Improving Palliative Care with Deep Learning

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.Comment: IEEE International Conference on Bioinformatics and Biomedicine 201

Prevention of ICU Delirium Through Implementation of a Sleep Promotion Bundle

Background: Intensive care unit (ICU) delirium is the prevalence of delirium in ICU patientswho do not have a history of drug/alcohol abuse, an admission for a mental status change, or anadmission to the ICU for less than 24 hours. Serious adverse outcomes have been linked to thepresence of ICU delirium resulting in overall longer hospital lengths of stay, longer duration ofmechanical ventilation, higher rates of mortality, and long-term neuropsychological deficits afterdischarge. At the site of this quality improvement project, the prevalence of ICU delirium was92.3% in a population determined to be high risk using the PRE-DELIRIC screening tool. Aim: The aim of this quality improvement project was to decrease the prevalence rate of ICUdelirium ICU through the implementation of a sleep-wake cycle bundle. Methods: The process began with screening new admissions within twenty-four hours ofadmission to determine whether intervention is needed. Intervention ended at their dischargefrom the unit, death, or the designation of “comfort measures only (CMO)” by the physician. The site of this quality improvement project was a surgical/trauma ICU in a large urban teachinghospital. ICU delirium prevalence rates were determined through a retrospective chart reviewover a period of thirty days. Using the PDSA framework, new admissions to the ICU werescreened using the PRE-DELIRIC model over a period of 30 days to determine their percent riskof developing delirium. Patients with a score of greater than 40% were enrolled in the projectand had a sleep promotion bundle initiated. These patients were followed throughout their ICUstay and presence of delirium was tracked. Compliance with the sleep promotion bundle wasalso tracked. Results: The 30 day rate of ICU delirium was reduced by 47.3% (p = 0.019).Conclusion: Limitations and implications of this quality improvement project will be discussed.Recommendations for practice will be made and the role of the Clinical Nurse Leader (CNL)will be addressed

Using Shock Index as a Predictor of ICU Readmission: A Quality Iimprovement Project

Background: Adverse events will occur in one-third of patients discharged from the intensivecare unit (ICU) and evidence shows that ICU readmissions increase a patient’s length of stay,mortality, hospital costs, and nosocomial infections, as well as decrease long-term survival.Specific predictive factors that will accurately predict which patients are at risk of adverseevents requiring readmission are needed.Aim: The specific aim of this project was to identify if shock index (SI) values higher than 0.7at the time of transfer from the ICU are a useful predictor of ICU readmission.Methods: Using the Plan, Do, Study, Act (PDSA) framework, a retrospective chart review wasperformed using a matched cohort of 34 patients readmitted with 72 hours of discharge from theICU and 34 controls to obtain SI values at admission, transfer from and readmission to the ICU.A second PDSA cycle looked for SI trends within 24 hours prior to discharge from the ICU.Results: An odds ratio calculating the risk of readmission of patients with an elevated SI was2.96 (Confidence Interval (CI) 1.1 to 7.94, p-value=0.03). The odds ratio for an 80% SIelevation over 24 hours prior to discharge was 1.56 (CI 0.36 to 6.76, p-value=0.55).Conclusion and Implications for CNL Practice: Patients with elevated SIs at the time oftransfer are three times more likely to be readmitted to the ICU. Patients with elevations in atleast 80% of the 24 hour pre-discharge SIs showed no significant differences between thecontrol and readmitted cohorts. Implications of these results for the clinical nurse leader will bediscussed

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454