Literature DB >> 21062514

Does intensive insulin therapy really reduce mortality in critically ill surgical patients? A reanalysis of meta-analytic data.

Jan O Friedrich1, Clarence Chant, Neill K J Adhikari.   

Abstract

Two recent systematic reviews evaluating intensive insulin therapy (IIT) in critically ill patients grouped randomized controlled trials (RCTs) by type of intensive care unit (ICU). The more recent review found that IIT reduced mortality in patients admitted to a surgical ICU, but not in those admitted to medical ICUs or mixed medical-surgical ICUs, or in all patients combined. Our objective was to determine whether IIT saves lives in critically ill surgical patients regardless of the type of ICU. Pooling mortality data from surgical and medical subgroups in mixed-ICU RCTs (16 trials) with RCTs conducted exclusively in surgical ICUs (five trials) and in medical ICUs (five trials), respectively, showed no effect of IIT in the subgroups of surgical patients (risk ratio = 0.85, 95% confidence interval (CI) = 0.69 to 1.04, P = 0.11; I2 = 51%, 95% CI = 1 to 75%) or of medical patients (risk ratio = 1.02, 95% CI = 0.95 to 1.09, P = 0.61; I2 = 0%, 95% CI = 0 to 41%). There was no differential effect between subgroups (interaction P = 0.10). There was statistical heterogeneity in the surgical subgroup, with some trials demonstrating significant benefit and others demonstrating significant harm, but no surgical subgroup consistently benefited from IIT. Such a reanalysis suggests that IIT does not reduce mortality in critically ill surgical patients or medical patients. Further insights may come from individual patient data meta-analyses or from future large multicenter RCTs in more narrowly defined subgroups of surgical patients.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 21062514      PMCID: PMC3219247          DOI: 10.1186/cc9240

Source DB:  PubMed          Journal:  Crit Care        ISSN: 1364-8535            Impact factor:   9.097


Introduction

Two recent systematic reviews that evaluated intensive insulin therapy (IIT) in critically ill patients grouped the included randomized controlled trials (RCTs) by type of intensive care unit (ICU): surgical versus medical versus mixed medical-surgical [1,2]. Both reviews found no mortality reduction among all critically ill patients. The more recent review by Griesdale and colleagues, however, found that IIT reduced mortality in patients admitted to surgical ICUs, but not in patients admitted to medical ICUs or mixed medical-surgical ICUs [2]. Potential explanations to support the beneficial effects of IIT among critically ill surgical patients were proposed in the accompanying editorial: a greater use of central and arterial lines in surgical ICUs, which allows for more accurate monitoring and correction of blood glucose; acute hyperglycemia in surgical patients, who are more likely to benefit from correction than medical patients with chronic elevations and adaptive responses; and better achievement of target glucose levels in surgical ICU studies compared with medical ICU or mixed ICU studies [3]. In contrast to the finding of the most recent review, however, the large NICE-SUGAR RCT enrolling over 6,000 critically ill patients suggested increased mortality both overall and among the subgroup of surgical patients [4]. (This largest trial to date was included in the most recent review but was analyzed among the mixed medical-surgical ICU group of trials [2].) These contrasting results between the meta-analyses [1,2] and the most recent trial [4] may stem from sensitivity of the meta-analytic results to methodologic decisions. In particular, the decision to group trials by type of ICU rather than by type of patient may not be intuitive for clinicians, for whom the important question is whether IIT saves lives in critically ill surgical patients regardless of the type of ICU in which they are treated, which depends on hospital organization. The objective of the present viewpoint article was therefore to determine whether IIT has a differential effect in surgical compared with medical critically ill patients by incorporating all available outcomes data from surgical and medical subgroups in mixed ICU trials.

Categorizing surgical and medical subgroups by type of patient rather than type of ICU

We considered all trials of IIT included in the two recent systematic reviews [1,2]. Our primary analysis used the RCTs included in the more recent review [2], which found differential effects between patients admitted to medical ICUs and surgical ICUs. The review's primary outcome was 90-day mortality - or, if not available, then hospital mortality, 28-day mortality, or ICU mortality (in descending order of preference; two trials reported only 6-month mortality). Since both reviews were published recently, we did not update the literature search; for included conference abstracts, however, we searched for and used data from subsequently published full reports. For trials conducted in mixed ICUs, we extracted mortality data separately for surgical and medical subgroups, and contacted authors to request subgroup data when not reported in the original publication. We grouped these outcomes with data reported in trials conducted exclusively in surgical ICUs and in medical ICUs. We used the categorization of surgical patients and medical patients by the authors of the mixed ICU RCTs and assumed that trials conducted in surgical ICUs and medical ICUs included exclusively surgical patients and medical patients, respectively. For one RCT, classified differently in the two systematic reviews [1,2], we confirmed with the study authors that the trial was conducted in a mixed ICU [5]. For our primary analysis, we constructed a surgical subgroup including trial-level data from the surgical ICU trials and surgical group-level data from the mixed ICU trials. We used a similar approach for the medical subgroup. Mortality data in each subgroup were pooled using random-effects models, which incorporate between-study heterogeneity (Review Manager; Cochrane Collaboration, Oxford, UK), expressed as risk ratios (RRs) with 95% confidence intervals (CIs). Pooled RRs in the surgical and medical subgroups were compared using a z test, with a significance level of 0.05. Statistical between-trial heterogeneity within each subgroup was assessed using the I2 measure with 95% CIs [6]. We conducted three sensitivity analyses. The first included only trials conducted in mixed ICUs that enrolled both surgical patients and medical patients. This analysis addresses the possibility that differences between trials other than patient population could explain differential effects. The second analysis included trials in the first systematic review by Wiener and colleagues [1] that were excluded by the more recent review by Griesdale and colleagues [2]. The third analysis included only trials that actually achieved tight glucose control, as defined by a mean blood glucose of 4.4 to 6.1 mM (the most commonly targeted range) in the intervention group. Of the 16 RCTs conducted in mixed ICUs [4,5,7-20], mortality data for surgical and medical subgroups were available for 14 RCTs [4,5,7-18] and were unavailable for one RCT [19] after author contact; we were unable to contact the authors of one study [20]. These 14 RCTs provided data for 9,935/10,206 (97%) of patients randomized in mixed ICU trials [4,5,7-18]. These data were combined with the five RCTs (1,972 patients) conducted exclusively in surgical ICUs [21-25] and the five RCTs (1,371 patients) in medical ICUs [26-30] included in the most recent review. For each included trial, Table 1 presents the target and mean achieved blood glucose values for both treatment groups and the mortality time point analyzed.
Table 1

Target and achieved blood glucose and mortality outcome time point by trial

Intervention groupControl group


StudyGlucose target (mM)Mean achieved glucose (mM)Glucose target (mM)Mean achieved glucose (mM)Mortality outcome time point
Studies included in the more recent systematic review [2]
Surgical ICU studies
 Van den Berghe and colleagues [21]4.4 to 6.15.710.0 to 11.18.5Hospital
 Grey and Perdrizet [22]4.4 to 6.76.910.0 to 12.29.9Hospital
 Bilotta and colleagues (SAH) [23]4.4 to 6.75.0< 12.28.36-month
 He and colleagues [24]4.4 to 8.36.710.0 to 11.110.0Hospital
 Bilotta and colleagues (TBI) [25]4.4 to 6.75.1< 12.28.26-month
Medical ICU studies
 Bland and colleagues [26]4.4 to 6.15.810.0 to 11.19.828-day
 Van den Berghe and colleagues [27]4.4 to 6.16.210.0 to 11.18.590-day
 Walters and colleagues [28]5.0 to 8.06.9≤15.08.130-day
 Oksanen and colleagues [29]4.0 to 6.05.06.0 to 8.06.430-day
 Bruno and colleagues [30]5.0 to 7.27.4< 11.110.690-day
Mixed medical-surgical ICU studies
 Mitchell and colleagues [8]4.4 to 6.15.410.0 to 11.17.9Hospital
 Azevedo and colleagues [9]4.4 to 6.77.4< 10.08.0ICU
 Preiser and colleagues [11]4.4 to 6.16.67.8 to 10.08.2Hospital
 Brunkhorst and colleagues [12]4.4 to 6.16.210.0 to 11.18.490-day
 Iapichino and colleagues [13]4.4 to 6.16.110.0 to 11.19.190-day
 He and colleagues [14]4.4 to 6.15.110.0 to 11.110.6ICU
 Zhang and colleagues [15]4.4 to 6.16.110.0 to 11.17.7Hospital
 De La Rosa and colleagues [16]4.4 to 6.16.510.0 to 11.18.2Hospital
 Arabi and colleagues [17]4.4 to 6.16.410.0 to 11.19.5Hospital
 Mackenzie and colleagues [18]4.0 to 6.07.0< 11.08.4Hospital
 NICE-SUGAR [4]4.5 to 6.06.4< 10.08.090-day
 Farah and colleagues [5]6.1 to 7.87.97.8 to 11.19.728-day
 Yu and colleagues [7]4.4 to 6.15.710.0 to 11.111.1Hospital
 McMullin and colleagues [10]5.0 to 7.07.18.0 to 10.09.4Hospital
Additional studies included only in the earlier systematic review [1]
Surgical ICU studies
 Stecher and colleagues [32]4.4 to 6.1n/a7.8 to 10.0n/an/a
 Kia and colleagues [33]4.2 to 6.46.010.0 to 11.18.090-day
 Chan and colleagues [34]4.4 to 6.77.0< 11.19.3Hospital
Medical ICU studies
 Fernandez and colleagues [35]4.4 to 6.16.7< 8.311.4Hospital
 Davies and colleagues [36]4.0 to 8.010.3< 10.010.7Hospital
 Gray and colleagues [37]4.0 to 7.06.3< 17.06.890-day

ICU, intensive care unit; n/a, not available; SAH, subarachnoid hemorrhage; TBI, traumatic brain injury.

Target and achieved blood glucose and mortality outcome time point by trial ICU, intensive care unit; n/a, not available; SAH, subarachnoid hemorrhage; TBI, traumatic brain injury. Meta-analyses showed no effect of IIT in the subgroups of surgical patients (RR = 0.85, 95% CI = 0.69 to 1.04, P = 0.11) or of medical patients (RR = 1.02, 95% CI = 0.95 to 1.09, P = 0.61) (Figure 1 and Table 2). There was no evidence of a differential effect between subgroups (P = 0.10). There was moderate statistical heterogeneity in the surgical subgroup (I2 = 51%, 95% CI = 1 to 75%) but none in the medical subgroup (I2 = 0%, 95% CI = 0 to 41%). Considering surgical patients, the effect of IIT appeared consistent in the subgroup of surgical ICU trials, in which the point estimate for I2 is 0%. However, the 95% confidence interval of this estimate of heterogeneity (0 to 70%) is wide and similar to the I2 confidence interval for both the surgical subgroup of the mixed ICU studies and the entire surgical patient population (see Figure 1a). This suggests that substantial heterogeneity cannot be excluded [31], even in the subgroup of surgical ICU trials.
Figure 1

Effect of intensive insulin therapy on mortality in surgical and medical patients. A z test of interaction between the risk ratio (RR) for mortality in (A) all surgical patients and (B) all medical patients was not statistically significant (P = 0.10), indicating that treatment effects did not differ between these two groups. This was also the case if one compares medical and surgical patients only within the same - that is, mixed intensive care unit (ICU) - trials (P = 0.66). Of the 14 trials conducted in mixed ICUs [4,5,7-18], one enrolled only surgical patients [7] and one enrolled only medical patients [10]. Preiser and colleagues' article [11] is the full publication of the abstract included in the most recent review [2]. After accounting for readmissions, subgroup-specific outcomes data were available for 991 out of 1,078 patients randomized. Compared with data presented in the most recent systematic review [2], subgroup-specific outcomes data are complete for all other trials except for 1/535 patients with missing data in one trial [12]. CI, confidence interval; I2, percentage of total variation across studies due to between-study heterogeneity rather than chance; IIT, intensive insulin therapy; n/N = number of deaths/number of patients randomized; SAH, subarachnoid hemorrhage; TBI, traumatic brain injury.

Table 2

Summary of pooled results of primary and sensitivity analyses

Pooled results

AnalysisAll trialsaSurgical patient subgroupMedical patient subgroupP valueb
Primary
 Trials included in more recent review [2]c0.93 (0.84 to 1.04, P = 0.20),I2 = 45% (2 to 69%);26 trials; 13,549 patients0.85 (0.69 to 1.04, P = 0.11),I2 = 51% (1 to 75%);18 trials; 6,164 patients1.02 (0.95 to 1.09, P = 0.61),I2 = 0% (0 to 41%);18 trials; 7,113 patients0.10
Sensitivity
 Only mixed ICU trials enrolling both surgical and medical patients0.97 (0.85 to 1.11, P = 0.66),I2 = 54% (0 to 79%);14 trials; 10,121 patients0.98 (0.80 to 1.19, P = 0.82),I2 = 40% (0 to 75%);12 trials; 4,137 patients1.03 (0.94 to 1.13, P = 0.51),I2 = 8% (0 to 62%);12 trials; 5,722 patients0.66
 Incorporating additional trials included in earlier review [1]0.96 (0.87 to 1.06, P = 0.43),I2 = 36% (0 to 61%);32 trials; 15,051 patients0.89 (0.74 to 1.08, P = 0.24),I2 = 45% (0 to 71%);21 trials; 6,644 patients1.02 (0.96 to 1.09, P = 0.46),I2 = 0% (0 to 31%);21 trials; 8,135 patients0.18
 Only trials achieving mean blood glucose 4.4 to 6.1 mM in IIT group0.80 (0.60 to 1.07, P = 0.14),I2 = 43% (0 to 76%);12 trials; 2,879 patients0.76 (0.57 to 1.01, P = 0.06),I2 = 10% (0 to 68%);9 trials; 2,474 patients1.04 (0.71 to 1.53, P = 0.82),I2 = 7% (0 to 76%);6 trials; 289 patients0.20

Data presented as risk ratio (95% confidence interval). I2, percentage of total variation across studies due to between-study heterogeneity rather than chance; ICU, intensive care unit; IIT, intensive insulin therapy. aIncludes also mixed ICU trials for which separate surgical and medical subgroup data were not available [19,20]. bSurgical versus medical interaction. cSee also Figure 1.

Effect of intensive insulin therapy on mortality in surgical and medical patients. A z test of interaction between the risk ratio (RR) for mortality in (A) all surgical patients and (B) all medical patients was not statistically significant (P = 0.10), indicating that treatment effects did not differ between these two groups. This was also the case if one compares medical and surgical patients only within the same - that is, mixed intensive care unit (ICU) - trials (P = 0.66). Of the 14 trials conducted in mixed ICUs [4,5,7-18], one enrolled only surgical patients [7] and one enrolled only medical patients [10]. Preiser and colleagues' article [11] is the full publication of the abstract included in the most recent review [2]. After accounting for readmissions, subgroup-specific outcomes data were available for 991 out of 1,078 patients randomized. Compared with data presented in the most recent systematic review [2], subgroup-specific outcomes data are complete for all other trials except for 1/535 patients with missing data in one trial [12]. CI, confidence interval; I2, percentage of total variation across studies due to between-study heterogeneity rather than chance; IIT, intensive insulin therapy; n/N = number of deaths/number of patients randomized; SAH, subarachnoid hemorrhage; TBI, traumatic brain injury. Summary of pooled results of primary and sensitivity analyses Data presented as risk ratio (95% confidence interval). I2, percentage of total variation across studies due to between-study heterogeneity rather than chance; ICU, intensive care unit; IIT, intensive insulin therapy. aIncludes also mixed ICU trials for which separate surgical and medical subgroup data were not available [19,20]. bSurgical versus medical interaction. cSee also Figure 1. Results of sensitivity analyses were similar to those of the primary analysis (Table 2). First, the analysis restricted to 12 mixed ICU trials enrolling both surgical and medical patients found RR = 0.98 (95% CI = 0.80 to 1.19, P = 0.82; I2 = 40%) in surgical patients and RR = 1.03 (95% CI = 0.94 to 1.13, P = 0.51; I2 = 8%) in medical patients (P = 0.66 for comparison of RRs). Second, the analysis adding the results of the three surgical ICU trials [32-34] and the three medical ICU trials [35-37] included only in the earlier systematic review [1] found RR = 0.89 (95% CI = 0.74 to 1.08, P = 0.24; I2 = 45%) in surgical patients and RR = 1.02 (95% CI = 0.96 to 1.09, P = 0.46; I2 = 0%) in medical patients (P = 0.18 for comparison of RRs). Finally, the analysis of trials achieving tight glucose control (four out of eight surgical ICU trials, two out of eight medical ICU trials, and five out of 14 mixed ICU trials) found RR = 0.76 (95% CI = 0.57 to 1.01, P = 0.06; I2 = 10%) in surgical patients and RR = 1.04 (95% CI = 0.71 to 1.53, P = 0.82; I2 = 7%) in medical patients (P = 0.20 for comparison of RRs). This last subgroup analysis is dominated by the largest surgical ICU trial [21] and excludes the six other largest trials (one in a medical ICU [27] and five in mixed ICUs [4,11,12,16,17]) that targeted the same blood glucose range in the intervention group (4.4 to 6.1 mM) but achieved slightly higher mean values (6.2 to 6.6 mM). Although there was a nonsignificant trend to benefit of IIT in the surgical subgroup considered in isolation for this sensitivity analysis, there is no evidence that the effect differed from medical patients. Given this lack of difference between surgical and medical subgroups in any of the primary or secondary analyses, the best estimate of IIT effect in both subgroups is the overall effect, which is nil (see Table 2).

Discussion and conclusions

Our analysis shows no effect of IIT in surgical or medical critically ill patients. We found moderate between-trial differences in the effect of IIT in the surgical subgroup, reflecting the contrasting results of two trials enrolling the most surgical patients: the study by Van den Berghe and colleagues [21] and the NICE-SUGAR study [4]. As noted by other studies [1,2,21,38,39], multiple factors may have contributed to the positive result in the single-center trial by Van den Berghe and colleagues that mainly enrolled cardiac surgery patients [21]: patient population (higher control group mortality than expected), local care practices (in particular, routine use of intravenous glucose and parenteral nutrition [40]), early stopping after an interim analysis showed benefit, and a higher target glucose range in the control group compared with other trials. Furthermore, our analysis reveals the variable definitions of surgical patients that may also have contributed to between-trial heterogeneity: some trials included only postoperative patients, while others also included patients who required ICU readmission from surgical wards or nonoperative patients with surgical diagnoses such as pancreatitis or trauma. Based on the available data, there does not appear to be any obvious subgroup of surgical patients that consistently benefits from IIT. Of the two trials conducted in patients after cardiac surgery, Van den Berghe and colleagues found a mortality benefit [21], but the much smaller trial by Chan and colleagues did not [34]. Moreover, Van den Berghe and colleagues' trial included patients who required ICU readmission from surgical wards in addition to immediately postoperative patients. Other trials classified such patients as medical, and no trial suggested benefit in medical patients. Furthermore, in the NICE-SUGAR trial, operative patients were defined as immediately postoperative ICU admissions - and this trial actually suggested harm in such patients [4]. In summary, we analyzed the effect of IIT in surgical patients, regardless of the type of ICU to which they were admitted, and found no effect on mortality - similar to the effect for critically ill medical patients and all critically ill patients combined [1,2]. We therefore do not recommend this intervention for critically ill surgical patients or critically ill medical patients. Further insights into the effects of this intervention in surgical patients may come from individual patient data meta-analyses, acknowledging the challenges of ensuring availability and comparability of data among trials and obtaining expert statistical support. Alternatively, future large multicenter RCTs in specific patient subgroups, such as cardiac surgical patients, may further refine our understanding of the role of IIT in the ICU.

Abbreviations

CI: confidence interval; I2: percentage of total variation across studies due to between-study heterogeneity rather than chance; ICU: intensive care unit; IIT: intensive insulin therapy; RCT: randomized controlled trial; RR: risk ratio.

Competing interests

The authors declare that they have no competing interests.
  34 in total

1.  Intensive insulin therapy in critically ill patients.

Authors:  G van den Berghe; P Wouters; F Weekers; C Verwaest; F Bruyninckx; M Schetz; D Vlasselaers; P Ferdinande; P Lauwers; R Bouillon
Journal:  N Engl J Med       Date:  2001-11-08       Impact factor: 91.245

2.  Intensive insulin therapy in critical illness.

Authors:  Derek C Angus; Edward Abraham
Journal:  Am J Respir Crit Care Med       Date:  2005-12-01       Impact factor: 21.405

3.  Intensive versus modified conventional control of blood glucose level in medical intensive care patients: a pilot study.

Authors:  David Kelvin Bland; Yvonne Fankhanel; Eileen Langford; Martha Lee; Scott W Lee; Colleen Maloney; Mark Rogers; Grenith Zimmerman
Journal:  Am J Crit Care       Date:  2005-09       Impact factor: 2.228

4.  Glycemic control in the intensive care unit: why we should wait for NICE-SUGAR.

Authors:  Rinaldo Bellomo; Moritoki Egi
Journal:  Mayo Clin Proc       Date:  2005-12       Impact factor: 7.616

5.  Intensive insulin therapy in the medical ICU.

Authors:  Greet Van den Berghe; Alexander Wilmer; Greet Hermans; Wouter Meersseman; Pieter J Wouters; Ilse Milants; Eric Van Wijngaerden; Herman Bobbaers; Roger Bouillon
Journal:  N Engl J Med       Date:  2006-02-02       Impact factor: 91.245

6.  A randomised, controlled pilot study to investigate the potential benefit of intervention with insulin in hyperglycaemic acute ischaemic stroke patients.

Authors:  M R Walters; C J Weir; K R Lees
Journal:  Cerebrovasc Dis       Date:  2006-05-09       Impact factor: 2.762

7.  [Influence and mechanism of a tight control of blood glucose by intensive insulin therapy on human sepsis].

Authors:  Wen-kui Yu; Wei-qin Li; Xiao-dong Wang; Xiao-wen Yan; Xiao-ping Qi; Ning Li; Jie-shou Li
Journal:  Zhonghua Wai Ke Za Zhi       Date:  2005-01-01

Review 8.  Toward understanding tight glycemic control in the ICU: a systematic review and metaanalysis.

Authors:  Paul E Marik; Jean-Charles Preiser
Journal:  Chest       Date:  2009-12-16       Impact factor: 9.410

9.  Reduction of nosocomial infections in the surgical intensive-care unit by strict glycemic control.

Authors:  Neil J Grey; George A Perdrizet
Journal:  Endocr Pract       Date:  2004 Mar-Apr       Impact factor: 3.443

10.  Metabolic control in diabetic subjects following myocardial infarction: difficulties in improving blood glucose levels by intravenous insulin infusion.

Authors:  R R Davies; R W Newton; G P McNeill; B M Fisher; C M Kesson; D Pearson
Journal:  Scott Med J       Date:  1991-06       Impact factor: 0.729

View more
  15 in total

1.  The Japanese guidelines for the management of sepsis.

Authors:  Shigeto Oda; Mayuki Aibiki; Toshiaki Ikeda; Hitoshi Imaizumi; Shigeatsu Endo; Ryoichi Ochiai; Joji Kotani; Nobuaki Shime; Osamu Nishida; Takayuki Noguchi; Naoyuki Matsuda; Hiroyuki Hirasawa
Journal:  J Intensive Care       Date:  2014-10-28

Review 2.  Intensive insulin therapy in critically ill hospitalized patients: making it safe and effective.

Authors:  David C Klonoff
Journal:  J Diabetes Sci Technol       Date:  2011-05-01

Review 3.  Glycemic control, mortality, and hypoglycemia in critically ill patients: a systematic review and network meta-analysis of randomized controlled trials.

Authors:  Tomohide Yamada; Nobuhiro Shojima; Hisashi Noma; Toshimasa Yamauchi; Takashi Kadowaki
Journal:  Intensive Care Med       Date:  2016-09-16       Impact factor: 17.440

4.  Evaluation of a continuous blood glucose monitoring system using central venous microdialysis.

Authors:  Fanny Schierenbeck; Anders Franco-Cereceda; Jan Liska
Journal:  J Diabetes Sci Technol       Date:  2012-11-01

5.  Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016.

Authors:  Andrew Rhodes; Laura E Evans; Waleed Alhazzani; Mitchell M Levy; Massimo Antonelli; Ricard Ferrer; Anand Kumar; Jonathan E Sevransky; Charles L Sprung; Mark E Nunnally; Bram Rochwerg; Gordon D Rubenfeld; Derek C Angus; Djillali Annane; Richard J Beale; Geoffrey J Bellinghan; Gordon R Bernard; Jean-Daniel Chiche; Craig Coopersmith; Daniel P De Backer; Craig J French; Seitaro Fujishima; Herwig Gerlach; Jorge Luis Hidalgo; Steven M Hollenberg; Alan E Jones; Dilip R Karnad; Ruth M Kleinpell; Younsuk Koh; Thiago Costa Lisboa; Flavia R Machado; John J Marini; John C Marshall; John E Mazuski; Lauralyn A McIntyre; Anthony S McLean; Sangeeta Mehta; Rui P Moreno; John Myburgh; Paolo Navalesi; Osamu Nishida; Tiffany M Osborn; Anders Perner; Colleen M Plunkett; Marco Ranieri; Christa A Schorr; Maureen A Seckel; Christopher W Seymour; Lisa Shieh; Khalid A Shukri; Steven Q Simpson; Mervyn Singer; B Taylor Thompson; Sean R Townsend; Thomas Van der Poll; Jean-Louis Vincent; W Joost Wiersinga; Janice L Zimmerman; R Phillip Dellinger
Journal:  Intensive Care Med       Date:  2017-01-18       Impact factor: 17.440

6.  The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2016 (J-SSCG 2016).

Authors:  Osamu Nishida; Hiroshi Ogura; Moritoki Egi; Seitaro Fujishima; Yoshiro Hayashi; Toshiaki Iba; Hitoshi Imaizumi; Shigeaki Inoue; Yasuyuki Kakihana; Joji Kotani; Shigeki Kushimoto; Yoshiki Masuda; Naoyuki Matsuda; Asako Matsushima; Taka-Aki Nakada; Satoshi Nakagawa; Shin Nunomiya; Tomohito Sadahiro; Nobuaki Shime; Tomoaki Yatabe; Yoshitaka Hara; Kei Hayashida; Yutaka Kondo; Yuka Sumi; Hideto Yasuda; Kazuyoshi Aoyama; Takeo Azuhata; Kent Doi; Matsuyuki Doi; Naoyuki Fujimura; Ryota Fuke; Tatsuma Fukuda; Koji Goto; Ryuichi Hasegawa; Satoru Hashimoto; Junji Hatakeyama; Mineji Hayakawa; Toru Hifumi; Naoki Higashibeppu; Katsuki Hirai; Tomoya Hirose; Kentaro Ide; Yasuo Kaizuka; Tomomichi Kan'o; Tatsuya Kawasaki; Hiromitsu Kuroda; Akihisa Matsuda; Shotaro Matsumoto; Masaharu Nagae; Mutsuo Onodera; Tetsu Ohnuma; Kiyohiro Oshima; Nobuyuki Saito; So Sakamoto; Masaaki Sakuraya; Mikio Sasano; Norio Sato; Atsushi Sawamura; Kentaro Shimizu; Kunihiro Shirai; Tetsuhiro Takei; Muneyuki Takeuchi; Kohei Takimoto; Takumi Taniguchi; Hiroomi Tatsumi; Ryosuke Tsuruta; Naoya Yama; Kazuma Yamakawa; Chizuru Yamashita; Kazuto Yamashita; Takeshi Yoshida; Hiroshi Tanaka; Shigeto Oda
Journal:  Acute Med Surg       Date:  2018-02-05

Review 7.  The optimal target for acute glycemic control in critically ill patients: a network meta-analysis.

Authors:  Tomoaki Yatabe; Shigeaki Inoue; Masahiko Sakaguchi; Moritoki Egi
Journal:  Intensive Care Med       Date:  2016-09-29       Impact factor: 17.440

8.  Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012.

Authors:  R P Dellinger; Mitchell M Levy; Andrew Rhodes; Djillali Annane; Herwig Gerlach; Steven M Opal; Jonathan E Sevransky; Charles L Sprung; Ivor S Douglas; Roman Jaeschke; Tiffany M Osborn; Mark E Nunnally; Sean R Townsend; Konrad Reinhart; Ruth M Kleinpell; Derek C Angus; Clifford S Deutschman; Flavia R Machado; Gordon D Rubenfeld; Steven Webb; Richard J Beale; Jean-Louis Vincent; Rui Moreno
Journal:  Intensive Care Med       Date:  2013-01-30       Impact factor: 17.440

9.  The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2016 (J-SSCG 2016).

Authors:  Osamu Nishida; Hiroshi Ogura; Moritoki Egi; Seitaro Fujishima; Yoshiro Hayashi; Toshiaki Iba; Hitoshi Imaizumi; Shigeaki Inoue; Yasuyuki Kakihana; Joji Kotani; Shigeki Kushimoto; Yoshiki Masuda; Naoyuki Matsuda; Asako Matsushima; Taka-Aki Nakada; Satoshi Nakagawa; Shin Nunomiya; Tomohito Sadahiro; Nobuaki Shime; Tomoaki Yatabe; Yoshitaka Hara; Kei Hayashida; Yutaka Kondo; Yuka Sumi; Hideto Yasuda; Kazuyoshi Aoyama; Takeo Azuhata; Kent Doi; Matsuyuki Doi; Naoyuki Fujimura; Ryota Fuke; Tatsuma Fukuda; Koji Goto; Ryuichi Hasegawa; Satoru Hashimoto; Junji Hatakeyama; Mineji Hayakawa; Toru Hifumi; Naoki Higashibeppu; Katsuki Hirai; Tomoya Hirose; Kentaro Ide; Yasuo Kaizuka; Tomomichi Kan'o; Tatsuya Kawasaki; Hiromitsu Kuroda; Akihisa Matsuda; Shotaro Matsumoto; Masaharu Nagae; Mutsuo Onodera; Tetsu Ohnuma; Kiyohiro Oshima; Nobuyuki Saito; So Sakamoto; Masaaki Sakuraya; Mikio Sasano; Norio Sato; Atsushi Sawamura; Kentaro Shimizu; Kunihiro Shirai; Tetsuhiro Takei; Muneyuki Takeuchi; Kohei Takimoto; Takumi Taniguchi; Hiroomi Tatsumi; Ryosuke Tsuruta; Naoya Yama; Kazuma Yamakawa; Chizuru Yamashita; Kazuto Yamashita; Takeshi Yoshida; Hiroshi Tanaka; Shigeto Oda
Journal:  J Intensive Care       Date:  2018-02-02

Review 10.  Accuracy of blood-glucose measurements using glucose meters and arterial blood gas analyzers in critically ill adult patients: systematic review.

Authors:  Shigeaki Inoue; Moritoki Egi; Joji Kotani; Kiyoshi Morita
Journal:  Crit Care       Date:  2013-03-18       Impact factor: 9.097

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.