The predictive value of medical big data for the prognosis of elderly patients with pneumonia: based on the result of clinical database of a Beijing Chaoyang Hospital Consortium Chaoyang Emergency Ward / 中华危重病急救医学

ABSTRACT

Objective:To explore a medical big data algorithm to screen the core indicators in clinical database that can be used to evaluate the prognosis of elderly patients with pneumonia.Methods:Based on the clinical database of a Beijing Chaoyang Hospital Consortium Chaoyang Emergency Ward in Beijing Chaoyang Hospital, Capital Medical University, patients with pulmonary infection were selected through the big data retrieval technology. According to the prognosis at the time of discharge, they were divided into death group and survival group. The general data of patients were collected, including gender, age, blood gas and laboratory indices. A computer language called Python was used to make batch calculations of key indicators that affect mortality in elderly patients with pneumonia. Logistic regression analysis was used to analyze the relationship between laboratory indicators and patients' prognosis. Receiver operating characteristic curve (ROC curve) was drawn to analyze the predictive value of screening method for patients' prognosis.Results:A total of 265 patients were included in the study, 64 died and 201 survived. The data of the first detection indexes of each patient after admission were collected, and 23 key indicators with significant differences were selected from 472 indicators blood routine indicators ( n = 7), blood gas indicators ( n = 3), tumor markers indicators ( n = 3),coagulation related indicators ( n = 4), and nutrition and organ function indicators ( n = 6). ① The key indicators of blood gas in patients died of pneumonia Cl - was 97-111 mmol/L in 51.6% (33 cases) of patients, lactic acid (Lac) was 0.5-2.5 mmol/L in 81.2% (52 cases) of patients, and H + was 0-46 mmol/L in 87.5% (56 cases) of patients. ② The key indicators of blood routine of patients died of pneumonia hemoglobin count (Hb) of 46.9% (30 cases) patients was 80-109 g/L, the eosinophils proportions (EOS%) in 67.2% (43 cases) patients was 0.000-0.009, the lymphocytes proportions (LYM%) in 51.6% (33 cases) patients was 0.00-0.09, the red blood cell count (RBC) in 50.0% (32 cases) patients was (3.0-3.9)×10 12/L, the white blood cell count (WBC) in 54.7% (35 cases) patients was (0.0-9.9)×10 9/L, and the red blood cell volume distribution width coefficientof variability (RDW-CV) in 48.4% (31 cases) patients was 10.0%-14.9%, serum C-reactive protein (CRP) was 0.0-49.9 mg/L in 48.4% (31 cases) patients. ③ The key indicators of tumor markers in patients died of pneumonia 76.6% (49 cases) of patients had negative free prostate specific antigen/total prostate specific antigen (FPSA/TPSA, the ratio was 0), 92.2% (59 cases) had cytokeratin 19 fragment (CYFRA21-1) between 0.0-11.0 μg/L, and 75.0% (48 cases) had carbohydrate antigen 125 (CA125) between 0-104 kU/L.④ The key coagulation indexes of patients died of pneumonia 68.8% (44 cases) of patients had activated partial thromboplastin time (APTT) of 57-96 s, 73.4% (47 cases) of patients had D-dimer of 0-6 mg/L, 93.8% (60 cases) of patients had thrombin time (TT) of 14-22 s, and 89.1% (57 cases) of patients had adenosine diphosphate (ADP) inhibition rate of 0%-53%. ⑤ Nutrition and organ function key indicatorsin patients died of pneumonia 92.2% (59 cases) of brain natriuretic peptide (BNP) in patients with 0, 46.9% (30 cases) of patients had prealbumin (PA) of 71-140 mg/L, 90.6% (58 cases) of the patients with uric acid (UA) for 21-41 μmol/L, 75.0% (48 cases) of the patients with albumin (Alb) to 10-20 g/L, 93.5% (60 cases) of patients had albumin/globulin ratio (A/G ratio) of 0-0.9, 84.4% (54 cases) of the patients with lactate dehydrogenase (LDH) from 0-6.68 μmol/L·s -1·L -1. ⑥ Logistic regression analysis and ROC curve analysis: Logistic regression analysis showed that PA and Lac were the prognostic factors. PA could reduce the risk of death by 0.9%, Lac could increase the risk of death by 69.4%; the area under ROC curve (AUC) between laboratory indicators and the prediction effect of death prediction model for patients' prognosis was 0.80, which showed that the classification effect was better, and this study model could better predict the prognosis of elderly patients with pneumonia. Conclusion:By using big data technology, 23 core indicators for evaluating the prognosis of elderly patients with pneumonia can be screened from the clinical database of emergency ward, which provides a new perspective and method for clinical evaluation of the prognosis of elderly patients with pneumonia.