Prediction of ICU Delirium: Validation of Current Delirium Predictive Models in Routine Clinical Practice.

OBJECTIVES: To investigate the ability of available delirium risk assessment tools to identify patients at risk of delirium in an Australian tertiary ICU. DESIGN: Prospective observational study. SETTING: An Australian tertiary ICU. PATIENTS: All patients admitted to the study ICU between May 8, 2017, and December 31, 2017, were assessed bid for delirium throughout their ICU stay using the Confusion Assessment Method for ICU. Patients were included in this study if they remained in ICU for over 24 hours and were excluded if they were delirious on ICU admission, or if they were unable to be assessed using the Confusion Assessment Method for ICU during their ICU stay. Delirium risk was calculated for each patient using the prediction of delirium in ICU patients, early prediction of delirium in ICU patients, and Lanzhou models. Data required for delirium predictor models were obtained retrospectively from patients medical records. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 803 ICU admissions during the study period, of which 455 met inclusion criteria. 35.2% (n = 160) were Confusion Assessment Method for ICU positive during their ICU admission. Delirious patients had significantly higher Acute Physiology and Chronic Health Evaluation III scores (median, 72 vs 54; p < 0.001), longer ICU (median, 4.8 vs 1.8 d; p < 0.001) and hospital stay (16.0 vs 8.16 d; p < 0.001), greater requirement of invasive mechanical ventilation (70% vs 21.4%; p < 0.001), and increased ICU mortality (6.3% vs 2.4%; p = 0.037). All models included in this study displayed moderate to good discriminative ability. Area under the receiver operating curve for the prediction of delirium in ICU patients was 0.79 (95% CI, 0.75-0.83); recalibrated prediction of delirium in ICU patients was 0.79 (95% CI, 0.75-0.83); early prediction of delirium in ICU patients was 0.72 (95% CI, 0.67-0.77); and the Lanzhou model was 0.77 (95% CI, 0.72-0.81). CONCLUSIONS: The predictive models evaluated in this study demonstrated moderate to good discriminative ability to predict ICU patients' risk of developing delirium. Models calculated at 24-hours post-ICU admission appear to be more accurate but may have limited utility in practice.

Indicators of maximal hiking performance in Laser sailors.

Laser class sailors have to hike out, i.e. hook their feet under the toe straps near the centreline of the boat and hold their upper bodies over the edge of the boat, to counteract the heeling forces generated by the sails. To identify the parameters that are associated with maximal hiking performance, this cross-sectional observational study measures various knee extensor and hip flexor muscle performance characteristics in 55 Laser sailors and correlates each with the area-under-the-curve hiking moment over 3 min of hiking on a hiking dynamometer (HM180). Our results showed that higher body mass and HM180 were significantly associated with better race scores (Spearman's rho = - 0.69 and - 0.62, respectively, both P < 0.01) in male sailors who participated in the National Inter-School Laser competition. Body mass (Pearson's correlation coefficient, r > or = 0.95, P < 0.01 in both males and females), maximum voluntary isometric strength of the quadriceps (r > or = 0.80, P < 0.01 in both males and females), and 3-RM knee extension strength (r > or = 0.80, P < 0.01 in both males and females) were associated with a higher HM180. The correlations between height, abdominal muscle endurance (crunches), explosive lower body strength (vertical jumps), cycling time-to-exhaustion, quadriceps strength endurance, or isometric quadriceps endurance with incremental loads (bucket test), and HM180 were weaker (r < 0.60). HM180 may be a useful performance indicator for Laser racing. Since strength measures correlated well with HM180, greater emphasis should be placed on developing maximum strength in the quadriceps to improve maximal hiking performance.