Search

Introduction: Chemotherapy plus immunotherapy is the standard of care for patients with metastatic NSCLC. No study has evaluated the outcomes of second-line chemotherapy treatments after progression following first-line chemo-immunotherapy. Method: This multicenter retrospective study evaluated the efficacy of second line (2L) chemotherapies after progression under first-line (1L) chemo-immunotherapy, measured by overall survival (2L-OS) and progression free survival (2L-PFS). Results: A total of 124 patients were included. The mean age was 63.1 years, 30.6 % of the patients were female, 72.6 % had an adenocarcinoma and 43.5 % had a poor ECOG-performance status prior to 2L initiation. Sixty-four (52.0 %) patients were considered resistant to first line chemo-immunotherapy. (1L-PFS < 6 months). In 2L treatments, 57 (46.0 %) patients received taxane monotherapy, 25 (20.1 %) taxane plus anti-angiogenic, 12 (9.7 %) platinum-based chemotherapy and 30 (24.2 %) other chemotherapy.

Background/Aims Rheumatic and musculoskeletal diseases (RMDs) are some of the most common indications for prescribed opioids. It is unclear how opioid prescribing has changed in the UK for RMDs, especially during the COVID-19 pandemic with limited healthcare access and cancelled elective-surgical interventions, which could impact prescribing in either direction. We aimed to investigate trends in opioid prescribing in RMDs and assess the impact of the pandemic in the UK. Methods Adult patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), axial spondyloarthritis (AxSpA), systemic lupus erythematosus (SLE), osteoarthritis (OA) and fibromyalgia with opioid prescriptions between 01/Jan/2006–31/Aug/2021 without prior cancer in the UK Clinical Practice Research Datalink (CPRD) were included. We calculated age- and gender-standardised yearly rates of people with opioid prescriptions between 2006–2021, and identified change points in trends by checking whether the rate of change of standardised rates crossed zero. For people with opioid prescriptions, monthly measures of mean morphine milligram equivalents (MME)/day were calculated between 2006–2021. To assess the impact of the pandemic, we fitted regression models to the monthly number of people with opioid prescriptions between Jan/2015–Aug/2021. The time coefficient reflects the trend pre-pandemic and the interaction term coefficient represents the change in the trend during the pandemic. Results We included 1,313,519 patients: 36,932 with RA, 12,649 with PsA, 6,811 with AxSpA, 6,423 with SLE, 1,255,999 with OA, and 66,944 with fibromyalgia. People with opioid prescriptions increased from 2006 to 2018 for OA, to 2019 for RA, AxSpA and SLE, to 2020 for PsA, and to 2021 for fibromyalgia, and all plateaued/decreased afterwards. OA patients on opioids increased from 466.8/10,000 persons in 2006 to a peak of 703.0 in 2018, followed by a decline to 575.3 in 2021. From 2006 to 2021, there was a 4.5-fold increase in fibromyalgia opioid users (17.7 vs.78.5/10,000 persons). In this period, MME/day increased for all RMDs, with the highest for fibromyalgia (≥35). During COVID-19 lockdowns, RA, PsA and fibromyalgia showed significant changes in the trend of people with opioid prescriptions. With a decreasing trend for RA (-0.001,95%CI=-0.002,-0.001) and a decreasing-to-flat curve for PsA (0.0010,95%CI=0.0006,0.0015) pre-pandemic until Feb/2020, the trends changed by -0.005 (95%CI=-0.008,-0.002) for RA and -0.003 (95%CI=-0.006,-0.0003) for PsA, leading to steeper decreasing trends during the pandemic (Mar/2020–Aug/2021). Fibromyalgia, conversely, had an increasing trend (0.009,95%CI=0.008,0.009) pre-pandemic, and this trend started decreasing by -0.009 (95%CI=-0.011,-0.006) during the pandemic. Conclusion The plateauing/decreasing trend of people with opioid prescriptions in RMDs after 2018 may reflect the efforts to tackle the rising opioid prescribing in UK primary care. Of all RMDs, fibromyalgia patients had the highest MME/day throughout the study period. COVID-19 lockdowns contribute to fewer people on opioids for most RMDs, reassuring there was no sudden increase in opioid prescribing during the pandemic. Disclosure Y. Huang: None. D.A. Jenkins: None. B.B. Yimer: None. J. Benitez-Aurioles: None. N. Peek: None. M. Lunt: None. W.G. Dixon: Consultancies; WGD has received consultancy fees from Google unrelated to this work. M. Jani: None.

Background/Aims There has been a sharp rise in the use of opioids for non-cancer pain globally. Despite increased awareness of adverse effects, they remain commonly prescribed in the UK. Clinical prediction models offer the possibility of assessing individual risk for a given outcome allowing better allocation of resources towards those at risk. Machine learning (ML) approaches can address nonlinear relationships and complex interactions between variables and are increasingly used to develop these models. Our objective is to develop, validate, and compare the performance of three clinical prediction models based on regression and ML, which leverage primary care data to estimate the risk of opioid-related death in patients prescribed opioids for non-cancer pain. Methods Patients ≥18 years old without prior cancer who were prescribed any opioid between 01/01/2006 and 31/12/2017 were identified in the Clinical Practice Research Datalink (CPRD). Only new opioid users were included. Index date was date of first prescription, with censoring at withdrawal from the CPRD or after not having an opioid prescription for two years. Baseline data were extracted from each patient’s records, including demographic information, comorbidities, concomitant medications, and the opioid type being prescribed, collecting 49 candidate predictors. These were used to train three competing risk models: a Fine&Gray regression model with LASSO regularisation, a survival random forest (RF), and a neural network (DeepHit). The outcome was opioid-related mortality and other cause mortality the competing event, defined using a curated ICD-10 codelist. Predictive performance of the models, such as the area under the receiver characteristic operator curve (AUC-ROC), were calculated using 5-fold cross validation. Results We included a total of 1,029,681 patients, of which 1,240 experienced an opioid-related death, and 52,833 experienced a competing death. The Fine&Gray, RF and DeepHit models achieved average AUC-ROC values of 0.83(95% CI: 0.81-0.85), 0.78(0.77-0.79) and 0.81(0.80-0.82) respectively. At the optimum risk cut point, as per Youden’s index, the models achieved sensitivities of 0.82(0.78-0.85), 0.75(0.67-0.82) and 0.80(0.78-0.83), and specificities of 0.78(0.73-0.82), 0.75(0.68-0.83) and 0.78(0.75-0.80) when predicting 12-month risk, respectively. In the Fine&Gray model, factors associated with an increased risk were history of substance use disorder (hazards ratio [HR]: 3.40, 95% CI:3.12-3.69) and alcohol abuse (HR:3.07, 95% CI:2.93-3.22). COPD (HR:1.53, 95% CI:1.48-1.58) and moderate liver disease (HR:1.31, 95% CI:0.99-1.63) were the comorbidities associated with highest risk. Morphine (HR:2.39, 95% CI:2.08-2.69) and oxycodone (HR:1.10, 95% CI:1.00-1.20) at initiation and concomitant gabapentinoids (HR:1.99, 95% CI:1.80-2.18) and benzodiazepines (HR:1.30, 95% CI:1.24-1.36) were associated with an increased risk. HR for rheumatologic diseases was 1.08 (95% CI:1.01-1.14). Conclusion The Fine&Gray and DeepHit models exhibited comparable discriminative performance. Substance abuse, lung and liver comorbidities, morphine or oxycodone at initiation and co-prescription of gabapentinoids and benzodiazepines, were some of the factors associated with a higher risk of opioid-related mortality. Disclosure J. Benitez-Aurioles: None. D. Jenkins: None. Y. Huang: None. C. Ramirez Medina: None. N. Peek: None. M. Jani: None.