Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study

Courtright, Katherine R.; Chivers, Corey; Becker, Michael; Regli, Susan Harkness; Pepper, Linnea C; Draugelis, Michael; O’Connor, Nina

doi:10.1007/s11606-019-05169-2

Cited by 64 publications

(58 citation statements)

References 46 publications

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“…Another study developed an EMR model of 6-month mortality using sophisticated machine learning techniques to trigger early palliative care consults on hospital day 2, increasing palliative care referrals. 20 Similarly, we are planning a pilot study to deploy our model as a part of the IU-LHSI project to identify patients at high risk of in-hospital mortality who may benefit from formal SIC interventions, including formal palliative care and hospice consultations as appropriate. Although the absolute percentage of deaths identified with this model is around 34% for those with a score above the threshold, this positive predictive value is reasonable for identifying patients that may benefit from early palliative care and hospice intervention.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

et al. 2021

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BACKGROUND: Predicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients. OBJECTIVE: Develop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC). DESIGN: Retrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195). PARTICIPANTS: Ten thousand three hundred eightynine patients greater than 18 years transferred to the Indiana University (IU)-Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017. MAIN MEASURES: Sensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model. KEY RESULTS: The final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X 2 (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89-0.92, p < 0.0001). We identified a risk threshold score of −2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold. CONCLUSION: This model can use EMR and administrative data within 24 h of transfer to predict the risk of 30day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

et al. 2021

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“…We chose the 30% risk threshold based on expert consensus from the clinicians in the study and a previous analysis of a similar algorithm used to help direct inpatient palliative care consults. 28 For 4 consecutive weeks in October 2018, we provided 15 clinicians with printed lists of high-risk oncology patients in the practice who had been identified as having high risk by the algorithm and had appointments in the upcoming week. At a weekly practice meeting, clinicians indicated yes or no for each patient appointment in the upcoming week to indicate whether that patient was appropriate for a conversation about goals and end-of-life preferences.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer

et al. 2019

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Key PointsQuestionCan machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?FindingsIn this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.MeaningIn this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.

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“…Some have even begun to predict adverse outcomes at the individual level, knowing in advance that a patient may be at high risk for hospitalization or death. 4 However, even with the technological advances in the use of big data and predictive modeling, there is still uncertainty about how to best respond. In a survey of a large, representative sample of accountable care organizations (ACOs), Bleser et al discovered that 94% employ measures to define their seriously ill populations, but that only 8% to 21% of ACOs have either "partially" or "widely" implemented clinical programs targeting these groups.…”

Section: F R O N T I E R S O F C a R Ementioning

confidence: 99%

Why palliative care is the answer to the serious illness question in payment reform

2020

Am J Manag Care

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Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study

Cited by 64 publications

References 46 publications

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer

Why palliative care is the answer to the serious illness question in payment reform

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