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Objectives The objectives of this study were to describe current sedative drug utilization patterns in critically ill patients undergoing mechanical ventilation (MV) in intensive care units (ICUs) in Japanese hospitals and to elucidate the relationship of these utilization patterns with patient clinical outcomes. Method Analysis of hospital claims data derived from the Quality Indicator/Improvement Project identified 12,395 critically ill adult patients who had undergone MV while hospitalized in the ICUs of 114 Japanese hospitals and had been discharged between April 2008 and March 2010. Descriptive statistics were calculated for the daily utilization of sedative drugs, opioids, and muscle relaxants in this patient sample, and the relationship between drug utilization and patient outcomes using Cox proportional hazards analysis were examined. Results Of the 12,395 patients included in the analysis, 7300 (58.9 %), 580 (4.7 %), and 671 (5.4 %) received sedative drugs, opioids, and muscle relaxants, respectively, for [greater than or equal to]2 days after intubation. Compared to the other patient groups, there was a higher proportion of males in the group given sedative drugs and the patients were significantly younger (P < 0.001). Propofol was the most frequently used sedative drug, followed by benzodiazepines, barbiturates, and dexmedetomidine. The mortality rate was lower and ventilator weaning was earlier among patients who received only propofol than among those who received only benzodiazepines. Muscle relaxants were associated with increased duration of MV. Conclusions This is the first study based on a large-scale analysis in Japan to elucidate sedative drug utilization patterns and their relationship with outcomes in critically ill patients. The most commonly used sedative was propofol, which was associated with favorable patient outcomes. Further prospective research must be conducted to discern effective sedative drug utilization., Author(s): Kenshi Hayashida [sup.1], Takeshi Umegaki [sup.2], Hiroshi Ikai [sup.3], Genki Murakami [sup.1], Masaji Nishimura [sup.4], Yuichi Imanaka [sup.3] Author Affiliations: (1) grid.271052.3, 0000000403745913, Department of Medical Informatics and Management, [...]

Purpose To compare the attitudes of physicians towards withholding and withdrawing life-sustaining treatments in intensive care units (ICUs) in low-middle-income Asian countries and regions with those in high-income ones, and to explore differences in the role of families and surrogates, legal risks, and financial considerations between these countries and regions. Methods Questionnaire study conducted in May-December 2012 on 847 physicians from 255 ICUs in 10 low-middle-income countries and regions according to the World Bank's classification, and 618 physicians from 211 ICUs in six high-income countries and regions. Results After we accounted for personal, ICU, and hospital characteristics on multivariable analyses using generalised linear mixed models, physicians from low-middle-income countries and regions were less likely to limit cardiopulmonary resuscitation, mechanical ventilation, vasopressors and inotropes, tracheostomy and haemodialysis than those from high-income countries and regions. They were more likely to involve families in end-of-life care discussions and to perceive legal risks with limitation of life-sustaining treatments and do-not-resuscitate orders. Nonetheless, they were also more likely to accede to families' requests to withdraw life-sustaining treatments in a patient with an otherwise reasonable chance of survival on financial grounds in a case scenario (adjusted odds ratio 5.05, 95 % confidence interval 2.69-9.51, P < 0.001). Conclusions Significant differences in ICU physicians' self-reported practice of limiting life-sustaining treatments, the role of families and surrogates, perception of legal risks and financial considerations exist between low-middle-income and high-income Asian countries and regions., Author(s): Jason Phua [sup.1], Gavin M. Joynt [sup.2], Masaji Nishimura [sup.3], Yiyun Deng [sup.4], Sheila Nainan Myatra [sup.5], Yiong Huak Chan [sup.6], Nguyen Gia Binh [sup.7], Cheng Cheng Tan [sup.8], [...]

Author(s): Takeshi Umegaki [sup.1], Masaji Nishimura [sup.2], Kimitaka Tajimi [sup.3], Kiyohide Fushimi [sup.4], Hiroshi Ikai [sup.5], Yuichi Imanaka [sup.5] Author Affiliations: (1) grid.410783.9, Department of Anesthesiology, Kansai Medical University, , [...], Objective To develop an equation model of in-hospital mortality for mechanically ventilated patients in adult intensive care using administrative data for the purpose of retrospective performance comparison among intensive care units (ICUs). Design Two models were developed using the split-half method, in which one test dataset and two validation datasets were used to develop and validate the prediction model, respectively. Nine candidate variables (demographics: age; gender; clinical factors hospital admission course; primary diagnosis; reason for ICU entry; Charlson score; number of organ failures; procedures and therapies administered at any time during ICU admission: renal replacement therapy; pressors/vasoconstrictors) were used for developing the equation model. Setting In acute-care teaching hospitals in Japan: 282 ICUs in 2008, 310 ICUs in 2009, and 364 ICUs in 2010. Participants Mechanically ventilated adult patients discharged from an ICU from July 1 to December 31 in 2008, 2009, and 2010. Main Outcome Measures: The test dataset consisted of 5,807 patients in 2008, and the validation datasets consisted of 10,610 patients in 2009 and 7,576 patients in 2010. Two models were developed: Model 1 (using independent variables of demographics and clinical factors), Model 2 (using procedures and therapies administered at any time during ICU admission in addition to the variables in Model 1). Using the test dataset, 8 variables (except for gender) were included in multiple logistic regression analysis with in-hospital mortality as the dependent variable, and the mortality prediction equation was constructed. Coefficients from the equation were then tested in the validation model. Results Hosmer-Lemeshow [chi].sup.2 are values for the test dataset in Model 1 and Model 2, and were 11.9 (P = 0.15) and 15.6 (P = 0.05), respectively; C-statistics for the test dataset in Model 1and Model 2 were 0.70 and 0.78, respectively. In-hospital mortality prediction for the validation datasets showed low and moderate accuracy in Model 1 and Model 2, respectively. Conclusions Model 2 may potentially serve as an alternative model for predicting mortality in mechanically ventilated patients, who have so far required physiological data for the accurate prediction of outcomes. Model 2 may facilitate the comparative evaluation of in-hospital mortality in multicenter analyses based on administrative data for mechanically ventilated patients.