Cost-Effectiveness Analysis of a Procalcitonin-Guided Decision Algorithm for Antibiotic Stewardship Using Real-World U.S. Hospital Data

Artificial intelligence in drug resistance management

This review highlights the application of artificial intelligence (AI), particularly deep learning and machine learning (ML), in managing antimicrobial resistance (AMR). Key findings demonstrate that AI models, such as Naïve Bayes, Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), have significantly advanced the prediction of drug resistance patterns and the identification of novel antibiotics. These algorithms have effectively optimized antibiotic use, predicted resistance phenotypes, and identified new drug candidates. AI has also facilitated the detection of AMR-associated mutations, offering new insights into the spread of resistance and potential interventions. Despite data privacy and algorithm transparency challenges, AI presents a promising tool in combating AMR, with implications for improving patient outcomes, enhancing disease management, and addressing global public health concerns. However, realizing its full potential requires overcoming issues related to data scarcity, ethical considerations, and fostering interdisciplinary collaboration.

GRADE-based procalcitonin guideline for emergency departments

Procalcitonin is a useful biomarker for infection. Over the past two decades, there has been much research on the clinical applications of procalcitonin, yet the majority of these studies have been conducted in the intensive care setting. Despite the extensive use of procalcitonin in emergency departments, there have been no guidelines focusing specifically on these clinical settings. Additionally, previous guidelines were predominantly shaped by expert consensus and rarely incorporate evidence-based medicine concepts. To address these shortcomings, the current guideline adopts a novel approach. Initially, we identified the most critical questions regarding the use of procalcitonin in emergency settings through expert voting. This was followed by a systematic literature review and the evaluation of evidence levels using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) methodology. Key characteristics of individual studies will be summarized and evaluated by the guideline development group to determine the overall quality of evidence. The GRADE working group's categorization system will be employed to rate evidence quality into four levels. Recommendations will be formulated based on explicit consideration of established criteria. This structured approach ensures that guideline recommendations are founded on robust evidence and transparently assessed for strength and potential caveats. This is the first guideline on the use of procalcitonin to be applied in emergency departments that adopts the principles of evidence-based medicine and encompasses the up-to-date literatures, and it marks an advancement in providing guidance on the utilization of procalcitonin in emergency departments.

Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development

Artificial intelligence (AI) has transformative potential in veterinary pathology in tasks ranging from cell enumeration and cancer detection to prognosis forecasting, virtual staining techniques, and individually tailored treatment plans. Preclinical testing and validation of AI systems (AIS) are critical to ensure diagnostic safety, efficacy, and dependability. In this two-part series, challenges such as the AI chasm (ie, the discrepancy between the AIS model performance in research settings and real-world applications) and ethical considerations (data privacy, algorithmic bias) are reviewed and underscore the importance of tailored quality assurance measures that address the nuances of AI in veterinary pathology. This review advocates for a multidisciplinary approach to AI development and implementation, focusing on image-based tasks, highlighting the necessity for collaboration across veterinarians, computer scientists, and ethicists to successfully navigate the complex landscape of using AI in veterinary medicine. It calls for a concerted effort to bridge the AI chasm by addressing technical, ethical, and regulatory challenges, facilitating AI integration into veterinary pathology. The future of veterinary pathology must balance harnessing AI's potential while intentionally mitigating its risks, ensuring the welfare of animals and the integrity of the veterinary profession are safeguarded. Part I of this review focuses on considerations for model development, and Part II focuses on external validation of AI.

Cost-effectiveness of Procalcitonin (PCT) guidance for antibiotics management of adult sepsis patients in the Egyptian context

BACKGROUND

Sepsis, which is described as a life-threatening organ malfunction brought on by an unbalanced host response to infection, continues to be a significant healthcare issue that affects millions of individuals each year. It is well-known that sepsis can affect anyone around the world, but the employed survey results showed that there are significant regional variations in sepsis incidence as well as mortality rates. Although there are no definite estimates for Egypt, the highest rates were in Low-Middle-Income Countries (LMICs). Procalcitonin (PCT) is a host response marker with high specificity for bacterial infections, unlike C-reactive protein (CRP) or white blood cell count (WBC), which represent the traditional methods of detecting inflammation and infection. Its dynamic profile and superior prognostic prediction make it invaluable for assessing response to antibiotic treatment and improving clinical care for sepsis patients. Our main purpose was to evaluate the cost-effectiveness of PCT guidance compared to no PCT guidance in the antibiotic management of adult sepsis patients according to the Egyptian context.

METHODS

We developed a decision tree model to compare the PCT-guided antibiotic management duration endpoint versus the conventional laboratory culture-based antibiotic management in adult sepsis patients. We employed the"Delphi technique" to reach a satisfactory consensus regarding the resources attributed to each compared alternative. The primary measure of the study was the additional cost associated with each Quality-Adjusted Life Year (QALY) gained by sepsis survivors over a one-year time horizon. Base-case, deterministic and probabilistic sensitivity analyses were conducted using TreeAge, Software.

RESULTS

Base-case analysis showed no dominance for either alternative and resulted in an Incremental Cost-Effectiveness Ratio (ICER) value of 297,783.57 Egyptian Pounds per Quality Adjusted Life Year (L.E/QALY) in favor of the PCT guidance alternative, Deterministic sensitivity analysis revealed that the highest impact magnitudes on ICER reside with seven input parameters, the top two parameters that had the most significant influence were the costs of ICU stay with and without PCT guidance. The CEAC showed a slightly higher probability in terms of acceptability in favor of the no PCT guidance choice along the WTP scale till reaching equal probabilities at the willingness-to-pay (WTP) value point of 390,000 (state currency) after which the - probability supports the PCT guidance choice.

CONCLUSIONS

In the Egyptian context, PCT guidance has no cost-effectiveness domination over no PCT guidance in Antibiotics management for adult sepsis patients. This may be attributed to the high cost of PCT investigation that shall be resolved by standardization of its cost when applying the approach of DRG cost packages.

Procalcitonin for safe reduction of unnecessary blood cultures in the emergency department: Development and validation of a prediction model

OBJECTIVES

Blood cultures (BCs) are commonly ordered in emergency departments (EDs), while a minority yields a relevant pathogen. Diagnostic stewardship is needed to safely reduce unnecessary BCs. We aimed to develop and validate a bacteremia prediction model for ED patients, with specific focus on the benefit of incorporating procalcitonin.

METHODS

We included adult patients with suspected bacteremia from a Dutch ED for a one-year period. We defined 23 candidate predictors for a "full model", of which nine were used for an automatable "basic model". Variations of both models with C-reactive protein and procalcitonin were constructed using LASSO regression, with bootstrapping for internal validation. External validation was done in an independent cohort of patients with confirmed infection from 71 Spanish EDs. We assessed discriminative performance using the C-statistic and calibration with calibration curves. Clinical usefulness was evaluated by sensitivity, specificity, saved BCs, and Net Benefit.

RESULTS

Among 2111 patients in the derivation cohort (mean age 63 years, 46% male), 273 (13%) had bacteremia, versus 896 (20%) in the external cohort (n = 4436). Adding procalcitonin substantially improved performance for all models. The basic model with procalcitonin showed most promise, with a C-statistic of 0.87 (0.86-0.88) upon external validation. At a 5% risk threshold, it showed a sensitivity of 99% and could have saved 29% of BCs while only missing 10 out of 896 (1.1%) bacteremia patients.

CONCLUSIONS

Procalcitonin-based bacteremia prediction models can safely reduce unnecessary BCs at the ED. Further validation is needed across a broader range of healthcare settings.

Inflammatory biomarkers as predictors of systemic vs isolated pocket infection in patients undergoing transvenous lead extraction

Background

Cardiovascular implantable electronic device (CIED) infections are a common indication for device extraction. Early diagnosis and complete system removal are crucial to reduce morbidity and mortality. The lack of clear infectious symptoms makes the diagnosis of pocket infections challenging and may delay referral for extraction.

Objective

We aimed to determine if inflammatory biomarkers can help diagnose CIED isolated pocket infection.

Methods

We performed a retrospective analysis of all patients undergoing transvenous lead extraction for CIED infection at the University of California San Diego from 2012 to 2022 (N = 156). Patients were classified as systemic infection (n = 88) or isolated pocket infection (n = 68). Prospectively collected preoperative procalcitonin (PCT), C-reactive protein, and white blood cell count were compared between groups.

Results

Pairwise comparisons revealed that the systemic infection group had a higher PCT than the control group (P < .001) and the pocket infection group (P = .009). However, there was no significant difference in PCT value between control subjects and isolated pocket infection subjects. Higher white blood cell count was only associated with systemic infection when compared with our control group (P = .018).

Conclusion

In patients diagnosed with CIED infections requiring extraction, inflammatory biomarkers were not elevated in isolated pocket infection. Inflammatory markers are not predictive of the diagnosis of pocket infections, which ultimately requires a high level of clinical suspicion.

Costs-effectiveness and cost components of pharmaceutical and non-pharmaceutical interventions affecting antibiotic resistance outcomes in hospital patients: a systematic literature review

INTRODUCTION

Limited information on costs and the cost-effectiveness of hospital interventions to reduce antibiotic resistance (ABR) hinder efficient resource allocation.

METHODS

We conducted a systematic literature review for studies evaluating the costs and cost-effectiveness of pharmaceutical and non-pharmaceutical interventions aimed at reducing, monitoring and controlling ABR in patients. Articles published until 12 December 2023 were explored using EconLit, EMBASE and PubMed. We focused on critical or high-priority bacteria, as defined by the WHO, and intervention costs and incremental cost-effectiveness ratio (ICER). Following Preferred Reporting Items for Systematic review and Meta-Analysis guidelines, we extracted unit costs, ICERs and essential study information including country, intervention, bacteria-drug combination, discount rates, type of model and outcomes. Costs were reported in 2022 US dollars ($), adopting the healthcare system perspective. Country willingness-to-pay (WTP) thresholds from Woods et al 2016 guided cost-effectiveness assessments. We assessed the studies reporting checklist using Drummond's method.

RESULTS

Among 20 958 articles, 59 (32 pharmaceutical and 27 non-pharmaceutical interventions) met the inclusion criteria. Non-pharmaceutical interventions, such as hygiene measures, had unit costs as low as $1 per patient, contrasting with generally higher pharmaceutical intervention costs. Several studies found that linezolid-based treatments for methicillin-resistant Staphylococcus aureus were cost-effective compared with vancomycin (ICER up to $21 488 per treatment success, all 16 studies' ICERs

CONCLUSION

Robust information on ABR interventions is critical for efficient resource allocation. We highlight cost-effective strategies for mitigating ABR in hospitals, emphasising substantial knowledge gaps, especially in low-income and middle-income countries. Our study serves as a resource for guiding future cost-effectiveness study design and analyses.PROSPERO registration number CRD42020341827 and CRD42022340064.

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Key Words

• Control strategies
• Infections, diseases, disorders, injuries
• Prevention strategies
• Public Health
• Systematic review

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MESH Headings

• Humans
• Methicillin-Resistant Staphylococcus aureus
• Checklist
• Drug Resistance, Microbial
• Hospitals
• Pharmaceutical Preparations

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Grants

• FONDECYT
• Asociación Nacional de Investigación y Desarrollo (ANID)
• Agencia Nacional de Investigación y Desarrollo ANID/FONDAP CIGIDEN
• National Institute for Health Research Health Protection Research Unit

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Affiliation(s)

Kasim Allel Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK Institute for Global Health, University College London, London, UK Department of Health and Community Sciences, University of Exeter, Exeter, UK
María José Hernández-Leal Department of Community, Maternity and Paediatric Nursing, University of Navarra, Pamplona, Spain Millennium Nucleus on Sociomedicine, Santiago, Chile
Nichola R Naylor Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London, UK
Eduardo A Undurraga Escuela de Gobierno, Pontificia Universidad Catolica de Chile, Santiago, Chile CIFAR Azrieli Global Scholars program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada
Gerard Joseph Abou Jaoude Institute for Global Health, University College London, London, UK
Priyanka Bhandari Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
Ellen Flanagan Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
Hassan Haghparast-Bidgoli Institute for Global Health, University College London, London, UK
Koen B Pouwels Nuffield Department of Population Health, University of Oxford, Oxford, UK The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
Laith Yakob Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK

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Micromotor-based dual aptassay for early cost-effective diagnosis of neonatal sepsis

Given the long-life expectancy of the newborn, research aimed at improving sepsis diagnosis and management in this population has been recognized as cost-effective, which at early stages continues to be a tremendous challenge. Despite there is not an ideal-specific biomarker, the simultaneous detection of biomarkers with different behavior during an infection such as procalcitonin (PCT) as high specificity biomarker with one of the earliest biomarkers in sepsis as interleukin-6 (IL-6) increases diagnostic performance. This is not only due to their high positive predictive value but also, since it can also help the clinician to rule out infection and thus avoid the use of antibiotics, due to their high negative predictive value. To this end, we explore a cutting-edge micromotor (MM)-based OFF-ON dual aptassay for simultaneous determination of both biomarkers in 15 min using just 2 μL of sample from low-birth-weight neonates with gestational age less than 32 weeks and birthweight below 1000 g with clinical suspicion of late-onset sepsis. The approach reached the high sensitivities demanded in the clinical scenario (LODPCT = 0.003 ng/mL, LODIL6 = 0.15 pg/mL) with excellent correlation performance (r > 0.9990, p < 0.05) of the MM-based approach with the Hospital method for both biomarkers during the analysis of diagnosed samples and reliability (Er < 6% for PCT, and Er < 4% for IL-6). The proposed approach also encompasses distinctive technical attributes in a clinical scenario since its minimal sample volume requirements and expeditious results compatible with few easy-to-obtain drops of heel stick blood samples from newborns admitted to the neonatal intensive care unit. This would enable the monitoring of both sepsis biomarkers within the initial hours after the manifestation of symptoms in high-risk neonates as a valuable tool in facilitating prompt and well-informed decisions about the initiation of antibiotic therapy.These results revealed the asset behind micromotor technology for multiplexing analysis in diagnosing neonatal sepsis, opening new avenues in low sample volume-based diagnostics.

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

BACKGROUND

The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence.

METHOD

This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases.

RESULTS

Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area.

CONCLUSION

AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.

The budget impact of procalcitonin-guided antibiotic stewardship compared to standard of care for patients with suspected sepsis admitted to the intensive care unit in Belgium

In Belgium, antibiotic resistance leads to approximately 530 deaths with a €24 million financial burden annually. This study estimated the impact of procalcitonin-guided antibiotic stewardship programs to reduce antibiotic consumption versus standard of care in patients with suspected sepsis. A decision analytic tree modelled health and budget outcomes of procalcitonin-guided antibiotic stewardship programs for patients admitted to the intensive care unit (ICU). A literature search, a survey with local clinical experts, and national database searches were conducted to obtain model input parameters. The main outcomes were total budget impact per patient, reduction in number of antibiotic resistance cases, and cost per antibiotic day avoided. To evaluate the impact of parameter uncertainty on the source data, a deterministic sensitivity analysis was performed. A scenario analysis was conducted to investigate budget impact when including parameters for reduction in length of ICU stay and mechanical ventilation duration, in addition to base-case parameters. Based on model predictions, procalcitonin-guided antibiotic stewardship programs could reduce the number of antibiotic days by 66,868, resulting in €1.98 million savings towards antibiotic treatment in current clinical practice. Antibiotic resistance cases could decrease by 7.7% (6.1% vs 9.2%) in the procalcitonin-guided setting compared with standard of care. The base-case budget impact suggests an investment of €1.90 per patient. The sensitivity analysis showed uncertainty, as the main drivers can alter potential cost savings. The scenario analysis indicated a saving of €1,405 per patient, with a reduction of 1.5 days in the ICU (14.8 days vs 12.8 days), and a reduction of 22.7% (18.1-27.2%) in mechanical ventilation duration. The associated sensitivity analysis was shown to be robust in all parameters. Procalcitonin-guided antibiotic stewardship programs are associated with clinical benefits that positively influence antimicrobial resistance in Belgium. A small investment per patient to implement procalcitonin testing may lead to considerable cost savings.

A real-world cost-effectiveness study of vancomycin versus linezolid for the treatment of late-onset neonatal sepsis in the NICU in China

BACKGROUND AND OBJECTIVE

Currently, the detection rates of methicillin-resistant S. aureus (MRSA) and methicillin-resistant coagulase-negative staphylococci (MRCoNS) in the blood cultures of neonates with sepsis exceed the national average drug resistance level, and vancomycin and linezolid are the primary antibacterial drugs used for these resistant bacteria according to the results of etiological examinations. However, a comprehensive evaluation of their costs and benefits in late-onset neonatal sepsis in a neonatal intensive care unit (NICU) has not been conducted. This study aimed to compare the cost and effectiveness of vancomycin and linezolid in treating neonatal sepsis in the NICU.

METHODS

A cost-effectiveness analysis of real-world data was carried out by retrospective study in our hospital, and the cost and effectiveness of vancomycin and linezolid were compared by establishing a decision tree model. The drug doses in the model were 0.6 g for linezolid and 0.5 g for vancomycin. The cost break down included cost of medical ward, NICU stay, intravenous infusion of vancomycin or linezolid, all monitoring tests, culture tests and drugs. The unit costs were sourced from hospital information systems. The effectiveness rates were obtained by cumulative probability analysis. One-way sensitivity analysis was used to analyze uncertain influencing factors.

RESULTS

The effectiveness rates of vancomycin and linezolid in treating neonatal sepsis in the NICU were 89.74% and 90.14%, respectively, with no significant difference. The average cost in the vancomycin group was ¥12261.43, and the average cost in the linezolid group was ¥17227.96. The incremental cost effectiveness was ¥12416.33 cost per additional neonate with treatment success in the linezolid group compared to vancomycin group at discharge. Factors that had the greatest influence on the sensitivity of the incremental cost-effectiveness ratio were the price of linezolid and the effectiveness rates.

CONCLUSIONS

The cost for treatment success of one neonate in linezolid group was ¥5449.17 more than that in vancomycin group, indicating that vancomycin was more cost-effective. Therefore, these results can provide a reference for a cost effectiveness treatment scheme for neonatal sepsis in the NICU.

A systematic literature review of economic evaluation studies of interventions impacting antimicrobial resistance

BACKGROUND

Antimicrobial resistance (AMR) is accelerated by widespread and inappropriate use of antimicrobials. Many countries, including those in low- and middle- income contexts, have started implementing interventions to tackle AMR. However, for many interventions there is little or no economic evidence with respect to their cost-effectiveness. To help better understand the scale of this evidence gap, we conducted a systematic literature review to provide a comprehensive summary on the value for money of different interventions affecting AMR.

METHODS

A systematic literature review was conducted of economic evaluations on interventions addressing AMR. a narrative synthesis of findings was produced. Systematic searches for relevant studies were performed across relevant databases and grey literature sources such as unpublished studies, reports, and other relevant documents. All identified economic evaluation studies were included provided that they reported an economic outcome and stated that the analysed intervention aimed to affect AMR or antimicrobial use in the abstract. Studies that reported clinical endpoints alone were excluded. Selection for final inclusion and data extraction was performed by two independent reviewers. A quality assessment of the evidence used in the included studies was also conducted.

RESULTS

28,597 articles were screened and 35 articles were identified that satisfied the inclusion criteria. The review attempted to answer the following questions: (1) What interventions to address AMR have been the subject of an economic evaluation? (2) In what types of setting (e.g. high-income, low-income, regions etc.) have these economic evaluations been focused? (3) Which interventions have been estimated to be cost-effective, and has this result been replicated in other settings/contexts? (4) What economic evaluation methods or techniques have been used to evaluate these interventions? (5) What kind and quality of data has been used in conducting economic evaluations for these interventions?

DISCUSSION

The review is one of the first of its kind, and the most recent, to systematically review the literature on the cost-effectiveness of AMR interventions. This review addresses an important evidence gap in the economics of AMR and can assist AMR researchers' understanding of the state of the economic evaluation literature, and therefore inform future research. Systematic review registration PROSPERO (CRD42020190310).

Guidelines for the Use of Procalcitonin for Rational Use of Antibiotics

How to cite this article: Khilnani GC, Tiwari P, Zirpe KG, Chaudhary D, Govil D, Dixit S, et al. Guidelines for the Use of Procalcitonin for Rational Use of Antibiotics. Indian J Crit Care Med 2022;26(S2):S77-S94.

Practical Concerns about the Metrics and Methods of Financial Outcome Measurement in Antimicrobial Stewardship Programs: A Narrative Review

Emerging pathogens in the meantime of paucity of new antibiotics discovery, put antimicrobial stewardship in the center of attention, to preserve the existing antimicrobial effect. Implementation of antimicrobial stewardship programs, however, needs approval from healthcare system managers. The approval process can be enhanced, when the beneficial effects of stewardship programs are supported by both clinical and financial evidence. Focusing on the financial outcome evaluation, the practitioners who run the stewardship programs, may choose certain methods and metrics, depending on the clinical setting scale and type, available human resources, and budget. The wise selection of the methods and metrics warrants a comprehensive insight of the existing methods and metrics, deployed by typically published works that set good examples to follow. This review is an attempt to provide such an insight along with typical relevant examples for each metric and method.

Prognostic Value of Serum Procalcitonin Based Model in Moderate to Severe Traumatic Brain Injury Patients

Objective

Procalcitonin (PCT) is an acknowledged marker of systemic inflammatory response. Previous studies have not reached agreement on the association between serum PCT and outcome of traumatic brain injury (TBI) patients. We designed this study to confirm the prognostic value of PCT in isolated TBI and those with extracranial injury, respectively.

Methods

Patients hospitalized in our hospital for moderate-to-severe TBI between March 2015 and December 2019 were included. Logistic regression analysis was performed to validate the association between PCT and in-hospital mortality in these patients. AUC (area under the receiver operating characteristics curve) of PCT and constructed model were calculated and compared.

Results

Among the included 211 patients, 81 patients suffered a poor outcome, with a mortality rate of 38.4%. Non-survivors had a higher level of serum PCT (2.73 vs 0.72, p<0.001) and lower GCS (5 vs 7, p<0.001) on admission than survivors. AUC of single PCT for predicting mortality in isolated TBI and those with extracranial injury were 0.767 and 0.553, respectively. Multivariate logistic regression showed that GCS (OR=0.744, p=0.008), glucose (OR=1.236, p<0.001), cholesterol (OR=0.526, p=0.002), and PCT (OR=1.107, p=0.022) were independently associated with mortality of isolated TBI. The AUC of the prognostic model composed of GCS, glucose, cholesterol, and PCT was 0.868 in isolated TBI.

Conclusion

PCT is an efficient marker of outcome in isolated moderate-to-severe TBI but not those with extracranial injury. A prognostic model incorporating PCT is useful for clinicians to make early risk stratification for isolated TBI.

Artificial intelligence to improve antimicrobial prescribing: A protocol for a systematic review

Introduction: The inappropriate use of antimicrobials is a threat to their effectiveness and often results in antimicrobial resistance (AMR) and difficult to treat infections. Different methods have been implemented to control AMR, and in recent years, artificial intelligence (AI) has been used to improve antimicrobial prescribing. However, there is insufficient information about the contribution of AI in improving antimicrobial prescribing. This systematic review aims to determine whether the use of AI can improve antimicrobial prescribing for human patients. Methods: Observational studies that examine the potential or actual use of AI in improving antimicrobial prescribing cited in IEEE Xplore, ScienceDirect, Scopus, Web of Science, OVID, EMBASE and ACM will be included in this systematic review. There will be no restriction on language, nor the setting (i.e.: primary care or hospital) nor the time when the studies included were conducted. The primary outcome of this systematic review is the relative reduction in prescribed antimicrobials, while the secondary outcome is the relative reduction in patients’ consultations, whether for infection recurrence or worsening of symptoms. Data will be meta-analyzed with a Random Effects Model. The I2 statistic for heterogeneity will be calculated and the Newcastle Ottawa Scale Tool will be used to assess risk of bias. Dissemination: The results will be disseminated through a peer-reviewed publication and scientific sessions. PROSPERO Registration: This protocol has been registered in PROSPERO online database (CRD42022329049; 14 May 2022).

Pancreatic stone protein point-of-care testing can reduce healthcare expenditure in sepsis

BACKGROUND

Sepsis is a life-threatening organ dysfunction in response to infection. Early recognition and rapid treatment are critical to patient outcomes and cost savings, but sepsis is difficult to diagnose because of its non-specific symptoms. Biomarkers such as pancreatic stone protein (PSP) offer rapid results with greater sensitivity and specificity than standard laboratory tests.

METHODS

This study developed a decision tree model to compare a rapid PSP test to standard of care in the emergency department (ED) and intensive care unit (ICU) to diagnose patients with suspected sepsis. Key model parameters included length of hospital and ICU stay, readmission due to infection, cost of sepsis testing, length of antibiotic treatment, antibiotic resistance, and clostridium difficile infections. Model inputs were determined by review of sepsis literature.

RESULTS

The rapid PSP test was found to reduce costs by $1688 per patient in the ED and $3315 per patient in the ICU compared to standard of care. Cost reductions were primarily driven by the specificity of PSP in the ED and the sensitivity of PSP in the ICU.

CONCLUSIONS

The results of the model indicate that PSP testing is cost saving compared to standard of care in diagnosis of sepsis. The abundance of sepsis cases in the ED and ICU make these findings important in the clinical field and further support the potential of sensitive and specific markers of sepsis to not only improve patient outcomes but also reduce healthcare expenditures.

Defining the decision problem: a scoping review of economic evaluations for Clostridioides difficile interventions

BACKGROUND

Clostridioides difficile infection is the leading cause of healthcare-associated infectious diarrhoea. Several preventative and treatment interventions exist; however, decisions for their use are typically made independent of other interventions along the care pathway.

AIM

To assess how the scope of the decision problem is defined in economic evaluations of C. difficile interventions.

METHODS

A scoping review was conducted following the Joanna Briggs Institute framework using a comprehensive literature search with C. difficile and economic evaluation as key search concepts. Study selection and extraction were performed independently by two reviewers. An in-depth analysis of all cost-utility and cost-effectiveness analyses was conducted. Care pathway domains (i.e. infection prevention and control, antimicrobial stewardship programmes, prevention, diagnostics, treatment) were defined iteratively, and each study was classified according to the scope of the decision problem: (i) one intervention, one domain; (ii) one intervention, multiple domains; (iii) multiple interventions, one domain; and (iv) multiple interventions, multiple domains.

RESULTS

In total, 3886 studies were identified. Of these, 116 studies were included in the descriptive overview, and 46 were included in the in-depth analysis. Most studies limited the scope of the decision problem to one intervention (43/46; 93%). Only three studies (3/46; 7%) assessed multiple interventions - either as bundled vs standalone interventions for prevention (i.e. a single domain), or as sequences of treatments for initial and recurrent infection (i.e. multiple domains). No study assessed multiple interventions across prevention and treatment domains.

CONCLUSIONS

Economic evaluations for C. difficile infection assess narrowly defined decision problems which may have implications for optimal healthcare resource allocation.

Cost impact analysis of novel host-response diagnostic for patients with community-acquired pneumonia in the emergency department

BACKGROUND

There is significant over-prescription of antibiotics for suspected community-acquired pneumonia (CAP) patients as bacterial and viral pathogens are difficult to differentiate. To address this issue, a host response diagnostic called MeMed BV (MMBV) was developed that accurately differentiates bacterial from viral infection at the point of need by integrating measurements of multiple biomarkers. A literature-based cost-impact model was developed that compared the cost impact and clinical benefits between using the standard of care diagnostics combined with MMBV relative to standard of care diagnostics alone.

METHODS

The patient population was stratified according to the pneumonia severity index, and cost savings were considered from payer and provider perspectives. Four scenarios were considered. The main analysis considers the cost impact of differences in antibiotic stewardship and resulting adverse events. The first, second, and third scenarios combine the impacts on antibiotic stewardship with changes in hospital admission probability, length of hospital stay and diagnosis related group (DRG) reallocation, and hospital admission probability, length of stay, and DRG reallocation in combination, respectively.

RESULTS

The main analysis results show overall per-patient savings of $37 for payers and $223 for providers. Scenarios 1, 2, and 3 produced savings of $137, $189, and $293 for payers, and $339, $713, and $809 for providers, respectively.

LIMITATIONS

Models are simulations of real-world clinical processes, and are not sensitive to variations in clinical practice driven by differences in physician practice styles, differences in facility-level practice patterns, and patient comorbidities expected to exacerbate the clinical impact of CAP. Hospital models are limited to costs and do not consider differences in revenue associated with each approach.

CONCLUSIONS

Introducing MMBV to the current SOC diagnostic process is likely to be cost-saving to both hospitals and payers when considering impacts on antibiotic distribution, hospital admission rate, hospital LOS, and DRG reallocation.

Impact of a Procalcitonin-Based Protocol on Antibiotic Exposure and Costs in Critically Ill Patients

Supplemental Digital Content is available in the text.

To examine the impact before and after adoption of a procalcitonin-based protocol to guide sepsis management has on antibiotic use, care costs, and outcomes of critically ill patients.

Big Data and Real-World Data based Cost-Effectiveness Studies and Decision-making Models: A Systematic Review and Analysis

Background: Big data and real-world data (RWD) have been increasingly used to measure the effectiveness and costs in cost-effectiveness analysis (CEA). However, the characteristics and methodologies of CEA based on big data and RWD remain unknown. The objectives of this study were to review the characteristics and methodologies of the CEA studies based on big data and RWD and to compare the characteristics and methodologies between the CEA studies with or without decision-analytic models. Methods: The literature search was conducted in Medline (Pubmed), Embase, Web of Science, and Cochrane Library (as of June 2020). Full CEA studies with an incremental analysis that used big data and RWD for both effectiveness and costs written in English were included. There were no restrictions regarding publication date. Results: 70 studies on CEA using RWD (37 with decision-analytic models and 33 without) were included. The majority of the studies were published between 2011 and 2020, and the number of CEA based on RWD has been increasing over the years. Few CEA studies used big data. Pharmacological interventions were the most frequently studied intervention, and they were more frequently evaluated by the studies without decision-analytic models, while those with the model focused on treatment regimen. Compared to CEA studies using decision-analytic models, both effectiveness and costs of those using the model were more likely to be obtained from literature review. All the studies using decision-analytic models included sensitivity analyses, while four studies no using the model neither used sensitivity analysis nor controlled for confounders. Conclusion: The review shows that RWD has been increasingly applied in conducting the cost-effectiveness analysis. However, few CEA studies are based on big data. In future CEA studies using big data and RWD, it is encouraged to control confounders and to discount in long-term research when decision-analytic models are not used.

Epigenetics and the International Classification of Functioning, Disability and Health Model: Bridging Nature, Nurture, and Patient-Centered Population Health

Epigenetic processes enable environmental inputs such as diet, exercise, and health behaviors to reversibly tag DNA with chemical "marks" that increase or decrease the expression of an individual's genetic template. Over time, epigenetic adaptations enable the effects of healthy or unhealthy stresses to become stably expressed in the tissue of an organism, with important consequences for health and disease. New research indicates that seemingly non-biological factors such as social stress, poverty, and childhood hardship initiate epigenetic adaptations in gene pathways that govern inflammation and immunity, two of the greatest contributors to chronic diseases such as diabetes and obesity. Epigenetic processes therefore provide a biological bridge between the genome-an individual's genetic inheritance-and the Social Determinants of Health-the conditions in which they are born, grow, live, work, and age. This Perspective paper argues that physical therapy clinicians, researchers, and educators can use the theoretical framework provided by the International Classification of Functioning, Disability, and Health (ICF model) to harmonize new discoveries from both public health research and medically focused genomic research. The ICF model likewise captures the essential role played by physical activity and exercise, which initiate powerful and widespread epigenetic adaptations that promote health and functioning. In this proposed framework, epigenetic processes transduce the effects of the social determinants of health and behaviors such as exercise into stable biological adaptations that affect an individual's daily activities and their participation in social roles. By harmonizing "nature" and "nurture," physical therapists can approach patient care with a more integrated perspective, capitalizing on novel discoveries in precision medicine, rehabilitation science, and in population-level research. As the experts in physical activity and exercise, physical therapists are ideally positioned to drive progress in the new era of patient-centered population health care.

Cost impact of procalcitonin-guided decision making on duration of antibiotic therapy for suspected early-onset sepsis in neonates

BACKGROUNDS

The large, international, randomized controlled NeoPInS trial showed that procalcitonin (PCT)-guided decision making was superior to standard care in reducing the duration of antibiotic therapy and hospitalization in neonates suspected of early-onset sepsis (EOS), without increased adverse events. This study aimed to perform a cost-minimization study of the NeoPInS trial, comparing health care costs of standard care and PCT-guided decision making based on the NeoPInS algorithm, and to analyze subgroups based on country, risk category and gestational age.

METHODS

Data from the NeoPInS trial in neonates born after 34 weeks of gestational age with suspected EOS in the first 72 h of life requiring antibiotic therapy were used. We performed a cost-minimization study of health care costs, comparing standard care to PCT-guided decision making.

RESULTS

In total, 1489 neonates were included in the study, of which 754 were treated according to PCT-guided decision making and 735 received standard care. Mean health care costs of PCT-guided decision making were not significantly different from costs of standard care (€3649 vs. €3616). Considering subgroups, we found a significant reduction in health care costs of PCT-guided decision making for risk category 'infection unlikely' and for gestational age ≥ 37 weeks in the Netherlands, Switzerland and the Czech Republic, and for gestational age < 37 weeks in the Czech Republic.

CONCLUSIONS

Health care costs of PCT-guided decision making of term and late-preterm neonates with suspected EOS are not significantly different from costs of standard care. Significant cost reduction was found for risk category 'infection unlikely,' and is affected by both the price of PCT-testing and (prolonged) hospitalization due to SAEs.

A Veterans' Healthcare Administration (VHA) antibiotic stewardship intervention to improve outpatient antibiotic use for acute respiratory infections: A cost-effectiveness analysis

OBJECTIVES

The Core Elements of Outpatient Antibiotic Stewardship provides a framework to improve antibiotic use, but cost-effectiveness data on implementation of outpatient antibiotic stewardship interventions are limited. We evaluated the cost-effectiveness of Core Element implementation in the outpatient setting.

METHODS

An economic simulation model from the health-system perspective was developed for patients presenting to outpatient settings with uncomplicated acute respiratory tract infections (ARI). Effectiveness was measured as quality-adjusted life years (QALYs). Cost and utility parameters for antibiotic treatment, adverse drug events (ADEs), and healthcare utilization were obtained from the literature. Probabilities for antibiotic treatment and appropriateness, ADEs, hospitalization, and return ARI visits were estimated from 16,712 and 51,275 patient visits in intervention and control sites during the pre- and post-implementation periods, respectively. Data for materials and labor to perform the stewardship activities were used to estimate intervention cost. We performed a one-way and probabilistic sensitivity analysis (PSA) using 1,000,000 second-order Monte Carlo simulations on input parameters.

RESULTS

The proportion of ARI patient-visits with antibiotics prescribed in intervention sites was lower (62% vs 74%) and appropriate treatment higher (51% vs 41%) after implementation, compared to control sites. The estimated intervention cost over a 2-year period was $133,604 (2018 US dollars). The intervention had lower mean costs ($528 vs $565) and similar mean QALYs (0.869 vs 0.868) per patient compared to usual care. In the PSA, the intervention was dominant in 63% of iterations.

CONCLUSIONS

Implementation of the CDC Core Elements in the outpatient setting was a cost-effective strategy.

Procalcitonin in Pediatric Sepsis: What Is It Good for?

Procalcitonin has several advantages over traditional biomarkers in the management of pediatric sepsis. In combination with other clinical/laboratory data or measured over time, procalcitonin can support earlier antibiotic discontinuations and reduce unnecessary antibiotic use.

Artificial Intelligence for Clinical Decision Support in Sepsis

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review

BACKGROUND

Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice.

OBJECTIVE

The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice.

METHODS

We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings.

RESULTS

We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation.

CONCLUSIONS

This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.

Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture

Data on comprehensive population-based surveillance of antimicrobial resistance is lacking. In low- and middle-income countries, the challenges are high due to weak laboratory capacity, poor health systems governance, lack of health information systems, and limited resources. Developing countries struggle with political and social dilemma, and bear a high health and economic burden of communicable diseases. Available data are fragmented and lack representativeness which limits their use to advice health policy makers and orientate the efficient allocation of funding and financial resources on programs to mitigate resistance. Low-quality data means soaring rates of antimicrobial resistance and the inability to track and map the spread of resistance, detect early outbreaks, and set national health policy to tackle resistance. Here, we review the barriers and limitations of conducting effective antimicrobial resistance surveillance, and we highlight multiple incremental approaches that may offer opportunities to strengthen population-based surveillance if tailored to the context of each country.

A qualitative process evaluation of the introduction of procalcitonin testing as an antimicrobial stewardship intervention

Background Successful antimicrobial stewardship interventions are imperative in today's environment of antimicrobial resistance. New antimicrobial stewardship interventions should include qualitative analysis such as a process evaluation to determine which elements within an intervention are effective and provide insight into the context in which the intervention is introduced. Objective To assess the implementation process and explore the contextual factors which influenced implementation. Setting An academic teaching hospital in Cork, Ireland. Methods A process evaluation was conducted on completion of a feasibility study of the introduction of a procalcitonin antimicrobial stewardship intervention. The process evaluation consisted of semi-structured face-to-face interviews of key stakeholders including participating (senior) doctors (5), medical laboratory scientists (3) and a hospital administrator. The Consolidated Framework for Implementation Research was used to guide data collection, analysis, and interpretation. Main outcome measures Qualitative assessment of the intervention implementation process, the contextual factors which influenced implementation and identification of improvements to the intervention and its implementation and determine if proceeding to a randomised controlled trial would be appropriate. Results Analysis of the interviews identified three main themes. (1) The procalcitonin intervention and implementation process was viewed positively to support prescribing decisions. Participants identified modifications to procalcitonin processing and availability to improve implementation and allow procalcitonin to be "more of a clinical influence". (2) In the antimicrobial stewardship context the concept of fear of missing an infection and risks of potentially serious outcomes for patients emerged. (3) The hospital context consisted of barriers such as available resources and facilitators including the hospital culture of quality improvement. Conclusion This process evaluation provides a detailed analysis of the implementation of procalcitonin testing as an antimicrobial stewardship intervention. The positive findings of this process evaluation and feasibility study should be built upon and a full randomised controlled trial and economic evaluation should be conducted in a variety of hospital settings to confirm the effectiveness of procalcitonin as an antimicrobial stewardship intervention.