Prediction model for death in patients with pulmonary tuberculosis accompanied by respiratory failure in ICU: retrospective study

Abstract

Background: The mortality rate of pulmonary tuberculosis (TB) patients with respiratory failure remains high. This study aimed to identify factors contributing to death in these patients, and develop a mortality prediction model for pulmonary TB patients with respiratory failure.Methods: A retrospective study of patients admitted consecutively to the medical intensive care unit (ICU) of Beijing Chest Hospital, (Beijing, China), Chaoyang Fourth Hospital (Chaoyang, China) and Hebi Third People's Hospital (Hebi, China) from May 2018 to May 2019 was conducted. 153 patients with pulmonary TB accompanied by respiratory failure were enrolled. A multivariate analysis was performed to identify risk factors for death. A predictive fatality score was determined. The utility of the score for predicting death was evaluated using receiver operating characteristic (ROC) curve analysis. Results: The patients' median age was 57.82±19.42 years (17.0-87.0 years) and there were 106 males (69.28%). The mortality rate was 39.22% (60 of 153). Independent predictive factors of mortality included the PaO 2 (hazard ratio 0.928, 95% CI: 0.882-0.976, P=0.004), Albumin (hazard ratio 0.881, 95% CI: 0.792-0.980, P=0.019), Apache II score (hazard ratio 1.120, 95% CI: 1.017-1.234, P=0.022) and C-reactive protein (hazard ratio 1.012, 95% CI: 1.004-1.019, P=0.003). Establishing a logistic model of the death risk grade of pulmonary TB with respiratory failure was Y=1.710 − 0.068*PaO 2 −0.163* albumin + 0.215* Apache II +0.012* C-reactive protein. The value was Y=−0.494. If the Y value was greater than or equal to −0.494, the patients belonged to the deceased group, and if less than −0.494 the patients belonged to the survival group. AUC=0.818, The sensitivity was 83.3%; specificity was 73.1%. Conclusions: Pulmonary TB patients with respiratory failure have a high mortality rate and poor prognosis, particularly those with high Apache II scores, high C-reactive protein levels, low PaO 2 admission to ICU and low albumin level. The prediction model will help assess the risk of death in patients with TB and respiratory failure.

In-hospital mortality of pulmonary tuberculosis with acute respiratory failure and related clinical risk factors

“…The in-hospital mortality rate in the present study was 47.75% (53 out of 111), which is consistent to those previously reported (26–59%) though mechanical ventilated patients was markedly greater than 90% [6] , [9] , [22] , [23] , [24] . These variety rate were associated with clinical characteristics baseline, progressivity and respiratory support.…”

Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach

“…Due to the COVID-19 pandemic, there was a great growth of publications focused on machine learning models for predicting ICU mortality in a disease-specific manner, such as those by Pan et al [ 16 ], Lichtner et al [ 26 ], and Subudhi et al [ 27 ]. Meanwhile, many of the previous studies in this field also focus on predicting ICU outcomes for specific diseases or morbid conditions, like sepsis or death from pulmonary tuberculosis[ 11 , 13 , 28 ], which lead to an assessment of parameters specific for the disease studied, somewhat restricting the research. Many of the renowned models and scales for ICU mortality prediction demand a series of measurements to make their use possible, but not always all the data required are available.…”

“…The use of machine learning has been consolidated as an alternative for the development of predictive models of mortality in the critical care setting. An example is the retrospective study by Liu et al [ 11 ], who developed a logistic model of the death risk grade in patients with pulmonary tuberculosis using data from patients admitted to ICUs in three hospitals. In this multivariate analysis study, where the sensitivity was 83.3% and specificity was 73.1%, the Apache II score, C-reactive protein levels, albumin levels, and pressure of oxygen in arterial blood (PaO 2 ) were considered the main factors influencing the outcome.…”

Factors Affecting Mortality in Critically Ill Patients With Tuberculosis: A Systematic Review and Meta-Analysis

“…Information regarding positivity rate for acidfast bacilli was available in ten studies. The average acid-fast bacilli smear positivity rate was 89.05% (n = 366/411) (6,14,(16)(17)(18)(19)(20). All the studies had included patients with active tuberculosis only.…”

“…A statistically significant difference was observed between groups with higher CRP levels among nonsurvivors (I 2 = 84%; p = 0.006; MD = -35.58 [-61.21 to -9.96]). Five studies compared the requirement for renal replacement therapy (8,(16)(17)(18)22). The requirement for renal replacement therapy was significantly higher in nonsurvivors (I 2 = 72%; p = 0.01; OR = 0.18 [0.05-0.67).…”