Payer budget impact of an artificial intelligence <i>in vitro</i> diagnostic to modify diabetic kidney disease progression

To evaluate the U.S. payer budget-impact of KidneyIntelX, an artificial intelligence-enabled in vitro diagnostic to predict kidney function decline in Type 2 Diabetic Kidney Disease (T2DKD) patients, stages 1–3b. We developed an Excel-based model according to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) good practices to assess U.S. payer budget impact associated with the use of the KidneyIntelX test to optimize therapy T2DKD patients compared to standard of care (SOC) (without KidneyIntelX). A hypothetical cohort of 100,000 stages 1–3b T2DKD patients was followed for 5 years. Peer-reviewed publications were used to identify model parameter estimates. KidneyIntelX costs incremental to SOC (without KidneyIntelX) included test cost, additional prescription medication use, specialist referrals and PCP office visits. Patients managed with KidneyIntelX experienced a 20% slowed progression rate compared to SOC (without KidneyIntelX) attributed to slowed DKD progression, delayed or prevented dialysis and transplants, and reduced dialysis crashes. Associated costs were compared to SOC (without KidneyIntelX). Sensitivity analyses were conducted by varying the definition of progression and the DKD progression rate associated with KidneyIntelX testing and related interventions. Projected undiscounted base case 5-year savings for 100,000 patients tested with KidneyIntelX were $1.052 billion, attributed mostly to slowed progression through DKD stages. The breakeven point for the health plan adopting KidneyIntelX is expected to occur prior to year 2 after adoption. Sensitivity analysis based on the assessment of the most conservative definition of progression and a 5$145 million associated with KidneyIntelX. Limitations included reliance on literature-based parameter estimates, including effect size of delayed progression supported by the literature. Incorporating KidneyIntelX in contemporary care of early-stage T2DKD patients is projected to result in substantial savings to payers.</p

Additional file 4 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

Supplementary Material

Additional file 5 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

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Additional file 1 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

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Additional file 6 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

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Additional file 2 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

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Additional file 3 of The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

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Additional file 6 of Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

Additional file 6. Supplementary Table 3: Demographics of Combined Training and Validation Oncotype Dataset

Additional file 1 of Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

Additional file 1. Methods

Additional file 7 of Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

Additional file 7. Supplementary Table 4A-D: Oncotype models