Junyong In

Prediction of Plasma Hemoglobin Concentration by Near-Infrared Spectroscopy

The estimation of plasma hemoglobin concentration (Hb) is among one of the daily activities in the practice of clinical anesthesiology. The near-infrared spectroscopy of the brain (rSO2) represents a balance between cerebral oxygen delivery and consumption. This study was designed to assess the value of rSO2 in the prediction of the Hb level while other variables were mathematically controlled. Thirty healthy adult patients undergoing spine surgery, expected to have a moderate degree of intraoperative bleeding, were enrolled in this study. General anesthesia was given and ventilation was mechanically controlled. Measurement of Hb and PaCO2 were performed at random periods of time. We obtained a total of 97 data combinations for the 30 study patients. The Hb was regressed by independent variables including rSO2 and PaCO2. A multilinear regression analysis was performed and the final regression equation was expressed only with statistically significant variables. The measured Hb was tightly regressed with three variables. The final regression equation was Hb=+8.580+0.238 rSO2-0.338 PaCO2-0.004 anesthetic exposure duration (Tmin) (p=0.000, r2=0.809). Near-infrared spectroscopy was shown to be a valuable predictor of plasma Hb in the clinical anesthesiology setting.

Standard deviation and standard error of the mean

In most clinical and experimental studies, the standard deviation (SD) and the estimated standard error of the mean (SEM) are used to present the characteristics of sample data and to explain statistical analysis results. However, some authors occasionally muddle the distinctive usage between the SD and SEM in medical literature. Because the process of calculating the SD and SEM includes different statistical inferences, each of them has its own meaning. SD is the dispersion of data in a normal distribution. In other words, SD indicates how accurately the mean represents sample data. However the meaning of SEM includes statistical inference based on the sampling distribution. SEM is the SD of the theoretical distribution of the sample means (the sampling distribution). While either SD or SEM can be applied to describe data and statistical results, one should be aware of reasonable methods with which to use SD and SEM. We aim to elucidate the distinctions between SD and SEM and to provide proper usage guidelines for both, which summarize data and describe statistical results.

Gastric Distension With SLIPA Versus LMA ProSeal During Laparoscopic Cholecystectomy: A Randomized Trial

Background: We compared the quantitative clinical performances of the streamlined liner of the pharynx airway (SLIPA) and the ProSeal laryngeal mask airway (LMA ProSeal) regarding intensity of gastric distension in patients undergoing laparoscopic cholecystectomy. Methods: A total of 124 anesthetized, paralyzed patients (ASA 1 to 2; aged, 18 to 80 y) were randomly allocated for airway management with the SLIPA or LMA ProSeal. After induction of general anesthesia using total intravenous anesthesia and rocuronium, the intensity of gastric distension was accessed twice by 2 raters, respectively. We also compared the fiberoptic bronchoscopic view of the glottis, the severity of blood stain, and postoperative sore throat. Results: There were no statistically significant differences between groups for each gastric size. The change of gastric size within the SLIPA group was not statistically significant for both raters. Change within the LMA ProSeal group was significant in rater 2 (P=0.045) and marginally significant for rater 1 (P=0.056). Anatomic fit, complications during emergence, and the severity of blood stain and postoperative sore throat were similar in both groups. Conclusions: SLIPA is as efficacious as LMA ProSeal for use in patients without severe complications who are undergoing laparoscopic cholecystectomy.

Analysis of factors related to patient refusal of spinal anesthesia

BACKGROUND Spinal anesthesia is a anesthetic technique that can be easily used and practically applied according to patients preference and physiologic status, surgical procedures and so forth. The purpose of the present study is to analyze factors related to patient refusal of spinal anesthesia, arising from the previous spinal anesthesia experience associated with side effects or unsatisfactory senses after spinal anesthesia. METHODS One hundred ninety four patients undergoing various surgical procedures under spinal anesthesia were enrolled. We made a questionnaire that consisted of examination items and question items, and checked it during spinal anesthesia and about 24 hours after spinal anesthesia. Factors related to patient refusal of spinal anesthesia were analyzed with multiple logistic regression. RESULTS Thirty one out of 194 patients (16%) rejected to receive spinal anesthesia if they would have chance to have it again. Significant factors associated with refusal of spinal anesthesia were low back pain (P = 0.005), needle type (Quincke) (P = 0.025) and tingling sensation in the lower extremities immediately after spinal anesthesia induction (P = 0.003). Low back pain was significantly associated with the number of attempts of spinal block (P = 0.023). CONCLUSIONS Factors related to patient refusal of spinal anesthesia are low back pain, needle type and tingling sensation. Low back pain is related to the number of attempts of spinal block. Practitioners should give patients appropriate information about spinal anesthesia preoperatively and consider using Whitacre needle and avoid multiple attempts of spinal block so as to increase patients compliance with spinal anesthesia.

Assessment of risk of bias in quasi-randomized controlled trials and randomized controlled trials reported in the Korean Journal of Anesthesiology between 2010 and 2016

Bias affects the true intervention effect in randomized controlled trials (RCTs), making the results unreliable. We evaluated the risk of bias (ROB) of quasi-RCTs or RCTs reported in the Korean Journal of Anesthesiology (KJA) between 2010 and 2016. Six kinds of bias (selection, performance, detection, attrition, reporting, and other biases) were evaluated by determining low, unclear, or high ROB for eight domains (random sequence generation, allocation concealment, blinding of participants, blinding of personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other bias) according to publication year. We identified 296 quasi-RCTs or RCTs. Random sequence generation was performed better than allocation concealment (51.7% vs. 20.9% for the proportion of low ROB, P < 0.001 and P = 0.943 for trend, respectively). Blinding of outcome assessment was superior to blinding of participants and personnel (42.9% vs. 15.5% and 23.0% for the proportion of low ROB, P = 0.026 vs. P = 0.003 and 0.896 for trend, respectively). Handling of incomplete outcome data was performed best with the highest proportion of low ROB (84.8%). Selective reporting had the lowest proportion of low ROB (4.7%). However, the ROB improved year by year (P < 0.001 for trend). Authors and reviewers should consider allocation concealment after random sequence generation, blinding of participants and personnel, and full reporting of results to improve the quality of RCTs submitted hereafter for publication in the KJA.

Considerations when calculating the sample size for an inequality test

Click here for Korean Translation. Calculating the sample size is a vital step during the planning of a study in order to ensure the desired power for detecting clinically meaningful differences. However, estimating the sample size is not always straightforward. A number of key components should be considered to calculate a suitable sample size. In this paper, general considerations for conducting sample size calculations for inequality tests are summarized.

Statistical review of 95 studies employing repeated-measures analysis of variance published in the Korean Journal of Anesthesiology.

Repeated-measures analysis of variance (RMANOVA) continues to be used widely for the analysis of repeated-measures data in anesthetic research. RMANOVA, like other parametric statistical tests, specifies several assumptions and requires specific description in publications. A recent article in the Korean Journal of Anesthesiology (KJA) provided valid information on RMANOVA [1].

Emergence cerebral oxygen desaturation without hemodynamic compromise in pediatric patients

Background The regional cerebral oxygen saturation (rSO2) decreases significantly during ordinary anesthetic recovery in pediatric patients anesthetized with sevoflurane or desflurane. The present study examined the relationship between rSO2 and the clinical parameters associated with the degree of anesthetic recovery. Methods Twelve pediatric patients with American Society of Anesthesiologists physical status 1 were assigned randomly to receive anesthesia with sevoflurane or desflurane. All children underwent general anesthesia for minor surgery. After surgery, the rSO2, the age-adjusted MAC fraction of anesthetic concentration (FE), and the bispectral index (BIS) were recorded over a 10-minute period. The correlations between rSO2 and candidate predictors, such as FE, BIS, anesthetic, and duration of anesthesia, were analyzed. Results All children recovered uneventfully. The lowest observed rSO2 reached 63% and the maximum decrease in rSO2 was 24%. The mean blood pressure and heart rate were maintained within clinical ranges. The decrease in rSO2 correlated positively with the FE (r = 0.25, P = 0.00) and the duration of anesthesia (r = 0.24, P = 0.01), and inversely with the use of sevoflurane (r = -0.30, P = 0.00). Conclusions Despite normal parameters, cerebral desaturation occurred during the emergence of ordinary general anesthesia even without hemodynamic compromise or arterial desaturation. Cerebral desaturation might be associated with the degree of anesthetic recovery and the use of sevoflurane.

Hypocapnia Attenuates, and Nitrous Oxide Disturbs the Cerebral Oximetric Response to the Rapid Introduction of Desflurane

The aim of this study was to develop a nonlinear mixed-effects model for the increase in cerebral oximetry (rSO2) during the rapid introduction of desflurane, and to determine the effect of hypocapnia and N2O on the model. Twelve American Society of Anesthesiologist physical status class 1 and 2 subjects were allocated randomly into an Air and N2O group. After inducing anesthesia, desflurane was then increased abruptly from 4.0 to 12.0%. The PETCO2, PETDESF and rSO2 were recorded at 12 predetermined periods for the following 10 min. The maximum increase in rSO2 reached +24-25% during normocapnia. The increase in rSO2 could be fitted to a four parameter logistic equation as a function of the logarithm of PETDESF. Hypocapnia reduced the maximum response of rSO2, shifted the EC50 to the right, and increased the slope in the Air group. N2O shifted the EC50 to the right, and reduced the slope leaving the maximum rSO2 unchanged. The N2O-effects disappeared during hypocapnia. The cerebrovascular reactivity of rSO2 to CO2 is still preserved during the rapid introduction of desflurane. N2O slows the response of rSO2. Hypocapnia overwhelms all the effects of N2O.

Survival analysis: Part I — analysis of time-to-event

Length of time is a variable often encountered during data analysis. Survival analysis provides simple, intuitive results concerning time-to-event for events of interest, which are not confined to death. This review introduces methods of analyzing time-to-event. The Kaplan-Meier survival analysis, log-rank test, and Cox proportional hazards regression modeling method are described with examples of hypothetical data.