Impact of plasma glucose and duration of type 2 diabetes mellitus on SYNTAX Score II in patients suffering from non ST-elevation myocardial infarction

Aim    The objective was to assess the correlation of fasting plasma glucose (FPG), HbA1c, and the duration of type 2 diabetes mellitus (T2DM) with SYNTAX score (SS) II in patients with non-ST elevation myocardial infarction (NSTEMI).Material and methods    FPG and HbA1C were measured in 398 patients presenting with NSTEMI at admission. SS II was calculated using an online calculator. Patients were stratified according to SS II (≤21.5, 21.5-30.6, and ≥30.6), defined as SS II low, mid, and high, respectively.Results    37.7 % of subjects were diabetic. Correlations of FPG (R=0.402, R2=0.162, p<0.001) and HbA1c (R=0.359, R2=0.129, p<0.001) with SS II were weak in the overall population. Duration of T2DM showed very strong correlation with SS II (R=0.827, R2=0.347). For the prediction of high SS II in the study population, FPG≥98.5 mg / dl demonstrated a sensitivity of 58 % and a specificity of 60 %, and HbA1c ≥6.05 demonstrated a sensitivity of 63 % and a specificity of 69 %. Duration of T2DM (adjusted odds ratio (OR): 1.182; 95 % confidence interval (CI): 1.185-2.773) and FPG (OR: 0.987; 95 % CI: 0.976-0.9959) were significantly associated with high SS II after controlling for other risk factors. Duration of T2DM (Beta=0.439) contributed strongly to variance of SS II, whereas HbA1c (Beta=0.063) contributed weakly.Conclusion    Duration of T2DM is a very important risk factor for severity of coronary artery disease.

Morphological processing of physiological signals for feature extraction

The paper proposes a novel method of extracting features from physiological signals using intrinsic mode decomposition (IMD) and morphological signal processing (MSP). The complex, nonlinear and non-stationary biomedical signals are first decomposed into intrinsic mode functions (IMF). Next each IMF is subjected to MSP for extracting features, namely, pattern spectrum entropy, that characterize the shape-size complexity of the component signals. These along with other features like energy and sample entropy are extracted from the individual IMF as well as the cumulative sums of IMF for characterizing the signals. The procedure is illustrated using heart sound signals digitally recorded during cardiac auscultation representing different cardiac conditions.

Prediction of periventricular leukomalacia. Part I: Selection of hemodynamic features using logistic regression and decision tree algorithms

OBJECTIVE

Periventricular leukomalacia (PVL) is part of a spectrum of cerebral white matter injury which is associated with adverse neurodevelopmental outcome in preterm infants. While PVL is common in neonates with cardiac disease, both before and after surgery, it is less common in older infants with cardiac disease. Pre-, intra-, and postoperative risk factors for the occurrence of PVL are poorly understood. The main objective of the present work is to identify potential hemodynamic risk factors for PVL occurrence in neonates with complex heart disease using logistic regression analysis and decision tree algorithms.

METHODS

The postoperative hemodynamic and arterial blood gas data (monitoring variables) collected in the cardiac intensive care unit of Children's Hospital of Philadelphia were used for predicting the occurrence of PVL. Three categories of datasets for 103 infants and neonates were used-(1) original data without any preprocessing, (2) partial data keeping the admission, the maximum and the minimum values of the monitoring variables, and (3) extracted dataset of statistical features. The datasets were used as inputs for forward stepwise logistic regression to select the most significant variables as predictors. The selected features were then used as inputs to the decision tree induction algorithm for generating easily interpretable rules for prediction of PVL.

RESULTS

Three sets of data were analyzed in SPSS for identifying statistically significant predictors (p<0.05) of PVL through stepwise logistic regression and their correlations. The classification success of the Case 3 dataset of extracted statistical features was best with sensitivity (SN), specificity (SP) and accuracy (AC) of 87, 88 and 87%, respectively. The identified features, when used with decision tree algorithms, gave SN, SP and AC of 90, 97 and 94% in training and 73, 58 and 65% in test. The identified variables in Case 3 dataset mainly included blood pressure, both systolic and diastolic, partial pressures pO(2) and pCO(2), and their statistical features like average, variance, skewness (a measure of asymmetry) and kurtosis (a measure of abrupt changes). Rules for prediction of PVL were generated automatically through the decision tree algorithms.

CONCLUSIONS

The proposed approach combines the advantages of statistical approach (regression analysis) and data mining techniques (decision tree) for generation of easily interpretable rules for PVL prediction. The present work extends an earlier research [Galli KK, Zimmerman RA, Jarvik GP, Wernovsky G, Kuijpers M, Clancy RR, et al. Periventricular leukomalacia is common after cardiac surgery. J Thorac Cardiovasc Surg 2004;127:692-704] in the form of expanding the feature set, identifying additional prognostic factors (namely pCO(2)) emphasizing the temporal variations in addition to upper or lower values, and generating decision rules. The Case 3 dataset was further investigated in Part II for feature selection through computational intelligence.

Prediction of periventricular leukomalacia. Part II: Selection of hemodynamic features using computational intelligence

OBJECTIVE

The objective of Part II is to analyze the dataset of extracted hemodynamic features (Case 3 of Part I) through computational intelligence (CI) techniques for identification of potential prognostic factors for periventricular leukomalacia (PVL) occurrence in neonates with congenital heart disease.

METHODS

The extracted features (Case 3 dataset of Part I) were used as inputs to CI based classifiers, namely, multi-layer perceptron (MLP) and probabilistic neural network (PNN) in combination with genetic algorithms (GA) for selection of the most suitable features predicting the occurrence of PVL. The selected features were next used as inputs to a decision tree (DT) algorithm for generating easily interpretable rules of PVL prediction.

RESULTS

Prediction performance for two CI based classifiers, MLP and PNN coupled with GA are presented for different number of selected features. The best prediction performances were achieved with 6 and 7 selected features. The prediction success was 100% in training and the best ranges of sensitivity (SN), specificity (SP) and accuracy (AC) in test were 60-73%, 74-84% and 71-74%, respectively. The identified features when used with the DT algorithm gave best SN, SP and AC in the ranges of 87-90% in training and 80-87%, 74-79% and 79-82% in test. Among the variables selected in CI, systolic and diastolic blood pressures, and pCO(2) figured prominently similar to Part I. Decision tree based rules for prediction of PVL occurrence were obtained using the CI selected features.

CONCLUSIONS

The proposed approach combines the generalization capability of CI based feature selection approach and generation of easily interpretable classification rules of the decision tree. The combination of CI techniques with DT gave substantially better test prediction performance than using CI and DT separately.

Intrapartum transcervical amnioinfusion for meconium-stained amniotic fluid

OBJECTIVE

To assess the rates of cesarean deliveries and perinatal outcome following intrapartum transcervical amnioinfusion in women with meconium-stained amniotic fluid (MSAF) in a setting with no electronic fetal monitoring or specialized neonatal care.

MATERIALS AND METHODS

In this prospective comparative study with 150 women who were in labor and had MSAF, 50 of the women received a transcervical amnioinfusion and the remaining 100 women received standard care. The inclusion criteria were a pregnancy of at least 37 weeks' duration, a single live fetus in cephalic presentation, no major medical or obstetric complications, and no known fetal malformation. The amnioinfusion was performed with 1000 mL of normal saline solution through a red rubber catheter.

RESULTS

Amnioinfusion was associated with a significant decrease in the incidence of low Apgar score (<7) at 1 min (12% vs. 47%; relative risk [RR], 0.26; 95% confidence interval [CI], 0.12-0.56); low Apgar score at 5 min (4% vs. 23%; RR, 0.17; 95% CI, 0.04-0.71); and meconium aspiration syndrome (4% vs. 18%; RR, 0.22; 95% CI, 0.05-0.92). There was also a trend towards a lesser incidence of cesarean deliveries (18% vs. 30%; RR, 0.6; 95% CI, 0.31-1.16) and perinatal deaths (4% vs. 13%; RR, 0.31; 95% CI, 0.07-1.31). The incidence of maternal hospital stays longer than 3 days was significantly lower in the amnioinfusion than in the control group (24% vs. 48%; RR, 0.5; 95% CI, 0.29-0.85). There were no major complications related to amnioinfusion.

CONCLUSIONS

Intrapartum amnioinfusion for MSAF is a simple, safe, effective, and inexpensive procedure feasible in settings where intrapartum monitoring is limited. It is associated with improved perinatal outcome and could lower cesarean delivery rates in low-resource countries.

Efficacy of the phosphorylation of synthetic peptides by purified catalytic subunit of PKA (PKAcat) from bovine lens depends on the amino acid sequence of the peptides

Protein kinase (PK) A catalytic (PKAcat) subunit was purified to homogeneity from bovine lens using a 100-kDa cut-off membrane filtration followed by different chromatographic procedures. The molecular weight of PKAcat was found to be 41 kDa. The kinase phosphorylates histone IIIs and other synthetic modified peptides of VRKRTLRRL with different amino acid environment. The extent of phosphorylation depends not only on the presence of Ser or Thr (phosphorylating residues) but also on other surrounding amino acid residues. Although some peptides compete in phosphorylating histone, they are not very significant. The result suggests that the extent of phosphorylation depends on the amino acid residue(s) surrounding phosphorylable residue(s) on the peptide.

{4-Bromo-2-[2-(5-bromo-2-oxidobenzylideneaminomethyl)phenyliminomethyl]phenolato-κ4O,N,N′,O′}nickel(II)

In the title complex, [Ni(C(21)H(14)Br(2)N(2)O(2))], the Ni(II) atom is coordinated by the two imine N and two phenolate O atoms of the Schiff base ligand in a tetrahedrally distorted square-planar geometry. The Ni-N and Ni-O distances are within the ranges expected for Ni-Schiff base derivatives. Intermolecular C-H...O hydrogen bonds link the molecules into centrosymmetric dimers, forming R(2)(2)(12) (A) and R(2)(2)(10) (B) rings. These dimers combine to form a supramolecular ABAB... aggregate which propagates along the [100] direction.