COVID-19 death risk predictors in Brazil using survival tree analysis: a retrospective cohort from 2020 to 2022

PURPOSE

This study analyses the survival of hospitalized patients with Severe Acute Respiratory Syndrome (SARS) due to COVID-19 and identifies the risk groups for death due to COVID-19 from the identification of potential interactions between its predictors.

METHODS

This was a retrospective longitudinal study with data from 1,756,917 patients reported in the Influenza Epidemiological Surveillance Information System from 26 February 2020 to 31 December 2022. In this study, all adult and older (≥ 20 years) patients were hospitalized with SARS due to COVID-19, with death as the outcome. Survival tree analysis was used to identify potential interactions between the predictors. A model was built for each year of study.

RESULTS

Hospital lethalitywas 33.2%. The worst survival curve was observed among those who underwent invasive mechanical ventilation and were aged 80 years or older in the three years of the pandemic. Black and brown race/color were predictors of deaths in the years 2020 and 2021 when there was greater demand from the health system due to the greater number of cases.

CONCLUSION

By applying survival tree analysis we identified several numbers of homogeneous subgroups with different risks for mortality from COVID-19. These findings show the effects of wide inequalities of access by the population, requiring effective policies for the reduction and adequate management of the disease.

Application of survival tree analysis for exploration of potential interactions between predictors of incident chronic kidney disease: a 15-year follow-up study

Background

Chronic kidney disease (CKD) is a growing public health challenges worldwide. Various studies have investigated risk factors of incident CKD; however, a very few studies examined interaction between these risk factors. In an attempt to clarify the potential interactions between risk factors of CKD, we performed survival tree analysis.

Methods

A total of 8238 participants (46.1% men) aged > 20 years without CKD at baseline [(1999–2001) and (2002–2005)], were followed until 2014. The first occurrence of CKD, defined as the estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m², was set as the main outcome. Multivariable Cox proportional hazard (Cox PH) regression was used to identify significant independent predictors of CKD; moreover, survival tree analysis was performed to gain further insight into the potential interactions between predictors.

Results

The crude incidence rates of CKD were 20.2 and 35.2 per 1000 person-years in men and women, respectively. The Cox PH identified the main effect of significant predictors of CKD incidence in men and women. In addition, using a limited number of predictors, survival trees identified 12 and 10 subgroups among men and women, respectively, with different survival probability. Accordingly, a group of men with eGFR > 74 ml/min/1.73 m², age ≤ 46 years, low level of physical activity, waist circumference ≤ 100 cm and FPG ≤ 4.7 mmol/l had the lowest risk of CKD incidence; while men with eGFR ≤ 63.4 ml/min/1.73 m², age > 50 years had the highest risk for CKD compared to men in the lowest risk group [hazard ratio (HR), 70.68 (34.57–144.52)]. Also, a group of women aged ≤ 45 years and eGFR > 83.5 ml/min/1.73 m² had the lowest risk; while women with age > 48 years and eGFR ≤ 69 ml/min/1.73 m² had the highest risk compared to low risk group [HR 27.25 (19.88–37.34)].

Conclusion

In this post hoc analysis, we found the independent predictors of CKD using Cox PH; furthermore, by applying survival tree analysis we identified several numbers of homogeneous subgroups with different risk for incidence of CKD. Our study suggests that two methods can be used simultaneously to provide new insights for intervention programs and improve clinical decision making.