Risk Factors For Pulmonary Infection Research Articles

Development and validation of a predictive model for pulmonary infection risk in patients with traumatic brain injury in the ICU: a retrospective cohort study based on MIMIC-IV

ObjectiveTo develop a nomogram for predicting occurrence of secondary pulmonary infection in patients with critically traumatic brain injury (TBI) during their stay in the intensive care unit, to further optimise...

Analysis of Risk Factors for Pulmonary Infection in Patients with Acute Leukemia after Chemotherapy

To investigate the risk factors of pulmonary infection in patients with acute leukemia (AL) after chemotherapy. A total of 294 patients with AL were collected and divided into infection group (n=93) and control group (n=201) according to whether the pulmonary infection occurred after chemotherapy. Analyze the correlation between sociodemographic data (sex, age, BMI), clinical data (disease type, ECOG score, invasive procedure, underlying disease, hormone therapy, empirical use of antibiotics, prognosis stratification, chemotherapy intensity, primitive cell count, white blood cell count, neutrophil count, duration of granulocyte deficiency, platelet count, hemoglobin, and albumin and pulmonary infection after chemotherapy. COX regression method was used to analyze the risk factors of pulmonary infection in AL patients after chemotherapy. Among 294 patients with AL, 11 died within 30 days after pulmonary infection. There were statistically significant differences in age, smoking history, ECOG score, invasive procedure, hormone therapy, empirical use of antibiotics, prognosis stratification, chemotherapy intensity, primitive cell count, neutrophil count, duration of granulocyte deficiency, platelet count, hemoglobin, albumin and fasting blood glucose between the 2 groups (P <0.05). COX regression analysis showed that smoking history, invasive procedure, unexperienced use of antibiotics, poor prognosis, long duration of granulocytopenia, low platelet level and low albumin were high risk factors for pulmonary infection in AL patients after chemotherapy (P <0.05). Smoking, invasive procedures, unexperienced use of antibiotics, poor prognosis, long duration of granulodeficiency, low platelet levels and low albumin are risk factors for pulmonary infection in AL patients after chemotherapy.

Construction of a prediction model for pulmonary infection and its risk factors in Intensive Care Unit patients.

To identify independent risk factors of pulmonary infection in intensive care unit (ICU) patients, and to construct a prediction model. Medical data of 398 patients treated in the ICU of Jiaxing Hospital of Traditional Chinese Medicine from January 2019 to January 2023 were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for pulmonary infection in ICU patients. R software was used to construct a nomogram prediction model, and the prediction model was internally validated using computer simulation bootstrap method. Predictive value of the model was analyzed using the receiver operating characteristic (ROC) curve. A total of 97 ICU patients (24.37%) developed pulmonary infection. Age, ICU stay time, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness were all identified as risk factors for pulmonary infection. The calibration curve of the constructed nomogram prediction model showed a good consistency between the predicted value of the model and the actual observed value. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.784 (95% CI: 0.731-0.837), indicating a certain predictive value. Age, length of stay in ICU, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness are risk factors for pulmonary infection in ICU patients. The nomogram prediction model constructed based on the above risk factors has shown a good predictive value.

Predicting the risk of pulmonary infection in patients with chronic kidney failure: A-C2GH2S risk score-a retrospective study.

The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients. This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855-0.927) and a Brier score of 0.012. Furthermore, the Hosmer-Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856-0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5. A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification.

Prediction of pulmonary infection in patients with severe myelitis by NPAR combined with spinal cord lesion segments.

To investigate the risk factors of pulmonary infection in patients with severe myelitis and construct a prediction model. The clinical data of 177 patients with severe myelitis at admission from the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2022 were retrospectively analyzed. The predicting factors associated with pulmonary infection were screened by multivariate logistic regression analysis, and the nomogram model was constructed, and the predictive efficiency of the model was evaluated, which was verified by calibration curve, Hosmer-Lemeshow goodness-of-fit test and decision curve analysis. Of the 177 patients with severe myelitis, 38 (21.5%) had pulmonary infection. Multivariate logistic regression analysis showed that neutrophil percentage to albumin ratio (NPAR) (OR = 6.865, 95%CI:1.746-26.993, p = 0.006) and high cervical cord lesion (OR = 2.788, 95%CI:1.229-6.323, p = 0.014) were independent risk factors for pulmonary infection, and the combined nomogram could easily predict the occurrence of pulmonary infection, with a C-index of 0.766 (95% CI: 0.678-0.854). The calibration curve, Hosmer-Lemeshow goodness-of-fit test (χ2 = 9.539, p = 0.299) and decision curve analysis showed that the model had good consistency and clinical applicability. The nomogram model constructed based on NPAR combined with high cervical cord lesion at admission has good clinical application value in predicting pulmonary infection in patients with severe myelitis, which is conducive to clinicians' evaluation of patients.

Analysis of risk factors for pulmonary infection in acute ischemic stroke patients following intravenous thrombolysis with alteplase.

To identify the risk factors for pulmonary infection in acute ischemic stroke patients treated with intravenous thrombolysis using alteplase. A retrospective analysis was conducted on 110 acute ischemic stroke patients who received intravenous alteplase thrombolysis between January 2019 and November 2022. The patients were categorized into a pulmonary infection group (40 cases) and a non-infection group (70 cases). Multivariate logistic regression analysis identified the following independent risk factors for pulmonary infection: age, National Institutes of Health Stroke Scale (NIHSS) score at admission, underlying lung disease, hypertension, mechanical ventilation, aspiration, confusion, and elevated C-reactive protein (CRP) levels (all P<0.05). The sensitivity and specificity of CRP ifor predicting pulmonary infection were 88.57% and 75.00%, respectively. The NIHSS score demonstrated a sensitivity of 87.14% and a specificity of 70.00%. Further stratification of patients into a good prognosis group (75 cases) and a poor prognosis group (35 cases) revealed that high NIHSS scores at admission, increased fibrinogen (FIB) levels, a thrombolysis window exceeding 3 hours, and concurrent pulmonary infection were independent risk factors for poor prognosis. The area under the ROC curve for NIHSS in predicting prognosis was 0.890, and for FIB, it was 0.854 (P<0.001). The sensitivity and specificity of NIHSS for predicting poor prognosis were 89.33% and 82.86%, respectively, while for FIB, they were 84.00% and 82.86%. These findings indicate that factors such as age, NIHSS score, underlying lung disease, hypertension, and elevated CRP levels significantly contribute to the risk of pulmonary infection in acute ischemic stroke patients. Clinicians should closely monitor these values to manage the risk of pulmonary infection effectively.

Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model.

This study analyzes the distribution of pathogenic bacteria and their antimicrobial susceptibilities in elderly patients with cardiovascular diseases to identify risk factors for pulmonary infections. A risk prediction model is established, aiming to serve as a clinical tool for early prevention and management of pulmonary infections in this vulnerable population. A total of 600 patients were categorized into infected and uninfected groups. Independent risk factors such as older age, diabetes history, hypoproteinemia, invasive procedures, high cardiac function grade, and a hospital stay of ≥10 days were identified through logistic regression. A predictive model was constructed, with a Hosmer-Lemeshow goodness of fit (P = 0.236) and an area under the receiver operating characteristic curve of 0.795, demonstrating good discriminative ability. The model had 63.40% sensitivity and 82.80% specificity, with a cut-off value of 0.13. Our findings indicate that the risk score model is valid for identifying high-risk groups for pulmonary infection among elderly cardiovascular patients. The study contributes to the early prevention and control of pulmonary infections, potentially reducing infection rates in this vulnerable population.

Risk Factors for Pulmonary Infection and Nursing Interventions Post-Tracheostomy in Patients with Spinal Cord Injury.

We analyzed the characteristics and risk factors for pulmonary infection in patients with spinal cord injury who underwent tracheostomy and propose measures to help in early detection and intervention to reduce mortality and improve prognosis. We collected data retrospectively from January 1, 2018, to December 31, 2022. The inclusion criteria were: Patients aged 18 years or more with a spinal cord injury who underwent tracheostomy, were treated with mechanical ventilation for over 48 hours, and were diagnosed as having a pulmonary infection. Sputum samples were cultured and analyzed. 101 cases of pulmonary infection were analyzed, and the incidence was 32.17%. Diabetes (OR 2.302, 95% CI 1.285-3.972), hypoproteinemia (OR 1.992, 95% CI 1.125-3.101), administration of glucocorticoids (OR 2.934, 95% CI 1.412-4.661), ASIA grade A (OR 3.672, 95% CI 1.988-5.046), mechanical ventilation for ≥ 6 days (OR 2.108, 95% CI 1.385-4.751), and length of hospital stay for ≥ 20 days (OR 2.137, 95% CI 1.092-3.842) were risk factors for pulmonary infection in patients with spinal cord injury post-tracheostomy. Among 213 pathogenic bacteria, 52 (51.48%) were Gram-negative and 24 (23.76%) were Gram-positive. Klebsiella pneumoniae (15.84%) and Staphylococcus aureus (8.91%) were the most common pathogenic bacteria. The mortality rate of patients with gram-positive infection was higher than that of patients with gram-negative infection. K. pneumoniae and S. aureus were sensitive to cefoperazone, meropenem, and levofloxacin. Pulmonary infection is a complication post-tracheostomy in patients with spinal cord injury. Diabetes, hypoproteinemia, administration of glucocorticoids, mechanical ventilation for ≥ 6 days, length of hospital stay for ≥ 20 days were risk factors for pulmonary infection. Pulmonary infection was mainly caused by gram-negative bacteria. Timely and effective measures for managing risk factors are essential for improving the prognosis of pulmonary infection post-tracheostomy in patients with spinal cord injuries.

Factors associated with pulmonary infection in kidney and kidney-pancreas transplant recipients: a case-control study.

To evaluate the etiology of and factors associated with pulmonary infection in kidney and kidney-pancreas transplant recipients. This was a single-center case-control study conducted between December of 2017 and March of 2020 at a referral center for kidney transplantation in the city of Belo Horizonte, Brazil. The case:control ratio was 1:1.8. Cases included kidney or kidney-pancreas transplant recipients hospitalized with pulmonary infection. Controls included kidney or kidney-pancreas transplant recipients without pulmonary infection and matched to cases for sex, age group, and donor type (living or deceased). A total of 197 patients were included in the study. Of those, 70 were cases and 127 were controls. The mean age was 55 years (for cases) and 53 years (for controls), with a predominance of males. Corticosteroid use, bronchiectasis, and being overweight were associated with pulmonary infection risk in the multivariate logistic regression model. The most common etiologic agent of infection was cytomegalovirus (in 14.3% of the cases), followed by Mycobacterium tuberculosis (in 10%), Histoplasma capsulatum (in 7.1%), and Pseudomonas aeruginosa (in 7.1%). Corticosteroid use, bronchiectasis, and being overweight appear to be risk factors for pulmonary infection in kidney/kidney-pancreas transplant recipients, endemic mycoses being prevalent in this population. Appropriate planning and follow-up play an important role in identifying kidney and kidney-pancreas transplant recipients at risk of pulmonary infection.

Risk Factors for Post-Operative Pulmonary Infection in Patients With Brain Tumors: A Systematic Review and Meta-Analysis.

Background: This study aims to analyze the risk factors for post-operative pulmonary infection in patients with brain tumors by meta-analysis to provide a reference for its prevention. Methods: PubMed, Embase, Web of Science, Cochrane Library, Ovid, and four Chinese databases (CNKI, SinoMed, VIP, and Wanfang databases) were searched for studies covering risk factors of pulmonary infection in patients with brain tumors, limited to the duration from the dates of inception of the respective databases to December 31, 2022. The Newcastle-Ottawa scale was used to assess the evidence. A meta-analysis of the factors affecting the incidence of pulmonary infection was performed using Revman 5.4 software. Results: Twelve studies were selected, covering 35,615 patients with brain tumors, among whom pulmonary infection occurred in 1,635 cases with an accumulated incidence of 4.6%, including 38 related risk factors. Meta-analysis results indicated: history of chronic pulmonary disease (odds ratio [OR], 5.74; 95% confidence interval [CI], 1.34-24.51; p = 0.02], diabetes mellitus (OR, 1.58; 95% CI, 1.29-1.95; p < 0.0001), history of cardiovascular disease (OR, 3.97; 95% CI, 2.18-7.24; p < 0.00001), age ≥60 years (OR, 1.55; 95% CI, 1.12-2.15; p = 0.009)], operation time ≥3 hours (OR, 1.03; 95% CI, 1.00-1.05; p = 0.03], Glasgow Coma Scale (GCS) score <13 (OR, 3.5; 95% CI, 1.90-6.46; p < 0.0001), and the American Society of Anesthesiologists classification (ASA) ≥3 (OR, 2.03; 95% CI, 1.68-2.46; p < 0.00001) as independent risk factors. Conclusions: History of chronic pulmonary disease, diabetes mellitus, history of cardiovascular disease, age ≥60 years, operation time ≥3 hours, GCS score <13, and the ASA grade ≥3 are independent risk factors for post-operative pulmonary infection in patients with brain tumors, which nursing staff should be aware of.

Retracted: Clinical Characteristics and Risk Factors for Pulmonary Infection in Emergency ICU Patients.

[This retracts the article DOI: 10.1155/2022/7711724.].

Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma.

There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC. To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management. We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses. Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively. Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.

Factors influencing pulmonary infection in elderly neurocritical patients and their predictive values: a data analysis for consecutive four-year

To analyze the factors influencing pulmonary infections in elderly neurocritical patients in the intensive care unit (ICU) and to explore the predictive value of risk factors for pulmonary infections. The clinical data of 713 elderly neurocritical patients [age ≥ 65 years, Glasgow coma score (GCS) ≤ 12 points] admitted to the department of critical care medicine of the Affiliated Hospital of Guizhou Medical University from 1 January 2016 to 31 December 2019 were retrospectively analyzed. According to whether or not they had HAP, the elderly neurocritical patients were divided into hospital-acquired pneumonia (HAP) group and non-HAP group. The differences in baseline data, medication and treatment, and outcome indicators between the two groups were compared. Logistic regression analysis was used to analyze the factors influencing the occurrence of pulmonary infection. The receiver operator characteristic curve (ROC curve) was plotted for risk factors and a predictive model was constructed to evaluate the predictive value for pulmonary infection. A total of 341 patients were enrolled in the analysis, including 164 non-HAP patients and 177 HAP patients. The incidence of HAP was 51.91%. According to univariate analysis, compared with the non-HAP group, mechanical ventilation time, the length of ICU stay and total hospitalization in the HAP group were significantly longer [mechanical ventilation time (hours): 171.00 (95.00, 273.00) vs. 60.17 (24.50, 120.75), the length of ICU stay (hours): 263.50 (160.00, 409.00) vs. 114.00 (77.05, 187.50), total hospitalization (days): 29.00 (13.50, 39.50) vs. 27.00 (11.00, 29.50), all P < 0.01], the proportion of open airway, diabetes, proton pump inhibitor (PPI), sedative, blood transfusion, glucocorticoids, and GCS ≤ 8 points were significantly increased than those in HAP group [open airway: 95.5% vs. 71.3%, diabetes: 42.9% vs. 21.3%, PPI: 76.3% vs. 63.4%, sedative: 93.8% vs. 78.7%, blood transfusion: 57.1% vs. 29.9%, glucocorticoids: 19.2% vs. 4.3%, GCS ≤ 8 points: 83.6% vs. 57.9%, all P < 0.05], prealbumin (PA) and lymphocyte count (LYM) decreased significantly [PA (g/L): 125.28±47.46 vs. 158.57±54.12, LYM (×109/L): 0.79 (0.52, 1.23) vs. 1.05 (0.66, 1.57), both P < 0.01]. Logistic regression analysis showed that open airway, diabetes, blood transfusion, glucocorticoids and GCS ≤ 8 points were independent risk factors for pulmonary infection in elderly neurocritical patients [open airway: odds ratio (OR) = 6.522, 95% confidence interval (95%CI) was 2.369-17.961; diabetes: OR = 3.917, 95%CI was 2.099-7.309; blood transfusion: OR = 2.730, 95%CI was 1.526-4.883; glucocorticoids: OR = 6.609, 95%CI was 2.273-19.215; GCS ≤ 8 points: OR = 4.191, 95%CI was 2.198-7.991, all P < 0.01], and LYM, PA were the protective factors for pulmonary infection in elderly neurocritical patients (LYM: OR = 0.508, 95%CI was 0.345-0.748; PA: OR = 0.988, 95%CI was 0.982-0.994, both P < 0.01). ROC curve analysis showed that the area under the ROC curve (AUC) for predicting HAP using the above risk factors was 0.812 (95%CI was 0.767-0.857, P < 0.001), with a sensitivity of 72.3% and a specificity of 78.7%. Open airway, diabetes, glucocorticoids, blood transfusion, GCS ≤ 8 points are independent risk factors for pulmonary infection in elderly neurocritical patients. The prediction model constructed by the above mentioned risk factors has certain predictive value for the occurrence of pulmonary infection in elderly neurocritical patients.

Retracted: A Case-Control Study on Risk Factors of Pulmonary Infection in Patients with Type 2 Diabetes Mellitus and Its Implications for Clinical Intervention.

[This retracts the article DOI: 10.1155/2022/4492574.].

Pathogen Distribution,Imaging Characteristics,and Establishment and Verification of Risk Prediction Model of Pulmonary Infection with Multi-drug Resistant Organism in Patients with Severe Craniocerebral Injury

Objective To investigate the pathogen distribution,imaging characteristics,and risk factors of pulmonary infection with multi-drug resistant organism (MDRO) in patients with severe craniocerebral injury,and establish and verify the risk prediction model. Methods A total of 230 patients with severe craniocerebral injury complicated with pulmonary infection were collected retrospectively.According to the 7∶3 ratio,they were randomly assigned into a modeling group (161 patients) and a validation group (69 patients).The risk factors of MDRO pulmonary infection were predicted with the data of the modeling group for the establishment of the risk prediction model.The data of the validation group was used to validate the performance of the model. Results Among the 230 patients,68 patients developed MDRO pulmonary infection.The isolated drug-resistant bacteria mainly included multi-drug resistant Acinetobacter baumannii,multi-drug resistant Klebsiella pneumoniae,multi-drug resistant Pseudomonas aeruginosa,and methicillin-resistant Staphylococcus aureus,which accounted for 45.21%,23.29%,16.44%,and 15.07%,respectively.The imaging characteristics included pleural effusion,lung consolidation,and ground-glass shadow,which accounted for 72.06%,63.24%,and 45.59%,respectively.Multivariate Logistic regression analysis showed that the independent risk factors for MDRO pulmonary infection included age ≥60 years (P=0.003),history of diabetes (P=0.021),history of chronic obstructive pulmonary disease (P=0.038),mechanical ventilation ≥7 d (P=0.001),transfer from other hospitals (P=0.008),and coma (P=0.002).A risk scoring model was established with the β value (rounded to the nearest integer) corresponding to each index in the regression equation.Specifically,the β values of age ≥60 years,history of diabetes,history of chronic obstructive pulmonary disease,mechanical ventilation ≥7 d,transfer from other hospitals,and coma were 1,1,1,2,2,and 1,respectively (value ≥4 indicated a high-risk population).The areas under the receiver operating characteristic curve of the modeling group and validation group were 0.845 and 0.809,respectively. Conclusions Multi-drug resistant Acinetobacter baumannii is the most common pathogen of MDRO pulmonary infection in patients with severe craniocerebral injury.Pleural effusion,lung consolidation,and ground-glass shadow were the most common imaging characteristics.The established risk model has high discriminant validity in both the modeling group and the validation group.

Analysis of Microbiological and Clinical Characteristics of Bacterial Infection in Patients with Pulmonary Infection.

Objective Using data investigation, the microbiology of bacterial infection in patients with pulmonary infection was discussed, and its clinical characteristics were analyzed. Methods The clinical data of 160 patients with pulmonary infection in our hospital from March 2019 to March 2021 were collected and analyzed. Blood samples were collected and cultured, and the pathogens were identified. The distribution, constituent ratio, and drug resistance of pathogens in elderly patients with pulmonary infection were analyzed. Logistics regression analysis was adopted to analyze the risk factors of pulmonary infection. Results Of the 160 patients with pulmonary infection, 107 were males (66.88%) and 53 were females (33.13%). The age ranged from 12 to 97 years old, with an average of 63.82 ± 12.64 years old. Sevent-six patients (47.50%) were over 65 years old. Urban patients accounted for 71.88%, and rural patients accounted for 28.13%, of which workers accounted for 46.25%, and farmers and cadres each accounted for about 4%. 85.62% of smokers have smoked for more than 4 years. Eighty-five patients had chronic diseases such as coronary heart disease, hypertension, diabetes, and cerebrovascular disease. Heart failure occurred in 10.00%, old tuberculosis in 11.25%, and new tuberculosis in 5.63%. The average hospital stay of the patients was 14.93 days, and the improvement rate was 91.25%. Eleven patients died. Among the 160 patients with pulmonary infection, COPD, pneumonia, and lung cancer accounted for the highest proportions, and idiopathic pulmonary fibrosis, bronchitis dilatation, tuberculosis, and bronchial asthma also played an important role. Pathogenic bacteria were detected in 104 of the 160 elderly patients with pulmonary infection, and the detection rate was 65.00%. A total of 444 strains of pathogenic bacteria were detected, including 328 strains of Gram-negative bacteria (73.87%, mainly Klebsiella pneumoniae, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, and Serratia marcescens), 28 strains of Gram-positive bacteria (6.30%, mainly Staphylococcus aureus), and 88 strains of fungi (20.00%, mainly Candida albicans). Regarding Klebsiella pneumoniae in elderly patients with pulmonary infection, the drug resistance rates were 59.72% for amoxicillin-clavulanate potassium, 52.78% for ampicillin sodium-sulbactam sodium, and 51.39% for cefazolin sodium. Regarding Pseudomonas aeruginosa, the drug resistance rates were 29.31% for ticarcillin sodium-potassium clavulanate, 27.59% for piperacillin sodium, and 24.14% for gentamicin. Regarding Stenotrophomonas maltophilia, the drug resistance rates were 79.55% for ceftazidime, 38.64% for chloramphenicol, and 31.82% for levofloxacin. Regarding Serratia marcescens, the drug resistance rates from high to low were 74.42% for cefotaxime, 72.09% for moxifloxacin, and 69.77% for gentamicin. Regarding Staphylococcus aureus in elderly patients with pulmonary infection, the drug resistance rates were 100.00% for penicillin, 61.54% for erythromycin, 61.54% for clarithromycin, and 61.54% for azithromycin. Regarding Candida albicans, the drug resistance rates from high to low were 22.41% for caspofungin, 15.52% for itraconazole, and 9.09% for fluconazole. The results of univariate analysis of pulmonary bacterial infection indicated that there were no significant differences in sex and body mass index between nonbacterial infection group and bacterial infection group (P > 0.05). There were significant differences in terms of dust or harmful gas exposure, family member smoking, chronic lung disease history, age, smoking, family cooking, hospital stay, and indwelling catheter (P < 0.05). Exposure to dust or harmful gases, family cooking, age, history of chronic lung disease, indwelling catheter, and length of hospital stay were risk factors for pulmonary bacterial infection (P < 0.05). Conclusion Gram-negative bacteria are the main pathogens in elderly patients with pulmonary infection. Antibiotics should be administered reasonably according to the results of the drug sensitivity test. Older age, history of chronic lung disease, catheter indwelling, and length of stay are the risk factors for pulmonary bacterial infection.

Clinical Characteristics and Risk Factors for Pulmonary Infection in Emergency ICU Patients.

Background Pulmonary infection in the emergency ICUs increases patient morbidity, hospital stay, treatment costs, and the risk of related adverse events. Methods This study included 695 patients admitted to our emergency ICU between December 2019 and March 2021. Medical records of emergency ICU patients were reviewed to collect their clinical data, including antibiotic use, history of tracheostomy, history of mechanical ventilation, presence or absence of underlying disease, history of smoking, alcohol consumption, age, gender, and history of shock. Bacterial cultures were performed. The incidence, main clinical features, main pathogens, and risk factors of pulmonary infection in emergency ICU were analyzed. Results In this study, 69 of the 695 emergency ICU patients (9.93%) developed pulmonary infection. The main clinical features of patients with pulmonary infection included cough and expectoration (97.10%), shortness of breath and chest tightness (95.65%), leukocyte elevation (69.57%), confusion (31.88%), drowsiness (28.99%), persistent fever (27.54%), and nausea and vomiting (10.14%). The main pathogenic bacteria in those with pulmonary infection included Klebsiella pneumoniae (62.32%), Pseudomonas aeruginosa (49.28%), Streptococcus pneumoniae (21.74%), Staphylococcus aureus (39.13%), Candida albicans (7.25%), Pneumococcus pneumoniae (15.95%), Pseudomonas aeruginosa (24.64%), and lung diplococcus inflammatory (13.04%). Univariate analysis showed that there were no significant differences in the occurrence of pulmonary infection with regard to sex, smoking, and alcohol consumption, but there were significant differences with regard to age, basic disease, invasive surgery, and shock. Logistic regression analysis confirmed that age ≥ 80 years, invasive surgery, shock, and basic diseases ≥ 2 were important risk factors for pulmonary infection in emergency ICU patients. Conclusion Considering the clinical features and risk factors for pulmonary infection in the emergency ICU, preventive and control measures are required to minimize its occurrence and ensure good outcomes.

A Case-Control Study on Risk Factors of Pulmonary Infection in Patients with Type 2 Diabetes Mellitus and Its Implications for Clinical Intervention.

Objective To analyze the risk factors of pulmonary infection in patients with type 2 diabetes mellitus (T2DM) and its implications for clinical intervention. Methods One hundred and twenty-five patients with type 2 diabetes treated in our hospital from January 2019 to November 2021 were divided into simple T2DM group (n = 80) and infection group (n = 45) according to whether they were complicated with pulmonary infection or not. Sputum samples of patients with infection were collected and identified by bacterial culture. The general conditions (age, sex, body mass index, course of disease, and length of stay), pulmonary complications (chronic bronchitis, emphysema, and obstructive pulmonary disease,), blood glucose control (fasting blood glucose and glycosylated hemoglobin), and treatment (use of hormones and antibiotics and invasive operation) were compared between the two groups. Univariate and multivariate analyses were used to screen the risk factors of pulmonary infection in patients with T2DM. Results A total of 45 patients were found to be infected in this study. 68 pathogenic bacteria were detected in the sputum samples, of which 42 were Gram-negative (61.76%), 22 were Gram-positive (35.35%), and 4 were fungi (5.88%). Gram-negative bacteria were mainly Klebsiella pneumoniae, accounting for 25.00%, followed by Pseudomonas aeruginosa, Acinetobacter baumannii, and Escherichia coli. Gram-positive bacteria were mainly Staphylococcus aureus, accounting for 17.65%, followed by Streptococcus pneumoniae and Staphylococcus haemolyticus. The main fungi were Candida albicans (4.41%). The age, the course of T2DM, and the duration of hospitalization in the coinfection group were significantly higher than those in the T2DM group (P < 0.05). There was no significant difference in other indexes (P > 0.05). The number of patients with chronic bronchitis, emphysema, and obstructive pulmonary disease in the coinfection group was significantly higher than that in the T2DM group. The fasting blood glucose and glycosylated hemoglobin in the coinfection group were significantly higher than those in the T2DM group. The number of patients using hormone and antimicrobial agents and invasive operation in the coinfection group was higher than that in the simple T2DM group, and the difference was statistically significant (P < 0.05). Multivariate analysis showed that age, course of T2DM, length of hospital stay, complicated pulmonary disease, glycosylated hemoglobin, use of hormones and antibiotics, and invasive operation were all risk factors of pulmonary infection in patients with T2DM (P < 0.05). Conclusion Gram-negative bacteria are the main pathogens of T2DM complicated with pulmonary infection. Drug sensitivity test should be combined to understand the drug resistance of pathogenic bacteria and use drugs reasonably to patients. Among them, advanced age, long course of T2DM, long hospital stay, complicated pulmonary disease, high level of glycosylated hemoglobin, use of hormones and antibiotics, and invasive operation were all risk factors of pulmonary infection in patients with T2DM. In clinical treatment, under the premise of using insulin to control blood sugar in an appropriate range, antibiotics should be used reasonably, pulmonary complications should be treated actively, pulmonary ventilation function should be improved, and invasive operation should be avoided as far as possible, which can effectively prevent the occurrence of T2DM complicated with pulmonary infection.

Study on the Risk Factors of Pulmonary Infection after Laparoscopic Surgery and Analysis of the Detection Results of Drug-Resistant Bacteria.

This study aimed to investigate and analyze the risk of pulmonary infection after laparoscopic surgery and the detection results of drug-resistant bacteria. With the laparoscopic technology developing rapidly in recent years and people's minimally invasive concept improving continuously, laparoscopic radical surgery has been widely used in the treatment of a variety of diseases. Laparoscopic surgery has the probability of causing complications. In order to avoid this, the risk factors after surgery were analyzed, and the drug-resistant bacteria were analyzed for accurate prevention and treatment. A total of 600 patients who underwent elective laparoscopic surgery in our hospital from January 2017 to September 2021 were included in the study. The risk factors and pathogen distribution of pulmonary infection were analyzed. The risk factors of pulmonary infection after laparoscopic surgery were hypoproteinemia, diabetes mellitus, pulmonary disease history, and perioperative blood transfusion. The main pathogens were Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Streptococcus pneumoniae. In clinical work, relevant nursing intervention measures can be developed for the above factors, so as to reduce the incidence of pulmonary infection. This study finds the risk factors for pulmonary infection after surgery, and the common drug-resistant bacteria has an indicative and guiding effect on the formulation of nursing management measures.

Added value of systemic inflammation markers in predicting pulmonary infection in stroke patients: A retrospective study by machine learning analysis.

Exploring candidate markers to predict the clinical outcomes of pulmonary infection in stroke patients have a high unmet need. This study aimed to develop machine learning (ML)-based predictive models for pulmonary infection.Between January 2008 and April 2021, a retrospective analysis of 1397 stroke patients who had CT angiography from skull to diaphragm (including CT of the chest) within 24 hours of symptom onset. A total of 21 variables were included, and the prediction model of pulmonary infection was established by multiple ML-based algorithms. Risk factors for pulmonary infection were determined by the feature selection method. Area under the curve (AUC) and decision curve analysis were used to determine the model with the best resolution and to assess the net clinical benefits associated with the use of predictive models, respectively.A total of 889 cases were included in this study as a training group, while 508 cases were as a validation group. The feature selection indicated the top 6 predictors were procalcitonin, C-reactive protein, soluble interleukin-2 receptor, consciousness disorder, dysphagia, and invasive procedure. The AUCs of the 5 models ranged from 0.78 to 0.87 in the training cohort. When the ML-based models were applied to the validation set, the results also remained reconcilable, and the AUC was between 0.891 and 0.804. The decision curve analysis also showed performed better than positive line and negative line, indicating the favorable predictive performance and clinical values of the models.By incorporating clinical characteristics and systemic inflammation markers, it is feasible to develop ML-based models for the presence and consequences of signs of pulmonary infection in stroke patients, and the use of the model may be greatly beneficial to clinicians in risk stratification and management decisions.