Validation study of the modified Drug Resistance in Pneumonia (mDRIP) score in identifying infections with drug-resistant pathogens among hospitalized patients with community-acquired pneumonia at the Chinese General Hospital and Medical Center
DOI:
https://doi.org/10.70172/pjcd38114477Keywords:
mDRIP score, community-acquired pneumonia, drug resistance, validation studyAbstract
Background: Community-acquired pneumonia (CAP) continues to be the leading cause of infection-related deaths globally and is the fourth leading cause of both morbidity and mortality in the Philippines. Despite the development of novel vaccines, antibiotics, and rapid diagnostic tests, managing CAP remains challenging, especially with the emergence of drug-resistant pathogens (DRP). The modified Drug Resistance in Pneumonia (mDRIP) score is a scoring system which was derived from locally-relevant clinical risk factors that can predict infections with DRP. The performance of mDRIP score in identifying infections with DRP among patients with CAP was evaluated in this cross-sectional study.
Methodology: A total of 127 participants with CAP were included. The mDRIP score was calculated upon admission. Antimicrobial culture results were later obtained and clinical outcomes were ascertained. The mDRIP score performance was assessed by determining the relevant performance metrics using area under the receiver operating characteristic curve (AUC-ROC). Clinical outcomes were compared between patients with DRP and those without.
Results: The prevalence of drug-resistant pathogens in the study was 40.16%. The most common organism isolated was Klebsiella pneumoniae. Among the major and minor risk factors for drug resistance included in the mDRIP score, the most common were recent antibiotic use (46.46%) and poor functional status (47.24%), respectively. The discrimination performance of the mDRIP score was good, with an AUC-ROC value of 0.868 (95% CI 0.801 to 0.935). There was no statistically significant difference in the length of hospital stay and hospital outcome between those with and without DRP.
Conclusions: The mDRIP score demonstrates good performance in identifying infections due to DRP in CAP. It is an accessible and effective risk stratification tool that can be utilized by clinicians for appropriate selection of antibiotics in CAP, especially in resource-limited settings.
References
1. Ferreira-Coimbra J, Sarda C, Rello, J. Burden of community-acquired pneumonia and unmet clinical needs. Adv. Ther. 2020, 37, 1302–1318. https://doi.org/10.1007/s12325-020-01248-7 DOI: https://doi.org/10.1007/s12325-020-01248-7
2. WHO. The top 10 causes of death. 2020. Accessed May 4, 2026. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
3. Philippine Statistics Authority . 2024 Causes of Death in the Philippines. Accessed June 16, 2026. https://psa.gov.ph/content/2024-causes-death-philippines-provisional-30-november-2024
4. Tumanan-Mendoza BA, Mendoza VL, Punzalan FER, Reganit PFM, Bacolcol SAA. Economic burden of community-acquired pneumonia among Aadults in the Philippines: Its equity and policy implications in the case rate payments of the Philippine Health Insurance Corporation. Value Health Reg Issues. 2015 May; 6:118-125. Epub 2015 May 16. PMID: 29698182. https://doi.org/10.1016/j.vhri.2015.03.003 DOI: https://doi.org/10.1016/j.vhri.2015.03.003
5. Metlay JP, Waterer GW, Long AC, et al. Diagnosis and treatment of adults with community-acquired pneumonia. An official clinical practice guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med. 2019;200(7):e45-67. https://doi.org/10.1164/rccm.201908-1581ST DOI: https://doi.org/10.1164/rccm.201908-1581ST
6. Fadrian F, Chen K, Kumalawati J, Rumende CM, Shatri H, Nelwan EJ. The validation of drug resistance in pneumonia (DRIP) score in predicting infections due to drug-resistant pathogens in community-acquired pneumonia at Cipto Mangunkusumo Hospital, Jakarta, Indonesia. Acta Med Indones. 2021 Oct;53(4):416-422. PMID: 35027488. https://doi.org/10.1128/AAC.01482-20
7. Webb BJ, Jones B, Dean NC. Empiric antibiotic selection and risk prediction of drug-resistant pathogens in community-onset pneumonia. Curr Opin Infect Dis. 2016;29(2):167-77. https://doi.org/10.1097/QCO.0000000000000254 DOI: https://doi.org/10.1097/QCO.0000000000000254
8. Shindo Y, Hasegawa Y. Regional differences in antibiotic‐resistant pathogens in patients with pneumonia: Implications for clinicians. Respirology. 2017;22(8):1536-46. https://doi.org/10.1111/resp.13135 DOI: https://doi.org/10.1111/resp.13135
9. Webb BJ, Dascomb K, Stenehjem E, et al. Derivation and multicenter validation of the drug resistance in pneumonia clinical prediction score. Antimicrobial Agents and Chemotherapy. 2016;60(5):2652-63. https://doi.org/ 10.1128/AAC.03071-15 DOI: https://doi.org/10.1128/AAC.03071-15
10. Farkas A, Sassine J, Mathew JP, et al. Outcomes associated with the use of a revised risk assessment strategy to predict antibiotic resistance in community-onset pneumonia: a stewardship perspective. J Antimicrobial Chemother. 2018;73(9):2555-8. https://doi.org/10.1093/jac/dky202 DOI: https://doi.org/10.1093/jac/dky202
11. Villalobos MJ, Zotomayor R, Gerodias A, et al. Validation study of the drug resistance in pneumonia (DRIP) score in predicting the risk of drug-resistant pathogens among patients with pneumonia: A single center cross-sectional study [Abstract only]. Clinical Diagnosis, Prediction and Outcomes of Lung Infections. Am J Respir Crit Care Med. 2020;A2130-A2130). Accessed May 4, 2026 https://doi.org/10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2130 DOI: https://doi.org/10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2130
12. Oliver MB, Fong K, Certain L, Spivak ES, Timbrook TT. Validation of a community-acquired pneumonia score To improve empiric antibiotic selection at an academic medical center. Antimicrobial Agents and Chemotherapy. 2021;65(2),e01482-20. https://doi.org/10.1128/AAC.01482-20 DOI: https://doi.org/10.1128/AAC.01482-20
13. Hamilton LC. 2013. Statistics with STATA: Updated for Version 12 (8th Ed.). Boston, USA: Brooks/Cole Cengage Learning.
14. Larsson DGJ, Flach CF. Antibiotic resistance in the environment. Nat Rev Microbiol. 2022 May;20(5):257-269. Epub 2021 Nov 4. PMID: 34737424; PMCID: PMC8567979. https://doi.org/10.1038/s41579-021-00649-x DOI: https://doi.org/10.1038/s41579-021-00649-x
15. Roved J, Westerdahl H, Hasselquist D. Sex differences in immune responses: Hormonal effects, antagonistic selection, and evolutionary consequences. Horm Behav. 2017 Feb;88:95-105. Epub 2016 Dec 9. PMID: 27956226. https://doi.org/ 10.1016/j.yhbeh.2016.11.017 DOI: https://doi.org/10.1016/j.yhbeh.2016.11.017
16. Costelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis. BMJ. 2010;340:c2096. https://doi.org/ 10.1136/bmj.c2096 DOI: https://doi.org/10.1136/bmj.c2096
17. Goldstein RC, Husk G, Jodlowski T, Mildvan D, Perlman DC, Ruhe JJ. Fluoroquinolone- and ceftriaxone-based therapy of community-acquired pneumonia in hospitalized patients: the risk of subsequent isolation of multidrug-resistant organisms. Am J Infect Control. 2014;42:539-541. https://doi.org/ 10.1016/j.ajic.2014.01.005 DOI: https://doi.org/10.1016/j.ajic.2014.01.005
18. Shindo Y, Ito R, Kobayashi D, et al. Risk factors for drug-resistant pathogens in community-acquired and healthcare-associated pneumonia. Am J Respir Crit Care Med. 2013;188:985-995. https://doi.org/10.1164/rccm.201301-0079OC DOI: https://doi.org/10.1164/rccm.201301-0079OC
19. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York City, USA: Springer; 2008. https://doi.org/10.1007/978-0-387-77244-8 DOI: https://doi.org/10.1007/978-0-387-77244-8
20. Brown B, Cheraghi-Sohi S, Jaki T, Su TL, Buchan I, Sperrin M. Understanding clinical prediction models as ‘innovations’: a mixed methods study in UK family practice. BMC Med Inform Decis Mak. 2016;16:106. https://doi.org/10.1186/s12911-016-0343-y DOI: https://doi.org/10.1186/s12911-016-0343-y
21. Wallace E, Johansen ME. Clinical prediction rules: challenges, barriers, and promise. Ann Fam Med. 2018;16:390–392. https://doi.org/10.1370/ afm.2303. DOI: https://doi.org/10.1370/afm.2303
22. Elligsen M, Pinto R, Leis JA, Walker SAN, MacFadden DR, Daneman N. Using prior culture results to improve initial empiric antibiotic prescribing: an evaluation of a simple clinical heuristic. Clin Infect Dis. 2021 May 18;72(10):e630-e638. https://doi.org/10.1093/cid/ciaa1397 DOI: https://doi.org/10.1093/cid/ciaa1397
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Cynthia N. Charmino, MD, Cristalle Geraldine C. Dy-Sebastian, MD
This work is licensed under a Creative Commons Attribution 4.0 International License.
