CURB-65 pneumonia severity assessment adapted for electronic decision support

Pneumonia diagnosis performance in the emergency department: a mixed-methods study about clinicians' experiences and exploration of individual differences and response to diagnostic performance feedback

OBJECTIVES

We sought to (1) characterize the process of diagnosing pneumonia in an emergency department (ED) and (2) examine clinician reactions to a clinician-facing diagnostic discordance feedback tool.

MATERIALS AND METHODS

We designed a diagnostic feedback tool, using electronic health record data from ED clinicians' patients to establish concordance or discordance between ED diagnosis, radiology reports, and hospital discharge diagnosis for pneumonia. We conducted semistructured interviews with 11 ED clinicians about pneumonia diagnosis and reactions to the feedback tool. We administered surveys measuring individual differences in mindset beliefs, comfort with feedback, and feedback tool usability. We qualitatively analyzed interview transcripts and descriptively analyzed survey data.

RESULTS

Thematic results revealed: (1) the diagnostic process for pneumonia in the ED is characterized by diagnostic uncertainty and may be secondary to goals to treat and dispose the patient; (2) clinician diagnostic self-evaluation is a fragmented, inconsistent process of case review and follow-up that a feedback tool could fill; (3) the feedback tool was described favorably, with task and normative feedback harnessing clinician values of high-quality patient care and personal excellence; and (4) strong reactions to diagnostic feedback varied from implicit trust to profound skepticism about the validity of the concordance metric. Survey results suggested a relationship between clinicians' individual differences in learning and failure beliefs, feedback experience, and usability ratings.

DISCUSSION AND CONCLUSION

Clinicians value feedback on pneumonia diagnoses. Our results highlight the importance of feedback about diagnostic performance and suggest directions for considering individual differences in feedback tool design and implementation.

ICU Utilization After Implementation of Minor Severe Pneumonia Criteria in Real-Time Electronic Clinical Decision Support

OBJECTIVES

To determine if the implementation of automated clinical decision support (CDS) with embedded minor severe community-acquired pneumonia (sCAP) criteria was associated with improved ICU utilization among emergency department (ED) patients with pneumonia who did not require vasopressors or positive pressure ventilation at admission.

DESIGN

Planned secondary analysis of a stepped-wedge, cluster-controlled CDS implementation trial.

SETTING

Sixteen hospitals in six geographic clusters from Intermountain Health; a large, integrated, nonprofit health system in Utah and Idaho.

PATIENTS

Adults admitted to the hospital from the ED with pneumonia identified by: 1) discharge International Classification of Diseases , 10th Revision codes for pneumonia or sepsis/respiratory failure and 2) ED chest imaging consistent with pneumonia, who did not require vasopressors or positive pressure ventilation at admission.

INTERVENTIONS

After implementation, patients were exposed to automated, open-loop, comprehensive CDS that aided disposition decision (ward vs. ICU), based on objective severity scores (sCAP).

MEASUREMENTS AND MAIN RESULTS

The analysis included 2747 patients, 1814 before and 933 after implementation. The median age was 71, median Elixhauser index was 17, 48% were female, and 95% were Caucasian. A mixed-effects regression model with cluster as the random effect estimated that implementation of CDS utilizing sCAP increased 30-day ICU-free days by 1.04 days (95% CI, 0.48-1.59; p < 0.001). Among secondary outcomes, the odds of being admitted to the ward, transferring to the ICU within 72 hours, and receiving a critical therapy decreased by 57% (odds ratio [OR], 0.43; 95% CI, 0.26-0.68; p < 0.001) post-implementation; mortality within 72 hours of admission was unchanged (OR, 1.08; 95% CI, 0.56-2.01; p = 0.82) while 30-day all-cause mortality was lower post-implementation (OR, 0.71; 95% CI, 0.52-0.96; p = 0.03).

CONCLUSIONS

Implementation of electronic CDS using minor sCAP criteria to guide disposition of patients with pneumonia from the ED was associated with safe reduction in ICU utilization.

Diagnosis and Treatment of Pneumonia in Urgent Care Clinics: Opportunities for Improving Care

Background

Community-acquired pneumonia is a well-studied condition; yet, in the urgent care setting, patient characteristics and adherence to guideline-recommended care are poorly described. Within Intermountain Health, a nonprofit integrated US health care system based in Utah, more patients present to urgent care clinics (UCCs) than emergency departments (EDs) for pneumonia care.

Methods

We performed a retrospective cohort study 1 January 2019 through 31 December 2020 in 28 UCCs within Utah. We extracted electronic health record data for patients aged ≥12 years with ICD-10 pneumonia diagnoses entered by the bedside clinician, excluding patients with preceding pneumonia within 30 days or missing vital signs. We compared UCC patients with radiographic pneumonia (n = 4689), without radiographic pneumonia (n = 1053), without chest imaging (n = 1472), and matched controls with acute cough/bronchitis (n = 15 972). Additional outcomes were 30-day mortality and the proportion of patients with ED visits or hospital admission within 7 days after the index encounter.

Results

UCC patients diagnosed with pneumonia and possible/likely radiographic pneumonia by radiologist report had a mean age of 40 years and 52% were female. Almost all patients with pneumonia (93%) were treated with antibiotics, including those without radiographic confirmation. Hospital admissions and ED visits within 7 days were more common in patients with radiographic pneumonia vs patients with "unlikely" radiographs (6% vs 2% and 10% vs 6%, respectively). Observed 30-day all-cause mortality was low (0.26%). Patients diagnosed without chest imaging presented similarly to matched patients with cough/acute bronchitis. Most patients admitted to the hospital the same day after the UCC visit (84%) had an interim ED encounter. Pneumonia severity scores (pneumonia severity index, electronic CURB-65, and shock index) overestimated patient need for hospitalization.

Conclusions

Most UCC patients with pneumonia were successfully treated as outpatients. Opportunities to improve care include clinical decision support for diagnosing pneumonia with radiographic confirmation and development of pneumonia severity scores tailored to the UCC.

Limitations in the use of automated mental status detection for clinical decision support

BACKGROUND

Clinical decision support (CDS) tools improve adherence to evidence-based practices but are dependent upon data quality in the electronic health record (EHR). Mental status is an integral component of many risk stratification scores, but it is not known whether EHR-measures of altered mental status are reliable. The Glasgow Coma Scale (GCS) is a measure of altered mentation that is widely adopted and entered in the EHR in structured format. We sought to determine the accuracy GCS < 15 as an EHR-measure of altered mentation compared to ED provider documentation.

METHODS

In patients presenting to an academic Emergency Department (ED) with pneumonia we abstracted GCS values entered by nurses during routine care and in a randomly selected subset manually reviewed provider documentation for evidence of altered mental status. We defined eConfusion as present if GCS < 15 at any point during the ED encounter. We then calculated the CURB-65 score and corresponding suggested disposition using each method. Performance of eConfusion and corresponding CURB-65 compared to manual versions was measured using agreement (Cohen's K), sensitivity, and specificity.

RESULTS

Among 300 randomly selected encounters, 47 (16 %) had eConfusion present and 46 (15 %) had evidence of altered mental status in provider documentation with Cohen's K 0.73. eConfusion had 78 % sensitivity and 96 % specificity for provider documented altered mental status. When input into CURB-65 to recommend inpatient disposition, eConfusion had 95 % sensitivity, and recommended discordant disposition for 3 %.

CONCLUSIONS

There was modest agreement between eConfusion and provider documentation of altered mental status. eConfusion had good specificity but low sensitivity which resulted in under-estimation of the CURB-65 score and occasional inappropriate disposition recommendations compared to provider documentation. These data do not support the use of GCS as a measure for altered mentation for use in CDS tools in the ED.

Predictive value of arterial blood lactate to serum albumin ratio for in-hospital mortality of patients with community-acquired pneumonia admitted to the Intensive Care Unit

OBJECTIVE

To investigate the predictive value of the arterial blood lactate to serum albumin ratio (LAR) on in-hospital mortality of patients with community-acquired pneumonia (CAP) admitted to the Intensive Care Unit (ICU).

METHODS

Clinical datasets of 1720 CAP patients admitted to ICU from MIMIC-IV database were retrospectively analyzed. Patients were randomly assigned to the training cohort (n=1204) and the validation cohort (n=516) in a ratio of 7:3. X-tile software was used to find the optimal cut-off value for LAR. The receiver operating curve (ROC) analysis was conducted to compare the performance between LAR and other indicators. Univariate and multivariate Cox regression analyses were applied to select prognostic factors associated with in-hospital mortality. Based on the observed prognostic factors, a nomogram model was created in training cohort, and the validation cohort was utilized to further validate the nomogram.

RESULTS

The optimal cut-off value for LAR in CAP patients admitted to ICU was 1.6 (the units of lactate and albumin were, respectively, 'mmol/L' and 'g/dL'). The ROC analysis showed that the discrimination abilities of LAR were superior to other indicators except Sequential Organ Failure Assessment score and Simplified acute physiology score (SAPSII), which had the same abilities. Age, mean arterial pressure, SpO2, heart rate, SAPSII score, neutrophil-to-lymphocyte ratio, and LAR were found to be independent predictors of poor overall survival in the training cohort by multivariate Cox regression analysis and were incorporated into the nomogram for in-hospital mortality as independent factors. The nomogram model, exhibiting medium discrimination, had a C-index of 0.746 (95% CI = 0.715-0.777) in the training cohort and 0.716 (95% CI = 0.667-0.765) in the validation cohort.

CONCLUSION

LAR could predict in-hospital mortality of patients with CAP admitted to ICU independently as a readily accessible biomarker. The nomogram that included LAR with other independent factors performed well in predicting in-hospital mortality.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers

Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission.

Machine-Learning Model for Mortality Prediction in Patients With Community-Acquired Pneumonia: Development and Validation Study

BACKGROUND

Artificial intelligence tools and techniques such as machine learning (ML) are increasingly seen as a suitable manner in which to increase the prediction capacity of currently available clinical tools, including prognostic scores. However, studies evaluating the efficacy of ML methods in enhancing the predictive capacity of existing scores for community-acquired pneumonia (CAP) are limited. We aimed to apply and validate a causal probabilistic network (CPN) model to predict mortality in patients with CAP.

RESEARCH QUESTION

Is a CPN model able to predict mortality in patients with CAP better than the commonly used severity scores?

STUDY DESIGN AND METHODS

This was a derivation-validation retrospective study conducted in two Spanish university hospitals. The ability of a CPN designed to predict mortality in sepsis (SepsisFinder [SeF]), and adapted for CAP (SeF-ML), to predict 30-day mortality was assessed and compared with other scoring systems (Pneumonia Severity Index [PSI], Sequential Organ Failure Assessment [SOFA], quick Sequential Organ Failure Assessment [qSOFA], and CURB-65 criteria [confusion, urea, respiratory rate, BP, age ≥ 65 years]). The SeF models are proprietary software. Differences between receiver operating characteristic curves were assessed by the DeLong method for correlated receiver operating characteristic curves.

RESULTS

The derivation cohort comprised 4,531 patients, and the validation cohort consisted of 1,034 patients. In the derivation cohort, the areas under the curve (AUCs) of SeF-ML, CURB-65, SOFA, PSI, and qSOFA were 0.801, 0.759, 0.671, 0.799, and 0.642, respectively, for 30-day mortality prediction. In the validation study, the AUC of SeF-ML was 0.826, concordant with the AUC (0.801) in the derivation data (P = .51). The AUC of SeF-ML was significantly higher than those of CURB-65 (0.764; P = .03) and qSOFA (0.729, P = .005). However, it did not differ significantly from those of PSI (0.830; P = .92) and SOFA (0.771; P = .14).

INTERPRETATION

SeF-ML shows potential for improving mortality prediction among patients with CAP, using structured health data. Additional external validation studies should be conducted to support generalizability.

How and when to manage respiratory infections out of hospital

Lower respiratory infections include acute bronchitis, influenza, community-acquired pneumonia, acute exacerbation of COPD and acute exacerbation of bronchiectasis. They are a major cause of death worldwide and often affect the most vulnerable: children, elderly and the impoverished. In this paper, we review the clinical presentation, diagnosis, severity assessment and treatment of adult outpatients with lower respiratory infections. The paper is divided into sections on specific lower respiratory infections, but we also dedicate a section to COVID-19 given the importance of the ongoing pandemic. Lower respiratory infections are heterogeneous entities, carry different risks for adverse events, and require different management strategies. For instance, while patients with acute bronchitis are rarely admitted to hospital and generally do not require antimicrobials, approximately 40% of patients seen for community-acquired pneumonia require admission. Clinicians caring for patients with lower respiratory infections face several challenges, including an increasing population of patients with immunosuppression, potential need for diagnostic tests that may not be readily available, antibiotic resistance and social aspects that place these patients at higher risk. Management principles for patients with lower respiratory infections include knowledge of local surveillance data, strategic use of diagnostic tests according to surveillance data, and judicious use of antimicrobials.

Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients

The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people’s lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.

Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning

The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8 + T Lymphocyte (Count), D-dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C-reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID-19 signs in CT scans included ground-glass opacity (GGO), followed by crazy-paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models.

Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study

Background

Computed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for the diagnosis of pulmonary embolism (PE), while posing risk for contrast-induced nephropathy and radiation-induced malignancy.

Objective

We aimed to create an automated process to calculate the Wells score for pulmonary embolism for patients in the ED, which could potentially reduce unnecessary CTPA testing.

Methods

We designed an automated process using electronic health records data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Wells scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at 2 tertiary care hospitals in New York, over a 2-month period. To validate the automated process, the scores were compared to those derived from a 2-clinician chart review.

Results

A total of 202 ED encounters resulted in a completed CTPA to form the retrospective study cohort. Patients classified as “PE likely” by the automated process (126/202, 62%) had a PE prevalence of 15.9%, whereas those classified as “PE unlikely” (76/202, 38%; Wells score >4) had a PE prevalence of 7.9%. With respect to classification of the patient as “PE likely,” the automated process achieved an accuracy of 92.1% when compared with the chart review, with sensitivity, specificity, positive predictive value, and negative predictive value of 93%, 90.5%, 94.4%, and 88.2%, respectively.

Conclusions

This was a successful development and validation of an automated process using electronic health records data elements, including free-text fields, to classify risk for PE in ED visits.

Influence of overweight and obesity on the mortality of hospitalized patients with community-acquired pneumonia

BACKGROUND

Obesity is associated with a better prognosis in patients with community-acquired pneumonia (the so-called obesity survival paradox), but conflicting results have been found.

AIM

To investigate the relationship between all-cause mortality and body mass index in patients with community-acquired pneumonia.

METHODS

This retrospective study included patients with community-acquired pneumonia hospitalized in the First Hospital of Qinhuangdao from June 2013 to November 2018. The patients were grouped as underweight (< 18.5 kg/m²), normal weight (18.5-23.9 kg/m²), and overweight/obesity (≥ 24 kg/m²). The primary outcome was all-cause hospital mortality.

RESULTS

Among 2327 patients, 297 (12.8%) were underweight, 1013 (43.5%) normal weight, and 1017 (43.7%) overweight/obesity. The all-cause hospital mortality was 4.6% (106/2327). Mortality was lowest in the overweight/obesity group and highest in the underweight group (2.8%, vs 5.0%, vs 9.1%, P < 0.001). All-cause mortality of overweight/obesity patients was lower than normal-weight patients [odds ratio (OR) = 0.535, 95% confidence interval (CI) = 0.334-0.855, P = 0.009], while the all-cause mortality of underweight patients was higher than that of normal-weight patients (OR = 1.886, 95%CI: 1.161-3.066, P = 0.010). Multivariable analysis showed that abnormal neutrophil counts (OR = 2.38, 95%CI: 1.55-3.65, P < 0.001), abnormal albumin levels (OR = 0.20, 95%CI: 0.06-0.72, P = 0.014), high-risk Confusion-Urea-Respiration-Blood pressure-65 score (OR = 2.89, 95%CI: 1.48-5.64, P = 0.002), and intensive care unit admission (OR = 3.11, 95%CI: 1.77-5.49, P < 0.001) were independently associated with mortality.

CONCLUSION

All-cause mortality of normal-weight patients was higher than overweight/ obesity patients, lower than that of underweight patients. Neutrophil counts, albumin levels, Confusion-Urea-Respiration-Blood pressure-65 score, and intensive care unit admission were independently associated with mortality in patients with community-acquired pneumonia.

Clinical Implication of Candida Score in Multidrug-Resistant Pneumonia with Airway Candida Colonization

Background

The growth of Candida in respiratory secretions is usually considered colonization, and antifungal therapy is rarely required. The role of Candida colonization in the progression of bacterial pneumonia remains controversial. The aim of this study was to identify the clinical implication of Candida score by analyzinge the relationship with multidrug-resistant (MDR) pneumonia and prognosis in patients with airway Candida colonization.

Materials and Methods

This study was a retrospective review of patients with airway Candida colonization by bronchial washing or bronchoalveolar lavage. The Candida score was calculated according to the four factors (severe sepsis, surgery at baseline, total parenteral nutrition, and multifocal Candida colonization). Pneumonia related mortality or hopeless discharge expecting death was defined as a poor outcome.

Results

A total of 148 patients were enrolled in the study. In a multivariate analysis model, Candida score was identified as an independent predictor of poor outcomes (odds ratio 2.23; 95% confidential interval 1.57 – 3.17; P <0.001) in pneumonia patients with airway Candida colonization. With a Candida score of three or higher compared with low score group, it was associated with bacterial pneumonia, especially methicillin-resistant Staphylococcus aureus (MRSA) infection (0.0% vs. 15.2%, P = 0.004). In addition, patients with a high Candida score had a longer hospital stay (13 vs. 38 days, P <0.001), longer duration of intensive care (7 vs. 18 days, P <0.001), and higher pneumonia-related mortality (0.0% vs. 45.5%, P <0.001) as compared to the low Candida score group. The Candida score showed a positive correlation with other pneumonia severity scales such as CURB-65 (Confusion, Urea, Respiratory rate, Blood pressure, and age ≥65 years) (r = 0.461, P <0.001), Pneumonia Severity Index (r = 0.397, P <0.001), and predisposition, insult, response, and organ dysfunction (PIRO) score (r = 0.425, P <0.001).

Conclusion

This study revealed that Candida is no longer a bystander of airway colonization, and that it affects the progression of bacterial pneumonia, including multidrug-resistant pathogens, particularly MRSA infection. Also Candida score can be used to predict the prognosis of patients with pneumonia.

Deploying an Electronic Clinical Decision Support Tool for Diagnosis and Treatment of Pneumonia Into Rural and Critical Access Hospitals: Utilization, Effect on Processes of Care, and Clinician Satisfaction

PURPOSE

Electronic clinical decision support (CDS) for treatment of community-acquired pneumonia (ePNa) is associated with improved guideline adherence and decreased mortality. How rural providers respond to CDS developed for urban hospitals could shed light on extending CDS to resource-limited settings.

METHODS

ePNa was deployed into 10 rural and critical access hospital emergency departments (EDs) in Utah and Idaho in 2018. We reviewed pneumonia cases identified through ICD-10 codes after local deployment to measure ePNa utilization and guideline adherence. ED providers were surveyed to assess quantitative and qualitative aspects of satisfaction.

FINDINGS

ePNa was used in 109/301 patients with pneumonia (36%, range 0%-67% across hospitals) and was associated with appropriate antibiotic selection (93% vs 65%, P < .001). Fifty percent of survey recipients responded, 87% were physicians, 87% were men, and the median ED experience was 10 years. Mean satisfaction with ePNa was 3.3 (range 1.7-4.8) on a 5-point Likert scale. Providers with a favorable opinion of ePNa were more likely to favor implementation of additional CDS (P = .005). Satisfaction was not associated with provider type, age, years of experience or experience with ePNa. Ninety percent of respondents provided qualitative feedback. The most common theme in high and low utilization hospitals was concern about usability. Compared to high utilization hospitals, low utilization hospitals more frequently identified concerns about adaptation for local needs.

CONCLUSIONS

ePNa deployment to rural and critical access EDs was moderately successful and associated with improved antibiotic use. Concerns about usability and adapting ePNa for local use predominated the qualitative feedback.

Guía de práctica clínica para el manejo de la neumonía adquirida en la comunidad

La neumonía sigue siendo una de las principales causas de consulta y de hospitalización a la que, además de su un alto impacto en términos de morbilidad y mortalidad, se suma la actual problemática de resistencia a los antimicrobianos, por lo que establecer directrices que permitan su adecuado diagnóstico y tratamiento es de gran importancia para obtener mejores desenlaces clínicos y promover un uso racional de antibióticos en estos pacientes. La presente guía de práctica clínica (GPC) contiene recomendaciones basadas en la evidencia para el diagnóstico y tratamiento de la neumonía adquirida en la comunidad en adultos, las cuales fueron realizadas mediante el proceso de adaptación de GPC basadas en la evidencia para el contexto colombiano.

Comparison of Frailty Index to Pneumonia Severity Measures in Older Patients With Pneumonia

OBJECTIVES

Risk stratification tools are useful to provide appropriate clinical care for older patients with pneumonia. This study aimed to compare a Frailty Index (FI) with pneumonia severity measures, CURB-65, and the Pneumonia Severity Index (PSI), for predicting mortality and persistent disability after pneumonia.

DESIGN

Single-center prospective cohort study.

SETTING AND PARTICIPANTS

The study included 190 patients aged ≥65 years who were hospitalized with pneumonia at a university hospital in Korea between October 2019 and September 2020.

METHODS

At admission, a 50-item deficit-accumulation FI (range: 0-1), CURB-65 (range: 0-5), and PSI (range: 0-395) scores were calculated. The outcomes were death and a composite outcome of death or decline in ability to perform daily activities and physical task 6 months later.

RESULTS

The median age was 79 years (interquartile range: 74-85), and 70 (36.8%) patients were women. The patients who died (n = 53) had higher FI (median, 0.46 vs 0.20; P < .011), CURB-65 score (median, 3 vs 2; P = .001), and PSI score (median, 149 vs 116; P < .001) than those who did not. The C-statistics (95% confidence intervals) for 6-month mortality were 0.69 (0.61-0.77) for the FI, 0.62 (0.53-0.71) for CURB-65, and 0.71 (0.62-0.79) for the PSI (P = .019). The C-statistics for the 6-month composite outcome were 0.73 (0.65-0.81) for the FI, 0.64 (0.55-0.73) for CURB-65, and 0.69 (0.60-0.77) for the PSI (P = .096). The C-statistics improved when the FI was added to CURB-65 (from 0.64 to 0.74; P = .003) and to the PSI (from 0.69 to 0.75; P = .044) for the composite outcome.

CONCLUSIONS AND IMPLICATIONS

Measuring frailty provides additive value to widely used pneumonia severity measures in predicting death or persistent hospitalization-associated disability in older adults after pneumonia hospitalization. Early recognition of frailty may be useful to identify those who require in-hospital and post-acute care interventions for functional recovery.

Computerized Mortality Prediction for Community-acquired Pneumonia at 117 Veterans Affairs Medical Centers

Rationale: Computerized severity assessment for community-acquired pneumonia could improve consistency and reduce clinician burden. Objectives: To develop and compare 30-day mortality-prediction models using electronic health record data, including a computerized score with all variables from the original Pneumonia Severity Index (PSI) except confusion and pleural effusion ("ePSI score") versus models with additional variables. Methods: Among adults with community-acquired pneumonia presenting to emergency departments at 117 Veterans Affairs Medical Centers between January 1, 2006, and December 31, 2016, we compared an ePSI score with 10 novel models employing logistic regression, spline, and machine learning methods using PSI variables, age, sex and 26 physiologic variables as well as all 69 PSI variables. Models were trained using encounters before January 1, 2015; tested on encounters during and after January 1, 2015; and compared using the areas under the receiver operating characteristic curve, confidence intervals, and patient event rates at a threshold PSI score of 970. Results: Among 297,498 encounters, 7% resulted in death within 30 days. When compared using the ePSI score (confidence interval [CI] for the area under the receiver operating characteristic curve, 0.77-0.78), performance increased with model complexity (CI for the logistic regression PSI model, 0.79-0.80; CI for the boosted decision-tree algorithm machine learning PSI model using the Extreme Gradient Boosting algorithm [mlPSI] with the 19 original PSI factors, 0.83-0.85) and the number of variables (CI for the logistic regression PSI model using all 69 variables, 0.84-085; CI for the mlPSI with all 69 variables, 0.86-0.87). Models limited to age, sex, and physiologic variables also demonstrated high performance (CI for the mlPSI with age, sex, and 26 physiologic factors, 0.84-0.85). At an ePSI score of 970 and a mortality-risk cutoff of <2.7%, the ePSI score identified 31% of all patients as being at "low risk"; the mlPSI with age, sex, and 26 physiologic factors identified 53% of all patients as being at low risk; and the mlPSI with all 69 variables identified 56% of all patients as being at low risk, with similar rates of mortality, hospitalization, and 7-day secondary hospitalization being determined. Conclusions: Computerized versions of the PSI accurately identified patients with pneumonia who were at low risk of death. More complex models classified more patients as being at low risk of death and as having similar adverse outcomes.

Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia

The soluble form of the suppression of tumorigenicity-2 (sST2) is a biomarker for risk classification and prognosis of heart failure, and its production and secretion in the alveolar epithelium are significantly correlated with the inflammation-inducing in pulmonary diseases. However, the predictive value of sST2 in pulmonary disease had not been widely studied. This study investigated the potential value in prognosis and risk classification of sST2 in patients with community-acquired pneumonia. Clinical data of ninety-three CAP inpatients were retrieved and their sST2 and other clinical indices were studied. Cox regression models were constructed to probe the sST2's predictive value for patients' restoring clinical stability and its additive effect on pneumonia severity index and CURB-65 scores. Patients who did not reach clinical stability within the defined time (30 days from hospitalization) have had significantly higher levels of sST2 at admission (P < 0.05). In univariate and multivariate Cox regression analysis, a high sST2 level (≥72.8 ng/mL) was an independent reverse predictor of clinical stability (P < 0.05). The Cox regression model combined with sST2 and CURB-65 (AUC: 0.96) provided a more accurate risk classification than CURB-65 (AUC:0.89) alone (NRI: 1.18, IDI: 0.16, P < 0.05). The Cox regression model combined with sST2 and pneumonia severity index (AUC: 0.96) also provided a more accurate risk classification than pneumonia severity index (AUC:0.93) alone (NRI: 0.06; IDI: 0.06, P < 0.05). sST2 at admission can be used as an independent early prognostic indicator for CAP patients. Moreover, it can improve the predictive power of CURB-65 and pneumonia severity index score.

Novel outpatient management of mild to moderate COVID-19 spares hospital capacity and safeguards patient outcome: The Geneva PneumoCoV-Ambu study

BACKGROUND

Severe Acute Respiratory Coronavirus 2 (SARS-CoV-2), the novel coronavirus that causes coronavirus disease (COVID-19), is creating an unprecedented burden on health care systems across the world due to its high rate of pneumonia-related hospitalizations. This study presents recommendations for the outpatient management of moderate SARS-CoV-2 pneumonia implemented at the Geneva University Hospital, Switzerland, from April 4 to June 30, 2020 and evaluated the impact of these recommendations on patient safety, patient satisfaction, and overall hospital capacity.

METHODS

Recommendations for the outpatient management of moderate pneumonia implemented in the Geneva University Hospital (PneumoCoV-Ambu) between April 4 and June 30, 2020, were evaluated prospectively. The primary endpoint was hospitalization. Secondary endpoints were: severity of COVID-19 disease based on a 7-points ordinal scale assessed at 1 and 2 months following SARS-CoV-2 infection; patient satisfaction using a satisfaction survey and the analysis of number of beds and costs potentially averted.

RESULTS

A total of 36 patients with COVID-19-related pneumonia were followed between April 4 and May 5, 2020. Five patients (14%) were hospitalized and none died over a median of 30 days follow-up. The majority of patients (n = 31; 86%) were satisfied with the ambulatory care they received. These novel recommendations for outpatient management resulted in sparing an estimated potential 124 hospital bed-nights and CHF 6'826 per capita averted hospitalization costs over the three months period.

CONCLUSIONS

Recommendations developed for the outpatient management of COVID-19-related pneumonia were able to spare hospital capacity without increasing adverse patient outcomes. Widely implementing such recommendations is crucial in preserving hospital capacity during this pandemic.

Finasteride in hospitalized adult males with COVID-19: A risk factor for severity of the disease or an adjunct treatment: A randomized controlled clinical trial

Background: There is controversy about the efficacy of 5-alpha-reductase inhibitors in COVID-19 patients. Some assumed that finasteride might be a risk factor for deterioration and others proposed it as a possible adjunct treatment for moderate to severe COVID-19 infection in the elderly.

Methods: We performed a randomized controlled clinical trial (registration ID IRCT20200505047318N1) on 80 hospitalized male patients aged ≥50 years diagnosed with COVID-19 pneumonia in a tertiary hospital in Qazvin (Iran) from April to July 2020. The patients were randomized into one of the 2 treatment groups using simple randomization. Treatment group patients underwent routine drug therapy and 5 mg finasteride once daily for 7 days. The primary endpoint was mortality rate and length of hospital stay (LOS), and secondary endpoints were peripheral capillary oxygen saturation, respiratory rate, and inflammatory markers changes. The study protocol was approved by the medical ethics committee of Qazvin University of Medical Sciences (registration ID IR.QUMS.REC.1399.080). Data were analyzed by statistical tests and SPSS version 25. Also, p<0.05 was considered to be statistically significant.

Results: We found a significant difference on O2 saturation among the 2 study groups on fifth day compared with the admission time (p= 0.018). The results did not show significant differences in mortality rate (2.5% vs 10%; p= 0.166) and LOS (p= 0.866) between patients in the finasteride and the control group.

Conclusion: A short course of finasteride administration partially improves O2 saturation but does not influence other outcomes in hospitalized male patients aged ≥50 years with COVID-19 pneumonia. Further research in a large scale with longer follow-up is required to help clarify the role of finasteride in this setting.

Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score

Objective

Develop and validate a risk score using variables available during an Emergency Department (ED) encounter to predict adverse events among patients with suspected COVID-19.

Methods

A retrospective cohort study of adult visits for suspected COVID-19 between March 1 – April 30, 2020 at 15 EDs in Southern California. The primary outcomes were death or respiratory decompensation within 7-days. We used least absolute shrinkage and selection operator (LASSO) models and logistic regression to derive a risk score. We report metrics for derivation and validation cohorts, and subgroups with pneumonia or COVID-19 diagnoses.

Results

26,600 ED encounters were included and 1079 experienced an adverse event. Five categories (comorbidities, obesity/BMI ≥ 40, vital signs, age and sex) were included in the final score. The area under the curve (AUC) in the derivation cohort was 0.891 (95% CI, 0.880–0.901); similar performance was observed in the validation cohort (AUC = 0.895, 95% CI, 0.874–0.916). Sensitivity ranging from 100% (Score 0) to 41.7% (Score of ≥15) and specificity from 13.9% (score 0) to 96.8% (score ≥ 15). In the subgroups with pneumonia (n = 3252) the AUCs were 0.780 (derivation, 95% CI 0.759–0.801) and 0.832 (validation, 95% CI 0.794–0.870), while for COVID-19 diagnoses (n = 2059) the AUCs were 0.867 (95% CI 0.843–0.892) and 0.837 (95% CI 0.774–0.899) respectively.

Conclusion

Physicians evaluating ED patients with pneumonia, COVID-19, or symptoms suspicious for COVID-19 can apply the COVAS score to assist with decisions to hospitalize or discharge patients during the SARS CoV-2 pandemic.

Prognostic Implications of Preoperative Pneumonia for Geriatric Patients Undergoing Hip Fracture Surgery or Arthroplasty

Objective

To report outcomes of geriatric patients undergoing hip fracture surgery or arthroplasty with or without preoperative pneumonia and to evaluate the influence of pneumonia severity on patient prognosis.

Methods

In this single center retrospective study, we included geriatric patients (≥60 years old) who had undergone hip fracture surgery or arthroplasty at Peking University People's Hospital from January 2008 to September 2018. Patients with fractures caused by neoplasms or patients with incomplete clinical data were excluded. Using logistic regression and the CURB‐65 (confusion, uremia, respiratory rate, blood pressure, and age ≥65 years) score as a prediction tool of 1‐year mortality, the effect of preoperative pneumonia on 1‐year mortality was evaluated. Survival of patients with different response to pneumonia‐specific therapy and survival of patients with different pneumonia severity (evaluated with CURB‐65 score) were analyzed using Cox regression.

Results

A total of 1386 patients were included; among them, 109 patients (7.86%) were diagnosed with preoperative pneumonia. Outcomes were evaluated in August 2019 (at least 1 year after surgery for all patients). Compared to patients without preoperative pneumonia, patients with this condition had higher 30‐day mortality (11.9% vs 5%, P = 0.002) and 1‐year mortality rates (33.9% vs 16.3%, P < 0.001) and higher incidence of acute heart failure (7.3% vs 3.4%, P = 0.034) and acute kidney injury (5.5% vs 1.8%, P = 0.009). In multivariate regression, preoperative pneumonia was identified as an independent predictor of 1‐year mortality (odds ratio [OR], 1.45; 95% confidence interval [CI] 1.39–3.52; P = 0.021), with other factors including age (≥84 years, OR, 1.46; 95% CI 1.08–1.60; P = 0.027), body mass index (<18.5 kg/m², OR 2.23; 95% CI 1.52–3.17, P < 0.001), anesthesia type (regional, OR 0.87; 95% CI 0.19–0.97, P = 0.042), preoperative pneumonia (OR 1.45; 95% CI 1.39–3.52; P = 0.002), congestive heart failure (OR 2.05, 95% CI 1.57–6.21, P < 0.001), chronic kidney disease (OR 1.73; 95% CI 1.50–2.62; P < 0.001). There was a trend of increased 1‐year mortality as the CURB‐65 score elevated (P for trend = 0.006). Cox regression reveals a higher risk of mortality in patient with preoperative pneumonia, especially in patients with no radiologic improvements after therapy (log‐rank, P = 0.035). Analysis of the impact of pneumonia severity on patient survival using Cox regression reveals that a CURB‐65 score ≥3 indicated a lower rate of survival (CURB‐65 score of 3: hazard ratio [HR] 3.12, 95% CI 1.39–7.03, P = 0.006; score of 4: HR 3.41, 95% CI 1.69–6.92, P = 0.001; score of 5: HR 6.28, 95% CI 2.95–13.35, P < 0.001).

Conclusion

In this single center retrospective study, preoperative pneumonia was identified as an independent risk factor of 1‐year mortality in geriatric patients undergoing hip fracture surgery or arthroplasty. A CURB‐65 score ≥3 indicated a higher risk of mortality.

Diagnosis and Treatment of Adults with Community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America

Background: This document provides evidence-based clinical practice guidelines on the management of adult patients with community-acquired pneumonia.

Methods: A multidisciplinary panel conducted pragmatic systematic reviews of the relevant research and applied Grading of Recommendations, Assessment, Development, and Evaluation methodology for clinical recommendations.

Results: The panel addressed 16 specific areas for recommendations spanning questions of diagnostic testing, determination of site of care, selection of initial empiric antibiotic therapy, and subsequent management decisions. Although some recommendations remain unchanged from the 2007 guideline, the availability of results from new therapeutic trials and epidemiological investigations led to revised recommendations for empiric treatment strategies and additional management decisions.

Conclusions: The panel formulated and provided the rationale for recommendations on selected diagnostic and treatment strategies for adult patients with community-acquired pneumonia.

Implementation of Real-Time Electronic Clinical Decision Support for Emergency Department Patients with Pneumonia Across a Healthcare System

A real-time electronic CDS for pneumonia (ePNa) identifies possible pneumonia patients, measures severity and antimicrobial resistance risk, and then recommends disposition, antibiotics, and microbiology studies. Use is voluntary, and clinicians may modify treatment recommendations. ePNa was associated with lower mortality in emergency department (ED) patients versus usual care (Annals EM 66:511). We adapted ePNa for the Cerner EHR, and implemented it across Intermountain Healthcare EDs (Utah, USA) throughout 2018. We introduced ePNa through didactic, interactive presentations to ED clinicians; follow-up visits identified barriers and facilitators to use. Email reminded clinicians and answered questions. Hospital admitting clinicians encouraged ePNa use to smooth care transitions. Audit-and-feedback measured utilization, showing variations from best practice when ePNa and associated electronic order sets were not used. Use was initially low, but gradually increased especially at larger hospitals. A user-friendly interface, frequent reminders, audit-and- feedback, a user survey, a nurse educator, and local physician champions are additive towards implementation success.

Broad-spectrum antibiotic use and poor outcomes in community-onset pneumonia: a cohort study

QUESTION

Is broad-spectrum antibiotic use associated with poor outcomes in community-onset pneumonia after adjusting for confounders?

METHODS

We performed a retrospective, observational cohort study of 1995 adults with pneumonia admitted from four US hospital emergency departments. We used multivariable regressions to investigate the effect of broad-spectrum antibiotics on 30-day mortality, length of stay, cost and Clostridioides difficile infection (CDI). To address indication bias, we developed a propensity score using multilevel (individual provider) generalised linear mixed models to perform inverse-probability of treatment weighting (IPTW) to estimate the average treatment effect in the treated. We also manually reviewed a sample of mortality cases for antibiotic-associated adverse events.

RESULTS

39.7% of patients received broad-spectrum antibiotics, but drug-resistant pathogens were recovered in only 3%. Broad-spectrum antibiotics were associated with increased mortality in both the unweighted multivariable model (OR 3.8, 95% CI 2.5-5.9; p<0.001) and IPTW analysis (OR 4.6, 95% CI 2.9-7.5; p<0.001). Broad-spectrum antibiotic use by either analysis was also associated with longer hospital stay, greater cost and increased CDI. Healthcare-associated pneumonia was not associated with mortality independent of broad-spectrum antibiotic use. In manual review we identified antibiotic-associated events in 17.5% of mortality cases.

CONCLUSION

Broad-spectrum antibiotics appear to be associated with increased mortality and other poor outcomes in community-onset pneumonia.

Short-Term Air Pollution and Incident Pneumonia. A Case-Crossover Study

RATIONALE

The relationship between air pollution and pneumonia is poorly understood.

OBJECTIVES

To examine relationships between short-term air pollution exposure and number and severity of pneumonia cases along the Wasatch Front in Utah, a region with periodic high levels of outdoor air pollution.

METHODS

We applied time-stratified case-crossover analyses with distributed lag to patients presenting to seven emergency departments with pneumonia over a 2-year period. We compared levels of particulate matter less than or equal to 2.5 μm in aerodynamic diameter, nitrogen dioxide, and ozone at patient residences with emergency department cases, hospitalizations, objectively defined severe pneumonia, and mortality. We calculated direct cost impacts of particulate matter less than or equal to 2.5 μm in aerodynamic diameter reduction.

RESULTS

We evaluated 4,336 pneumonia cases in seven hospitals. Among adults aged 65 years and older, we found consistently positive associations between particulate matter less than or equal to 2.5 μm in aerodynamic diameter within 6 days of presentation and instances of pneumonia (Lag Day 1 adjusted odds ratio, 1.35 per 10 μg/m³ over 12 μg/m³; 95% confidence interval, 1.16-1.57), severe pneumonia (Lag Day 1 adjusted odds ratio, 1.38; 95% confidence interval, 1.06-1.80), and inpatient mortality (Lag Day 5 adjusted odds ratio, 1.50; 95% confidence interval, 1.03-2.16). Smaller associations were found between nitrogen dioxide exposure and pneumonia occurrence, severity, and inpatient and 30-day mortality. Ozone exposure was modestly associated with increased instance and severity of pneumonia in younger adults. Particulate matter less than or equal to 2.5 μm in aerodynamic diameter and nitrogen dioxide effects were greatest in colder months, and ozone effects were greatest in warmer months. Reduction of particulate matter less than or equal to 2.5 μm in aerodynamic diameter levels to less than 12.0 mg/m³ could prevent 76-112 cases of pneumonia per year in these hospitals serving approximately half of the Wasatch Front's population, reducing direct medical facility costs by $807,000 annually.

CONCLUSIONS

Among older adults, short-term ambient particulate matter less than or equal to 2.5 μm in aerodynamic diameter exposure is associated with more emergency department visits and hospitalizations for pneumonia, severe pneumonia, increased mortality, and increased healthcare costs. Nitrogen dioxide and ozone modestly increase pneumonia risk and illness severity.

Long-Term Mortality and Prognostic Factors in Aspiration Pneumonia

OBJECTIVES

Aspiration pneumonia is a leading cause of death among older patients; however, little is known about the long-term mortality in aspiration pneumonia. The purpose of this study was to evaluate long-term mortality and its associated factors in patients with aspiration pneumonia.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

In total, 550 patients with aspiration pneumonia (median age: 78.0 years, 66.4% male) with compatible clinical symptoms and chest computed tomography images were enrolled at a single tertiary center from 2006 to 2016.

MEASURES

The 1-, 3-, and 5-year mortality rates were evaluated for all patients. The prognostic factors for 1-year and 5-year mortality were also evaluated using Cox proportional hazard models.

RESULTS

A total of 441 (80.2%) patients died during a median follow-up of 50.7 weeks. The 1-, 3-, and 5-year mortality rates were 49.0%, 67.1%, and 76.9%, respectively. Multivariate analysis identified 5 risk factors for 1-year mortality of male sex [hazard ratio (HR) 1.533, P = .003], low body mass index (HR 0.934, P = .002), hypoalbuminemia, anemia (0.973, P = .032), and mechanical ventilation (HR 2.052, P < .001), which were also independent prognostic factors for 5-year mortality. During the follow-up period, 133 (24.2%) patients experienced recurrent aspiration pneumonia. However, Kaplan-Meier analysis showed no significant differences in survival curves between patients with single and recurrent aspiration pneumonia (P = .371).

CONCLUSIONS/IMPLICATIONS

Long-term prognosis of aspiration pneumonia was poor as a result of underlying morbidity instead of the aspiration pneumonia itself. Our findings suggest that prognostic indices for patients with aspiration pneumonia including the patient's underlying conditions should be devised.

Antibiotic Use and Outcomes After Implementation of the Drug Resistance in Pneumonia Score in ED Patients With Community-Onset Pneumonia

BACKGROUND

To guide rational antibiotic selection in community-onset pneumonia, we previously derived and validated a novel prediction tool, the Drug-Resistance in Pneumonia (DRIP) score. In 2015, the DRIP score was integrated into an existing electronic pneumonia clinical decision support tool (ePNa).

METHODS

We conducted a quasi-experimental, pre-post implementation study of ePNa with DRIP score (2015) vs ePNa with health-care-associated pneumonia (HCAP) logic (2012) in ED patients admitted with community-onset pneumonia to four US hospitals. Using generalized linear models, we used the difference-in-differences method to estimate the average treatment effect on the treated with respect to ePNa with DRIP score on broad-spectrum antibiotic use, mortality, hospital stay, and cost, adjusting for available patient-level confounders.

RESULTS

We analyzed 2,169 adult admissions: 1,122 in 2012 and 1,047 in 2015. A drug-resistant pathogen was recovered in 3.2% of patients in 2012 and 2.8% in 2015; inadequate initial empirical antibiotics were prescribed in 1.1% and 0.5%, respectively (P = .12). A broad-spectrum antibiotic was administered in 40.1% of admissions in 2012 and 33.0% in 2015 (P < .001). Vancomycin days of therapy per 1,000 patient days in 2012 were 287.3 compared with 238.8 in 2015 (P < .001). In the primary analysis, the average treatment effect among patients using the DRIP score was a reduction in broad-spectrum antibiotic use (OR, 0.62; 95% CI, 0.39-0.98; P = .039). However, the average effects for ePNa with DRIP on mortality, length of stay, and cost were not statistically significant.

CONCLUSIONS

Electronic calculation of the DRIP score was more effective than HCAP criteria for guiding appropriate broad-spectrum antibiotic use in community-onset pneumonia.

CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support

BACKGROUND

Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process.

OBJECTIVES

This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations.

METHODS

We implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis.

RESULTS

Among 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection.

CONCLUSION

While adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system.

Performance of the CURB-65 Score in Predicting Critical Care Interventions in Patients Admitted With Community-Acquired Pneumonia

STUDY OBJECTIVE

Confusion, uremia, elevated respiratory rate, hypotension, and aged 65 years or older (CURB-65) is a clinical prediction rule intended to stratify patients with pneumonia by expected mortality. We assess the predictive performance of CURB-65 for the proximal endpoint of receipt of critical care intervention in emergency department (ED) patients admitted with community-acquired pneumonia.

METHODS

We performed a retrospective analysis of electronic health records from a single tertiary center for ED patients admitted as inpatients with a primary diagnosis of pneumonia from 2010 to 2014. Patients with a history of malignancy, tuberculosis, bronchiectasis, HIV, or readmission within 14 days were excluded. We assessed the predictive accuracy of CURB-65 for receipt of critical care interventions (ie, vasopressors, large-volume intravenous fluids, invasive catheters, assisted ventilation, insulin infusions, or renal replacement therapy) and inhospital mortality. Logistic regression was performed to assess the increase in odds of critical care intervention or inhospital mortality by increasing CURB-65 score.

RESULTS

There were 2,322 patients admitted with community-acquired pneumonia in the study cohort; 630 (27.1%) were admitted to the ICU within 48 hours of ED triage and 343 (14.8%) received a critical care intervention. Of patients with a CURB-65 score of 0 to 1, 181 (15.6%) were admitted to the ICU, 74 (6.4%) received a critical care intervention, and 7 (0.6%) died. Of patients with a CURB-65 score of 2, 223 (27.0%) were admitted to the ICU, 127 (15.4%) received a critical care intervention, and 47 (5.7%) died. Among patients with CURB-65 score greater than or equal to 3, 226 (67.0%) were admitted to the ICU, 142 (42.1%) received a critical care intervention, and 43 (12.8%) died. The areas under the receiver operating characteristic for CURB-65 as a predictor of critical care intervention and mortality were 0.73 and 0.77, whereas sensitivity of CURB-65 score greater than or equal to 2 in predicting critical care intervention was 78.4%; for mortality, 92.8%.

CONCLUSION

Patients with CURB-65 score less than or equal to 2 were often admitted to the ICU and received critical care interventions. Given this finding and the relatively low sensitivity of CURB-65 for critical care intervention, clinicians should exercise caution when using CURB-65 to guide disposition. Future ED-based clinical prediction rules may benefit from calibration to proximal endpoints.

Automated versus Manual Data Extraction of the Padua Prediction Score for Venous Thromboembolism Risk in Hospitalized Older Adults

OBJECTIVE

Venous thromboembolism (VTE) prophylaxis is an important consideration for hospitalized older adults, and the Padua Prediction Score (PPS) is a risk prediction tool used to prioritize patient selection. We developed an automated PPS (APPS) algorithm using electronic health record (EHR) data. This study examines the accuracy of APPS and its individual components versus manual data extraction.

METHODS

This is a retrospective cohort study of hospitalized general internal medicine patients, aged 70 and over. Fourteen clinical variables were collected to determine their PPS; APPS used EHR data exports from health system databases, and a trained abstractor performed manual chart abstractions. We calculated sensitivity and specificity of the APPS, using manual PPS as the gold standard for classifying risk category (low vs. high). We also examined performance characteristics of the APPS for individual variables.

RESULTS

PPS was calculated by both methods on 311 individuals. The mean PPS was 3.6 (standard deviation, 1.8) for manual abstraction and 2.8 (1.4) for APPS. In detecting patients at high risk for VTE, the sensitivity and specificity of the APPS algorithm were 46 and 94%, respectively. The sensitivity for APPS was poor (range: 6-34%) for detecting acute conditions (i.e., acute myocardial infarction), moderate (range: 52-74%) for chronic conditions (i.e., heart failure), and excellent (range: 94-98%) for conditions of obesity and restricted mobility. Specificity of the automated extraction method for each PPS variable was > 87%.

CONCLUSION

APPS as a stand-alone tool was suboptimal for classifying risk of VTE occurrence. The APPS accurately identified high risk patients (true positives), but lower scores were considered indeterminate.

Broad- versus Narrow-Spectrum Oral Antibiotic Transition and Outcomes in Health Care-associated Pneumonia

RATIONALE

Guidelines recommend a switch from intravenous to oral antibiotics once patients who are hospitalized with pneumonia achieve clinical stability. However, little evidence guides the selection of an oral antibiotic for patients with health care-associated pneumonia, especially where no microbiological diagnosis is made.

OBJECTIVES

To compare outcomes between patients who were transitioned to broad- versus narrow-spectrum oral antibiotics after initially receiving broad-spectrum intravenous antibiotic coverage.

METHODS

We performed a secondary analysis of an existing database of adults with community-onset pneumonia admitted to seven Utah hospitals. We identified 220 inpatients with microbiology-negative health care-associated pneumonia from 2010 to 2012. After excluding inpatient deaths and treatment failures, 173 patients remained in which broad-spectrum intravenous antibiotics were transitioned to an oral regimen. We classified oral regimens as broad-spectrum (fluoroquinolone) versus narrow-spectrum (usually a β-lactam). We compared demographic and clinical characteristics between groups. Using a multivariable regression model, we adjusted outcomes by severity (electronically calculated CURB-65), comorbidity (Charlson Index), time to clinical stability, and length of intravenous therapy.

MEASUREMENTS AND MAIN RESULTS

Age, severity, comorbidity, length of intravenous therapy, and clinical response were similar between the two groups. Observed 30-day readmission (11.9 vs. 21.4%; P = 0.26) and 30-day all-cause mortality (2.3 vs. 5.3%; P = 0.68) were also similar between the narrow and broad oral antibiotic groups. In multivariable analysis, we found no statistically significant differences for adjusted odds of 30-day readmission (adjusted odds ratio, 0.56; 95% confidence interval, 0.06-5.2; P = 0.61) or 30-day all-cause mortality (adjusted odds ratio, 0.55; 95% confidence interval, 0.19-1.6; P = 0.26) between narrow and broad oral antibiotic groups.

CONCLUSIONS

On the basis of analysis of a limited number of patients observed retrospectively, our findings suggest that it may be safe to switch from broad-spectrum intravenous antibiotic coverage to a narrow-spectrum oral antibiotic once clinical stability is achieved for hospitalized patients with health care-associated pneumonia when no microbiological diagnosis is made. A larger retrospective study with propensity matching or regression-adjusted test of equivalence or ideally a prospective comparative effectiveness study will be necessary to confirm our observations.

Community-acquired pneumonia management and outcomes in the era of health information technology

Pneumonia continues to be a leading cause of hospitalization and mortality. Implementation of health information technology (HIT) can lead to cost savings and improved care. In this review, we examine the literature on the use of HIT in the management of community-acquired pneumonia. We also discuss barriers to adoption of technology in managing pneumonia, the reliability and quality of electronic health data in pneumonia research, how technology has assisted pneumonia diagnosis and outcomes research. The goal of using HIT is to develop and deploy generalizable, real-time, computerized clinical decision support integrated into usual pneumonia care. A friendly user interface that does not disrupt efficiency and demonstrates improved clinical outcomes should result in widespread adoption.

Thoracic Complications in Chronic Lymphocytic Leukemia

BACKGROUND

Chronic lymphocytic leukemia (CLL) is the most common lymphoproliferative disorder worldwide. Although thoracic complications are frequent in CLL, only limited data exist regarding the etiologies of these complications.

MATERIALS AND METHODS

A retrospective chart review was performed on all patients admitted to a tertiary care, CLL referral center, with CLL and a respiratory complaint from 2001 through 2013, to categorize pulmonary complaints and diagnoses.

RESULTS

There were 277 patients with CLL admitted on 409 occasions with respiratory complaints. The median age was 73 years, with a male to female ratio of 2:1. The majority of patients had a high-risk Rai classification and had received prior treatment. Common presenting symptoms included dyspnea, cough, and sputum production. The most common diagnoses were pneumonia (62.8%), with an identified organism in 44.7%, pleural effusions (31.8%), lung cancer (6.9%), and leukemic infiltrates (5.9%). Invasive procedures were performed 138 times: 70 bronchoscopies, 24 surgical lung biopsies, 10 computed tomography-guided lung biopsies, and 34 thoracenteses. In-hospital mortality was 24.9%. In a multivariable analysis, an elevated blood urea nitrogen level and creatinine, thrombocytopenia, and a presenting symptom of dyspnea correlated significantly with in-hospital mortality.

CONCLUSION

Thoracic manifestations in CLL are common among hospitalized patients. Although infectious pneumonia remains most common, unusual or opportunistic infections may be increasing, and direct lung damage owing to CLL itself or to newer biologic agents are being diagnosed with lung tissue sampling. Recognition of these complications will allow earlier diagnosis, which may change management including removal of offending biologic agents or augmentation of treatment for CLL when infiltrative leukemic cells are present.

Pneumococcal urinary antigen test use in diagnosis and treatment of pneumonia in seven Utah hospitals

The pneumocococcal urine antigen test increases specific microbiological diagnosis over conventional culture methods in pneumonia patients. Data are limited regarding its yield and effect on antibiotic prescribing among patients with community-onset pneumonia in clinical practice.

We performed a secondary analysis of 2837 emergency department patients admitted to seven Utah hospitals over 2 years with international diagnostic codes version 9 codes and radiographic evidence of pneumonia.

Mean age was 64.2 years, 47.2% were male and all-cause 30-day mortality was 9.6%. Urinary antigen testing was performed in 1110 (39%) patients yielding 134 (12%) positives. Intensive care unit patients were more likely to undergo testing, and have a positive result (15% versus 8.8% for ward patients; p<0.01). Patients with risk factors for healthcare-associated pneumonia had fewer urinary antigen tests performed, but 8.4% were positive. Physicians changed to targeted antibiotic therapy in 20 (15%) patients, de-escalated antibiotic therapy in 76 patients (57%). In 38 (28%) patients, antibiotics were not changed. Only one patient changed to targeted therapy suffered clinical relapse. Length of stay and mortality were lower in patients receiving targeted therapy.

Pneumococcal urinary antigen testing is an inexpensive, noninvasive test that favourably influenced antibiotic prescribing in a “real world”, multi-hospital observational study.

Pneumococcal urinary antigen test in pneumoniahttp://ow.ly/sm8R303lOe0

Derivation and Multicenter Validation of the Drug Resistance in Pneumonia Clinical Prediction Score

The health care-associated pneumonia (HCAP) criteria have a limited ability to predict pneumonia caused by drug-resistant bacteria and favor the overutilization of broad-spectrum antibiotics. We aimed to derive and validate a clinical prediction score with an improved ability to predict the risk of pneumonia due to drug-resistant pathogens compared to that of HCAP criteria. A derivation cohort of 200 microbiologically confirmed pneumonia cases in 2011 and 2012 was identified retrospectively. Risk factors for pneumonia due to drug-resistant pathogens were evaluated by logistic regression, and a novel prediction score (the drug resistance in pneumonia [DRIP] score) was derived. The score was then validated in a prospective, observational cohort of 200 microbiologically confirmed cases of pneumonia at four U.S. centers in 2013 and 2014. The DRIP score (area under the receiver operator curve [AUROC], 0.88 [95% confidence interval {CI}, 0.82 to 0.93]) performed significantly better (P = 0.02) than the HCAP criteria (AUROC, 0.72 [95% CI, 0.64 to 0.79]). At a threshold of ≥4 points, the DRIP score demonstrated a sensitivity of 0.82 (95% CI, 0.67 to 0.88), a specificity of 0.81 (95% CI, 0.73 to 0.87), a positive predictive value (PPV) of 0.68 (95% CI, 0.56 to 0.78), and a negative predictive value (NPV) of 0.90 (95% CI, 0.81 to 0.93). By comparison, the performance of HCAP criteria was less favorable: sensitivity was 0.79 (95% CI, 0.67 to 0.88), specificity was 0.65 (95% CI, 0.56 to 0.73), PPV was 0.53 (95% CI, 0.42 to 0.63), and NPV was 0.86 (95% CI, 0.77 to 0.92). The overall accuracy of the HCAP criteria was 69.5% (95% CI, 62.5 to 75.7%), whereas that of the DRIP score was 81.5% (95% CI, 74.2 to 85.6%) (P = 0.005). Unnecessary extended-spectrum antibiotics were recommended 46% less frequently by applying the DRIP score (25/200, 12.5%) than by use of HCAP criteria (47/200, 23.5%) (P = 0.004), without increasing the rate at which inadequate treatment recommendations were made. The DRIP score was more predictive of the risk of pneumonia due to drug-resistant pathogens than HCAP criteria and may have the potential to decrease antibiotic overutilization in patients with pneumonia. Validation in larger cohorts of patients with pneumonia due to all causes is necessary.

CURB-65 Performance Among Admitted and Discharged Emergency Department Patients With Community-acquired Pneumonia

OBJECTIVES

Pneumonia severity tools were primarily developed in cohorts of hospitalized patients, limiting their applicability to the emergency department (ED). We describe current community ED admission practices and examine the accuracy of the CURB-65 to predict 30-day mortality for patients, either discharged or admitted with community-acquired pneumonia (CAP).

METHODS

A retrospective, observational study of adult CAP encounters in 14 community EDs within an integrated healthcare system. We calculated CURB-65 scores for all encounters and described the use of hospitalization, stratified by each score (0-5). We then used each score as a cutoff to calculate sensitivity, specificity, positive predictive value, negative predictive value (NPV), positive likelihood ratios, and negative likelihood ratios for predicting 30-day mortality.

RESULTS

The sample included 21,183 ED encounters for CAP (7,952 discharged and 13,231 admitted). The C-statistic describing the accuracy of CURB-65 for predicting 30-day mortality in the full sample was 0.761 (95% confidence interval [CI], 0.747-0.774). The C-statistic was 0.864 (95% CI, 0.821-0.906) among patients discharged from the ED compared with 0.689 (95% CI, 0.672-0.705) among patients who were admitted. Among all ED encounters a CURB-65 threshold of ≥1 was 92.8% sensitive and 38.0% specific for predicting mortality, with a 99.9% NPV. Among all encounters, 62.5% were admitted, including 36.2% of those at lowest risk (CURB-65 = 0).

CONCLUSIONS

CURB-65 had very good accuracy for predicting 30-day mortality among patients discharged from the ED. This severity tool may help ED providers risk stratify patients to assist with disposition decisions and identify unwarranted variation in patient care.

Pleural Effusions at First ED Encounter Predict Worse Clinical Outcomes in Patients With Pneumonia

BACKGROUND

Pleural effusions are present in 15% to 44% of hospitalized patients with pneumonia. It is unknown whether effusions at first presentation to the ED influence outcomes or should be managed differently.

METHODS

We studied patients in seven hospital EDs with International Statistical Classification of Disease and Health Related Problems-Version 9 codes for pneumonia, or empyema, sepsis, or respiratory failure with secondary pneumonia. Patients with no confirmatory findings on chest imaging were excluded. Pleural effusions were identified with the use of radiographic imaging.

RESULTS

Over 24 months, 4,771 of 458,837 adult ED patients fulfilled entry criteria. Among the 690 (14.5%) patients with pleural effusions, their median age was 68 years, and 46% were male. Patients with higher Elixhauser comorbidity scores (OR, 1.13 [95% CI, 1.09-1.18]; P < .001), brain natriuretic peptide levels (OR, 1.20 [95% CI, 1.12-1.28]; P < .001), bilirubin levels (OR, 1.07 [95% CI, 1.00-1.15]; P = .04), and age (OR, 1.15 [95% CI, 1.09-1.21]; P < .001) were more likely to have parapneumonic effusions. In patients without effusion, electronic version of CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years accurately predicted mortality (4.7% predicted vs 5.0% actual). However, eCURB underestimated mortality in those with effusions (predicted 7.0% vs actual 14.0%; P < .001). Patients with effusions were more likely to be admitted (77% vs 57%; P < .001) and had a longer hospital stay (median, 2.8 vs 1.3 days; P < .001). After severity adjustment, the likelihood of 30-day mortality was greater among patients with effusions (OR, 2.6 [CI, 2.0-3.5]; P < .001), and hospital stay was disproportionately longer (coefficient, 0.22 [CI, 0.14-0.29]; P < .001).

CONCLUSIONS

Patients with pneumonia and pleural effusions at ED presentation in this study were more likely to die, be admitted, and had longer hospital stays. Why parapneumonic effusions are associated with adverse outcomes, and whether different management of these patients might improve outcome, needs urgent investigation.

Why we need a new definition of sepsis

On April 23, 2015, Kaukonen and colleagues published an article in the New England Journal of Medicine entitled "Systemic inflammatory response syndrome criteria in defining severe sepsis", which investigated the sensitivity and validity of using SIRS criteria to define intensive care unit (ICU) patients with severe sepsis. This study used admission data of over 100,000 patients in order to investigate patients with severe sepsis who either met or didn't meet SIRS criteria. The investigators found that in-hospital mortality increased linearly with the number of SIRS criteria met; raising concern that SIRS criterion is not sensitive enough. This study of SIRS criteria raises important questions about the recognition and diagnosis of severe sepsis.

The Accuracy of an Electronic Pulmonary Embolism Severity Index Auto-Populated from the Electronic Health Record: Setting the stage for computerized clinical decision support

BACKGROUND

The Pulmonary Embolism (PE) Severity Index identifies emergency department (ED) patients with acute PE that can be safely managed without hospitalization. However, the Index comprises 11 weighted variables, complexity that can impede its integration into contextual workflow.

OBJECTIVE

We designed a computerized version of the PE Severity Index (e-Index) to automatically extract the required variables from discrete fields in the electronic health record (EHR). We tested the e-Index on the study population to determine its accuracy compared with a gold standard generated by physician abstraction of the EHR on manual chart review.

METHODS

This retrospective cohort study included adults with objectively-confirmed acute PE in four community EDs from 2010-2012. Outcomes included performance characteristics of the e-Index for individual values, the number of cases requiring physician editing, and the accuracy of the e-Index risk category (low vs. higher).

RESULTS

For the 593 eligible patients, there were 6,523 values automatically extracted. Fifty one of these needed physician editing, yielding an accuracy at the value-level of 99.2% (95% confidence interval [CI], 99.0%-99.4%). Sensitivity was 96.9% (95% CI, 96.0%-97.9%) and specificity was 99.8% (95% CI, 99.7%-99.9%). The 51 corrected values were distributed among 47 cases: 43 cases required the correction of one variable and four cases required the correction of two. At the risk-category level, the e-Index had an accuracy of 96.8% (95% CI, 95.0%-98.0%), under-classifying 16 higher-risk cases (2.7%) and over-classifying 3 low-risk cases (0.5%).

CONCLUSION

Our automated extraction of variables from the EHR for the e-Index demonstrates substantial accuracy, requiring a minimum of physician editing. This should increase user acceptability and implementation success of a computerized clinical decision support system built around the e-Index, and may serve as a model to automate other complex risk stratification instruments.

Impact of an Electronic Clinical Decision Support Tool for Emergency Department Patients With Pneumonia

STUDY OBJECTIVE

Despite evidence that guideline adherence improves clinical outcomes, management of pneumonia patients varies in emergency departments (EDs). We study the effect of a real-time, ED, electronic clinical decision support tool that provides clinicians with guideline-recommended decision support for diagnosis, severity assessment, disposition, and antibiotic selection.

METHODS

This was a prospective, controlled, quasi-experimental trial in 7 Intermountain Healthcare hospital EDs in Utah's urban corridor. We studied adults with International Classification of Diseases, Ninth Revision codes and radiographic evidence for pneumonia during 2 periods: baseline (December 2009 through November 2010) and post-tool deployment (December 2011 through November 2012). The tool was deployed at 4 intervention EDs in May 2011, leaving 3 as usual care controls. We compared 30-day, all-cause mortality adjusted for illness severity, using a mixed-effect, logistic regression model.

RESULTS

The study population comprised 4,758 ED pneumonia patients; 14% had health care-associated pneumonia. Median age was 58 years, 53% were female patients, and 59% were admitted to the hospital. Physicians applied the tool for 62.6% of intervention ED study patients. There was no difference overall in severity-adjusted mortality between intervention and usual care EDs post-tool deployment (odds ratio [OR]=0.69; 95% confidence interval [CI] 0.41 to 1.16). Post hoc analysis showed that patients with community-acquired pneumonia experienced significantly lower mortality (OR=0.53; 95% CI 0.28 to 0.99), whereas mortality was unchanged among patients with health care-associated pneumonia (OR=1.12; 95% CI 0.45 to 2.8). Patient disposition from the ED postdeployment adhered more to tool recommendations.

CONCLUSION

This study demonstrates the feasibility and potential benefit of real-time electronic clinical decision support for ED pneumonia patients.

Characteristics associated with clinician diagnosis of aspiration pneumonia: a descriptive study of afflicted patients and their outcomes

BACKGROUND

Aspiration pneumonia is a common disease, although less well characterized than other pneumonia syndromes.

OBJECTIVE

We analyzed patient-level covariates associated with clinician-defined aspiration pneumonia.

METHODS

We used the Community-Acquired Pneumonia Organization database, a multicenter, international population of patients with community-acquired pneumonia, using data from 2001 to 2012. Aspiration pneumonia was determined by the treating clinician. We analyzed covariates associated with clinician-defined diagnosis of aspiration pneumonia using logistic regression. We compared aspiration pneumonia patients to propensity-matched cases with nonaspiration pneumonia.

RESULTS

We studied 5185 patients. Four hundred fifty-one of these patients had aspiration pneumonia. Patients with aspiration pneumonia were older, had greater disease severity, and more comorbidities than patients with nonaspiration pneumonia. They were more likely cared for in the intensive care unit (19% vs 13%, P = 0.002), had longer unadjusted hospital length of stay (9 vs 7 days, P < 0.001), and took longer to achieve clinical stability (unadjusted 8 vs 4 days, P < 0.001). Confusion, nursing home residence, and cerebrovascular disease were most associated with clinician diagnosis of aspiration pneumonia (odds ratio: 4.4, 2.9, 2.3, respectively). Unadjusted inpatient mortality was higher (23% vs 9%, P < 0.001). Aspiration pneumonia conferred a 2.3 odds ratio for inpatient mortality after adjusting for age, disease severity, and comorbidities.

CONCLUSIONS

Among pneumonia patients, confusion, nursing home residence, and cerebrovascular disease are associated with a clinician diagnosis of aspiration. Aspiration pneumonia is associated with greater mortality among patients with community-acquired pneumonia, which is not explained by older age, measured indices of severity, or comorbidities.

Validating hospital admission criteria for decision support in pneumonia

Background

We evaluated our previously derived admission criteria for agreement with physician decisions and outpatient failure among patients presenting to emergency departments (EDs) with pneumonia.

Methods

Among patients presenting to seven Intermountain EDs in the urban region of Utah with pneumonia December 1 2009-December 1 2010, we measured hospital admission rates and outpatient failure, defined as either 7-day secondary hospitalization or death in 30 days for patients initially discharged home from the ED. We measured our admission criteria’s ability to predict hospital admission and its hypothetical rates of admission and outpatient failure with strict adherence to the criteria. We compared our admission criteria to other electronically calculable criteria, CURB-65 and A-DROP.

Results

In 2,308 patients, admission rate was 57%, 30-day mortality 6.1%, 7-day secondary hospitalization 5.8%, and outpatient failure rate 6.4%. Our admission criteria predicted hospital admission with an AUC of 0.77, compared to 0.73 for CURB-65 ≥ 2 and 0.78 for A-DROP≥ 2. Hypothetical 100% concordance with our admission criteria decreased the hospitalization rate to 52% and reduced the outpatient failure rate to 3.9%, slightly better than A-DROP ≥ 2 (54% and 4.3%) and CURB-65 ≥ 2 (49% and 5.1%).

Conclusions

Our admission criteria agreed acceptably with overall observed admission decisions for patients presenting to EDs with pneumonia, but may safely reduce hospital admission rates and increase recognition of patients at risk for outpatient failure compared to CURB-65 ≥ 2 or A-DROP ≥ 2.

Admission decisions and outcomes of community-acquired pneumonia in the homeless population: a review of 172 patients in an urban setting

OBJECTIVES

We compared admission rates, outcomes, and performance of the CURB-65 mortality prediction score of homeless patients and nonhomeless patients with community-acquired pneumonia (CAP).

METHODS

We compared homeless (n = 172) and nonhomeless (n = 1897) patients presenting to a Salt Lake City, Utah, emergency department with CAP from 1996 to 2006. In the homeless cohort, we measured referral from and follow-up with the local homeless health care clinic and arrangement of medical housing.

RESULTS

Homeless patients were younger (44 vs 59 years; P < .001) and had lower CURB-65 scores and higher hospitalization risk (severity-adjusted odds ratio = 1.89; 95% confidence interval = 1.33, 2.69) than did nonhomeless patients, with a similar length of stay, median inpatient cost, and median outpatient cost, even after severity adjustment. Of homeless patients, 22% were referred from the homeless health care clinic to the emergency department; 54% of outpatients and 51% of hospital patients were referred back to the clinic, and medical housing was arranged for 23%.

CONCLUSIONS

A large cohort of homeless patients with CAP demonstrated higher hospitalization risk than but similar length of stay and costs as nonhomeless patients. The strong relationship between the hospital and homeless health care clinic may have contributed to this finding.

Automated identification of pneumonia in chest radiograph reports in critically ill patients

BACKGROUND

Prior studies demonstrate the suitability of natural language processing (NLP) for identifying pneumonia in chest radiograph (CXR) reports, however, few evaluate this approach in intensive care unit (ICU) patients.

METHODS

From a total of 194,615 ICU reports, we empirically developed a lexicon to categorize pneumonia-relevant terms and uncertainty profiles. We encoded lexicon items into unique queries within an NLP software application and designed an algorithm to assign automated interpretations ('positive', 'possible', or 'negative') based on each report's query profile. We evaluated algorithm performance in a sample of 2,466 CXR reports interpreted by physician consensus and in two ICU patient subgroups including those admitted for pneumonia and for rheumatologic/endocrine diagnoses.

RESULTS

Most reports were deemed 'negative' (51.8%) by physician consensus. Many were 'possible' (41.7%); only 6.5% were 'positive' for pneumonia. The lexicon included 105 terms and uncertainty profiles that were encoded into 31 NLP queries. Queries identified 534,322 'hits' in the full sample, with 2.7 ± 2.6 'hits' per report. An algorithm, comprised of twenty rules and probability steps, assigned interpretations to reports based on query profiles. In the validation set, the algorithm had 92.7% sensitivity, 91.1% specificity, 93.3% positive predictive value, and 90.3% negative predictive value for differentiating 'negative' from 'positive'/'possible' reports. In the ICU subgroups, the algorithm also demonstrated good performance, misclassifying few reports (5.8%).

CONCLUSIONS

Many CXR reports in ICU patients demonstrate frank uncertainty regarding a pneumonia diagnosis. This electronic tool demonstrates promise for assigning automated interpretations to CXR reports by leveraging both terms and uncertainty profiles.

Mortality, morbidity, and disease severity of patients with aspiration pneumonia

BACKGROUND

Aspiration pneumonia is a common syndrome, although less well characterized than other pneumonia syndromes. We describe a large population of patients with aspiration pneumonia.

METHODS

In this retrospective population study, we queried the electronic medical records at a tertiary-care, university-affiliated hospital from 1996 to 2006. Patients were initially identified by International Classification of Diseases, 9th Revision code 507.x; subsequent physician chart review excluded patients with aspiration pneumonitis and those without a confirmatory radiograph. Patients with community-acquired aspiration pneumonia were compared to a contemporaneous population of community-acquired pneumonia (CAP) patients. We compared CURB-65 (a clinical prediction rule based on Confusion, Uremia, Respiratory rate, Blood Pressure, and age)-predicted mortality with actual 30-day mortality.

RESULTS

We identified 628 patients with aspiration pneumonia, of which 510 were community-acquired. Median age was 77 years, with 30-day mortality of 21%. Compared to CAP patients, patients with community-acquired aspiration pneumonia had more frequent inpatient admission (99% vs 58%) and intensive care unit admission (38% vs 14%), higher Charlson comorbidity index (3 vs 1), and higher prevalence of do not resuscitate/intubate orders (24% vs 11%). CURB-65 predicted mortality poorly in aspiration pneumonia patients (area under the curve, 0.66).

CONCLUSIONS

Patients with community-acquired aspiration pneumonia are older, have more comorbidities, and demonstrate higher mortality than CAP patients, even after adjustment for age and comorbidities. CURB-65 poorly predicts mortality in this population.