Identification rate of Legionella species in non-purulent sputum culture is comparable to that in purulent sputum culture in Legionella pneumonia.

Many Legionella pneumonia patients do not produce sputum, and it is unknown whether purulent sputum is required for the identification of Legionella species. This study aimed to evaluate the identification rate of Legionella species based on sputum quality and the factors predictive of Legionella infection. This study included Legionella pneumonia patients at Kurashiki Central Hospital from November 2000 to December 2022. Sputum quality, based on gram staining, was classified as the following: Geckler 1/2, 3/6 and 4/5. Geckler 4/5 was defined as purulent sputum. The sputa of 104 of 124 Legionella pneumonia patients were cultured. Fifty-four patients (51.9%) were identified with Legionella species, most of which were Legionella pneumophila serogroup 1 (81.5%). The identification rates of Legionella species according to sputum quality were 57.1% (16/28) in Geckler 1/2 sputum, 50.0% (34/68) in Geckler 3/6 sputum, and 50.0% (4/8) in Geckler 4/5 sputum, which were not significantly different (P = 0.86). On multivariate analysis, pre-culture treatment with anti-Legionella antimicrobials (odds ratio [OR] 0.26, 95% confidence interval [CI] 0.06-0.91), Pneumonia Severity Index class ≥IV (OR 2.57 [95% CI 1.02-6.71]), and intensive care unit admission (OR 3.08, 95% CI 1.06-10.09) correlated with the ability to identify Legionella species, but sputum quality did not (OR 0.88, 95% CI 0.17-4.41). The identification rate of Legionella species in non-purulent sputum was similar to that in purulent sputum. For the diagnosis of Legionella pneumonia, sputum should be collected before administering anti-Legionella antibiotics and cultured regardless of sputum quality.

Association of gut microbiome with COPD in Japanese male residents: the SESSA study.

Background: Altered gut microbiota may contribute to COPD development or progression. Herein, we investigated the association of gut microorganisms with COPD, taking into account the impact of smoking status. Methods: This cross-sectional observational study was a part of the Shiga Epidemiological Study of Subclinical Atherosclerosis, a population-based cohort study of Japanese men aged 46-76 years, conducted from 2010 to 2016. The gut microbiome, determined using 16S rRNA gene sequencing, was compared among 99 never-smokers, 306 non-COPD ever-smokers and 76 patients with COPD while adjusting for age, body mass index, ethanol consumption and treatment for type 2 diabetes mellitus. Results: The abundance of phylum Firmicutes was comparable between patients with COPD and non-COPD ever-smokers but tended to be higher in never-smokers. Similarly, the α- and ß-diversity analysis showed similarity between patients with COPD and non-COPD ever-smokers, which tended to differ from never-smokers. Discriminant analysis identified the genus [Prevotella] to be more prevalent in patients with COPD than in never-smokers or non-COPD ever-smokers. Post hoc analysis confirmed similarity of gut microbiome between COPD Global Initiative for Chronic Obstructive Lung Disease (GOLD) I and non-COPD ever-smokers, which was different from GOLD II. Conclusion: Smoking may alter the overall gut microbial composition, but gut microbial composition itself may not play a role in the development of COPD. Rather, specific gut bacteria, such as [Prevotella], could be a risk factor for the development of COPD; this may be a potential therapeutic target.

Autobullectomy with COVID-19 in a patient with chronic obstructive pulmonary disease.

A 72-year-old man with chronic obstructive pulmonary disease (COPD) was admitted for coronavirus disease 2019 (COVID-19). He was discharged on day 30; however, he was readmitted 6 days later due to a left lung organizing pneumonia secondary to COVID-19. After methylprednisolone treatment, the patient was discharged on day 15. One year later, computed tomography showed shrinkage of emphysematous lesions, and both total lung capacity measured using computed tomography and fraction of low attenuation volume decreased in the left lung compared to that before COVID-19. Here, we report a rare case of autobullectomy with COVID-19 in a patient with COPD.

Inspiratory and Expiratory Computed Tomography Imaging Clusters Reflect Functional Characteristics in Chronic Obstructive Pulmonary Disease.

Purpose: Disease probability measure (DPM) is a useful voxel-wise imaging assessment of gas-trapping and emphysematous lesions in patients with chronic obstructive pulmonary disease (COPD). To elucidate the progression of COPD, we performed a cluster analysis using the following DPM parameters: normal (DPMNormal), gas-trapping (DPMGasTrap), and emphysematous lesions (DPMEmph). Our findings revealed the characteristics of each cluster and the 3-year disease progression using imaging parameters. Patients and Methods: Inspiratory and expiratory chest computed tomography (CT) images of 131 patients with COPD were examined, of which 84 were followed up for 3 years. The percentage of low attenuation volume (LAV%) and the square root of the wall area of a hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10) were quantitatively measured using inspiratory chest CT. A hierarchical cluster analysis was performed using the DPM parameters at baseline. Five clusters were named according to the dominant DPM parameters: normal (NL), normal-GasTrap (NL-GT), GasTrap (GT), GasTrap-Emphysema (GT-EM), and Emphysema (EM). Results: Women were predominantly diagnosed with GT. Forced expiratory volume in 1 s gradually decreased in the following order: NL, NL-GT, GT, GT-EM, and EM. DPMEmph correlated well with LAV%. Four clusters other than NL showed significantly higher values of √Aaw at Pi10 than NL; however, no significant differences were observed among them. In all clusters, DPMEmph increased after 3 years. DPMNormal only increased in the GT cluster. Conclusion: Clusters using DPM parameters may reflect the characteristics of COPD and help understand the pathophysiology of the disease.

Predictors of longitudinal changes in body weight, muscle and fat in patients with and ever-smokers at risk of COPD.

BACKGROUND AND OBJECTIVE: Weight and muscle loss are predictors of poor outcomes in chronic obstructive pulmonary disease. However, to our knowledge, no study has investigated the predictors of longitudinal weight loss or its composition from functional and morphological perspectives. METHODS: This longitudinal observational study with a median follow-up period of 5 years (range: 3.0-5.8 years) included patients with COPD and ever-smokers at risk of COPD. Using chest computed tomography (CT) images, airway and emphysematous lesions were assessed as the square root of the wall area of a hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10) and the percentage of low attenuation volume (LAV%). Muscle mass was estimated using cross-sectional areas (CSAs) of the pectoralis and erector spinae muscles, and fat mass was estimated using the subcutaneous fat thickness at the level of the 8th rib measured using chest CT images. Statistical analyses were performed using the linear mixed-effects models. RESULTS: In total, 114 patients were enrolled. Their body mass index remained stable during the study period while body weight and muscle CSA decreased over time and the subcutaneous fat thickness increased. Reduced forced expiratory volume in 1 s and peak expiratory flow (PEF) at baseline predicted the future decline in muscle CSA. CONCLUSION: Severe airflow limitation predicted future muscle wasting in patients with COPD and ever-smokers at risk of COPD. Airflow limitation with a PEF slightly below 90% of the predicted value may require intervention to prevent future muscle loss.

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method.

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.

How Do Carbon Taxes Affect Emissions? Plant-Level Evidence from Manufacturing.

This paper investigates how carbon taxes affect emissions by examining British Columbia's revenue-neutral carbon tax in the manufacturing sector. We theoretically demonstrate that carbon taxes can achieve emission reductions while increasing production. Recycling carbon tax revenues to lower corporate income tax rates encourages investments, allowing plants to emit less per unit of output. Using detailed confidential plant-level data, we evaluate this theoretical prediction by exploiting the treatment intensity through plants' emission intensity. We find that the carbon tax lowers emissions by 4 percent. Furthermore, we find that the policy had a positive output effect and negative emission intensity effect, suggesting that the carbon tax encouraged plants to produce more with less energy. We provide initial evidence showing how a revenue-neutral carbon tax may achieve emission reductions while stimulating the economy.

Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.

OBJECTIVES: To evaluate the usefulness of deep learning image reconstruction (DLIR) to improve the image quality of dual-energy computed tomography (DECT) of the abdomen, compared to hybrid iterative reconstruction (IR). METHODS: This study included 40 patients who underwent contrast-enhanced DECT of the abdomen. Virtual monochromatic 40-, 50-, and 70-keV and iodine density images were reconstructed using three reconstruction algorithms, including hybrid IR (ASiR-V50%) and DLIR (TrueFidelity) at medium- and high-strength level (DLIR-M and DLIR-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. The contrast-to-noise ratio (CNR) for the portal vein on portal venous phase CT was calculated. The vessel conspicuity and overall image quality were graded on a 5-point scale ranging from 1 (poor) to 5 (excellent). The comparative scale of lesion conspicuity in 47 abdominal solid lesions was evaluated on a 5-point scale ranging from 0 (best) to -4 (markedly inferior). RESULTS: The image noise of virtual monochromatic 40-, 50 -, and 70-keV and iodine density images was significantly decreased by DLIR compared to hybrid IR (p < 0.0001). The CNR was significantly higher in DLIR-H and DLIR-M than in hybrid IR (p < 0.0001). The vessel conspicuity and overall image quality scores were also significantly greater in DLIR-H and DLIR-M than in hybrid IR (p < 0.05). The lesion conspicuity scores for DLIR-M and DLIR-H were significantly higher than those for hybrid IR in the virtual monochromatic image of all energy levels (p ≤ 0.001). CONCLUSIONS: DLIR improves vessel conspicuity, CNR, and lesion conspicuity of virtual monochromatic and iodine density images in abdominal contrast-enhanced DECT, compared to hybrid IR. KEY POINTS: • Deep learning image reconstruction (DLIR) is useful for reducing image noise and improving the CNR of visual monochromatic 40-, 50-, and 70-keV images in dual-energy CT. • DLIR can improve lesion conspicuity of abdominal solid lesions on virtual monochromatic images compared to hybrid iterative reconstruction. • DLIR can also be applied to iodine density maps and significantly improves their image quality.

Inflammatory biomarkers are not useful for predicting prognosis in nursing and healthcare-associated pneumonia: A prospective, cohort study.

INTRODUCTION: Whether inflammatory biomarkers including procalcitonin (PCT) and C-reactive protein (CRP) are useful for predicting prognosis in nursing and healthcare-associated pneumonia (NHCAP) is unknown. The aim of the present study was to investigate the utility of serial PCT and CRP measurements for predicting prognosis and treatment efficacy for hospitalized NHCAP patients. METHODS: This prospective, observational, cohort study enrolled consecutive NHCAP patients hospitalized at Kurashiki Central Hospital from October 2010 to September 2017. PCT and CRP were measured twice, once on admission and again within 48-72 h after admission. The primary outcome was 30-day all-cause mortality, and the secondary outcome was initial treatment failure. RESULTS: A total of 299 patients were included. The 30-day mortality rate was 8.4% (25/299), and the initial treatment failure rate was 15.4% (46/299). On multivariate analysis, performance status [odds ratio (OR) (95% confidence interval (CI)): 2.25 (1.34-3.77), P = 0.002], temperature [OR (95%CI): 0.53 (0.32-0.88), P = 0.02], heart rate [OR (95%CI): 1.03 (1.01-1.06), P = 0.007], albumin [OR (95%CI): 0.42 (0.18-0.95), P = 0.04], and blood urea nitrogen [OR (95%CI): 1.02 (1.00-1.05), P = 0.04] were significant prognostic factors, and CRP D3 [OR (95%CI): 1.07 (1.02-1.11), P = 0.003] and PSI [OR (95%CI): 1.01 (1.00-1.02), P = 0.01] were the predictors of initial treatment failure. Consecutive measurements of PCT and CRP were not significant predictors of 30-day mortality. CONCLUSIONS: Inflammatory biomarkers including PCT and CRP were not useful for predicting prognosis and treatment efficacy in NHCAP patients. We should carefully evaluate the patients' vital signs and comorbidities when managing NHCAP patients.

[Single-phase Pulmonary Arteriovenous Separation CT Protocol with Estimated Time of Arrival Method Using 3rd Generation Dual Source CT: Its Success Rate of Pulmonary Arteriovenous Separation in Our Experience of 223 Cases].

We retrospectively investigated the success rate of pulmonary arteriovenous separation in a single-phase computed tomography (CT) protocol using the estimated time of arrival (ETA) method. A total of 223 patients who underwent a single-phase CT protocol using the ETA method for pulmonary arteriovenous separation were included in the analysis. Dual source CT (SOMATOM Force, SIEMENS) was used for imaging. The tube voltage was 80 kVp, and the scan mode was turbo flash spiral mode. CT values of main pulmonary artery (MPA), peripheral pulmonary artery (pPA), peripheral pulmonary vein (pPV), left atrium (LA), ascending aorta (AAo) and descending aorta (DAo) were measured. When the difference in CT values on the central side was 100 Hounsfield unit (HU) or more, it was judged that the separation was successful. The mean CT values were 671.9±154 HU for MPA, 424.4±81.2 HU for LA, 551.1±142.6 HU for pPA, 351.6±94.0 HU for pPV, 362.2±75.8 HU for AAo, and 282.7±83.7 HU for DAo. The mean difference in CT values of the pulmonary artery and vein was 247.5±138.9 HU on the central side and 199.5±133.0 HU on the peripheral side. There were 90.1% of cases where the difference in CT values on the central side was 100 HU or more. In addition, a strong positive correlation (r=0.849, p<0.001) was found between the CT value of MPA and the CT value difference on the central side. The success rate of pulmonary arteriovenous separation by the ETA method, which is a method that enables stable pulmonary arteriovenous separation, was 90.1%.

Time Trend of the Sensitivity of the Pneumococcal Urinary Antigen Test for Diagnosing Pneumococcal Community-Acquired Pneumonia: An Analysis of 15-Year, Prospective Cohort Data.

INTRODUCTION: Whether the sensitivity of the BinaxNOW Streptococcus pneumoniae urinary antigen test kit (BinaxNOW), adjusted by some variables including vital signs, laboratory examinations and pneumonia severity, has been decreasing is unknown. The aim of the present study was to investigate whether BinaxNOW sensitivity has decreased recently and to identify the predictors of the BinaxNOW result, including the time trend. METHODS: This prospective cohort study enrolled consecutive patients with pneumococcal community-acquired pneumonia who were hospitalised at Kurashiki Central Hospital from January 2001 to December 2015. Pneumococcal community-acquired pneumonia was defined as positive blood or pleural effusion or sputum culture results. To evaluate the effect of the time trend for the sensitivity of BinaxNOW, time series regression analysis was performed. In addition, predictors of the BinaxNOW result were examined by multivariable analysis using variables such as sex, vital signs, blood tests such as C-reactive protein, albumin, blood urea nitrogen, creatinine, white blood cell count, haematocrit and platelets, antibiotic pre-treatment, bacteraemia, and pneumonia severity, in addition to time trend and seasonality. RESULTS: A total of 446 patients were included. BinaxNOW sensitivity showed a significant, gradual decrease from 2001 (81.3%) to 2015 (48.7%). On multivariable analysis [odds ratio (95% confidence interval)], bacteraemia [2.516 (1.387-4.561), P = 0.002] was a predictor of a positive BinaxNOW result, whereas male sex [0.467 (0.296-0.736), P = 0.001], white blood cell count [0.959 (0.930-0.989), P = 0.008] and the time trend per year [0.900 (0.859-0.943), P < 0.001] were predictors of a negative BinaxNOW result. CONCLUSIONS: The sensitivity of BinaxNOW decreased over a 15-year period. We should be careful when interpreting BinaxNOW results in daily clinical practice, and the development of a new kit with good sensitivity is anticipated. TRIAL REGISTRATION NUMBER: UMIN000004353.

Predictors and usefulness of targeted therapy for pneumococcal community-acquired pneumonia diagnosed by the urinary antigen test: a prospective, observational cohort study.

The aim of the present study was to investigate the predictors of targeted therapy (TT) for pneumococcal community-acquired pneumonia (PCAP) with a positive urinary antigen test (UAT) and compare the outcomes with those of nontargeted therapy. This prospective cohort study enrolled consecutive PCAP patients with a positive UAT who were hospitalized at Kurashiki Central Hospital from October 2010 to November 2019. A total of 286 patients were included. Of them, 56 patients (19.6%) were included in the TT group. On multivariate analysis, identification of Gram-positive diplococci by Gram stain (OR [95% CI]: 2.46 [1.32-4.63]) was a positive predictor, whereas aspiration pneumonia (0.17 [0.03-0.59]) and CURB-65 score (0.59 [0.42-0.81]) were negative predictors of TT. Initial treatment failure and 30-day mortality were not significantly different. The UAT is not used enough for TT, and TT for PCAP did not have worse outcomes.

Usefulness of ß-lactam and macrolide combination therapy for treating community-acquired pneumonia patients hospitalized in the intensive care unit: Propensity score analysis of a prospective cohort study.

INTRODUCTION: Whether ß-lactam and macrolide combination therapy reduces mortality in severe community-acquired pneumonia (SCAP) patients hospitalized in the intensive care unit (ICU) is controversial. The aim of the present study was to evaluate the usefulness of ß-lactam and macrolide combination therapy for SCAP patients hospitalized in the ICU. METHODS: A prospective, observational, cohort study of hospitalized pneumonia patients was performed. Hospitalized SCAP patients admitted to the ICU within 24 h between October 2010 and October 2017 were included for analysis. The primary outcome was 30-day mortality, and secondary outcomes were 14-day mortality and ICU mortality. Inverse probability of treatment weighting (IPTW) analysis as a propensity score analysis was used to reduce biases, including six covariates: age, sex, C-reactive protein, albumin, Pneumonia Severity Index score, and APACHE II score. RESULTS: A total of 78 patients were included, with 48 patients in the non-macrolide-containing ß-lactam therapy group and 30 patients in the macrolide combination therapy group. ß-lactam and macrolide combination therapy significantly decreased 30-day mortality (16.7% vs. 43.8%; P = 0.015) and 14-day mortality (6.7% vs. 31.3%; P = 0.020), but not ICU mortality (10% vs 27.1%, P = 0.08) compared with non-macrolide-containing ß-lactam therapy. After adjusting by IPTW, macrolide combination therapy also decreased 30-day mortality (odds ratio, 0.29; 95%CI, 0.09-0.96; P = 0.04) and 14-day mortality (odds ratio, 0.19; 95%CI, 0.04-0.92; P = 0.04), but not ICU mortality (odds ratio, 0.34; 95%CI, 0.08-1.36; P = 0.13). CONCLUSIONS: Combination therapy with ß-lactam and macrolides significantly improved the prognosis of SCAP patients hospitalized in the ICU compared with a non-macrolide-containing ß-lactam regimen.

Increased risk of Legionella pneumonia as community-acquired pneumonia after heavy rainfall in 2018 in west Japan.

INTRODUCTION: Japan experienced a heavy rainfall event from June 28 to July 8, 2018, and many casualties were caused by both heavy rainfall and flooding. Few studies have investigated patients' characteristics and the causative pathogens of community-acquired pneumonia before and after heavy rainfall events. The aim of the present study was to evaluate the causative pathogens and clinical characteristics of hospitalized patients with community-acquired pneumonia before and after the heavy rainfall event using prospective cohort data. METHODS: The study was divided into two periods: July to November 2013-2017 (before heavy rainfall) and July to November 2018 (after heavy rainfall). The patients' clinical characteristics and causative pathogens before and after the heavy rainfall were investigated. Regarding the causative pathogens, adjustments were made for precipitation and seasonal patterns. RESULTS: There were no significant differences in the number and clinical characteristics of patients before and after heavy rainfall. However, the frequency of Legionella pneumonia was significantly higher after than before the heavy rainfall event (8.9% vs 3.0%, P = 0.02) and remained significant after adjusting for precipitation and season. Three of 7 Legionella pneumonia patients engaged in reconstruction work and 2 Legionella pneumonia patients had soil exposure. CONCLUSIONS: An increased risk of Legionella pneumonia after not only rainfall and serious flooding, but also following recovery work or soil exposure should be considered.

Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction.

PURPOSE: To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique. METHOD: Pre and post-contrast abdominal CT images in 50 patients were reconstructed with 2 different algorithms: hybrid iterative reconstruction (hybrid IR: ASiR-V 50%) and DLIR (TrueFidelity). Standard deviation of attenuation in normal liver parenchyma was measured as the image noise on pre and post-contrast CT. The contrast-to-noise ratio (CNR) for the aorta, and the signal-to-noise ratio (SNR) of the liver were calculated on post-contrast CT. The overall image quality was graded on a 5-point scale ranging from 1 (poor) to 5 (excellent). RESULTS: The image noise was significantly decreased by DLIR compared to hybrid-IR [hybrid IR, median 8.3 Hounsfield unit (HU) (interquartile range (IQR) 7.6-9.2 HU); DLIR, median 5.2 HU (IQR 4.6-5.8), P < 0.0001 for post-contrast CT]. The CNR and SNR were significantly improved by DLIR [CNR, median 4.5 (IQR 3.8-5.6) vs 7.3 (IQR 6.2-8.8), P < 0.0001; SNR, median 9.4 (IQR 8.3-10.1) vs 15.0 (IQR 13.2-16.4), P < 0.0001]. The overall image quality score was also higher for DLIR compared to hybrid-IR (hybrid IR 3.1 ± 0.6 vs DLIR 4.6 ± 0.5, P < 0.0001 for post-contrast CT). CONCLUSIONS: Image noise, overall image quality, CNR and SNR for abdominal CT images are improved with DLIR compared to hybrid IR.

Is antipseudomonal antibiotic treatment needed for all nursing and healthcare-associated pneumonia patients at risk for antimicrobial resistance?

OBJECTIVES: Nursing and healthcare-associated pneumonia (NHCAP) was proposed by the Japanese Respiratory Society to refer to healthcare-associated pneumonia. This study aimed to investigate whether antipseudomonal antibiotic therapy improved the prognosis of NHCAP patients at high risk for antimicrobial-resistant pathogens. METHODS: Consecutive hospitalised NHCAP patients in Kurashiki Central Hospital between October 2010 and December 2016 were prospectively enrolled. NHCAP patients who were at high risk for antimicrobial resistance were defined as those who received antimicrobials in the preceding 90 days and/or were on tube feeding. The patients who received antipseudomonal antibiotics were defined as the guideline-concordant (GC) therapy group, and the others were defined as the guideline-discordant (GD) therapy group. The primary outcome was 30-day mortality. Inverse probability of treatment weighting (IPTW) analysis was used to reduce biases. RESULTS: There were 277 patients with NHCAP; a majority (78.0%) were discharged from a hospital in the preceding 90 days. There were 52 patients in the GC group and 225 patients in the GD group. The 30-day mortality rate was significantly higher in the GC group than in the GD group (17.3%, 9/52 vs. 7.1%, 16/225; P = 0.03). After IPTW analysis, the GC therapy, compared with GD therapy, did not improve the 30-day mortality (OR 1.71, 95% CI 0.65-4.47; P = 0.28). CONCLUSIONS: Not all NHCAP patients, even those at high risk for antimicrobial resistance, need antipseudomonal antimicrobial treatment. The treatment strategy for NHCAP patients should be individualised, according to the pneumonia severity, risk for antimicrobial-resistant pathogens, and antibiogram in each hospital.

An index of the fractal characteristic of an airway tree is associated with airflow limitations and future body mass index reduction in COPD patients.

Disorders of the fractality of an airway tree and a vessel tree have been studied in pulmonary diseases. Here we successfully applied Mishima's D to the bronchial minimal inner cross-sectional area (iCSA) measured in multidetector computed tomography (MDCT) images of chronic obstructive pulmonary disease (COPD) and non-COPD smokers (n = 162), by defining D in the following formula: logN(≥X) = -D × logX + c, where X is a certain iCSA value, N(≥X) is the number of airway branches having iCSA greater than or equal to X, and c is a constant. Mathematically, this D of iCSA was associated with the expected reduction ratio of iCSA at bifurcations, which can be estimated by 2-1/D. This D of iCSA also correlated weakly with the box-counting fractal dimension and Weibel's reduction ratio over airway generations, which indicated that the airway tree was not a perfect fractal object and that the branch bifurcation was asymmetric. The D of iCSA showed positive correlations with lung function measurements of airflow limitation in study participants. In addition, D of iCSA representing the periphery showed an association with future body mass index reduction, most likely as an indicator of energy efficacy for breathing as predicted by Hess-Murray's law. D of iCSA may be helpful to understanding the pathogenesis of obstructive lung diseases.NEW & NOTEWORTHY An airway tree is a fractal object. We showed that the distribution of minimal inner cross-sectional area (iCSA) of airway branches can be expressed by a fractal index, D, of minimal iCSA. This D was correlated with airflow limitation and future body mass index reduction in chronic obstructive pulmonary disease patients, as predicted by Hess-Murray's law.

Evaluation of pneumonia severity scoring systems in nursing and healthcare-associated pneumonia for predicting prognosis: A prospective, cohort study.

The usefulness of existing pneumonia severity indices for predicting mortality in nursing and healthcare-associated pneumonia (NHCAP) is unclear. This study compared the usefulness of existing pneumonia severity indices for predicting mortality in NHCAP and community-acquired pneumonia (CAP). Consecutive hospitalized pneumonia patients including NHCAP and CAP patients were prospectively enrolled between October 2010 and November 2017. Admission pneumonia severity was assessed using CURB-65, Pneumonia Severity Index (PSI), A-DROP, Infectious Diseases Society of America (IDSA)/American Thoracic Society (ATS) severe pneumonia criteria, and I-ROAD. The primary outcome was 30-day mortality. The discriminatory ability of each severity index was evaluated by receiver operating characteristic curve analysis. Overall, 828 patients had NHCAP, and 1330 patients had CAP. Thirty-day mortality was 12.8% and 5.6% in NHCAP and CAP patients, respectively. The area under the curve of PSI (0.717, 95% confidence interval 0.673-0.761) was the highest among all pneumonia severity indices, with significant differences compared with CURB-65 (0.651, 95% confidence interval 0.598-0.705, P = 0.02) and IDSA/ATS severe pneumonia criteria (0.659, 95% confidence interval 0.612-0.707, P = 0.03). The predictive abilities for 30-day mortality of the pneumonia severity indices, excluding PSI and I-ROAD, were significantly inferior for NHCAP than for CAP. PSI may be the most useful pneumonia severity score for predicting mortality in NHCAP. However, the predictive ability for mortality of each pneumonia severity score was worse for NHCAP than for CAP; therefore, the prognostic factors in NHCAP need to be identified for better management of NHCAP patients.