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Seriously ill surgical patients require complex and integrated surgical, interventional, and medical management to balance the risks and benefits that complicate decision-making. Palliative care principles can aid surgeons in these cases. To illustrate this, we describe a scenario of a patient with unresectable hepatocellular carcinoma with portal vein tumor thrombus causing portal hypertension. We discuss options for managing the sequelae of portal hypertension, including varices and ascites. We explore the interventional and surgical options for mitigating or palliating the underlying portal hypertension. Advances in interventional radiological techniques can facilitate the creation of transjugular intrahepatic portosystemic shunts (TIPSs), even with extensive portal vein thrombus. If interventional approaches are not possible, surgical shunts can be considered but carry significant risks that must be weighed against the benefits. To communicate effectively, we outline key steps to breaking bad news. To make shared decisions in challenging cases, we describe how to elicit a patient’s hopes, expectations, concerns, and preferences; how to synthesize goals of care from these stated values; and how to use those goals to guide decision-making.

There is growing interest in, and need for, integrating palliative care (PC) into the care of patients undergoing emergency surgery and those with traumatic injury. Thus, PC consults for these populations will likely grow in the coming years. Understanding the nuances and unique characteristics of these two acutely ill populations will improve the care that PC clinicians can provide. Using a modified Delphi technique, this article offers 10 tips that experts in the field, based on their broad clinical experience, believe PC clinicians should know about the care of trauma and emergency surgery patients.

The goal of this review is to synthesize a strategy for difficult conversations using evidence from the literature and our experience in surgical and palliative care practice. Several published and established resources can inform surgeons’ communication skills. Vital Talk and the Serious Illness Care Program are formal training programs of four days and a half-day in length, respectively, for improving providers’ communication skills. The Best Case/Worst Case tool is a decision aid developed by surgeons for surgeons guiding patients through difficult choices. Published frameworks such as SPIKES and ABCDE outline key steps to navigate challenging conversations and break bad news. Numerous factors contribute to quality of communication, including setting, positioning of participants, open-ended questions, clear and concise language, responding to emotion, and engaging in shared decision-making. We describe a strategy for difficult conversations that is tailored for use by surgeons and modeled on surgical principles.

Background Surgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients’ diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making. Methods Cochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Results Requirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences. Conclusion Traditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

Context Patients with significant burn injuries likely have palliative care needs. Objectives We performed a systematic review of existing evidence concerning the palliative care needs of burn patients. Methods Through November 26, 2018, we systematically searched PubMed, CINAHL, Embase, Web of Science, and Scopus, using terms representing burn injuries and the eight domains of quality palliative care as outlined by the National Consensus Project for Quality Palliative Care. Eligible articles involved burn-injured patients treated with an intervention targeting at least one of the eight domains. Results Our searches yielded 7532 unique records, which led to 238 articles for full review and 88 studies that met inclusion criteria. Seventy-five studies addressed the domain physical aspects of care and merit a separate systematic review; 13 studies were included in our final review. Four of the seven domains—processes of care, psychologic symptoms, social aspects, and end of life—were addressed by studies but three domains—spiritual, cultural, or ethics—were unaddressed. Included studies highlight potential benefits from peridischarge self-care education programs, peer support, and group therapy in improving quality of life. In patients with severe injuries, end-of-life decision-making protocols were associated with increased utilization of comfort-focused treatments. Conclusion Most existing palliative care-related research in burn patients addresses interventions for physical symptoms with minimal literature concerning other domains. Opportunities exist for further research of palliative care in burn populations with emphasis on addressing interventions for all domains and better standardizing the language and outcomes for the palliative care interventions.

Background/Objectives The P.A.L.Li.A.T.E. (prognostic assessment of life and limitations after trauma in the elderly) consortium has previously created a prognosis calculator for mortality after geriatric injury based on age, injury severity, and transfusion requirement called the geriatric trauma outcome score (GTOS). Here, we sought to create and validate a prognosis calculator called the geriatric trauma outcome score ii (GTOS II) estimating probability of unfavorable discharge. Design Retrospective cohort. Setting Four geographically diverse Level 1 trauma centers. Participants Trauma admissions aged 65 to 102 years surviving to discharge from 2000 to 2013. Intervention None. Measurements Age, injury severity score (ISS), transfusion at 24 hours post-admission, discharge dichotomized as favorable (home/rehabilitation) or unfavorable (skilled nursing/long term acute care/hospice). Training and testing samples were created using the holdout method. A multiple logistic mixed model (GTOS II) was created to estimate the odds of unfavorable disposition then re-specified using the GTOS II as the sole predictor in a logistic mixed model using the testing sample. Results The final dataset was 16,114 subjects (unfavorable discharge status = 15.4%). Training (n = 8,057) and testing (n = 8,057) samples had similar demographics. The formula based on the training sample was (GTOS II = Age + [0.71 × ISS] + 8.79 [if transfused by 24 hours]). Misclassification rate and AUC were 15.63% and 0.67 for the training sample, respectively, and 15.85% and 0.67 for the testing sample. Conclusion GTOS II estimates the probability of unfavorable discharge in injured elders with moderate accuracy. With the GTOS mortality calculator, it can help in goal setting conversations after geriatric injury.

The Geriatric Trauma Outcome Score (GTOS; [age] + [2.5 × Injury Severity Score] + 22 [if packed RBC transfused within ≤24 hours of admission]), was developed and validated as a prognostic indicator for in-hospital mortality in elderly trauma patients. However, GTOS neither provides information about post-discharge outcomes nor discriminates between patients dying with and without care restrictions. Isolating the latter, GTOS prediction performance was examined during admission and 1-year post discharge in a mature European trauma registry.All trauma admissions 65 years of age and older in a university hospital during 2007 to 2011 were considered. Data on age, Injury Severity Score, packed RBC transfusion within ≤24 hours, therapy restrictions, discharge disposition, and mortality were collected. In-hospital deaths with therapy restrictions and patients discharged to hospice were excluded. The GTOS was the sole predictor in a logistic regression model estimating mortality probabilities. Performance of the model was assessed by misclassification rate, Brier score, Tjur RThe study population was 1,080 patients with a median age of 75 years, mean Injury Severity Score of 10, and packed RBCs transfused in 8.2%. In-hospital mortality was 14.9% and 7.7% after exclusions. Misclassification rate fell from 14% to 6.5% and Brier score from 0.09 to 0.05, and area under the curve increased from 0.87 to 0.88. Equivalent values for the original GTOS sample were 9.8%, 0.07, and 0.82, respectively. One-year mortality follow-up showed a misclassification rate of 17.6% and Brier score of 0.13.Excluding patients with care restrictions and discharged to hospice improved GTOS performance for in-hospital mortality prediction. The GTOS is not adept at predicting 1-year mortality.

A prognostic tool for geriatric mortality after injury called the Geriatric Trauma Outcome Score (GTOS), where GTOS = [age] + [ISS × 2.5] + [22 if transfused any PRBCs by 24 hours after admission], was previously developed based on 13 years of data from geriatric trauma patients admitted to Parkland Hospital. We sought to validate this model.Four Level I centers identified subjects who are 65 years or older for the period of the original study. The GTOS model was first specified using the formula [GTOS = age + (ISS × 2.5) + 22 (if given PRBC by 24 hours)] developed from the Parkland sample and then used as the sole predictor in a logistic mixed model estimating probability of mortality in the validation sample, accounting for site as a random effect. We estimated the misclassification (error) rate, Brier score, Tjur R, and the area under the curve in evaluating the predictive performance of the GTOS model.The original Parkland sample (n = 3,841) had a mean (SD) age of 76.6 (8.1) years, mean (SD) ISS of 12.4 (9.9), mortality of 10.8%, and 11.9% receiving PRBCs at 24 hours. The validation sample (n = 18,282) had a mean (SD) age of 77.0 (8.1) years, mean (SD) ISS of 12.3 (10.6), mortality of 11.0%, and 14.1% receiving PRBCs at 24 hours. Fitting the GTOS model to the validation sample revealed that the parameter estimates from the validation sample were similar to those of fitting it to the Parkland sample with highly overlapping 95% confidence limits. The misclassification (error) rate for the GTOS logistic model applied to the validation sample was 9.97%, similar to that of the Parkland sample (9.79%). Brier score, Tjur R, and the area under the curve for the GTOS logistic model when applied to the validation sample were 0.07, 0.25, and 0.86, respectively, compared with 0.08, 0.20, and 0.82, respectively, for the Parkland sample.With the use of the data available at 24 hours after injury, the GTOS accurately predicts in-hospital mortality for the injured elderly.Prognostic study, level III.