Remifentanil as analgesia for labour pain

Aims: To collect updated information about pharmacological labour analgesia in Norway, especially systemic opioids and epidural. Evaluation of efficacy and safety with remifentanil IVPCA (intravenous patient-controlled analgesia) for pain relief during labour. To compare remifentanil IVPCAwith epidural analgesia (EDA) regarding efficacy and safety during labour. Methods: In paper I, two national surveys identified Norwegian labour analgesia methods and changes during the study period (2005-2008). Paper II is a prospective, observational study of analgesic efficacy and safety with remifentanil IVPCA. Paper III is a prospective, randomized controlled trial comparing remifentanil IVPCA with EDA regarding analgesic efficacy and safety. Results: The surveys in paper I found the frequency of EDA in Norwegian hospitals to be increasing, but still low (25.9%) compared to other western countries. Nitrous oxide and traditional systemic opioids, like pethidine, were frequently used. In paper II remifentanil IVPCA was found to give satisfactory labour analgesia in more than 90% of the parturients with an average maximal pain reduction of 60%. Maternal oxygen desaturation and sedation were acceptable, and neonatal data reassuring. In paper III, a randomized controlled trial found remifentanil IVPCA and EDA to be comparable both regarding analgesic efficacy (pain reduction) and maternal satisfaction. Remifentanil IVPCA produced more maternal sedation and oxygen desaturation, neonatal outcome was reassuring in both groups. Conclusions: The frequency of epidural labour analgesia in Norway has increased, but is still relatively low. Nitrous oxide and traditional systemic opioids are frequently used. The clinical practice seems conservative, newer short-acting opioids are seldom used for systemic labour analgesia. The studies on remifentanil IVPCA revealed adequate pain relief, high maternal satisfaction, and no serious neonatal side effects. There were no differences in analgesic efficacy, maternal satisfaction, and neonatal outcome when comparing remifentanil IVPCA with EDA. However, remifentanil caused maternal sedation and oxygen desaturation. We recommend the use of IVPCA remifentanil as labour analgesia instead of traditional opioids as pethidine and morphine when EDA is not an option. The presence of skilled personnel and close monitoring is mandatory

Combining Unsupervised, Supervised, and Rule-based Algorithms for Text Mining of Electronic Health Records - A Clinical Decision Support System for Identifying and Classifying Allergies of Concern for Anesthesia During Surgery

Undisclosed allergic reactions of patients are a major risk when undertaking surgeries in hospitals. We present our early experience and preliminary findings for a Clinical Decision Support System (CDSS) being developed in a Norwegian Hospital Trust. The system incorporates unsupervised and supervised machine learning algorithms in combination with rule-based algorithms to identify and classify allergies of concern for anesthesia during surgery. Our approach is novel in that it utilizes unsupervised machine learning to analyze large corpora of narratives to automatically build a clinical language model containing words and phrases of which meanings and relative meanings are also learnt. It further implements a semi-automatic annotation scheme for efficient and interactive machine-learning, which to a large extent eliminates the substantial manual annotation (of clinical narratives) effort necessary for the training of supervised algorithms. Validation of system performance was performed through comparing allergies identified by the CDSS with a manual reference standard

Knowledge, safety, and teamwork: a qualitative study on the experiences of anaesthesiologists and nurse anaesthetists working in the preanaesthesia assessment clinic

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Effectiveness of pre-anaesthetic assessment clinic: a systematic review of randomised and non-randomised prospective controlled studies

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Effectiveness of pre-anaesthetic assessment clinic: a systematic review of randomised and non-randomised prospective controlled studies.

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Effectiveness of pre-anaesthetic assessment clinic: a systematic review of randomised and non-randomised prospective controlled studies

Objectives The aim of this systematic review was to examine the effectiveness of pre-anaesthesia assessment clinics (PACs) in improving the quality and safety of perioperative patient care. Design Systematic review. Data sources The electronic databases CINAHL Plus with Full Text (EBSCOhost), Medline and Embase (OvidSP) were systematically searched on 11 September 2018 and updated on 3 February 2020 and 4 February 2021. Eligibility criteria The inclusion criteria for this study were studies published in English or Scandinavian language and scientific original research that included randomised or non-randomised prospective controlled studies. Additionally, studies that reported the outcomes from a PAC consultation with the patient present were included. Data extraction and synthesis Titles, abstracts and full texts were screened by a team of three authors. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist for quasi-experimental studies. Data extraction was performed by one author and checked by four other authors. Results were synthesised narratively owing to the heterogeneity of the included studies. Results Seven prospective controlled studies on the effectiveness of PACs were included. Three studies reported a significant reduction in the length of hospital stay and two studies reported a significant reduction in cancellation of surgery for medical reasons when patients were seen in the PAC. In addition, the included studies presented mixed results regarding anxiety in patients. Most studies had a high risk of bias. Conclusion This systematic review demonstrated a reduction in the length of hospital stay and cancellation of surgery when the patients had been assessed in the PAC. There is a need for high-quality prospective studies to gain a deeper understanding of the effectiveness of PACs

The association between intrapartum opioid fentanyl and early breastfeeding: A prospective observational study

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Effectiveness of pre-anaesthetic assessment clinic: a systematic review of randomised and non-randomised prospective controlled studies

OBJECTIVES: The aim of this systematic review was to examine the effectiveness of pre-anaesthesia assessment clinics (PACs) in improving the quality and safety of perioperative patient care. DESIGN: Systematic review. DATA SOURCES: The electronic databases CINAHL Plus with Full Text (EBSCOhost), Medline and Embase (OvidSP) were systematically searched on 11 September 2018 and updated on 3 February 2020 and 4 February 2021. ELIGIBILITY CRITERIA: The inclusion criteria for this study were studies published in English or Scandinavian language and scientific original research that included randomised or non-randomised prospective controlled studies. Additionally, studies that reported the outcomes from a PAC consultation with the patient present were included. DATA EXTRACTION AND SYNTHESIS: Titles, abstracts and full texts were screened by a team of three authors. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist for quasi-experimental studies. Data extraction was performed by one author and checked by four other authors. Results were synthesised narratively owing to the heterogeneity of the included studies. RESULTS: Seven prospective controlled studies on the effectiveness of PACs were included. Three studies reported a significant reduction in the length of hospital stay and two studies reported a significant reduction in cancellation of surgery for medical reasons when patients were seen in the PAC. In addition, the included studies presented mixed results regarding anxiety in patients. Most studies had a high risk of bias. CONCLUSION: This systematic review demonstrated a reduction in the length of hospital stay and cancellation of surgery when the patients had been assessed in the PAC. There is a need for high-quality prospective studies to gain a deeper understanding of the effectiveness of PACs. PROSPERO REGISTRATION NUMBER: CRD42019137724

Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records

Abstract Background Data mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efficiency, or transparency. Methods In this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplified and accelerated clinical vocabulary and concept building, and deterministic rules for fine-grained control of information extraction. The clinical language is automatically learnt, and vocabulary, concepts, and rules supporting a variety of NLP downstream tasks can further be built with only minimal manual feature engineering and tagging required from clinical experts. Together, these steps create an open processing pipeline that gradually refines the data in a transparent way, which greatly improves the interpretable nature of our method. Data transformations are thus made transparent and predictions interpretable, which is imperative for healthcare. The combined method also has other advantages, like potentially being language independent, demanding few domain resources for maintenance, and able to cover misspellings, abbreviations, and acronyms. To test and evaluate the combined method, we have developed a clinical decision support system (CDSS) named Information System for Clinical Concept Searching (ICCS) that implements the method for clinical concept tagging, extraction, and classification. Results In empirical studies the method shows high performance (recall 92.6%, precision 88.8%, F-measure 90.7%), and has demonstrated its value to clinical practice. Here we employ a real-life EHR-derived dataset to evaluate the method’s performance on the task of classification (i.e., detecting patient allergies) against a range of common supervised learning algorithms. The combined method achieves state-of-the-art performance compared to the alternative methods we evaluate. We also perform a qualitative analysis of common word embedding methods on the task of word similarity to examine their potential for supporting automatic feature engineering for clinical NLP tasks. Conclusions Based on the promising results, we suggest more research should be aimed at exploiting the inherent synergies between unsupervised, supervised, and rule-based paradigms for clinical NLP