UK student midwives' theoretical knowledge, confidence, and experience of intermittent auscultation of the fetal heart rate during labour: An online cross-sectional survey.

AIM: This study aimed to explore student midwives' theoretical knowledge of intrapartum intermittent auscultation, their confidence in, and their experience of this mode of fetal monitoring. DESIGN AND SETTING: An online cross-section survey with closed and open questions. Descriptive statistics were used to analyse participants' intermittent auscultation knowledge, confidence, and experience. Reflexive thematic analysis was used to identify patterns within the free text about participants' experiences. PARTICIPANTS: Undergraduate midwifery students (n = 303) from Nursing and Midwifery Council-approved educational institutions within the United Kingdom. FINDINGS: Most participants demonstrated good theoretical knowledge. They had witnessed the technique being used in clinical practice, and when performed, the practice was reported to be in line with national guidance. In closed questions, participants reported feeling confident in their intermittent auscultation skills; however, these data contrasted with free-text responses. CONCLUSION: This cross-sectional survey found that student midwives possess adequate knowledge of intermittent auscultation. However, reflecting individual clinical experiences, their confidence in their ability to perform intermittent auscultation varied. A lack of opportunity to practice intermittent auscultation, organisational culture, and midwives' preferences have caused student midwives to question their capabilities with this essential clinical skill, leaving some with doubt about their competency close to registration.

Mucoepidermoid carcinoma of the bronchus: a rare early diagnosis.

Salivary gland tumours of the tracheobronchial tree are rare and early diagnosis in T1 stage is further rare. We report a case of a young 21-year-old male medical student diagnosed and treated for the same prompted by a detailed respiratory examination.

Accuracy and inter-rater reliability of lung auscultation by bovine practitioners when compared with ultrasonographic findings.

In practice, veterinary surgeons frequently rely on lung auscultation as a confirmation test for pneumonia. To what extent diagnostic accuracy of lung auscultation varies between different practitioners is currently unknown. In this diagnostic test study, 49 Dutch veterinarians each auscultated between 8 and 10 calves, and communicated whether they would decide to treat the animal with antimicrobials or not. They were not allowed to perform any other aspect of the clinical examination. Their decisions were compared with lung ultrasonography findings. The average sensitivity and specificity of lung auscultation were 0.63 (sd=0.2; range=0.2-1.0) and 0.46 (sd=0.3; range=0.0-1.0), respectively. Of the participants, 8.2 per cent were 100 per cent sensitive, 16.3 per cent were 100 per cent specific, and only 4.0 per cent were perfect. The Krippendorff's alpha was 0.18 (95 per cent confidence interval: -0.01 to 0.38), signifying poor reliability between multiple raters. Regardless of the poor diagnostic accuracy in this study, especially the large variation in a confirmation test between different practitioners could potentially cause professional damage as well as misuse of antimicrobials. This study could be seen as a gentle stimulus to regularly evaluate one's diagnostic skills. Both complementary training and the use of more accurate techniques with less inter-rater variation could improve the situation.

Comparison of infant heart rate assessment by auscultation, ECG and oximetry in the delivery room.

Clinical assessment of an infant's heart rate (HR) in the delivery room (DR) has been reported to be inaccurate. We compared auscultation of the HR using a stethoscope with electrocardiography (ECG) and pulse oximetry (PO) for determining the HR in 92 low-risk newborn infants in the DR. Caregivers auscultated the HR while masked to the HR on the monitor. Auscultation underestimated ECG HR (mean difference (95% CI) by -9 (-15 to -2) beats per minute (bpm)) and PO HR (mean difference (95% CI) by -5 (-12 to 2) bpm). The median (IQR) time to HR by auscultation was 14 (10-18) s. As HR was determined quickly and with reasonable accuracy by auscultation in low-risk newborns, study in high-risk infants is warranted.

[Establishment of prediction model of acute gastrointestinal injury classification of critically ill patients based on digital gastrointestinal sounds monitoring].

OBJECTIVE: To develop the prediction model of acute gastrointestinal injury (AGI) classification of critically ill patients. METHODS: The binary channel gastrointestinal sounds (GIS) monitor system was used to gather and analyze the GIS of 60 consecutive critically ill patients who were admitted in Critical Care Medicine of PLA General Hospital from April 2015 to November 2015 (patients with chronic gastrointestinal disease or history of gastrointestinal surgery were excluded). Meanwhile, the AGI grades were evaluated according to the ESICM guidelines of AGI grading system. Correlations between GIS and AGI classification were examined with Spearman rank correlation. Then principal component analysis was performed on the significantly correlated parameters after standardization. The top 3 post-normalized main components were selected for back-propagation (BP) neural network training to establish primary AGI grade model of critically ill patients based on the neural network model. RESULTS: A total of 1 132 GIS and 333 AGI were collected from 60 patients. The number (P = 0.0005), percentage of time (P = 0.0004), mean power (P = 0.0088), maximum power (P = 0.0101) and maximum time (P = 0.0025) of GIS wave from the channel located at the stomach were negatively correlated with the AGI grades, while the parameters of GIS wave from the channel located at the intestine had no significant correlation with the AGI grades(all P > 0.05). Three main components were selected after principal component analysis of these five correlated parameters. An AGI grade network model including 9 hide layers, with a fitting degree of 0.981 64 was built by BP artificial neural network based on the analysis of these three main components of GIS. The accuracy rate of the model to predict the AGI grade was 70.83%. CONCLUSION: The preliminary model based on GIS in classifying AGI grade is established successfully, which can help predict the classification of AGI grade of critically ill patients.

The consequences of using advanced physical assessment skills in medical and surgical nursing: A hermeneutic pragmatic study.

AIMS AND OBJECTIVES: The aim of this study was to explore the consequences of the nurse's use of advanced assessment skills on medical and surgical wards. BACKGROUND: Appropriate, accurate, and timely assessment by nurses is the cornerstone of maintaining patient safety in hospitals. The inclusion of "advanced" physical assessment skills such as auscultation, palpation, and percussion is thought to better prepare nurses for complex patient presentations within a wide range of clinical situations. DESIGN: This qualitative study used a hermeneutic pragmatic approach. METHOD: Unstructured interviews were conducted with five experienced medical and surgical nurses to obtain 13 detailed narratives of assessment practice. Narratives were analyzed using Van Manen's six-step approach to identify the consequences of the nurse's use of advanced assessment skills. RESULTS: The consequences of using advanced assessment skills include looking for more, challenging interpretations, and perseverance. The use of advanced assessment skills directs what the nurse looks for, what she sees, interpretation of the findings, and her response. It is the interpretation of what is seen, heard, or felt within the full context of the patient situation, which is the advanced skill. CONCLUSION: Advanced assessment skill is the means to an accurate interpretation of the clinical situation and contributes to appropriate diagnosis and medical management in complex patient situations. RELEVANCE TO CLINICAL PRACTICE: The nurse's use of advanced assessment skills enables her to contribute to diagnostic reasoning within the acute medical and surgical setting.

Inter-Rater Agreement of Auscultation, Palpable Fremitus, and Ventilator Waveform Sawtooth Patterns Between Clinicians.

BACKGROUND: Clinicians often use numerous bedside assessments for secretion retention in participants who are receiving invasive mechanical ventilation. This study aimed to evaluate inter-rater agreement between clinicians when using standard clinical assessments of secretion retention and whether differences in clinician experience influenced inter-rater agreement. METHODS: Seventy-one mechanically ventilated participants were assessed by a research clinician and by one of 13 ICU clinicians. Each clinician conducted a standardized assessment of lung auscultation, palpation for chest-wall (rhonchal) fremitus, and ventilator inspiratory/expiratory flow-time waveforms for the sawtooth pattern. RESULTS: On the presence of breath sounds, agreement ranged from absolute to moderate in the upper zones and the lower zones, respectively. Kappa values for abnormal and adventitious lung sounds achieved moderate agreement in the upper zones, less than chance agreement to substantial agreement in the middle zones, and moderate agreement to almost perfect agreement in the lower zones. Moderate to almost perfect agreement was established for palpable fremitus in the upper zones, moderate to substantial agreement in the middle zones, and less than chance to moderate agreement in the lower zones. Inter-rater agreement on the presence of expiratory sawtooth pattern identification showed moderate agreement. The level of percentage agreement between the research and ICU clinicians for each respiratory assessment studied did not relate directly to level of clinical experience. CONCLUSIONS: Inter-rater agreement for all assessments showed variability between lung regions but maintained reasonable percentage agreement in mechanically ventilated participants. The level of percentage agreement achieved between clinicians did not directly relate to clinical experience for all respiratory assessments. Therefore, these respiratory assessments should not necessarily be viewed in isolation but interpreted within the context of a full clinical assessment.

Analysis of bowel sounds application status for gastrointestinal function monitoring in the intensive care unit.

The auscultation of bowel sounds (BS) has been neglected as a reliable tool for determining gastrointestinal (GI) functioning in the critically ill patient. This article considers the validity of BS auscultation in the assessment and management of critically ill patients and outlines how the information may be utilized for monitoring GI function. A descriptive, cross-sectional design with self-administered questionnaires was used to survey 132 nurses and 38 doctors in the 6 general intensive care units in Beijing hospitals. Descriptive statistics and chi-square test analyses were used to assess the level of knowledge about BS among Chinese doctors and nurses and to determine how they applied their BS auscultation findings in the care of critically ill patients. Bowel sounds were found to be the primary indicator for determining GI functioning in the unconscious, critically ill patient. However, only 11.4% of nurses and 47.6% of doctors could make correct clinical judgments on the basis of their auscultatory findings. The attitudes of nurses and doctors differed significantly on whether BS auscultation was needed to monitor GI function for unconscious patients. Bowel sounds auscultation remains an important indicator of GI function. Distinct and feasible standards regarding BS auscultation and results interpretation need to be established.

Management of COPD exacerbations in primary care: a clinical cohort study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) exacerbations are associated with lung function decline, lower quality of life, and increased mortality, and can be prevented by pharmacological treatment and rehabilitation. AIMS: To examine management including examination, treatment, and planned follow-up of COPD exacerbation visits in primary care patients and to explore how measures and management at exacerbation visits are related to subsequent exacerbation risk. METHODS: A clinical population of 775 COPD patients was randomly selected from 56 Swedish primary healthcare centres. Data on patient characteristics and management of COPD exacerbations were obtained from medical record review and a patient questionnaire. In the study population of 458 patients with at least one exacerbation, Cox regression analyses estimated the risk of a subsequent exacerbation with adjustment for age and sex. RESULTS: During a follow-up period of 22 months, 238 patients (52%) had a second exacerbation. A considerable proportion of the patients were not examined and treated as recommended by guidelines. Patients with a scheduled extra visit to an asthma/COPD nurse following an exacerbation had a decreased risk of further exacerbations compared with patients with no extra follow-up other than regularly scheduled visits (adjusted hazard ratio 0.60 (95% confidence interval 0.37 to 0.99), p=0.045). CONCLUSIONS: Guidelines for examination and emergency treatment at COPD exacerbation visits are not well implemented. Scheduling an extra visit to an asthma/COPD nurse following a COPD exacerbation may be associated with a decreased risk of further exacerbations in primary care patients.

Blind insertion of feeding tubes in intensive care units: a national survey.

BACKGROUND: Although most critically ill patients experience at least 1 blind insertion of a feeding tube during their stay in an intensive care unit, little is known about the types of health care personnel who perform these insertions or about methods used to determine proper positioning of the tubes. OBJECTIVES: To describe results from a national survey of critical care nurses about feeding tube practices in their adult intensive care units. The questions asked included who performs blind insertions of feeding tubes and what methods are used to determine if the tubes are properly positioned. METHODS: Data were collected from members of the American Association of Critical-Care Nurses via pencil-and-paper and online surveys. Results from both forms were combined for data analysis and were compared with practice recommendations of national-level organizations. RESULTS: A total of 2298 responses were obtained. Physicians perform more blind insertions of styleted feeding tubes than do nurses; in contrast, nurses place more nonstyleted tubes. Radiographic confirmation of correct position is mandated more often for blindly inserted styleted tubes (92.3%) than for nonstyleted tubes (57.5%). The 3 most commonly used bedside methods to determine tube location are auscultation for air injected via the tube, appearance of feeding tube aspirate, and observation for indications of respiratory distress. CONCLUSIONS: Recommendations from multiple national-level organizations to obtain radiographic confirmation that each blindly inserted feeding tube is correctly positioned before the first use of the tube are not adequately implemented. Auscultation is widely used despite recommendations to the contrary.

Assessment of ICA algorithms for the analysis of crackles sounds.

Blind source separation by independent component analysis has been applied extensively in the biomedical field for extracting different contributing sources in a signal. Regarding lung sounds analysis to isolate the adventitious sounds from normal breathing sound is relevant. In this work the performance of FastICA, Infomax, JADE and TDSEP algorithms was assessed using different scenarios including simulated fine and coarse crackles embedded in recorded normal breathing sounds. Our results pointed out that Infomax obtained the minimum Amari index (0.10037) and the maximum signal to interference ratio (1.4578e+009). Afterwards, Infomax was applied to 25 channels of recorded normal breathing sound where simulated fine and coarse crackles were added including acoustic propagation effects. A robust blind crackle separation could improve previous results in generating an adventitious acoustic thoracic imaging.

An ultra-sensitive wearable accelerometer for continuous heart and lung sound monitoring.

This paper presents a chest-worn accelerometer with high sensitivity for continuous cardio-respiratory sound monitoring. The accelerometer is based on an asymmetrical gapped cantilever which is composed of a bottom mechanical layer and a top piezoelectric layer separated by a gap. This novel structure helps to increase the sensitivity by orders of magnitude compared with conventional cantilever based accelerometers. The prototype with a resonant frequency of 1100Hz and a total weight of 5 gram is designed, constructed and characterized. The size of the prototype sensor is 35mm×18mm×7.8mm (l×w×t). A built-in charge amplifier is used to amplify the output voltage of the sensor. A sensitivity of 86V/g and a noise floor of 40ng/√Hz are obtained. Preliminary tests for recording both cardiac and respiratory signals are carried out on human body and the new sensor exhibits better performance compared with a high-end electronic stethoscope.

Medication administration via enteral tubes: a survey of nurses' practices.

AIM: This article is a report of a study examining the practices of acute care nurses when administering medication via enteral tubes. BACKGROUND: Administering medication via enteral tubes is predominantly a nursing responsibility across countries. It is important to establish what nurses actually do when giving enteral medication to inform policy and continuing education development. METHOD: In 2007, a survey was conducted using a random sample of acute care nurses at two large metropolitan hospitals in Melbourne, Australia. There were 181 Registered Nurses who participated in the study; 92 (50.8%) practised in intensive care units, 52 (28.7%) in surgical areas, 30 (16.6%) in medical areas and 7 (3.9%) were from combined medical-surgical areas. The questionnaire was developed by the researchers and a pilot study was conducted in August 2006 to test reliability, face validity and user-friendliness of the tool. RESULTS: Nurses reported using a range of methods to verify enteral tube position prior to administering enteral medication; some were unreliable methods. A majority reported administering enteric-coated and slow or extended release forms of medication, and giving solid forms of medication when liquid form was available. Nearly all (96%) reported flushing a tube after giving medication, 28% before, and 12% always flushed between each medication. CONCLUSION: Enteral medication administration practices are inconsistent. Some nurses are using unsafe practices and may therefore compromise patient care.

Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes.

In this paper, we present the pattern recognition methods proposed to classify respiratory sounds into normal and wheeze classes. We evaluate and compare the feature extraction techniques based on Fourier transform, linear predictive coding, wavelet transform and Mel-frequency cepstral coefficients (MFCC) in combination with the classification methods based on vector quantization, Gaussian mixture models (GMM) and artificial neural networks, using receiver operating characteristic curves. We propose the use of an optimized threshold to discriminate the wheezing class from the normal one. Also, post-processing filter is employed to considerably improve the classification accuracy. Experimental results show that our approach based on MFCC coefficients combined to GMM is well adapted to classify respiratory sounds in normal and wheeze classes. McNemar's test demonstrated significant difference between results obtained by the presented classifiers (p<0.05).

A filter bank-based source extraction algorithm for heart sound removal in respiratory sounds.

In this paper, we consider the problem of heart sounds (HS) removal from respiratory sounds (RS), and a novel semi-blind single-channel source extraction algorithm is proposed. The proposed method is able to extract the underlying pure RS from the HS corrupted noisy input signals by incorporating the filter banks and template-based matching using FIR filters. For performance evaluation of the presented method, the average power spectral densities (PSD) of the input RS segments without HS have been compared with the PSD of the reconstructed signals over six selected frequency bands from 20 to 800Hz. The proposed method is tested for various types of RS recordings and found effective by yielding an overall maximum spectral difference of 2.8707+/-0.9875dB for a frequency range below 800Hz.

Modulation filtering for heart and lung sound separation from breath sound recordings.

Separation of heart and lung sounds from breath sound recordings is a challenging task due to the temporal and spectral overlap of the two signals. In this paper, the use of a spectro-temporal representation to improve signal separation is investigated. The representation is obtained by means of a frequency decomposition (termed modulation frequency) of temporal trajectories of short-term spectral components. Experiments described herein suggest that improved separability of heart (HS) and lung sounds (LS) is attained in the modulation frequency domain. Bandpass and bandstop modulation filters are designed to separate HS and LS signals from breath sound recordings, respectively. Visual and auditory inspection, quantitative analysis, as well as algorithm execution time are used to assess algorithm performance. Log-spectral distances below 1 dB corroborate our listening test which found no audible artifacts in separated heart and lung sound signals.

Crackles detection using a time-variant autoregressive model.

Several techniques have been explored to detect automatically fine and coarse crackles; however, the solution for automatic detection of crackles remains insufficient. The purpose of this work was to explore the capacity of the time-variant autoregressive (TVAR) model to detect and to provide an estimate number of fine and coarse crackles in lung sounds. Thus, simulated crackles inserted in normal lung sounds and real lung sounds containing adventitious sounds were processed with TVAR and by an expert that based crackle detection on time-expanded waveform-analysis. The coefficients of the TVAR were obtained by an adaptive filtering prediction scheme. The adaptive filter used the recursive least squares algorithm with a forgetting factor of 0.97 and the model order was four. TVAR model showed an efficiency to detect crackles over 90% even with crackles overlapping and amplitudes as low as 1.5 of the standard deviation of background lung sounds, where expert presented an efficiency around 30%. In conclusion, TVAR model is a proper alternative to detect and to provide an estimate number of fine and coarse crackles, even in presence of crackles overlapping and crackles with low amplitude, conditions where crackles detection based on time-expanded waveform-analysis reveals evident limitations.

Analysis of discontinuous adventitious lung sounds by Hilbert-Huang spectrum.

It is now widely accepted that crackles are associated with different pulmonary pathologies and different efforts have been done to detect and to extract them. Consequently, due to the difficulty for their characterization, the selection of an adequate time-frequency representation (TFR) for the analysis of their time-frequency dynamics is important. Traditionally, normal and abnormal lung sounds have been analyzed by the Spectrogram (SP). However, this analysis tool has certain disadvantages when one deals with nonstationary signals. As an effort to point out the appropriate analysis tool for crackles, this paper shows the performance of the Hilbert-Huang spectrum (HHS) for the analysis of fine and coarse crackles, simulated and real ones. The HHS allowed to analyze the evolving time-frequency of crackle sounds straightforward with good resolution compared with SP. Beside this enhanced time-frequency course, HHS could be useful to establish a signature to detect and separate fine from coarse crackles in order to screen pathologies and their progress during medication.

Elimination of vesicular sounds from pulmonary crackle waveforms.

Pulmonary crackles and their parameters are very useful in the diagnosis of pulmonary disorders. A new automatic method has been proposed for the elimination of background vesicular sound from crackle signal with a view to introduce minimum distortion to crackle parameters. A region of interest is designated and a distortion metric based on the correlation between raw and filtered waveforms in that region is defined. Filter cut-off frequency is estimated based on the distortion metric. To reduce computational cost, a regression analysis is also realized which predicts a new fitted cut-off frequency from the estimated cut-off frequency. As a comparison basis, wavelet filtering is also applied on the same data. The algorithm is validated on simulated crackles superimposed on recorded vesicular sound with results indicating that filtering is achieved with minimal distortion of crackle parameters. The algorithm is also applied on real crackles from subjects with various respiratory disorders. The results show the extent of the effect of vesicular sound on crackle parameters, emphasizing the significance of proper filtering in crackle studies.

Neural classification of lung sounds using wavelet coefficients.

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.