To develop a regional ICU mortality prediction model during the first 24 h of ICU admission utilizing MODS and NEMS with six other independent variables from the Critical Care Information System (CCIS) Ontario, Canada

Weak Correlation Between Perceived and Measured Intensive Care Unit Nursing Workload: An Observational Study

BACKGROUND

Efficient management of nursing workload in the intensive care unit (ICU) is essential for patient safety, care quality, and nurse well-being. Current ICU-specific workload assessment scores lack comprehensive coverage of nursing activities and perceived workload.

PURPOSE

The purpose of this study was to assess the correlation between ICU nurses' perceived workload and the Nine Equivalents of Nursing Manpower Use Score (NEMS).

METHODS

In a 45-bed adult ICU at a tertiary academic hospital, nurses' perceived shift workload (measured with an 11-point Likert scale) was correlated with the NEMS, calculated manually and electronically.

RESULTS

The study included 1734 observations. The perceived workload was recorded for 77.6% of observations. A weak positive correlation was found between perceived and objectively measured workload.

CONCLUSION

Findings indicate a need to consider the multifaceted nature of nursing activities and individual workload perceptions in the ICU.

Red blood cell distribution width and Charlson comorbidity index help to identify frail polytraumatized patients : Experiences from a level I trauma center

INTRODUCTION

Little is known about the potential impact of the red blood cell distribution width (RDW) and pre-existing comorbidities on the late-phase survival of polytraumatized patients.

METHODS

A total of 173 polytraumatized patients were included retrospectively in this cohort study in a level I trauma center from January 2012 to December 2015. The Charlson comorbidity index (CCI) scores and RDW values were evaluated.

RESULTS

Out of all polytraumatized patients (n = 173), 72.8% (n = 126) were male, the mean ISS was 31.7 points (range 17-75) and the mean age was 45.1 years (range 18-93 years). Significantly higher RDW values (13.90 vs. 13.37; p = 0.006) and higher CCI scores (3.38 vs. 0.49; p < 0.001) were seen in elderly polytraumatized patients (age > 55 years). RDW values > 13.75% (p = 0.033) and CCI scores > 2 points (p = 0.001) were found to have a significant influence on the late-phase survival of polytraumatized patients. Age > 55 years (p = 0.009, HR 0.312; 95% confidence interval (CI) 0.130-0.749) and the presence of severe traumatic brain injury (TBI) (p = 0.007; HR 0.185; 95% CI 0.054-0.635) remained as independent prognostic factors on the late-phase survival after multivariate analysis.

CONCLUSION

Even younger elderly polytraumatized patients (> 55 years of age) showed significant higher RDW values and higher CCI scores. In addition to the presence of severe TBI and age > 55 years, RDW value > 13.75% on admission and CCI score > 2 might help to identify the "younger" frail polytraumatized patient at risk.

Forecasting ICU Census by Combining Time Series and Survival Models

Capacity planning of ICUs is essential for effective management of health safety, quality of patient care, and the allocation of ICU resources. Whereas ICU length of stay (LOS) may be estimated using patient information such as severity of illness scoring systems, ICU census is impacted by both patient LOS and arrival patterns. We set out to develop and evaluate an ICU census forecasting algorithm using the Multiple Organ Dysfunction Score (MODS) and the Nine Equivalents of Nursing Manpower Use Score (NEMS) for capacity planning purposes.

DESIGN

Retrospective observational study.

SETTING

We developed the algorithm using data from the Medical-Surgical ICU (MSICU) at University Hospital, London, Canada and validated using data from the Critical Care Trauma Centre (CCTC) at Victoria Hospital, London, Canada.

PATIENTS

Adult patient admissions (7,434) to the MSICU and (9,075) to the CCTC from 2015 to 2021.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We developed an Autoregressive integrated moving average time series model that forecasts patients arriving in the ICU and a survival model using MODS, NEMS, and other factors to estimate patient LOS. The models were combined to create an algorithm that forecasts ICU census for planning horizons ranging from 1 to 7 days. We evaluated the algorithm quality using several fit metrics. The root mean squared error ranged from 2.055 to 2.890 beds/d and the mean absolute percentage error from 9.4% to 13.2%. We show that this forecasting algorithm provides a better fit when compared with a moving average or a time series model that directly forecasts ICU census. Additionally, we evaluated the performance of the algorithm using data during the global COVID-19 pandemic and found that the error of the forecasts increased proportionally with the number of COVID-19 patients in the ICU.

CONCLUSIONS

It is possible to develop accurate tools to forecast ICU census. This type of algorithm may be important to clinicians and managers when planning ICU capacity as well as staffing and surgical demand planning over a short time horizon.

External Validation of Mortality Prediction Models for Critical Illness Reveals Preserved Discrimination but Poor Calibration

OBJECTIVES

In a recent scoping review, we identified 43 mortality prediction models for critically ill patients. We aimed to assess the performances of these models through external validation.

DESIGN

Multicenter study.

SETTING

External validation of models was performed in the Simple Intensive Care Studies-I (SICS-I) and the Finnish Acute Kidney Injury (FINNAKI) study.

PATIENTS

The SICS-I study consisted of 1,075 patients, and the FINNAKI study consisted of 2,901 critically ill patients.

MEASUREMENTS AND MAIN RESULTS

For each model, we assessed: 1) the original publications for the data needed for model reconstruction, 2) availability of the variables, 3) model performance in two independent cohorts, and 4) the effects of recalibration on model performance. The models were recalibrated using data of the SICS-I and subsequently validated using data of the FINNAKI study. We evaluated overall model performance using various indexes, including the (scaled) Brier score, discrimination (area under the curve of the receiver operating characteristics), calibration (intercepts and slopes), and decision curves. Eleven models (26%) could be externally validated. The Acute Physiology And Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS)-Reduced (SAPS-R)' and Simplified Mortality Score for the ICU models showed the best scaled Brier scores of 0.11' 0.10' 0.10' and 0.06' respectively. SAPS II, APACHE II, and APACHE IV discriminated best; overall discrimination of models ranged from area under the curve of the receiver operating characteristics of 0.63 (0.61-0.66) to 0.83 (0.81-0.85). We observed poor calibration in most models, which improved to at least moderate after recalibration of intercepts and slopes. The decision curve showed a positive net benefit in the 0-60% threshold probability range for APACHE IV and SAPS-R.

CONCLUSIONS

In only 11 out of 43 available mortality prediction models, the performance could be studied using two cohorts of critically ill patients. External validation showed that the discriminative ability of APACHE II, APACHE IV, and SAPS II was acceptable to excellent, whereas calibration was poor.

DECAF: An interpretable deep cascading framework for ICU mortality prediction

Medical risk detection is an important topic and a challenging task to improve the performance of clinical practices in Intensive Care Units (ICU). Although many bio-statistical learning and deep learning approaches have provided patient-specific mortality predictions, these existing methods lack interpretability that is crucial to gain adequate insight on why such predictions would work. In this paper, we introduce cascading theory to model the physiological domino effect and provide a novel approach to dynamically simulate the deterioration of patients' conditions. We propose a general DEep CAscading Framework (DECAF) to predict the potential risks of all physiological functions at each clinical stage. Compared with other feature-based and/or score-based models, our approach has a range of desirable properties, such as being interpretable, applicable with multi prediction tasks, and learnable from medical common sense and/or clinical experience knowledge. Experiments on a medical dataset (MIMIC-III) of 21,828 ICU patients show that DECAF reaches up to 89.30 % on AUROC, which surpasses the best competing methods for mortality prediction.

Assistive Relief Tool for Early Childhood and Special Psychological Symptom Groups during the Pandemic: Clothing Design Based on the Virtual Contact Principle

During the COVID-19 pandemic, some special populations-groups of early childhood and people with autism, among others-faced more profound challenges than the common people. The lack of real physical contact such as embracing greatly affected the effectiveness of development, psychiatric treatment, and other processes for these populations. This study is aimed at developing clothing with appropriate contact pressure based on the contact comfort principle of psychology and providing a type of pressure clothing that can relieve the wearer's tension by simulating hugging, alleviating the lack of physical contact for early childhood education and special education groups during the pandemic. First, the elementary requirements of clothing design are attained using a questionnaire survey and test method. The analysis revealed that clothing should fulfill the four requirements of pressure comfort, fabric softness, wearing and taking off comfort, and visual beauty. Second, we realized the performance requirements in the fabric and accessories, style design, structure design, and functional design. Finally, the product experience is proposed through a fitting, and the reasonable opinions were fed back to the product design to enhance the functionality of clothing. The research shows that clothing can simulate hugging and can ease the loneliness of the wearer. This study can be used as a good tool to assist during the pandemic for early childhood education and special psychological symptom groups, as well as a broader group of people living alone at home, to play an adjunctive treatment and loneliness relief functions.

Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury

Background

Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising ‘electronic biomarker’ of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system.

Results

A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application.

Conclusions

The prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.

Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19

OBJECTIVES

To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.

METHODS

A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results.

RESULTS

Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%).

CONCLUSIONS

Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.

External validation of a prognostic model for intensive care unit mortality: a retrospective study using the Ontario Critical Care Information System

PURPOSE

To externally validate an intensive care unit (ICU) mortality prediction model that was created using the Ontario Critical Care Information System (CCIS), which includes the Multiple Organ Dysfunction Score (MODS).

METHODS

We applied the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) recommendations to a prospective longitudinal cohort of patients discharged between 1 July 2015 and 31 December 31 2016 from 90 adult level-3 critical care units in Ontario. We used multivariable logistic regression with measures of discrimination, calibration-in-the-large, calibration slope, and flexible calibration plots to compare prediction model performance of the entire data set and for each ICU subtype.

RESULTS

Among 121,201 CCIS records with ICU mortality of 11.3%, the C-statistic for the validation data set was 0.805. The C-statistic ranged from 0.775 to 0.846 among the ICU subtypes. After intercept recalibration to adjust the baseline risk, the mean predicted risk of death matched actual ICU mortality. The calibration slope was close to 1 with all CCIS data and ICU subtypes of cardiovascular and community hospitals with low ventilation rates. Calibration slopes significantly less than 1 were found for ICUs in teaching hospitals and community hospitals with high ventilation rates whereas coronary care units had a calibration slope significantly higher than 1. Calibration plots revealed over-prediction in high risk groups to a varying degree across all cohorts.

CONCLUSIONS

A risk prediction model primarily based on the MODS shows reproducibility and transportability after intercept recalibration. Risk adjusting models that use existing and feasible data collection can support performance measurement at the individual ICU level.

Mortality prediction models in the adult critically ill: A scoping review

BACKGROUND

Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients.

METHODS

Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication.

RESULTS

In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R² (4.7%).

CONCLUSIONS

Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

A classification model for prediction of clinical severity level using qSOFA medical score

Purpose

The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method.

Design/methodology/approach

To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score.

Findings

From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification.

Originality/value

This paper helps in identifying patient' clinical status.

The association between the patient and the physician genders and the likelihood of intensive care unit admission in hospital with restricted ICU bed capacity

BACKGROUND

Despite the evidence that the patient gender is an important component in the intensive care unit (ICU) admission decision, the role of physician gender and the interaction between the two remain unclear.

OBJECTIVE

To investigate the association of both the patient and the physician gender with ICU admission rate of critically ill emergency department (ED) medical patients in a hospital with restricted ICU bed capacity operates with 'closed door' policy.

METHODS

A retrospective population-based cohort analysis. We included patients above 18 admitted to an ED resuscitation room (RR) of a tertiary hospital during 2011-12. Data on medical, laboratory and clinical characteristics were obtained. We used an adjusted multivariable logistic regression to analyze the association between both the patient and the physician gender to the ICU admission decision.

RESULTS

We included 831 RR admissions, 388 (46.7%) were female patients and 188 (22.6%) were treated by a female physicians. In adjusted multivariable analysis (adjusted for age, diabetes, mode of hospital transportation, first pH and patients who were treated with definitive airway and vasso-pressors in the RR), female-female combination (patient-physician, respectively) showed the lowest likelihood to be admitted to ICU (adjusted OR: 0.21; 95% CI: 0.09-0.51) compared to male-male combination, in addition to a smaller decrease among female-male (adjusted OR: 0.53; 95% CI: 0.32-0.86) and male-female (adjusted OR: 0.43; 95% CI: 0.21-0.89) combinations.

CONCLUSION

We demonstrated the existence of the possible gender bias where female gender of the patient and treating physician diminish the likelihood of the restricted health resource use.

Assessing contemporary intensive care unit outcome: development and validation of the Australian and New Zealand Risk of Death admission model

The Australian and New Zealand Risk of Death (ANZROD) model currently used for benchmarking intensive care units (ICUs) in Australia and New Zealand utilises physiological data collected up to 24 hours after ICU admission to estimate the risk of hospital mortality. This study aimed to develop the Australian and New Zealand Risk of Death admission (ANZROD₀) model to predict hospital mortality using data available at presentation to ICU and compare its performance with the ANZROD in Australian and New Zealand hospitals. Data pertaining to all ICU admissions between 1 January 2006 and 31 December 2015 were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Hospital mortality was modelled using logistic regression with development (two-thirds) and validation (one-third) datasets. All predictor variables available at ICU admission were considered for inclusion in the ANZROD₀ model. Model performance was assessed using Brier score, standardised mortality ratio and area under the receiver operating characteristic curve. The relationship between ANZROD₀ and ANZROD predicted risk of death was assessed using linear regression. After standard exclusions, 1,097,416 patients were available for model development and validation. Observed mortality was 9.5%. Model performance measures (Brier score, standardised mortality ratio and area under the receiver operating characteristic curve) for the ANZROD₀ and ANZROD in the validation dataset were 0.069, 1.0 and 0.853; 0.057, 1.0 and 0.909, respectively. There was a strong positive correlation between the mortality predictions with an overall R² of 0.73. We found that the ANZROD₀ model had acceptable calibration and discrimination. Predictions from the models had high correlations in all major diagnostic groups, with the exception of cardiac surgery and possibly trauma and sepsis.