Useful Tool For Clinical Decision Research Articles

A Prognostic Model for Predicting the Impact of Comorbidities on Survival of Patients with Myelodysplastic Syndromes.

Myelodysplastic syndromes (MDS) occur mainly in older persons, and these patients are likely to have comorbidities. We studied the impact of comorbidities on non-leukemic death (NLD) and overall survival (OS) in MDS patients with the aim of developing a specific prognostic index. Eight hundred forty consecutive patients receiving a diagnosis of MDS at Policlinico San Matteo, Pavia, Italy, between 1992 and 2006 were retrospectively evaluated. One or more comorbidities were present in 455/840 (54%) patients: the older the age, the higher their prevalence (P<0.001). Cardiac disease was observed in 25% of patients, liver disease in 16%, diabetes in 11%, prior solid tumor in 10%, nephropathy and pulmonary disease in 4%. Non-leukemic causes of death included cardiac failure (63%), infection (24%) and hemorrhage (7%). In a Cox analysis with age, sex, WHO category, cytogenetics and transfusion-dependency as time-dependent covariates, the presence of one or more comorbidities significantly affected both the risk of NLD (HR=1.91, P=0.001) and OS (HR=1.51, P=0.01), while it did not influence the risk of leukemic progression. The negative effect of comorbidities on OS was more evident in patients without excess of blasts (HR=1.8 P=0.007), while it retained a borderline significance in patients with more advanced disease (P=0.05). By including comorbidities as distinct entities in multivariable analysis, cardiac failure, liver or pulmonary disease, and solid tumors were found to independently affect the risk of NLD (HR=3.7, HR=2.08, HR=2.07 HR=2.23, respectively; P values from <0.001 to 0.033). Based on results of uni- and multivariable analysis, we developed a prognostic model for predicting the effect of comorbidities on NLD and OS. For each comorbidity, risk scores were estimated from the coefficients of the Cox regression. This MDS-specific comorbidity index (MDS-CI) allowed us to identify 3 groups of patients with different probability of NLD and OS (HR 2.78, P<0.001; HR 1.67 P=0.001), and provided a better stratification than the available non MDS-specific indices. Focusing on WPSS categories [J Clin Oncol 2007; 25:3503–10], MDS-CI significantly stratified survival of patients with very-low, low and intermediate risk groups (P<0.001), while it had no effect in high and very-high risk groups. We then investigated the relationship between transfusion-dependency, secondary iron overload and comorbidities. Heart failure (28% vs. 18% P=0.001) and cardiac death (69% vs 55% P=0.03) were significantly more frequent in transfusion-dependent patients. In a Cox analysis with time-dependent covariates, transfusion-dependent patients showed an increased risk of NLD (HR=2.12 P=<0.001), heart failure (HR 1.34 P=0.03), and cardiac death (HR 2.99 P=0.01). The development of secondary iron overload significantly affected the risk of NLD and OS (HR=1.25 and 1.16 respectively, P<0.001), and this effect was maintained after adjusting for transfusion burden. Iron overload specifically increased the risk of developing heart failure (HR=1.17, P<0.001). In summary, the presence of non-hematological comorbidities significantly worsens the survival of MDS patients. Transfusion-dependency and secondary iron overload are associated with an increased risk of cardiac complications and cardiac death. The MDS-CI might be a useful tool for clinical decision making in patients with myelodysplastic syndromes.

784 Exercise echocardiography: an useful tool in clinical decision making of moderate mitral stenosis

784 Exercise echocardiography: an useful tool in clinical decision making of moderate mitral stenosis

External validation of a prognostic model for predicting survival of cirrhotic patients with refractory ascites

OBJECTIVE: Cirrhotic patients with refractory ascites (RA) have a poor prognosis, although individual survival varies greatly. A model that could predict survival for patients with RA would be helpful in planning treatment. Moreover, in cases of potential liver transplantation, a model of these characteristics would provide the bases for establishing priorities of organ allocation and the selection of patients for a living donor graft. Recently, we developed a model to predict survival of patients with RA. The aim of this study was to establish its generalizability for predicting the survival of patients with RA. METHODS: The model was validated by assessing its performance in an external cohort of patients with RA included in a multicenter, randomized, controlled trial that compared large-volume paracentesis and peritoneovenous shunt. The values for actual and model-predicted survival of three risk groups of patients, established according to the model, were compared graphically and by means of the one-sample log-rank test. RESULTS: The model provided a very good fit to the survival data of the three risk groups in the validation cohort. We also found good agreement between the survival predicted from the model and the observed survival when patients treated with peritoneovenous shunt and with paracentesis were considered separately. CONCLUSION: Our survival model can be used to predict the survival of patients with RA and may be a useful tool in clinical decision making, especially in deciding priority for liver transplantation.