Rapid detection of bacterial meningitis using a point-of-care glucometer

Rousseau, Geoffroy; Asmolov, Romain; Grammatico‐Guillon, Leslie; Auvet, Adrien; Laribi, Sai͏̈d; Garot, Denis; Jouan, Youenn; Dequin, Pierre‐François; Guillon, Antoine

doi:10.1097/mej.0000000000000495

Cited by 27 publications

(12 citation statements)

References 19 publications

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“…We compared the concordance between the predictions and the final microbial etiologies for the experts and for the algorithm and calculated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and likelihood ratios (LRs) for the predictions [12]. Given the importance of this diagnostic prediction in the patient's therapeutic management, we determined that the discriminant properties should be "high" (LR + > 10 and/ or LR-< 0.1) for the prediction to be considered useful for clinical practice [13,14]. Table 1 summarizes the LR cutoff values defining the discriminant properties of the predictions [13].…”

Section: Discussionmentioning

confidence: 99%

“…Step 1: patient data collection Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Section: Methodsmentioning

confidence: 99%

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Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

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Background: Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia?Methods: We included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant. Results: We included 153 patients with CAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

Self Cite

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show abstract

“…We compared the concordance between the predictions and the final microbial etiologies for the experts and for the algorithm and calculated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and likelihood ratios (LRs) for the predictions [11]. Given the importance of this diagnostic prediction in the patient's therapeutic management, we determined that the discriminant properties should be "high" (LR +> 10 and/or LR-< 0.1) for the prediction to be considered useful for clinical practice [12,13]. Table 1 summarizes the LR cutoff values defining the discriminant properties of the predictions [12].…”

Section: Discussionmentioning

confidence: 99%

“…Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Section: Step 1: Patient Data Collectionmentioning

confidence: 99%

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Lhommet

Garot

Grammatico‐Guillon

et al. 2019

Preprint

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Background Severe community-acquired pneumonia (sCAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia? Methods We included patients hospitalized for sCAP and recorded clinical/paraclinical data available in the first 3-hour period of care. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: ( i ) a panel of three experts and ( ii ) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values >10 and negative LR values <0.1 were considered clinically relevant.Results We included 153 patients with sCAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]). The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).Conclusion Neither experts nor an AI algorithm can predict the microbial etiology of sCAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.

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“…18 A simple concurrent bedside glucose measurement in the samples from blood and CSF can allow rapid identification of AVM. 31 The best cut off point for the proportion of CSF over blood glucose produced via a point-of-care glucometer has been noted as 0.46, (it has 94.1% sensitivity, 91% specificity, and a positive likelihood ratio of 10 in discerning these two entities). The above mentioned practical modality carries the potential to provide faster diagnostic evaluation of the disease.…”

Section: Etiologic Diagnosis Of Tbe Is Only Made On Basis Of Laboratomentioning

confidence: 99%

Diagnostic Advances in Acute Viral Meningitis: Any Novelty for the Physician?

Karcıoğlu¹,

Yılmaz²

2018

Emerg Med Open J

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Acute viral meningitis (AVM) constitute an important cause of mortality and neurological morbidity within the last decades, in spite of all diagnostic and therapeutic advances in medical technology. Diagnostic procedures consist of lumbar puncture, hemoculture, complete blood cell count (including, leukocytes), procalcitonin, lactate and glucose to give an overall insight for disease; while polymerase chain reaction (PCR) and cerebrospinal fluid (CSF) analyses (urine reagent strips, glucose, protein, and microscopic examination) are used to diagnose herpetic meningeal involvement and most other viral infections affecting central nervous system (CNS). Findings in blood and CSF work-up after a clinical index of suspicion result in a high yield of diagnosis. Technological advances should be coupled with clinical findings to produce an accurate clinical management in the target population. This review is intended to analyze the epidemiology, clinical manifestation, findings on examination, and diagnostic tools to be utilized in patients suspected to have AVM in light of recent advances and research data, and to culminate up-to-date concept of AVM for primary care and emergency physicians.

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Rapid detection of bacterial meningitis using a point-of-care glucometer

Abstract: A glucometer accurately detects an abnormal CSF/blood glucose ratio immediately after the lumbar puncture. This cheap point-of-care method has the potential to speed up the diagnostic process of patients with bacterial meningitis.

Cited by 27 publications

References 19 publications

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Diagnostic Advances in Acute Viral Meningitis: Any Novelty for the Physician?

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