Mikkelsen, Mark E.

Objectives: Data are lacking regarding implementation of novel strategies such as follow-up clinics and peer support groups, to reduce the burden of postintensive care syndrome. We sought to discover enablers that helped hospital-based clinicians establish post-ICU clinics and peer support programs, and identify barriers that challenged them. Design: Qualitative inquiry. The Consolidated Framework for Implementation Research was used to organize and analyze data. Setting: Two learning collaboratives (ICU follow-up clinics and peer support groups), representing 21 sites, across three continents. Subjects: Clinicians from 21 sites. Measurement and Main Results: Ten enablers and nine barriers to implementation of “ICU follow-up clinics” were described. A key enabler to generate support for clinics was providing insight into the human experience of survivorship, to obtain interest from hospital administrators. Significant barriers included patient and family lack of access to clinics and clinic funding. Nine enablers and five barriers to the implementation of “peer support groups” were identified. Key enablers included developing infrastructure to support successful operationalization of this complex intervention, flexibility about when peer support should be offered, belonging to the international learning collaborative. Significant barriers related to limited attendance by patients and families due to challenges in creating awareness, and uncertainty about who might be appropriate to attend and target in advertising. Conclusions: Several enablers and barriers to implementing ICU follow-up clinics and peer support groups should be taken into account and leveraged to improve ICU recovery. Among the most important enablers are motivated clinician leaders who persist to find a path forward despite obstacles.

Objectives: Patients and caregivers can experience a range of physical, psychologic, and cognitive problems following critical care discharge. The use of peer support has been proposed as an innovative support mechanism. Design: We sought to identify technical, safety, and procedural aspects of existing operational models of peer support, among the Society of Critical Care Medicine Thrive Peer Support Collaborative. We also sought to categorize key distinctions between these models and elucidate barriers and facilitators to implementation. Subjects and Setting: Seventeen Thrive sites from the United States, United Kingdom, and Australia were represented by a range of healthcare professionals. Measurements and Main Results: Via an iterative process of in-person and email/conference calls, members of the Collaborative defined the key areas on which peer support models could be defined and compared, collected detailed self-reports from all sites, reviewed the information, and identified clusters of models. Barriers and challenges to implementation of peer support models were also documented. Within the Thrive Collaborative, six general models of peer support were identified: community based, psychologist-led outpatient, models-based within ICU follow-up clinics, online, groups based within ICU, and peer mentor models. The most common barriers to implementation were recruitment to groups, personnel input and training, sustainability and funding, risk management, and measuring success. Conclusions: A number of different models of peer support are currently being developed to help patients and families recover and grow in the postcritical care setting.

Abstract Rationale Accurate, early identification of patients at risk for developing acute lung injury (ALI) provides the opportunity to test and implement secondary prevention strategies. Objectives To determine the frequency and outcome of ALI development in patients at risk and validate a lung injury prediction score (LIPS). Methods In this prospective multicenter observational cohort study, predisposing conditions and risk modifiers predictive of ALI development were identified from routine clinical data available during initial evaluation. The discrimination of the model was assessed with area under receiver operating curve (AUC). The risk of death from ALI was determined after adjustment for severity of illness and predisposing conditions. Measurements and Main Results Twenty-two hospitals enrolled 5,584 patients at risk. ALI developed a median of 2 (interquartile range 1–4) days after initial evaluation in 377 (6.8%; 148 ALI-only, 229 adult respiratory distress syndrome) patients. The frequency of ALI varied according to predisposing conditions (from 3% in pancreatitis to 26% after smoke inhalation). LIPS discriminated patients who developed ALI from those who did not with an AUC of 0.80 (95% confidence interval, 0.78–0.82). When adjusted for severity of illness and predisposing conditions, development of ALI increased the risk of in-hospital death (odds ratio, 4.1; 95% confidence interval, 2.9–5.7). Conclusions ALI occurrence varies according to predisposing conditions and carries an independently poor prognosis. Using routinely available clinical data, LIPS identifies patients at high risk for ALI early in the course of their illness. This model will alert clinicians about the risk of ALI and facilitate testing and implementation of ALI prevention strategies. Clinical trial registered with www.clinicaltrials.gov (NCT00889772).