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INFECTIOUS DISEASES: Edited by Michael S. Niederman and Alimuddin Zumla

Strategies for prediction of drug-resistant pathogens and empiric antibiotic selection in community-acquired pneumonia

Gil, Ryana; Webb, Brandon J.b,c

Author Information
Current Opinion in Pulmonary Medicine: May 2020 - Volume 26 - Issue 3 - p 249-259
doi: 10.1097/MCP.0000000000000670
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Abstract

INTRODUCTION

Community-acquired pneumonia (CAP) remains one of the most common infectious diseases and remains an important cause of morbidity, mortality, and healthcare resource utilization [1,2]. The primary goal of current management is to improve patient-centered outcomes. Timely administration of narrow-spectrum antibiotic regimens targeting typical CAP pathogens such as recommended by the Infectious Disease Society of America (IDSA) and American Thoracic Society (ATS) guidelines [3▪▪], have been shown to improve mortality [4]. However, increasing antibiotic resistance over the last 2 decades has led to changes in prescribing patterns and introduced new challenges in the appropriate management of CAP. This article summarizes the current state of knowledge regarding patient-level risk factors for drug resistant bacterial pathogens (DRP) and currently available risk factor-based prediction models that may be used clinically to guide empiric antibiotic selection.

Box 1
Box 1:
no caption available

ANTIBIOTIC RESISTANCE AND COMMUNITY-ACQUIRED PNEUMONIA MANAGEMENT

In response to observations that inappropriate initial antibiotic spectrum was associated with increased mortality, and concerns about rising DRP incidence [5], the 2005 ATS/IDSA guidelines recommended extended-spectrum therapy to cover methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa and other DRP for patients meeting criteria for healthcare-associated pneumonia (HCAP) [6]. The 2008 Surviving Sepsis Campaign Guidelines also recommended the use of initial extended-spectrum antibiotic regimens for patients with sepsis [7]. Consequently, use of anti-MRSA and extended-spectrum Gram-negative beta-lactam agents such as piperacillin-tazobactam and carbapenems doubled in the United States over the following 10 years [8]. Complicating this trend, it is now recognized that broad-spectrum antibiotic use is associated with drug-toxicity [9,10▪,11▪], Clostridioides difficile infection [12▪▪], antimicrobial resistance [13], and disruptions of the microbiome [10▪], and both inadequate initial spectrum and unnecessary broad-spectrum use have been associated with poor outcomes in CAP [12▪▪].

In contrast to earlier reports, recent etiological studies of bacterial pathogens causing CAP [1,8,14,15] suggest that the incidence of DRP is lower than previously reported [8,14]. The EPIC study was a large prospective, multicenter study that conducted active microbiological surveillance to characterize the cause of CAP in the United States [1]. Of 2320 patients enrolled, the most common bacterial pathogen detected was Streptococcus pneumoniae, with an incidence of 5%. The incidence of MRSA was only 0.7%. The Global Initiative for Methicillin-resistant S. aureus pneumonia (GLIMP) study was a multinational, observational cohort study published in 2016 that aimed to identify the rates of MRSA pneumonia globally. The global prevalence of MRSA was 3%, with a higher rate of 5% noted in the United States [14]. Similarly, a Veterans Affairs study identified rates of Pseudomonas and MRSA of less than 2%, respectively [16].

RISK FACTORS FOR DRUG-RESISTANT PATHOGENS

To balance the competing risks of inadequate and unnecessary broad-spectrum antibiotic use, better methods of patient-tailored empiric antibiotic selection are needed. A largely conserved set of risk factors associated with DRP has now been reproducibly identified by studies from around the world, classifiable into four categories (Fig. 1): pathogen acquisition, persistence of colonization, selective pressure favoring resistant organisms, and lower respiratory tract invasion.

FIGURE 1
FIGURE 1:
Risk factors for drug-resistant bacterial pathogens (Reproduced by permission. Webb, Jones and Dean, Curr Opin Infect Dis 2016, 29:167–177.

Pathogen acquisition is largely related to healthcare exposure, and includes recent hospitalization and residence in a long-term care facility [17–19], wound care [20] tube feeding [21], and poor functional status, [22,23] all of which increase oropharyngeal colonization with DRP. Once colonized, several factors favor or reflect persistent colonization, including immunosuppression [17,24], chronic lung disease [19,25,26], or known colonization or prior infection with DRP [27–29]. Prior antibiotic exerts selective pressure on the oropharyngeal flora, varying by antibiotic spectrum and timing of exposure [13,18,27]. Finally, factors that alter host physiology lead to invasion of lower respiratory tract by DRP if present in the oropharynx, such as age [30], aspiration risk [25], cognitive impairment [31], neurological disease [32], poor functional status, [33] and gastric acid suppression [28]. Some risk factors, such as prior infection or colonization with DRP and recent antibiotic use have a stronger association than other factors, and patient-level risk increases with accumulating factors [17,20,34].

RISK-PREDICTION METHODS FOR DRUG-RESISTANT PATHOGENS

Because the HCAP criteria demonstrated poor specificity and sensitivity for DRP, this category is no longer included in ATS/IDSA guidelines [3▪▪]. Updated guidelines emphasize avoidance of unnecessary use of broad-spectrum therapy in CAP and recommend using local epidemiology of pathogens and locally validated prediction tools to guide antibiotic use. To that end, multiple prediction tools have been published. Here, we review currently available tools, summarizing test performance characteristics and derivation, validation and implementation populations.

We performed a systematic search of bibliographic databases including PubMed, Ovid and Embase using combinations of key words including ‘Community-Acquired Infection, Pneumonia, Antibacterial agents, MRSA, drug-resistant, antibiotic resistance, prediction’ to identify studies describing development of prediction tools for DRP. A total of 1303 studies were identified. We then narrowed results by excluding duplicates (n = 172), articles prior to 2000 (n = 213), articles involving pharmacology (n = 86), basic science (n = 29), pediatric populations (n = 14), economic comparisons and articles published in languages other than English (n = 733). We also reviewed relevant references from identified studies. We ultimately identified 36 articles, of which 14 described the derivation of unique prediction tools for DRP and the others reporting external validation or implementation of these tools. We utilized the QUADAS-2 method to assess the quality of each prediction model (Fig. 2) [35].

FIGURE 2
FIGURE 2:
Risk of bias and applicability – QUADAS-2 criteria.

RISK PREDICTION MODELS

Of the 14 published prediction tools for DRP [17,21,22,26–31,36–40] (Table 1), seven [Shorr, Brito/Niederman, Aliberti, Shorr (MRSA), Shindo, Prina (PES - Pseudomonas, Extended-spectrum Beta-lactamase, Staphylococcus aureus) and Webb (Drug Resistance in Pneumonia (DRIP))] have been robustly externally validated in populations outside of the derivation cohort. Prospective implementation results have been published for only two of the tools (Brito/Niederman and DRIP). While all of the tools have demonstrated superior test performance characteristics compared with HCAP, most favor sensitivity over specificity and therefore overtreatment to varying degrees. Reported positive and negative predictive values of each tool vary depending on the background rates of DRP in the validation population and whether the cohort was restricted to microbiology-positive cases [significantly enriches positive predictive value (PPV)]. Only one of the models attempts to distinguish risk for MRSA and Gram-negative DRP [28].

Table 1
Table 1:
Prediction scores for drug-resistant pathogens in community-acquired pneumonia
Table 1
Table 1:
Prediction scores for drug-resistant pathogens in community-acquired pneumonia
Table 1
Table 1:
Prediction scores for drug-resistant pathogens in community-acquired pneumonia

The Shorr score was one of the first models developed, derived in an urban US cohort [37]. It is probabilistic score that includes recent hospitalization or antibiotic use, nursing home, hemodialysis. It has been observationally validated in several US and European settings (Table 1). At a cutoff of at least 1, the score favors sensitivity (75–89%) over specificity (40–69%). In a large observational US culture-positive validation cohort, this score would have resulted in unnecessary extended-spectrum antibiotic use in 24% and inadequate therapy in 2.4% of cases [41]. Prina et al.[30] prospectively derived a novel score, PES, in a large population in Spain that has also been externally validated (Table 1). This probabilistic model includes common risk factors as well as several variables that are less specific to DRP, such as age, sex, altered mental status and fever. Sensitivity (70–100%) and specificity (72–90%) are balanced in this model; however, in the derivation cohort, the score was estimated to recommend overtreatment in 24% of cases.

Another European score, derived by Aliberti et al.[26] using conserved risk factors for DRP (Table 1) has been validated in several large cohorts with very low DRP incidence where sensitivity ranged between 71 and 88% and specificity of 47 and 71%. Shindo et al.[28] also developed a novel prediction tool for DRP using a set of well established risk factors in a Japanese cohort. The Shindo score has been validated in several Japanese and US populations, where it demonstrated improved specificity (60–91%) and preserved sensitivity (45–84%). This cumulative model uniquely incorporates several MRSA-specific risk factors, which trigger MRSA-coverage when copresent with other factors.

The Brito/Niederman model is a cumulative model based on two sets of equally weighted healthcare exposure and DRP risk factors [36]. This model features severity stratification, where the threshold to treat for DRP is lower for patients with severe illness and favors sensitivity (45–94%) over specificity (53–86%). The Niederman model was prospectively implemented in a Japanese cohort comprising both community-onset as well as hospital-associated and ventilator-associated pneumonias [44▪▪] In a subgroup of 894 all-cause community-onset cases (6.7% DRP incidence), adherence was 80.3%. The protocol recommended broad-spectrum antibiotics for 16.3% of patients, although 28.9% actually received a broad-spectrum regimen; 2.7% received inadequate spectrum and 30-day mortality was 5.4%.

Webb et al.[29] described a novel probabilistic model (the DRIP score) comprised of four major risk factors: antibiotic use within 60 days, long-term care, enteral feeding or prior history of DRP infections and six minor risk factors: recent hospitalization, chronic pulmonary disease, poor functional status, gastric acid suppression, wound care, and known MRSA colonization. The score was originally validated in a multicenter US cohort where it demonstrated balanced sensitivity (82%) and specificity (81%) and outperformed HCAP (AUROC 0.88 vs. 0.72). In several external validation cohorts (Table 1), sensitivity ranged from 70 to 82% and specificity 71–82%. In one observational Veterans Affairs study, DRIP recommended broad spectrum antibiotics in 21.8% fewer patients than HCAP; interestingly in this study intensive antimicrobial stewardship on admission resulted in even greater reductions in antibiotic use [54]. DRIP has now been implemented in several US health systems. In two single-center studies, a significant reduction in the use of anti-MRSA and antipseudomonal antibiotics without a subsequent increase in readmissions was observed after transitioning from HCAP to DRIP-score-based algorithms [46▪,51]. Finally, in a large, multihospital DRIP implementation study where DRP prevalence was 2.8%, broad-spectrum antibiotics were recommended in 18% of patients and overall use was reduced significantly (adjusted odds ratio 0.62) [52▪▪].

Lastly, the 2019 ATS/IDSA guidelines include a new risk-factor-based algorithm as an alternative to using locally validated prediction models. This model yet to be validated. We applied the ATS/IDSA algorithm to a cohort of 894 consecutive CAP admissions from a previously published cohort [52▪▪]. Overall DRP incidence was 2.5% and was equally distributed between ICU and wards admissions; 21.1% of patients were admitted to the ICU. Overall this algorithm heavily favors specificity (91.6%) over sensitivity (31.8%); negative predictive value 98.2%, PPV 8.8%. This performance was magnified in nonsevere CAP (sensitivity 10%, specificity 91.6%) and more balanced in ICU admissions (sensitivity 50%, specificity 83.1%).

METHICILLIN-RESISTANT STAPHYLOCOCCUS AUREUS-SPECIFIC PREDICTION

Most scores, with the exception of the Shindo model, do not differentiate between MRSA and other gram negative DRP. In 2013, Shorr et al.[31] developed a probabilistic model specifically targeting MRSA pneumonia. The model includes extremes of age, recent hospitalization, skilled-nursing residence, prior intravenous antibiotics, ICU admission, cerebrovascular accident, dementia and female with diabetes mellitus. In a number of validation cohorts, this MRSA-specific prediction tool demonstrated sensitivity and specificity ranges of 50–97% and 30–64%, respectively, with excellent negative predictive value (NPV) (96%). Similarly, the GLIMP investigators recently reported validation of several scores for MRSA, demonstrating that in a cohort with very low MRSA prevalence, all had very poor PPV but excellent NPV, suggesting utility in ruling out MRSA CAP.

Another more widely method for ruling out MRSA in CAP is the PCR assay to detect MRSA from the nares. This has emerged as an important tool for guiding safe discontinuation of anti-MRSA therapy, owing to the test's excellent negative predictive value (96–99%) and rapid (several hours in most centers) turn-around time [55,56▪▪,57▪]. In centers where the turn-around time is rapid enough to be considered nearly point-of-care, the assay has also been used to inform whether to initiate or withhold initial empiric vancomycin [3▪▪]. The ATS/IDSA guidelines and many centers currently employ a two-step screening process in which patient-level risk factors for MRSA, or a validated clinical prediction rule is used to guide ordering of the MRSA PCR [3▪▪]. In this strategy (Fig. 3), the linked diagnostic value of clinical prediction and the MRSA PCR is cumulative, resulting in enhanced negative predictive value [58], and, surprisingly, a posttest MRSA pneumonia prevalence of 28–57% [57▪,58].

FIGURE 3
FIGURE 3:
Linked diagnostic value of drug-resistance prediction tool and methicillin-resistant Staphylococcus aureus PCR Nasal Swab for methicillin-resistant Staphylococcus aureus pneumonia.

CONCLUSION

Current data suggest that in most areas, the incidence of DRP is relatively low and that most patients with CAP can be appropriately treated with narrow-spectrum antibiotics. However, because both inadequate and unnecessary antibiotic spectrum are associated with poor outcomes, accurately predicting which patients require DRP coverage is an important clinical objective. The 2019 ATS/IDSA CAP guidelines endorse using locally validated prediction models for DRP. Institutions now have multiple options to choose from; although most risk-factors for DRP are generally conserved, the relative predictive value differs by population, highlighting the importance of locally evaluating existing tools. Clinicians should also recognize that even the most specific of the tools still recommend broad-spectrum antibiotics in 15–18% of patients, far exceeding DRP in most centers. The algorithm proposed in the latest ATS/IDSA guideline recommendations appear to be even more restrictive, recommending broad spectrum in around 10% of patients, likely at the expense of initial inadequate therapy in nonsevere patients. Both the MRSA nasal swab and published prediction scores have a strong NPV and can be used to withhold or discontinue anti-MRSA therapy. Severity of illness should be used as modifier in the clinical decision regarding the threshold at which to select empiric antibiotic spectrum covering DRP; in critically ill patients, the clinical margin for error is lower, justifying a lower predicted posttest probability threshold above which broad-spectrum therapy is needed. De-escalation with negative cultures remains a necessary companion step in pneumonia management when broad spectrum antibiotics are initiated. Future research should focus on comparative effectiveness for broad versus narrow and clarify whether a delay in providing adequate bacterial spectrum in nonsevere CAP is associated with adverse outcomes.

Acknowledgements

The authors wish to thank Dr Nathan C. Dean and the Intermountain Research and Medical Foundation for support of pneumonia research at Intermountain Healthcare.

Financial support and sponsorship

None.

Conflicts of interest

There are no conflicts of interest.

REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest

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      Keywords:

      antibiotic; antimicrobial stewardship; community-acquired; drug-resistance; healthcare-associated; pneumonia

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