ABSTRACT
Objective:To study the influence of time-dependent acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score on 14-day death risk in patients with severe stroke, and to provide reference for clinical diagnosis and treatment.Methods:Data of 3 229 patients with severe stroke were enrolled from Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ). According to the main types of stroke, the patients were divided into subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), ischemic stroke (IS) and other groups. According to age, patients were divided into > 60 years old and ≤ 60 years old subgroups. According to the baseline of sequential organ failure assessment (SOFA) score, they were divided into subgroups of > 3 and ≤ 3. The daily measured values of APACHE Ⅱ scores in each patient were recorded. And all-cause death within 14 days after admission to intensive care unit (ICU) was used as the outcome index to obtain the survival status and survival time of patients. Joint models for longitudinal and time-to-event data were established to evaluate the effect of APACHE Ⅱ score measured at multiple time points on the death risk of patients, and a subgroup analysis was performed.Results:Among the joint models, the one which include APACHE Ⅱ score, and the interaction items between APACHE Ⅱ and age showed the better fitting. Further analysis showed that APACHE Ⅱ score was affected by age, gender, hospital admission, baseline SOFA score and smoking history. After controlling for these confounding factors, APACHE Ⅱ score was significantly associated with 14-day all-cause death in patients with severe stroke [hazard ratio ( HR) = 1.48, 95% confidence interval (95% CI) was 1.31-1.66, P < 0.001], which indicated that the risk of death increased by 48% (95% CI was 31%-66%) for each 1-point increase in APACHE Ⅱ score. Subgroup analysis showed that for different types of severe stroke patients, APACHE Ⅱ score had a greater impact on the risk of 14-day death in SAH patients ( HR = 1.43, 95% CI was 1.10-1.85), but had a smaller impact on ICH and IS groups [HR (95% CI) was 1.37 (1.15-1.64) and 1.35 (1.06-1.71), respectively]. There was no significant difference in APACHE Ⅱ score on the risk of 14-day death between the patients aged > 60 years old and those aged ≤ 60 years old [ HR (95% CI): 1.37 (1.08-1.72) vs. 1.35 (1.07-1.70), respectively]. Compared with patients with SOFA score > 3, APACHE Ⅱ score had a greater impact on the risk of 14-day death in patients with SOFA score ≤ 3 [ HR (95% CI): 1.40 (1.16-1.70) vs. 1.34 (1.16-1.55)]. Conclusion:Time-dependent APACHE Ⅱ score is an important indicator to evaluate the risk of death in patients with severe stroke.
ABSTRACT
In prospective cohort study, multi follow up is often necessary for study subjects, and the observed values are correlated with each other, usually resulting in time-dependent confounding. In this case, the data generally do not meet the application conditions of traditional multivariate regression analysis. Sequential conditional mean model (SCMM) is a new approach that can deal with time-dependent confounding. This paper mainly summarizes the basic theory, steps and characteristics of SCMM.
ABSTRACT
In prospective cohort study, multi follow up is often necessary for study subjects, and the observed values are correlated with each other, usually resulting in time-dependent confounding. In this case, the data generally do not meet the application conditions of traditional multivariate regression analysis. Sequential conditional mean model (SCMM) is a new approach that can deal with time-dependent confounding. This paper mainly summarizes the basic theory, steps and characteristics of SCMM.
ABSTRACT
In the studies of modem epidemiology,exposure in a short term cannot fully elaborate the mechanism of the development of diseases or health-related events.Thus,lights have been shed on to life course epidemiology,which studies the exposures in early life time and their effects related to the development of chronic diseases.When exploring the mechanism leading from one exposure to an outcome and its effects through other factors,due to the existence of time-variant effects,conventional statistic methods could not meet the needs of etiological analysis in life course epidemiology.This paper summarizes the dynamic path analysis model,including the model structure and significance,and its application in life course epidemiology.Meanwhile,the procedure of data processing and etiology analyzing were introduced.In conclusion,dynamic path analysis is a useful tool which can be used to better elucidate the mechanisms that underlie the etiology of chronic diseases.
ABSTRACT
In the studies of modem epidemiology,exposure in a short term cannot fully elaborate the mechanism of the development of diseases or health-related events.Thus,lights have been shed on to life course epidemiology,which studies the exposures in early life time and their effects related to the development of chronic diseases.When exploring the mechanism leading from one exposure to an outcome and its effects through other factors,due to the existence of time-variant effects,conventional statistic methods could not meet the needs of etiological analysis in life course epidemiology.This paper summarizes the dynamic path analysis model,including the model structure and significance,and its application in life course epidemiology.Meanwhile,the procedure of data processing and etiology analyzing were introduced.In conclusion,dynamic path analysis is a useful tool which can be used to better elucidate the mechanisms that underlie the etiology of chronic diseases.