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Purpose <p>Identifying patients for clinical studies evaluating strategies to reduce unnecessary antibiotic usage in hospitals is challenging. This study aimed to develop a predictive score to identify newly hospitalized patients with high likelihood of receiving antibiotics, thus improving patient inclusion in future studies focusing on antimicrobial stewardship (AMS) programs.</p> Methods <p>This retrospective analysis used data from the PILGRIM study (NCT03765528), which included 1,600 patients across ten international sites. Predictive variables for antibiotic treatment during hospitalization were computed, and an additive score model was developed using logistic regression and 10-fold cross-validation. The PILGRIM score was validated in an independent cohort (validation cohort), with performance metrics assessed.</p> Results <p>Data from 1,258 patients was included. In the development cohort 52.8% (n =&#xa0;445) and in the validation cohort 42.4% (n =&#xa0;134) of patients received antibiotics. Key predictors included hematologic malignancies, immunosuppressive medication, and past hospitalization. The logistic regression model demonstrated an area under the curve of 0.74 in the validation. The final additive score incorporated these predictors plus “planned elective surgery” achieving a specificity of 92%, a positive predictive value of 78%, a sensitivity of 41%, and a negative predictive value (NPV) of 69%in validation set.</p> Conclusion <p>The PILGRIM score effectively identifies newly hospitalized patients likely to receive antibiotics, demonstrating high specificity and PPV. Its application can improve future AMS programs and trial recruitment by facilitating targeted inclusion of patients, especially in the hematological and oncological setting. Further -external and prospective- validation is needed to broaden the model’s applicability.</p>

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Identifying patients at high risk for antibiotic treatment following hospital admission: a predictive score to improve antimicrobial stewardship measures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月15日

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  • Moritz Beck,
  • Carolin Koll,
  • Uga Dumpis,
  • Christian G. Giske,
  • Siri Göpel,
  • Silje Bakken Jørgensen,
  • Johanna Kessel,
  • Lars Kaare Kleppe,
  • Dorthea Hagen Oma,
  • Noa Eliakim Raz,
  • Makeda Semret,
  • Gunnar Skov Simonsen,
  • Maria J. G. T. Vehreschild,
  • Kerstin Albus,
  • Lena M. Biehl,
  • Jörg J. Vehreschild,
  • Annika Y. Classen,
  • Pauls Aldins,
  • Per Espen Akselsen,
  • Anne Mette Asfeldt,
  • Nadine Conzelmann,
  • Kelly Davison,
  • Thilo Dietz,
  • Simone Eisenbeis,
  • Lucas J. Fein,
  • Fe dja Farowski,
  • Romina Georghe,
  • Maayan Huberman Samuel,
  • Barbara Ann Jardin,
  • Merve Kaya,
  • Christian Kjellander,
  • Zane Linde Ozola,
  • Leonard Leibovici,
  • Nick Schulze,
  • Hannes Wåhlin,
  • Aija Vilde,
  • Viesturs Zvirbulis

摘要

Purpose

Identifying patients for clinical studies evaluating strategies to reduce unnecessary antibiotic usage in hospitals is challenging. This study aimed to develop a predictive score to identify newly hospitalized patients with high likelihood of receiving antibiotics, thus improving patient inclusion in future studies focusing on antimicrobial stewardship (AMS) programs.

Methods

This retrospective analysis used data from the PILGRIM study (NCT03765528), which included 1,600 patients across ten international sites. Predictive variables for antibiotic treatment during hospitalization were computed, and an additive score model was developed using logistic regression and 10-fold cross-validation. The PILGRIM score was validated in an independent cohort (validation cohort), with performance metrics assessed.

Results

Data from 1,258 patients was included. In the development cohort 52.8% (n = 445) and in the validation cohort 42.4% (n = 134) of patients received antibiotics. Key predictors included hematologic malignancies, immunosuppressive medication, and past hospitalization. The logistic regression model demonstrated an area under the curve of 0.74 in the validation. The final additive score incorporated these predictors plus “planned elective surgery” achieving a specificity of 92%, a positive predictive value of 78%, a sensitivity of 41%, and a negative predictive value (NPV) of 69%in validation set.

Conclusion

The PILGRIM score effectively identifies newly hospitalized patients likely to receive antibiotics, demonstrating high specificity and PPV. Its application can improve future AMS programs and trial recruitment by facilitating targeted inclusion of patients, especially in the hematological and oncological setting. Further -external and prospective- validation is needed to broaden the model’s applicability.