B-type natriuretic peptide concentrations and myocardial dysfunction in critical illness

B-type natriuretic peptide (BNP) is the first biomarker of proven value in screening for left ventricular dysfunction. The availability of point-of-care testing has escalated clinical interest and the resultant research is defining a role for BNP in the investigation and treatment of critically ill patients. This review was undertaken with the aim of collecting and assimilating current evidence regarding the use of BNP assay in the evaluation of myocardial dysfunction in critically ill humans. The information is presented in a format based upon organ system and disease category. BNP assay has been studied in a spectrum of clinical conditions ranging from acute dyspnoea to subarachnoid haemorrhage. Its role in diagnosis, assessment of disease severity, risk stratification and prognostic evaluation of cardiac dysfunction appears promising, but requires further elaboration. The heterogeneity of the critically ill population appears to warrant a range of cut-off values. Research addressing progressive changes in BNP concentration is hindered by infrequent assay and appears unlikely to reflect the critically ill patient's rapidly changing haemodynamics. Multi-marker strategies may prove valuable in prognostication and evaluation of therapy in a greater variety of illnesses. Scant data exist regarding the use of BNP assay to alter therapy or outcome. It appears that BNP assay offers complementary information to conventional approaches for the evaluation of cardiac dysfunction. Continued research should augment the validity of BNP assay in the evaluation of myocardial function in patients with life-threatening illness.

Diagnosis of diabetes mellitus: case of different cutoff values: A data mining approach

Objective: An estimation of cut-off points for the diagnosis of diabetes mellitus (DM) based on individual risk factors. Methods: A subset of the 1991 Oman National Diabetes Survey is used, including all patients with a 2h post glucose load >= 200 mg/dl (278 subjects) and a control group of 286 subjects. All subjects previously diagnosed as diabetic and all subjects with missing data values were excluded. The data set was analyzed by use of the SPSS Clementine data mining system. Decision Tree Learners (C5 and CART) and a method for mining association rules (the GRI algorithm) are used. The fasting plasma glucose (FPG), age, sex, family history of diabetes and body mass index (BMI) are input risk factors (independent variables), while diabetes onset (the 2h post glucose load >= 200 mg/dl) is the output (dependent variable). All three techniques used were tested by use of crossvalidation (89.8%). Results: Rules produced for diabetes diagnosis are: A- GRI algorithm (1) FPG>=108.9 mg/dl, (2) FPG>=107.1 and age>39.5 years. B- CART decision trees: FPG >=110.7 mg/dl. C- The C5 decision tree learner: (1) FPG>=95.5 and 54, (2) FPG>=106 and 25.2 kg/m2. (3) FPG>=106 and =133 mg/dl. The three techniques produced rules which cover a significant number of cases (82%), with confidence between 74 and 100%. Conclusion: Our approach supports the suggestion that the present cut-off value of fasting plasma glucose (126 mg/dl) for the diagnosis of diabetes mellitus needs revision, and the individual risk factors such as age and BMI should be considered in defining the new cut-off value.