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Background: Community-acquired pneumonia (CAP) is a common cause of hospitalization, characterized by high mortality, morbidity, and cost and has serious human health implications. Various scoring criteria have been used to predict the severity of pneumonia, including the CURB-65 score, Pneumonia Severity Index (PSI) score, and Severe Community-Acquired Pneumonia (SCAP) score. However, these scoring criteria have shortcomings such as low sensitivity, cumbersome clinical application, and limited application. This study aimed to construct a nomogram model to predict the severity of CAP patients by blood indicators., Methods: This is a retrospective study. Patients with CAP were enrolled and then tested by routine blood, coagulation series, biochemical and inflammatory indicators, and general information such as imaging findings and disease history of the patients were recorded. The main observation was the progression of the patients' disease. Univariate analysis and binary logistic regression analysis were used to explore the independent risk factors for the severity of CAP patients, followed by plotting a nomogram to obtain the prediction model and constructing calibration curves to assess the authenticity and accuracy of the prediction model. Finally, the sensitivity, specificity, and other evaluation indexes were calculated by the receiver operating characteristic curve (ROC) to evaluate the clinical application value of the nomogram prediction model., Results: Univariate analysis and binary logistic regression analysis of 277 hospitalized patients yielded platelet to lymphocyte ratio (PLR) and serum amyloid A (SAA) as independent risk factors for the severity of CAP patients. The AUC of the nomogram model for PLR and SAA was 0.889 (95% CI 0.845 - 0.932). The sensitivity was 77.3%, and the specificity was 85.3%, which had an excellent predictive value., Conclusions: The nomogram model based on PLR and SAA to predict the severity of CAP patients has a high specificity and sensitivity.

Background: The study aimed at investigating the effectiveness of the BAP-65 score combined with D-dimer and procalcitonin (PCT) in predicting admission of acute exacerbation chronic obstructive pulmonary disease (AECOPD) patients to the intensive care unit (ICU)., Methods: We conducted a retrospective study. We analyzed data from 369 patients over the age of 40 years ad-mitted to our hospital with AECOPD. All patients received blood routine measurements and BAP-65 score calculation on admission. Receiver operating characteristic curves (ROC) were used to assess the sensitivity and specificity of D-dimer, PCT, and BAP-65 scores and combined metrics in predicting the risk of admissions to the ICU of AECOPD patients., Results: We found that the percentage of patients with AECOPD admitted to the ICU was 32.25% (119/369). The area under the curve (AUC) of D-dimer, PCT, and BAP-65 score in individually predicting the probability of entering the ICU of AECOPD patients were 0.74 (95% CI 0.68 - 0.80), 0.83 (95% CI 0.78 - 0.88), and 0.72 (95% CI 0.66 - 0.79), respectively. The sensitivities of D-dimer, PCT, and BAP-65 score were 0.51, 0.65, and 0.52, respectively. The specificities of D-dimer, PCT, and BAP-65 score were 0.90, 0.91, and 0.92, respectively. The AUC of D-dimer and PCT combined with BAP-65 score was 0.90 (95% CI 0.86 - 0.94), the sensitivity and specificity were 0.90 and 0.80, respectively., Conclusions: D-dimer and procalcitonin improve the sensitivity of the BAP-65 score in predicting the probability of AECOPD patients entering the ICU while having a good specificity.

Background: The CURB-65 scoring system is a simple tool for assessment and prognosis prediction for community-acquired pneumonia (CAP) patients. However, the variations in the performance of CURB-65 in young and elderly patients, underestimation, or overestimation of the severity have often been reported. It is worth noting that the application of biomarkers is helpful for improving the accuracy of the scoring system. In recent years, more and more reports and studies paid attention to procalcitonin (PCT) in respiratory infectious diseases, and its clinical value has attracted increasing attention. The study aimed at investigating the effectiveness of the CURB-65 score combined with PCT in predicting admission of CAP patients to intensive care units (ICU)., Methods: We conducted a retrospective study. We analyzed data from 520 non-immune individuals over the age of 18 in this study. All patients received blood indicators measurement and CURB-65 score calculation on admission. The primary outcome used to assess the probability of a CAP patient was who would get a bed in general ward or ICU. Receiver operating characteristic curves (ROC) were used to evaluate the sensitivity and specificity of the CURB-65 model and PCT combined CURB-65 augmented model in predicting the main outcomes., Results: After analyzing the data from 520 patients, we found that the probability of entering the ICU was 22.1% (115/520). The AUC of Combination 1 (PCT&CURB-65 scores), Combination 2 (WBC&CURB-65 scores), Combination 3 (hs-CRP&CURB-65 scores) and Combination 4 (D-dimer&CURB-65 scores) for predicting CAP patients entering the ICU was 0.92 (95% CI 0.88 - 0.95), 0.91 (95% CI 0.87 - 0.94), 0.89 (95% CI 0.85 - 0.92), and 0.90 (95% CI 0.87 - 0.94), respectively, with statistically significant differences (p = 0.00); the sensitivities were 0.83, 0.82, 0.77 and 0.77, respectively, and the specificities were 0.92, 0.84, 0.90 and 0.91, respectively. PCT was superior to other indexes to improve the sensitivity and specificity of the CURB-65 score., Conclusions: Procalcitonin improves the accuracy and sensitivity of the CURB-65 score in predicting the probability of CAP patients entering the ICU, and PCT was superior to other indexes to improve the sensitivity and specificity of the CURB-65 score.

Background: As a serious and common out-of-hospital infectious disease, community-acquired pneumonia (CAP) ranks among the leading causes of death in both developing and developed countries. In recent years, the increasing incidence of CAP has led to an increase in the number of hospitalizations. Although CURB-65 (or CRB-65) and pneumonia severity Index (PSI) scoring systems are widely used in CAP prognostic scoring systems, each score had some limitations in predicting whether patients with CAP would require prolonged hospitalization. The aim of this study was to analyze serum inflammatory biomarkers combined with age to establish a novel predictive model for predicting prolonged hospitalization in patients with CAP., Methods: In a retrospective study, serum inflammatory biomarkers were collected from all enrolled CAP patients, including white blood cell count (WBC), high-sensitivity C-reactive protein (hs-CRP), erythrocyte sedimentation rate (ESR), D-dimer, procalcitonin (PCT), fibrinogen (FIB), and ICU treatment. Length of hospital stay and age were also recorded. The 75th percentile of length of stay in the enrolled population was defined as long hospitalization over time, and the primary predictor of outcome was prolonged hospitalization. Univariate analysis and binary logistic regression analysis were used to explore the independent risk factors which could be components of a new predicting model for prolonged hospitalization in CAP patients. ROC curves were used to evaluate the sensitivity and specificity of the new model, which consisted of the combination of all independent risk factors in predicting the main outcomes., Results: The results showed that among 364 patients with CAP, 85 had extended hospitalization (85/364). Further analysis showed that age, white blood cell, fibrinogen, and high-sensitivity C-reactive protein were independent risk factors for extended hospitalization in patients with CAP. Finally, the AUC of the ROC curve of the new prediction model (the joint model consists of age, WBC, FIB, and hs-CRP) was 0.93 (95% CI 0.90 - 0.96), and the sensitivity and specificity were 87.1% and 87.8%, respectively., Conclusions: Serum inflammatory biomarkers combined age have high specificity and sensitivity in predicting prolonged hospitalization in adult CAP patients.

Background: AECOPD is the most common cause of death among infectious diseases in developing countries, and also an important cause of mortality and morbidity in developed countries. In recent years, related scoring systems such as the mMRC score and CAT questionnaire have been widely used to assess the severity of AECOPD. However, they both have some shortcomings in predicting the admission of AECOPD patients to the ICU. This study aimed to develop a new prediction model to predict the admission of AECOPD patients to the ICU based on objective blood indicators., Methods: This was a retrospective study. Enrolled patients with AECOPD underwent blood gas analysis as well as biomarker testing for serum inflammatory markers, including white blood cell count (WBC), neutrophils, D-dimer, procalcitonin (PCT), high-sensitivity C-reactive protein (hs-CRP), and erythrocyte sedimentation rate (ESR). General characteristics such as age and gender were also recorded. The main observation was admission to the ICU. Univariate analysis and binary logistic regression analysis were used to explore independent risk factors for admission to the ICU in patients with AECOPD, which could be used as components of a new predictive model. Subject receiver operating characteristic curves (ROC) were used to assess the sensitivity and specificity of the new model, which consisted of all independent risk factors predicting the primary outcome., Results: Initially, 369 patients with AECOPD were admitted to the general ward, of which 119 were subsequently transferred to the ICU (119/369). PaCO2, WBC, D-dimer, PCT, and hs-CRP were independent risk factors for admission to the ICU in patients with AECOPD. The AUC of the new prediction model (combined model consisting of PaCO2, WBC, D-dimer, PCT, and hs-CRP) was 0.94 (95% CI 0.92 - 0.97). The sensitivity was 80.7% and the specificity was 94.8%., Conclusions: The model for predicting the admission of AECOPD patients to the ICU based on blood indicators has a high specificity and sensitivity.