Early Mobility Index and Patient Outcomes: A Retrospective Study in Multiple Intensive Care Units.

BACKGROUND: Early mobility interventions in intensive care units (ICUs) are safe and improve outcomes in subsets of critically ill adults. However, implementation varies, and the optimal mobility dose remains unclear. OBJECTIVE: To test for associations between daily dose of out-of-bed mobility and patient outcomes in different ICUs. METHODS: In this retrospective cohort study of electronic records from 7 adult ICUs in an academic quarternary hospital, multivariable linear regression was used to examine the effects of out-of-bed events per mobility-eligible day on mechanical ventilation duration and length of ICU and hospital stays. RESULTS: In total, 8609 adults hospitalized in ICUs from 2015 through 2018 were included. Patients were mobilized out of bed on 46.5% of ICU days and were eligible for mobility interventions on a median (IQR) of 2.0 (1-3) of 2.7 (2-9) ICU days. Median (IQR) out-of-bed events per mobility-eligible day were 0.5 (0-1.2) among all patients. For every unit increase in out-of-bed events per mobility-eligible day before extubation, mechanical ventilation duration decreased by 10% (adjusted coefficient [95% CI], -0.10 [-0.18 to -0.01]). Daily mobility increased ICU stays by 4% (adjusted coefficient [95% CI], 0.04 [0.03-0.06]) and decreased hospital stays by 5% (adjusted coefficient [95% CI], -0.05 [-0.07 to -0.03]). Effect sizes differed among ICUs. CONCLUSIONS: More daily out-of-bed mobility for ICU patients was associated with shorter mechanical ventilation duration and hospital stays, suggesting a dose-response relationship between daily mobility and patient outcomes. However, relationships differed across ICU subpopulations.

Using the ROX Index to Predict Treatment Outcome for High-Flow Nasal Cannula and/or Noninvasive Ventilation in Patients With COPD Exacerbations.

BACKGROUND: The ratio of oxygen saturation index (ROX index; or SpO2 /FIO2 /breathing frequency) has been shown to predict risk of intubation after high-flow nasal cannula (HFNC) support among adults with acute hypoxemic respiratory failure primarily due to pneumonia. However, its predictive value for other subtypes of respiratory failure is unknown. This study investigated whether the ROX index predicts liberation from HFNC or noninvasive ventilation (NIV), intubation with mechanical ventilation, or death in adults admitted for respiratory failure due to an exacerbation of COPD. METHODS: We performed a retrospective study of 260 adults hospitalized with a COPD exacerbation and treated with HFNC and/or NIV (continuous or bi-level). ROX index scores were collected at treatment initiation and predefined time intervals throughout HFNC and/or NIV treatment or until the subject was intubated or died. A ROX index score of ≥ 4.88 was applied to the cohort to determine if the same score would perform similarly in this different cohort. Accuracy of the ROX index was determined by calculating the area under the receiver operator curve. RESULTS: A total of 47 subjects (18%) required invasive mechanical ventilation or died while on HFNC/NIV. The ROX index at treatment initiation, 1 h, and 6 h demonstrated the best prediction accuracy for avoidance of invasive mechanical ventilation or death (area under the receiver operator curve 0.73 [95% CI 0.66-0.80], 0.72 [95% CI 0.65-0.79], and 0.72 [95% CI 0.63-0.82], respectively). The optimal cutoff value for sensitivity (Sn) and specificity (Sp) was a ROX index score > 6.88 (sensitivity 62%, specificity 57%). CONCLUSIONS: The ROX index applied to adults with COPD exacerbations treated with HFNC and/or NIV required higher scores to achieve similar prediction of low risk of treatment failure when compared to subjects with hypoxemic respiratory failure/pneumonia. ROX scores < 4.88 did not accurately predict intubation or death.

Work of Breathing During Proportional Assist Ventilation as a Predictor of Extubation Failure.

BACKGROUND: Despite decades of research on predictors of extubation success, use of ventilatory support after extubation is common and 10-20% of patients require re-intubation. Proportional assist ventilation (PAV) mode automatically calculates estimated total work of breathing (total WOB). Here, we assessed the performance of total WOB to predict extubation failure in invasively ventilated subjects. METHODS: This prospective observational study was conducted in 6 adult ICUs at an academic medical center. We enrolled intubated subjects who successfully completed a spontaneous breathing trial, had a rapid shallow breathing index < 105 breaths/min/L, and were deemed ready for extubation by the primary team. Total WOB values were recorded at the end of a 30-min PAV trial. Extubation failure was defined as any respiratory support and/or re-intubation within 72 h of extubation. We compared total WOB scores between groups and performance of total WOB for predicting extubation failure with receiver operating characteristic curves. RESULTS: Of 61 subjects enrolled, 9.8% (n = 6) required re-intubation, and 50.8% (n = 31) required any respiratory support within 72 h of extubation. Median total WOB at 30 min on PAV was 0.9 J/L (interquartile range 0.7-1.3 J/L). Total WOB was significantly different between subjects who failed or were successfully extubated (median 1.1 J/L vs 0.7 J/L, P = .004). The area under the curve was 0.71 [95% CI 0.58-0.85] for predicting any requirement of respiratory support and 0.85 [95% CI 0.69-1.00] for predicting re-intubation alone within 72 h of extubation. Total WOB cutoff values maximizing sensitivity and specificity equally were 1.0 J/L for any respiratory support (positive predictive value [PPV] 70.0%, negative predictive value [NPV] 67.7%) and 1.3 J/L for re-intubation (PPV 26.3%, NPV 97.6%). CONCLUSIONS: The discriminative performance of a PAV-derived total WOB value to predict extubation failure was good, indicating total WOB may represent an adjunctive tool for assessing extubation readiness. However, these results should be interpreted as preliminary, with specific thresholds of PAV-derived total WOB requiring further investigation in a large multi-center study.

Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.

To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data. DESIGN: Retrospective, observational cohort study. SETTING: Academic medical center ICU. PATIENTS: Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6). CONCLUSIONS: Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

Standardisation, multi-measure, data quality and trending: A qualitative study on multidisciplinary perspectives to improve intensive care early mobility monitoring.

OBJECTIVE: To explore multi-clinician perspectives on intensive care early mobility, monitoring and to assess the perceived value of technology-generated mobility metrics to provide user feedback to inform research, practice improvement, and technology development. METHODS: We performed a qualitative descriptive study. Three focus groups were conducted with critical care clinicians, including nurses (n = 10), physical therapists (n = 8) and physicians (n = 8) at an academic medical centre that implemented an intensive care early mobility programme in 2012. Qualitative thematic analysis was used to code transcripts and identify overarching themes. FINDINGS: Along with reaffirming the value of performing early mobility interventions, four themes for improving mobility monitoring emerged, including the need for: 1) standardised indicators for documenting mobility; 2) inclusion of both quantitative and qualitative metrics to measure mobility 3) a balance between quantity and quality of data; and 4) trending mobility metrics over time. CONCLUSION: Intensive care mobility monitoring should be standardised and data generated should be high quality, capable of supporting trend analysis, and meaningful. By improving measurement and monitoring of mobility, future researchers can examine the arc of activity that patients in the intensive care unit undergo and develop models to understand factors that influence successful implementation.