RESUMEN
Many patients with primary myelofibrosis (PMF) become red blood cell (RBC) transfusion dependent (TD), risking iron overload (IOL). Iron chelation therapy (ICT) may decrease the risk of haemosiderosis associated organ dysfunction, though its benefit in PMF is undefined. To assess the effect of TD and ICT on survival in PMF, we retrospectively reviewed 41 patients. Clinical data were collected from the database and by chart review. The median age at PMF diagnosis was 64 (range 43-86) years. Median white blood cell (WBC) count at diagnosis was 7.6 (range 1.2-70.9) x 10(9)/L; haemoglobin 104 (62-145) G/L; platelets 300 (38-2088) x 10(9)/L. Lille, Strasser, Mayo and International Prognostic System (IPS) scores were: low risk, n = 15, 8, 11, 3; intermediate, n = 15, 19, 9, 16; high, n = 5, 11, 5, 7; respectively. Primary PMF treatment was: supportive care, n = 23; hydroxyurea, n = 10; immunomodulatory, n = 4; splenectomy, n = 2. Sixteen patients were RBC transfusion independent (TI) and 25 TD; of these 10 received ICT for a median of 18.3 (0.1-117) months. Pre-ICT ferritin levels were a median of 2318 (range 263-8400) and at follow up 1571 (1005-3211 microg/L (p = 0.01). In an analysis of TD patients, factors significant for overall survival (OS) were: WBC count at diagnosis (p = 0.002); monocyte count (p = 0.0001); Mayo score (p = 0.05); IPS (p = 0.02); number of RBC units (NRBCU) transfused (p = 0.02) and ICT (p = 0.003). In a multivariate analysis, significant factors were: NRBCU (p = 0.001) and ICT (p = 0.0001). Five year OS for TI, TD-ICT and TD-NO ICT were: 100, 89 and 34%, respectively (p = 0.003). The hazard ratio (HR) for receiving >20 RBCU was 7.6 (95% Confidence Intervals [CI] 1.2-49.3) and for ICT was 0.15 (0.03-0.77). In conclusion, 61% of PMF patients developed RBC-TD which portended inferior OS; however patients receiving ICT had comparatively improved OS, suggesting a clinical benefit. Prospective studies of IOL and the impact of ICT in PMF are warranted.
Asunto(s)
Terapia por Quelación/mortalidad , Transfusión de Eritrocitos/mortalidad , Ferritinas/metabolismo , Quelantes del Hierro/uso terapéutico , Sobrecarga de Hierro/mortalidad , Mielofibrosis Primaria/mortalidad , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Sobrecarga de Hierro/tratamiento farmacológico , Sobrecarga de Hierro/etiología , Masculino , Persona de Mediana Edad , Mielofibrosis Primaria/sangre , Mielofibrosis Primaria/terapia , Pronóstico , Estudios Retrospectivos , Tasa de Supervivencia , Resultado del TratamientoRESUMEN
BACKGROUND: Total shoulder arthroplasty (TSA) has demonstrated good long-term survivorship but early implant failure can occur. This study identified factors associated with shoulder arthroplasty revision and constructed a risk score for revision surgery following shoulder arthroplasty. METHODS: A validated algorithm was used to identify all patients who underwent anatomic TSA between 2002 and 2012 using population-based data. Demographic variables included shoulder implant type, age and sex, Charlson comorbidity score, income quintile, diagnosis, and surgeon arthroplasty volume. The associations of covariates with time to revision were measured while treating death as a competing risk and were expressed in the Shoulder Arthroplasty Revision Risk Score (SARRS). RESULTS: During the study period, 4079 patients underwent TSA. Revision risk decreased in a nonlinear fashion as patients aged and in the absence of osteoarthritis with no influence from surgery type or other covariables. The SARRS ranged from -21 points (5-year revision risk 0.75%) to 30 points (risk 11.4%). Score discrimination was relatively weak 0.55 (95% confidence interval: 0.530.61) but calibration was very good with a test statistic of 5.77 (df = 8, P = .762). DISCUSSION: The SARRS model accurately predicted the 5-year revision risk in patients undergoing TSA. Validation studies are required before this score can be used clinically to predict revision risk. Further study is needed to determine if the addition of detailed clinical data including functional outcome measures and the severity of glenohumeral arthrosis increases the model's discrimination.