ABSTRACT
Background: Adequate reimbursement is considered a prerequisite for adoption of new diagnostic technologies that facilitate patient access to better treatments. Detailed longitudinal investigation of the adoption of new HCPCS codes and the factors influencing it are scarce, although the availability of large-scale claims databases should facilitate such studies. We examined claims for three CPT codes used for next generation sequencing (NGS): 81445, 81450 and 81455 in a large database of claims data from CMS and attempted to correlate presumptive drivers of test adoption such as coverage decisions and payments with test volume. Methods: CMS claims data were accessed using CMS' Virtual Research Data Center (VRDC) under data use agreement 50486. Any claim with a CPT code of 81445, 81450 or 81455 was extracted from the data and analysed using SAS Enterprise Guide with results summarised in Microsoft Excel. Data relating to national/local coverage determinations were located by internet searches. Results: Test volumes for all 3 codes showed significant variability, including a large decrease around Q1-2 of 2020, likely due to the COVID-19 pandemic. Utilization of the 3 CPT codes varied by patient diagnosis. Details of the top 5 diagnoses for each CPT are given in the Table. The top 30 diagnoses for each CPT code accounted for 80.33%-88.45% of patients. Conclusions: Utilisation of NGS testing from 2016-2021 was highly variable, confounding attempts to match potential drivers to changes in monthly test volumes. A relatively small number of conditions accounted for >80% test use. Increased use of 81445 and 81450 from 2019 onwards may be related to CMS LCD issued in March 2018, suggesting that it can take 8-9 months or more for a LCD to filter through to testing practice. Decreases in test volume around March 2020 coincide with decreased patient presentation and testing for cancer because of the COVID-19 pandemic indicating that factors beyond reimbursement can significantly affect test use. Changes in reimbursement or adoption of proprietary lab analysis (PLA) codes covering specific NGS tests may have caused the drop in test volumes in the latter half of 2021. This study demonstrates that determination of factors affecting adoption of a test technology can be problematic due to wide variation in claims over a relatively short space of time. However, determination of these factors is important as they ultimately affect patient access to testing and potentially to therapy. (Table Presented).
ABSTRACT
Introduction Measures taken to mitigate infection spread during the 2020 COVID-19 pandemic are considered to have caused significant unintended consequences on other diseases. Large decreases in the numbers of symptomatic and asymptomatic people presenting for diagnosis of heart disease, diabetes and cancer have been observed. A recent analysis of solid tumors showed up to 70% reduction in the number of patients presenting for diagnosis. The potential exists for significantly increased morbidity and mortality for these missed or delayed presenting patients. Further, it is important to determine whether infection spread mitigation measures affected the diagnostic testing and treatment decisions for these patients. This study aimed to determine whether pandemic control measures affected presentation, testing and treatment of patients across eight different hematologic cancers. Methods CMS claims data were analyzed for the presence of diagnostic (DX) ICD 10 codes indicative of hematologic cancer. Patients with a DX code first appearing in 2019 or in 2020 were selected to provide newly diagnosed pre-COVID-19 and during COVID-19 cohorts for comparison, with unique patient counts being calculated for each month. A “COVID-19 dip” i.e. a decrease in the number of patients was calculated as the change in number of patients diagnosed in a given month relative to the number for JAN2020. Dip duration was calculated only when the decrease was >10% of the JAN2020 figure. Patients who received treatment via a “J” code Healthcare Common Procedure Coding System (HCPCS) code were extracted from the cohorts and the time taken from initial diagnosis to first treatment calculated. Results Eight hematologic cancers: AML, CLL, CML, HEME (a group of different hematologic cancers), Hodgkins (HOG), Myelodysplasia (MDS), Non-Follicular Lymphomas (NFL), and Non-Hodgkins Lymphoma (NHL) showed a decrease in the number of patients being diagnosed during the early part of 2020 (Fig.1) Fig.1. Change in new patient diagnoses for selected hematologic cancers as a proportion of their JAN2020 value There was some variation in the depth and duration of the COVID-19 dip (Table 1) with MDS having both the longest and deepest dip. Median depth and duration of the dip was 33% and 3.5 months, respectively, with all dips starting either in FEB or MAR2020. Table 1. Duration and depth of COVID-19 dips for selected hematological cancers The proportions of patients receiving therapy via J HCPCS code (JRX) are shown in Table 2 Table 2. Proportions of patients receiving J code therapy Conclusions The decline in new patient diagnoses for heme cancers during the period when COVID-19 control measures were implemented is similar to that seen with solid tumors, although the depth of the COVID-19 dip was generally larger in the latter. There is no evidence of “catch up” diagnosis occurring i.e. patients missing from Q2 2020 are not reappearing en masse in subsequent quarters. The decline for MDS patients has, except for SEP to OCT2020, remained. Collectively, (depending on the calculation method), the COVID-19 dip for these eight heme cancers represents 16,584-33,671 patients who will likely have significantly increased rates of morbidity and mortality due to delayed diagnosis. Analysis of J code treatments show little difference between the proportions of patients receiving these treatments in 2020 compared to 2019 suggesting that at least some aspects of treatment e.g. infused chemotherapy, IO drugs for these patients was relatively unchanged by pandemic control measures. It also suggests that the main cause for decreased patient numbers treated is due to decreased testing for diagnosis, rather than not being treated once diagnosed. This aligns with findings from studies in the US and UK. The results of this study indicate that there may be a “backlog” of tens of thousands of people with cancer whose diagnosis has been significantly delayed and who urgently need to be identified in order to get on proper treatment to lessen the impact of that delay. [F rmula presented] Disclosures: No relevant conflicts of interest to declare.