Clinical prediction rules in Staphylococcus aureus bacteremia demonstrate the usefulness of reporting likelihood ratios in infectious diseases

Bai, Anthony D.; Showler, Adrienne; Burry, Lisa; Steinberg, Marilyn; Tomlinson, George; Bell, Chaim M.; Morris, Andrew M.

doi:10.1007/s10096-016-2711-z

Cited by 13 publications

(13 citation statements)

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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].…”

Section: Discussionmentioning

confidence: 99%

“…", as the discriminant ability between the two diagnoses was considered low. We interpreted our results mainly based on the calculation of likelihood ratios, as recommended for reports of a diagnostic test for an infectious disease [12]. Likelihood ratios incorporate both sensitivity and specificity and, unlike predictive values, do not vary with prevalence, making them good statistical tools to facilitate translation of knowledge from research to clinical practice [12].…”

Section: Discussionmentioning

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: Discussionmentioning

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

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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].…”

Section: Discussionmentioning

confidence: 99%

“…", as the discriminant ability between the two diagnoses was considered low. We interpreted our results mainly based on the calculation of likelihood ratios, as recommended for reports of a diagnostic test for an infectious disease [11]. Likelihood ratios incorporate both sensitivity and specificity and, unlike predictive values, do not vary with prevalence, making them good statistical tools to facilitate translation of knowledge from research to clinical practice [11].…”

Section: Discussionmentioning

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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“…Still, there are only a few studies in which the optimal ndings have been reported to differentiate between them by Gram stain. 11 Therefore, the current study aims to investigate the contributable ndings in Gram stain to identify CoNS and distinguish it from SA.…”

Section: Introductionmentioning

confidence: 99%

“A Four-Leaf Clover” Sign for Rapid Identification of Coagulase-Negative Staphylococci by Gram Staining From Positive Blood Culture: A Cross-Sectional Study

Matsuo¹,

Hayashi²,

Sakurai³

et al. 2020

Preprint

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Background: Coagulase-negative staphylococci (CoNS) are one of the most common contaminant microorganisms isolated from blood cultures. Few studies exploring the use of Gram staining to distinguish between Staphylococcus aureus (SA) and CoNS have been reported. Here, this study aimed to explore whether morphological features of Gram staining could identify SA or CoNS.Methods: This study was conducted at St. Luke’s International Hospital from November 2016 to September 2017. The positive blood cultures for which the Gram staining showed gram-positive cocci (GPC) in clusters were included in our study. The direct smear of Gram staining obtained from positive blood culture bottles were examined within 24 hours of positivity. We have identified and characterized the following two signs: “four-leaf clover (FLC)” if 4 GPC gathered like a planar four-leaf clover and “grapes” if the GPC gathered like grapes in a three-dimensional form. The number of fields with FLC and grapes signs in 10 fields per slide with ×1,000 power was counted, and the results in a total of 20 fields with ×1,000 power were combined. We performed a logistic regression analysis to assess whether these signs could serve as factors distinguishing between SA and CoNS. The predictive ability of these signs was evaluated based on the sensitivity, specificity, positive predictive value, and negative predictive value for CoNS via receiver operating curve analysis.Results: In total, 106 blood cultures for which Gram staining showed GPC in clusters were examined; 46 (43%) were SA, and 60 (57%) were CoNS samples. The result of multivariate logistic regression analysis showed that the FLC sign was a statistically significant marker of CoNS with an odds ratio of 1.31 (95 % confidential interval (CI): 1.07–1.61, p<0.05). In aerobic bottles, sensitivity, specificity, positive predictive value, and negative predictive value for CoNS were 0.67, 0.91, 0.92, and 0.65, respectively, and the value of area under the curve was 0.79 (95% CI: 0.67–0.91).Conclusions: To our knowledge, this is the first study to show that the FLC could be a rapid and useful indicator to identify CoNS in aerobic bottles. Thus, the presence of FLC sings could help clinicians to suspect the possibility of CoNS before the final identification by cultures.

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Clinical prediction rules in Staphylococcus aureus bacteremia demonstrate the usefulness of reporting likelihood ratios in infectious diseases

Cited by 13 publications

References 14 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?

“A Four-Leaf Clover” Sign for Rapid Identification of Coagulase-Negative Staphylococci by Gram Staining From Positive Blood Culture: A Cross-Sectional Study

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