Prediction of complications associated with general surgery using a Bayesian network.

BACKGROUND: Numerous attempts have been made to identify risk factors for surgery complications, but few studies have identified accurate methods of predicting complex outcomes involving multiple complications. METHODS: We performed a prospective cohort study of general surgical inpatients who attended 4 regionally representative hospitals in China from January to June 2015 and January to June 2016. The risk factors were identified using logistic regression. A Bayesian network model, consisting of directed arcs and nodes, was used to analyze the relationships between risk factors and complications. Probability ratios for complications for a given node state relative to the baseline probability were calculated to quantify the potential effects of risk factors on complications or of complications on other complications. RESULTS: We recruited 19,223 participants and identified 21 nodes, representing 9 risk factors and 12 complications, and 55 direct relationships between these. Respiratory failure was at the center of the network, directly affected by 5 risk factors, and directly affected 7 complications. Cardiopulmonary resuscitation and sepsis or septic shock also directly affected death. The area under the receiver operating characteristic curve for the ability of the network to predict complications was >0.7. Notably, the probability of other severe complications or death significantly increased when a severe complication occurred. Most importantly, there was a 141-fold higher risk of death when cardiopulmonary resuscitation was required. CONCLUSION: We have created a Bayesian network that displays how risk factors affect complications and their interrelationships and permits the accurate prediction of complications and the creation of appropriate preventive guidelines.

Smoking-related cancer death among men and women in an ageing society (China 2020-2040): a population-based modelling study.

BACKGROUND: China is experiencing a postpeak smoking epidemic with accelerating population ageing. Understanding the impacts of these factors on the future cancer burden has widespread implications. METHODS: We developed predictive models to estimate smoking-related cancer deaths among men and women aged ≥35 years in China during 2020-2040. Data sources for model parameters included the United Nations World Population Prospects, China Death Surveillance Database, national adult tobacco surveys and the largest national survey of smoking and all causes of death to date. The main assumptions included stable sex-specific and age-specific cancer mortality rates and carcinogenic risks of smoking over time. RESULTS: In a base-case scenario of continuing trends in current smoking prevalence (men: 57.4%-50.5%; women: 2.6%-2.1% during 2002-2018), the smoking-related cancer mortality rate with population ageing during 2020-2040 would rise by 44.0% (from 337.2/100 000 to 485.6/100 000) among men and 52.8% (from 157.3/100 000 to 240.4/100 000) among women; over 20 years, there would be 8.6 million excess deaths (0.5 million more considering former smoking), and a total of 117.3 million smoking-attributable years of life lost (110.3 million (94.0%) in men; 54.1 million (46.1%) in working-age (35-64 years) adults). An inflection point may occur in 2030 if smoking prevalence were reduced to 20% (Healthy China 2030 goal), and 1.4 million deaths would be averted relative to the base-case scenario if the trend were maintained through 2040. CONCLUSIONS: Coordinated efforts are urgently needed to curtail a rising tide of cancer deaths in China, with intensified tobacco control being key.

Polymorphisms in a disintegrin and metalloprotease 33 gene and the risk of chronic obstructive pulmonary disease: a meta-analysis.

A number of polymorphisms in a disintegrin and metalloprotease 33 (ADAM33) gene have been implicated in susceptibility to chronic obstructive pulmonary disease (COPD). However, results to date have been inconclusive. We conducted meta-analyses to investigate the associations between multiple polymorphisms in ADAM33 gene and COPD susceptibility. PubMed, Embase and Chinese databases (Wanfang and China National Knowledge Infrastructure) were searched for eligible case-control studies. We extracted data and used meta-analysis to calculate pooled odds ratios with 95% confidence intervals to evaluate the strength of associations. Twelve studies containing six ADAM33 polymorphisms (F+1, S1, S2, T1, V4 and Q-1) were identified, which involved 2630 cases and 4376 controls. ADAM33 S1 polymorphism showed stable and significant associations with COPD risks among the Chinese and smoking populations, and Q-1 polymorphism showed stable and significant associations with COPD risks among the overall populations. In subgroup analyses, T1 and Q-1 polymorphisms were significantly associated with COPD risks among the Chinese and smoking populations, and among the Chinese, Caucasians and smoking populations, respectively. However, none of the significant results was stable in sensitivity analyses. With respect to F+1, S2 or V4 polymorphism, there was no evidence of any significant association with COPD risks in either the overall or the subgroup analysis. The results of this meta-analysis indicate that ADAM33 S1 polymorphism is a risk factor for COPD among the Chinese and smoking populations, and that Q-1 polymorphism is a risk factor for COPD among the overall populations.