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Pragmatic methods to avoid intensive care unit admission when it does not align with patient and family goals.

ABSTRACT: For patients with chronic, life-limiting illnesses, admission to the intensive care unit (ICU) near the end of life might not improve patient outcomes or be consistent with patient and family values, goals, and preferences. In this context, advance care planning and palliative care interventions designed to clarify patients' values, goals, and preferences have the potential to reduce provision of high-intensity interventions that are unwanted or non-beneficial. In this Series paper, we have assessed interventions that are effective at helping patients with chronic, life-limiting illnesses to avoid an unwanted ICU admission. The evidence found was largely from observational studies, with considerable heterogeneity in populations, methods, and types of interventions. Results from randomised trials of interventions to improve communication about goals of care are scarce, of variable quality, and mixed. Although observational studies show that advance care planning and palliative care interventions are associated with a reduced number of ICU admissions at the end of life, causality has not been well established. Using the available evidence we suggest recommendations to help to avoid ICU admission when it does not align with patient and family values, goals, and preferences and conclude with future directions for research.

SUBMITTER: Khandelwal N

PROVIDER: S-EPMC6733408 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Pragmatic methods to avoid intensive care unit admission when it does not align with patient and family goals.

Khandelwal Nita N Long Ann C AC Lee Robert Y RY McDermott Cara L CL Engelberg Ruth A RA Curtis J Randall JR

The Lancet. Respiratory medicine 20190520 7

For patients with chronic, life-limiting illnesses, admission to the intensive care unit (ICU) near the end of life might not improve patient outcomes or be consistent with patient and family values, goals, and preferences. In this context, advance care planning and palliative care interventions designed to clarify patients' values, goals, and preferences have the potential to reduce provision of high-intensity interventions that are unwanted or non-beneficial. In this Series paper, we have asse ...[more]

PMID: 31122895

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Project description:ObjectiveTo compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours.BackgroundPrediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models.MethodsAll patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospital (SGH) between 1 January 2012 and 31 October 2016 were included. Patient demographics, comorbidities, preoperative laboratory results, and surgery details were obtained from their electronic medical records. Seventy percent of the observations were randomly selected for training, leaving 30% for testing. Baseline models were CARES and ASA-PS. Candidate models were trained using random forest, adaptive boosting, gradient boosting, and support vector machine. Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).ResultsA total of 90,785 patients were included, of whom 539 (0.6%) died within 30 days and 1264 (1.4%) required ICU admission >24 hours postoperatively. Baseline models achieved high AUROCs despite poor sensitivities by predicting all negative in a predominantly negative dataset. Gradient boosting was the best performing model with AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes respectively.ConclusionsMachine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.

Project description:IntroductionEngaging patients and family members as partners in research increases the relevance of study results and enhances patient-centered care; how to best engage patients and families in research is unknown.MethodsWe tested a novel research approach that engages and trains patients and family members as researchers to see if we could understand and describe the experiences of patients admitted to the intensive care unit (ICU) and their families. Former patients and family members conducted focus groups and interviews with patients (n = 11) and families of surviving (n = 14) and deceased (n = 7) patients from 13 ICUs in Alberta Canada, and analyzed data using conventional content analysis. Separate blinded qualitative researchers conducted an independent analysis.ResultsParticipants described three phases in the patient/family "ICU journey"; admission to ICU, daily care in ICU, and post-ICU experience. Admission to ICU was characterized by family shock and disorientation with families needing the presence and support of a provider. Participants described five important elements of daily care: honoring the patient's voice, the need to know, decision-making, medical care, and culture in ICU. The post-ICU experience was characterized by the challenges of the transition from ICU to a hospital ward and long-term effects of critical illness. These "ICU journey" experiences were described as integral to appropriate interactions with the care team and comfort and trust in the ICU, which were perceived as essential for a community of caring. Participants provided suggestions for improvement: 1) provide a dedicated family navigator, 2) increase provider awareness of the fragility of family trust, 3) improve provider communication skills, 4) improve the transition from ICU to hospital ward, and 5) inform patients about the long-term effects of critical illness. Analyses by independent qualitative researchers identified similar themes.ConclusionsPatient and family member-led research is feasible and can identify opportunities for improving care.

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Pragmatic methods to avoid intensive care unit admission when it does not align with patient and family goals.

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Pragmatic methods to avoid intensive care unit admission when it does not align with patient and family goals.

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