Background Predicting risk for community-acquired pneumonia due to drug-resistant pathogens (CAP-DRP) is challenging. Healthcare-associated pneumonia (HCAP) criteria have limited predictive value. However, a more accurate model has not yet been validated in a U.S. cohort. Methods In a retrospective derivation cohort of 200 culture-positive CAP and HCAP cases, previously identified risk factors for CAP-DRP were analyzed by logistic regression. A novel prediction tool, the DRIP score, was derived from 10 of these factors, weighted according to AOR. A validation cohort of 200 culture-positive cases was identified prospectively at four U.S. centers (May 2013 - May 2014). The performance of DRIP, HCAP and other prediction models was evaluated in this group. Results In the derivation group CAP-DRP prevalence was 25%. Prior antibiotic use (60 days) (AOR 7.3, p<0.01), tube feeding (AOR 25.6 p=0.02), long term care (AOR 4.1 p=0.05), and prior CAP-DRP (AOR 7.2 p=0.079) were associated with CAP-DRP. The DRIP score best differentiated high and low risk at a threshold of ≥4 points with a PPV of 73.0, NPV 92.0, accuracy of 87% and AUROC of 0.896. This was superior to HCAP (AUROC 0.599, accuracy 72%), and all other predictive models . In the validation cohort, DRIP was again most predictive: AUROC of 0.860, PPV 68.0, NPV 89.0, accuracy 80.5%. CAP-DRP prevalence was 33%. Compared to HCAP, the DRIP model would reduce over treatment by 46% with no difference in rates of under treatment. Conclusion In this prospective multi-center U.S. study, the DRIP score was more predictive of CAP-DRP than HCAP and other models and has potential to decrease antibiotic over-utilization. Further validation is needed.

Updated Abstract

Test Performance - Derivation Cohort

• The DRIP score is a novel cumulative, probabilistic model for predicting risk of pneumonia due to CAP-DRP

• DRIP was derived using a more comprehensive and refined set of risk factors for drug resistance than HCAP, with major factors assigned increased weight.

• DRIP shows strong correlation with CAP-DRP at all scoring levels as demonstrated by excellent AUROC (.896 derivation, .860 validation)

• At a cut-off of ≥4 points, DRIP optimally differentiates high and low risk (PPV of 73.0, NPV 92.0, accuracy of 87%), supporting its utility as a clinical decision tool to guide empiric antibiotic selection.

• In the derivation group, DRIP was superior to HCAP (AUROC 0.599, accuracy 72%), and all other predictive models.

• In the multi-center validation cohort, DRIP was again most predictive: AUROC of 0.860, PPV 68.0, NPV 89.0, accuracy 80.5%.

• Compared to HCAP, the DRIP model would reduce unnecessary extended-spectrum antibiotic use by 46% with no difference in rates of inadequate empiric treatment.

Discussion

Demographic Data

Derivation Cohort • A retrospective cohort of 200 culture-positive pneumonia cases was

identified from 1450 patients consecutively admitted with pneumonia to 7 community and tertiary-care hospitals in Utah in 2012.

• Charts were manually reviewed and cases only included if they met clinical and radiographic criteria for PNA with an organism consistent with a respiratory bacterial pathogen.

• Demographics, microbiology, and risk factor data were gathered. Patients were contacted by telephone if risk factor data was not available in the chart.

• 25 risk factors previously described for CAP-DRP were analyzed by univariate analysis and 12 were included in logistic regression. Risk factors were entered into a predictive score model using multiple stepwise sequential iterations . Several methods of weighting risk factors according to regression ß- coefficient were evaluated. Test performance characteristic s were measured for each step until an optimum model, the DRIP score, was derived.

Validation Cohort • We conducted a multi-center, non-interventional prospective

validation study of the DRIP score from May 2013 to June 2014. • 200 patients admitted to four study hospitals with pneumonia

determined by investigators AND a positive culture consistent with a respiratory pathogen were enrolled in the study. Risk factor data was obtained from EMR and patient interviews.

• Test performance characteristics of the DRIP score was compared to other prediction models including HCAP.

Methods DRIP Score

• In this prospective multi-center U.S. study, the novel DRIP score was more predictive of CAP-DRP than HCAP and other models and has potential to decrease antibiotic over-utilization.

• Prospective implementation studies in populations with varying rates of CAP-DRP are needed.

Conclusions

1. Buie VC OM, DeFrances CJ, Golosinskiy A. National Hospital Discharge Survey: 2006 summary. National Center for Health Statistics. Vital Health Stat 2010;13(168). 2. Heron MP HD, Murphy SL, Xu JQ, Kochanek KD, Tejada-Vera B. . Deaths: Final data for 2006. National vital statistics reports. 2009;57(14). 3. Kollef MH, Shorr A, Tabak YP, Gupta V, Liu LZ, Johannes RS. Epidemiology and outcomes of health-care-associated pneumonia: results from a large US database of culture-positive pneumonia. Chest. Dec 2005;128(6):3854-3862. 4. Zilberberg MD, Shorr AF, Micek ST, Mody SH, Kollef MH. Antimicrobial therapy escalation and hospital mortality among patients with health-care-associated pneumonia: a single-center experience. Chest. Nov 2008;134(5):963-968. 5. ATS/IDSA. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. American journal of respiratory and critical care medicine. Feb 15 2005;171(4):388-416. 6. Attridge RT, Frei CR, Restrepo MI, et al. Guideline-concordant therapy and outcomes in healthcare-associated pneumonia. Eur Respir J. Oct 2012;38(4):878-887 7. Carratala J, Mykietiuk A, Fernandez-Sabe N, et al. Health care-associated pneumonia requiring hospital admission: epidemiology, antibiotic therapy, and clinical outcomes. Archives of internal medicine. Jul 9 2007;167(13):1393-1399. 8. Chalmers JD, Taylor JK, Singanayagam A, et al. Epidemiology, antibiotic therapy, and clinical outcomes in health care-associated pneumonia: a UK cohort study. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America. Jul 15 2011;53(2):107-113. 9. Grenier C, Pepin J, Nault V, et al. Impact of guideline-consistent therapy on outcome of patients with healthcare-associated and community-acquired pneumonia. The Journal of antimicrobial chemotherapy. Jul 2011;66(7):1617-1624. 10. Madaras-Kelly KJ, Remington RE, Fan VS, Sloan KL. Predicting antibiotic resistance to community-acquired pneumonia antibiotics in culture-positive patients with healthcare-associated pneumonia. Journal of hospital medicine : an official publication of the Society of Hospital Medicine. Mar 2012;7(3):195-202. 11. Shorr AF, Zilberberg MD, Reichley R, et al. Validation of a Clinical Score for Assessing the Risk of Resistant Pathogens in Patients With Pneumonia Presenting to the Emergency Department. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America. Nov 21 2010. 12. Venditti M, Falcone M, Corrao S, Licata G, Serra P. Outcomes of patients hospitalized with community-acquired, health care-associated, and hospital-acquired pneumonia. Ann Intern Med. Jan 6 2009;150(1):19-26. 13. Webb BJ, Dangerfield BS, Pasha JS, Agrwal N, Vikram HR. Guideline-concordant antibiotic therapy and clinical outcomes in healthcare-associated pneumonia. Respiratory medicine. Nov 2012;106(11):1606-1612. 14. Berger A, Edelsberg J, Oster G, Huang X, Weber DJ. Patterns of initial antibiotic therapy for community-acquired pneumonia in U.S. hospitals, 2000 to 2009. The American journal of the medical sciences. May 2014;347(5):347-356. 15. Chen JI, Slater LN, Kurdgelashvili G, Husain KO, Gentry CA. Outcomes of health care-associated pneumonia empirically treated with guideline-concordant regimens versus community-acquired pneumonia guideline-concordant regimens for patients admitted to acute care wards from home. Ann Pharmacother. Jan 2013;47(1):9-19 16. El Solh AA, Akinnusi ME, Alfarah Z, Patel A. Effect of antibiotic guidelines on outcomes of hospitalized patients with nursing home-acquired pneumonia. Journal of the American Geriatrics Society. Jun 2009;57(6):1030-1035. 17. Taylor SP, Taylor BT. Health care-associated pneumonia in haemodialysis patients: clinical outcomes in patients treated with narrow versus broad spectrum antibiotic therapy. Respirology. Feb 2013;18(2):364-368. 18. Burgess LD, Drew RH. Comparison of the incidence of vancomycin-induced nephrotoxicity in hospitalized patients with and without concomitant piperacillin-tazobactam. Pharmacotherapy. Jul 2014;34(7):670-676. 19. Jeffres MN, Isakow W, Doherty JA, Micek ST, Kollef MH. A retrospective analysis of possible renal toxicity associated with vancomycin in patients with health care-associated methicillin-resistant Staphylococcus aureus pneumonia. Clin Ther. Jun 2007;29(6):1107-1115. 20. Chalmers JD, Al-Khairalla M, Short PM, Fardon TC, Winter JH. Proposed changes to management of lower respiratory tract infections in response to the Clostridium difficile epidemic. The Journal of antimicrobial chemotherapy. Apr 2010;65(4):608-618. 21.McCabe C, Kirchner C, Zhang H, Daley J, Fisman DN. Guideline-concordant therapy and reduced mortality and length of stay in adults with community-acquired pneumonia: playing by the rules. Archives of internal medicine. Sep 14 2009;169(16):1525-1531. 22. Kett DH, Cano E, Quartin AA, et al. Implementation of guidelines for management of possible multidrug-resistant pneumonia in intensive care: an observational, multicentre cohort study. The Lancet. Infectious diseases. Mar 2011;11(3):181-189. 23. Troitino AX, Porhomayon J, El-Solh AA. Guideline-concordant antimicrobial therapy for healthcare-associated pneumonia: a systematic review and meta-analysis. Lung. Jun 2013;191(3):229-237 24. Chalmers JD, Rother C, Salih W, Ewig S. Healthcare-associated pneumonia does not accurately identify potentially resistant pathogens: a systematic review and meta-analysis. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America. Feb 2014;58(3):330-339. 25. Gross, AE, Van Schooneveld TC, Olsen KM, et al. Epidemiology and Predictors of Multidrug-Resistant Communiy-Acquired and Heatlh Care-Associated Pneumonia. Antimicrob Agents Chemo. Sept 2014;58(9):5262-5268.

References Background

Drug Resistance in Pneumonia • Pneumonia remains the leading infectious cause of death in the United

States and a major contributor to healthcare spending.(1,2) • Correct initial antibiotic selection improves outcomes in community-

acquired pneumonia, but increasing rates of CAP-DRP makes identifying patients at risk of drug-resistance a significant clinical challenge.(3,4)

Healthcare-associated Pneumonia (HCAP) Criteria • 2005 ATS/IDSA guidelines recommend extended-spectrum antibiotic

coverage for patients meeting the HCAP criteria, which identify a population with frequent healthcare exposure, multiple co-morbidities and elevated mortality.(5-13)

• As a result, use of vancomycin and broad –spectrum gram-negative agents for pneumonia treatment has increased dramatically.(14)

• Widespread use of extended-spectrum antibiotics is associated with increased cost (6,15), length of stay(6,16,17), rates of drug-toxicity(18,19), and Clostridium difficile infection(20).

Limitations of HCAP • Unlike CAP, where data supports the mortality benefit of guideline-

concordant antibiotics (21), outcomes in HCAP may actually be worse with guideline-concordant therapy.(6,22,23)

• The predictive value of HCAP for CAP-DRP is poor.(11,24,25) • A recent meta-analysis confirmed the relatively poor performance of

HCAP as a predictor of CAP-DRP: overall sensitivity of 53.7, specificity of 71.2 and AUROC of 0.70.(24)

Microbiology

AUROC – Validation Cohort

DRIP – Drug Resistance In Pneumonia: Derivation and Prospective Multi-center Validation of a Scoring Model to Predict Drug-Resistant Pathogens

Brandon J. Webb MD1, Kristin Dascomb MD1, Edward Stenehjem MD1, Holenarasipur R. Vikram MD2, Neera Agrwal MD2, Kenneth Sakata MD2, Kathryn Williams MD2, Bruno Bockorny MD3 , Kavitha Bagavathy MD3, Shireen Mirza MD3, Mark Metersky MD3, Nathan Dean MD4

1Intermountain Healthcare, Salt Lake City, UT, 2Mayo Clinic in Arizona, Phoenix, AZ, 3University of Connecticut Medical Center, Farmington, CT, 4Division of Pulmonary and Critical Care Medicine at Intermountain Medical Center and the University of Utah, Salt Lake City, UT

Derivation

Test Performance - Validation Cohort

Acknowledgments

• This study was supported by an academic institutional grant from the Intermountain Research and Medical Foundation.

• Institutional review board approval was obtained at all participating centers.

Poster #47798 Brandon.Webb@imail.org

Phone: (801) 507-7781 Fax: (801) 507-7780

AUROC – Derivation Cohort

Accuracy: Recommended Antibiotic Spectrum Accuracy: Recommended Antibiotic Spectrum

Risk Factor AOR p-value

Chronic Pulmonary Disease

2.26 0.109

Long-term Facility<31d

4.08 0.047

Antibiotics <61d 7.27 0.001

Tube Feeding 25.8 0.015

History of CAP-DRP Infection <1yr

7.20 0.079

Test Cut-off Score

Sens % Spec % PPV % NPV % AUROC (95% CI)

DRIP 4 0.79 0.81 0.68 0.89 0.860 (0.804-

0.916)

HCAP 1 0.79 0.65 0.53 0.86 0.719 (0.644-

0.793)

Schreiber 2 0.68 0.66 0.50 0.81 0.727 (0.653-

0.800)

Shorr 1 0.88 0.40 0.42 0.87 0.774 (0.702-

0.845)

Niederman 2 0.91 0.53 0.49 0.92 0.802 (0.739-

0.866)

Shindo 2 0.83 0.60 0.50 0.88 0.787 (0.720-

0.854)

Aliberti 0.5 0.88 0.55 0.49 0.90 0.727 (0.656-

0.799)

Park 3 0.77 0.72 0.57 0.86 0.812 (0.749-

0.875)

7

7

10.5

4

3

5.5

4

7.5

80.5

69.5

67

55.5

65.5

67.5

66

73.5

12.5

23.5

22.5

40.5

31.5

27

30

19

DRIP

HCAP

Schreiber

Shorr

Niederman

Shindo

Aliberti

Park

Inadequate Spectrum % Unnecessary spectrum % Overtreatment

6

16

12

2.5

4.5

9

7.5

12.5

87

72

72

44

70

75.5

60

77

7

12

16

53.5

25.5

15.5

32.5

10.5

DRIP

HCAP

Schreiber

Shorr

Niederman

Shindo

Aliberti

Park

Inadequate Spectrum % Overall Accuracy % Unnecessary spectrum %

Test Cut-off Score

Sens % Spec % PPV % NPV % AUROC (95% CI)

DRIP 4 0.76 0.91 0.73 0.92 0.896 (0.841-

0.952)

HCAP 1 0.36 0.84 0.43 0.80 0.599 (0.504-

0.695)

Schreiber 2 0.52 0.79 0.45 0.83 0.682 (0.596-

0.769)

Shorr 1 0.9 0.29 0.30 0.90 0.714 (0.628-

0.800)

Niederman 2 0.82 0.66 0.45 0.92 0.795 (0.724-

0.865)

Shindo 2 0.64 0.79 0.51 0.87 0.769 (0.684-

0.855)

Aliberti 0.5 0.7 0.57 0.35 0.85 0.655 (0.571-

0.739)

Park 3 0.5 0.86 0.54 0.84 0.754 (0.669-

0.839)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

Pe

rce

nt

Derivation

Validation

Cumulative

CAP-DRP

Risk

Selective Antibiotic

Pressure

Intrinsic (Host)

Factors Chronic Lung Disease

Immunosuppression

Aspiration

Gastric Acid Suppression

Cognitive impairment

Cerebrovascular disease

MRSA Colonization

Prior CAP-DRP

Tobacco Use

Diabetes

Extrinsic

(Environment)

Factors Prior Hospitalization

Long Term Care

Poor Functional Status

Tube Feeding

Wound Care

Infusion Therapy

Indwelling Catheter

Hemodialysis

Risk Factors for CAP-DRP