A 180-day mortality predictive score based on frailty syndrome in elderly patients with sepsis: a Logistic regression analysis model / 中华危重病急救医学

ABSTRACT

Objective:To establish a 180-day mortality predictive score based on frailty syndrome in elderly sepsis patients [elderly sepsis score (ESS)].Methods:A prospective study for sepsis patients aged 60 years and above who were admitted to a medical intensive care unit of the General Hospital of Southern Theatre Command from January 1st, 2018 to December 31st, 2018 was conducted. Univariate analysis was performed on 19 independent variables including gender, age, body mass index (BMI), tumor, charlson comorbidity index (CCI), activity of daily living (ADL), instrumental activity of daily living (IADL), mini-mental state examination (MMSE), geriatric depression scale (GDS), clinical frail scale (CFS), sequential organ failure assessment (SOFA), Glasgow coma scale (GCS), acute physiology and chronic health evaluation (APACHEⅡ, APACHEⅣ), modified NUTRIC score (MNS), multiple drug resistance (MDR), mechanical ventilation (MV), continuous renal replacement therapy (CRRT) and palliative care. Continuous independent variables were converted into classified variables. Multivariate binary regression analysis of risk factors was conducted to screen independent risk factors which affecting 180-day mortality in elderly sepsis patients. Then a 180-day mortality predictive score was established, and the discrimination of the mortality of patients using CFS, SOFA, GCS, APACHEⅡ, APACHEⅣ, MNS scores were compared.Results:A total of 257 patients were enrolled, with a 180-day mortality of 60.7%. Univariate analysis showed that age, tumor, CCI, ADL, IADL, MMSE, CFS, SOFA, GCS, APACHEⅡ, APACHEⅣ, MNS, MDR, MV, CRRT, palliative care were risk factors of 180-day mortality in elderly sepsis patients [age odds ratio ( OR) = 1.027, 95% confidence interval (95% CI) was 1.005-1.050, P = 0.018; tumor OR =2.001, 95% CI was 1.022-3.920, P = 0.043; CCI OR = 1.193, 95% CI was 1.064-1.339, P = 0.003; ADL OR = 0.851, 95% CI was 0.772-0.940, P = 0.001; IADL OR = 0.894, 95% CI was 0.826-0.967, P = 0.005; MMSE OR = 0.962, 95% CI was 0.937-0.988, P = 0.004; CFS OR = 1.303, 95% CI was 1.089-1.558, P = 0.004; SOFA OR = 1.112, 95% CI was 1.038-1.191, P = 0.003; GCS OR = 0.918, 95% CI was 0.863-0.977, P = 0.007; APACHEⅡ OR = 1.098, 95% CI was 1.053-1.145, P < 0.001; APACHEⅣ OR = 1.032, 95% CI was 1.020-1.044, P < 0.001; MNS OR = 1.315, 95% CI was 1.159-1.493, P < 0.001; MDR OR = 2.029, 95% CI was 1.197-3.437, P = 0.009; MV OR = 6.408, 95% CI was 3.480-11.798, P < 0.001, CRRT OR = 2.744, 95% CI was 1.529-4.923, P = 0.001, palliative care OR = 5.760, 95% CI was 2.177-15.245, P < 0.001]. By binary regression analysis, CFS stratification ( OR = 1.934, 95% CI was 1.267-2.953, P = 0.002), MV ( OR = 4.531, 95% CI was 2.376-8.644, P < 0.001), CRRT ( OR = 2.471, 95% CI was 1.285-4.752, P = 0.007), palliative care ( OR = 6.169, 95% CI was 2.173-17.515, P = 0.001) were independent risk factors of 180-day mortality in elderly patients with sepsis. The model of "ESS = 0.660×CFS stratification+1.511×MV+0.905×CRRT+1.820×palliative care" was established. Receiver operating characteristic curve (ROC curve) analysis showed that the area under the ROC curve (AUC) for predicting 180-day mortality by ESS was 0.785 (95% CI was 0.730-0.834, P < 0.001). When the best cut-off value was 2.2 points, its sensitivity was 78.9%, specificity was 70.3%, the positive predictive value was 80.4%, and the negative predictive value was 68.3%. Simplified ESS was defined as "0.5×CFS stratification+1.5×MV+1×CRRT+2×palliative care". ROC curve analysis showed that AUC for predicting 180-day mortality by simplified ESS was 0.784 (95% CI was 0.729-0.833, P < 0.001). When the best cut-off value was 2.0 points, sensitivity was 76.9%, specificity was 70.3%, the positive predictive value was 80.0%, and the negative predictive value was 66.4%. Compared with CFS, SOFA, GCS, APACHEⅡ, APACHEⅣ and MNS, ESS had a significant difference in discriminating 180-day mortality in elderly patients with sepsis (AUC was 0.785 vs. 0.607, 0.607, 0.600, 0.664, 0.702, 0.657, 95% CI 0.730-0.734 vs. 0.537-0.678, 0.537-0.677, 0.529-0.671, 0.598-0.730, 0.638-0.766, 0.590-0.725, all P < 0.05). Conclusions:CFS, MV, CRRT, and palliative care are independent risk factors of 180-day mortality in elderly patients with sepsis. We established ESS based on these risk factors. The ESS model has good discrimination and can be used as a reference and assessment tool for prediction and treatment guidance in elderly patients with sepsis.