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April 13, 2026BMC Infectious Diseases0 citationsOpen Access

Dynamic regression forecasting of carbapenem-resistant Klebsiella spp. based on carbapenem consumption with observed data from 2020 to 2023 and projections up to 2029 in a tertiary hospital in Alexandria, Egypt

EEEhab ElmonguiAZAdel ZakiAEAmel Elsheredy

Key Points

  • The aim was to predict trends of carbapenem-resistant Klebsiella spp. using consumption data to enhance antimicrobial stewardship.
  • Analyzed hospital and ICU carbapenem consumption and resistance data from 2019 to 2023.
  • Used cross-correlation analyses to identify lag structures between consumption and resistance.
  • Incorporated lagged consumption measures as exogenous regressors in dynamic regression time-series models.
  • Hospital resistance increased from 30.2% in 2020 to 57.5% in 2023, with consumption rising from 30 to 131 DDD/1000PD.
  • ICU resistance rose from 25.8% in 2019 to 67.0% in 2023, with consumption escalating from 196 to 473 DOT/1000PD.
  • Strong correlations were found (r = 0.80 for hospital, r = 0.86 for ICU) between lagged consumption and resistance rates.

Abstract

Carbapenem-resistant Klebsiella spp. is a major global health threat. Overuse of carbapenem in healthcare settings, particularly ICUs, has driven the rise of these resistant bacteria. This study aimed to apply dynamic regression forecasting to predict future trends of carbapenem-resistant Klebsiella spp., with analysis of the temporal relationship between carbapenem consumption and resistance serving as the foundation for building accurate forecasts to inform antimicrobial stewardship (AMS) strategies. This retrospective study analyzed hospital- and ICU-level carbapenem consumption and resistance data for 1,727 Klebsiella spp. isolates (1,043 ICU) from a tertiary care hospital in Alexandria, Egypt (2019–2023). Cross-correlation analyses identified lag structures between consumption and resistance, and these lagged consumption measures — hospital carbapenem use expressed as defined daily doses per 1,000 patient-days (DDD/1000PD) per quarter and ICU use expressed as days of therapy per 1,000 patient-days (DOT/1000PD) per semester —were incorporated as exogenous regressors in dynamic regression time-series models to forecast future resistance trends. Carbapenem resistance exhibited by Klebsiella spp. in the hospital significantly increased from 30.2% (95% CI: 20.6% to 39.8%) in quarter 1 of 2020 to 57.5% (95% CI: 49.3% to 65.7%) in quarter 4 of 2023, accompanied by a parallel increase in the burden of resistant isolates and a substantial rise in carbapenem consumption from 30 DDD/1000PD in quarter 1 of 2019 to 131 DDD/1000PD in quarter 4 of 2023. Similarly, in the ICU, carbapenem resistance jumped from 25.8% (95% CI: 18.5% to 33.1%) in semester 1 of 2019 to 67.0% (95% CI: 58.6% to 75.3%) in semester 2 of 2023, accompanied by an increase in the burden of resistant isolates and by a surge in carbapenem consumption from 196 DOT/1000PD in semester 1 of 2019 to 473 DOT/1000PD in semester 4 of 2023. Strong correlations were found between one lagged period of carbapenem consumption and resistance rates (r = 0.80 for hospital, r = 0.86 for ICU). A marked increase in hospital carbapenem consumption was observed following the onset of the COVID-19 pandemic, with a post-pandemic level change of 61.82 DDD/1000 patient-days. Dynamic regression forecasting predicted stabilization of carbapenem resistance at approximately 50% at the hospital level (95% prediction interval PI: 32.6–67.1), while ICU-level models projected a continued upward trajectory in resistance over time (95% PI: 31.3–97.6). Dynamic regression forecasting revealed persistently high carbapenem resistance in Klebsiella spp. at the hospital level and a continued rising trend in the ICU, occurring in temporal association with carbapenem use. These projections support antimicrobial stewardship by informing trend awareness and highlighting the need for targeted interventions and sustained surveillance to mitigate further escalation.

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Cite This Study

Elmongui et al. (2026) studied this question.

synapsesocial.com/papers/69dc89473afacbeac03eb168https://doi.org/10.1186/s12879-026-13168-y
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