Dataset Information

Use of procalcitonin for the diagnosis of pneumonia in patients presenting with a chief complaint of dyspnoea: results from the BACH (Biomarkers in Acute Heart Failure) trial.

ABSTRACT: Biomarkers have proven their ability in the evaluation of cardiopulmonary diseases. We investigated the utility of concentrations of the biomarker procalcitonin (PCT) alone and with clinical variables for the diagnosis of pneumonia in patients presenting to emergency departments (EDs) with a chief complaint of shortness of breath.The BACH trial was a prospective, international, study of 1641 patients presenting to EDs with dyspnoea. Blood samples were analysed for PCT and other biomarkers. Relevant clinical data were also captured. Patient outcomes were assessed at 90 days. The diagnosis of pneumonia was made using strictly validated guidelines. A model using PCT was more accurate [area under the curve (AUC) 72.3%] than any other individual clinical variable for the diagnosis of pneumonia in all patients, in those with obstructive lung disease, and in those with acute heart failure (AHF). Combining physician estimates of the probability of pneumonia with PCT values increased the accuracy to >86% for the diagnosis of pneumonia in all patients. Patients with a diagnosis of AHF and an elevated PCT concentration (>0.21 ng/mL) had a worse outcome if not treated with antibiotics (P = 0.046), while patients with low PCT values (<0.05 ng/mL) had a better outcome if they did not receive antibiotic therapy (P = 0.049).Procalcitonin may aid in the diagnosis of pneumonia, particularly in cases with high diagnostic uncertainty. Importantly, PCT may aid in the decision to administer antibiotic therapy to patients presenting with AHF in which clinical uncertainty exists regarding a superimposed bacterial infection.

SUBMITTER: Maisel A

PROVIDER: S-EPMC3284113 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Use of procalcitonin for the diagnosis of pneumonia in patients presenting with a chief complaint of dyspnoea: results from the BACH (Biomarkers in Acute Heart Failure) trial.

Maisel Alan A Neath Sean-Xavier SX Landsberg Judd J Mueller Christian C Nowak Richard M RM Peacock W Frank WF Ponikowski Piotr P Möckel Martin M Hogan Christopher C Wu Alan H B AH Richards Mark M Clopton Paul P Filippatos Gerasimos S GS Di Somma Salvatore S Anand Inder I Ng Leong L LL Daniels Lori B LB Christenson Robert H RH Potocki Mihael M McCord James J Terracciano Garret G Hartmann Oliver O Bergmann Andreas A Morgenthaler Nils G NG Anker Stefan D SD

European journal of heart failure 20120202 3

<h4>Aims</h4>Biomarkers have proven their ability in the evaluation of cardiopulmonary diseases. We investigated the utility of concentrations of the biomarker procalcitonin (PCT) alone and with clinical variables for the diagnosis of pneumonia in patients presenting to emergency departments (EDs) with a chief complaint of shortness of breath.<h4>Methods and results</h4>The BACH trial was a prospective, international, study of 1641 patients presenting to EDs with dyspnoea. Blood samples were ana ...[more]

PMID: 22302662

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Project description:Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper, we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naïve Bayes (MNB) and a support vector machine (SVM). The MNB classifier is one of only two machine learning algorithms currently being used for syndromic surveillance. All four models are trained to predict diagnostic code groups as defined by Clinical Classification Software, first to predict from discharge diagnosis, and then from chief complaint fields. The classifiers are trained on 3.6 million de-identified emergency department records from a single United States jurisdiction. We compare performance of these models primarily using the F1 score, and we measure absolute model performance to determine which conditions are the most amenable to surveillance based on chief complaint alone. Using discharge diagnoses, the LSTM classifier performs best, though all models exhibit an F1 score above 96.00. Using chief complaints, the GRU performs best (F1 = 47.38), and MNB with bigrams performs worst (F1 = 39.40). We also note that certain syndrome types are easier to detect than others. For example, chief complaints using the GRU model predicts alcohol-related disorders well (F1 = 78.91) but predicts influenza poorly (F1 = 14.80). In all instances, the RNN models outperformed the bag-of-words classifiers suggesting deep learning models could substantially improve the automatic classification of unstructured text for syndromic surveillance.

Project description:Reduction in emergency department (ED) use is frequently viewed as a potential source for cost savings. One consideration has been to deny payment if the patient's diagnosis upon ED discharge appears to reflect a "nonemergency" condition. This approach does not incorporate other clinical factors such as chief complaint that may inform necessity for ED care.To determine whether ED presenting complaint and ED discharge diagnosis correspond sufficiently to support use of discharge diagnosis as the basis for policies discouraging ED use.The New York University emergency department algorithm has been commonly used to identify nonemergency ED visits. We applied the algorithm to publicly available ED visit data from the 2009 National Hospital Ambulatory Medical Care Survey (NHAMCS) for the purpose of identifying all "primary care-treatable" visits. The 2009 NHAMCS data set contains 34,942 records, each representing a unique ED visit. For each visit with a discharge diagnosis classified as primary care treatable, we identified the chief complaint. To determine whether these chief complaints correspond to nonemergency ED visits, we then examined all ED visits with this same group of chief complaints to ascertain the ED course, final disposition, and discharge diagnoses.Patient demographics, clinical characteristics, and disposition associated with chief complaints related to nonemergency ED visits.Although only 6.3% (95% CI, 5.8%-6.7%) of visits were determined to have primary care-treatable diagnoses based on discharge diagnosis and our modification of the algorithm, the chief complaints reported for these ED visits with primary care-treatable ED discharge diagnoses were the same chief complaints reported for 88.7% (95% CI, 88.1%-89.4%) of all ED visits. Of these visits, 11.1% (95% CI, 9.3%-13.0%) were identified at ED triage as needing immediate or emergency care; 12.5% (95% CI, 11.8%-14.3%) required hospital admission; and 3.4% (95% CI, 2.5%-4.3%) of admitted patients went directly from the ED to the operating room.Among ED visits with the same presenting complaint as those ultimately given a primary care-treatable diagnosis based on ED discharge diagnosis, a substantial proportion required immediate emergency care or hospital admission. The limited concordance between presenting complaints and ED discharge diagnoses suggests that these discharge diagnoses are unable to accurately identify nonemergency ED visits.

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Dataset Information

Use of procalcitonin for the diagnosis of pneumonia in patients presenting with a chief complaint of dyspnoea: results from the BACH (Biomarkers in Acute Heart Failure) trial.

Publications

Use of procalcitonin for the diagnosis of pneumonia in patients presenting with a chief complaint of dyspnoea: results from the BACH (Biomarkers in Acute Heart Failure) trial.

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