Can a multistage approach improve individual tree mortality predictions across the complex mixed-species and managed forests of eastern North America?

Tree mortality plays a fundamental role in the dynamics of forest ecosystems,yet it is one of the most difficult phenomena to accurately predict.Various modeling strategies have been developed to improve individual tree mortality predictions.One less explored strategy is the use of a multistage modeling approach.Potential improvements from this approach have remained largely unknown.In this study,we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation.Extensive permanent plot data(n=9442)covering the Acadian Region of North America and over multiple decades(1965–2014)were used in this study.Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach.The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach.In addition,tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations,whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data.The new multistage approach also predictions of zero mortality in individual plots,a result not possible in conventional models.Finally,the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values.Overall,this new multistage approach deserves to be considered and tested in future studies.

Generalized or general mixed-effect modelling of tree morality of Larix gmelinii subsp.principis-rupprechtii in Northern China

Tree mortality models play an important role in predicting tree growth and yield,but existing mortality models for Larix gmelinii subsp.principis-rupprechtii,an important species used for regeneration and afforestation in northern China,have overlooked potential regional influences on tree mortality.This study used data acquired from 102 temporary sample plots(TSPs)in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest(n=67)and state-owned Boqiang Forest(n=35)in northern China.To model stand-level tree mortality,we compared seven model forms of county data.Three continuous(dominant height,plot mean diameter,and basal area per hectare)and one dummy variable with two levels(region)were used as fixed effects variables.Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models.Results showed that tree mortality significantly positively correlated with stand basal area and dominant height,but negatively correlated with stand mean diameter.Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements,and Hurdle Poisson mixed-effects model showed the most attractive fit statistics(largest R^(2)and smallest RMSE)when employing leave-one-out cross-validation.These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.

Clinical characteristics and mortality risk prediction model in children with acute myocarditis

Background Acute myocarditis(AMC)can cause poor outcomes or even death in children.We aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital admission.Methods This was a single-center retrospective cohort study of AMC children hospitalized between January 2016 and January 2020.The demographics,clinical examinations,types of AMC,and laboratory results were collected at hospital admission.In-hospital survival or death was documented.Clinical characteristics associated with death were evaluated.Results Among 67 children,51 survived,and 16 died.The most common symptom was digestive disorder(67.2%).Based on the Bayesian model averaging and Hosmer–Lemeshow test,we created a final best mortality prediction model(acute myocarditis death risk score,AMCDRS)that included ten variables(male sex,fever,congestive heart failure,left-ventricular ejection fraction<50%,pulmonary edema,ventricular tachycardia,lactic acid value>4,fulminant myocarditis,abnormal creatine kinase-MB,and hypotension).Despite differences in the characteristics of the validation cohort,the model discrimination was only marginally lower,with an AUC of 0.781(95%confidence interval=0.675–0.852)compared with the derivation cohort.Model calibration likewise indicated acceptable fit(Hosmer‒Lemeshow goodness-of-fit,P¼=0.10).Conclusions Multiple factors were associated with increased mortality in children with AMC.The prediction model AMCDRS might be used at hospital admission to accurately identify AMC in children who are at an increased risk of death.

A good prognostic predictor for liver transplantation recipients with benign end-stage liver cirrhosis

BACKGROUND： Post-transplant model for predicting mortality（PMPM, calculated as-5.359＋1.988×ln（serum creatinine [mg/d L]）＋1.089×ln（total bilirubin [mg/d L]）） score has been proved to be a simple and accurate model for predicting the prognosis after liver transplantation（LT） in a single center study. Here we aim to verify this model in a large cohort of patients.METHODS： A total of 2727 patients undergoing LT with endstage liver cirrhosis from January 2003 to December 2010 were included in this retrospective study. Data were collected from the China Liver Transplant Registry（CLTR）. PMPM score was calculated at 24-h and 7-d following LT. According to the PMPM score at 24-h, all patients were divided into the low-risk group（PMPM score ≤-1.4, n=2509） and the high-risk group（PMPM score 〉-1.4, n=218）. The area under receiver operator characteristic curve（AUROC） was calculated for evaluating the prognostic accuracy.RESULTS： The 1-, 3-, and 5-year patient survival rates in the low-risk group were significantly higher than those in the high-risk group（90.23%, 88.01%, and 86.03% vs 63.16%, 59.62%, and 56.43%, respectively, P〈0.001）. In the high-risk group, 131 patients had a decreased PMPM score（≤-1.4） at 7-d, and their cumulative survival rate was significantly higher than the other 87 patients with sustained high PMPM score（〉-1.4）（P〈0.001）. For predicting 3-month mortality, PMPM score showed a much higher AUROC than post-transplant MELD score（P〈0.05）.CONCLUSION： PMPM score is a simple and effective tool to predict short-term mortality after liver transplantation in patients with benign liver diseases, and an indicator for prompt salvaging treatment as well.

What MELD score mandates use of entecavir for ACLF-HBV HBeAg-negative patients?

AIM: To investigate optimal timing for therapeutic efficacy of entecavir for acute-on-chronic hepatitis B liver failure (ACLF-HBV) in hepatitis B e antigen (HBeAg)negative patients. METHODS: A total of 109 inpatients with ACLF-HBV were recruited from the Department of Infectious Diseases of the Third Affiliated Hospital, Sun Yat-sen University from October 2007 to October 2010. Entecavir 0.5 mg/d was added to each patient's comprehensive therapeutic regimen. Patients were divided into threegroups according to model for end-stage liver disease (MELD) score: high (≥ 30, 20 males and 4 females, mean age 47.8 ± 13.5 years); intermediate (22-30, 49 males and 5 females, 45.9 ± 12.4 years); and low (≤ 22, 28 males and 3 females, 43.4 ± 9.4 years). Statistical analysis were performed using SPSS 11.0 software. Data with normal distribution were expressed as mean ± SD and comparisons were made with Student's t tests. A value of P < 0.05 was considered statistically significant. Viral loads were related exponentially and logarithmic data were used for analysis. RESULTS: For 24 patients with MELD score ≥ 30, treatment lasted 17.2 ± 16.5 d. Scores before and after treatment were significantly different (35.97 ± 4.87 and 40.48 ± 8.17, respectively, t = -2.762, P = 0.011); HBV DNA load was reduced (4.882 ± 1.847 copies log10/mL to 3.685 ± 1.436 copies log10/mL); and mortality rate was 95.83% (23/24). Of 54 patients with scores of 22-30, treatment lasted for 54.0 ± 43.2 d; scores before and after treatment were 25.87 ± 2.33 and 25.82 ± 13.92, respectively (t = -0.030, P = 0.976); HBV DNA load decreased from 6.308 ± 1.607 to 3.473 ± 2.097 copies log10/mL; and mortality was 51.85% (28/54). Of 31 patients with scores ≤ 22, treatment lasted for 66.1 ± 41.9 d; scores before and after treatment were 18.88 ± 2.44 and 12.39 ± 7.80, respectively, (t = 4.860, P = 0.000); HBV DNA load decreased from 5.841 ± 1.734 to 2.657 ± 1.154 copies log10/mL; and mortality was 3.23% (1/31). CONCLUSION: For HBeAg-negative patients with ACLF-HBV, when entecavir was added to comprehensive therapy, a MELD score ≥ 30 predicted very poor prognosis due to fatal liver failure.