Trends in Health Care Expenditures and Patient Out-of-Pocket Expenses in Primary Anterior Cruciate Ligament Reconstruction.

BACKGROUND: Anterior cruciate ligament reconstruction (ACLR) is one of the most commonly performed orthopaedic procedures in the United States, and the number of procedures is increasing annually, as is the cost. Patients are expected to shoulder a larger out-of-pocket expenditure. PURPOSE: To answer the following questions: (1) How is reimbursement changing for ACLR, and how is this affecting patients' out-of-pocket expenditures? (2) How are reimbursements from payers and patients' out-of-pocket expenses for ACLR distributed, and how is this changing? (3) Does performing ACLR in an ambulatory surgery center (ASC) result in lower costs for payers and patients? STUDY DESIGN: Economic and decision analysis study; Level of evidence, 4. METHODS: A total of 37,763 patients who underwent outpatient primary arthroscopic ACLR in the United States between 2013 and 2017 were identified using the IBM MarketScan Commercial Claims and Encounters Database. Patients with concomitant procedures and revision ACLR were excluded. Recorded outcomes were total patient payments and reimbursed claim totals in US dollars. RESULTS: Day-of-surgery reimbursement decreased 4.3% from $11,536 in 2013 to $11,044 in 2017, while patient out-of-pocket expenses increased 36% from $1085 in 2013 to $1480 in 2017. Day-of-surgery charges were the highest expense for patients, followed by physical therapy and magnetic resonance imaging (MRI) costs. Total reimbursement for MRI decreased 22.5%, while patient out-of-pocket expenses for MRI increased 166%. ACLR performed in an outpatient hospital resulted in 61% greater day-of-surgery expenditure for payers compared with ACLR performed in an ASC; however, the median total patient out-of-pocket savings for ACLRs performed in an ASC versus outpatient hospital was only $11. CONCLUSION: Out-of-pocket expenses for patients are increasing as they are forced to cover a larger percentage of their health care costs despite overall payer reimbursement decreasing. High-deductible health plans reimbursed the least out of all insurance types while having the highest patient out-of-pocket expenditure.

A modular master regulator landscape controls cancer transcriptional identity.

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses.

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.