Clinical cure rates in subjects treated with azithromycin for community-acquired respiratory tract infections caused by azithromycin-susceptible or azithromycin-resistant<i>Streptococcus pneumoniae</i>: analysis of Phase 3 clinical trial data—authors' response: Figure 1.
Bibliographic record
Abstract
Sir, Kirby1 comments on our publication2 that found clinical isolates of Streptococcus pneumoniae with azithromycin MICs of ≥2 mg/L, when compared with isolates with MICs of <0.5 mg/L, predict worse outcomes for patients receiving azithromycin to treat pneumococcal community-acquired respiratory tract infections (CARTIs). We determined that outcomes were not different for isolates with azithromycin MICs of 2–8, ≥16 or ≥64 mg/L.2 Kirby1 states that the relationship we observed between MIC and outcome for azithromycin-resistant isolates was not a linear dose (MIC)–response relationship. To improve the clarity of our data, we have graphically analysed the clinical cure rate as a function of azithromycin MIC for azithromycin-non-susceptible S. pneumoniae (Figure 1), treating MIC as a semi-continuous variable using all subjects eligible for analysis, including subjects with azithromycin-intermediate isolates. In the graphical analysis, if the MIC was recorded as ‘>X’, the numerical value was plotted as 2X; e.g. if the MIC was recorded as ‘>256 mg/L’, the value of the MIC plotted was 512 mg/L (Figure 1). Figure 1 depicts a linear relationship with clinical cure rates as a function of azithromycin MIC for all CARTI subjects with azithromycin-non-susceptible S. pneumoniae (MIC >0.5 mg/L; n = 124). The logistic regression model predicts no meaningful change in cure rate with increasing azithromycin MIC for subjects with CARTIs with azithromycin-non-susceptible S. pneumoniae. In our original publication we explained that the underlying reason for the apparently weak relationship between in vitro susceptibility and clinical cure rate (for azithromycin-non-susceptible S. pneumoniae) in patients treated with azithromycin could not be fully explained by the data available to us.2 We hypothesized that our observation may be due to a variety of reasons, including the unusual pharmacokinetic/pharmacodynamic properties of macrolides; specifically, that the azalide azithromycin is concentrated in tissues where CARTI occurs, as well as the reported ability of macrolides to exert immunomodulatory and anti-inflammatory effects. Kirby1 also suggests that a multivariate analysis is required to determine whether patient factors (e.g. age, comorbidities, previous episodes of RTI or macrolide treatment) may explain the observed association between azithromycin resistance and outcome in the treatment of S. pneumoniae RTI. Although it is difficult to argue that the results of additional analysis would not be helpful, the data were not available to perform such an analysis. That said, we do not believe that the results from such an analysis would have provided data that would change our original conclusion. As can be observed in Figure 1, there is a linear relationship between clinical cure rates and azithromycin MIC for all CARTI subjects with azithromycin-non-susceptible S. pneumoniae. This linear relationship has a small slope that may, or may not, become even flatter when patient factors are accounted for. The fact is, the line (relationship) is currently quite flat as it is. Logistic regression modelling of clinical cure rate for azithromycin-treated CARTI subjects with non-susceptible S. pneumoniae, as a function of azithromycin MIC. In closing, we do in part agree with Kirby1 that ‘MIC criteria defining resistance to azithromycin treatment of S. pneumoniae respiratory tract infections may be unhelpful in predicting an individual's risk of treatment failure’.1 When using oral azithromycin for the treatment of outpatient infections such as CARTI, the results of in vitro susceptibility testing may have limited clinical predictive value. K. D. W. is a full-time employee of Pfizer and owns Pfizer stock. Both other authors have none to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".