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Introduction:The Society of Critical Care Medicine Tele-Critical Care (TCC) Committee has identified the need for rigorous comparative research of different TCC delivery models to support the development of best practices for staffing, application, and approaches to workflow. Our objective was to describe and compare outcomes between two TCC delivery models, TCC with 24/7 Bedside Intensivist (BI) compared with TCC with Private Daytime Attending Intensivist (PI) in relation to intensive care unit (ICU) and hospital mortality, ICU and hospital length of stay (LOS), cost, and complications across the spectrum of routine ICU standards of care. Methods:Observational cohort study at large health care system in 12 ICUs and included patients, ≥18, with Acute Physiology and Chronic Health Evaluation (APACHE) IVa scores and predictions (October 2016–June 2019). Results:Of the 19,519 ICU patients, 71.7% (n = 13,993) received TCC with 24/7 BI while 28.3% (n = 5,526) received TCC with PI. ICU and Hospital mortality (4.8% vs. 3.1%, p < 0.0001; 12.6% vs. 8.1%, p < 0.001); and ICU and Hospital LOS (3.2 vs. 2.4 days, p < 0.001; 9.8 vs. 7.2 days, p < 0.001) were significantly higher among 24/7 BI compared with PI. The APACHE observed/expected ratios (odds ratio [OR]; 95% confidence interval [CI]) for ICU mortality (0.62; 0.58–0.67) vs. (0.53; 0.46–0.61) and Hospital mortality (0.95; 0.57–1.48) vs. (0.77; 0.70–0.84) were significantly different for 24/7 BI compared with PI. Multivariate mixed models that adjusted for confounders demonstrated significantly greater odds of (OR; 95% CI) ICU mortality (1.58; 1.28–1.93), Hospital mortality (1.52; 1.33–1.73), complications (1.55; 1.18–2.04), ICU LOS [3.14 vs. 2.59 (1.25; 1.19–1.51)], and Hospital LOS [9.05 vs. 7.31 (1.23; 1.21–1.25)] among 24/7 BI when compared with PI. Sensitivity analyses adjusting for ICU admission within 24 h of hospital admission, receiving active ICU treatments, nighttime admission, sepsis, and highest third acute physiology score indicated significantly higher odds for 24/7 BI compared with PI. Conclusion:Our comparison demonstrated that TCC delivery model with PI provided high-quality care with significant positive effects on outcomes. This suggests that TCC delivery models have broad-ranging applicability and benefits in routine critical care, thus necessitating progressive research in this direction. [ABSTRACT FROM AUTHOR]

Objectives: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance. Design: Retrospective, multicenter study of ICU patients randomized in 3:2 fashion into development and validation cohorts. Generalized additive models (GAM) with features designed to mitigate biases introduced from documentation of admission diagnosis, Glasgow Coma Scale (GCS), and extreme vital signs were developed using clinical features representing the first 24 hours of ICU admission. Setting: eICU Research Institute database derived from ICUs participating in the Philips eICU telecritical care program. Patients: A total of 572,985 adult ICU stays discharged from the hospital between January 1, 2017, and December 31, 2018, were included, yielding 509,586 stays in the final cohort; 305,590 and 203,996 in development and validation cohorts, respectively. Interventions: None. Measurements and Main Results: Model discrimination was compared against Acute Physiology and Chronic Health Evaluation (APACHE) IVa/IVb models on the validation cohort using the area under the receiver operating characteristic (AUROC) curve. Calibration assessed by actual/predicted ratios, calibration-in-the-large statistics, and visual analysis. Performance metrics were further stratified by subgroups of admission diagnosis and ICU characteristics. Historic data from two health systems with abrupt changes in Glasgow Coma Scale (GCS) documentation were assessed in the year prior to and after data shift. CCOPM outperformed APACHE IVa/IVb for ICU mortality (AUROC, 0.925 vs 0.88) and hospital mortality (AUROC, 0.90 vs 0.86). Better calibration performance was also attained among subgroups of different admission diagnoses, ICU types, and over unique ICU-years. The CCOPM provided more stable predictions compared with APACHE IVa within an external cohort of greater than 120,000 patients from two health systems with known changes in GCS documentation. Conclusions: These mortality risk models demonstrated excellent performance compared with APACHE while appearing to mitigate bias introduced through major shifts in GCS documentation at two large health systems. This provides evidence to support using automated capture rather than trained personnel for capture of GCS data used in benchmarking ICUs on mortality performance. [ABSTRACT FROM AUTHOR]

Background: Failure to rescue (FTR) is a benchmark of quality care. Limited evidence exists examining the influence of telemedicine intensive care units (tele-ICU) nursing interventions in preventing FTR. The purpose of this study was to characterize tele-ICU nursing interventions and to determine which combination of documented tele-ICU nursing interventions (DTNI) best predicts prevention of FTR in ICU patients with hospital-acquired conditions (HACs). Materials and Methods: We used convergent parallel mixed methods design to conduct qualitative interviews with a purposive sample of tele-ICU nurses ( n  = 19) from 11 US tele-ICU centers. Quantitative data, including demographics, DTNIs, severity of illness scores, and video assessment times from January 2016 to December 2016 were retrieved for ICU patients discharged from a multihospital health system with a tele-ICU center ( n  = 861). Findings from both qualitative and quantitative analyses were merged, compared, and contrasted. Results: FTR patients had higher severity of illness, longer video assessment by tele-ICU nurses, and were more likely to have DTNIs related to hemodynamic instability. Four themes emerged from qualitative analysis: fundamental tele-ICU nurse attributes, proactive clinical practice, effective collaborative relationships, and strategic use of advanced technology. Mixed methods analysis revealed convergence between DTNIs and tele-ICU nurses' characterizations of their practice. Conclusions: Tele-ICU nurses' characterizations of their practice closely align with DTNIs. Tele-ICU nursing practice to prevent FTR involves systems thinking and integration of many complex factors. Tele-ICU nurses can reduce the odds of FTR with focus on support and clinical coordination interventions that avoid hemodynamic instability in ICU patients with a diagnosed HAC.