Adult Postoperative Open-Heart Patients: Anemia and 30-Day Hospital Readmission

Background. In 2013 alone, more than 4% (3.9 million) of patients discharged from a hospital were readmitted. Anemia following a surgical procedure is associated with early hospital readmission. Purpose/Aims. The following were specific aims of this dissertation: Aim 1. To develop an operational definition of the term condition-based maintenance as applied to health care and discuss the applicability and effectiveness of condition-based maintenance within health care. Aim 2. To identify the number of adult patients undergoing elective open-heart surgery with preoperative anemia. Aim 3. To examine the relationship between preoperative anemia, sociodemographics, and 30-day hospital readmission rates among postoperative open-heart adult patients. Aim 4. To explain the development and impact of the Hospital Readmissions Reduction Program (HRRP) and discuss the political, social, and economic implications of CABG as a newly targeted condition within the HRRP. Approach. To address aim 1, the Walker and Avant model for concept analysis was used to review and analyze relevant literature, create a basic operational definition, and clarify related concepts. To address aims 2 and 3, a retrospective cross‐sectional study was conducted using the STS Database to identify 1,353 surgical cases between August 2014 and July 2018. Cross-tabs and multivariable logistic regression analysis were used to assess the prevalence of preoperative anemia and association with 30-day hospital readmission. To address aim 4, a policy analysis was performed in accordance with Bardach and Patashnik’s procedure. Findings. From the concept analysis process, the notion of condition-based maintenance emerged, holding promise in advancing symptom science through development of personalized strategies to treat and prevent adverse symptoms of illness. The prevalence of preoperative anemia was 43.7% (n = 591), and 177 (13%) had a 30-day hospital readmission. Patients with preoperative anemia had 1.88 (95% CI 1.36, 2.58) times higher odds of being readmitted. Through policy analysis, a correlation between insurance and 30-day hospital readmission following a CABG procedure was identified. Currently, penalty programs may be adjusted to better capture sociodemographic differences. Implications. The findings from this study suggest preoperative anemia is associated with increased risk for 30-day hospital readmission. These results provide a basis for further risk reduction strategies and preoperative optimization

Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial

Objective To evaluate the impact of telling patients their estimated spirometric lung age as an incentive to quit smoking.Design Randomised controlled trial.Setting Five general practices in Hertfordshire, England.Participants 561 current smokers aged over 35.Intervention All participants were offered spirometric assessment of lung function. Participants in intervention group received their results in terms of "lung age" (the age of the average healthy individual who would perform similar to them on spirometry). Those in the control group received a raw figure for forced expiratory volume at one second (FEV1). Both groups were advised to quit and offered referral to local NHS smoking cessation services.Main outcome measures The primary outcome measure was verified cessation of smoking by salivary cotinine testing 12 months after recruitment. Secondary outcomes were reported changes in daily consumption of cigarettes and identification of new diagnoses of chronic obstructive lung disease.Results Follow-up was 89%. Independently verified quit rates at 12 months in the intervention and control groups, respectively, were 13.6% and 6.4% (difference 7.2%, P=0.005, 95% confidence interval 2.2% to 12.1%; number needed to treat 14). People with worse spirometric lung age were no more likely to have quit than those with normal lung age in either group. Cost per successful quitter was estimated at 280 pound ((euro) 365, $556). A new diagnosis of obstructive lung disease was made in 17% in the intervention group and 14% in the control group; a total of 16% (89/561) of participants.Conclusion Telling smokers their lung age significantly improves the likelihood of them quitting smoking, but the mechanism by which this intervention achieves its effect is unclear.Trial registration National Research Register N0096173751

Validity of physical activity monitors during daily life in patients with COPD.

Symptoms during physical activity (PA) and physical inactivity are COPD. Our aim was to evaluate the validity and usability of six activity in patients with COPD against the doubly labelled water (DLW) indirect calorimetry method.Eighty COPD patients (age 68+/-6 years, FEV1 57+/-19% predicted) recruited in four centres each wore simultaneously three or six commercially available monitors validated in chronic conditions for consecutive days. A priori validity criteria were defined. These ability to explain total energy expenditure (TEE) variance through regression analysis, using TEE as the dependent variable with total body (TBW) plus several PA monitors outputs as independent variables; and with DLW measured activity energy expenditure (AEE).The Actigraph GT3X DynaPort MoveMonitor best explained the majority of the TEE variance not explained by TBW (53% and 70% respectively) and showed the most correlations with AEE (r=0.71 p<0.001, r=0.70 p<0.0001, of this study should guide users in choosing valid activity monitors for or for clinical use in patients with chronic diseases such as COPD

Personalized functional health and fall risk prediction using electronic health records and in-home sensor data

Research has shown the importance of Electronic Health Records (EHR) and in-home sensor data for continuous health tracking and health risk predictions. With the increased computational capabilities and advances in machine learning techniques, we have new opportunities to use multi-modal health big data to develop accurate health tracking models. This dissertation describes the development, evaluation, and testing of systems for predicting functional health and fall risks in community-dwelling older adults using health data and machine learning techniques. In an initial study, we focused on organizing and de-identifying EHR data for analysis using HIPAA regulations. The dataset contained nine years of structured and unstructured EHR data obtained from TigerPlace, a senior living facility at Columbia, MO. The de-identification of this data was done using custom automated algorithms. The de-identified EHR data was used in several studies described in this dissertation. We then developed personalized functional health tracking models using geriatric assessments in the EHR data. Studies show that higher levels of functional health in older adults lead to a higher quality of life and improves the ability to age-in-place. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking the personalized functional health of older adults using a combination of these assessments. In this study, data from 150 older adult residents were used to develop a composite functional health prediction model using Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Tracking functional health objectively could help clinicians to make decisions for interventions in case of functional health deterioration. We next constructed models for fall risk prediction in older adults using geriatric assessments, demographic data, and GAITRite assessment data. A 6-month fall risk prediction model was developed with data from 93 older adult residents. Explainable AI techniques were used to provide explanations to the model predictions, such as which specific features increased the risk of fall in a particular model prediction. Such explanations to model predictions provide valuable insights for targeted interventions. In another study, we developed deep neural network models to predict fall risk from de-identified nursing notes data from 162 older adult residents from TigerPlace. Clinical nursing notes have been shown to contain valuable information related to fall risk factors. This analysis provides the groundwork for future experiments to predict fall risk in older adults using clinical notes. In addition to using EHR data to predict functional health and fall risk in older adults, two studies were conducted to predict fall and functional health from in-home sensor data. Models for in-home fall prediction using depth sensor imagery have been successfully used at TigerPlace. However, the model is prone to false fall alarms in several scenarios, such as pillows thrown on the floor and pets jumping from couches. A secondary fall analysis was performed by analyzing fall alert videos to further identify and remove false alarms. In the final study, we used in-home sensor data streaming from depth sensors and bed sensors to predict functional health and absolute geriatric assessment values. These prediction models can be used to predict the functional health of residents in absence of sparse and infrequent geriatric assessments. This can also provide continuous tracking of functional health in older adults using the streaming in-home sensor data

Associations of pulmonary parameters with accelerometer data

Some papers of this thesis are not available in Munin: Paper 2. Dias, A.; Gorzelniak, L.; Jorres, R.; Fischer, R.; Hartvigsen, G.; Horsch,A.: 'Assessing Physical Activity in the daily life of cystic fibrosis patients', Journal of Pervasive Computing (2012), vol. 8(6):837–844. Available at http://dx.doi.org/10.1016/j.pmcj.2012.08.001 Paper 3. Gorzelniak, L.; Dias, A.; Schultz,K.; Wittmann, M.; Karrasch, S.; Jorres, R.; Horsch,A.: 'Comparison of recording positions of physical activity in severe COPD', Journal Of Chronic Obstructive Pulmonary Disease (2012), vol. 9(5):528-537. Available at http://dx.doi.org/10.3109/15412555.2012.708066 Paper 4. Dias, A.; Gorzelniak, L.; Schultz,K.;Wittmann, M.; Rudnik, J.;Jorres, R.; Horsch,A.: 'Classification of exacerbation episodes in Chronic Obstructive Pulmonary Disease patients' (manuscript) Paper 5. Ortlieb, S.; Gorzelniak, L.; Dias,A.; Schulz, H.; Horsch,A.: 'Recommendations for Collecting and Processing Accelerometry Data in Older Healthy People' (manuscript) Additional paper 1. Dias, A.; Gorzelniak, L.; Doring, A.; Hartvigsen, G.; Horsch, A.: 'Extracting Gait Parameters from Raw Data Accelerometers', Studies in Health Technology and Informatics (2011), vol. 169:445-449. Additional paper 2. Gorzelniak, L.; Dias, A.; Soyer, H.; Knoll, A.; Horsch, A.; 'Using a Robotic Arm to Assess the Variability of Motion Sensors', Studies in Health Technology and Informatics (2011), vol. 169:897-901. Additional paper 3. Chen, C.; Dias, A.; Knoll, A.; Horsch, A.: 'A Prototype of a Wireless Body Sensor Network for Healthcare Monitoring', Medical informatics in Europe (2011). Additional paper 4. Skrovseth, S.; Dias, A.; Gorzelniak, L.; Godtliebsen, F.; Horsch, A.: 'Scale-space methods for live processing of sensor data', Medical informatics in Europe (2012). Additional paper 7. Peters A, Döring A, Ladwig KH, Meisinger C, Linkohr B, Autenrieth C, Baumeister SE, Behr J, Bergner A, Bickel H, Bidlingmaier M, Dias A, Emeny RT, Fischer B, Grill E, Gorzelniak L, Hänsch H, Heidbreder S, Heier M, Horsch A, Huber D, Huber RM, Jörres RA, Kääb S, Karrasch S, Kirchberger I, Klug G, Kranz B, Kuch B, Lacruz ME, Lang O, Mielck A, Nowak D, Perz S, Schneider A, Schulz H, Müller M, Seidl H, Strobl R, Thorand B, Wende R, Weidenhammer W, Zimmermann AK, Wichmann HE, Holle R.: 'Multimorbidity and successful aging: the populationbased KORA-Age study', Zeitschrift für Gerontologie und Geriatrie (2011), vol. 44(2):41-54. Available at http://dx.doi.org/10.1007/s00391-011-0245-7In Europe it is estimated that the number of elderly people aged above 65 will have doubled by 2060. In several chronic pulmonary diseases patients can suffer recurrent exacerbation episodes that can lead to severe breathing or death. In this thesis we explore the association of physical activity to lung health parameters, focusing on cystic fibrosis and chronic obstructive pulmonary disease patients and a group of the general population. The main goals of the thesis were to assess the feasibility of classifying exacerbation episodes in cystic fibrosis and chronic obstructive pulmonary disease patients and to implement new parameters in the context of a cohort study. We conducted four distinct studies involving in total over 250 subjects. We asked them to wear a set of accelerometers, including GT3X and RT3, recording physical activity for up to 14 days. The data was processed and several features extracted that were used as inputs in three different classification algorithms: logarithmic regression, neural networks and support vector machines. We achieved an area under the curve of 67% with logarithmic regression, 83% with neural networks and 90% with support vector machines when classifying exacerbation episodes in chronic obstructive pulmonary disease. A neural network was achieved an accuracy of 85% distinguishing cystic fibrosis patients from healthy controls. We proposed, extracted and tested a large set of physical activity parameters for use in KORA-Age. The work on classification of exacerbations in COPD patients is, to our knowledge, the first attempt based on features from accelerometer data. Overall SVM showed to be the most robust classifier with an area under the curve of 90%. Nevertheless the number of patients and episodes is too low to draw definitive conclusions. The next step to classify exacerbations in COPD is to design a study with a statistically significant number of exacerbation episodes