Dynamic microsimulation to model multiple outcomes in cohorts of critically ill patients

Assessment of Dynamic Intracranial Compliance in Children with Severe Traumatic Brain Injury: Proof-of-Concept

Background and Aims

Intracranial compliance refers to the relationship between a change in intracranial volume and the resultant change in intracranial pressure (ICP). Measurement of compliance is useful in managing cardiovascular and respiratory failure; however, there are no contemporary means to assess intracranial compliance. Knowledge of intracranial compliance could complement ICP and cerebral perfusion pressure (CPP) monitoring in patients with severe traumatic brain injury (TBI) and may enable a proactive approach to ICP management. In this proof-of-concept study, we aimed to capitalize on the physiologic principles of intracranial compliance and vascular reactivity to CO₂, and standard-of-care neurocritical care monitoring, to develop a method to assess dynamic intracranial compliance.

Methods

Continuous ICP and end-tidal CO₂ (ETCO₂) data from children with severe TBI were collected after obtaining informed consent in this Institutional Review Board-approved study. An intracranial pressure-PCO₂ Compliance Index (PCI) was derived by calculating the moment-to-moment correlation between change in ICP and change in ETCO₂. As such, “good” compliance may be reflected by a lack of correlation between time-synched changes in ICP in response to changes in ETCO₂, and “poor” compliance may be reflected by a positive correlation between changes in ICP in response to changes in ETCO₂.

Results

A total of 978 h of ICP and ETCO₂ data were collected and analyzed from eight patients with severe TBI. Demographic and clinical characteristics included patient age 7.1 ± 5.8 years (mean ± SD); 6/8 male; initial Glasgow Coma Scale score 3 [3–7] (median [IQR]); 6/8 had decompressive surgery; 7.1 ± 1.4 ICP monitor days; ICU length of stay (LOS) 16.1 ± 6.8 days; hospital LOS 25.9 ± 8.4 days; and survival 100%. The mean PCI for all patients throughout the monitoring period was 0.18 ± 0.04, where mean ICP was 13.7 ± 2.1 mmHg. In this cohort, PCI was observed to be consistently above 0.18 by 12 h after monitor placement. Percent time spent with PCI thresholds > 0.1, 0.2, and 0.3 were 62% [24], 38% [14], and 23% [15], respectively. The percentage of time spent with an ICP threshold > 20 mmHg was 5.1% [14.6].

Conclusions

Indirect assessment of dynamic intracranial compliance in TBI patients using standard-of-care monitoring appears feasible and suggests a prolonged period of derangement out to 5 days post-injury. Further study is ongoing to determine if the PCI—a new physiologic index, complements utility of ICP and/or CPP in guiding management of patients with severe TBI.

Electronic supplementary material

The online version of this article (10.1007/s12028-020-01004-3) contains supplementary material, which is available to authorized users.

Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review

OBJECTIVE

We systematically reviewed models to predict adult ICU length of stay.

DATA SOURCES

We searched the Ovid EMBASE and MEDLINE databases for studies on the development or validation of ICU length of stay prediction models.

STUDY SELECTION

We identified 11 studies describing the development of 31 prediction models and three describing external validation of one of these models.

DATA EXTRACTION

Clinicians use ICU length of stay predictions for planning ICU capacity, identifying unexpectedly long ICU length of stay, and benchmarking ICUs. We required the model variables to have been published and for the models to be free of organizational characteristics and to produce accurate predictions, as assessed by R across patients for planning and identifying unexpectedly long ICU length of stay and across ICUs for benchmarking, with low calibration bias. We assessed the reporting quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.

DATA SYNTHESIS

The number of admissions ranged from 253 to 178,503. Median ICU length of stay was between 2 and 6.9 days. Two studies had not published model variables and three included organizational characteristics. None of the models produced predictions with low bias. The R was 0.05-0.28 across patients and 0.01-0.64 across ICUs. The reporting scores ranged from 49 of 78 to 60 of 78 and the methodologic scores from 12 of 22 to 16 of 22.

CONCLUSION

No models completely satisfy our requirements for planning, identifying unexpectedly long ICU length of stay, or for benchmarking purposes. Physicians using these models to predict ICU length of stay should interpret them with reservation.

Temporal trends of postinjury multiple-organ failure: still resource intensive, morbid, and lethal

BACKGROUND

While the incidence of postinjury multiple-organ failure (MOF) has declined during the past decade, temporal trends of its morbidity, mortality, presentation patterns, and health care resources use have been inconsistent. The purpose of this study was to describe the evolving epidemiology of postinjury MOF from 2003 to 2010 in multiple trauma centers sharing standard treatment protocols.

METHODS

"Inflammation and Host Response to Injury Collaborative Program" institutions that enrolled more than 20 eligible patients per biennial during the 2003 to 2010 study period were included. The patients were aged 16 years to 90 years, sustained blunt torso trauma with hemorrhagic shock (systolic blood pressure < 90 mm Hg, base deficit ≥ 6 mEq/L, blood transfusion within the first 12 hours), but without severe head injury (motor Glasgow Coma Scale [GCS] score < 4). MOF temporal trends (Denver MOF score > 3) were adjusted for admission risk factors (age, sex, body max index, Injury Severity Score [ISS], systolic blood pressure, and base deficit) using survival analysis.

RESULTS

A total of 1,643 patients from four institutions were evaluated. MOF incidence decreased over time (from 17% in 2003-2004 to 9.8% in 2009-2010). MOF-related death rate (33% in 2003-2004 to 36% in 2009-2010), intensive care unit stay, and mechanical ventilation duration did not change over the study period. Adjustment for admission risk factors confirmed the crude trends. MOF patients required much longer ventilation and intensive care unit stay, compared with non-MOF patients. Most of the MOF-related deaths occurred within 2 days of the MOF diagnosis. Lung and cardiac dysfunctions became less frequent (57.6% to 50.8%, 20.9% to 12.5%, respectively), but kidney and liver failure rates did not change (10.1% to 12.5%, 15.2% to 14.1%).

CONCLUSION

Postinjury MOF remains a resource-intensive, morbid, and lethal condition. Lung injury is an enduring challenge and should be a research priority. The lack of outcome improvements suggests that reversing MOF is difficult and prevention is still the best strategy.

LEVEL OF EVIDENCE

Epidemiologic study, level III.

Clinical course of sepsis, severe sepsis, and septic shock in a cohort of infected patients from ten Colombian hospitals

Background

Sepsis has several clinical stages, and mortality rates are different for each stage. Our goal was to establish the evolution and the determinants of the progression of clinical stages, from infection to septic shock, over the first week, as well as their relationship to 7-day and 28-day mortality.

Methods

This is a secondary analysis of a multicenter cohort of inpatients hospitalized in general wards or intensive care units (ICUs). The general estimating equations (GEE) model was used to estimate the risk of progression and the determinants of stages of infection over the first week. Cox regression with time-dependent covariates and fixed covariates was used to determine the factors related with 7-day and 28-day mortality, respectively.

Results

In 2681 patients we show that progression to severe sepsis and septic shock increases with intraabdominal and respiratory sources of infection [OR = 1,32; 95%IC = 1,20-1,46 and OR = 1.21, 95%CI = 1,11-1,33 respectively], as well as according to Acute Physiology and Chronic Health Evaluation II (APACHE II) [OR = 1,03; 95%CI = 1,02-1,03] and Sequential Organ Failure Assessment (SOFA) [OR = 1,16; 95%CI = 1,14-1,17] scores. The variables related with first-week mortality were progression to severe sepsis [HR = 2,13; 95%CI = 1,13-4,03] and septic shock [HR = 3,00; 95%CI = 1,50-5.98], respiratory source of infection [HR = 1,76; 95%IC = 1,12-2,77], APACHE II [HR = 1,07; 95% CI = 1,04-1,10] and SOFA [HR = 1,09; 95%IC = 1,04-1,15] scores.

Conclusions

Intraabdominal and respiratory sources of infection, independently of SOFA and APACHE II scores, increase the risk of clinical progression to more severe stages of sepsis; and these factors, together with progression of the infection itself, are the main determinants of 7-day and 28-day mortality.

Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges

The complexity of the systemic inflammatory response and the lack of a treatment breakthrough in the treatment of pathogenic infection demand that advanced tools be brought to bear in the treatment of severe sepsis and trauma. Systems medicine, the translational science counterpart to basic science's systems biology, is the interface at which these tools may be constructed. Rapid initial strides in improving sepsis treatment are possible through the use of phenomenological modelling and optimization tools for process understanding and device design. Higher impact, and more generalizable, treatment designs are based on mechanistic understanding developed through the use of physiologically based models, characterization of population variability, and the use of control-theoretic systems engineering concepts. In this review we introduce acute inflammation and sepsis as an example of just one area that is currently underserved by the systems medicine community, and, therefore, an area in which contributions of all types can be made.

Prediction of clinical conditions after coronary bypass surgery using dynamic data analysis

This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the time they need to reach a stable state after coronary bypass surgery: less or more than 9 h. On the basis of five physiological variables (heart rate, systolic arterial blood pressure, systolic pulmonary pressure, blood temperature and oxygen saturation), different dynamic features were extracted, namely the means and standard deviations at different moments in time, coefficients of multivariate autoregressive models and cepstral coefficients. These sets of features served subsequently as inputs for a Gaussian process and the prediction results were compared with the case where only admission data was used for the classification. The dynamic features, especially the cepstral coefficients (aROC: 0.749, Brier score: 0.206), resulted in higher performances when compared to static admission data (aROC: 0.547, Brier score: 0.247). The differences in performance are shown to be significant. In all cases, the Gaussian process classifier outperformed to logistic regression.

Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit

In intensive care medicine close monitoring of organ failure status is important for the prognosis of patients and for choices regarding ICU management. Major challenges in analyzing the multitude of data pertaining to the functioning of the organ systems over time are to extract meaningful clinical patterns and to provide predictions for the future course of diseases. With their explicit states and probabilistic state transitions, Markov models seem to fit this purpose well. In complex domains such as intensive care a choice is often made between a simple model that is estimated from the data, or a more complex model in which the parameters are provided by domain experts. Our primary aim is to combine these approaches and develop a set of complex Markov models based on clinical data. In this paper we describe the design choices underlying the models, which enable them to identify temporal patterns, predict outcomes, and test clinical hypotheses. Our models are characterized by the choice of the dynamic hierarchical Bayesian network structure and the use of logistic regression equations in estimating the transition probabilities. We demonstrate the induction, inference, evaluation, and use of these models in practice in a case-study of patients with severe sepsis admitted to four Dutch ICUs.

Between prediction, education, and quality control: simulation models in critical care

Today, computer-aided strategies in social sciences are an indispensable component of teaching programs. In recent years, microsimulation modeling has gained attention in its ability to represent predicted physiological developments visually, thus providing the user with a full understanding of the impacts of a proposed scheme. There are several microsimulation models in human medicine, and they can be either dynamic or static. If the model is dynamic the course of variables changes over time; in contrast, in the static case time constancy is assumed. In critical care there have been several approaches to implement microsimulation models to predict outcome. This commentary describes current approaches for predicting disease progression by using dynamic microsimulation in pneumonia-related sepsis.

Use of dynamic microsimulation to predict disease progression in patients with pneumonia-related sepsis

Introduction

Sepsis is the leading cause of death in critically ill patients and often affects individuals with community-acquired pneumonia. To overcome the limitations of earlier mathematical models used to describe sepsis and predict outcomes, we designed an empirically based Monte Carlo model that simulates the progression of sepsis in hospitalized patients over a 30-day period.

Methods

The model simulates changing health over time, as represented by the Sepsis-related Organ Failure Assessment (SOFA) score, as a function of a patient's previous health state and length of hospital stay. We used data from patients enrolled in the GenIMS (Genetic and Inflammatory Markers of Sepsis) study to calibrate the model, and tested the model's ability to predict deaths, discharges, and daily SOFA scores over time using different algorithms to estimate the natural history of sepsis. We evaluated the stability of the methods using bootstrap sampling techniques.

Results

Of the 1,888 patients originally enrolled, most were elderly (mean age 67.77 years) and white (80.72%). About half (47.98%) were female. Most were relatively ill, with a mean Acute Physiology and Chronic Health Evaluation III score of 56 and Pneumonia Severity Index score of 73.5. The model's estimates of the daily pattern of deaths, discharges, and SOFA scores over time were not statistically different from the actual pattern when information about how long patients had been ill was included in the model (P = 0.91 to 0.98 for discharges; P = 0.26 to 0.68 for deaths). However, model estimates of these patterns were different from the actual pattern when the model did not include data on the duration of illness (P < 0.001 for discharges; P = 0.001 to 0.040 for deaths). Model results were stable to bootstrap validation.

Conclusion

An empiric simulation model of sepsis can predict complex longitudinal patterns in the progression of sepsis, most accurately by models that contain data representing both organ-system levels of and duration of illness. This work supports the incorporation into mathematical models of disease of the clinical intuition that the history of disease in an individual matters, and represents an advance over several prior simulation models that assume a constant rate of disease progression.

Predicting Hospital Mortality, Length of Stay, and Transfer to Long-Term Care for Injured Patients

BACKGROUND

Using hospital length of stay (LOS) to measure trauma care efficiency is complicated by short LOS resulting from early mortality or transfer to long-term care (LTC).

METHODS

Records from the 1999 to 2003 National Trauma Data Bank were used to create a multistate model divided into four time periods, each with constant rates of death, discharge home, and LTC transfer. Estimated hospital mortality and LOS for patient subgroups were calculated from this model, and time-varying covariate effects were estimated.

RESULTS

A total of 369,829 cases with adequate data were available. Early mortality was increased most by hypotension or coma, and also by anatomic injury severity or penetrating mechanism, but these effects diminished with time; age remained a strong predictor of mortality at any time but sex was insignificant. Rates of discharge home decreased with time, whereas rates of LTC transfer peaked at 6 to 11 days. Increased age strongly predicted transfer to LTC, whereas penetrating or burn mechanisms made it less likely. Predicted and observed outcomes were similar for multiple subgroups, and about 17% of individual variation in LOS was explained by the model.

CONCLUSIONS

Multistate models of patient status can accurately predict mortality and resource use after injury, and describe time-varying effects of other factors.

SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

OBJECTIVE

To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data.

DESIGN

Prospective multicentre, multinational cohort study.

PATIENTS AND SETTING

A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002.

MEASUREMENTS AND RESULTS

ICU admission data (recorded within +/-1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test H=10.56, p=0.39, C=14.29, p=0.16). Customized equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit.

CONCLUSIONS

The SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels.