Modeling longitudinal data in acute illness

Biomarkers of sepsis could allow early identification of high-risk patients, in whom aggressive interventions can be life-saving. Among those interventions are the immunomodulatory therapies, which will hopefully become increasingly available to clinicians. However, optimal use of such interventions will probably be patient specific and based on longitudinal profiles of such biomarkers. Modeling techniques that allow proper interpretation and classification of these longitudinal profiles, as they relate to patient characteristics, disease progression, and therapeutic interventions, will prove essential to the development of such individualized interventions. Once validated, these models may also prove useful in the rational design of future clinical trials and in the interpretation of their results. However, only a minority of mathematicians and statisticians are familiar with these newer techniques, which have undergone remarkable development during the past two decades. Interestingly, critical illness has the potential to become a key testing ground and field of application for these emerging modeling techniques, given the increasing availability of point-of-care testing and the need for titrated interventions in this patient population

La santé des employés de bureau : le cas de la fonction publique québécoise

Résumé des résultats d'une enquête de santé effectuée auprès d'un échantillon de 1 300 employés du gouvernement du Québec. Les données furent recueillies à l'aide d'examens médicaux et de questionnaires concernant les habitudes de vie et de travail des participants. Les résultats indiquent que les troubles psychiques et la consommation excessive de somnifères et de tranquillisants sont des problèmes réels que l'employeur et les syndicats concernés doivent considérer de plus prèsThis is the first study of its kind in Québec to present a veritable overview of health, based on a vast sample of office employees and compared with results obtained from other groups. The employees concerned were normally not exposed to chemical, physical or biological dangers at works. They were, nevertheless, subject to psycho-social dangers which are, of course, more difficult to determine.Health problems among these employees were detected as a result of a medical examination including: blood pressure tests, biological analyses and, for those 40 and over, electrocardiograms. The medical history of each participant was established in which particular attention was devoted to the taking of medication, treatment for diabetes, hypercholesterolemia or arterial tension, as well as previous Personal and family characteristics.Life habits were examined through replies to a questionnaire filled in by each participant before his medical examination. The following variables were tested: level of physical activity; consumption of alcohol, tobacco and medication. Mental health was examined in two ways: medical history and medical interview on the one hand, and a scale of psychological health included in the questionnaire on the other. Finally, work environment was examined with reference to scales of works satisfaction, organizational climate, and job characteristics.More that 1 300 Québec Government civil servants participated in this inquiry and undertook the required medical examination. Results include: contrary to pre-conceived notions about sedentary employees, the cardio-vascular system of civil servants is in good condition. The same cannot be said for the nervous System, where the main problems detected are psychic disorders and excessive consumption of soporifics and tranquilizers. The article describes these results in détail

From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses

The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information

Predicting late anemia in critical illness

INTRODUCTION: Identifying critically ill patients most likely to benefit from pre-emptive therapies will become increasingly important if therapies are to be used safely and cost-effectively. We sought to determine whether a predictive model could be constructed that would serve as a useful decision support tool for the pre-emptive management of intensive care unit (ICU)-related anemia. METHODS: Our cohort consisted of all ICU patients (n = 5,170) admitted to a large tertiary-care academic medical center during the period from 1 July 2000 to 30 June 2001. We divided the cohort into development (n = 3,619) and validation (n = 1,551) sets. Using a set of demographic and physiologic variables available within six hours of ICU admission, we developed models to predict patients who either received late transfusion or developed late anemia. We then constructed a point system to quantify, within six hours of ICU admission, the likelihood of developing late anemia. RESULTS: Models showed good discrimination with receiver operating characteristic curve areas ranging from 0.72 to 0.77, although predicting late transfusion was consistently less accurate than predicting late anemia. A five-item point system predicted likelihood of late anemia as well as existing clinical trial inclusion criteria but resulted in pre-emptive intervention more than two days earlier. CONCLUSION: A rule-based decision support tool using information available within six hours of ICU admission may lead to earlier and more appropriate use of blood-sparing strategies

A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside

RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis

INTRODUCTION: The lack of a standard definition for acute kidney injury has resulted in a large variation in the reported incidence and associated mortality. RIFLE, a newly developed international consensus classification for acute kidney injury, defines three grades of severity – risk (class R), injury (class I) and failure (class F) – but has not yet been evaluated in a clinical series. METHODS: We performed a retrospective cohort study, in seven intensive care units in a single tertiary care academic center, on 5,383 patients admitted during a one year period (1 July 2000–30 June 2001). RESULTS: Acute kidney injury occurred in 67% of intensive care unit admissions, with maximum RIFLE class R, class I and class F in 12%, 27% and 28%, respectively. Of the 1,510 patients (28%) that reached a level of risk, 840 (56%) progressed. Patients with maximum RIFLE class R, class I and class F had hospital mortality rates of 8.8%, 11.4% and 26.3%, respectively, compared with 5.5% for patients without acute kidney injury. Additionally, acute kidney injury (hazard ratio, 1.7; 95% confidence interval, 1.28–2.13; P < 0.001) and maximum RIFLE class I (hazard ratio, 1.4; 95% confidence interval, 1.02–1.88; P = 0.037) and class F (hazard ratio, 2.7; 95% confidence interval, 2.03–3.55; P < 0.001) were associated with hospital mortality after adjusting for multiple covariates. CONCLUSION: In this general intensive care unit population, acute kidney 'risk, injury, failure', as defined by the newly developed RIFLE classification, is associated with increased hospital mortality and resource use. Patients with RIFLE class R are indeed at high risk of progression to class I or class F. Patients with RIFLE class I or class F incur a significantly increased length of stay and an increased risk of inhospital mortality compared with those who do not progress past class R or those who never develop acute kidney injury, even after adjusting for baseline severity of illness, case mix, race, gender and age