Development and validation of prognostic model for predicting mortality of COVID-19 patients in Wuhan, China

Abstract

Novel coronavirus 2019 (COVID-19) infection is a global public health issue, that has now affected more than 200 countries worldwide and caused a second wave of pandemic. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with a high risk of mortality. However, prognostic factors predicting poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain under intensive investigation. We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the end of the follow up period was the primary outcome. Factors predicting mortality were also assessed and a prognostic model was developed, calibrated and validated. The study included 492 patients with SARS-CoV-2 who were divided into three cohorts: the training cohort (n = 237), the validation cohort 1 (n = 120), and the validation cohort 2 (n = 135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including advanced age [odds ratio (OR), 1.1/years increase (p < 0.001)], increased neutrophil-to-lymphocyte ratio [(NLR) OR, 1.14/increase (p < 0.001)], elevated body temperature on admission [OR, 1.53/°C increase (p = 0.005)], increased aspartate transaminase [OR, 2.47 (p = 0.019)], and decreased total protein [OR, 1.69 (p = 0.018)]. Furthermore, the prognostic model drawn from the training cohort was validated with validation cohorts 1 and 2 with comparable area under curves (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell’s C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other. A validated prognostic model was developed to assist in determining the clinical prognosis for SARS-CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted to be in the low risk group had a higher probability of survival.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

“…Complete case analysis. Mei ( Mei et al, 2020 ) Age, NLR, admission body temperature, AST, total protein Yes Points-based score 0.912 (95% CI 0.878-0.947) VC1 = 0.928 (95% CI 0.884-0.971) and VC2 = 0.883 (0.815-0.952) Risk of selection bias due to inclusion/exclusion criteria, included only patients from Wuhan. Small sample size for development and validation.…”

New evidence for supplementary crop production, foddering and fuel use by Bronze Age transhumant pastoralists in the Tianshan Mountains

“…Dates calibrated in OxCal 4.3, using IntCal13 calibration curve 83 , 84 . Site Material Calibrated Date at 95.4% (BC) References Tongtiandong Wheat 3262–2917 Zhou et al 18 Barley 3347–3097 Barley 3336–2945 Barley 2461–2210 Broomcorn Millet 2199–1981 Broomcorn Millet 1623–1460 Broomcorn Millet 1623–1460 Xintala Wheat 1972–1694 Dodson et al 10 Wupaer Wheat 1506–1303 Wheat 1186–909 Yang et al 20 Wheat 1506–1300 Barley 1501–1320 Xicaozi Wheat 1381–1047 Dodson et al 10 Sidaogou Wheat 1496–1132 Barley …”

“…Early evidence of crops consists of a few grains of wheat and barley from the Altai region (i.e., Tongtiandong Cave, Jeminay County) dated to the late fourth millennium BC 18 . However, a consistent expansion and diversification of crop use occurred in the IAMC in the period 2500–1500 BC 7 , 19 , 20 . A variety of grains—including naked barley, broomcorn millet, wheat and pea—were recovered from archaeobotanical assemblages in present-day Kazakhstan, showing dietary diversity in the regional Bronze Age communities 21 , 22 .…”

Associations of community, famliy and early individual factors with body mass index z-scores trajectories among Chinese children and adolescents

“…A small number of studies on the trajectory of overweight/obesity in Chinese children are available. Nevertheless, some of them only studied the trajectory of average BMI z-scores 7 , the heterogeneity of trajectory was not considered. Although some other studies 6 , 8 used dichotomous variables such as overweight or obesity to estimate different trajectories, they did not comprehensively demonstrate the process of weight change by using the continuous variable of BMI z-scores.…”