Objectives: Suspected infection and sepsis are common conditions seen among older ICU patients. Frailty has prognostic importance among critically ill patients, but its impact on outcomes and resource utilization in older patients with suspected infection is unknown. We sought to evaluate the association between patient frailty (defined as a Clinical Frailty Scale ≥ 5) and outcomes of critically ill patients with suspected infection. We also evaluated the association between frailty and the quick Sequential Organ Failure Assessment score. Design: Analysis of a prospectively collected registry. Setting: Two hospitals within a single tertiary care level hospital system between 2011 and 2016. Patients: We analyzed 1,510 patients 65 years old or older (at the time of ICU admission) and with suspected infection at the time of ICU admission. Of these, 507 (33.6%) were categorized as “frail” (Clinical Frailty Scale ≥ 5). Interventions: None. Measurements and Main Results: The primary outcome was in-hospital mortality. A total of 558 patients (37.0%) died in-hospital. Frailty was associated with increased risk of in-hospital death (adjusted odds ratio, 1.81 [95% CIs, 1.34–2.49]). Frailty was also associated with higher likelihood of discharge to long-term care (adjusted odds ratio, 2.06 [95% CI, 1.50–2.64]) and higher likelihood of readmission within 30 days (adjusted odds ratio, 1.83 [95% CI, 1.38–2.34]). Frail patients had increased ICU resource utilization and total costs. The combination of frailty and quick Sequential Organ Failure Assessment greater than or equal to 2 further increased the risk of death (adjusted odds ratio, 7.54 [95% CI, 5.82–9.90]). Conclusions: The presence of frailty among older ICU patients with suspected infection is associated with increased mortality, discharge to long-term care, hospital readmission, resource utilization, and costs. This work highlights the importance of clinical frailty in risk stratification of older ICU patients with suspected infection.

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Purpose. Delirium frequently affects critically ill patients in the intensive care unit (ICU). The purpose of this study is to evaluate the impact of delirium on ICU and hospital length of stay (LOS) and perform a cost analysis. Materials and Methods. Prospective studies and randomized controlled trials of patients in the ICU with delirium published between January 1, 2015, and December 31, 2020, were evaluated. Outcome variables including ICU and hospital LOS were obtained, and ICU and hospital costs were derived from the respective LOS. Results. Forty-one studies met inclusion criteria. The mean difference of ICU LOS between patients with and without delirium was significant at 4.77 days ( p < 0.001 ); for hospital LOS, this was significant at 6.67 days ( p < 0.001 ). Cost data were extractable for 27 studies in which both ICU and hospital LOS were available. The mean difference of ICU costs between patients with and without delirium was significant at $3,921 ( p < 0.001 ); for hospital costs, the mean difference was $5,936 ( p < 0.001 ). Conclusion. ICU and hospital LOS and associated costs were significantly higher for patients with delirium, compared to those without delirium. Further research is necessary to elucidate other determinants of increased costs and cost-reducing strategies for critically ill patients with delirium. This can provide insight into the required resources for the prevention of delirium, which may contribute to decreasing healthcare expenditure while optimizing the quality of care.

Lung ultrasound (LUS) is an accurate thoracic imaging technique distinguished by its handheld size, low-cost, and lack of radiation. User dependence and poor access to training have limited the impact and dissemination of LUS outside of acute care hospital environments. Automated interpretation of LUS using deep learning can overcome these barriers by increasing accuracy while allowing point-of-care use by non-experts. In this multicenter study, we seek to automate the clinically vital distinction between A line (normal parenchyma) and B line (abnormal parenchyma) on LUS by training a customized neural network using 272,891 labelled LUS images. After external validation on 23,393 frames, pragmatic clinical application at the clip level was performed on 1162 videos. The trained classifier demonstrated an area under the receiver operating curve (AUC) of 0.96 (±0.02) through 10-fold cross-validation on local frames and an AUC of 0.93 on the external validation dataset. Clip-level inference yielded sensitivities and specificities of 90% and 92% (local) and 83% and 82% (external), respectively, for detecting the B line pattern. This study demonstrates accurate deep-learning-enabled LUS interpretation between normal and abnormal lung parenchyma on ultrasound frames while rendering diagnostically important sensitivity and specificity at the video clip level.