PREDICTING HEALTH CARE NEEDS FOLLOWING LUMBAR SPINE SURGERY

Low back pain is one of the most common health problems globally, having significant impact on individuals, community, and health care system. Lumbar Spine Surgery (LSS) is usually considered a treatment of low back pain when conservative management fails. In the United States, there has been an increase in the prevalence of LSS, with a similar increase in surgery costs and related post-surgical care. Although LSS is often considered to be a more efficient treatment than the nonsurgical management, the improvements gained from LSS are not optimal, resulting in no change or even worsening of symptoms in some cases. Investigating and understanding variables associated with surgical outcomes would be cost effective and clinically significance. Limited studies have attempted to examine patient recovery in acute state of LSS. Specifically studies are lacking to identify predictors of length of hospital stay (LOS), and little is known about predictors of discharge placement (DP) and outcomes as early as 2 weeks post discharge after having LSS. Bridging the gap in knowledge related to predictors of short-term LSS outcomes is the goal of this work. Identifying these predictors and early measures of recovery may lead to better utilization of resources and improved patient care and efforts to optimize LSS outcomes, Chapter 2 sought to identify predictors of LOS using various potential surgical and non-surgical variables. We used structural equation modeling analysis to study the direct effect of three latent factors on LOS: presurgical, surgical, and postsurgical factors. The three latent factors were constructed from potential predictor variables (indicators) that had significant direct effect on their related factors. Results showed that higher age diminished prior level of function, needing assistive devices, and low hemoglobin level were significant indicators of presurgical factors and associated with longer LOS. Secondly, high illness severity, increased complications, and need for intensive care unit stay after surgery were significant indicators of surgical factors and associated with higher LOS. Finally, inpatient physical therapy assessment, including low sitting and standing balance score, higher dependency in bed mobility transfer and mobility, and less distance walked during physical therapy sessions, were significant indicators of postsurgical factors and associated with longer LOS. The model explained approximately 50% of the variation in LOS. Postsurgical factors constructed from physical therapy assessment measured the highest percentage of the variation in LOS, followed by surgical factors, and finally presurgical factors that individually explained minor percentage of variation in LOS. Prospective studies are needed to confirm these results, and should consider including standardized clinical testing, especially at baseline to improve the prediction accuracy. Given that discharge placement (DP) predictors has been studies after many surgeries and conditions including total knee, total hip replacement, stroke and brain injury, little is known regarding the predictors of DP following LSS. Chapter 3 sought to address this gap in knowledge. Results showed that younger age, longer distance walked during hospital stay, and shorter length of hospital stay predicted greater likelihood of being discharged to home. Further analysis suggested that those living along, have inferior level of function prior to their surgery, and required longer hospital stay are likely to need skilled assistance (i.e. home health care or outpatient services) after being discharged to home ,. Prospective studies with more potential variables should be conducted to confirm these results. Short-and long-term outcomes following LSS were studied extensively following LSS. However, to our knowledge no study has investigated surgery outcomes earlier than 6 weeks post-hospital discharge. Therefore, chapter 4 explored the changes in patients' clinical status at 2 weeks following hospital discharge, and predictors of patient- outcomes during this short follow-up period. Results revealed that patients had significant reduction in back pain intensity, leg pain intensity, and improvement in function. However, there was no significant reduction in somatic perception and change in the type of analgesics used. High somatic perception predicted higher back pain, poor function, and inferior quality of life. Longer symptom duration was associated with higher postoperative back pain, while diagnosis of spondylolisthesis and preoperative use of opioids predicted higher postoperative leg pain intensity. Having high functional level at baseline was associated with high functional level postoperatively. Experiencing higher back and leg pain intensity, having depression symptoms, smoking, and receiving worker's compensation were significant factors associated with negative patient-perception of surgery outcomes. The study showed that multiple variables should be considered when predicting short-term LSS outcomes. In summary this dissertation work presented that LSS is effective in management of patients' pain, and improving function and quality of life for short-term follow up. Multiple variables showed to predict LOS, DP, and surgery outcomes after 2 weeks of post discharge after LSS. These variables could be presurgical variables including sociodemographic variables, cognitive behavioral variables, presurgical clinical status, presurgical functional level, or surgical including severity of illness, complications, longer intensive care unit and total hospital LOS, and postsurgical which including physical therapy functional assessment measures. The new knowledge presented in this work is important in guiding patients' selection criteria, establishing realistic expectations from surgery, and designing strategies to optimize surgery outcomes. Prospective studies with larger sample are needed to fully understand determinant of LSS success

Development of a Composite Health Index in Children with Cystic Fibrosis: A Pipeline for Data Processing, Machine Learning, and Model Implementation using Electronic Health Records

Cystic Fibrosis (CF) is a heterogeneous multi-faceted genetic condition that primarily affects the lungs and digestive system. For children and young people living with CF, timely management is necessary to prevent the establishment of severe disease. Modern data capture through electronic health records (EHR) have created an opportunity to use machine learning algorithms to classify subgroups of disease to understand health status and prognosis. The overall aim of this thesis was to develop a composite health index in children with CF. An iterative approach to unsupervised cluster analysis was developed to identify homogeneous clusters of children with CF in a pre-existing encounter-based CF database from Toronto Canada. An external validation of the model was carried out in a historical CF dataset from Great Ormond Street Hospital (GOSH) in London UK. The clusters were also re-created and validated using EHR data from GOSH when it first became accessible in 2021. The interpretability and sensitivity of the GOSH EHR model was explored. Lastly, a scoping review was carried out to investigate common barriers to implementation of prognostic machine learning algorithms in paediatric respiratory care. A cluster model was identified that detailed four clusters associated with time to future hospitalisation, pulmonary exacerbation, and lung function. The clusters were also associated with different disease related variables such as comorbidities, anthropometrics, microbiology infections, and treatment history. An app was developed to display individualised cluster assignment, which will be a useful way to interpret the cluster model clinically. The review of prognostic machine learning algorithms identified a lack of reproducibility and validations as the major limitation to model reporting that impair clinical translation. EHR systems facilitate point-of-care access of individualised data and integrated machine learning models. However, there is a gap in translation to clinical implementation of machine learning models. With appropriate regulatory frameworks the health index developed for children with CF could be implemented in CF care

A Systems Approach to Healthcare: Agent-based Modeling, Community Mental Health, and Population Well-being

Purpose Explore whether agent-based modeling and simulation can help healthcare administrators discover interventions that increase population wellness and quality of care while, simultaneously, decreasing costs. Since important dynamics often lie in the social determinants outside the health facilities that provide services, this study thus models the problem at three levels (individuals, organizations, and society). Methods The study explores the utility of translating an existing (prize winning) software for modeling complex societal systems and agent\u27s daily life activities (like a Sim City style of software), into a desired decision support system. A case study tests if the 3 levels of system modeling approach is feasible, valid, and useful. The case study involves an urban population with serious mental health and Philadelphia\u27s Medicaid population (n = 527,056), in particular. Results Section 3 explains the models using data from the case study and thereby establishes feasibility of the approach for modeling a real system. The models were trained and tuned using national epidemiologic datasets and various domain expert inputs. To avoid co-mingling of training and testing data, the simulations were then run and compared (Section 4.1) to an analysis of 250,000 Philadelphia patient hospital admissions for the year 2010 in terms of re-hospitalization rate, number of doctor visits, and days in hospital. Based on the Student t-test, deviations between simulated vs. real world outcomes are not statistically significant. Validity is thus established for the 2008–2010 timeframe. We computed models of various types of interventions that were ineffective as well as 4 categories of interventions (e.g., reduced per-nurse caseload, increased check-ins and stays, etc.) that result in improvement in well-being and cost. Conclusions The 3 level approach appears to be useful to help health administrators sort through system complexities to find effective interventions at lower costs

Readmission prediction via deep contextual embedding of clinical concepts

Objective Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. Materials and methods We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. Results The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. Discussion Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. Conclusion This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model

J Investig Med

Prescriptions for biologic therapy for treatment of Crohn's disease (CD) and ulcerative colitis (UC) have increased during the past two decades; however, trends are less clear regarding corticosteroid prescriptions in this context. We designed a cross-sectional study using the IQVIA Ambulatory Electronic Medical Records databases. Weighted linear regressions by age group were used to estimate annual percentage change from 2011 to 2020 in prescriptions for biologics and for corticosteroids among patients with or without biologic prescriptions within the same calendar year. Using 2019 data, we compared patient demographic and lifestyle risk factors using \u3c7| test for biologic prescriptions and corticosteroids with or without biologics prescriptions. There was an 11% (CD) and 16% (UC) annual increase in the percentage of patients prescribed biologics during the study period. The percentage of patients with biologics prescriptions prescribed corticosteroids decreased by 2% (CD) and 3% (UC) annually after 2015, while the percentage remained unchanged for corticosteroid prescriptions among patients without biologics. In 2019, differences in medication prescriptions existed by patient's demographic and lifestyle factors for patients with CD (n=52,892) and UC (n=52,280), including a higher percentage prescribed biologics among younger patients, men, those with fewer comorbidities, and current alcohol drinkers, and a higher percentage prescribed corticosteroids without biologics among women, those with more comorbidities, and a history of smoking. While medications continue to evolve during the biologic era, it is important to continue to monitor trends and differences in prescription patterns to assess progress toward optimizing treatment for patients with CD or UC.CC999999/ImCDC/Intramural CDC HHSUnited States

Characteristics of Patients Experiencing Extrapyramidal Symptoms or Other Movement Disorders Related to Dopamine Receptor Blocking Agent Therapy

Purpose/background: Dopamine receptor blocking agents (DRBAs), also known as antipsychotics, are medications widely used to treat a growing number of mental health diagnoses. However, their utility is limited by the potential to cause serious adverse movement reactions. Akathisia, dystonia, parkinsonism, and tardive dyskinesia (collectively known as extrapyramidal symptoms or EPSs) are associated with reduced social and occupational functioning, negative patient attitudes toward treatment, and nonadherence to pharmacotherapy. Neuroleptic malignant syndrome is a life-threatening reaction that can result from DRBA use and cause musculoskeletal dysfunction. The aim of this study is to profile patients who have developed DRBA-related movement adverse effects and identify risk factors significantly associated with each subtype of EPSs or other movement disorders (OMDs) such as neuroleptic malignant syndrome. Methods/procedures: A report of all potential DRBA-related EPSs or OMDs occurrences within a large community hospital network was generated using International Classification of Diseases, Ninth Revision (ICD-9) and 10th Revision (ICD-10) billing codes. Each patient encounter was manually reviewed to confirm that a documented case of DRBA-related EPSs or OMDs had indeed occurred and subsequently determine the likely causative agent(s). Findings/results: The resultant cohort of 148 patients experiencing unique DRBA-related EPS or OMD events was analyzed. The average patient was female, middle-aged, and overweight. The most common DRBAs precipitating EPSs or OMDs were haloperidol and quetiapine. In the population studied, age was significantly associated with the subtype of EPSs experienced such that those patients with akathisia and dystonia tended to be younger, whereas those with tardive dyskinesia tended to be older. Body mass index (BMI) category was also negatively correlated with the incidence of dystonia. In addition, it was observed that exposure to specific DRBAs, classes, and routes of administration significantly affected the risk of developing different subtypes of EPSs or OMDs in the study population. Implications/conclusions: To our knowledge, this is the first study to describe an association between age and BMI with the risk of akathisia and dystonia, respectively, in patients taking DRBAs. Other trends observed with age and BMI in patients developing DRBA-related EPSs support previously reported findings. Expanding the knowledge base of individual characteristics associated with the risk of developing different subtypes of EPSs or OMDs can help providers and patients anticipate and attempt to mitigate these reactions, and may ultimately improve adherence to DRBA therapy

Appalachia\u27s Children: The Challenge of Mental Health

This thoughtful, compassionate book makes a major contribution to our understanding of the Southern Appalachian child—his mental disorders and his adaptive strengths. Drawing upon his extensive fieldwork as a clinical child psychiatrist in Eastern Kentucky, Dr. Looff suggests means by which these children can be helped to bridge the gap between their subculture and the mainstream of American life today. The children described in this book, the author points out, are in a real sense not “all children.” Since no child grows up in a vacuum, the children of Eastern Kentucky cannot be understood apart from the historical, geographic, and socioeconomic characteristics of the area in which they grow. Knowledge of the children requires some knowledge of the lives of parent, teachers, and the many others upon whom they are dependent. That is to say, mental disorder—or mental health—is embedded in a social matrix. Dr. Looff therefore examines the milieu of these Southern Appalachian children, their future as adults, and how they can achieve their potential—whether in their native or an urban setting. In viewing the children within their own cultural framework, Dr. Looff shows how they develop toward mental health or psychopathology, suggesting supportive techniques that build upon the strengths inherent in each child. These strengths, he suggests, rise out of the same culture that burdens the child with handicaps. Dr. Looff’s position is one of guarded optimism, based on the successes of the techniques he has used and observed in seven years of work in Appalachian field clinics. Although he details instances of mental disorder in children, and instances of failure in family functioning, he notes at the same time family strengths and sees these strengths as sources of hope. Although this book is based on fieldwork techniques within a specific area and culture, it is paradigmatically suggestive of wider application. Dr. Looff demonstrates effectively and clearly the profound need for increased concern about what is happening to the rising generation—the children of Eastern Kentucky, the children of the Southern Appalachian region, and the children of the rural south. David H. Looff, a Fellow of the American Psychiatric Association, is an associate professor of child psychiatry at the University of Kentucky.https://uknowledge.uky.edu/upk_psychology/1000/thumbnail.jp

Self-referred walk-in patients in the emergency department - who and why? Consultation determinants in a multicenter study of respiratory patients in Berlin, Germany

Background: Emergency department (ED) consultations are on the rise, and frequently consultations by non-urgent patients have been held accountable. Self-referred walk-in (SRW) consulters supposedly represent a predominantly less urgent patient population. The EMACROSS study aimed to explore consultation determinants and motives in SRW patients with respiratory symptoms. Methods: Multicenter survey of adult ED patients with respiratory complaints in eight emergency departments in central Berlin, Germany. Secondary hospital records data including diagnoses was additionally assessed. Characteristics of SRW and non-SRW patients were compared. Determinants of SRW consultation were evaluated by binary logistic regression. Consultation motives were analyzed descriptively. As a supplemental approach, network analysis (lasso-regularized mixed graphical model) was performed to explore connections between consultation determinants, consultation features and motives. Results: Between June 2017 and November 2018, n = 472 participants were included, the median age was 55 years (range 18–96), 53.2% of patients were male and n = 185 cases (39.2%) were SRW consulters. The SRW group showed lower proportions of potentially severe (pneumonia and respiratory failure, p < 0.001, χ2 test) and chronic pulmonary conditions. Determinants of SRW consultation identified by logistic regression were younger age (p < 0.001), tertiary education (p = 0.032), being a first-generation migrant (p = 0.002) or tourist (p = 0.008), having no regular primary care provider (p = 0.036) and no chronic pulmonary illness (p = 0.017). The area under the curve (AUC) for the model was 0.79. Personal distress and access problems in ambulatory care were stated most frequently as consultation motives in the SRW group; network analysis showed the scarcity of associations between demographic and medical SRW determinants and motives triggering the actual decision to consult. Conclusions: As to "who" consults, this study identified demographic and medical predictors of SRW utilization. The said markers seem only remotely connected to "why" people decide for SRW visits. To alleviate ED crowding by addressing frequent SRW consultation motives, interventions focused on the ability for symptom self-assessment and at better-accessible alternative care seem sensible