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Journal articleGeeson C, Wei L, Franklin BD, 2019,
Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists' input to prevent medication-related problems
, BMJ QUALITY & SAFETY, Vol: 28, Pages: 645-656, ISSN: 2044-5415- Cite
- Citations: 27
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Journal articleBalinskaite V, Johnson AP, Holmes A, et al., 2019,
The impact of a national antimicrobial stewardship programme on antibiotic prescribing in primary care: an interrupted time series analysis
, Clinical Infectious Diseases, Vol: 69, Pages: 227-232, ISSN: 1058-4838Background: The Quality Premium was introduced in 2015 to financially reward local commissioners of healthcare in England for targeted reductions in antibiotic prescribing in primary care. Methods: We used a national antibiotic prescribing dataset from April 2013 till February 2017 to examine the number of antibiotic items prescribed, the total number of antibiotic items prescribed per STAR-PU (Specific Therapeutic Group Age-sex Related Prescribing Units), the number of broad-spectrum antibiotic items prescribed and broad-spectrum antibiotic items prescribed expressed as a percentage of the total number of antibiotic items. To evaluate the impact of the Quality Premium on antibiotic prescribing, we used a segmented regression analysis of interrupted time series data. Results: During the study period, over 140 million antibiotic items were prescribed in primary care. Following the introduction of the Quality Premium, antibiotic items prescribed decreased by 8.2%, representing 5,933,563 fewer antibiotic items prescribed during the 23 post-intervention months compared with the expected numbers based on the trend in the pre-intervention period. After adjusting for the age and sex distribution in the population, the segmented regression model also showed a significant relative decrease in antibiotic items prescribed per STAR-PU. A similar effect was found for broad-spectrum antibiotics (comprising 10.1% of total antibiotic prescribing), with an 18.9% reduction in prescribing. Conclusions: This study shows that the introduction of financial incentives for local commissioners of healthcare to improve the quality of prescribing was associated with a significant reduction in both total and broad-spectrum antibiotic prescribing in primary care in England.
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Journal articleBalinskaite V, Bou-Antoun S, Johnson AP, et al., 2019,
An assessment of potential unintended consequences following a national antimicrobial stewardship programme in England: an interrupted time series analysis
, Clinical Infectious Diseases, Vol: 69, Pages: 233-242, ISSN: 1058-4838Background: The 'Quality Premium' (QP) introduced in England in 2015 aimed to financially reward local healthcare commissioners for targeted reductions in primary care antibiotic prescribing. We aimed to evaluate possible unintended clinical outcomes related to this QP. Methods: Using Clinical Practice Research Datalink and Hospital Episode Statistics datasets, we examined general practitioner (GP) consultations (visits) and emergency hospital admissions related to a series of pre-defined conditions of unintended consequences of reduced prescribing. Monthly age and sex-standardised rates were calculated using a direct method of standardisation. We used segmented regression analysis of interrupted time series to evaluate the impact of the QP on seasonally adjusted outcome rates. Results: We identified 27,334 GP consultations and over five million emergency hospital admissions with pre-defined conditions. There was no evidence that the QP was associated with changes in GP consultation and hospital admission rates for the selected conditions combined. However, when each condition was considered separately, a significant increase in hospital admission rates was noted for quinsy, and significant decreases were seen for hospital-acquired pneumonia, scarlet fever, pyelonephritis and complicated urinary tract conditions. A significant decrease in GP consultation rates was estimated for empyema and scarlet fever. No significant changes were observed for other conditions. Conclusions: Findings from this study show that overall there was no significant association between the intervention and unintended clinical consequences, with the exception of a few specific conditions, most of which could be explained through other parallel policy changes or should be interpreted with caution due to small numbers.
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Journal articleCharani E, Ahmad R, Rawson T, et al., 2019,
The differences in antibiotic decision-making between acute surgical and acute medical teams: An ethnographic study of culture and team dynamics
, Clinical Infectious Diseases, Vol: 69, Pages: 12-20, ISSN: 1058-4838BackgroundCultural and social determinants influence antibiotic decision-making in hospitals. We investigated and compared cultural determinants of antibiotic decision-making in acute medical and surgical specialties.MethodsAn ethnographic observational study of antibiotic decision-making in acute medical and surgical teams at a London teaching hospital was conducted (August 2015–May 2017). Data collection included 500 hours of direct observations, and face-to-face interviews with 23 key informants. A grounded theory approach, aided by Nvivo 11 software, analyzed the emerging themes. An iterative and recursive process of analysis ensured saturation of the themes. The multiple modes of enquiry enabled cross-validation and triangulation of the findings.ResultsIn medicine, accepted norms of the decision-making process are characterized as collectivist (input from pharmacists, infectious disease, and medical microbiology teams), rationalized, and policy-informed, with emphasis on de-escalation of therapy. The gaps in antibiotic decision-making in acute medicine occur chiefly in the transition between the emergency department and inpatient teams, where ownership of the antibiotic prescription is lost. In surgery, team priorities are split between 3 settings: operating room, outpatient clinic, and ward. Senior surgeons are often absent from the ward, leaving junior staff to make complex medical decisions. This results in defensive antibiotic decision-making, leading to prolonged and inappropriate antibiotic use.ConclusionsIn medicine, the legacy of infection diagnosis made in the emergency department determines antibiotic decision-making. In surgery, antibiotic decision-making is perceived as a nonsurgical intervention that can be delegated to junior staff or other specialties. Different, bespoke approaches to optimize antibiotic prescribing are therefore needed to address these specific challenges.
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Journal articleHarkanen M, Vehvilainen-Julkunen K, Murrells T, et al., 2019,
Medication administration errors and mortality: Incidents reported in England and Wales between 2007-2016
, RESEARCH IN SOCIAL & ADMINISTRATIVE PHARMACY, Vol: 15, Pages: 858-863, ISSN: 1551-7411- Author Web Link
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- Citations: 45
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Journal articleMing DK, Otter JA, Ghani R, et al., 2019,
Clinical risk stratification and antibiotic management of NDM and OXA-48 carbapenemase-producing Enterobacteriaceae bloodstream infections in the UK
, Journal of Hospital Infection, Vol: 102, Pages: 95-97, ISSN: 0195-6701 -
Journal articleCourtenay M, Castro-Sanchez E, Fitzpatrick M, et al., 2019,
Tackling antimicrobial resistance 2019–2024 – The UK's five-year national action plan
, Journal of Hospital Infection, Vol: 101, Pages: 426-427, ISSN: 0195-6701 -
Journal articleRawson TM, Hernandez B, Moore L, et al., 2019,
Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study
, Journal of Antimicrobial Chemotherapy, Vol: 74, Pages: 1108-1115, ISSN: 0305-7453BackgroundInfection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital.MethodsAn SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160 203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored.ResultsOne hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21–98) years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20–0.40). ROC AUC was 0.84 (95% CI: 0.76–0.91).ConclusionsAn SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.
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Journal articleMcLeod M, Ahmad R, Shebl NA, et al., 2019,
A whole-health-economy approach to antimicrobial stewardship: Analysis of current models and future direction
, PLoS Medicine, Vol: 16, ISSN: 1549-1277In a Policy Forum, Alison Holmes and colleagues discuss coordinated approaches to antimicrobial stewardship.
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Journal articlePhillips CJ, Gilchrist M, Cooke FJ, et al., 2019,
Adherence to antibiotic guidelines and reported penicillin allergy: pooled cohort data on prescribing and allergy documentation from two English National Health Service (NHS) trusts
, BMJ Open, Vol: 9, ISSN: 2044-6055OBJECTIVE: To investigate documentation of antimicrobial allergy and to determine prescribing adherence to local antibiotic guidelines for inpatients with and without reported penicillin allergy treated for infection in a National Health Service (NHS) context. SETTING: Data were collected at two English hospital NHS trusts over two time-periods: June 2016 and February 2017. DESIGN: Cohort study. Trust 1 data were sourced from prospective point prevalence surveys. Trust 2 data were extracted retrospectively from an electronic report. PARTICIPANTS: Inpatients treated for urinary tract infection (UTI), community-acquired pneumonia (CAP), hospital-acquired pneumonia (HAP) and skin and soft tissue infection (SSTI). Data on allergy were collected, and antibiotic selection assessed for adherence to trust guidelines with differences between groups presented as adjusted ORs. RESULTS: A total of 1497 patients were included, with 2645 antibiotics orders. Patients were treated for CAP (n=495; 33.1%), UTI (407; 27.2%), HAP (330; 22%) and SSTI (265; 17.7%). There were 240 (16%) patients with penicillin allergy. Penicillin allergy was recorded as allergy (n=52; 21.7%), side effect (27; 11.3%) and no documentation (161; 67.1%). Overall, 2184 (82.6%) antibiotic orders were guideline-adherent. Adherence was greatest for those labelled penicillin allergy (453 of 517; 87.6%) versus no allergy (1731 of 2128; 81.3%) (OR 0.52 (95% CI 0.37 to 0.73) p<0.001). Guideline-adherence for CAP was higher if penicillin allergy (151 of 163; 92.6%) versus no allergy (582 of 810; 71.9%) (OR 0.20 (95% CI 0.10 to 0.37) p<0.001). There was no difference in adherence between those with and without penicillin allergy for UTI, HAP or SSTI treatment. CONCLUSIONS: A relatively high proportion of patients had a penicillin allergy and two thirds of these had no description of their allergy, which has important implications for patient safety. Patients with penicillin allergy treated for CAP
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