Development and Validation of eRADAR: A Tool Using EHR Data to Detect Unrecognized Dementia.

ObjectivesEarly recognition of dementia would allow patients and their families to receive care earlier in the disease process, potentially improving care management and patient outcomes, yet nearly half of patients with dementia are undiagnosed. Our aim was to develop and validate an electronic health record (EHR)-based tool to help detect patients with unrecognized dementia (EHR Risk of Alzheimer's and Dementia Assessment Rule [eRADAR]).DesignRetrospective cohort study.SettingKaiser Permanente Washington (KPWA), an integrated healthcare delivery system.ParticipantsA total of 16 665 visits among 4330 participants in the Adult Changes in Thought (ACT) study, who undergo a comprehensive process to detect and diagnose dementia every 2 years and have linked KPWA EHR data, divided into development (70%) and validation (30%) samples.MeasurementsEHR predictors included demographics, medical diagnoses, vital signs, healthcare utilization, and medications within the previous 2 years. Unrecognized dementia was defined as detection in ACT before documentation in the KPWA EHR (ie, lack of dementia or memory loss diagnosis codes or dementia medication fills).ResultsOverall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 498 (49%) were unrecognized in the KPWA EHR. The final 31-predictor model included markers of dementia-related symptoms (eg, psychosis diagnoses, antidepressant fills), healthcare utilization pattern (eg, emergency department visits), and dementia risk factors (eg, cerebrovascular disease, diabetes). Discrimination was good in the development (C statistic = .78; 95% confidence interval [CI] = .76-.81) and validation (C statistic = .81; 95% CI = .78-.84) samples, and calibration was good based on plots of predicted vs observed risk. If patients with scores in the top 5% were flagged for additional evaluation, we estimate that 1 in 6 would have dementia.ConclusionThe eRADAR tool uses existing EHR data to detect patients with good accuracy who may have unrecognized dementia. J Am Geriatr Soc 68:103-111, 2019

Prediction Screening to Identify Heart Failure Patients at High Risk for Readmission

Background: There is an increased need to identify factors associated with higher risk for excessive HF re-hospitalizations due to hospitals receiving financial penalties related to these re-hospitalizations and poorer patient outcomes. Identifying HF patients at highest risk for re-hospitalization with a screening instrument upon admission to the hospital would allow for early implementation of interventions tailored around reducing risk factors for re-hospitalization. Objectives: The specific aims of this study were to 1) identify characteristics that were predictive of HF re-hospitalization; and 2) use those characteristics to create a screening instrument. Methods: A total of 158 patients (age=63±13; 50.6% female; 73.4% Caucasian; 63.3% NYHA class III/IV) admitted with a primary or secondary diagnosis of HF were included in this study. Patient’s knowledge of HF symptoms, along with socio-demographic, biophysical, and cognitive information was assessed by data collected with validated instruments as well as the electronic medical record. Chi square tests and independent t-tests were used to examine bivariate differences in the readmitted and the non readmitted groups. Cox proportional hazards modeling was used to predict the outcome, or time to hospitalization, based on the predictor variables. Results: The mean time to re-hospitalization was 68 days. Only 8 patients were re-hospitalized within the first 30 days. Depressive symptoms scores was the only variable identified as being significantly different (p Conclusions: Screening HF patients at highest risk for re-hospitalization and those with depressive symptoms will allow healthcare providers to individualize interventions to improve HF patient outcomes and reduce costly hospital re-hospitalizations

Epidemiology of Colorectal Cancer Comorbidities and Stage at Diagnosis, Survival, and Second Primary Malignancies in Kentucky, 2003-2016

Background: Colorectal cancer (CRC) is the third most common type of cancer and the third most common cause of cancer death among men and women in the United States.1-3 The American Cancer Society estimates that there will be 147,950 new cases of CRC and 53,200 CRC related deaths in the U.S. for the year 2020.3 Kentucky CRC incidence for 2012-2016 was the highest in the nation, and the mortality rate for years 2013-2017 was ranked 5th in the nation.4-6 Risk factors for CRC include lifestyle factors, genetics, and disease status (comorbidities and treatment).2, 7 Diabetes has been found to be the most prevalent comorbidity among CRC patients, and the risk of developing CRC in patients with diabetes is 25% higher than those without diabetes.8, 9 Aim: The purpose of this study is to explore if comorbidities impacts CRC progression, CRC outcomes, and the development of second primary malignancy among CRC patients age 18 and older in Kentucky diagnosed between January 1, 2003 and December 31, 2016. Methods: Two studies were performed using CRC data from Kentucky Cancer Registry, one was a retrospective cohort study and the other was a case control study. There were 20,571 cases included in the cohort study with the primary outcomes was all-cause mortality, CRC mortality, and second primary cancer. There were 18,170 total, 9,085 cases and controls in the second study. This study examined the geographical distribution of late-stage CRC and comorbidities. Results Chapter 3: Logistic regression models show that comorbidities increased the odds of death or late-stage CRC. The Cox proportional hazard models of all-cause and CRC mortalities and second primary show that comorbidities, patient factors, and treatments can be protective or increase the hazards of dying or having a second primary cancer. The Kaplan Meier curve demonstrates the survival of early-stage at diagnosis CRC versus late-stage at diagnosis CRC. Results Chapter 4: The geographical distribution maps of the four positively associated morbidities (electrolyte disorders, liver disease, weight loss, and deficiency anemia) do not demonstrate any patterns resembling the cluster, the comorbidity distribution appears to be random. The map of comorbidities among CRC patients show that a large percentage experience a burden of two or more comorbidities. Conclusion: The results indicate that comorbidities do play a role in the stage of CRC diagnosis, with the data showing greater odds of being diagnosed with early-stage cancer for many of the individual comorbidities. The space-time analysis found a significant high rate cluster of late-stage CRC, however, mapping the distribution of positively associated comorbidities did not demonstrate a pattern matching the cluster. Further research is needed to examine the impact of comorbidities and CRC stage at diagnosis

Multimorbidity as specific disease combinations, an important predictor factor for mortality in octogenarians: the Octabaix study

BACKGROUND: The population is aging and multimorbidity is becoming a common problem in the elderly. OBJECTIVE: To explore the effect of multimorbidity patterns on mortality for all causes at 3- and 5-year follow-up periods. MATERIALS AND METHODS: A prospective community-based cohort (2009-2014) embedded within a randomized clinical trial was conducted in seven primary health care centers, including 328 subjects aged 85 years at baseline. Sociodemographic variables, sensory status, cardiovascular risk factors, comorbidity, and geriatric tests were analyzed. Multimorbidity patterns were defined as combinations of two or three of 16 specific chronic conditions in the same individual. RESULTS: Of the total sample, the median and interquartile range value of conditions was 4 (3-5). The individual morbidities significantly associated with death were chronic obstructive pulmonary disease (COPD; hazard ratio [HR]: 2.47; 95% confidence interval [CI]: 1.3; 4.7), atrial fibrillation (AF; HR: 2.41; 95% CI: 1.3; 4.3), and malignancy (HR: 1.9; 95% CI: 1.0; 3.6) at 3-year follow-up; whereas dementia (HR: 2.04; 95% CI: 1.3; 3.2), malignancy (HR: 1.84; 95% CI: 1.2; 2.8), and COPD (HR: 1.77; 95% CI: 1.1; 2.8) were the most associated with mortality at 5-year follow-up, after adjusting using Barthel functional index (BI). The two multimorbidity patterns most associated with death were AF, chronic kidney disease (CKD), and visual impairment (HR: 4.19; 95% CI: 2.2; 8.2) at 3-year follow-up as well as hypertension, CKD, and malignancy (HR: 3.24; 95% CI: 1.8; 5.8) at 5 years, after adjusting using BI. CONCLUSION: Multimorbidity as specific combinations of chronic conditions showed an effect on mortality, which would be higher than the risk attributable to individual morbidities. The most important predicting pattern for mortality was the combination of AF, CKD, and visual impairment after 3 years. These findings suggest that a new approach is required to target multimorbidity in octogenarians

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality

Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease history to predict sepsis mortality. We benefit from data in electronic medical records covering all hospital encounters in Denmark from 1996 to 2014. This data set included 6.6 million patients of whom almost 120,000 were diagnosed with the ICD-10 code: A41 ‘Other sepsis’. Interestingly, patients following recurrent trajectories of time-ordered co-morbidities had significantly increased sepsis mortality compared to those who did not follow a trajectory. We identified trajectories which significantly altered sepsis mortality, and found three major starting points in a combined temporal sepsis network: Alcohol abuse, Diabetes and Cardio-vascular diagnoses. Many cancers also increased sepsis mortality. Using the trajectory based stratification model we explain contradictory reports in relation to diabetes that recently have appeared in the literature. Finally, we compared the predictive power using 18.5 years of disease history to scoring based on within-admission clinical measurements emphasizing the value of long term data in novel patient scores that combine the two types of data

Assessing Prevalence of Known Risk Factors in a Regional Central Kentucky Medical Center Heart Failure Population as an Approach to Assessment of Needs for Development of a Program to Provide Targeted Services to Reduce 30 Day Readmissions

Abstract Objectives: Determine demographic, physiologic, and laboratory characteristics at time of admission of the heart failure (HF) population in a regional acute care facility in Central Kentucky through review of patient electronic medical records. Determine which HF population characteristics are significantly associated with readmissions to the hospital. Provide identification of the statistically significant common characteristics of the HF population to this facility so that they may work towards development of an electronic risk for readmission predictive instrument. Design: Retrospective chart review. Setting: Regional acute care facility in Central Kentucky. Participants: All patients (n = 175) with a diagnosis or history of HF (to include diagnosis related group (DRG) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.1, 428.41, 428.23, 428.43, 428.31, 428.33, 428.1, 428.20, 428.22, 428.30, 428.32, 428.40, 428.40, 428.42, 428.0, and 428.9; The Joint Commission, 2013) admitted to the acute care setting of a regional hospital in the Central Kentucky area between the dates of January 1, 2013 and July 31, 2013. Eligible participants were identified via an electronic discharge report listing all patients discharged during the study time period with a HF code. Main Outcome Measure: A chart review was performed to define the HF population within the regional acute care facility. Abstracted information was collected on data instruments (Appendices A,B, and C) and analyzed to define the overall HF population (n = 175). The data was then analyzed to determine significance between patient characteristics (demographic, physiologic, and laboratory) and 30 day readmissions. The data was examined both on the individual patient level and independent of patient level looking at each admission independently. Results: An in depth description of the HF patient population in this facility was obtained. Several patient characteristics including a history of anemia, COPD, ischemic heart disease, diabetes, and the laboratory values creatinine and BNP outside of the reference range were found to have a significant association with 30 day readmissions. Discharge to a skilled nursing facility (SNF) was also found to be a significant predictor of 30 day readmissions. Some social variables such as marital status were not found to have a significant relationship to 30 day readmissions. Conclusion: This investigation is a stepping stone to creating an electronic tool designed to reflect the characteristics of HF population admitted to a single facility and predict risk of HF readmissions within 30 days at the time of admission. Implementation of a plan of care designed to meet the needs of this HF population as well as identify those patients at high risk for will allow for provision of a comprehensive and timely individualized plan of care to reduce the incidence of 30 day readmissions

Predictors of 6-month mortality among nursing home residents: Diagnoses maybe more predictive than functional disability

Objective: Loss of daily living functions can be a marker for end of life and possible hospice eligibility. Unfortunately, data on patient\u27s functional abilities is not available in all settings. In this study we compare predictive accuracy of two indices designed to predict 6-month mortality among nursing home residents. One is based on traditional measures of functional deterioration and the other on patients\u27 diagnoses and demography. Methods: We created the Hospice ELigibility Prediction (HELP) Index by examining mortality of 140,699 Veterans Administration (VA) nursing home residents. For these nursing home residents, the available data on history of hospital admissions were divided into training (112,897 cases) and validation (27,832 cases) sets. The training data were used to estimate the parameters of the HELP Index based on (1) diagnoses, (2) age on admission, and (3) number of diagnoses at admission. The validation data were used to assess the accuracy of predictions of the HELP Index. The cross-validated accuracy of the HELP Index was compared with the Barthel Index (BI) of functional ability obtained from 296,052 VA nursing home residents. A receiver operating characteristic curve was used to examine sensitivity and specificity of the predicted odds of mortality. Results: The area under the curve (AUC) for the HELP Index was 0.838. This was significantly (α \u3c0.01) higher than the AUC for the BI of 0.692. Conclusions: For nursing home residents, comorbid diagnoses predict 6-month mortality more accurately than functional status. The HELP Index can be used to estimate 6-month mortality from hospital data and can guide prognostic discussions prior to and following nursing home admission