C-reactive protein or erythrocyte sedimentation rate results reliably exclude invasive bacterial infections

Paydar‐Darian, Niloufar; Kimia, Amir A.; Monuteaux, Michael C.; Michelson, Kenneth A.; Landschaft, Assaf; Maulden, Alexandra B.; Chenard, Rachel L.; Nigrovic, Lise E.

doi:10.1016/j.ajem.2018.11.011

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…Therefore, it has been suggested that CRP values may be useful as an adjuvant marker until the results of bacterial cultures are obtained. 11,17,18 Based on our results, we suggest that clinicians should be alert to the risk of SBI when neutropenia is severe and high level of CRP is detected, in addition to clinical findings and fever in neutropenic patients.…”

Section: Discussionmentioning

confidence: 69%

Clinical Picture, Outcome, and Risk of Serious Bacterial Infections in Immunocompetent Previously Healthy Neutropenic Children

Arslantaş

Ayçicek

Türkoğlu

et al. 2021

Journal of Pediatric Infectious Diseases

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Objective In childhood, the cause of neutropenia is a challenging diagnosis with a spectrum of underlying etiologies. This study was performed to investigate the clinical picture and the outcomes associated with the new onset neutropenia in previously healthy children, and to determine the risk of serious bacterial infection (SBI) in those patients. Methods Patients presenting between January 2018 and September 2018 with an absolute neutrophil count (ANC) <1,500/μL were retrospectively evaluated. Patients with known underlying chronic disease or immunosuppressive conditions were excluded. Neutropenia was categorized into three groups: mild, 1,000–1,500/μL; moderate, 500 to <1,000/μL; and severe <500/μL. Results A total of 423 patients were investigated. There were 156 (36.9%), 193 (45.6%), and 74 (17.5%) patients in the mild, moderate, and severe groups, respectively. Bacteremia was detected in one (0.02%) patient and SBI in 21 (4.9%) patients. No significant correlation was found between the incidence of SBI and bacterial infection rate among different age groups (p > 0.05). The incidence of SBI varied significantly according to the severity of the neutropenia (p = 0.012) and as the neutropenia became more severe, the incidence of SBI increased (p = 0.015). Conclusion The clinical outcome of neutropenia in previously healthy and immunocompetent children is generally good with a relatively low incidence of SBI. We suggest that aggressive therapy and frequent follow-up should be reserved for previously healthy neutropenic children with SBI.

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Section: Discussionmentioning

confidence: 69%

Clinical Picture, Outcome, and Risk of Serious Bacterial Infections in Immunocompetent Previously Healthy Neutropenic Children

Arslantaş

Ayçicek

Türkoğlu

et al. 2021

Journal of Pediatric Infectious Diseases

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“…In a systematic review, Sanders et al could show that CRP gave moderate information in both ruling in and out serious bacterial infections in children with fever in an outpatient setting [21]. In later studies, the same results have been confirmed for CRP, both as a single marker [22][23][24] but also together with other markers in clinical algorithms, such as the "step-by-step" approach [25]. The diagnostic accuracy for discriminating viral from bacterial etiologies is however limited, especially in early stages [21,26,27].…”

Section: Routine Biomarkersmentioning

confidence: 91%

Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis

2021

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Differentiating viral from bacterial infections in febrile children is challenging and often leads to an unnecessary use of antibiotics. There is a great need for more accurate diagnostic tools. New molecular methods have improved the particular diagnostics of viral respiratory tract infections, but defining etiology can still be challenging, as certain viruses are frequently detected in asymptomatic children. For the detection of bacterial infections, time consuming cultures with limited sensitivity are still the gold standard. As a response to infection, the immune system elicits a cascade of events, which aims to eliminate the invading pathogen. Recent studies have focused on these host–pathogen interactions to identify pathogen-specific biomarkers (gene expression profiles), or “pathogen signatures”, as potential future diagnostic tools. Other studies have assessed combinations of traditional bacterial and viral biomarkers (C-reactive protein, interleukins, myxovirus resistance protein A, procalcitonin, tumor necrosis factor-related apoptosis-inducing ligand) to establish etiology. In this review we discuss the performance of such novel diagnostics and their potential role in clinical praxis. In conclusion, there are several promising novel biomarkers in the pipeline, but well-designed randomized controlled trials are needed to evaluate the safety of using these novel biomarkers to guide clinical decisions.

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“…We use a Graphic User Interface (GUI) that allows the lay user to begin with keywords and progressively develop RegEx related to the keywords chosen. As one example, RegEx can expand the keyword “ulcer” to capture “ulceration” while omitting negative expressions such as “no ulcer,” and unrelated expressions such as “peptic ulcer” or “mouth ulcer.” Our home-grown GUI is called DrT (Document Review Tool), which has been applied in numerous prior studies including patient safety surveillance as well as cohort identification for clinical and health services research 30–33 . Regular expressions can be used for combinations of words and combining expressions; for example, if the user inputs “pressure ulcer” and “decubitus sores” to describe pressure injuries, the RegEx might evolve to “(pressure|decub\w*)\s*(\bsore(?)\b|ulcer(s)?…”

Section: Methodsmentioning

confidence: 99%

“…Our home-grown GUI is called DrT (Document Review Tool), which has been applied in numerous prior studies including patient safety surveillance as well as cohort identification for clinical and health services research. [30][31][32][33] Regular expressions can be used for combinations of words and combining expressions; for example, if the user inputs "pressure ulcer" and "decubitus sores" to describe pressure injuries, the RegEx might evolve to "(pressure| decub\w*)\s*(\bsore(?)\b|ulcer(s)? )," which would capture combinations including "pressure sore," "pressure sores," "pressure ulcer," "pressure ulcers," "decub ulcer," "decubitus sores," and more, while omitting expressions such as "resolved pressure ulcer" and "unlikely to be a pressure sore," among others.…”

Section: Establishing the Very First Data Set From Abstract To Regula...mentioning

confidence: 99%

Enhancing Pressure Injury Surveillance Using Natural Language Processing

Milliren,

Ozonoff,

Fournier

et al. 2023

J Patient Saf

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Objective This study assessed the feasibility of nursing handoff notes to identify underreported hospital-acquired pressure injury (HAPI) events. Methods We have established a natural language processing–assisted manual review process and workflow for data extraction from a corpus of nursing notes across all medical inpatient and intensive care units in a tertiary care pediatric center. This system is trained by 2 domain experts. Our workflow started with keywords around HAPI and treatments, then regular expressions, distributive semantics, and finally a document classifier. We generated 3 models: a tri-gram classifier, binary logistic regression model using the regular expressions as predictors, and a random forest model using both models together. Our final output presented to the event screener was generated using a random forest model validated using derivation and validation sets. Results Our initial corpus involved 70,981 notes during a 1-year period from 5484 unique admissions for 4220 patients. Our interrater human reviewer agreement on identifying HAPI was high (κ = 0.67; 95% confidence interval [CI], 0.58–0.75). Our random forest model had 95% sensitivity (95% CI, 90.6%–99.3%), 71.2% specificity (95% CI, 65.1%–77.2%), and 78.7% accuracy (95% CI, 74.1%–83.2%). A total of 264 notes from 148 unique admissions (2.7% of all admissions) were identified describing likely HAPI. Sixty-one described new injuries, and 64 describe known yet possibly evolving injuries. Relative to the total patient population during our study period, HAPI incidence was 11.9 per 1000 discharges, and incidence rate was 1.2 per 1000 bed-days. Conclusions Natural language processing–based surveillance is proven to be feasible and high yield using nursing handoff notes.

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C-reactive protein or erythrocyte sedimentation rate results reliably exclude invasive bacterial infections

Cited by 9 publications

References 27 publications

Clinical Picture, Outcome, and Risk of Serious Bacterial Infections in Immunocompetent Previously Healthy Neutropenic Children

Clinical Picture, Outcome, and Risk of Serious Bacterial Infections in Immunocompetent Previously Healthy Neutropenic Children

Novel Biomarkers Differentiating Viral from Bacterial Infection in Febrile Children: Future Perspectives for Management in Clinical Praxis

Enhancing Pressure Injury Surveillance Using Natural Language Processing

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