Preoperative preparation and postoperative care in children in thoracic surgery.

Anesthesia for pediatric patients undergoing thoracic surgery continues to be distinctive due to differing anatomical and physiological characteristics compared to adults. Adequate preoperative preparation, appropriate tool selection for providing one-lung ventilation, perioperative pain management, and a multidisciplinary approach can ensure higher quality postoperative care. In this review, the perioperative anesthesia management for pediatric patients undergoing thoracic surgery will be discussed, starting from the preoperative preparation phase. Additionally, the issues related to the application and management of one-lung ventilation will also be assessed.

The effect of body mass index on thoracic paravertebral block analgesia after video-assisted thoracoscopic surgery; a prospective interventional study.

BACKGROUND: To investigate the effects of body mass index (BMI) on intensity postoperative pain in patients who underwent thoracic paravertebral block (TPVB) for postoperative analgesia after video-assissted thoracoscopic surgery (VATS). METHODS: Patients aged 18-80 years, ASA I-III, and BMI 18-40 kg/m2 who underwent elective VATS were included in the study. The patients were divided into 3 groups according to their BMI levels. TPVB was performed under ultrasound-guidance at the fifth thoracic vertebrae, and 30 ml of 0.25% bupivacaine was injected. The patient-controlled analgesia (PCA) was performed by using morphine and multimodal analgesia was performed. As a rescue analgesic agent, 0.5 mg/kg tramadol was given to patients intravenously when a score of visual analog scale (VAS) at rest was ≥ 4. The primary outcome was determined as VAS scores at rest and cough. Secondary outcomes were determined as postoperative morphine consumption, additional analgesic requirement, and side effects. RESULTS: The post-hoc test revealed that the VAS resting scores at the 4th hour (p: 0.007), 12th hour (p: 0.014), and 48th hour (p: 0.002) were statistically significantly lower in Group I compared to Group II. Additionally, VAS resting scores were also statistically significantly lower in Group I compared to Group III at all time points (p < 0.05). Similarly, the post-hoc test indicated that the VAS coughing scores at the 4th hour (p: 0.023), 12th hour (p: 0.011), and 48th hour (p: 0.019) were statistically significantly lower in Group I compared to Group II. Moreover, VAS coughing scores were statistically significantly lower in Group I compared to Group III at all time points (p < 0.001). Furthermore, there were statistically significant differences in terms of additional analgesic use between the groups (p: 0.001). Additionally, there was a statistically significant difference in terms of morphine consumption via PCA and morphine milligram equivalent consumption between the groups (p < 0.001). CONCLUSIONS: Higher postoperative VAS scores with TPVB applied in obese patients and the consequent increase in additional analgesics and complications require more specific postoperative management in this patient group.

Comparison of the effects of one-level and bi-level pre-incisional erector spinae plane block on postoperative acute pain in video-assisted thoracoscopic surgery; a prospective, randomized, double-blind trial.

BACKGROUND: This prospective, randomized, double-blind trial aimed to compare the postoperative analgesic efficacy of One-Level pre-incisional erector spinae plane block (ESPB) and Bi-Level pre-incisional ESPB in patients undergoing video-assisted thoracic surgery (VATS). METHODS: This pilot trial was conducted between April 2022 and February 2023 with sixty patients. The patients were randomly divided into two groups. In One-Level ESPB Group (n = 30) block was performed at the thoracal(T)5 level with the 30 ml 0.25% bupivacaine. In the Bi-Level ESPB Group (n = 30) block was performed at T4 and T6 levels by using 15 ml of 0.25% bupivacaine for each level. In the postoperative period, 50 mg dexketoprofen every 12 h and 1 g paracetamol every 8 h were given intravenously (IV). Patient-controlled analgesia (PCA) prepared with morphine was applied to the patients. 0.5 mg/kg of tramadol was administered via IV for rescue analgesia. Visual analog scale (VAS) scores were recorded in the postoperative 1st, 2nd, 4th, 12th, 24th, and 48th -hours. The need for additional analgesics and side effects were recorded. In two groups, patients' demographics and postoperative hemodynamic data were recorded. RESULTS: VAS scores at resting were statistically significantly higher at the 1st (p: 0.002) and 4th -hour (p: 0.001) in the One-Level ESPB. When the groups were evaluated in terms of VAS coughing scores, the 4th -hour (p: 0.001) VAS coughing scores results were found to be statistically significantly higher in the One-Level ESPB group. In terms of VAS values evaluated during follow-up, the rates of VAS coughing score > 3 values were found to be statistically significantly lower in the Bi-Level ESPB group (p: 0.011). There was no statistically significant difference between the groups in terms of side effects, morphine consumption, and additional analgesic use (p > 0.05). CONCLUSIONS: Adequate analgesia was achieved in the early postoperative period in the group treated with Bi-Level ESPB with similar morphine consumption and side effects. This may be an advantage, especially in the early postoperative period when the pain is quite intense.

Non-interventional Feasibility Assessment for Fast-Track Cardiac Anesthesia.

Background The introduction of fast-track extubation procedures following cardiac surgery has significantly shortened hospitalization duration in intensive care units (ICUs). Early extubation is the most crucial step in getting out of the ICU early and providing ideal patient circulation. In times of crisis such as pandemics, it is vital to provide rapid flow through the hospital to prevent the postponement or inability to operate on patients awaiting surgery. This study aimed to determine the obstacles to early extubation in patients undergoing cardiac surgery and the perioperative characteristics that were affected in terms of fast-track extubation. Methodology This was an observational, cross-sectional study with data collected prospectively from October 1 to November 30, 2021. Preoperative data and comorbidities were recorded. Intraoperative and postoperative data were recorded and analyzed. Intraoperative cross-clamp duration, cardiopulmonary bypass duration, length of operation, and erythrocytes (red blood cells) transfused were recorded for each patient. Early postoperative clinical conditions were defined in patients whose mechanical ventilation duration exceeded eight hours (such as pulmonary complications, cardiovascular complications, renal complications, neurological conditions, and infective complications ). The length of ICU stay (hours), length of hospital stay (days), return to the ICU, reasons for return to the ICU, and overall hospital mortality were investigated. A total of 226 patients were included in the study. Patients were divided into two groups: extubated within eight hours (FTCA, fast-track cardiac anesthesia) and late extubation (after eight hours) postoperatively, and the data were evaluated accordingly. Results While 138 (61.1%) of the patients were extubated in eight hours or less, 88 (38.9%) patients were extubated after more than eight hours. The most common complications (55.7%) in patients with late extubation were cardiovascular complications, followed by respiratory complications (15.9%), and the surgeon's refusal (15.9%). In the logistic model created with the independent variables affecting the extubation time, the American Society of Anesthesiologists score and red blood cell transfusion were risk factors for longer extubation time. Conclusions In our research to reveal the feasibility of and barriers to FTCA, it was found that cardiac and respiratory problems were the most common reasons for delayed extubation. Due to the refusal of the surgical team, it was observed that some patients remained intubated despite meeting the FTCA requirements. It was considered the most improvable obstacle. Regarding cardiovascular complications, the team should aim to optimally control patient comorbidities in the preoperative period, reduce the use of red blood cell transfusions, and ensure that the entire team is updated on current extubation protocols, in particular surgeons and anesthesiologists.

Extending exploratory diagnostic classification models: Inferring the effect of covariates.

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.

First-Order Learning Models With the GDINA: Estimation With the EM Algorithm and Applications.

In learning environments, understanding the longitudinal path of learning is one of the main goals. Cognitive diagnostic models (CDMs) for measurement combined with a transition model for mastery may be beneficial for providing fine-grained information about students' knowledge profiles over time. An efficient algorithm to estimate model parameters would augment the practicality of this combination. In this study, the Expectation-Maximization (EM) algorithm is presented for the estimation of student learning trajectories with the GDINA (generalized deterministic inputs, noisy, "and" gate) and some of its submodels for the measurement component, and a first-order Markov model for learning transitions is implemented. A simulation study is conducted to investigate the efficiency of the algorithm in estimation accuracy of student and model parameters under several factors-sample size, number of attributes, number of time points in a test, and complexity of the measurement model. Attribute- and vector-level agreement rates as well as the root mean square error rates of the model parameters are investigated. In addition, the computer run times for converging are recorded. The result shows that for a majority of the conditions, the accuracy rates of the parameters are quite promising in conjunction with relatively short computation times. Only for the conditions with relatively low sample sizes and high numbers of attributes, the computation time increases with a reduction parameter recovery rate. An application using spatial reasoning data is given. Based on the Bayesian information criterion (BIC), the model fit analysis shows that the DINA (deterministic inputs, noisy, "and" gate) model is preferable to the GDINA with these data.

Computerized Adaptive Testing for Cognitively Based Multiple-Choice Data.

Cognitive diagnosis models (CDMs) are latent class models that hold great promise for providing diagnostic information about student knowledge profiles. The increasing use of computers in classrooms enhances the advantages of CDMs for more efficient diagnostic testing by using adaptive algorithms, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT). When multiple-choice items are involved, CD-CAT can be further improved by using polytomous scoring (i.e., considering the specific options students choose), instead of dichotomous scoring (i.e., marking answers as either right or wrong). In this study, the authors propose and evaluate the performance of the Jensen-Shannon divergence (JSD) index as an item selection method for the multiple-choice deterministic inputs, noisy "and" gate (MC-DINA) model. Attribute classification accuracy and item usage are evaluated under different conditions of item quality and test termination rule. The proposed approach is compared with the random selection method and an approximate approach based on dichotomized responses. The results show that under the MC-DINA model, JSD improves the attribute classification accuracy significantly by considering the information from distractors, even with a very short test length. This result has important implications in practical classroom settings as it can allow for dramatically reduced testing times, thus resulting in more targeted learning opportunities.