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R Spanedda

Publications and source records attributed to R Spanedda.

58 records · Page 4Linked to original sources
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Primary gastric lymphoma: distribution and clinical relevance of different epidemiological factors.

BACKGROUND: Over the last 10 years the incidence of primary gastric lymphomas (PGL), and in particular those of MALT origin, has significantly increased. Recent works correlated this epidemiological observation to Helicobacter pylori (HP) infection. On the other hand, new evidence demonstrating that occupational exposure to pesticides and solvents has played an important role in the pathogenesis of non-Hodgkin lymphomas (NHL) has emerged from studies involving large series of patients. METHODS: Thirty PGL patients, observed between 1986-1992, were subdivided according to HP infection, history of previous gastric disturbances (G) and exposure to pesticides and solvents (T). RESULTS: On the basis of these parameters we divided the patients into three groups: T+HP+ (8), T+HP- (7), T-HP+ (9). T+ patients had a positive history of gastric problems or a positive histological biopsy in 13.3% of cases, versus 66.7% in T- patients. The incidence of HP infection in the T+ group was 53%, which proved to be comparable to the statistics for northeastern Italy, while in the T- group the incidence of infection was 100%. CONCLUSIONS: On the whole these data suggest that HP infection could be considered a pathogenetic factor in 34% of patients, while occupational exposure to pesticides and solvents could have played a more important role in 66% of these cases.

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Study of prognosis in acute myeloid leukemias (AML) by cluster analysis.

BACKGROUND: Cluster analysis is particularly effective in detecting homogeneous subgroups among large series of observations. We applied this relatively uncommon approach to the study of prognosis in 137 patients affected by acute myeloid leukemia (AML). METHODS AND RESULTS: Employing simple presentation parameters (age, WBC, splenomegaly, hepatomegaly) we used cluster analysis to define 3 groups with different overall survival (p = 0.0019). This classification was obtained following a rescaling of the variables and principal component analysis. Validation was performed through random definition of a control group. With the same variables, univariate analysis demonstrated age was the only prognostic factor, while Cox's model was not significant. CONCLUSIONS: In our series cluster analysis allowed a better definition of prognosis than Cox's analysis. Since the 3 groups are well identifiable, each patient can be rapidly classified and his allocation confirmed by discriminant functions. For cluster 2 we were able to project a possible myelodysplastic evolution, while cluster 3 was more frequently associated with a monocytic blastic component. We think that cluster analysis deserves consideration as an alternative statistical approach in the analysis of large series of data; its usefulness lies in its power to define homogeneous prognostic or biologic subgroups and to elaborate further hypotheses for new studies.

Acute Disease↗