Machine Learning for Benchmarking Critical Care Outcomes

Predicting outcomes using neural networks in the intensive care unit

Patients in intensive care units (ICUs) require rapid critical decision making. Modern ICUs are data rich, where information streams from diverse sources. Machine learning (ML) and neural networks (NN) can leverage the rich data for prognostication and clinical care. They can handle complex nonlinear relationships in medical data and have advantages over traditional predictive methods. A number of models are used: (1) Feedforward networks; and (2) Recurrent NN and convolutional NN to predict key outcomes such as mortality, length of stay in the ICU and the likelihood of complications. Current NN models exist in silos; their integration into clinical workflow requires greater transparency on data that are analyzed. Most models that are accurate enough for use in clinical care operate as 'black-boxes' in which the logic behind their decision making is opaque. Advances have occurred to see through the opacity and peer into the processing of the black-box. In the near future ML is positioned to help in clinical decision making far beyond what is currently possible. Transparency is the first step toward validation which is followed by clinical trust and adoption. In summary, NNs have the transformative ability to enhance predictive accuracy and improve patient management in ICUs. The concept should soon be turning into reality.

Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data

Background: A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). Methods: The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data. Performance comparisons were conducted with RF and the proposed MixRFb algorithms based solely on SAPS scoring, with additional evaluation using a descriptive receiver operating characteristic curve incorporating RDW's predictive mortality ability. Results: MixRFb, incorporating RDW and other covariates, outperforms the SAPS-based variant, achieving an area under the curve of 0.882 compared to 0.814. Age and RDW were identified as the most significant predictors of ICU mortality, as reported by the variable importance plot analysis. Conclusions: The MixRFb algorithm demonstrates superior efficacy in predicting in-hospital mortality and identifies age and RDW as primary predictors. Implementation of this algorithm could facilitate patient selection for clinical trials, thereby improving trial outcomes and strengthening ethical standards. Future research should focus on enriching algorithm robustness, expanding its applicability across diverse clinical settings and patient demographics, and integrating additional predictive markers to improve patient selection capabilities.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

BACKGROUND

In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2.

METHODS

We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability.

MEASUREMENTS AND MAIN RESULTS

The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2.

CONCLUSIONS

The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.