1. Predicting failure of hematopoietic stem cell mobilization before it starts: the Predicted Poor Mobilizer (pPM) score
- Author
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Nicola Piccirillo, Paolo Corradini, Domenico Pastore, Roberta Nuccorini, Giorgina Specchia, Francesco Saraceni, Massimo Martino, Massimo Pini, Sarah Marktel, Andrea Mengarelli, Pietro Pioltelli, Giuseppe Milone, Francesco Zallio, Monica Poiani, Elvira Di Nardo, Saveria Capria, Sara Pasquina Pascale, Tiziana Moscato, Gianluca Gaidano, Immacolata Attolico, Pellegrino Musto, Paolo Perseghin, Francesco Merli, Lucia Farina, Luca Nassi, Martina Chiarucci, Simona Sica, Giuseppe Mele, Jacopo Olivieri, Francesco Lanza, Attilio Olivieri, Fabio Ciceri, Katia Codeluppi, Olivieri, Jacopo, Attolico, Immacolata, Nuccorini, Roberta, Pascale, Sara Pasquina, Chiarucci, Martina, Poiani, Monica, Corradini, Paolo, Farina, Lucia, Gaidano, Gianluca, Nassi, Luca, Sica, Simona, Piccirillo, Nicola, Pioltelli, Pietro Enrico, Martino, Massimo, Moscato, Tiziana, Pini, Massimo, Zallio, Francesco, Ciceri, Fabio, Marktel, Sarah, Mengarelli, Andrea, Musto, Pellegrino, Capria, Saveria, Merli, Francesco, Codeluppi, Katia, Mele, Giuseppe, Lanza, Francesco, Specchia, Giorgina, Pastore, Domenico, Milone, Giuseppe, Saraceni, Francesco, Di Nardo, Elvira, Perseghin, Paolo, and Olivieri, Attilio
- Subjects
Oncology ,Adult ,Male ,medicine.medical_specialty ,Multivariate analysis ,Adolescent ,lymphoma ,Filgrastim ,Likelihood ratios in diagnostic testing ,NO ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Predictive Value of Tests ,Risk Factors ,Internal medicine ,medicine ,Humans ,Child ,Hematopoietic Stem Cell Mobilization ,Aged ,Retrospective Studies ,Transplantation ,Mobilization ,Receiver operating characteristic ,business.industry ,Plerixafor ,Patient Selection ,Area under the curve ,poor mobiliser ,Hematology ,Middle Aged ,stem cell mobilization, lymphoma, myeloma, poor mobiliser, oredicting clinical score ,stem cell mobilization ,Settore MED/15 - MALATTIE DEL SANGUE ,myeloma ,oredicting clinical score ,030220 oncology & carcinogenesis ,Area Under Curve ,Child, Preschool ,Female ,business ,Multiple Myeloma ,030215 immunology ,medicine.drug - Abstract
Predicting mobilization failure before it starts may enable patient-tailored strategies. Although consensus criteria for predicted PM (pPM) are available, their predictive performance has never been measured on real data. We retrospectively collected and analyzed 1318 mobilization procedures performed for MM and lymphoma patients in the plerixafor era. In our sample, 180/1318 (13.7%) were PM. The score resulting from published pPM criteria had sufficient performance for predicting PM, as measured by AUC (0.67, 95%CI: 0.63â0.72). We developed a new prediction model from multivariate analysis whose score (pPM-score) resulted in better AUC (0.80, 95%CI: 0.76â0.84, p < 0001). pPM-score included as risk factors: increasing age, diagnosis of NHL, positive bone marrow biopsy or cytopenias before mobilization, previous mobilization failure, priming strategy with G-CSF alone, or without upfront plerixafor. A simplified version of pPM-score was categorized using a cut-off to maximize positive likelihood ratio (15.7, 95%CI: 9.9â24.8); specificity was 98% (95%CI: 97â98.7%), sensitivity 31.7% (95%CI: 24.9â39%); positive predictive value in our sample was 71.3% (95%CI: 60â80.8%). Simplified pPM-score can ârule inâ patients at very high risk for PM before starting mobilization, allowing changes in clinical management, such as choice of alternative priming strategies, to avoid highly likely mobilization failure.
- Published
- 2018