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Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

ABSTRACT: Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were performed in eligible patients using the Critical-Care Pain Observation Tool (CPOT). Three machine-learning methods-random forest (RF), support vector machine (SVM), and logistic regression (LR)-were employed to predict pain using parameters, such as vital signs, age group, and sedation levels. Prediction accuracy was calculated as the harmonic mean of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Furthermore, 117,190 CPOT assessments were performed in 11,507 eligible patients (median age: 65 years; 58.0% males). We found that pain prediction was possible with all three machine-learning methods. RF demonstrated the highest AUROC for the test data (RF: 0.853, SVM: 0.823, and LR: 0.787). With this method, pain can be objectively, continuously, and semi-automatically evaluated in critically ill patients.

SUBMITTER: Kobayashi N

PROVIDER: S-EPMC7933166 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

Kobayashi Naoya N Shiga Takuya T Ikumi Saori S Watanabe Kazuki K Murakami Hitoshi H Yamauchi Masanori M

Scientific reports 20210304 1

Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were perform ...[more]

PMID: 33664391

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Project description:BackgroundResearch on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC.ObjectiveWe intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings.MethodsWe conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data.ResultsWe retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%).ConclusionsWe observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

Project description:ImportanceCognitive behavioral therapy for chronic pain (CBT-CP) is a safe and effective alternative to opioid analgesics. Because CBT-CP requires multiple sessions and therapists are scarce, many patients have limited access or fail to complete treatment.ObjectivesTo determine if a CBT-CP program that personalizes patient treatment using reinforcement learning, a field of artificial intelligence (AI), and interactive voice response (IVR) calls is noninferior to standard telephone CBT-CP and saves therapist time.Design, setting, and participantsThis was a randomized noninferiority, comparative effectiveness trial including 278 patients with chronic back pain from the Department of Veterans Affairs health system (recruitment and data collection from July 11, 2017-April 9, 2020). More patients were randomized to the AI-CBT-CP group than to the control (1.4:1) to maximize the system's ability to learn from patient interactions.InterventionsAll patients received 10 weeks of CBT-CP. For the AI-CBT-CP group, patient feedback via daily IVR calls was used by the AI engine to make weekly recommendations for either a 45-minute or 15-minute therapist-delivered telephone session or an individualized IVR-delivered therapist message. Patients in the comparison group were offered 10 therapist-delivered telephone CBT-CP sessions (45 minutes/session).Main outcomes and measuresThe primary outcome was the Roland Morris Disability Questionnaire (RMDQ; range 0-24), measured at 3 months (primary end point) and 6 months. Secondary outcomes included pain intensity and pain interference. Consensus guidelines were used to identify clinically meaningful improvements for responder analyses (eg, a 30% improvement in RMDQ scores and pain intensity). Data analyses were performed from April 2021 to May 2022.ResultsThe study population included 278 patients (mean [SD] age, 63.9 [12.2] years; 248 [89.2%] men; 225 [81.8%] White individuals). The 3-month mean RMDQ score difference between AI-CBT-CP and standard CBT-CP was -0.72 points (95% CI, -2.06 to 0.62) and the 6-month difference was -1.24 (95% CI, -2.48 to 0); noninferiority criterion were met at both the 3- and 6-month end points (P < .001 for both). A greater proportion of patients receiving AI-CBT-CP had clinically meaningful improvements at 6 months as indicated by RMDQ (37% vs 19%; P = .01) and pain intensity scores (29% vs 17%; P = .03). There were no significant differences in secondary outcomes. Pain therapy using AI-CBT-CP required less than half of the therapist time as standard CBT-CP.Conclusions and relevanceThe findings of this randomized comparative effectiveness trial indicated that AI-CBT-CP was noninferior to therapist-delivered telephone CBT-CP and required substantially less therapist time. Interventions like AI-CBT-CP could allow many more patients to be served effectively by CBT-CP programs using the same number of therapists.Trial registrationClinicalTrials.gov Identifier: NCT02464449.

Project description:BackgroundView classification is a key step toward building a fully automated system for interpretation of echocardiograms. However, compared with adult echocardiograms, creating a view classification model for pediatric echocardiograms poses additional challenges, such as greater variation in anatomy, structure size, and views. The aim of this study was to develop a computer vision model to autonomously perform view classification on pediatric echocardiographic images.MethodsUsing a training set of 12,067 echocardiographic images from patients aged 0 to 19 years, a convolutional neural network model was trained to identify 27 preselected standard pediatric echocardiographic views which included anatomic sweeps, color Doppler, and Doppler tracings. A validation set of 6,197 images was used for parameter tuning and model selection. A test set of 9,684 images from 100 different patients was then used to evaluate model accuracy. The model was also evaluated on a per study basis using a second test set consisting of 524 echocardiograms from children with leukemia to identify six preselected views pertinent to cardiac dysfunction surveillance.ResultsThe model identified the 27 preselected views with 90.3% accuracy. Accuracy was similar across age groups (89.3% for 0-4 years, 90.8% for 4-9 years, 90.0% for 9-14 years, and 91.2% for 14-19 years; P = .12). Examining the view subtypes, accuracy was 78.3% for the cine one location, 90.5% for sweeps with color Doppler, 82.2% for sweeps without color Doppler, and 91.1% for Doppler tracings. Among the leukemia cohort, the model identified the six preselected views on a per study basis with a positive predictive value of 98.7% to 99.2% and sensitivity of 76.9% to 94.8%.ConclusionsA convolutional neural network model was constructed for view classification of pediatric echocardiograms that was accurate across the spectrum of ages and view types. This work lays the foundation for automated quantitative analysis and diagnostic support to promote efficient, accurate, and scalable analysis of pediatric echocardiograms.

Dataset Information

Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

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Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

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