Perspective; recommendations for improved patient participation in decision-making for geriatric patients in acute surgical settings.

Geriatric patients often present to the hospital in acute surgical settings. In these settings, shared decision-making as equal partners can be challenging. Surgeons should recognize that geriatric patients, and frail patients in particular, may sometimes benefit from de-escalation of care in a palliative setting rather than curative treatment. To provide more person-centred care, better strategies for improved shared decision-making need to be developed and implemented in clinical practice. A shift in thinking from a disease-oriented paradigm to a patient-goal-oriented paradigm is required to provide better person-centred care for older patients. We may greatly improve the collaboration with patients if we move parts of the decision-making process to the pre-acute phase. In the pre-acute phase appointing legal representatives, having goals of care conversations, and advance care planning can help give physicians an idea of what is important to the patient in acute settings. When making decisions as equal partners is not possible, a greater degree of physician responsibility may be appropriate. Physicians should tailor the "sharedness" of the decision-making process to the needs of the patient and their family.

External validation of the U-HIP prediction model for in-hospital mortality in geriatric hip fracture patients.

INTRODUCTION: Identification of high-risk hip fracture patients in an early stage is vital for guiding surgical management and shared decision making. To objective of this study was to perform an external international validation study of the U-HIP prediction model for in-hospital mortality in geriatric patients with a hip fracture undergoing surgery. MATERIALS AND METHODS: In this retrospective cohort study, data were used from The American College of Surgeons National Surgical Quality Improvement Program. Patients aged 70 years or above undergoing hip fracture surgery were included. The discrimination (c-statistic) and calibration of the model were investigated. RESULTS: A total of 25,502 patients were included, of whom 618 (2.4%) died. The mean predicted probability of in-hospital mortality was 3.9% (range 0%-55%). The c-statistic of the model was 0.74 (95% CI 0.72-0.76), which was comparable to the c-statistic of 0.78 (95% CI 0.71-0.85) that was found in the development cohort. The calibration plot indicated that the model was slightly overfitted, with a calibration-in-the-large of 0.015 and a calibration slope of 0.780. Within the subgroup of patients aged between 70 and 85, however, the c-statistic was 0.78 (95% CI 0.75-0.81), with good calibration (calibration slope 0.934). DISCUSSION AND CONCLUSION: The U-HIP model for in-hospital mortality in geriatric hip fractures was externally validated in a large international cohort, and showed a good discrimination and fair calibration. This model is freely available online and can be used to predict the risk of mortality, identify high-risk patients and aid clinical decision making.

Does the Dutch Safety Management Program predict adverse outcomes for older patients in the emergency department?

PURPOSE: Frailty screening in the emergency department may identify frail patients at risk for adverse outcomes. This study investigated if the Dutch Safety Management Program (VMS) screener predicts outcomes in older patients in the emergency department. METHODS: In this prospective cohort study, patients aged 70 years or older presenting to the emergency department were recruited on workdays between 10:00 AM and 7:00 PM from May 2017 until August 2017. Patients were screened in four domains: activities of daily living, malnutrition, risk of delirium, and risk of falling. After 90 days of follow up, mortality, functional decline, living situation, falls, readmission to the emergency department, and readmission to the hospital were recorded. VMS was studied using the total VMS score as a predictor with ROC curve analysis, and using a cut-off point to divide patients into frail and non-frail groups to calculate positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 249 patients were included. Higher VMS score was associated with 90-day mortality (AUC 0.65, 95% CI 0.54-0.76) and falling (AUC 0.67, 95% CI 0.56-0.78). VMS frailty predicted mortality (PPV 0.15, NPV 0.94, p = 0.05) and falling (PPV 0.22, NPV 0.92, p = 0.02), but none of the other outcomes. CONCLUSION: In this selected group of patients, higher VMS score was associated with 90-day mortality and falls. The low positive predictive value shows that the VMS screener is unsuitable for identifying high-risk patients in the ED. The high negative predictive value indicates that the screener can identify patients not at risk for adverse medical outcomes. This could be useful to determine which patients should undergo additional screening.