Hyperglycaemia following immune checkpoint inhibitor therapy-Incidence, aetiology and assessment.

AIMS: We systematically studied the presence of hyperglycaemia during treatment with Immune Checkpoint Inhibitors (ICPI) for cancer, in those with and without diabetes at baseline, and determined the cause of new-onset hyperglycaemia, METHODS: Retrospective review of electronic records of those receiving an ICPI for melanoma, lung or renal cancer. RESULTS: Overall, 959 participants were included. In this study, 103 had diabetes at baseline (10.7%). Those with lung cancer had the highest frequency of diabetes; 131 people had hyperglycaemia (defined as at least one glucose ≥11.1 mmol/L) in the year after starting an ICPI. The incidence was 55% in those with diabetes at baseline, and 8.6% in those without baseline diabetes. Among 74 with new-onset hyperglycaemia (without pre-existing diabetes) 76% was attributable to steroid induced diabetes, with 9.5% due to ICPI Induced diabetes resembling type 1 diabetes. CONCLUSIONS: Hyperglycaemia is common in persons receiving an ICPI for cancer, including 8.6% of those without known diabetes. While much of this is due to glucocorticoid use, care is needed to avoid missing those with ICPI-induced diabetes who are at risk of diabetic ketoacidosis, which is a medical emergency.

Assessing the collection and reporting of patient-reported outcome data in interventional cancer trials: a single institution, retrospective systematic evaluation.

BACKGROUND: To understand our performance with respect to the collection and reporting of patient-reported outcome (PRO) measure (PROM) data, we examined the protocol content, data completeness and publication of PROs from interventional trials conducted at the Royal Marsden NHS Foundation Trust (RM) and explored factors associated with data missingness and PRO publication. DESIGN: From local records, we identified closed, intervention trials sponsored by RM that opened after 1995 and collected PROMs as primary, secondary or exploratory outcomes. Protocol data were extracted by two researchers and scored against the SPIRIT-PRO (PRO protocol content checklist; score 0-100, higher scores indicate better completeness). For studies with locally held datasets, the information team summarized for each study, PRO completion defined as the number of expected (as per protocol) PRO measurements versus the number of actual (i.e. completed) PRO measurements captured in the study data set. Relevant publications were identified by searching three online databases and chief investigator request. Data were extracted and each publication scored against the CONSORT-PRO (PRO manuscript content checklist; scored as SPIRIT-PRO above). Descriptive statistics are presented with exploratory comparisons of point estimates and 95% confidence intervals. RESULTS: Twenty-six of 65 studies were included in the review. Nineteen studies had accessible datasets and 18 studies published at least one article. Fourteen studies published PRO results. Most studies had a clinical (rather than PRO) primary outcome (16/26). Across all studies, responses in respect of 35 of 69 PROMs were published. Trial protocols scored on average 46.7 (range 7.1-92.9) on the SPIRIT-PRO. Among studies with accessible data, half (10/19) had less than 25% missing measurements. Publications scored on average 80.9 (range 36-100%) on the CONSORT-PRO. Studies that published PRO results had somewhat fewer missing measurements (19% [7-32%] vs 60% [- 26 to 146%]). For individual PROMs within studies, missing measurements were lower for those that were published (17% [10-24%] vs 41% [18-63%]). Studies with higher SPIRIT-PRO scores and PROs as primary endpoints (13% [4-22%] vs 39% [10-58%]) had fewer missing measurements. CONCLUSIONS: Missing data may affect publication of PROs. Extent of inclusion of SPIRIT-PRO protocol items and PROs as primary endpoints may improve data completeness. Preliminary evidence from the study suggests a future larger study examining the relationship between PRO completion and publication is warranted.

Establishing a colorectal cancer research database from routinely collected health data: the process and potential from a pilot study.

OBJECTIVE: Colorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source. METHODS: Clinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically. RESULTS: Three pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed. DISCUSSION: Algorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer. CONCLUSION: The data set has great potential to facilitate research into colorectal cancer.

Experience of Isavuconazole as a Salvage Therapy in Chronic Pulmonary Fungal Disease.

Background: Instances of resistant fungal infection are rising in pulmonary disease, with limited therapeutic options. Therapeutic drug monitoring of azole antifungals has been necessary to ensure safety and efficacy but is considered unnecessary for the newest triazole isavuconazole. Aims: To characterise the prevalence of isavuconazole resistance and use in a tertiary respiratory centre. Methods: A retrospective observational analysis (2016−2021) of adult respiratory patients analysing fungal culture, therapeutic drug monitoring, and outcome post-isavuconazole therapy. Results: During the study period, isavuconazole susceptibility testing was performed on 26 Aspergillus spp. isolates. A total of 80.8% of A. fumigatus isolates had isavuconazole (MIC > 1 mg/L, and 73.0% > 2 mg/L) with a good correlation to voriconazole MIC (r = 0.7, p = 0.0002). A total of 54 patients underwent isavuconazole therapy during the study period (median duration 234 days (IQR: 24−499)). A total of 67% of patients tolerated isavuconazole, despite prior azole toxicity in 61.8%, with increased age (rpb = 0.31; p = 0.021) and male sex (φc = 0.30; p = 0.027) being associated with toxicity. A total of 132 isavuconazole levels were performed with 94.8% > 1 mg/L and 72% > 2 mg/L. Dose change from manufacturer's recommendation was, however, required in 9.3% to achieve a concentration of >2 mg/L. Conclusion: We describe the use of isavuconazole as a salvage therapy in a chronic pulmonary fungal disease setting with a high prevalence of azole resistance. Therapeutic concentrations at standard dosing were high; however, results reinforce antifungal stewardship for optimization.

Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation. Objective: To automate lung nodule identification in a tertiary cancer centre. Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients. Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy. Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.