Predictive Models of Fever, ICU Transfer, and Mortality in Hospitalized Patients With Neutropenia: Erratum

ABSTRACT Empiric antibiotic prescribing can be supported by guidelines and/or local antibiograms, but these have limitations. We sought to use data from a comprehensive electronic health record to use statistical learning to develop predictive models for individual antibiotics that incorporate patient- and hospital-specific factors. This paper reports on the development and validation of these models with a large retrospective cohort. This was a retrospective cohort study including hospitalized patients with positive urine cultures in the first 48 h of hospitalization at a 1,500-bed tertiary-care hospital over a 4.5-year period. All first urine cultures with susceptibilities were included. Statistical learning techniques, including penalized logistic regression, were used to create predictive models for cefazolin, ceftriaxone, ciprofloxacin, cefepime, and piperacillin-tazobactam. These were validated on a held-out cohort. The final data set used for analysis included 6,366 patients. Final model covariates included demographics, comorbidity score, recent antibiotic use, recent antimicrobial resistance, and antibiotic allergies. Models had acceptable to good discrimination in the training data set and acceptable performance in the validation data set, with a point estimate for area under the receiver operating characteristic curve (AUC) that ranged from 0.65 for ceftriaxone to 0.69 for cefazolin. All models had excellent calibration. We used electronic health record data to create predictive models to estimate antibiotic susceptibilities for urinary tract infections in hospitalized patients. Our models had acceptable performance in a held-out validation cohort.

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De-escalation of Empiric Antibiotics Following Negative Cultures in Hospitalized Patients With Pneumonia: Rates and Outcomes

Clinical Infectious Diseases ◽

10.1093/cid/ciaa212 ◽

2020 ◽

Cited By ~ 1

Author(s):

Abhishek Deshpande ◽

Sandra S Richter ◽

Sarah Haessler ◽

Peter K Lindenauer ◽

Pei-Chun Yu ◽

...

Keyword(s):

Odds Ratio ◽

Adjusted Odds Ratio ◽

Methicillin Resistant Staphylococcus Aureus ◽

Empiric Therapy ◽

Multidrug Resistant ◽

Hospitalized Patients ◽

Transfer Length ◽

Multidrug Resistant Organisms ◽

Patients At Risk ◽

Icu Transfer

Abstract Background For patients at risk for multidrug-resistant organisms, IDSA/ATS guidelines recommend empiric therapy against methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas. Following negative cultures, the guidelines recommend antimicrobial de-escalation. We assessed antibiotic de-escalation practices across hospitals and their associations with outcomes in hospitalized patients with pneumonia with negative cultures. Methods We included adults admitted with pneumonia in 2010–2015 to 164 US hospitals if they had negative blood and/or respiratory cultures and received both anti-MRSA and antipseudomonal agents other than quinolones. De-escalation was defined as stopping both empiric drugs on day 4 while continuing another antibiotic. Patients were propensity adjusted for de-escalation and compared on in-hospital 14-day mortality, late deterioration (ICU transfer), length-of-stay (LOS), and costs. We also compared adjusted outcomes across hospital de-escalation rate quartiles. Results Of 14 170 patients, 1924 (13%) had both initial empiric drugs stopped by hospital day 4. Hospital de-escalation rates ranged from 2–35% and hospital de-escalation rate quartile was not significantly associated with outcomes. At hospitals in the top quartile of de-escalation, even among patients at lowest risk for mortality, the de-escalation rates were <50%. In propensity-adjusted analysis, patients with de-escalation had lower odds of subsequent transfer to ICU (adjusted odds ratio, .38; 95% CI, .18–.79), LOS (adjusted ratio of means, .76; .75–.78), and costs (.74; .72–.76). Conclusions A minority of eligible patients with pneumonia had antibiotics de-escalated by hospital day 4 following negative cultures and de-escalation rates varied widely between hospitals. To adhere to recent guidelines will require substantial changes in practice.

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1358: PREDICTIVE MODELS OF FEVER AND MORTALITY IN HOSPITALIZED PATIENTS WITH NEUTROPENIA

Critical Care Medicine ◽

10.1097/01.ccm.0000510032.91468.66 ◽

2016 ◽

Vol 44 (12) ◽

pp. 415-415

Author(s):

Elizabeth Gulleen ◽

Yuanbo Wang ◽

John Ainsworth ◽

Laura Barnes ◽

Christopher Moore

Keyword(s):

Predictive Models ◽

Hospitalized Patients

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Predictive Models of Fever and Adverse Outcomes in Hospitalized Patients with Neutropenia

Open Forum Infectious Diseases ◽

10.1093/ofid/ofx163.1873 ◽

2017 ◽

Vol 4 (suppl_1) ◽

pp. S698-S698

Author(s):

Elizabeth Gulleen ◽

Mawulolo Ameko ◽

John Ainsworth ◽

Prabhat Rayapati ◽

Laura Barnes ◽

...

Keyword(s):

Predictive Models ◽

Hospitalized Patients ◽

Adverse Outcomes

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1433. Predictive Models for Antibiotic Coverage of Gram-Negative Urinary Tract Infections

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.1297 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S523-S523

Author(s):

Courtney Hebert ◽

Yuan Gao ◽

Protiva Rahman ◽

Courtney M Dewart ◽

Nirav Shah ◽

...

Keyword(s):

Urinary Tract ◽

Urinary Tract Infections ◽

Predictive Models ◽

Hospitalized Patients ◽

Validation Dataset ◽

Point Estimate ◽

Gram Negative ◽

Validation Set ◽

Tract Infections ◽

Antibiotic Coverage

Abstract Background Providers use institutional recommendations, national guidelines, and antibiograms to decide on empiric antibiotics. As local antibiograms are most effective after organisms are known, we sought to use local microbiology and clinical data to develop predictive models for antibiotic coverage prior to identifying the organism. We focused on Gram-negative organisms as they are common urinary pathogens and are often the cause of sepsis originating in the urinary tract. As such, they are important to cover in hospitalized patients with urinary tract infections (UTI). Methods Hospitalized patients, with a diagnosis of UTI and a positive urine culture in the first 48 hours were included. Gram-positive organisms, yeast, and cultures without susceptibilities were excluded. Unknown susceptibilities were filled in using expert-derived rules. Clinical information from electronic health record (EHR) data were extracted on each patient. Penalized logistic regression with 10-fold cross validation was used to develop final models for coverage for five antibiotics (cefazolin, ceftriaxone, ciprofloxacin, cefepime, piperacillin–tazobactam). Final models were chosen based on their discrimination, calibration, and number of predictors, and then tested on a held-out validation dataset. Results Included were 5,096 patients (80% training; 20% validation). Coverage ranged from 65% for cefazolin to 90% for cefepime. Positive blood cultures were present in 544 (11%) with 388 (71%), including a urinary pathogen. In the first 24 hours, 2329 (46%) were hypotensive, 2179 (43%) had a respiratory rate > 22, 2049 (40%) had a WBC > 12, 1079 (21%) were febrile, and 584 (11%) required ICU care. Final model covariates included demographics, antibiotic exposure, prior resistant pathogens, and antibiotic allergies. The five predictive models had a point-estimate for the area under the ROC on the validation set that ranged from 0.70 for ciprofloxacin to 0.73 for ceftriaxone. Conclusion In this cohort of moderate to high acuity hospitalized patients with Gram-negative urinary pathogens, we used EHR data to develop 5 models that predict antibiotic coverage which could be used to support empiric prescribing. These models performed well in a held-out validation set. Disclosures All authors: No reported disclosures.

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