Christian Morgenstern

Evaluation of a Noninvasive Algorithm for Differentiation of Obstructive and Central Hypopneas

STUDY OBJECTIVES The clear discrimination of central and obstructive hypopneas is highly relevant to avoid misinterpretation and inappropriate treatment of complicated breathing patterns. Esophageal manometry is the accepted standard for the differentiation of the phenotypes of sleep apnea. However, it is limited in its use due to poor acceptance by patients and therefore rarely performed in routine clinical practice. Flattening of the inspiratory airflow curve, paradoxical breathing, arousal position, sleep stages, and breathing pattern at the end of the hypopnea can each give hints for the classification of hypopnea. The aim of this study was to evaluate a standardized algorithm combining these polysomnographic parameters for the discrimination of hypopneas in everyday practice. METHODS Polysomnography (PSG) and esophageal manometry were performed in 41 patients suspected of having sleep apnea (33 male, 52.3 ± 15.9 yr, body mass index 28.6 ± 4.5 kg/m(2)). Hypopneas were independently discriminated by blinded investigators based on esophageal pressure and the PSG-based algorithm. Only those hypopneas that could be differentiated with both methods were evaluated. RESULTS There were 1,175 of 1,837 hypopneas (64%) that could be defined by esophageal pressure, 1,812 (98.6%) by the PSG-based algorithm. Using esophageal pressure as a reference, the new algorithm correctly defined 76.9% of central and 60.5% of obstructive hypopneas. The overall accuracy was 68%. The isolated analysis of single PSG parameters revealed a lower accuracy compared with the combined algorithm. CONCLUSIONS The PSG-based algorithm allows for discrimination of most hypopneas. It is advantageous in comparison with esophageal pressure because it is noninvasive and less impaired by artefacts. Therefore, it is a potentially helpful tool for sleep specialists. CITATION Randerath WJ; Treml M; Priegnitz C; Stieglitz S; Hagmeyer L; Morgenstern C. Evaluation of a noninvasive algorithm for differentiation of obstructive and central hypopneas. SLEEP 2013;36(3):363-368.

Assessment of Changes in Upper Airway Obstruction by Automatic Identification of Inspiratory Flow Limitation During Sleep

New techniques for automatic invasive and noninvasive identification of inspiratory flow limitation (IFL) are presented. Data were collected from 11 patients with full nocturnal polysomnography and gold-standard esophageal pressure (Pes) measurement. A total of 38,782 breaths were extracted and automatically analyzed. An exponential model is proposed to reproduce the relationship between Pes and airflow of an inspiration and achieve an objective assessment of changes in upper airway obstruction. The characterization performance of the model is appraised with three evaluation parameters: mean-squared error when estimating resistance at peak pressure, coefficient of determination, and assessment of IFL episodes. The models results are compared to the two best-performing models in the literature. The obtained gold-standard IFL annotations were then employed to train, test, and validate a new noninvasive automatic IFL classification system. Discriminant analysis, support vector machines, and Adaboost algorithms were employed to objectively classify breaths noninvasively with features extracted from the time and frequency domains of the breathspsila flow patterns. The results indicated that the exponential model characterizes IFL and subtle relative changes in upper airway obstruction with the highest accuracy and objectivity. The new noninvasive automatic classification system also succeeded in identifying IFL episodes, achieving a sensitivity of 0.87 and a specificity of 0.85.

An Invasive and a Noninvasive Approach for the Automatic Differentiation of Obstructive and Central Hypopneas

The automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifier with nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p <; 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.

The learning curve in foraminal endoscopic discectomy: experience needed to achieve a 90% success rate.

Background We sought to construct a general methodology for objectively quantifying the learning curve associated with any surgical technique and to determine the number of cases needed to achieve a success rate of 90% for the technique of transforaminal endoscopic lumbar discectomy. To our knowledge, no other studies have observed the learning curve of endoscopic lumbar discectomy by transforaminal approach. Methods We studied the learning curve of 1 orthopedic surgeon who had had experience performing open spine surgery and knee and shoulder arthroscopic surgery, but not endoscopic spine surgery. We studied 144 patients who had an endoscopic lumbar discectomy by transforaminal approach (using the Yeung Endoscopic Surgery System). We evaluated results with modified MacNab criteria and used a questionnaire to determine the patients’ satisfaction with the surgery. The average follow-up period was 24 months. We used an algorithm, analyzing the patient outcome and the surgical time evolution, to determine the case at which a success rate of 90% good/excellent results was reached. Results The cut for the calculated learning curve was placed at case no. 72; i.e., the results in the first 72 cases were 75% good/excellent, 18% fair, and 7% poor, and the results in the following 72 cases were 90.3% good/excellent, 9.7% fair, and 0% poor. Conclusions A methodology to calculate the learning curve of a surgical procedure was developed. A learning curve of 72 cases was needed to reach the goal of 90% of good/excellent results for transforaminal endoscopic lumbar discectomy. Clinical Relevance The method developed to establish the learning curve of a surgical procedure, based on outcome and surgical time, may be used to assess any new procedure. With respect to the transforaminal endoscopic technique, the determination of a specific number of cases (72) needed to master (achieve 90% excellent/ good results) could help orient surgeons willing to adopt this technique.

Usefulness of an expandable interbody spacer for the treatment of foraminal stenosis in extremely collapsed disks: preliminary clinical experience with endoscopic posterolateral transforaminal approach.

Study Design Clinical series of patients with degenerative disk disease undergoing an endoscopic posterolateral transforaminal procedure that used a reaming foraminoplasty technique to enlarge the foramen coupled with insertion of the B-Twin expandable spacer. Objectives This retrospective analysis of 107 consecutive patients sought to assess the outcome of this surgical procedure. Summary of Background Data Reamed endoscopic foraminoplasty under direct endoscopic vision has been shown to be suitable for extremely collapsed disks (>50% total disk height) despite the difficult access, especially at L5-S1. The authors tried to investigate the efficacy of an expandable spacer being inserted by the endoscopic transforaminal approach to solve foraminal stenosis without bone fusion techniques. Methods The procedure consists of bone reaming under direct endoscopic control to wide the foramen followed by insertion of the B-Twin expandable device as a disk spacer to restore partially or to maintain the height of the collapsed disk. Outcome measures included visual analog scale (VAS) for pain, the Oswestry Disability Index (ODI) for functional disability, and radioimaging studies. Results Mean follow-up was 27.2 months. Clinical outcome was considered excellent in 64 patients, good in 25, fair in 10, and poor in 8. Results were similar in single and double B-Twin spacer insertions. Postoperative mean values for VAS and ODI scores improved significantly as compared with preoperative data. Mean VAS and ODI scores were significantly higher in patients with fair or poor results than in those with excellent or good outcome. In 2 cases, clear signs of end plate bone resorption in the control computed tomographic scans at 6 months and 12 months leading to a substantial loss of disk height were documented. Conclusions This preliminary study has shown the efficacy of an endoscopic surgical technique for the treatment of foraminal stenosis in extremely collapsed disks.

Characterization of inspiratory flow limitation during sleep with an exponential model

Assessing incidence and severity of inspiratory flow limitation (IFL) is of importance for patients suffering of sleep disordered breathing (SDB) in order to diagnose a spectrum of different pathologies. In this study a new exponential equation is proposed to characterize the pressure/flow relationship of IFL and non-IFL breaths. Classical and alternative criteria are applied on the models predictions in order to assess IFL, and its outcome is compared to the outcome of other models. The newly proposed exponential model seems to be promising, as it outperforms other models by achieving a global average sensitivity of 93% and specificity of 91%, and the lowest mean square error when estimating resistance at peak pressure. Additional statistical tests were performed on the exponential models coefficients in order to determine if a coefficient based classification is possible.

Comparison of upper airway respiratory resistance measurements with the esophageal pressure/airflow relationship during sleep

Measurement of upper airway resistance is of interest in sleep disordered breathing to estimate upper airway patency. Resistance is calculated with the airflow and respiratory effort signals. However, there is no consensus on a standard for upper airway resistance measurement. This study proposes a new benchmarking method to objectively compare different upper airway resistance measurement methods by objectively differentiating between breaths with inspiratory flow limitation (high resistance) and non-limited breaths (low resistance). Resistance was measured at peak-Pes, at peak-flow, at the linear portion of a polynomial equation, as an area comparative and as average resistance for an inspiration. A total of 20 patients with systematic, gold-standard esophageal pressure and nasal airflow acquisition were analyzed and 109,955 breaths were automatically extracted and evaluated. Relative resistance values in relationship to a reference resistance value obtained during wakefulness were also analyzed. The peak-Pes measurement method obtained the highest separation index with significant (p < 0.001) differences to the other methods, followed by the area comparative and the peak-flow methods. As expected, average resistances were significantly (p < 0.001) lower for the non-IFL than for the IFL group. Hence, we recommend employing the peak-Pes for accurate upper airway resistance estimation.

Automatic differentiation of obstructive and central hypopneas with esophageal pressure measurement during sleep

The differentiation between obstructive and central respiratory events is one of the most recurrent tasks in the diagnosis of sleep disordered breathing. Esophageal pressure measurement is the gold-standard method to assess respiratory effort and identify these events. But as its invasiveness discourages its use in clinical routine, non-invasisve systems have been proposed for differentiation. However, their adoption has been slow due to their limited clinical validation, as the creation of manual, gold-standard validation sets by human experts is a cumbersome procedure. In this study, a new system is proposed for an objective automatic, gold-standard differentiation between obstructive and central hypopneas with the esophageal pressure signal. First, an overall of 356 hypopneas of 16 patients were manually scored by a human expert to create a gold-standard validation set. Then, features were extracted from each hypopnea to train and test classifiers (Discriminant Analysis, Support Vector Machines and adaboost classifiers) to differentiate between central and obstructive hypopneas with the gold-standard esophageal pressure signal. The automatic differentiation system achieved promising results, with a sensitivity of 0.88, a specificity of 0.93 and an accuracy of 0.90. Hence, this system seems promising for an automatic, gold-standard differentiation between obstructive and central hypopneas.

Automatic non-invasive differentiation of obstructive and central hypopneas with nasal airflow compared to esophageal pressure

The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been shown with invasive techniques to be useful to differentiate between central and obstructive hypopneas. In this study we present a new method for the automatic non-invasive differentiation of obstructive and central hypopneas solely with nasal airflow. An overall of 36 patients underwent full night polysomnography with systematic Pes recording and a total of 1069 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the nasal airflow signal to train and test our automatic classifier (Discriminant Analysis). Flattening patterns were non-invasively assessed in the airflow signal using spectral and time analysis. The automatic non-invasive classifier obtained a sensitivity of 0.71 and an accuracy of 0.69, similar to the results obtained with a manual non-invasive classification algorithm. Hence, flattening airflow patterns seem promising for the non-invasive differentiation of obstructive and central hypopneas.