Chao Wen

Comparing fiberoptic bronchoscopy- and a tracheal tube-mounted camera-guided percutaneous dilatational tracheostomy

In a small, prospective, randomized trial comparing fiberoptic bronchoscopy (FOB) and an endotracheal tube-mounted camera (VivaSight TM-SL tube [VST]; ETView, Misgav, Israel) for optical guidance of percutaneous dilatational tracheostomy (PDT), Grensemann et al. [1] concluded that visualization of PDT with the VST is not non-inferior to the FOB, but ventilation is superior with less hypercarbia when using the VST. We note that in the FOB group, a thick FOB with an outer diameter of 4.9 mm (Olympus BF-P60; Olympus Medical Systems Corp., Tokyo, Japan) was used. The readers were not provided with the sizes of endotracheal tubes used in the FOB group, but placement of such a thick FOB into an adult endotracheal tube can significantly impair ventilation, especially for critically ill patients requiring PDT [2]. This may be a main reason for the inferior ventilation with more hypercarbia using FOB in this study. We argue that different results would have been obtained if a thin adult FOB, such as Olympus BF-DP with an outer diameter of 3.1 mm, was used. Furthermore, it was unclear whether a comparable fresh gas flow was used for mechanical ventilation during the PDT procedure. This could confuse the interpretation of more hypercarbia with the FOB. For critically ill patients, the time required for the PDT procedure is the main concern. In the Methods section, Grensemann et al. did not clearly define the procedure duration. In the Results section, the authors reported that the mean procedure duration from skin incision to insertion of tracheal cannula did not differ significantly between groups. This may not be an appropriate comparison. We believe that the total procedure duration of PDT should be the time taken from insertion of direct laryngoscopy for tube exchange to completion of PDT in the VST group and from insertion of FOB to completion of PDT in the FOB group. Finally, other than airway visualization, during the PDT procedure, the versatility of FOB can also offer invaluable advantages, such as determination of incision site, avoidance of accidental extubation, detection and management of complications, etc. [3, 4]. The recent evidence indicates that PDT combined with FOB not only is a time-saving, easy-to-operate technique with few complications, but can manage complications of posterior tracheal wall injury or perforation, tracheoesophageal fistula [5]. In the absence of high-quality evidence establishing the safety of the VST-guided PDT procedure, we believe that FOB is still the most frequently used technique for monitoring the PDT procedure while maintaining mechanical ventilation.

Assessment of flexible bronchoscopic intubation through the AuraGain™ laryngeal mask versus a slit Guedel tube

To the Editor, Moser et al. recently assessed the performance of flexible bronchoscopic intubation (FBI) via the AuraGain laryngeal mask (Ambu A/S, Ballerup, Denmark) vs a slit Guedel tube (Rüsch & Co, Kernen, Germany) in a non-inferiority randomized controlled trial. They showed that FBI through an AuraGain laryngeal mask is achieved at least as rapidly as standard FBI via the slit Guedel tube. Much of their study was well done, and we can learn from their example. We noted, however, an important issue that was not addressed by the authors. The study compared only the times required for FBI via the two studied devices. It did not compare the times required for the whole intubation procedure, which includes inserting each device before FBI, the FBI itself via each device, or removing each device after FBI. As a commonly used practice to assist FBI, insertion and removal of a slit Guedel tube before and after FBI are evidently easier and simpler than for an AuraGain laryngeal mask, which includes insertion, cuff inflation, position confirmation, cuff deflation, and its removal along the tracheal tube. Especially, removing the AuraGain laryngeal mask after successful intubation without dislodging the tracheal tube from the trachea should be a great challenge to intubators. As a result, when comparing the performance of FBI via these two devices, in addition to the times required for FBI using each device, the intubation time should also include the times required for inserting each device before FBI and its removal after FBI. We argue that unrealistic definitions of intubation times for the two groups could make interpretation of their findings questionable. Furthermore, early postoperative neck pain was used as a performance end point for comparing the two devices, but the readers were not provided with the postoperative pain strategy or the analgesic drug consumption in the two groups. It must be emphasized that when early postoperative neck pain is compared between groups, standardization of postoperative pain management is an essential component of the study design. Especially, the timing of analgesic drug administration in relation to the assessment of early postoperative neck pain should be clearly described as part of the method. Because of the lack of comparable postoperative pain management, the study findings and subsequent conclusions must be interpreted with caution as they may have been obtained using incomplete methodology. Finally, we were interested in knowing why a Unoflex reinforced tracheal tube (ConvaTec Ltd, Flintshire, UK) was used for FBI in the AuraGain laryngeal mask group even though it has a fixed connector that could prevent removal of the AuraGain laryngeal mask after successful intubation.

Assessing Predictive Value of Postoperative Elevated Lactate for Adverse Outcomes Following Hepatectomy

To the Editor, With great interest, we read the recent article by Lemke et al. [1] determining the association of early post-hepatectomy lactate (PHL) with adverse outcomes. They show that PHL is independently associated with postoperative 90-day mortality and 30-day morbidity. As lactate increases from 2 to[ 6 mmol/L, 30-day morbidity rises from 11.6 to 40.6%, and 90-day mortality from 0.7 to 22.7%. Furthermore, the cutoff values of PHL for predicting morbidity and mortality are 3.96 and 3.72 mmol/L, respectively. Other than the limitations described in the discussion, however, we note that several issues of this study are not well addressed. First, to determine discrimination ability of elevated PHL for postoperative morbidity and mortality, only providing odds ratio by multivariable analysis and cutoff values by the receiver operating characteristic curve is not enough. The authors should further provide the sensitivity, specificity, positive, and negative predictive values of elevated PHL for postoperative adverse outcomes in the validation and development sets. By providing the predicted probabilities and observed frequencies for postoperative morbidity and mortality based on the cutoff values of PHL, the readers can estimate whether there is a good overall agreement between predicted probabilities and observed frequencies in the development and the validation sets. Furthermore, preoperative comorbidity index is an accepted independent risk factor for mortality and morbidity after hepatectomy [2, 3] and has been associated significantly with postoperative mortality and morbidity in this study. A limitation of this study design is that there is no comparison for predictive value of elevated PHL for postoperative adverse outcomes with that of preoperative comorbidity index. Thus, an important question that can be unanswered by this study is whether predictive performance of elevated PHL to facilitate early identification of adverse outcomes after hepatectomy equals or surpasses these classical risk indexes. Second, hyperlactatemia is mainly related to a condition of insufficient oxygen delivery or inadequate tissue perfusion. It has been shown that highest lactate level during hepatectomy is attributable to the Pringle maneuver, especially in patients with chronic liver diseases [4]. The peak intraoperative blood lactate level during cardiac surgery is significantly associated with prolonged cardiopulmonary bypass time and aortic cross-clamp time [5]. In this study, we noted that changes of PHL over the first postoperative night in all patients were significantly different (decrease in 74.8% of patients, no change in 15.9% of cases, and increase in 9.3% of patients). As the readers were not provided with the details of postoperative events, it was unclear whether elevated PHL was due to intraoperative or postoperative factors. At least, the available evidence shows that postoperative hyperglycemia and epinephrine therapy are independent risk factors for hyperlactatemia after surgery [6]. Third, the authors did not describe whether first PHL level was measured at same postoperative time in all patients. It has been shown that dynamic changes in lactate levels during the postoperative first 24 h are Re: Lemke M, et al. Elevated lactate is independently associated with adverse outcomes following hepatectomy. World J Surg 2017; In Press: doi:10.1007/s00268-017-4118-0

Derivation and Validation of a Novel Physiological Emergency Surgery Acuity Score

To the Editor The recent article by Sangji et al. [1] derivating and validating a novel physiological emergency surgery acuity score (PESAS) was of great interest. They show that the PESAS can assess the acuity of disease at presentation in emergency surgery patients and is strongly associated with postoperative mortality. The main strengths of this study are the use of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database including a relatively large sample of emergency surgery patients. Furthermore, the authors had applied properly statistical methods to derivate the PESAS. Given that accurate prediction of postoperative mortality is important for emergency surgery quality improvement, their findings have the potential implications. However, we noted the several aspects of this study that were not well addressed. First, in designing the PESAS, it was unclear why Sangji et al. only included the preoperative variables related to health status and comorbidities of emergency surgery patients, but not the intraoperative and postoperative risk factors affecting mortality risk of emergency surgery patients. Other than preoperative health status and comorbidities, the surgical burden is an important determinant for postoperative mortality. It has been shown that the Surgical Apgar Score based on the intraoperative blood loss, lowest heart rate and lowest mean artery pressure is a good predictor of mortality after emergency high-risk surgery [2]. Furthermore, postoperative complications, such as acute kidney injury, pulmonary complications and myocardial infarction, have been strongly associated with mortality after emergency surgery. Specially, postoperative acute kidney injury and pulmonary complications are common among emergency surgery patients and have been significantly associated with increased risk of early postoperative mortality [3, 4]. In fact, postoperative complications have been regarded as important targets for emergency surgical quality improvement initiatives. In available literature, predictive performance of the models only using preoperative variables for postoperative mortality has been questioned [5]. Thus, we believe that discriminating ability and predictive value of the PESAS designed by authors would have further been improved, if the model design had included intraoperative and postoperative risk factors affecting postoperative mortality of emergency surgery patients. Second, when validation of the PESAS was performed using 2012 data, only c-statistic was measured. The receiver operating characteristic curve was established, but the relative analysis was not carried out. To assess the predictive performance of the PESAS for postoperative mortality in a validation set, only providing the c-statistic is not enough, especially for low-risk patients. The authors should perform the calibration assessment by the Hosmer-Lemeshow goodness-of-fit test. The calibration assessment can test the predictive ability of the PESAS to match the number of actual events across deciles of risk-stratified subgroups. By providing the predicted probabilities and observed frequencies for postoperative mortality based on the PESAS, the readers can estimate whether there is a good overall agreement between predicted probabilities and observed frequencies in the development and validation sets. In the calibration assessment, a P value of 0.05 indicates that the null hypothesis—that actual observed events occur at a frequency similar to events predicted by the PESAS—is false, & Fu-Shan Xue xuefushan@aliyun.com

Letter to the Editor. Use of the ACS NSQIP Surgical Risk Calculator for prediction of adverse outcomes after neurosurgery

TO THE EDITOR: The recent article by Vaziri et al.7 assessing the predictive performance of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator for adverse postoperative outcomes in neurosurgical patients was of great interest (Vaziri S, Wilson J, Abbatematteo J, et al: Predictive performance of the American College of Surgeons universal risk calculator in neurosurgical patients. J Neurosurg [epub ahead of print April 28, 2017. DOI: 10.3171/2016.11.JNS161377]). The authors showed that the risk calculator had a good predictive performance for mortality but a poor predictive capacity for other potential adverse events and clinical outcomes. Given that prediction of adverse postoperative outcomes is important for neurosurgical quality improvement, the authors’ findings have several potential implications. Other than the limitations described in the Discussion section, however, we noted two aspects of this study that needed to be clarified. First, the authors reported that the mortality rate in the entire study population was 3.479%. Because the duration of the follow-up or observation period for this mortality rate was not specified, it is unclear whether the rate reflects shortor long-term outcome. The available evidence shows that the causes of postoperative shortand longterm deaths are different. Furthermore, the predictive performance of the ACS NSQIP Surgical Risk Calculator for postoperative shortand long-term mortality may be variable.6 Second, the ACS NSQIP Surgical Risk Calculator was shown to have a poor discriminative performance for several adverse postoperative outcomes such as pneumonia, urinary tract infection, return to the operating room, and discharge to a rehabilitation or nursing facility. In addition to the possible explanations that the authors provide, another potential reason is that this risk calculator considers 21 preoperative factors but does not include intraoperative and surgery-specific variables, which have been shown to be significantly associated with the most common adverse postoperative outcomes. In fact, other than patients’ preoperative health status and comorbidities, the surgical burden is the most important determinant of adverse postoperative events and death. In the available literature, increased anesthesia and operative times, intraoperative blood loss, blood transfusion, change in hemoglobin level, and elevated serum lactate level are the independent risk factors for developing adverse postoperative events in patients undergoing neurosurgery.1–3 In fact, the surgical Apgar score, which is based on the estimated blood loss, lowest heart rate, and lowest mean arterial blood pressure value during surgery, has been demonstrated to be significantly associated with increased risks of postoperative complications and death in patients undergoing a neurosurgical procedure; namely, scores vary inversely with the risks of postoperative complications and mortality.8 To improve the prediction of morbidity and mortality after neurosurgery, thus, we argue that the ACS NSQIP Surgical Risk Calculator should integrate the intraoperative and surgery-specific risk factors. In fact, it has been shown that incorporation of surgery-specific risk factors into the ACS NSQIP Surgical Risk Calculator can improve the prediction of morbidity and mortality after pancreatoduodenectomy.5 Moreover, the predictive performance of the models that use only preoperative variables for postoperative complications and mortality has been questioned.4

Use of urine N-acetyl-ß-D-glucosaminidase for early identification of renal tubular injury: more questions than solutions

Sir, With great interest we read the recent article by Lannemyr et al. using urine N-acetyl-ß-D-glucosaminidase (NAG) increases for early identification of renal tubular injury during cardiopulmonary bypass (CPB). They showed that already 30 min after the onset of CPB, urine NAG-excretion was significantly increased, followed by a sixfold peak increase after discontinuation of CPB. However, the logistic regression analysis showed that peak urine NAG-excretion did not predict the development of acute kidney injury (AKI) defined by the KDIGO criteria based on changes in serum creatinine within 48 h after surgery. This is not in according with Zheng et al. study in infants and young children undergoing CPB surgery, in which urine NAG-excretion increase is a predictor of postoperative AKI and has the best predictive performance at 4 h after surgery. When confronting these inconsistent results, our questions are what clinical values of urine NAG-excretion increases during and after CPB in adult patients are, and what clinicians should do when this issue occurs. In our view, there were several issues in this study that were not well addressed. First, urine NAG levels were assayed by a spectrophotometric method. In such an exploring clinical trial with the single arm, only reporting and comparing mean urine NAGexcretion levels at different observed points may have of limited clinical value. Most importantly, the authors did not provide the cutoff value of urine NAG levels with spectrophotometric method for diagnosis of renal tubular injury. Thus, it was unclear whether any urine NAGexcretion increase means a renal tubular injury with clinical significance. Furthermore, we were very interesting in knowing how many patients had a higher urine NAG level than the cutoff value for diagnosis of renal tubular injury. For example, when using serum or urine neutrophil gelatinase–associated lipocalin to define postoperative AKI, a level of > 150 ng/ml can identify patients at high risk for AKI, and a level > 350 ng/ml, those at high risk for renal replacement therapy. Second, the binary logistic regression was used to test the predictive ability of urine NAGexcretion increases for postoperative AKI. We believe that the receiver operating characteristic curve (ROC) should be constructed, and the area under the ROC be measured to evaluate the predictive performances of urine NAG-excretion increases for postoperative AKI, as performed in previous study. The value of the area under the ROC with 0.90–1.0 can indicate excellent (0.75–0.89), good (0.60–0.74), fair (0.50– 0.59), or no predictive value. By the ROC analysis, the optimal cutoff value of urine NAGexcretion increases for postoperative AKI as well as its sensitivity, specificity, and positive and negative predictive values can also be obtained. The optimal cutoff value is the one that has the highest sensitivity and specificity combined. By providing the predicted probabilities and observed frequencies for postoperative AKI based on the cutoff values of urine NAGexcretion increases, the readers can estimate whether there is a good overall agreement between predicted probabilities and observed frequencies in the development and validation sets. Finally, the authors only assess the association between urine NAG-excretion increases and postoperative AKI, but not the relationship of urine NAG-excretion increases with postoperative outcomes of patients, such as incidence of complications and length of hospital stay. Thus, an important question that can be unanswered by this study is whether use of urine NAG-excretion increases to facilitate early identification of renal tubular injury in patients undergoing CPB can affect the postoperative