A dynamic model for individualized prognosis prediction in patients with avian influenza A H7N9.

Background: Avian influenza A H7N9 progresses rapidly and has a high case fatality rate. However, few models are available to predict the survival of individual patients with H7N9 infection in real-time. This study set out to construct a dynamic model for individual prognosis prediction based on multiple longitudinal measurements taken during hospitalization. Methods: The clinical and laboratory characteristics of 96 patients with H7N9 who were admitted to hospitals in Jiangsu between January 2016 and May 2017 were retrospectively investigated. A random forest model was applied to longitudinal data to select the biomarkers associated with prognostic outcome. Finally, a multivariate joint model was used to describe the time-varying effects of the biomarkers and calculate individual survival probabilities. Results: The random forest selected a set of significant biomarkers that had the lowest classification error rates in the feature selection phase, including C-reactive protein (CRP), blood urea nitrogen (BUN), procalcitonin (PCT), base excess (BE), lymphocyte count (LYMPH), white blood cell count (WBC), and creatine phosphokinase (CPK). The multivariate joint model was used to describe the effects of these biomarkers and characterize the dynamic progression of the prognosis. Combined with the covariates, the joint model displayed a good performance in discriminating survival outcomes in patients within a fixed time window of 3 days. During hospitalization, the areas under the curve were stable at 0.75. Conclusions: Our study has established a novel model that is able to identify significant indicators associated with the prognostic outcomes of patients with H7N9, characterize the time-to-event process, and predict individual-level daily survival probabilities after admission.

A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers.

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.

Association between dietary sodium, potassium intake and lung cancer risk: evidence from the prostate, lung, colorectal and ovarian cancer screening trial and the Women's Health Initiative.

BACKGROUND: Epidemiological studies have reported that dietary mineral intake plays an important role on lung cancer risk, but the association of sodium, potassium intake is still unclear. METHODS: We determined the association between dietary sodium, potassium intake and lung cancer risk based on the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial and the Women's Health Initiative (WHI). Totally 165,409 participants who completed the baseline questionnaire (BQ) and diet history questionnaire (DHQ) were included into the analytical dataset, including 92,984 (44,959 men and 48,025 women) from the PLCO trial and 72,425 (women only) from the WHI cohort. Multivariable Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of incident lung cancer associated with dietary potassium and sodium intake. The dose-response relationship was also described using the spline smoothed curve after adjusting covariates. RESULTS: After the median follow-up of 8.55 and 18.56 years, 1,278 and 1,631 new cases of lung cancer were identified in the PLCO trial and WHI cohort, respectively. Intake of sodium was significantly associated with the incidence of lung cancer in the PLCO trial after multivariate adjustment for men (HR: 1.19, 95% CI: 1.05-1.35; P for linear trend =0.044). There was a suggestion that lung cancer risk had a quadratic curve correlation with the increase of potassium intake for women (third vs. lowest quintile: HR: 0.72, 95% CI: 0.54-0.96; P for quadratic trend =0.042). The similar results showing an inverse association between potassium intake and lung cancer risk were also observed in the WHI cohort for women (highest vs. lowest quintile: HR: 0.82, 95% CI: 0.70-0.97; P for linear trend =0.009). CONCLUSIONS: Appropriate intake of potassium has a protective effect against lung cancer, while high consumption of sodium is associated with an increased risk of lung cancer.