Utilizing a PLASMIC score-based approach in the management of suspected immune thrombotic thrombocytopenic purpura: a cost minimization analysis within the Harvard TMA Research Collaborative.
Vivek A UpadhyayBenjamin P GeislerLova SunLynne UhlRichard M KaufmanChristopher StowellRobert S MakarPavan K BendapudiPublished in: British journal of haematology (2019)
The PLASMIC score is a recently described clinical scoring algorithm that rapidly assesses the probability of severe ADAMTS13 (a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13) deficiency among patients presenting with microangiopathic haemolytic anaemia. Using a large multi-institutional cohort, we explored whether an approach utilizing the PLASMIC score to risk-stratify patients with suspected immune thrombotic thrombocytopenic purpura (iTTP) could lead to significant cost savings. Our consortium consists of institutions with an unrestricted approach to ADAMTS13 testing (Group A) and those that require pre-approval by the transfusion medicine service (Group B). Institutions in Group A tested more patients than those in Group B (P < 0·001) but did not identify more cases of iTTP (P = 0·29) or have lower iTTP-related mortality (P = 0·84). Decision tree cost analysis showed that applying a PLASMIC score-based strategy to screen patients for ADAMTS13 testing in Group A would have reduced costs by approximately 27% over the 12-year period of our study compared to the current approach. Savings were primarily driven by a reduction in unnecessary therapeutic plasma exchanges, but lower utilization of ADAMTS13 testing and subspecialty consultations also contributed. Our data indicate that using the PLASMIC score to guide ADAMTS13 testing and the management of patients with suspected iTTP could be associated with significant cost savings.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- healthcare
- prognostic factors
- machine learning
- patient reported outcomes
- high throughput
- deep learning
- pulmonary embolism
- acute kidney injury
- coronary artery disease
- risk factors
- early onset
- quality improvement
- sickle cell disease
- data analysis
- decision making