Sare Merve Başağa, Ayşegül Ulu Kılıç, Zeynep Ture, Gökmen Zararsız, Serra İlayda Yerlitaş
{"title":"血液恶性肿瘤患者耐碳青霉烯革兰氏阴性细菌感染的床边风险评分","authors":"Sare Merve Başağa, Ayşegül Ulu Kılıç, Zeynep Ture, Gökmen Zararsız, Serra İlayda Yerlitaş","doi":"10.3390/idr17040092","DOIUrl":null,"url":null,"abstract":"<p><strong>Background/objectives: </strong>This study aimed to create a 'carbapenem resistance score' with the risk factors of carbapenem-resistant Gram-negative bacterial infections (GNBIs) in patients with hematological malignancies.</p><p><strong>Methods: </strong>Patients with carbapenem-resistant and susceptible GNBIs were included in this study and compared in terms of risk factors. Three models of \"carbapenem resistance risk scores\" were created with statistically significant variables.</p><p><strong>Results: </strong>The study included 154 patients with hospital-acquired GNBIs, of whom 64 had carbapenem-resistant GNBIs and 90 had carbapenem-susceptible GNBIs. Univariate and multivariate analyses identified several statistically significant risk factors for carbapenem resistance, including transfer from another hospital or clinic (<i>p</i> = 0.038), prior use of antibiotics like fluoroquinolones (<i>p</i> = 0.009) and carbapenems (<i>p</i> = 0.001), a history of carbapenem-resistant infection in the last six months (<i>p</i> < 0.001), rectal <i>Klebsiella pneumoniae</i> colonization (<i>p</i> < 0.001), hospitalization for ≥30 days (<i>p</i> = 0.001), and the presence of a urinary catheter (<i>p</i> = 0.002). Notably, the 14-day mortality rate was significantly higher in the carbapenem-resistant group (<i>p</i> < 0.001). Based on these findings, three risk-scoring models were developed. Common factors in all three models were fluoroquinolone use in the last six months, rectal <i>K. pneumoniae</i> colonization, and the presence of a urinary catheter. The fourth variable was transfer from another hospital (Model 1), a history of carbapenem-resistant infection (Model 2), or hospitalization for ≥30 days (Model 3). All models demonstrated strong discriminative power (AUC for Model 1: 0.830, Model 2: 0.826, Model 3: 0.831). For all three models, a cutoff value of >2.5 was adopted as the threshold to identify patients at high risk for carbapenem resistance, a value which yielded high positive and negative predictive values.</p><p><strong>Conclusions: </strong>This study successfully developed three practical risk-scoring models to predict carbapenem resistance in patients with hematological malignancies using common clinical risk factors. A cutoff score of >2.5 proved to be a reliable threshold for identifying high-risk patients across all models, providing clinicians with a valuable tool to guide appropriate empirical antibiotic therapy.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"17 4","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12385919/pdf/","citationCount":"0","resultStr":"{\"title\":\"Bedside Risk Scoring for Carbapenem-Resistant Gram-Negative Bacterial Infections in Patients with Hematological Malignancies.\",\"authors\":\"Sare Merve Başağa, Ayşegül Ulu Kılıç, Zeynep Ture, Gökmen Zararsız, Serra İlayda Yerlitaş\",\"doi\":\"10.3390/idr17040092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background/objectives: </strong>This study aimed to create a 'carbapenem resistance score' with the risk factors of carbapenem-resistant Gram-negative bacterial infections (GNBIs) in patients with hematological malignancies.</p><p><strong>Methods: </strong>Patients with carbapenem-resistant and susceptible GNBIs were included in this study and compared in terms of risk factors. Three models of \\\"carbapenem resistance risk scores\\\" were created with statistically significant variables.</p><p><strong>Results: </strong>The study included 154 patients with hospital-acquired GNBIs, of whom 64 had carbapenem-resistant GNBIs and 90 had carbapenem-susceptible GNBIs. Univariate and multivariate analyses identified several statistically significant risk factors for carbapenem resistance, including transfer from another hospital or clinic (<i>p</i> = 0.038), prior use of antibiotics like fluoroquinolones (<i>p</i> = 0.009) and carbapenems (<i>p</i> = 0.001), a history of carbapenem-resistant infection in the last six months (<i>p</i> < 0.001), rectal <i>Klebsiella pneumoniae</i> colonization (<i>p</i> < 0.001), hospitalization for ≥30 days (<i>p</i> = 0.001), and the presence of a urinary catheter (<i>p</i> = 0.002). Notably, the 14-day mortality rate was significantly higher in the carbapenem-resistant group (<i>p</i> < 0.001). Based on these findings, three risk-scoring models were developed. Common factors in all three models were fluoroquinolone use in the last six months, rectal <i>K. pneumoniae</i> colonization, and the presence of a urinary catheter. The fourth variable was transfer from another hospital (Model 1), a history of carbapenem-resistant infection (Model 2), or hospitalization for ≥30 days (Model 3). All models demonstrated strong discriminative power (AUC for Model 1: 0.830, Model 2: 0.826, Model 3: 0.831). For all three models, a cutoff value of >2.5 was adopted as the threshold to identify patients at high risk for carbapenem resistance, a value which yielded high positive and negative predictive values.</p><p><strong>Conclusions: </strong>This study successfully developed three practical risk-scoring models to predict carbapenem resistance in patients with hematological malignancies using common clinical risk factors. A cutoff score of >2.5 proved to be a reliable threshold for identifying high-risk patients across all models, providing clinicians with a valuable tool to guide appropriate empirical antibiotic therapy.</p>\",\"PeriodicalId\":13579,\"journal\":{\"name\":\"Infectious Disease Reports\",\"volume\":\"17 4\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12385919/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infectious Disease Reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/idr17040092\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infectious Disease Reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/idr17040092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
Bedside Risk Scoring for Carbapenem-Resistant Gram-Negative Bacterial Infections in Patients with Hematological Malignancies.
Background/objectives: This study aimed to create a 'carbapenem resistance score' with the risk factors of carbapenem-resistant Gram-negative bacterial infections (GNBIs) in patients with hematological malignancies.
Methods: Patients with carbapenem-resistant and susceptible GNBIs were included in this study and compared in terms of risk factors. Three models of "carbapenem resistance risk scores" were created with statistically significant variables.
Results: The study included 154 patients with hospital-acquired GNBIs, of whom 64 had carbapenem-resistant GNBIs and 90 had carbapenem-susceptible GNBIs. Univariate and multivariate analyses identified several statistically significant risk factors for carbapenem resistance, including transfer from another hospital or clinic (p = 0.038), prior use of antibiotics like fluoroquinolones (p = 0.009) and carbapenems (p = 0.001), a history of carbapenem-resistant infection in the last six months (p < 0.001), rectal Klebsiella pneumoniae colonization (p < 0.001), hospitalization for ≥30 days (p = 0.001), and the presence of a urinary catheter (p = 0.002). Notably, the 14-day mortality rate was significantly higher in the carbapenem-resistant group (p < 0.001). Based on these findings, three risk-scoring models were developed. Common factors in all three models were fluoroquinolone use in the last six months, rectal K. pneumoniae colonization, and the presence of a urinary catheter. The fourth variable was transfer from another hospital (Model 1), a history of carbapenem-resistant infection (Model 2), or hospitalization for ≥30 days (Model 3). All models demonstrated strong discriminative power (AUC for Model 1: 0.830, Model 2: 0.826, Model 3: 0.831). For all three models, a cutoff value of >2.5 was adopted as the threshold to identify patients at high risk for carbapenem resistance, a value which yielded high positive and negative predictive values.
Conclusions: This study successfully developed three practical risk-scoring models to predict carbapenem resistance in patients with hematological malignancies using common clinical risk factors. A cutoff score of >2.5 proved to be a reliable threshold for identifying high-risk patients across all models, providing clinicians with a valuable tool to guide appropriate empirical antibiotic therapy.