Application of a parametric model in the mortality risk analysis of ICU patients with severe COPD.
OBJECTIVE
To evaluate the mortality risk factors of patients with severe chronic obstructive pulmonary disease (COPD) in the intensive care unit (ICU) by utilizing parametric model of survival analysis, and to estimate patients' prognoses based on the risk and survival functions in the model.


METHODS
The clinical data and outcomes of COPD patients admitted to a respiratory intensive care unit (RICU) at Beijing Chao-Yang Hospital from January 2009 to June 2014 were prospectively collected. Based on the clinical outcomes, these patients were divided into a survival group and a death group. A univariate analysis was conducted to identify statistically significant variables. According to the distribution characteristics of these data, residual plots were prepared for an exponential model, a Weibull model and a Gamma model. The Gamma model was determined to be the best fitted for these data and was used as the analytical tool. Therefore, the variables with differences obtained from the univariate analysis were put into the Gamma model to determine the patients' mortality risk factors. The risk and survival functions were also generated according to the model formula.


RESULTS
A total of 160 patients (111 survival, 49 death) were enrolled. The univariate analysis showed that, compared with the survival group, patients in the death group had higher Charlson Comorbidity Index score (CCI), higher body temperature, higher white blood cell counts, higher proportions of neutrophils, higher serum creatinine levels and higher pH levels; lower resting carbon dioxide pressure (PCO2); and more cases complicated with invasive pulmonary aspergillosis (IPA). A further analysis with the Gamma model revealed that concurrent or non-concurrent IPA, white blood cell count, pH level and PCO2were associated with the patients' mortality risk and that the predicted value (PV) of the patients' mortality risk = 2.8655 + 0.4070 × IPA + 0.0048 × WBC + 0.0049 × pH - 0.0097 × PCO2. Moreover, the survival function formula was obtained according to the Gamma model: S(x) = 1 - I (0.7507 x, -3.3773), where I is an incomplete gamma function.


CONCLUSIONS
Formula generated from parametric model may help to predict the prognosis of patients with severe COPD in ICU, and this method need to be evaluated in further studies. According to our formula, IPA is an important risk factor of mortality for patients with severe COPD in the ICU.
Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, No.8 Gongti Nanlu, Beijing 100020, China.
Hangyong
H
He
Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, No.8 Gongti Nanlu, Beijing 100020, China.
Ying
Y
Sun
Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, No.8 Gongti Nanlu, Beijing 100020, China.
Bing
B
Sun
Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, No.2 Yinghuadongjie, Chaoyang district Beijing 100029, China.
Qingyuan
Q
Zhan
D018806:APACHE; D000368:Aged; D000369:Aged, 80 and over; D001230:Aspergillus / Q000302:isolation & purification; D002681:China / Q000453:epidemiology; D003422:Critical Care; D005260:Female; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D055744:Invasive Pulmonary Aspergillosis / Q000175:diagnosis / Q000401:mortality*; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications / Q000453:epidemiology / Q000401:mortality*; D012307:Risk Factors; D012720:Severity of Illness Index; D016019:Survival Analysis
chronic obstructive pulmonary disease; mortality risk; parametric model; survival analysis