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.

Objective The aim of this study was to evaluate whether PRDM16 gene polymorphisms were associated with dyslipidemia. Methods The polymorphisms of rs2651899, rs2236518, rs870171, and rs2282198 in PRDM16 gene in 528 participants were genotyped by the method of snapshot or ligase detection reaction. The genotype differences and the allele differences between the case group and the control group were analyzed. Linkage disequilibrium analysis was performed with SHE-sis online software. The interaction between rs2651899, rs2236518, rs870171, rs2282198 and gender, age, BMI were analyzed by MDR software. Results The frequency of allele A in rs2651899 locus was significantly higher in low HDL-C group compared with that in control group[OR(95%CI)=1.32(1.02-1.71), P=0.033]. The frequency of A/C genotype in rs870171 was significantly different between LDL-C abnormal group and control group[OR(95% CI)=1.97(1.01-3.86), P=0.037]. There may be interaction between rs2236518 and sex, which is a risk factor for low HDL-C[Model Ⅱ: OR(95% CI)=1.958(1.366-2.809), P<0.01]. There may be interactions among rs2651899, rs2236518, rs870171, and rs2282198, which seemed to be risk factors for lower HDL-C[Model Ⅳ: OR(95% CI)=3.991(2.707-5.884), P<0.01]. rs870171, rs2282198 may have interaction with age, which is a risk factor for high LDL-C [Model Ⅶ: OR(95%CI)=3.991(2.707-5.884), P<0.01]. Conclusion Allele A of rs2651899 may be a risk factor to low HDL-C. Under the codominant inheritance patterns, genotype A/C of rs870171 may be a risk factor to high LDL-C. In addition, there may be interaction between SNPs with gender and age.

Biomarkers ; Breast Neoplasms ; Breast ; Drug Therapy ; Humans ; Neoadjuvant Therapy ; Neoplasm, Residual ; Pathology ; Prognosis ; United States Food and Drug Administration

Purpose: Combining targeted agents with adjuvant chemotherapy prolongs survival in human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients, but also increases the risk of adverse effects. The updated results of 3 randomized controlled trials (RCTs) were reported in 2019. Given the lack of adequate head-to-head pairwise assessment for anti-HER2 agents, network meta-analysis facilitates obtaining more precise inference for evidence-based therapy. Methods: RCTs comparing at least 2 anti-HER2 regimens in an adjuvant setting for HER2-positive early-stage breast cancer (EBC) were included. Hazard ratios for overall survival (OS) and disease free survival (DFS), with respective 95% confidence intervals were pooled for assessment of efficacy. A Bayesian statistical model was used, and odds ratios (ORs) for adverse events (AEs) were used to pool effect sizes. Results: We demonstrated that 1-year trastuzumab plus chemotherapy had increased efficacy compared to shorter or longer treatment duration. The OR of cardiac events gradually increased from 6 months to 1 and 2-year trastuzumab arms, relative to chemotherapy only.Compared to trastuzumab plus chemotherapy, dual HER2-targeting therapies increased DFS, especially for hormone receptor negative patients. Dual anti-HER2 blockade regimens revealed an increased probability of gastrointestinal reactions. As a second agent, pertuzumab showed significantly higher DFS and OS. Conclusion We conclude that 1-year adjuvant trastuzumab should remain as the standard treatment for HER2-positive EBC patients, as it has greater efficacy and a manageable proportion of AEs. Clinical efficacy can be increased for hormone receptor-negative tumors by including a second HER2-targeted agent to the treatment regimen. For hormone receptorpositive cases with basal disease, it is acceptable to reduce the risk of cardiotoxicity by shortening the duration of trastuzumab.