Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Multicentre pilot randomised clinical trial of early in-bed cycle ergometry with ventilated patients.

Introduction: Acute rehabilitation in critically ill patients can improve post-intensive care unit (post-ICU) physical function. In-bed cycling early in a patient\u27s ICU stay is a promising intervention. The objective of this study was to determine the feasibility of recruitment, intervention delivery and retention in a multi centre randomised clinical trial (RCT) of early in-bed cycling with mechanically ventilated (MV) patients. Methods: We conducted a pilot RCT conducted in seven Canadian medical-surgical ICUs. We enrolled adults who could ambulate independently before ICU admission, within the first 4 days of invasive MV and first 7 days of ICU admission. Following informed consent, patients underwent concealed randomisation to either 30 min/day of in-bed cycling and routine physiotherapy (Cycling) or routine physiotherapy alone (Routine) for 5 days/week, until ICU discharge. Our feasibility outcome targets included: accrual of 1-2 patients/month/site; \u3e80% cycling protocol delivery; \u3e80% outcomes measured and \u3e80% blinded outcome measures at hospital discharge. We report ascertainment rates for our primary outcome for the main trial (Physical Function ICU Test-scored (PFIT-s) at hospital discharge). Results: Between 3/2015 and 6/2016, we randomised 66 patients (36 Cycling, 30 Routine). Our consent rate was 84.6 % (66/78). Patient accrual was (mean (SD)) 1.1 (0.3) patients/month/site. Cycling occurred in 79.3% (146/184) of eligible sessions, with a median (IQR) session duration of 30.5 (30.0, 30.7) min. We recorded 43 (97.7%) PFIT-s scores at hospital discharge and 37 (86.0%) of these assessments were blinded. Discussion: Our pilot RCT suggests that a future multicentre RCT of early in-bed cycling for MV patients in the ICU is feasible. Trial registration number: NCT02377830

Review and classification of variability analysis techniques with clinical applications

Abstract Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.</p