Pneumonia In Patients Research Articles

Magnetic resonance (MR) evaluation of deep venous thrombosis of 338 discharged viral pneumonia patients.

Viral pneumonia (VP) often leads to the development of deep vein thrombosis (DVT) in hospitalized patients. The aim of the study was to investigate the incidence of DVT in discharged patients with VP, and whether new and old DVT differ in transverse relaxation time. In this prospective cohort study in Wuhan, China, 338 consecutive discharged VP patients from February 2021 to March 2023 who underwent T2 weighted Sampling Perfection with Application Optimized Contrast Evolution (SPACE) were recruited to detect DVT. T2 mapping and T2* mapping were performed for the patients with DVT detected by magnetic resonance imaging (MRI). The minimum, maximum, mean of T2 time and T2* time of DVT were recorded as T2min, T2max, T2mean, T2*min, T2*max, and T2*mean, respectively. Clinical data and laboratory findings were compared between new and old DVT cases, which were defined based on the examination results before and after discharge. A Mann-Whitney test was used to compare transverse relaxation time parameters between new and old DVT. Twelve percent of VP patients (40/338) developed new DVT after discharge. Thirty-three out of 104 DVTs did not resolve after discharge. Compared with patients with new DVT, patients with old DVT were older (67 vs. 59 years, P=0.003); and had a higher proportion of bedridden time >72 hours (72.7% vs. 37.0%, P<0.001). Patients with old DVT had a lower lymphocyte count (0.67×109/L vs. 0.97×109/L, P=0.01), higher C-reactive protein (59 vs. 35 mg/L, P=0.019), and higher levels of D-dimer (6.7 vs. 0.9 µg/mL, P<0.001) than patients with new DVT. Patients with old DVT received more invasive mechanical ventilation (30.3% vs. 7.4%, P<0.001) and had a higher proportion of acute respiratory distress syndrome (75.8% vs. 51.9%, P<0.001), and a higher proportion of cardiac injury (39.4% vs. 14.8%, P=0.033) than patients with new DVT. T2min, T2max, T2mean, and T2*max of new DVT were significantly greater than old DVT (17.6±10.4 vs. 13.2±5.9 ms, 94.9±44.9 vs. 42.3±23.6 ms, 46.8±24.0 vs. 25.0±12.6 ms, 22.5±12.4 vs. 10.7±3.5 ms, P<0.05 for all). There was no significant difference in T2*min or T2*mean between new and old DVT (3.2±0.4 vs. 3.1±0.4 ms, 8.2±4.9 vs. 5.5±1.5 ms, P>0.05 for both). T2 weighted SPACE magnetic resonance (MR) is valuable in the follow-up of thrombosis of discharged VP patients. T2 mapping distinguishes between new and old DVT.

Development and validation of machine learning-based prediction model for severe pneumonia: A multicenter cohort study

Severe pneumonia (SP) is a prevalent respiratory ailment characterized by high mortality and poor prognosis. Current scoring systems for pneumonia are not only time-consuming but also exhibit limitations in early SP prediction. To address this gap, this study aimed to develop a machine-learning model using inflammatory markers from peripheral blood for early prediction of SP. A total of 204 pneumonia patients from seven medical centers were studied, with 143 (68 SP cases) in the training cohort and 61 (32 SP cases) in the test cohort. Clinical characteristics and laboratory test results were collected at diagnosis. Various models including Logistic Regression, Random Forest, Naïve Bayes, XGBoost, Support Vector Machine, and Decision Tree were built and evaluated. Seven predictors—age, sex, WBC count, T-lymphocyte count, NLR, CRP, TNF-α, IL-4/IFN-γ ratio, IL-6/IL-10 ratio—were selected through LASSO regression and clinical insight. The XGBoost model, exhibiting best performance, achieved an AUC of 0.901 (95 % CI: 0.827 to 0.985) in the test cohort, with an accuracy of 0.803, sensitivity of 0.844, specificity of 0.759, and F1_score of 0.818. Indeed, SHAP analysis emphasized the significance of elevated WBC counts, older age, and elevated CRP as the top predictors. The use of inflammatory biomarkers in this concise predictive model shows significant potential for the rapid assessment of SP risk, thereby facilitating timely preventive interventions.

A tailored machine learning approach for mortality prediction in severe COVID-19 treated with glucocorticoids.

<sec><title>BACKGROUND</title>The impact of severe COVID-19 pneumonia on healthcare systems highlighted the need for accurate predictions to improve patient outcomes. Despite the established efficacy of glucocorticoids (GCs), variable patient responses are observed, and the existing clinical scores are limited in predicting non-responders. We propose a machine learning (ML) based approach to predict mortality in COVID-19 pneumonia treated with GCs.</sec><sec><title>METHODS</title>This is an ML-driven retrospective analysis involving 825 patients. We leveraged XGBoost to select the most appropriate features from the initial 52, including clinical and laboratory data. Six different ML techniques were compared. Shapley additive explanation (SHAP) values were used to describe the influence of each feature on classification. Internal validation was performed.</sec><sec><title>RESULTS</title>Nine key predictors of death were identified: increasing C-reactive protein (CRP), decreasing arterial partial pressure of oxygen to fraction of inspired oxygen ratio (PaO₂/FiO₂), age, coronary artery disease, invasive mechanical ventilation, acute renal failure, chronic heart failure, PaO₂/FiO₂ earliest value, and body mass index. Random forest achieved the highest test area under the receiver operating characteristic curve at 0.938 (95% CI 0.903-0.969). SHAP values highlighted age and PaO₂/FiO₂ improvement as the most influential features; the latter showed a higher impact than CRP reduction over time.</sec><sec><title>CONCLUSION</title>The proposed ML algorithm effectively predicts the risk of hospital death in COVID-19 pneumonia patients undergoing GCs. This approach can be adapted to datasets measuring similar clinical variables.</sec>.

Clinical characteristics and microbial signatures in the lower airways of diabetic and nondiabetic patients with pneumonia.

The microbial signatures in diabetes with pneumonia and the risk factors of severe pneumonia (SP) in diabetic patients are not clear. Our study explored microbial signatures and the association between clinical characteristics and SP then constructed a risk model to find effective biomarkers for predicting pneumonia severity. Our study was conducted among 273 patients with pneumonia diagnosed and treated in our hospital from January 2018 to May 2021. Bronchoalveolar lavage fluid (BALF) samples and clinical data were collected. Metagenomic sequencing was applied after extracting the DNA from samples. Appropriate statistical methods were used to compare the microbial signatures and clinical characteristics in patients with or without diabetes mellitus (DM). In total, sixty-one pneumonia patients with diabetes and 212 pneumonia patients without diabetes were included. Sixty-six differential microorganisms were found to be associated with SP in diabetic patients. Some microbes correlated with clinical indicators of SP. The prediction model for SP was established and the receiver operating characteristic (ROC) curve demonstrated its accuracy, with the sensitivity and specificity of 0.82 and 0.91, respectively. Some microorganisms affect the severity of pneumonia. We identified the microbial signatures in the lower airways and the association between clinical characteristics and SP. The predictive model was more accurate in predicting SP by combining microbiological indicators and clinical characteristics, which might be beneficial to the early identification and management of patients with SP.

Association of Raised C-Reactive Proteins With Prolonged ICU Stay in Children with Bronchopneumonia: A Cross Sectional Study

Objective To study the association of raised CRP with prolonged ICU stay in children with bronchopneumonia at Ghurki Trust Teaching Hospital, Lahore, Pakistan. Methodology This cross-sectional study was conducted in Paediatric Intensive Care Unit of Ghurki Trust Teaching Hospital, Lahore, Pakistan, for a duration of six months (June 2023 to December 2023). Out of 127 children admitted into ICU, 82 met the inclusion criteria, aged 2-60 months, diagnosed with bronchopneumonia. Upon admission, blood sample was taken for CRP levels and these levels were correlated with ICU stay, TLC count, O2 dependency, and antibiotics duration of the patients. Patients were divided into five groups based on CRP counts. CRP counts less than 0.3 mg/dL were taken as normal. CRP counts from 0.4-0.9 mg/dL were taken as mild, 1-10 mg/dL were considered moderate, 11-49 mg/dL were considered marked, and ≥50 mg/dL were considered severe. Results Out of 82 patients, 45 (54.88%) were males and 37 (45.12%) were females. This study found that raised CRP was present in 73 (89.02%) out of 82 children (p&lt;0.04). Amongst 73 (89.02%), 08 (10.96%) patients had mild elevation of CRP, 36 (49.32%) had moderate elevation, 22 (30.14%) had marked, 07 (9.59%) had severe elevation. Moreover, a direct relation was observed among CRP and ICU stay (3.82±3.12), O2 requirements (3.72±3.07), TLC (13.36±5.18), antibiotic treatment duration (9.52±3.94) of broncho pneumonic patients. Conclusion Higher CRP levels significantly result in longer ICU stays and higher oxygen requirements in children with bronchopneumonia.

Clinical characteristics of pediatric patients with pneumonia and Streptococcus pneumoniae serotype distribution at Hai Phong Children’s Hospital, Vietnam

Streptococcus pneumoniae (S. pneumoniae) is a major cause of pneumonia in pediatric health concerns. Effective vaccination strategies depend on understanding serotype distribution and its correlation with clinical manifestations. This study aims to describe the serotype distribution of Streptococcus pneumoniae strains at Hai Phong Children’s Hospital, Vietnam. Conducted at Hai Phong Children’s Hospital in Vietnam from November 2022 to September 2023, our cross-sectional study enrolled 428 pediatric pneumonia patients. Nasopharyngeal fluid specimens were collected to isolate S. pneumoniae by culture and confirmed by Real-time PCR. S. pneumoniae serotyping was performed by realtime PCR. Medical records provided symptom and medical history data. Out of 428 samples, 66.8% tested positive for S. pneumoniae. Among confirmed cases by real-time PCR, 82.6% were typeable, with predominant serotypes being 6A/B (16.4%), 19F (14.9%), 15B/C (11.4%), 19A (11.0%), and 15A/15F (7.8%). Notably, 70.7% of strains belonged to vaccine-covered serotypes. Cough (96.5%), tachypnea (78.7%), fever (56.1%), and wheezing (52.8%) were the most frequent symptoms. Notably, cough, tachypnea, wheezing, and chest indrawing (29.0%) significantly correlated with pneumococcal pneumonia. Positive S. pneumoniae cultures also correlated with chest X-ray findings. Our study reveals a high prevalence of S. pneumoniae in pediatric patients with pneumonia in Vietnam. Insights into serotype distribution, clinical characteristics of pneumonia cases offer valuable guidance for optimizing vaccination programs and enhancing disease management.

Evaluation of progressive pulmonary fibrosis in non-idiopathic pulmonary fibrosis-interstitial lung diseases: a cross-sectional study

BackgroundProgressive pulmonary fibrosis is the symptomatic, physiological, and radiological progression of interstitial lung diseases. The aim of this study was to examine the relationship between progressive pulmonary fibrosis and demographic characteristics and to evaluate the effect on clinical outcomes and mortality.MethodsThis cross-sectional study included 221 patients diagnosed with non-idiopathic pulmonary fibrosis interstitial lung diseases who were followed in the last 5 years. Patient symptoms, clinical, radiological, and demographic data were examined. Risk factors for the development of progressive pulmonary fibrosis and the relationship with clinical outcomes and mortality were examined.ResultsOf the patients, 33.0% (n = 73) had fibrotic idiopathic nonspecific interstitial pneumonia (iNSIP), 35.7% (n = 79) had fibrotic hypersensitivity pneumonia (HP), 18.1% (n = 40) had fibrotic connective tissue disease (CTD) interstitial lung diseases (ILD), and 13.1% (n = 29) had postinfectious fibrotic ILD. The progressive pulmonary fibrosis development rates of the subtypes were 46.5% iNSIP (n = 34), 86.0% fibrotic HP (n = 68), 42.5% fibrotic CTD-ILD (n = 17), and 20.7% postinfectious ILD (n = 6). The presence of progressive pulmonary fibrosis was associated with the development of respiratory failure and mortality (odds ratio [OR]: 2.70, 95% CI: 1.04–7.05 and OR: 2.13, 95% CI: 1.23–3.69). Progressive pulmonary fibrosis development was higher in hypersensitivity pneumonia patients with farmer’s lung (OR: 5.06, 95% CI: 1.02–25.18).ConclusionProgressive pulmonary fibrosis was more prevalent in older patients. Farming was an important risk factor in the development of hypersensitivity pneumonia-progressive pulmonary fibrosis. Respiratory failure and mortality were higher in those who developed progressive pulmonary fibrosis.

Surfactant derived protein type B: a new biomarker linked to respiratory failure and lung damage in mild to moderate SARS-CoV-2 pneumonia

BackgroundThe COVID-19 pandemic has led to significant concern due to its impact on human health, particularly through pneumonia-induced lung damage. Surfactant proteins A and D (SP-A and SP-D) are implicated in COVID-19 lung damage, but the role of surfactant protein B (SP-B) remains unclear.MethodsWe conducted a single-center prospective observational study involving 73 hospitalised COVID-19 pneumonia patients. SP-B levels were measured within 48 h of admission, alongside SP-A and SP-D in a subset. Clinical data were collected, and follow-up visits were conducted after 6 months.ResultsAt hospitalisation, circulating immature SP-B levels measured in 73 patients (26.31 AU [14.27–41.31]), correlated significantly with lung involvement (r=0.447, p&lt;0.001) and oxygen support requirement (p=0.005). SP-B levels did not predict mechanical ventilation or ICU admission. SP-B decreased significantly (p&lt;0.001) from 25.53 AU [14.36–41.46] at the acute hospitalisation to 12.73 AU [9.12–20.23] at 6 months follow up, unlike SP-A and SP-D that did not change significantly. Immature SP-B (but not SP-A and SP-D) confirmed to be significantly associated with the need of oxygen support (n=26, 58%) during the hospitalisation (p&lt;0.05).ConclusionImmature SP-B emerges as a potential biomarker for COVID-19 pneumonia severity and prognosis. Its dynamic changes suggest utility in monitoring disease progression and long-term outcomes, despite limitations in predicting hard endpoints. Larger studies are needed to validate these findings and understand the underlying mechanisms of surfactant protein dysregulation in COVID-19 pathogenesis.

Machine Learning to Predict the Risk of Malnutrition in Hospitalized Patients with Pneumonia and Analysis of Related Prognostic Factor.

This study explored machine learning's potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patients in a Taiwan medical center from Jan 2016 to Feb 2022, excluding ICU cases. The average age was 77.6 years, with 10.2% well-nourished, 76.3% at-risk, and 13.5% malnourished. Machine learning models, particularly LightGBM and XGBoost, showed high accuracy in predicting hospital stays, mortality rates, and readmissions. These findings emphasize the role of data-driven methods in enhancing patient care and managing conditions like pneumonia more effectively.

FLUEnT: Transformer for detecting lung consolidations in videos using fused lung ultrasound encodings

Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings. The wide application of point-of-care ultrasound at the bedside is limited mainly due to a lack of training for data acquisition and interpretation. Artificial Intelligence can serve as a potential tool to automate and improve the LUS data interpretation process, which mainly involves analysis of hyper-echoic horizontal and vertical artifacts, and hypo-echoic small to large consolidations. This paper presents, Fused Lung Ultrasound Encoding-based Transformer (FLUEnT), a novel pediatric LUS video scoring framework for detecting lung consolidations using fused LUS encodings. Frame-level embeddings from a variational autoencoder, features from a spatially attentive ResNet-18, and encoded patient information as metadata combiningly form the fused encodings. These encodings are then passed on to the transformer for binary classification of the presence or absence of consolidations in the video. The video-level analysis using fused encodings resulted in a mean balanced accuracy of 89.3 %, giving an average improvement of 4.7 % points in comparison to when using these encodings individually. In conclusion, outperforming the state-of-the-art models by an average margin of 8 % points, our proposed FLUEnT framework serves as a benchmark for detecting lung consolidations in LUS videos from pediatric pneumonia patients.

Distribution characteristics of human herpes viruses in the lower respiratory tract and their impact on 30-day mortality in community-acquired pneumonia patients.

Human herpes viruses (HHVs) are commonly detected in community-acquired pneumonia (CAP) patients, particularly those with complex complications, attracting increased attention from clinical practitioners. However, the significance of detecting HHVs in bronchoalveolar lavage fluid (BALF) with CAP patients is still unclear. This study retrospectively analyzed BALF samples from 64 CAP patients at the Kunming Third People's Hospital between August 2021 and December 2023. Metagenomic next generation sequencing (mNGS) was conducted on BALF samples during CAP onset. Multivariate Cox regression models were used to identify independent risk factors for 30-day all-cause mortality in CAP. HHVs were found in 84.4% of CAP patients, which were the most common pathogens (45.1%), followed by bacteria (30.2%) and fungi (11.5%). Bacterial-viral co-infections were most common, occurring in 39 patients. Notably, there was no significant difference in HHV presence between severe and non-severe CAP patients (EBV: P = 0.431, CMV: P = 0.825), except for HHV-7 (P = 0.025). In addition, there was no significant difference in the 30-day mortality between HHV positive and HHV negative groups (P = 0.470), as well as between the HHV-7 positive and HHV-7 negative groups (P = 0.910). However, neither HHVs nor HHV-7 was independent risk factors for 30-day mortality in CAP patients (HHVs: HR 1.171, P = 0.888; HHV-7: HR 1.947, P = 0.382). In summary, among the prevalent presence of multiple HHVs, EBV and CMV were the most prevalent in CAP patients. Patients with sCAP were more susceptible to HHV-7 than those with non-sCAP. These results provide valuable insights for clinicians in guiding appropriate interventions for CAP treatment.

Pulmonary Function, Functional Capacity, Respiratory, and Locomotor Muscle Strength after Severe to Critically Ill COVID-19: A Long-Term Study.

The sequelae of post-coronavirus disease 2019 (COVID-19) pneumonia on lung function, exercise capacity, and quality of life were observed in both shortterm and long-term. However, the study about the respiratory and locomotor muscle strength in severe and critically ill COVID-19 survivors are still limited. Therefore, we aimed to examine long-term pulmonary function, functional capacities, and respiratory and locomotor body muscle strength in severe to critically ill post-COVID-19 survivors. A prospective observational study was conducted in 22 post-COVID-19 pneumonia and healthy adults. Clinical characteristics during admission, pulmonary function, functional capacity, respiratory muscles, and locomotor muscles strength were examined at 1, 3, and 6 months after discharge from the hospital. The generalized linear mixed model showed that percent predicted of forced expiratory volume in the 1 second (%FEV1), percent predicted of forced vital capacity (%FVC), maximum inspiratory pressure (MIP), handgrip strength, 6-minute walk distance, and five times sit to stand (5TSTS) were significantly lower in post-COVID-19 pneumonia patients than in healthy subjects during the follow-up period. The percent predicted of maximal voluntary ventilation (%MVV), and locomotor muscle strength were not different between the two groups throughout the follow-up period. Among post-COVID-19 pneumonia patients, %FEV1, %FVC, %MVV, 5TSTS, locomotor muscle strength significantly improved at three months compared to baseline at 1 month. Pulmonary function, functional capacity, respiratory, and locomotor muscle strength of survivors from COVID-19 were impaired and recovery was observed after three to six months. These emphasized the need to evaluate the long-term consequences of COVID-19.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

Serum calcium and mortality in pediatric pneumonia patients admitted to the PICU: a retrospective cohort study

This study investigates serum calcium’s prognostic value in pediatric pneumonia, focusing on its correlation with PICU mortality, to enhance understanding and treatment approaches in this field. Data from 414 pediatric pneumonia patients (2010–2019) admitted to the intensive care units at the Children’s Hospital, Zhejiang University School of Medicine were analyzed. The study utilized restricted cubic spline analysis, Cox proportional hazard regression, and Kaplan–Meier survival curve analysis to assess the relationship between serum calcium levels at admission and PICU mortality risk. After adjusting for multivariate factors, for each 1 mmol/dL increase in serum calcium, the risk of mortality decreased by 24% (HR: 0.76, 95% CI 0.67–0.87). Among the three levels of serum calcium groups, higher serum calcium levels were linked to a 63% reduction in the mortality rate compared to lower levels (HR: 0.37, 95% CI 0.16–0.84). The cumulative hazard estimates of mortality significantly differed across serum calcium groups (log-rank P = 0.032). This association was consistent across diverse subgroups (P for interaction > 0.05). Higher serum calcium levels are associated with decreased PICU mortality in pediatric pneumonia, highlighting its potential as a prognostic marker.

Impact of early myocardial injury on patients with severe pneumonia.

Pneumonia often causes myocardial damage. This study sought to understand how early myocardial injury affects severe pneumonia patients' prognoses. This multi-center prospective cohort study from March 2020 to October 2023 comprised severe pneumonia patients. Binary logistic regression analysis examined how myocardial damage affects cardiac complications and acute renal injury (AKI). We used Spearman correlation analysis to examine the relationship between troponin I levels and the vasoactive inotropic score (VIS) in shock patients with myocardial injury. We used the Kaplan-Meier survival curve to evaluate the impact of myocardial injury on 30-day and 1-year survival rates. Mediation investigations examined how AKI and cardiac complications mediate myocardial injury and death. This study included 363 severe pneumonia patients, of whom 204 (56.2%) developed myocardial damage, 132 (36.4%) had cardiac problems, and 146 (40.2%) had AKI. Myocardial damage independently elevated the incidence of cardiac complications (OR = 2.548, 95% CI = 1.404-4.303, P = 0.002) and AKI (OR = 1.946, 95% CI = 1.177-3.219, P = 0.009). There was a positive link between troponin I and VIS in myocardial injury and shock patients (r = 0.43, P < 0.001). COX regression found myocardial injury to be a death risk (HR = 1.472, 95% CI = 1.043-2.077, P = 0.028). Adjusted Kaplan-Meier survival analysis showed significantly decreased short-term and long-term survival rates with myocardial injury (log-rank test P < 0.05). The mediation study showed that cardiac complications and AKI mediated myocardial injury and death by 19.30% and 17.18%, respectively. Early myocardial injury in severe pneumonia patients raises the likelihood of cardiac problems, AKI, and refractory shock, reducing short- and long-term survival.

2246 Mind the HAP – an initiative to improve the diagnosis, management and prevention of hospital acquired pneumonia on elderly care

Abstract Introduction The commonest nosocomial infection in the UK is Hospital Acquired Pneumonia (HAP), associated with prolonged length of stay and mortality. The HAP incidence on Elderly care wards was &amp;gt;5% of admissions, exceeding the national average. An initiative ‘Mind the HAP’ was launched which included doctors, nurses, pharmacists, SLTs, physiotherapists and coders to improve HAP diagnosis, management and prevention. Methods To monitor the effectiveness of the interventions 3 audit cycles were performed between 2019 and 2023. Several interventions were implemented between 2019–2023.A multidisciplinary steering committee was formed with 3 work streams (diagnosis, management and prevention). To improve the accuracy of diagnosis and management of HAP, focused educational sessions were conducted for junior doctors with monthly meetings with coders. Nurses championed implementing the HAP prevention strategies i.e. hand hygiene, mouth care and positioning at 30–45 degrees. Regular comprehensive training sessions were held. HAP awareness and education campaign was launched. Daily nursing huddles helped to identify high risk patients. Physiotherapists provide chest physiotherapy to yield sputum sample collection among pneumonia patients. An electronic dashboard of incidence of HAP against the preventative measures and sputum culture reports has been launched with help from informatics. Information leaflets on HAP were created for patient awareness. An electronic HAP power plan to facilitate diagnosis and management of HAP will be launched from February 2024. Results HAP incidence has dropped to &amp;lt;2%, diagnostic accuracy improved from 35% to 81%, and sputum collection has increased from 9% to 24%. The HAP Quality Improvement Project received first prize for the most impactful Quality Improvement initiative at the Trust-wide conference in 2023. The results have been shared with the regional Microbiologists. Conclusion The collaborative efforts coupled with effective leadership and guidance, have been pivotal to the success of ‘Mind the HAP’ project.

Exploring the Interplay of Dataset Size and Imbalance on CNN Performance in Healthcare: Using X-rays to Identify COVID-19 Patients.

Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance-a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings. Initially, a CNN was developed to classify lung diseases using X-ray images, distinguishing between healthy individuals and COVID-19 patients. Later, the model was expanded to include pneumonia patients. To evaluate performance, numerous experiments were conducted with varied data sizes and imbalance ratios for both binary and ternary classifications, measuring various indices to validate the model's efficacy. The study revealed that increasing dataset size positively impacts CNN performance, but this improvement saturates beyond a certain size. A novel finding is that the data balance ratio influences performance more significantly than dataset size. The behavior of three-class classification mirrored that of binary classification, underscoring the importance of balanced datasets for accurate classification. This study emphasizes the fact that achieving balanced representation in datasets is crucial for optimal CNN performance in healthcare, challenging the conventional focus on dataset size. Balanced datasets improve classification accuracy, both in two-class and three-class scenarios, highlighting the need for data-balancing techniques to improve model reliability and effectiveness. Our study is motivated by a scenario with 100 patient samples, offering two options: a balanced dataset with 200 samples and an unbalanced dataset with 500 samples (400 healthy individuals). We aim to provide insights into the optimal choice based on the interplay between dataset size and imbalance, enriching the discourse for stakeholders interested in achieving optimal model performance. Recognizing a single model's generalizability limitations, we assert that further studies on diverse datasets are needed.

Efficacy and Safety of Dexmedetomidine in the Prone Position in Elderly Patients with Pneumonia: A Prospective, Double-Blind, Randomized Controlled Study.

We aimed to identify a safe and effective method to assist older adults with pneumonia in tolerating the prone position for a longer duration. This was a randomized, controlled, double-blinded study performed at the Shanghai Fourth People's Hospital. Eighty patients with pneumonia aged ≥ 65years were included. The patients were able to spontaneous breath in the prone position and were administered intravenous dexmedetomidine or an isotonic sodium chloride solution. The cumulative daily durations of prone positioning for all patients in the two groups were recorded. The primary outcome was the percentage of patients who completed ≥ 9h/day in the prone position. The secondary outcomes included the incidence of complications in the prone position and patient outcomes. Eighty patients were included (average age: 79.6 ± 8.9years). The percentage of patients who completed ≥ 9h/day in the prone position was significantly higher in the dexmedetomidine group than in the placebo group (P = 0.011). The percentage of patients who completed ≥ 12h/day in the prone position was also significantly greater in the dexmedetomidine group than in the placebo group (P = 0.008). There were no significant differences in other variables between the two groups. The results of this study demonstrate that intravenous dexmedetomidine injection can significantly prolong the duration of spontaneous breathing in the prone position in elderly pneumonia patients without obvious adverse events. We provide a safe and effective method to help patients with pneumonia, especially those with delirium or cognitive impairment, who cannot tolerate the length of time needed for spontaneous breathing in the prone position to be effective. The study was registered with the Chinese Clinical Trial Center (registration number: ChiCRT2300067383) on 2023-01-05.

Time-dependent cardiovascular risks following pneumonia in inpatient and outpatient settings: A register-based cohort study

BackgroundThe elevated long-term cardiovascular disease (CVD) risks associated with pneumonia have been observed among inpatients, yet the risks associated with outpatients are less understood. MethodsWe used register-based data and a matched cohort design, including 98,354 pneumonia inpatients and 44,486 outpatients, as well as a 5-fold number of matched healthy controls. Associations between pneumonia presentation (in inpatient and outpatient settings) and long-term CVD risks were measured by rate difference and hazard ratio (HR) using Poisson and Cox regressions in a time-dependent manner. ResultsDuring a maximum follow-up period of 5.7 years of ischemic heart disease (IHD), heart failure (HF), and stroke were documented among pneumonia inpatients.Relative to healthy controls, pneumonia patients showed increased risks of IHD, HF, and stroke. Women and young inpatients demonstrated stronger associations of CVD with pneumonia; inpatients aged 60 years or older showed the highest excessive CVD risks. ConclusionsPneumonia demanding outpatient and inpatient cares are intermediate-term and long-term risk factors of incident CVDs respectively, underscoring the need to plan setting-specific and time-dependent CVD-preventive cares following pneumonia presentation.

Impact of Outdoor Air Pollutants Exposure on the Severity and Outcomes of Community-Acquired Pneumonia in Gabes Region, Tunisia.

Background Acute community-acquired pneumonia (CAP) is considered the leading cause of infectious death worldwide. Air pollution and prolonged exposure to airborne contaminants have been implicated in various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). However, the specific impact of air pollution on pneumonia, particularly CAP, remains underexplored. Given the rising levels of urban air pollution and its potential health ramifications, our study aimed to examine the association between exposure to outdoor air pollution and severity as well as the outcomes of pneumonia cases requiring hospitalization. Methodology A cohort analytical study with retrospective data collection was carried out in the pulmonology department of the Gabès University Hospital between January and October 2022. We compared levels of particulate matter less than or equal to 10µm in aerodynamic diameter (PM10),sulfur dioxide (SO2),ozone (O3), moisture and ambient temperature with severity and outcomes ofpneumonia requiring hospitalization. The choice of these specific pollutants and environmental factors was based on their established impact on respiratory health and their prevalence in the study region. Results Increased sulfur dioxide (SO2) levels were associated with increased use of non-invasive ventilation (NIV) (r = 0.400). Higher levels of particulate matter (PM10) were significantly associated with the development of lung abscesses. Similarly, increased humidity and ambient temperature were strongly correlated with the development of lung abscesses. Increased air SO2 levels were correlated with a higher CURB65 score (r = 0.299). High outdoor SO2 levels and increasing moisture content were associated with increased Pneumonia Severity Index (PSI) score (r = 0.303 and = 0.310, respectively). Higher levels of PM10 were associated with an increased risk of pleural effusion, a serious complication of pneumonia. Finally, higher ambient temperatures were correlated with more extensive opacities on chest X-rays (r = 0.706), suggesting the severity of pneumonia. Conclusion This study highlights the significant associations between environmental factors and various clinical parameters in pneumonia patients. The findings underscore the importance of considering environmental exposures, such as air quality and weather conditions, in understanding and managing the severity of pneumonia.