Biomarkers and Fever in Children with Cancer: Kinetics and Levels According to Final Diagnosis

We investigated the kinetics of CRP, PCT, IL-6 and MR-proADM in a cohort of consecutive febrile patients with cancer in order to test the hypothesis that higher plasma concentrations and the absence of a rapid decrease in peak values would be associated with disease severity. (1) Method: A prospective descriptive and analytical study of patients with cancer and fever (≤18 years of age) at a University Hospital was carried out between January 2018 and December 2019. Information collected: sex, age, diagnosis, date and symptoms at diagnosis and medical history. The episodes were classified into three groups: bacterial infection, non-bacterial infection and systemic inflammatory response syndrome (SIRS). (2) Results: One hundred and thirty-four episodes were included. Bacterial infection criteria were met in 38 episodes. Biomarkers were measured at four different points: baseline, at 12–24 h, at 25–48 h and at 49–72 h. All the biomarkers evaluated decreased after the peak level was reached. IL-6 and MR-proADM showed a trend towards higher levels in the SIRS group although this rise was statistically significant only for IL-6 (p < 0.005). Bacterial infections more frequently presented values of PCT above the cut-off point (>0.5 ng/mL) at 12–24 h. (3) Conclusion: In our experience, IL-6 kinetics is faster than PCT kinetics and both are faster than CRP in patients with fever and cancer who present a good outcome. Patients with a good evolution show a rapid increase and decrease of PCT and particularly of IL-6 levels.

Characteristics and Treatment of Patients with Multiple Myeloma Who Received Daratumumab in a Large Claims Database in the United States

Introduction: Daratumumab (DARA) is a human IgGk monoclonal antibody targeting CD38 with a direct on-tumor and immunomodulatory mechanism of action. Since its approval for relapsed or refractory multiple myeloma (MM) in 2015 and newly diagnosed MM (NDMM) in 2018, DARA has become the foundation of treatment for MM. However, there is limited real-world evidence describing patients (pts) with MM treated with DARA-containing regimens in the US, especially for NDMM pts. Thus, using a large US claims database, we evaluated pt and treatment characteristics among MM pts who received DARA-containing regimens as first-line (1L) or second-line (2L) therapy, and separately for the subset who received DARA plus lenalidomide/dexamethasone (D-Rd) given its more recent approval in NDMM. Methods: This retrospective, observational cohort study evaluated pts aged ≥18 years at the index date (first observed treatment date with ≥1 medical or pharmacy claim for DARA) from the Optum Clinformatics Data Mart database between January 1, 2013 and December 31, 2020, with ≥1 diagnosis code for MM before the index date, and who were continuously enrolled in a medical benefit plan and pharmacy plan from ≥6 months before the initial MM diagnosis date to the index date. Pts with a hematopoietic stem cell transplant from 6 months before the observed initial MM diagnosis date to the index date were excluded. Eligible pts were categorized into 4 cohorts: pts with (1) a DARA-containing regimen as 1L (1L DARA-based cohort), (2) a DARA-containing regimen as 2L (2L DARA-based cohort), (3) D-Rd as 1L (1L D-Rd cohort), and (4) D-Rd as 2L (2L D-Rd cohort) after the observed initial MM diagnosis. Demographics were assessed on the index date; clinical characteristics, all-cause healthcare resource utilization (HCRU), and total all-cause healthcare costs (medical + pharmacy costs) were measured during the 6-month baseline period before the index date. Results: Among the 1L DARA-based cohort (n=235), 48 (20.4%) pts received D-Rd (1L D-Rd cohort). Overall, the most common 1L DARA-based regimens (with or without dexamethasone or prednisone) were D-R (28.5%), DARA plus bortezomib (V)/R (D-VR; 23.8%), D-V (18.7%), and D (14.9%). Among the 2L DARA-based cohort (n=310), 44 (14.2%) pts received D-Rd (2L D-Rd cohort). In the 1L DARA-based and 1L D-Rd cohorts, the mean (SD) age on the index date was 72.4 (9.0) and 76.1 (7.9) years, respectively. In the 2L DARA-based and 2L D-Rd cohorts, the mean (SD) age on the index date was 72.7 (9.3) and 70.5 (11.5) years, respectively. Mean baseline Quan-Charlson Comorbidity index was higher in the 2L cohorts versus 1L cohorts (1L DARA-based: 3.1; 1L D-Rd: 2.8; 2L DARA-based: 3.5; 2L D-Rd: 3.6). Hypercalcemia, renal impairment, anemia, and/or bone lesions (CRAB symptoms) were identified in about 50% of pts in all cohorts. Renal impairment was identified in 32.3%, 22.9%, 32.9%, and 34.1% of pts in the 1L DARA-based, 1L D-Rd, 2L DARA-based, and 2L D-Rd cohorts, respectively. Cardiovascular conditions (myocardial infarction, angina, peripheral vascular disease, and/or congestive heart failure) were found in 26.4% of pts in the 1L DARA-based cohort, 29.2% in the 1L D-Rd cohort, 34.5% in the 2L DARA-based cohort, and 47.7% in the 2L D-Rd cohort. The most common (&gt;5% of pts) 1L regimens for pts in the 2L D-Rd cohort were VRd (17 [38.6%] pts), Vd (6 [13.6%]), V/cyclophosphamide (C)/d (4 [9.1%]), Rd (4 [9.1%]), and VC (3 [6.8%]). In the 1L DARA-based, 1L D-Rd, 2L DARA-based, and 2L D-Rd cohorts, 47.7%, 41.7%, 47.7%, and 54.5% of pts, respectively, had ≥1 hospitalization, and mean numbers of outpatient encounters were 26.4, 25.1, 50.6, and 52.8 within the 6-month baseline period. Mean 6-month baseline total healthcare cost (in 2020 USD) was $53,874 (medical: $51,837; pharmacy: $2,038) in the 1L DARA-based cohort, $39,695 ($35,896; $3,799) in the 1L D-Rd cohort, $131,832 ($96,737; $35,095) in the 2L DARA-based cohort, and $136,218 ($97,380; $38,838) in the 2L D-Rd cohort. Conclusions: In this real-world assessment using claims data, pts with MM receiving DARA-based therapy in 1L or 2L had median age ≥70 years, with high clinical burden as observed by their comorbidities, HCRU, and costs. Understanding the characteristics of real-world pts with MM treated with DARA-based therapy can further aid in the understanding of effective use of DARA in the real world. Disclosures Feng: Janssen: Current Employment. Fu: Janssen: Current Employment. Khare: Janssen: Current Employment. Ogbonnaya: Janssen: Current Employment. Kaila: Janssen Scientific Affairs, LLC: Current Employment.

Real-World Patient Characteristics and Treatment Outcomes Among Newly Diagnosed Multiple Myeloma Patients Initiating Daratumumab, Lenalidomide, and Dexamethasone As First-Line Therapy

Introduction: Daratumumab, a CD38 monoclonal antibody, was approved for the treatment of heavily pretreated relapsed or refractory multiple myeloma (MM) in 2015 and for newly diagnosed MM (NDMM) in 2018. The phase 3 MAIA study demonstrated that daratumumab plus lenalidomide/dexamethasone (D-Rd) improved clinical outcomes, including overall survival, versus lenalidomide/dexamethasone (Rd) in transplant-ineligible NDMM (Facon T, et al. EHA Library. 2021). However, limited real-world data are available regarding patients with MM treated with daratumumab in the US. We evaluated patient characteristics and treatment outcomes among transplant-ineligible NDMM patients who received D-Rd as first-line therapy in US oncology practices. Methods: This retrospective, observational cohort study evaluated patients from the Flatiron MM Core Registry who received first-line D-Rd between November 1, 2015 and February 28, 2021. The Flatiron Health electronic health record (EHR)-derived deidentified database has longitudinal patient-level records for patients treated at community oncology practices and academic medical centers across the US. The index date was the date of the first observed record of the D-Rd regimen. Patients aged &lt;18 years on the index date, enrolled in a clinical trial on the index date, with other malignancies prior to the index date, with a diagnosis of amyloid light-chain amyloidosis prior to the index date, or with hematopoietic stem cell transplant from the diagnosis date to the index date were excluded. Patient characteristics analyzed included age at time of D-Rd initiation, race, Eastern Cooperative Oncology Group performance status (ECOG PS) score, International Staging System (ISS) stage, frailty, and presence of comorbidities such as diabetes and acute renal impairment. Time-to-next-treatment and progression-free survival (PFS) were analyzed using the Kaplan-Meier method. Results: From November 1, 2015 to February 28, 2021, 1,721 patients were identified from the Flatiron MM Core Registry as patients treated with a daratumumab-based regimen; 120 (7.0%) of the 1,721 patients were treated with a daratumumab-based regimen as first-line therapy, and 35 (29.2%) of the 120 patients were identified as eligible patients treated with D-Rd as first-line therapy. For patients treated with first-line D-Rd (n=35), the mean age as of the index date was 73.5 years (SD: 7.9). Most patients were White (72.4%), had an ECOG PS score of 1 (51.9%), and had an ISS stage of II (38.1%) or III (42.9%). Of these patients, 15 (42.9%) patients were ≥75 years of age, 4 (11.4%) were Black or African American, 4 (11.4%) had high-risk cytogenetics, 21 (60.0%) were frail, 6 (17.1%) had diabetes, and 6 (17.1%) had acute renal impairment. The median time from the initial diagnosis date to the index date was 1.2 months and the median follow-up was 8.0 months. The median PFS was not reached in this study. The estimated 6-month PFS rate was 91.6%, 9-month PFS rate was 81.0%, and 12-month PFS rate was 73.6% (Figure). After 15 months of follow-up, an estimated 80.5% of patients had not received their next treatment. Conclusions: The real-world population of patients who received D-Rd as first-line therapy is similar to the population studied in MAIA. The early trend of PFS is also similar to that in MAIA, with the majority of patients progression free at 6, 9, and 12 months. Greater understanding of long-term outcomes among transplant-ineligible NDMM patients who received D-Rd as first-line therapy at US oncology practices is needed to further facilitate the safe and effective use of daratumumab. Figure 1 Figure 1. Disclosures Tai: Janssen Scientific Affairs, LLC: Current Employment. Cai: Janssen: Current Employment. Fu: Janssen: Current Employment. Khare: Janssen: Current Employment. Kaila: Janssen Scientific Affairs, LLC: Current Employment.

Validation of algorithms for selecting rheumatoid arthritis patients in the Tuscan healthcare administrative databases

Validation of algorithms for selecting patients from healthcare administrative databases (HAD) is recommended. This PATHFINDER study section is aimed at testing algorithms to select rheumatoid arthritis (RA) patients from Tuscan HAD (THAD) and assessing RA diagnosis time interval between the medical chart date and that of THAD. A population was extracted from THAD. The information of the medical charts at the Rheumatology Unit of Pisa University Hospital represented the reference. We included first ever users of biologic disease modifying anti-rheumatic drugs (bDMARDs) between 2014 and 2016 (index date) with at least a specialist visit at the Rheumatology Unit of the Pisa University Hospital recorded from 2013 to the index date. Out of these, we tested four index tests (algorithms): (1) RA according to hospital discharge records or emergency department admissions (ICD-9 code, 714*); (2) RA according to exemption code from co-payment (006); (3) RA according to hospital discharge records or emergency department admissions AND RA according to exemption code from co-payment; (4) RA according to hospital discharge records or emergency department admissions OR RA according to exemption code from co-payment. We estimated sensitivity, specificity, positive and negative predicted values (PPV and NPV) with 95% confidence interval (95% CI) and the RA diagnosis median time interval (interquartile range, IQR). Two sensitivity analyses were performed. Among 277 reference patients, 103 had RA. The fourth algorithm identified 96 true RA patients, PPV 0.78 (95% CI 0.70–0.85), sensitivity 0.93 (95% CI 0.86–0.97), specificity 0.84 (95% CI 0.78–0.90), and NPV 0.95 (95% CI 0.91–0.98). The sensitivity analyses confirmed performance. The time measured between the actual RA diagnosis date recorded in medical charts and that assumed in THAD was 2.2 years (IQR 0.5–8.4). In conclusion, this validation showed the fourth algorithm as the best. The time interval elapsed between the actual RA diagnosis date in medical charts and that extrapolated from THAD has to be considered in the design of future studies.

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study

Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospital, Seattle, WA, and Children’s Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. Results Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. Conclusions Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Determining Diagnosis Date of Diabetes Using Structured Electronic Health Record (EHR) Data: The SEARCH for Diabetes in Youth Study

Background. Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes.Methods. A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospital, Seattle, WA, and Children’s Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. Results. Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p=0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. Conclusions. Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Cervical Cancer and COVID: A collaborative assessment of the effect of the COVID pandemic on the presentation of Cervical cancer in the North of England

Objective: To review the effect of the COVID-19 pandemic on the presentation of Cervical cancer. Design/ Setting: Retrospective study involving the Regional Cancer Centres in the M62 Group. Methods: Data was collected for two equal time periods. All cervical cancers were included and FIGO 2018 staging was used for the data collection. P values were calculated using binomial hypothesis test for the difference in staging. Time from symptoms to diagnosis was assessed using a normal distribution test. All other calculations were performed using chi-squared test. Statistical significance was considered if p values were <0.05. Main outcome measures: Histology, stage at diagnosis, date of onset of symptoms, investigation and type of treatment. Results: A total of 406 cases of cervical cancer were reviewed; 233 from May – October 2019 (pre-COVID) and 173 between May – October 2020 (post COVID); representing a significant reduction in new cervical cancer diagnoses of 25% post COVID (p<0.001) There was a 42% increase in the delay from start of symptoms to diagnosis Post COVID. Pre COVID, 27% of patients presented with Stage 3 or 4 disease, whilst during COVID this was 38%; statistically significant (p <0.001). When we evaluated the treatments received between the two time periods, this was also statistically significant (chi-squared, p=0.0005). Conclusions: This study has demonstrated a statistically significant increase in the stage of cervical cancer at diagnosis and a change in treatment for cervical cancer following the onset of COVID-19. The implications of this are discussed.

Comorbidity burden in the first three years after diagnosis in patients with rheumatoid arthritis, psoriatic arthritis or spondyloarthritis: a general practice registry-based study

ObjectivesRheumatoid arthritis (RA), psoriatic arthritis (PsA) and spondyloarthritis (SpA) are chronic inflammatory rheumatic conditions with high levels of comorbidity requiring additional therapeutic attention. We aimed to compare the 3-year comorbidity incidence and pain medication prescription in patients diagnosed with RA, PsA or SpA versus controls.MethodsData between 1999 and 2012 were obtained from Intego, a general practitioner (GP) morbidity registry in Flanders, Belgium. Cases were identified by International Classification of Primary Care (ICPC-2) codes representing ‘rheumatoid/seropositive arthritis (L88)’ or ‘musculoskeletal disease other (L99)’. The registered keywords mapped to these ICPC-2 codes were further verified and mapped to a RA/SpA/PsA diagnosis. Controls were matched on age, gender, GP practice and diagnosis date. We analysed the 3-year comorbidity burden in cases and controls, measured by the Rheumatic Diseases Comorbidity Index (RDCI). All electronically GP-prescribed drugs were registered.ResultsIn total, 738, 229 and 167 patients were included with a diagnosis of RA, SpA or PsA, respectively. Patients with RA or PsA had comparable median RDCI scores at baseline, but higher scores at year 3 compared with controls (RA: p=0.010; PsA: p=0.008). At baseline, depression was more prevalent in PsA patients vs controls (p<0.003). RA patients had a higher 3-year incidence of cardiovascular disease including myocardial infarction than controls (p<0.035). All disease population were given more prescriptions than controls for any pain medication type, even opioids excluding tramadol.ConclusionsThis study highlights the increasing comorbidity burden of patients with chronic inflammatory rheumatic conditions, especially for individuals with RA or PsA. The high opioid use in all populations was remarkable.

Multifactorial analysis of cancer patients’ willingness for COVID-19 vaccination.

12062 Background: Public health authorities advocate vaccinations for the general population, including cancer patients and survivors. Since the onset of the COVID-19 pandemic, its vaccine has been eagerly awaited, but the extent of cancer patients’ willingness to get vaccinated is not clear. As health promotion is crucial for these individuals, it is important to measure and analyze their willingness to receive the vaccine. Methods: A few days after the regulatory approval for the first COVID-19 vaccine, the CareAcross online interactive platforms were used to evaluate the willingness of patients to get vaccinated. Through an online survey, within a few hours, 1106 cancer patients selected either “Yes, I plan to get the vaccine” or “No, I will not get the vaccine”. The patients were from the UK, Germany, France, Spain or Italy; they had been diagnosed with breast, lung, prostate or colorectal cancer. Their responses were analyzed to determine how their cancer diagnosis (including date, metastatic status, and other aspects), and their country of origin, affected their reported willingness to get vaccinated. Results: Overall, 70.6% of patients indicated willingness to get the vaccine (WTV), and 29.4% reported the opposite (NWTV). The strongest determinant of WTV was the patient’s country of origin: patients in the UK, Spain, Italy, Germany and France reported WTV of 84.1%, 64.2%, 58.7%, 47.4% and 38.3%, respectively. The next strongest determinant was the time elapsed since the patient’s diagnosis: for the largest population with available diagnosis date (451 UK patients), the average time since diagnosis for patients with WTV vs NWTV was: breast, 3.5 vs 2.5 years; lung, 1.6 vs 1.4 years; prostate, 2.4 vs 3.3 years; colorectal, 1.9 vs 1.5 years. Among patients from other countries with available diagnosis date: as the time since diagnosis increased, among 148 Italian patients WTV gradually increased; among 94 Spanish patients, WTV substantially decreased; among 85 French patients, WTV gradually decreased; among 50 German patients, WTV substantially increased. There was no significant correlation of WTV percentages with cancer type; metastatic status; triple negative vs non-triple negative among breast cancer patients; non-small cell vs small cell among lung cancer patients. Conclusions: Despite long-standing efforts of the scientific community for health promotion through the COVID-19 vaccine, a substantial percentage of cancer patients reported no willingness to get vaccinated. This appeared to depend on each patient’s country of origin, and the time elapsed since their diagnosis. This patient input was collected shortly after the first vaccine’s approval. With increasing evidence of efficacy and safety through more vaccinations of citizens and patients, willingness is expected to increase. We are in the process of conducting a follow-up survey to track these changes and update the results to be reportable during ASCO.

Association of blood inflammatory/immune markers with outcomes in (neo)adjuvant breast cancer: A large single institutional study.

e12540 Background: An inflammatory state in various cancer populations may correlate with mortality. Neutrophil-lymphocyte ratio is a surrogate marker of an inflammatory state. A recent meta-analysis showed the predictive value of neutrophil to lymphocyte ratio in breast cancers, but series are generally small. We aim to study the associations of Neutrophil-Lymphocyte Ratio (NLR) with outcomes in stage I–III breast cancer in patients who received neo-adjuvant chemotherapy (NAC) or upfront surgery. The endpoints are overall survival (OS) and breast cancer-specific survival (BCSS). In the NAC group, association with pathological complete response (PCR) rate was also studied. Methods: Data of patients with stage I–III breast cancer treated from 2011–2017 were extracted from a prospectively maintained registry and merged with full blood count (FBC) results from a clinical management software. FBC were performed within one-month pre-chemo for NAC patients and one-month pre-surgery for upfront surgery patients. PCR is defined as stage ypT0/isN0M0. OS is defined as death from any cancer from diagnosis date, censored at last follow-up. BCSS is defined as death from breast cancer from diagnosis date, censored at last follow up or death from any cause. The NLR values with the maximal Youden’s indexes calculated for each outcome were used as cut-off, logistic regression was used to determine PCR association and cox regression and log rank for OS and BCSS. Results: A total of 2,479 eligible patients were analysed. Overall, treatment compliance was high (87.6% of ER+ patients received endocrine treatment, and 94.1% of HER2+ NAC patients had targeted therapy). In the NAC group (n = 357), 23% achieved PCR. NLR did not show any statistically significant association with PCR. In unadjusted analysis, high NLR was associated with worse BCSS (log-rank p = 0.003 figure 1). In multivariable analysis (MVA), only triple negative and HER-enriched cancers were significantly associated with PCR. In NAC patients, NLR was associated with OS (cut-off 2.63; OR 1.6, p = 0.077) and BCSS (cut-off 3.58; OR 2.2, p = 0.003) in MVA. In patients treated with up-front surgery (n = 2122), unadjusted analysis showed high NLR was associated with worse BCSS (OR 1.55, p = 0.05; figure 2). In MVA, NLR (cut-off 2.13, OR 1.57, p = 0.005), triple negative histology, stage and age were significant predictors of OS. BCSS was not significantly associated with NLR (OR 1.38 95%CI 0.90-2.12 p = 0.145). Conclusions: Using a large cohort of patients, a high NLR was found to be associated with worse outcomes in NAC and upfront surgery breast cancer patients.