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Aims: We aim to evaluate change in the use of prognostic guideline‐directed medical therapies (GDMTs) for heart failure (HF) before and after a cancer diagnosis as well as the matched non‐cancer controls, including renin‐angiotensin‐system inhibitors (RASIs), beta‐blockers, and mineralocorticoid receptor antagonists (MRAs). Methods and results: We conducted a longitudinal study in patients with HF in the UK Clinical Practice Research Datalink between 2005 and 2021. We selected patients with probable HF with reduced ejection fraction (HFrEF) based on diagnostic and prescription records. We described the longitudinal trends in the use and dosing of GDMTs before and after receiving an incident cancer diagnosis. HF patients with cancer were matched with a 1:1 ratio to HF patients without cancer to investigate the association between cancer diagnosis and treatment adherence, persistence, initiation, and dose titration as odds ratios (ORs) with 95% confidence intervals (CIs) using multivariable logistic regression models. Of 8504 eligible HFrEF patients with incident cancer, 4890 were matched to controls without cancer. The mean age was 75.7 (±8.4) years and 73.9% were male. In the 12 months following a cancer diagnosis, patients experienced reductions in the use and dosing of GDMT. Compared with the non‐cancer controls, patients with cancer had higher risks for poor adherence for all three medication classes (RASIs: OR = 1.51, 95% CI = 1.35–1.68; beta‐blockers: OR = 1.22, 95% CI = 1.08–1.37; MRAs: OR = 1.31, 95% CI = 1.08–1.59) and poor persistence (RASIs: OR = 2.04, 95% CI = 1.75–2.37; beta‐blockers: OR = 1.35, 95% CI = 1.12–1.63; MRAs: OR = 1.49, 95% CI = 1.16–1.93), and higher risks for dose down‐titration for RASIs (OR = 1.69, 95% CI = 1.40–2.04) and beta‐blockers (OR = 1.31, 95% CI = 1.05–1.62). Cancer diagnosis was not associated with treatment initiation or dose up‐titration. Event rates for HF hospitalization and mortality were higher in patients with poor adherence or persistence to GDMTs. Conclusions: Following a cancer diagnosis, patients with HFrEF were more likely to have reduced use of GDMTs for HF. [ABSTRACT FROM AUTHOR]

Introduction Chemotherapy forms the cornerstone of systemic treatment for advanced ovarian cancer, extending overall survival; however, drug-related toxicity can lead to treatment delays, potentially diminishing treatment efficacy. This study evaluated the impact of treatment delays on all-cause mortality of patients with ovarian cancer, to better inform decisions on patient management. Methods This retrospective, population-based cohort study included 1517 women with advanced-stage ovarian cancer, receiving first-line adjuvant or neoadjuvant chemotherapy in 2014 and 2015. The frequency of inter-cycle delays >7 days was calculated using drug administration dates. Kaplan-Meier estimates were used to compare 2-year overall survival (OS) between patients who were delayed and those treated to schedule. Cox proportional hazards regression was used to investigate the impact of treatment delay on all-cause mortality. Inverse probability of treatment weighting propensity scores were used to adjust for confounding variables. Results Delays >7 days occurred in 35.3% of patients. Two-year OS probability was 62.7% in patients who experienced treatment delays >7 days (95% CI, 58.7-66.9) compared to 69.1% in those treated to schedule (95% CI, 66.2-72.0). Delays were not significantly associated with all-cause mortality when adjusted for confounders (HR 1.00 95% CI, 0.83-1.20, P  = .9). Conclusions Delays to chemotherapy treatment were not significantly associated with worsened survival in patients with advanced-stage ovarian cancer. These results can inform clinical decision making that prioritize toxicity management and quality of life for those treated with chemotherapy. [ABSTRACT FROM AUTHOR]

Introduction: The incidence of immune-related adverse events (irAEs) from immune checkpoint inhibitors (ICI) is well described. However, the impact on emergency care services is not. This study investigated the incidence of irAEs out-of-hours, and the management used to mitigate symptoms and side effects. Methods: This retrospective cohort study reviewed all emergency presentations triaged by the acute oncology team between December 2021 and June 2022, between 5 pm and 9 am. Patients were identified from triage audit sheets and remaining data points were retrieved from electronic health records. Inclusion criteria included all adult patients admitted on an ICI at one tertiary centre. Results: In 7 months, 970 patients called the acute oncology helpline 11% (n = 109) of patients were on an ICI treatment. After clinical review, 78% (n = 70) resulted in hospital admissions, with length of stay cumulating to 496 bed days. 56% (n = 39) of patients delayed reporting symptoms, ranging between 12 hours and 10 days from symptom onset to seeking support. 49% (n = 34) patients received steroids to manage suspected irAEs. Dexamethasone was the most common steroid used in 71% (n = 24) of patients, and variation was found in prescribed doses. Conclusions: These results underline the urgent need to address patient and staff education on adverse effects related to ICI. Patients require a comprehensive understanding of the symptoms and importance of prompt reporting. Staff education on recognition and treatment management is needed to reduce variation in practice. Further research is needed to identify barriers in symptom reporting and focus on realtime reporting to reduce the out-of-hours burden on services. [ABSTRACT FROM AUTHOR]

Purpose: A recent observational study suggested statins could reduce cancer diagnosis in patients with heart failure (HF). The findings need to be validated using robust epidemiological methods. This study aimed to evaluate the effect of statin treatment on the risk of cancer in patients with HF. Methods: We conducted two target trial emulations using primary care data from IQVIA Medical Research Database‐UK (2000 to 2019) with a clone‐censor‐weight design. The first emulated trial addressed the treatment initiation effect: initiating within 1 year versus not initiating a statin after the HF diagnosis. The second emulated trial addressed the cumulative exposure effect: continuing a statin for ≤3 years, 3–6 years, and >6 years after initiation. The study outcomes were any incident cancer and site‐specific cancer diagnoses. Weighted pooled logistic regression models were used to estimate 10‐year risk ratios (RR). 95% confidence intervals (CIs) were estimated using non‐parametric bootstrapping. Results: The first emulated trial showed that, compared to no statin, statins did not reduce the cancer risk in patients with HF (RR, 1.05; 95% CI, 0.94–1.15). The second emulated trial showed that, compared to treatment ≤3 years, statins with longer durations did not reduce the cancer risk (3–6 years: RR, 0.94; 95% CI, 0.70–1.33. >6 years: RR, 0.97; 95% CI, 0.79–1.26). No significant risk difference was observed on any site‐specific cancer diagnoses. Conclusions: The results from the target trial emulations suggest that statin treatment is not associated with cancer risk in patients with HF. [ABSTRACT FROM AUTHOR]

Introduction: The coronavirus of 2019 pandemic has necessitated vast and rapid changes in the way oncology pharmacy services are delivered around the world. Methods/aims: An international survey of oncology pharmacists and technicians was conducted via the International Society of Oncology Pharmacy Practitioners and collaborating global pharmacy organisations to determine the impact that the coronavirus of 2019 has had on pharmacy service delivery, pharmacy practitioners and oncology practice. Results: The survey received 862 responses from 40 different countries from September to October 2020. The majority of respondents were pharmacists (n = 841, 97.6%), with 24% involved in the direct care of patients with the coronavirus of 2019. Of the survey participants, 55% increased their time working remotely, with remote activities including dispensing, patient assessment/follow-up and attending multi-disciplinary rounds. Respondents reported a 72% increase in the use of technology to perform remote patient interaction activities and that participation in educational meetings and quality improvement projects was reduced by 68% and 44%, respectively. Workforce impacts included altered working hours (50%), cancelled leave (48%) and forced leave/furloughing (30%). During the pandemic, respondents reported reduced access to intensive care (19%) and anti-cancer (15%) medications. In addition, 39% of respondents reported reduced access to personal protective equipment, including N95 masks for chemotherapy compounding. Almost half of respondents (49%) reported that cancer treatments were delayed or intervals were altered for patients being treated with curative intent. A third of practitioners (30%) believed that patient outcomes would be adversely impacted by changes to pharmacy services. Sixty-five percent of respondents reported impacts on their mental health, with 12% utilising support services. Conclusion: The coronavirus of 2019 pandemic has altered the way oncology pharmacy services are delivered. These results demonstrate the adaptability of the oncology pharmacy profession and highlight the importance of formal evaluation of the varied practice models to determine the evidence-based practices that enhance pharmacy services and, thus, should be reinstated as soon as practical and reasonable. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study is to investigate the rates of hypersensitivity reactions (HSRs) in patients receiving paclitaxel chemotherapy, with and without a histamine‐2 (H2) antagonists. Method: This prospective, multi‐centre, cohort study compared patients receiving paclitaxel treated with premedication regimens containing chlorphenamine, dexamethasone and an H2 antagonist vs patients treated without an H2 antagonist. Rates of HSRs were described and logistic multivariable regression was used to investigate any associations with H2 antagonist treatment, adjusting for confounding variables. Results: A total of 1043 individuals were included in the study; of these, 638 (61%) patients received an H2 antagonist and 405 (49%) were not given an H2 antagonist. Incidence of HSR in the cohort treated with H2 antagonists was 11.31% (n = 70) vs 9.86% (n = 41) in the cohort without. There was no statistically significant difference between the rates of HSR observed in those receiving and not receiving an H2 antagonist (odds ratio 1.04, 95% CI 0.65, 1.66, P =.9). Conclusions: Results presented within the study are consistent with other recently published evidence to suggest that H2 antagonists do not confer any advantage as part of premedication regimens in reducing the incidence of HSR in patients treated with paclitaxel. [ABSTRACT FROM AUTHOR]

Individuals with cancer are at increased risk of developing new‐onset diabetes mellitus and hyperglycaemia, and an estimated 20% of people with cancer already have an underlying diagnosis of diabetes mellitus. People with both cancer and diabetes may have an increased risk of toxicities, hospital admissions and morbidity, with hyperglycaemia potentially attenuating the efficacy of chemotherapy often secondary to dose reductions and early cessation. Numerous studies have demonstrated that hyperglycaemia is prognostic of worse overall survival and risk of cancer recurrence. These guidelines aim to provide the oncology/haemato‐oncology and diabetes multidisciplinary teams with the tools to manage people with diabetes commencing anti‐cancer/glucocorticoid therapy, as well as identifying individuals without a known diagnosis of diabetes who are at risk of developing hyperglycaemia and new‐onset diabetes. [ABSTRACT FROM AUTHOR]

Background: On average 20% of patients require a systemic anti-cancer treatment (SACT) delay.1 The risk of toxicity-related dose delays, with SACT, should be included as part of pre-treatment education and be considered by clinicians upon prescribing chemotherapy. An objective measure of individual risk could influence clinical decisions, such as the escalation of standard supportive care and stratification of some patients, to receive proactive toxicity monitoring. The understanding of this risk may also support the advanced preparation of treatments for those that have a lower risk of delay. Data available within hospital systems can support the understanding of risk through the development of a risk score. In this study, we developed and validated a score to assess the risk of a SACT dose delay of 7 days (delay-7 score). Objectives: To develop and internally validate a risk score to predict the occurrence of a 7-day dose delay for patients receiving cycle 1 treatments for breast and colorectal cancers and diffuse large b-cell lymphomas. To assess the discrimination and calibration of the model. Methods: Four hospitals were included in our study, recruited through BOPA. Data were collected for patients aged 18 or over, identified through the chemotherapy prescribing systems at each hospital for the period of 1 January 2013 to 1 January 2018. The first chemotherapy treatment date was used as the index date for entry to the cohort during the study period. The study data was restricted to the following three tumour groups: breast, colorectal and diffuse large B-Cell lymphoma. Data included hospital treatment, age at the start of SACT, gender, ethnicity, body mass index, cancer diagnosis, chemotherapy regimen, colony-stimulating factor use, first cycle dose modifications and baseline blood values. Baseline blood values included neutrophils, platelets, haemoglobin, creatinine and bilirubin. A risk prediction model was developed using multivariable logistic regression and all analysis methods complied with the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD).2 Shrinkage was used to adjust for over-optimism of predictor effects. For internal validation (of the full models in the development data) we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Results: A total of 4604 patients were included in our study of which 628 (13.6%) incurred a 7-day delay to the second cycle of chemotherapy. Delay-7 showed good discrimination and calibration, with a c-statistic of 0.68 [95% confidence interval (CI) 0.66–0.7], following internal validation and calibration-in-the-large of −0.006. Conclusions: Delay-7 predicts a patient’s individualised risk of a treatment-related delay at cycle two of treatment. The score can be used to stratify interventions to reduce the occurrence of treatment-related toxicity. However, to be used in the clinical setting a prospective study should be conducted to ensure the reliability of the score. Nonetheless, we have identified that the score can be useful to support advance preparation in pharmacy aseptic units and a small validation in this setting is planned. [ABSTRACT FROM AUTHOR]

Background: In those receiving cytotoxic chemotherapy, renal and hepatic dysfunction can increase the risk of toxicity and should therefore be monitored. However, only a small proportion of patients will experience a change in these functions (measured through creatinine and bilirubin levels) following a second cycle of treatment [1]. We have previously found that less than 10% of patients will encounter changes to their renal and hepatic function. Furthermore, we found the occurrence of one-grade changes does not change the timing of next treatment or dosing. Variation exists in hospital policies. Some hospitals would require renal and hepatic function, taken within 7 days, to be assessed by pharmacists before clinical verification, whereas other hospitals would verify based on blood from prior cycles. To support, clinicians, pharmacists and nurses streamline pathways and reduce national variations in practice, we aimed to develop a machine learning model. The objective of the model was to identify those patients that need closer monitoring of their renal and hepatic function, enabling a safer and more efficient service. Methods: Weused retrospective data, from a large academic hospital, for patients treated with chemotherapy for breast cancer, colorectal cancer and Diffuse-large B-cell lymphoma. The outcome of interest was any grade change in creatinine or bilirubin among patients included, at any cycle following cycle two. We chose bilirubin as an outcome measure rather than ALT; rises in ALT are common with a number of chemotherapeutic agents and rarely cause clinical concern or reflect dysfunction of the liver. The predictors incorporated into the development model were baseline, cycle one and cycle two blood results, patient demographics and details of treatment. The data enabled us to train and validate a model to predict the outcomes: unacceptable rises in bilirubin or creatinine. To assess the performance of the model, validation was performed using patient data from an independent hospital, containing the same variables. Using this dataset, we evaluated the sensitivity and specificity of our models. Results: We identified 1214 patients in total. Of these, 684 were included in the training set and 530 patients in validation. The training set had almost perfect sensitivity and specificity of > 0.95. The area under the curve was 0.99 (95% CI 0.98–1.00) for creatinine and 0.97 (95% CI: 0.95–0.99) for bilirubin. The validation set had good sensitivity (creatinine: 0.60, 95% CI: 0.55–0.64, bilirubin: 0.54, 95% CI: 0.52–0.56), and specificity (creatinine 0.98, 95% CI: 0.96–0.99, bilirubin 0.90, 95% CI: 0.87–0.94) and area under the curve (creatinine: 0.76, 95% CI: 0.70, 0.82, bilirubin 0.72, 95% CI: 0.68–0.76). Conclusion: We have demonstrated that a multi-layer perceptron model can be used within electronic prescribing systems to reduce the number of blood tests required for some patients and improve safety for others. Our study needs further validation and we have been awarded innovative UK funding to both validate the work and develop software that can be used by all hospitals in the UK, to redesign their pathways. [ABSTRACT FROM AUTHOR]

Introduction: HDMTX (dose > 1 g/m²) is used commonly as a treatment for high-grade lymphomas, ALL and sarcomas. Glucarpidase is available for urgent treatment of MTX-induced renal dysfunction, in patients with toxic plasma MTX levels, at risk of life threatening complications. Pharmacist peer discussion nationally, highlighted a recent requirement for glucarpidase in overweight adult patients, prompting this study. Aims and objectives: Primary objective: To investigate associations between high BMI and requirement for glucarpidase, a proxy measure for severe toxicity. Secondary objective: to examine national variation in glucarpidase dosing in patients with raised BMI. Methodology: We developed two cohorts to achieve our objectives: Cohort 1. Retrospective review of UK adult patients receiving glucarpidase between 01 January 2019 and 01 July 2021, identified via UK sales. A data collection form was developed and piloted, and hospitals having placed orders were invited to participate. Patient baseline demographics, clinical characteristics, MTX regimen/ dose and interacting medicines were collated. The glucarpidase dose calculation method and toxicity outcome were recorded. Cohort 2: A control group from three participating centres of all patients that received HDMTX, not requiring glucarpidase, during the same period. BMI was categorised according to WHO definitions for overweight and obesity; we described the % of patients in each category and BMI distributions. BMIs across the two cohorts were compared using an unpaired t-test. Results: 23/24 Adult Treatment Centres completed data collection on 34 patients – 32 patients received glucarpidase, and two orders were not administered. In the control group (N = 209), 61% had BMI > 25 kg/m², and 23%hadBMI > 30 kg/m² compared to 85% and 59% in the glucarpidase group respectively (p = < 0.0001). The BMI average of the control group was 26.9 kg/m², compared to 31.6 kg/m² in the glucarpidase-treated group. 29/32 patients received glucarpidase dosed according to total body weight (TBW), and three patients according to adjusted body weight (ABW40). Discussion: Full weight-based chemotherapy dosing is recommended practice for treating obese patients with cancer, especially with curative intent.1 Only four out of 20 patients with BMI ≥ 30 in the glucarpidase cohort did not receive TBW dose methotrexate, when the reason for dose modification was stated as obesity. There is a known association between obesity and risk of haematological malignancy, particularly DLBCL,2 however this small case series suggests obese patients are overly represented in the HDMTX UK patient population experiencing the most severe toxicity, proposing a significant association but not causation. One study has shown an association between overweight patients and increased serum creatinine post-HDMTX,3 conversely another showed no association between obesity and delayed MTX clearance.4 Study limitations and areas of further investigation: Further work is required, using a larger dataset, to determine if obesity is an independent risk factor for MTX-induced renal dysfunction and delayed clearance. Study outcomes are complicated by the fact the precise indication for glucarpidase is ambiguous. In our study, all three patients receiving glucarpidase dosed according to ABW40 successfully cleared methotrexate, a renal function returning to baseline, supported by published literature on the use of lower doses5 – an approach which may reduce treatment costs. Larger patient numbers are required to further investigate the optimal dose. [ABSTRACT FROM AUTHOR]

Introduction: Systemic anti-cancer treatment (SACT) requires toxicity assessment prior to initiation to ensure adequate bone marrow, renal and liver function. The timing of these assessments is determined locally, with the results guiding clinicians to delay treatment or request for repeated blood tests if the desired range is not attained. The time to obtain these tests is ideal on the day chemotherapy commences, however, this is not practical within the NHS. Common practice is for patients to undergo these assessments prior to the treatment day with some policies1 recommended earlier at five or six days prior to initiations with potential impact on patient experience. This research aims to study the main policies relating to pre-chemotherapy assessment in three cancer groups: early breast cancer, colorectal cancer and DLBCL. Objectives: (1) Examine the policies on the time-periods used to perform toxicity assessment prior to chemotherapy at hospitals in the United Kingdom (UK). (2) Identify if there is variation reported within the same hospital sites. Methods: A cross-sectional online survey (Qualtrics) was sent out to members of the BOPA and ACP society between 1 July 2020 and 31 July 2020. The survey was validated by four experts in survey design and chemotherapy regimen. The sample size was calculated based on the total population of hospitals in the UK (n = 256). Based on a 95% CI, n = 131 was needed. Data was exported and analysed on STATA 15 and presented as counts and percentages. Ethics was obtained and findings have been recently published in objective 1 only. Results: • 91 participants completed the questionnaire in full. • 25 hospitals had more than one profession/participant responding. • 12 hospitals had complete responses for each question from each participant. • Two hospitals had participants responding within the same time frame. In both settings, the responses were from the same profession. • Hospital A: × 2 participants (pharmacists) • Hospital B: ×4 participants (medical oncologists) • 10 hospitals had different responses despite the profession with a difference in the time frame from 1 to 4 days between respondents. Discussion/conclusion: Toxicity assessments guide clinicians on dose delay and dose intensity of chemotherapy. Policies on when to conduct these assessments vary across the UK irrespective of the chemotherapy in all three cancer groups. Results demonstrate there is variation at the same hospital site despite professional background. As cancer patient numbers are growing and advanced preparation is vital, many hospitals are investigating extending the validity period of blood. Depending on the chemotherapy regimen, between 13 and 15 sites are currently practicing using between 4 and 7 days. However, the more this is extended there may be a need for duplicate blood where patients do not reach desired thresholds.1 Further evidence is required in this area and the need to reduce variation in pre-chemotherapy blood assessments at the same site and across sites within the UK. This study was limited by its method of dissemination, with the majority of respondents receiving a response from their membership societies, where members pay to join. [ABSTRACT FROM AUTHOR]

Objectives: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich feature patient data sets more readily available. These large data stores lend themselves to use in modern machine learning (ML) models. This paper investigates the use of transformer-based models to identify critical deterioration in unplanned ED admissions, using free-text fields, such as triage notes, and tabular data, including early warning scores (EWS)., Design: A retrospective ML study., Setting: A large ED in a UK university teaching hospital., Methods: We extracted rich feature sets of routine clinical data from the EHR and systematically measured the performance of tree- and transformer-based models for predicting patient mortality or admission to critical care within 24 hours of presentation to ED. We compared our proposed models to the National EWS (NEWS)., Results: Models were trained on 174 393 admission records. We found that models including free-text triage notes outperform structured tabular data models, achieving an average precision of 0.92, compared with 0.75 for tree-based models and 0.12 for NEWS., Conclusions: Our findings suggests that machine learning models using free-text data have the potential to improve clinical decision-making in the ED; our techniques significantly reduce alert rate while detecting most high-risk patients missed by NEWS., Competing Interests: Competing interests: All authors have completed the Unified Competing Interest form and declare: MW, SBL, DG, MH and NAM report support from an HDR UK and NIHR Winter Pressures grant for this project. MW, PC and NAM also report support from an Innovate UK grant. PC reports research funding from Gilead and Pfizer that is unrelated to this research. MH reports three separate honoraria/payments from the Society for Acute Medicine, Welsh Acute Physicians Society and Doctors.NET for invited talks and/or educational packages on the National Early Warning Score. PC reports honoraria from GSK, unrelated to this research. PC reports support for attending educational meetings from Gilead, unrelated to this research. NAM is employed by Evergreen Life Ltd. All authors report no other financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)