Diagnosis and management of pneumonia in the emergency department.

Pneumonia is a condition that is often treated by emergency physicians. This article reviews the diagnosis and management of pneumonia in the emergency department and highlights dilemmas in diagnostic testing, use of blood and sputum cultures, hospital admission decisions, infection control, quality measures for pneumonia care, and empiric antimicrobial therapy

Identifying which septic patients have increased mortality risk using severity scores:a cohort study

Background: Early aggressive therapy can reduce the mortality associated with severe sepsis but this relies on prompt recognition, which is hindered by variation among published severity criteria. Our aim was to test the performance of different severity scores in predicting mortality among a cohort of hospital inpatients with sepsis. Methods: We anonymously linked routine outcome data to a cohort of prospectively identified adult hospital inpatients with sepsis, and used logistic regression to identify associations between mortality and demographic variables, clinical factors including blood culture results, and six sets of severity criteria. We calculated performance characteristics, including area under receiver operating characteristic curves (AUROC), of each set of severity criteria in predicting mortality. Results: Overall mortality was 19.4% (124/640) at 30 days after sepsis onset. In adjusted analysis, older age (odds ratio 5.79 (95% CI 2.87-11.70) for ≥80y versus <60y), having been admitted as an emergency (OR 3.91 (1.31-11.70) versus electively), and longer inpatient stay prior to sepsis onset (OR 2.90 (1.41-5.94) for >21d versus <4d), were associated with increased 30 day mortality. Being in a surgical or orthopaedic, versus medical, ward was associated with lower mortality (OR 0.47 (0.27-0.81) and 0.26 (0.11-0.63), respectively). Blood culture results (positive vs. negative) were not significantly association with mortality. All severity scores predicted mortality but performance varied. The CURB65 community-acquired pneumonia severity score had the best performance characteristics (sensitivity 81%, specificity 52%, positive predictive value 29%, negative predictive value 92%, for 30 day mortality), including having the largest AUROC curve (0.72, 95% CI 0.67-0.77). Conclusions: The CURB65 pneumonia severity score outperformed five other severity scores in predicting risk of death among a cohort of hospital inpatients with sepsis. The utility of the CURB65 score for risk-stratifying patients with sepsis in clinical practice will depend on replicating these findings in a validation cohort including patients with sepsis on admission to hospital

The Belgian policy of funding antimicrobial stewardship in hospitals and trends of selected quality indicators for antimicrobial use, 1999-2010: a longitudinal study

Objectives: In order to improve antimicrobial (AM) use, a policy of providing technical and financial support to AM management teams (AMTs) was rolled out in all Belgian hospitals between 2002 and 2008. We aimed to analyse the association of this policy with AM use for the two indications accounting for the largest number of patients receiving AM: prophylaxis for major lower limb orthopaedic surgery and pneumonia. Design, setting, participants: We used patient-level data routinely collected in all Belgian acute care hospitals between 1999 and 2010. We modelled trends for selected quality indicators (QIs) using the year of AMT implementation in each hospital as the main 'change point', with fine-tuned case-mix adjustment. Of all admissions for lower limb orthopaedic surgery, and pneumonia between 1999 and 2010, 90% (325 094) and 95% (327 635), respectively, were found eligible for analyses. Outcomes: The surgery QI was defined as: cefazolin, dose in the expected range, and no use of other AM. For pneumonia, QIs were: ratio of oral/parenteral defined daily doses (DDD, O/P QI), and mean number of DDD minus penicillin, per 100 days of hospitalisation (DDD QI). Results: Between 1999 and 2010, the surgery QI improved from 59% to 71%, the O/P QI from 0.72 to 0.97, and the DDD QI from 96 to 64. Heterogeneity between hospitals was high. Overall, no association was found with the year of implementation of the AMT. Conclusions: Improvements have been observed but could not be related at the national level to the policy under study. However, these results cannot be extrapolated to other QIs for AM use in hospitals. Our findings do not question the need for AMT, nor the need for continuation of AMT funding. Several recommendations can be made in order to make the best of Belgium's unique political and financial commitments in that field

Using data-driven rules to predict mortality in severe community acquired pneumonia

Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available. © 2014 Wu et al

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Systemic infections after acute stroke

After an acute stroke, systemic infection can complicate the recovery process and lead to a worse clinical outcome, including a higher risk of mortality. Post-stroke infection (PSI) is responsible for the majority of the mortality occurring between 1 week and 1 month after stroke, peaking towards the end of the second week. The effects of PSI on longer-term outcome and other aspects of recovery, such as cognition, mood and quality of life, are largely unknown. The cerebrovascular event itself may result in a systemic immunosuppressed state, hence lowering the threshold for subsequent systemic bacterial infections. Although there have been advances in the basic understanding of the pathophysiological mechanisms of PSI, clinical studies have not provided any clear guidelines on the best methods of managing or preventing PSI. This article provides a review of the current knowledge of the phenomenon of PSI and the possible future developments in the understanding and treatment of PSI

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

The prognostic ability of curb-65 in predicting outcomes of hospitalised patients with community acquired pneumonia in Hospital Universiti Sains Malaysia

Background Community Acquired pneumonia (CAP) is a common disease and many patients require admission, which is about 20-40% of patients. It also has high morbidity and mortality. CAP still remains one of the leading causes of death from infectious diseases. There are several validated tools to assess severity, predict mortality in patients admitted with CAP and guide clinical decision about the level of intervention required for better monitoring and treatment. The purpose of this study is to determine the prognostic ability of CURB-65 as a pneumonia severity score in predicting outcomes in hospitalized patients with CAP. Methodology The study was an observational retrospective cohort study performed for patients admitted to medical ward and intensive care unit (ICU) HUSM that fulfilled diagnosis for CAP, from June 2012 till May 2014. The clinical profiles for CAP in HUSM were elaborated in a descriptive study. The adverse outcomes that were investigated in this study were use of inotropic support, need of ventilation support, ICU admission and in hospital mortality. The prognostic ability of CURB-65 in predicting outcomes were analysed using 4 tests, i.e: Chi square test, SLR, ROC curve analysis and sensitivity, specificity and predictive values. The recommended cut off points to indicate higher CURB-65 score was 3 to 5. Results The majority of patients were Malay (95.4%) with almost equal male to female distribution and mean age of 63.29 (SD+16.55) years. The proportion of in hospital xvii mortality was 8.8%, use of inotropic support was 11.1% , need of ventilation support was 12.6% and need of ICU admission was 6.9%. CURB-65 score severity category demonstrated high sensitivity (89-100%), specificity (84-88%), negative predictive value (99-100%) and area under ROC curve; and significant association with all the adverse outcomes. It also had good to excellent discriminative values (0.853-0.938). Conclusion Our study showed CURB-65 had a prognostic ability in predicting outcomes i.e: used of inotropic support, need of ventilation support, need of ICU admission and inhospital mortality for hospitalised patients with community acquired pneumonia with high sensitivity and specificity