Predicting readmission and death after hospital discharge: a comparison of conventional frailty measurement with an electronic health record-based score

Abstract Background frailty measurement may identify patients at risk of decline after hospital discharge, but many measures require specialist review and/or additional testing. Objective to compare validated frailty tools with routine electronic health record (EHR) data at hospital discharge, for associations with readmission or death. Design observational cohort study. Setting hospital ward. Subjects consented cardiology inpatients ≥70 years old within 24 hours of discharge. Methods patients underwent Fried, Short Physical Performance Battery (SPPB), PRISMA-7 and Clinical Frailty Scale (CFS) assessments. An EHR risk score was derived from the proportion of 31 possible frailty markers present. Electronic follow-up was completed for a primary outcome of 90-day readmission or death. Secondary outcomes were mortality and days alive at home (‘home time’) at 12 months. Results in total, 186 patients were included (79 ± 6 years old, 64% males). The primary outcome occurred in 55 (30%) patients. Fried (hazard ratio [HR] 1.47 per standard deviation [SD] increase, 95% confidence interval [CI] 1.18–1.81, P < 0.001), CFS (HR 1.24 per SD increase, 95% CI 1.01–1.51, P = 0.04) and EHR risk scores (HR 1.35 per SD increase, 95% CI 1.02–1.78, P = 0.04) were independently associated with the primary outcome after adjustment for age, sex and co-morbidity, but the SPPB and PRISMA-7 were not. The EHR risk score was independently associated with mortality and home time at 12 months. Conclusions frailty measurement at hospital discharge identifies patients at risk of poorer outcomes. An EHR-based risk score appeared equivalent to validated frailty tools and may be automated to screen patients at scale, but this requires further validation.

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The readmission risk flag: Using the electronic health record to automatically identify patients at risk for 30-day readmission

Journal of Hospital Medicine ◽

10.1002/jhm.2106 ◽

2013 ◽

Vol 8 (12) ◽

pp. 689-695 ◽

Cited By ~ 35

Author(s):

Charles A. Baillie ◽

Christine VanZandbergen ◽

Gordon Tait ◽

Asaf Hanish ◽

Brian Leas ◽

...

Keyword(s):

At Risk ◽

Electronic Health Record ◽

Health Record ◽

Patients At Risk ◽

Electronic Health ◽

Readmission Risk

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Electronic Health Record–Based Monitoring of Primary Care Patients at Risk of Medication-Related Toxicity

The Joint Commission Journal on Quality and Patient Safety ◽

10.1016/s1553-7250(12)38027-6 ◽

2012 ◽

Vol 38 (5) ◽

pp. 216-AP2 ◽

Cited By ~ 4

Author(s):

David G. Bundy ◽

Jill A. Marsteller ◽

Albert W. Wu ◽

Lilly D. Engineer ◽

Sean M. Berenholtz ◽

...

Keyword(s):

Primary Care ◽

At Risk ◽

Electronic Health Record ◽

Health Record ◽

Patients At Risk ◽

Primary Care Patients ◽

Electronic Health

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1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1829 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S813-S814

Author(s):

Laura A Vonnahme ◽

Jonathan Todd ◽

Jon Puro ◽

Jee Oakley ◽

Matthew Jones ◽

...

Keyword(s):

At Risk ◽

Electronic Health Record ◽

Tuberculosis Infection ◽

Health Record ◽

Populations At Risk ◽

Cascade Of Care ◽

Patients At Risk ◽

Electronic Health ◽

Not At Risk ◽

Positive Results

Abstract Background Appropriate screening of individuals to detect latent tuberculosis infection (LTBI) is a critical step for achieving tuberculosis (TB) elimination in the US; >80% of TB cases are attributed to LTBI reactivation. TB infection testing and treatment must engage community health clinics where populations at risk seek care. However, there are significant data knowledge gaps in the current LTBI cascade of care (CoC) in this setting. We used an electronic health record (EHR) database from OCHIN, Inc., to characterize the LTBI CoC and identify potential future interventions. Methods We extracted a cohort of patients from 2012–2016 EHR data; we stratified by whether patients were at risk for TB based on meeting at least one of the following criteria: non-US born or non-English language preference, homelessness, encounter at correctional facility, history of close contact with a TB case, or being immunocompromised. Along each step of the LTBI CoC, we determined the proportions with a test for TB infection, with available test results, with a positive test, with an LTBI diagnosis, and with LTBI treatment prescribed. We used Χ 2 tests to compare the LTBI CoCs among patients at risk with those classified as not at risk. Results Of nearly 2.2 million patient records, 701,467 (32.0%) met criteria for being at risk for TB; 84,422 at risk (12.0%) were tested; 65,562 (77.7%) had available results, of whom 9,624 (14.7%) were positive. Among those with positive results, 6,958 (72.3%) had an LTBI diagnosis, of whom 1,732 (24.9%) were prescribed treatment. Among those classified as not at risk, fewer were tested (66,773 [4.5%], p< 0.001) and had positive results (2,500 [3.7%], p< 0.0001). Among those with positive results, 1,998 (80.0%) had an LTBI diagnosis, of whom 395 (19.8%) initiated treatment. Conclusion This study highlights gaps in the LTBI CoC, and where interventions are most needed. The largest gaps were in testing patients at risk, as 88% were not tested, and treatment, as 75% diagnosed with LTBI were not treated. Just under half (44%) of all TB tests appeared to be performed in persons with little risk for TB; this is a substantial amount of testing given very few begin treatment. Resources could be redirected to increase screening and treatment among populations at risk. Disclosures All Authors: No reported disclosures

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S3 Identification of Patients at Risk for Pancreatic Cancer in a 3-Year Timeframe Based on Machine Learning Algorithms in the Electronic Health Record

The American Journal of Gastroenterology ◽

10.14309/01.ajg.0000771992.23577.96 ◽

2021 ◽

Vol 116 (1) ◽

pp. S2-S2

Author(s):

Weicheng Zhu ◽

Mark B. Pochapin ◽

Aphinyanaphongs Yindalon ◽

Narges Razavian ◽

Tamas A. Gonda

Keyword(s):

Machine Learning ◽

Pancreatic Cancer ◽

At Risk ◽

Electronic Health Record ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Health Record ◽

Patients At Risk ◽

Electronic Health

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Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2020.103652 ◽

2021 ◽

Vol 113 ◽

pp. 103652

Author(s):

Maxwell Salvatore ◽

Lauren J. Beesley ◽

Lars G. Fritsche ◽

David Hanauer ◽

Xu Shi ◽

...

Keyword(s):

Pancreatic Cancer ◽

Electronic Health Record ◽

Risk Scores ◽

Health Record ◽

Electronic Health Record Data ◽

Data Discovery ◽

Record Data ◽

Electronic Health

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29 Predicting Unplanned Readmission and Death After Hospital Discharge: How Do Frailty Tools Compare to Electronic Health Record Frailty Markers?

Age and Ageing ◽

10.1093/ageing/afab029.08 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

pp. i7-i11

Author(s):

A Anand ◽

Y Yong Tew ◽

J Hao Chan ◽

P Keeling ◽

S D Shenkin ◽

...

Keyword(s):

Electronic Health Record ◽

Hospital Discharge ◽

Royal Infirmary ◽

Unplanned Readmission ◽

Health Record ◽

Clinical Frailty Scale ◽

Standard Deviation Increase ◽

Face To Face ◽

Hospitalised Patients ◽

Electronic Health

Abstract Introduction Numerous frailty tools and definitions have been described. Amongst hospitalised patients, the validity of face-to-face instruments may be confounded by acute illness. However, patient assessment after recovery at the point of hospital discharge, or recognition of electronic health record (EHR) frailty markers, may overcome this issuep. Methods In a consented, prospective observational cohort study, we recruited patients ≥70 years old within 24 hours of expected discharge from the cardiology ward of the Royal Infirmary of Edinburgh. Three established frailty instruments were tested: the Fried phenotype, Short Physical Performance Battery and nurse-administered Clinical Frailty Scale (CFS). An unweighted 32-item EHR score was generated using frailty markers (e.g. falls risk, continence, cognition) recorded within mandated admission documentation. Comorbidity was assessed by count of chronic health conditions. Outcomes were a 90-day composite of unplanned readmission or death and 12-month mortality. Adjusted Cox modelling determined the hazard ratio (HR) per standard deviation increase in each frailty score. Results 186 patients (mean age 79 ± 6 years, 64% male) were included, of whom 55 (30%) had a 90-day composite outcome, and 21 (11%) died within 12 months. All four frailty tools were moderately correlated with age and comorbidity (Pearson’s r 0.21 to 0.43, all p < 0.05). The Fried phenotype (HR 1.47, 95% CI 1.18–1.81), CFS (HR 1.24, 95% CI 1.01–1.51) and EHR score (HR 1.26, 95% CI 1.03–1.55) independently predicted 90-day readmission or death, after adjustment for age, sex and comorbidity. All frailty instruments were independent predictors of 12-month mortality, with age, sex and comorbidity losing predictive power (p > 0.05) once frailty was included in modelling. Conclusions At hospital discharge, the Fried phenotype and CFS added to age and comorbidity in risk prediction for future unplanned readmission or death. EHR frailty markers appeared comparable to face-to-face assessment. An automated trigger for high-risk patients using routine EHR data merits prospective evaluation.

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Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

10.1101/19011643 ◽

2019 ◽

Author(s):

Daniel M. Bean ◽

James Teo ◽

Honghan Wu ◽

Ricardo Oliveira ◽

Raj Patel ◽

...

Keyword(s):

Atrial Fibrillation ◽

Natural Language Processing ◽

Natural Language ◽

Electronic Health Record ◽

Open Source ◽

Language Processing ◽

Risk Scores ◽

Free Text ◽

Health Record ◽

Electronic Health

AbstractAtrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs.The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing.AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N=10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients.Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts).In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%).Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely-collected EHR data can replicate findings from large-scale curated registries.

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Abstract 14907: Clinically Feasible Electronic Health Record Tool for Stratification of 1- to 3-Year Post-Myocardial Infarction Risk of Cardiovascular Events: The Intermountain Acute Coronary Syndromes Risk Scores

Circulation ◽

10.1161/circ.132.suppl_3.14907 ◽

2015 ◽

Vol 132 (suppl_3) ◽

Author(s):

Benjamin D Horne ◽

Joseph B Muhlestein ◽

Durgesh Bhandary ◽

Greta L Hoetzer ◽

Naeem D Khan ◽

...

Keyword(s):

Myocardial Infarction ◽

Electronic Health Record ◽

Acute Coronary Syndromes ◽

Troponin I ◽

Risk Scores ◽

Health Record ◽

Additional Risk Factor ◽

Lipid Panel ◽

Coronary Syndromes ◽

Electronic Health

Background: Randomized trials report that prolonged (>1 year) use of P2Y12 inhibitors with aspirin after myocardial infarction (MI) reduces stent thrombosis and cardiovascular (CV) events, including new MI, stroke, and CV death. Post-MI patients may benefit to a differing extent from long-term dual anti-platelet therapy (DAPT); thus, a method is needed to identify those at higher risk of CV events. Hypothesis: A low-cost, easy-to-use, and highly predictive risk stratification tool can be created to differentiate risk of CV events 1-3 years after MI. Methods: Patients surviving ≥1 year after an index MI who had ≥1 additional risk factor for MI were studied. Cox regression models were used to derive sex-specific Intermountain Acute Coronary Syndromes (IMACS) risk scores in 70% of patients (N=1,342 females; 3,047 males). Validation of IMACS scores was performed in the other 30% of patients (N=576 females; 1,290 males). Variables used in model creation were age, troponin I, B-type natriuretic peptide, hemoglobin A1c, and all components of the lipid panel, complete blood count, and comprehensive metabolic panel. The primary end point was a composite of CV death, MI, or stroke. Results: Age averaged 68.7±12 and 69.8±12 for females in the derivation and validation groups, respectively, and 63.6±12 and 63.9±12 for males. IMACS scores ranged from 0-11 for females (grouping scores of 0-2, 3-6, and 7-11 into low-, moderate-, and high-risk) and 0-14 for males (0-2, 3-7, 8-14). In the validation groups, IMACS categories stratified CV event risk (Figure). IMACS c-statistics for females were c=0.675 and c=0.734 in derivation and validation groups, respectively, and for males c=0.715 and c=0.672. Conclusion: Sex-specific IMACS risk scores strongly stratified 1- to 3-year post-MI risk of CV events. IMACS is an inexpensive electronic health record tool that empowers the evaluation of which post-MI patients may be the best candidates for more aggressive therapeutic management.

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