Impact of an electronic sepsis initiative on antibiotic use and health care facility–onset Clostridium difficile infection rates

Hiensch, Robert; Poeran, Jashvant; Saunders-Hao, Patricia; Adams, Victoria; Powell, Charles A.; Glasser, Allison; Mazumdar, Madhu; Patel, Gopi

doi:10.1016/j.ajic.2017.04.005

Cited by 40 publications

(31 citation statements)

References 47 publications

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“…However, concerns have been raised regarding the poor sensitivity of the qSOFA possibly leading to delays in sepsis identification [40]. The high sensitivity of the SIRS criteria, on the other hand, could lead to over diagnosis of sepsis resulting in inappropriate antibiotics use [41]. Additionally, most of the investigated biomarkers failed to show discriminative power or clinical relevance [42,43].…”

Section: Performance and Clinical Relevance Of Individual Modelsmentioning

confidence: 99%

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

et al. 2020

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Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.

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Section: Performance and Clinical Relevance Of Individual Modelsmentioning

confidence: 99%

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

et al. 2020

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“…These data are needed to inform the rational use of antibiotics, given that overuse of broad-spectrum therapy may also confer harm by selecting for antibioticresistant bacteria, increasing the risk of adverse events, such as Clostridioides difficile infections, and raising costs. [8][9][10][11][12] Overtreatment has also been associated with higher mortality rates in some populations. [13][14][15] Therefore, we sought to elucidate the epidemiology of antibiotic-resistant pathogens in patients with culture-positive community-onset sepsis and the risks of both inadequate and unnecessarily broad antibiotic treatments in US hospitals.…”

Section: Introductionmentioning

confidence: 99%

Prevalence of Antibiotic-Resistant Pathogens in Culture-Proven Sepsis and Outcomes Associated With Inadequate and Broad-Spectrum Empiric Antibiotic Use

et al. 2020

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IMPORTANCE Broad-spectrum antibiotics are recommended for all patients with suspected sepsis to minimize the risk of undertreatment. However, little is known regarding the net prevalence of antibiotic-resistant pathogens across all patients with community-onset sepsis or the outcomes associated with unnecessarily broad empiric treatment. OBJECTIVE To elucidate the epidemiology of antibiotic-resistant pathogens and the outcomes associated with both undertreatment and overtreatment in patients with culture-positive communityonset sepsis. DESIGN, SETTING, AND PARTICIPANTS This cohort study included 17 430 adults admitted to 104 US hospitals between January 2009 and December 2015 with sepsis and positive clinical cultures within 2 days of admission. Data analysis took place from January 2018 to December 2019. EXPOSURES Inadequate empiric antibiotic therapy (ie, Ն1 pathogen nonsusceptible to all antibiotics administered on the first or second day of treatment) and unnecessarily broad empiric therapy (ie, active against methicillin-resistant Staphylococcus aureus [MRSA]; vancomycin-resistant Enterococcus [VRE]; ceftriaxone-resistant gram-negative [CTX-RO] organisms, including Pseudomonas aeruginosa; or extended-spectrum β-lactamase [ESBL] gram-negative organisms when none of these were isolated). MAIN OUTCOMES AND MEASURES Prevalence and empiric treatment rates for antibiotic-resistant organisms and associations of inadequate and unnecessarily broad empiric therapy with in-hospital mortality were assessed, adjusting for baseline characteristics and severity of illness. RESULTS Of 17 430 patients with culture-positive community-onset sepsis (median [interquartile range] age, 69 [57-81] years; 9737 [55.9%] women), 2865 (16.4%) died in the hospital. The most common culture-positive sites were urine (9077 [52.1%]), blood (6968 [40.0%]), and the respiratory tract (2912 [16.7%]). The most common pathogens were Escherichia coli (5873 [33.7%]), S aureus (3706 [21.3%]), and Streptococcus species (2361 [13.5%]). Among 15 183 cases in which all antibiotic-pathogen susceptibility combinations could be calculated, most (12 398 [81.6%]) received adequate empiric antibiotics. Empiric therapy targeted resistant organisms in 11 683 of 17 430 cases (67.0%; primarily vancomycin and anti-Pseudomonal β-lactams), but resistant organisms were uncommon (MRSA, 2045 [11.7%]; CTX-RO, 2278 [13.1%]; VRE, 360 [2.1%]; ESBLs, 133 [0.8%]). The net prevalence for at least 1 resistant gram-positive organism (ie, MRSA or VRE) was 13.6% (2376 patients), and for at least 1 resistant gram-negative organism (ie, CTX-RO, ESBL, or CRE), it was 13.2% (2297 patients). Both inadequate and unnecessarily broad empiric antibiotics were associated with (continued) Key Points Question What is the prevalence of antibiotic resistance in communityonset sepsis, and is there risk associated with the receipt of empiric broadspectrum antibiotics? Findings In this cohort study of 17 430 adults with culture-positive sepsis admitted to 104 US hospitals, 67.0% received empiric b...

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“…Obviously, the policy of "right first time" has been recommended by many academicians (4), but it is not exempt of the ecological potential risk of increasing emergence of resistance. As a matter of fact, excessive use of antibiotics, or unjustified prolonged use do increase the selection of resistant micro-organisms (4,5). A recent study reported that the implementation of antibiotic/ sepsis bundles in patients with a sepsis code was associated with increased Clostridium difficile infection rates (5).…”

mentioning

confidence: 99%

“…As a matter of fact, excessive use of antibiotics, or unjustified prolonged use do increase the selection of resistant micro-organisms (4,5). A recent study reported that the implementation of antibiotic/ sepsis bundles in patients with a sepsis code was associated with increased Clostridium difficile infection rates (5). Here is where diagnostic tests have an important role and a policy of antimicrobial stewardship focusing on reduced exposure to broad-spectrum antibiotic needs to be implemented (6).…”

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confidence: 99%

Best practice for sepsis

Rello

Rubulotta

2018

J. Thorac. Dis.

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Impact of an electronic sepsis initiative on antibiotic use and health care facility–onset Clostridium difficile infection rates

Cited by 40 publications

References 47 publications

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Prevalence of Antibiotic-Resistant Pathogens in Culture-Proven Sepsis and Outcomes Associated With Inadequate and Broad-Spectrum Empiric Antibiotic Use

Best practice for sepsis

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