Aloud real- time reading of intensive care unit diaries: A feasibility study

Johnson, Kimberly R.; Temeyer, Joseph P; Schulte, Phillip J.; Nydahl, Peter; Philbrick, Kemuel L.; Karnatovskaia, Lioudmila V.

doi:10.1016/j.iccn.2023.103400

Cited by 3 publications

(9 citation statements)

References 57 publications

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“…Thirty-six studies were included in the review. 17 – 52 The data extraction table for these studies is described in Table 2 . The majority of the studies were conducted in the USA (n = 20 studies), n = 6 studies were conducted in China, and n = 2 in India.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of study size, 5 studies used more than 30 000 patient records in the development of their models 20 , 28 , 29 , 33 , 53 ; with Johnson et al 20 using the largest number of patients of 50 488 in their model development. Nine studies used between 10 000 and 30 000 patient records.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of the data used, out of 36 studies included in this review, 21 of them developed their models using data from a version of the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database. 54 Eleven studies used the MIMIC-II, 17 – 19 , 21 – 24 , 26 , 48 , 50 , 52 6 studies used the MIMIC-III, 20 , 29 , 39 , 41 , 42 , 47 and 4 studies used the MIMIC-IV. 33 , 43 , 45 , 46 Two studies used the MIMIC-III for validating their models externally.…”

Section: Resultsmentioning

confidence: 99%

“…Most models predicting mortality focused on short-term mortality, specifically happening while in the ICU. 18 , 20 , 21 , 23 , 29 , 33 , 36 , 39 , 49 , 56 In Hug et al 19 the focus was broader however as it predicted the risk of both death while in the ICU but also within 30 days of discharge from the ICU. The 4 models of Lehman et al, Luo et al, Mao et al and Meyer et al focused on prediction of in-hospital mortality.…”

Section: Resultsmentioning

confidence: 99%

“…To take the serial nature of the data into account, the studies had different approaches. It was common to use summary statistics, such a mean or median, minimum and maximum, first and last values within predetermined time windows 19 , 20 , 28 , 31 , 33 , 34 , 36 – 38 , 42 – 46 . For some models, the classification method handled the time-series data.…”

Section: Resultsmentioning

confidence: 99%

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Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art

Lapp

Roper

Kavanagh

et al. 2023

J Intensive Care Med

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Introduction Intensive care units (ICUs) are high-pressure, complex, technology-intensive medical environments where patient physiological data are generated continuously. Due to the complexity of interpreting multiple signals at speed, there are substantial opportunities and significant potential benefits in providing ICU staff with additional decision support and predictive modeling tools that can support and aid decision-making in real-time. This scoping review aims to synthesize the state-of-the-art dynamic prediction models of patient outcomes developed for use in the ICU. We define “dynamic” models as those where predictions are regularly computed and updated over time in response to updated physiological signals. Methods Studies describing the development of predictive models for use in the ICU were searched, using PubMed. The studies were screened as per Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, and the data regarding predicted outcomes, methods used to develop the predictive models, preprocessing the data and dealing with missing values, and performance measures were extracted and analyzed. Results A total of n = 36 studies were included for synthesis in our review. The included studies focused on the prediction of various outcomes, including mortality (n = 17), sepsis-related complications (n = 12), cardiovascular complications (n = 5), and other complications (respiratory, renal complications, and bleeding, n = 5). The most common classification methods include logistic regression, random forest, support vector machine, and neural networks. Conclusion The included studies demonstrated that there is a strong interest in developing dynamic prediction models for various ICU patient outcomes. Most models reported focus on mortality. As such, the development of further models focusing on a range of other serious and well-defined complications—such as acute kidney injury—would be beneficial. Furthermore, studies should improve the reporting of key aspects of model development challenges.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art

Lapp

Roper

Kavanagh

et al. 2023

J Intensive Care Med

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Opening Pandora’s box by generating ICU diaries through artificial intelligence: A hypothetical study protocol

Peschel,

Krotsetis,

Seidlein

et al. 2024

Intensive and Critical Care Nursing

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ICU Patients’ Pattern Recognition and Correlation Identification of Vital Parameters Using Optimized Machine Learning Models

Yallabandi,

Mayya,

Jeganathan

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Early detection of patient deterioration in the Intensive Care Unit (ICU) can play a crucial role in improving patient outcomes. Conventional severity scales currently used to predict patient deterioration are based on a number of factors, the majority of which consist of multiple investigations. Recent advancements in machine learning (ML) within the healthcare domain offer the potential to alleviate the burden of continuous patient monitoring. In this study, we propose an optimized ML model designed to leverage variations in vital signs observed during the final 24 hours of an ICU stay for outcome predictions. Further, we elucidate the relative contributions of distinct vital parameters to these outcomes The dataset compiled in real-time encompasses six pivotal vital parameters: systolic (0) and diastolic (1) blood pressure, pulse rate (2), respiratory rate (3), oxygen saturation (SpO2) (4), and temperature (5). Of these vital parameters, systolic blood pressure emerges as the most significant predictor associated with mortality prediction. Using a fivefold cross-validation method, several ML classifiers are used to categorize the last 24 hours of time series data after ICU admission into three groups: recovery, death, and intubation. Notably, the optimized Gradient Boosting classifier exhibited the highest performance in detecting mortality, achieving an area under the receiver-operator curve (AUC) of 0.95. Through the integration of electronic health records with this ML software, there is the promise of early notifications regarding adverse outcomes, potentially several hours before the onset of hemodynamic instability.

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Aloud real- time reading of intensive care unit diaries: A feasibility study

Cited by 3 publications

References 57 publications

Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art

Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art

Opening Pandora’s box by generating ICU diaries through artificial intelligence: A hypothetical study protocol

ICU Patients’ Pattern Recognition and Correlation Identification of Vital Parameters Using Optimized Machine Learning Models

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