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BackgroundThe impact of different classes of microbial pathogens on mortality in severe community-acquired pneumonia is not well elucidated. Previous studies have shown significant variation in the incidence of viral, bacterial and mixed infections, with conflicting risk associations for mortality. We aimed to determine the risk association of microbial aetiologies with hospital mortality in severe CAP, utilising a diagnostic strategy incorporating molecular testing. Our primary hypothesis was that respiratory viruses were important causative pathogens in severe CAP and was associated with increased mortality when present with bacterial pathogens in mixed viral-bacterial co-infections.MethodsA retrospective cohort study from January 2014 to July 2015 was conducted in a tertiary hospital medical intensive care unit in eastern Singapore, which has a tropical climate. All patients diagnosed with severe community-acquired pneumonia were included.ResultsA total of 117 patients were in the study. Microbial pathogens were identified in 84 (71.8%) patients. Mixed viral-bacterial co-infections occurred in 18 (15.4%) of patients. Isolated viral infections were present in 32 patients (27.4%); isolated bacterial infections were detected in 34 patients (29.1%). Hospital mortality occurred in 16 (13.7%) patients. The most common bacteria isolated was Streptococcus pneumoniae and the most common virus isolated was Influenza A. Univariate and multivariate logistic regression showed that serum procalcitonin, APACHE II severity score and mixed viral-bacterial infection were associated with increased risk of hospital mortality. Mixed viral-bacterial co-infections were associated with an adjusted odds ratio of 13.99 (95% CI 1.30–151.05, p = 0.03) for hospital mortality.ConclusionsRespiratory viruses are common organisms isolated in severe community-acquired pneumonia. Mixed viral-bacterial infections may be associated with an increased risk of mortality.

Severe rhabdomyolysis is an uncommon but potentially fatal complication of dengue fever that is not well characterised and may be underreported. With the resurgence and continued rise of dengue cases worldwide, physicians must be aware of the less common but serious complications of dengue. Here, we report a patient who presented with severe rhabdomyolysis secondary to dengue fever with a serum creatine kinase of 742 900 U/L.

BackgroundChest radiograph (CXR) is a basic diagnostic test in community-acquired pneumonia (CAP) with prognostic value. We developed a CXR-based artificial intelligence (AI) model (CAP AI predictive Engine: CAPE) and prospectively evaluated its discrimination for 30-day mortality.MethodsDeep-learning model using convolutional neural network (CNN) was trained with a retrospective cohort of 2235 CXRs from 1966 unique adult patients admitted for CAP from 1 January 2019 to 31 December 2019. A single-centre prospective cohort between 11 May 2020 and 15 June 2020 was analysed for model performance. CAPE mortality risk score based on CNN analysis of the first CXR performed for CAP was used to determine the area under the receiver operating characteristic curve (AUC) for 30-day mortality.Results315 inpatient episodes for CAP occurred, with 30-day mortality of 19.4% (n=61/315). Non-survivors were older than survivors (mean (SD)age, 80.4 (10.3) vs 69.2 (18.7)); more likely to have dementia (n=27/61 vs n=58/254) and malignancies (n=16/61 vs n=18/254); demonstrate higher serum C reactive protein (mean (SD), 109 mg/L (98.6) vs 59.3 mg/L (69.7)) and serum procalcitonin (mean (SD), 11.3 (27.8) μg/L vs 1.4 (5.9) μg/L). The AUC for CAPE mortality risk score for 30-day mortality was 0.79 (95% CI 0.73 to 0.85, p<0.001); Pneumonia Severity Index (PSI) 0.80 (95% CI 0.74 to 0.86, p<0.001); Confusion of new onset, blood Urea nitrogen, Respiratory rate, Blood pressure, 65 (CURB-65) score 0.76 (95% CI 0.70 to 0.81, p<0.001), respectively. CAPE combined with CURB-65 model has an AUC of 0.83 (95% CI 0.77 to 0.88, p<0.001). The best performing model was CAPE incorporated with PSI, with an AUC of 0.84 (95% CI 0.79 to 0.89, p<0.001).ConclusionCXR-based CAPE mortality risk score was comparable to traditional pneumonia severity scores and improved its discrimination when combined.

Keywords: predictive model; prognosis; COVID-19; SARS-CoV-2; Deep-learning; Artificial intelligence; Chest radiograph Background: Chest radiography may be used together with deep-learning models to prognosticate COVID-19 patient outcomes Purpose: To evaluate the performance of a deep-learning model for the prediction of severe patient outcomes from COVID-19 pneumonia on chest radiographs. Methods: A deep-learning model (CAPE: Covid-19 AI Predictive Engine) was trained on 2337 CXR images including 2103 used only for validation while training. The prospective test set consisted of CXR images (n=70) obtained from RT-PCR confirmed COVID-19 pneumonia patients between 1 January and 30 April 2020 in a single center. The radiographs were analyzed by the AI model. Model performance was obtained by receiver operating characteristic curve analysis. Results: In the prospective test set, the mean age of the patients was 46 (+/- 16.2) years (84.2% male). The deep-learning model accurately predicted outcomes of ICU admission/mortality from COVID-19 pneumonia with an AUC of 0.79 (95% CI 0.79-0.96). Compared to traditional risk scoring systems for pneumonia based upon laboratory and clinical parameters, the model matched the EWS and MulBTSA risk scoring systems and outperformed CURB-65. Conclusions: A deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 on chest radiographs.

A 50‐year‐old immunocompetent man presented with intracranial space‐occupying lesions and a right lung mass. This was found to be disseminated Cryptococcus gattii infection. Following 15 months of anti‐fungal therapy, imaging showed reduction in the size of the pulmonary cryptococcoma and new multi‐lobar ground‐glass opacities interspersed with a crazy‐paving pattern. Surgical lung biopsy was performed after bronchoscopic evaluation was non‐yielding. Histology showed intra‐alveolar accumulation of foamy macrophages and airspaces containing periodic acid Schiff‐positive amorphous eosinophilic material with strong immune positivity for surfactant A, consistent with a diagnosis of pulmonary alveolar proteinosis (PAP). The majority of adult‐onset PAP is due to the presence of anti‐granulocyte macrophage colony‐stimulating factor antibodies. Opportunistic fungal and mycobacterial infections are known to occur in these patients due to alveolar macrophage and neutrophilic dysfunction. The onset of PAP may occur concurrently with, or be temporally distinct from, opportunistic infections. For patients with respiratory failure, whole lung lavage is a therapeutic strategy.

Introduction: Subjective indicators of health like self-rated health (SRH) have been shown to be a predictor of mortality and morbidity. We determined the prevalence of poor SRH in Singapore and its association with various lifestyle and socioeconomic factors and disease states. Materials and Methods: Cross-sectional survey by interviewer-administered questionnaire of participants aged 40 years and above. SRH was assessed from a standard question and categorised into poor, fair, good or excellent. Lifestyle factors, socioeconomic factors and presence of disease states were also assessed. Results: Out of 409 participants, 27.6% rated their health as poor or fair, 53.1% as good and 19.3% as excellent. Smaller housing-type (PRR: 1.64, 95% CI: 1.10-2.44) and lack of exercise (PRR: 1.54, 95% CI: 1.06-2.22) were found to be associated with poor SRH. Presence of chronic diseases such as coronary artery disease (PRR: 1.89, 95% CI: 1.13-3.17), diabetes mellitus (PRR: 1.85, 95% CI: 1.18-2.91), history of cancer (PRR: 2.15, 95% CI: 1.05-4.41) and depression (PRR: 1.73, 95% CI: 1.13-2.65) were associated with poor SRH. Conclusion: Prevalence and factors associated with poor SRH in Singapore was comparable to other developed countries. SRH is an important subjective outcome of health and has the potential for wider use in clinical practice in Singapore. Key words: Chronic diseases, Socioeconomic factors, Subjective health indicators

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