Genotyping N-acetyltransferase 2 (NAT2) is of high relevance for individualized dosing of antituberculosis drugs and bladder cancer epidemiology. In this study we compared a recently published tagging single nucleotide polymorphism (SNP) (rs1495741) to the conventional 7-SNP genotype (G191A, C282T, T341C, C481T, G590A, A803G and G857A haplotype pairs) and systematically analysed if novel SNP combinations outperform the latter. For this purpose, we studied 3177 individuals by PCR and phenotyped 344 individuals by the caffeine test. Although the tagSNP and the 7-SNP genotype showed a high degree of correlation (R=0.933, P<0.0001) the 7-SNP genotype nevertheless outperformed the tagging SNP with respect to specificity (1.0 vs. 0.9444, P=0.0065). Considering all possible SNP combinations in a receiver operating characteristic analysis we identified a 2-SNP genotype (C282T, T341C) that outperformed the tagging SNP and was equivalent to the 7-SNP genotype. The 2-SNP genotype predicted the correct phenotype with a sensitivity of 0.8643 and a specificity of 1.0. In addition, it predicted the 7-SNP genotype with sensitivity and specificity of 0.9993 and 0.9880, respectively. The prediction of the NAT2 genotype by the 2-SNP genotype performed similar in populations of Caucasian, Venezuelan and Pakistani background. A 2-SNP genotype predicts NAT2 phenotypes with similar sensitivity and specificity as the conventional 7-SNP genotype. This procedure represents a facilitation in individualized dosing of NAT2 substrates without losing sensitivity or specificity.

Twenty-nine months after a Whipple procedure for pancreatic carcinoma, a 47-year-old woman developed esophageal variceal bleeding. Percutaneous transhepatic portography revealed a severe stenosis of the portal vein with prehepatic portal hypertension and collateral circulation mainly to the gastric and esophageal veins. Percutaneous transhepatic balloon angioplasty was used to dilate the stenoses, but it did not remove the stenosis sufficiently. Therefore, an 8-mm, self-expandable stent was implanted, creating a nearly normal lumen without a pressure gradient. Portal hypertension was relieved, and the patient had no recurrent variceal bleeding for the 5 months up to her death.

Objectives: To determine how different mathematical time series approaches can be implemented for the detection of qualitative patterns in physiologic monitoring data, and which of these approaches could be suitable as a basis for future bedside time series analysis. Interventions: None. Measurements and results:Hemodynamic data were acquired in 1-minute intervals from a clinical information system and exported into statistical software for further analysis. Altogether, 134 time series for heart rate, mean arterial pressure and mean pulmonary artery pressure were visually classified by a senior intensivist into five patterns: no change, outlier, temporary level change, permanent level change, and trend. The same series were analyzed with low order autoregressive (AR) models and with phase space (PS) models. The resulting classifications from both models were compared to the initial classification. Outliers and level changes were detected in most instances with both methods. Trend detection could only be done indirectly. Both methods were more sensitive to pattern changes than they were clinically relevant. Especially with outlier detection, 95% confidence intervals were too close. AR models require direct user interaction, whereas PS models offer opportunities for fully automated time series analysis in this context. Conclusion:Statistical patterns in univariate intensive care time series can reliably be detected with AR models and with PS models. For most bedside problems both methods are too sensitive. AR models are highly interactive, and both methods require that users have an explicit knowledge of statistics. While AR models and PS models can be extremely useful in the scientific off-line analysis, routine bedside clinical use cannot yet be recommended.

Agreement between whole-body impedance cardiography and thermodilution in the measurement of cardiac output was unsatisfactory. Factors that can explain these differences are differences between the populations used for calibration of nCO and the study population, the influence of changing peripheral perfusion, and the effect of a supranormal hemodynamic state on the bioimpedance signal. Whole-body impedance cardiography cannot be recommended for assessing the hemodynamic state of high-risk surgical patients as studied in this investigation.

Hepatic resection is a chance for cure for primary and secondary liver tumors and a variety of benign diseases. Despite advances in surgical technique and patient care, preoperative and postoperative morbidity in patients undergoing liver resection remains high. Because a high morbidity represents a risk factor contributing to a fatal outcome of the surgical procedure, our study aimed to investigate the contribution of different risk factors to a fatal outcome and if mortality can be predicted by the presence of certain risk factors. Two hundred fifty-seven patients undergoing hepatic resection (curative and palliative) were analyzed preoperatively, immediately after surgery, and 10 days after surgery for 60 potential risk factors. Survivors (n = 238) and nonsurvivors (n = 19) were compared univariately. The analysis identified 14 variables to differentiate between groups. These variables were processed by multivariate logistic regression analysis. Three models to estimate 30-day mortality were identified, tested for statistical accuracy, and assessed for their receiver-operated characteristics (ROCs). The variables in the multivariate models were as follows: preoperatively, age, number of comorbid factors, and presence of cirrhosis; immediately after surgery, age, number of comorbid factors, and percentage of resected liver; and 10 days after surgery, age, hours of ventilation, and number of adverse events. Goodness of fit was 0. 863, 0.912, and 0.966, respectively. Areas under the ROC curves were 83.6%, 85.7%, and 98.0%. The specificity (probability to identify survivors correctly) was greater than 90% for all models, although sensitivity (probability to identify nonsurvivors correctly) was greater than 90% only for 10 days after surgery. We conclude that logistic regression is appropriate to assess the importance of risk factors in the course of hepatic resection and to identify patient groups at high risk.