95% CI [0.694-0.778]) and 0.766 (95% CI [0.724-0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients. CONCLUSION: The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

Alert for non-respiratory symptoms of coronavirus disease 2019 patients in epidemic period: A case report of familial cluster with three asymptomatic COVID-19 patients.

At present, coronavirus disease 2019 (COVID-19) is rampaging around the world. However, asymptomatic carriers intensified the difficulty of prevention and management. Here we reported the screening, clinical features, and treatment process of a family cluster involving three COVID-19 patients. The discovery of the first asymptomatic carrier in this family cluster depends on the repeated and comprehensive epidemiological investigation by disease control experts. In addition, the combination of multiple detection methods can help clinicians find asymptomatic carriers as early as possible. In conclusion, the prevention and control experience of this family cluster showed that comprehensive rigorous epidemiological investigation and combination of multiple detection methods were of great value for the detection of hidden asymptomatic carriers.

Establishment and evaluation of a simplified evaluation system of acute respiratory distress syndrome.

PURPOSE: In recent years, a variety of acute respiratory distress syndrome (ARDS) evaluation systems have been developed worldwide; however, they are not so convenient for the doctors clinically, we decided to establish and evaluate a simplified evaluation system of ARDS (SESARDS). MATERIALS AND METHODS: Data from 140 ARDS patients (derivation data set) were collected to screen for prognostic factors affecting outcomes in ARDS patients. By logistic regression analysis, scores were allocated to corresponding intervals of the variables, respectively, by means of analysis of the frequency distribution to establish a preliminary scoring system. Based on this primary scoring system, a definitive evaluation scheme was created through consultation with a panel of experts. The scores for the validation data set (92 cases) were assigned and calculated by their predictive mortality with the SESARDS and acute physiology and chronic health evaluation II (APACHE II). The performance of SESARDS was compared with that of APACHE II by means of statistical analysis. RESULTS: The factors of age, pH, Glasgow coma scale (GCS), oxygenation index (OI), and the lobes of lung were associated with prognosis of ARDS respectively. The sensitivity and specificity of SESARDS for the validation data set were 96.43% and 58.33%, respectively. On the AUC, no significant difference between APACHE II and SESARDS was detected. There were no significant differences between the prediction and the actuality obtained by SESARDS for the validation data set the SESARDS scores were positively correlated with the actual mortality. CONCLUSION: SESARDS was shown to be simple, accurate and effective in predicting ARDS progression.