Set Of Patient Characteristics Research Articles

Inclusion of patient-level emergency department characteristics to classify potentially redirectable visits to subacute care: a modified Delphi consensus study.

Most patients transported by Ontario paramedics to the emergency department have non-emergent conditions and may be more appropriately served by subacute community-based care centres. We sought to determine consensus on a set of patient characteristics that could be useful to classify retrospective emergency department visits that had a high probability of being primary care-like and potentially redirectable to a subacute care centre by paramedics. We conducted a modified Delphi study to assess expert consensus on characteristics of patients transported by paramedics to the emergency department from August to October 2021. An expert Delphi committee was constructed of emergency and family physicians in Ontario using purposive sampling. Experts rated whether each characteristic was useful to be included in a classification to identify potentially redirectable visits retrospectively, as well as characteristic details (e.g., upper and lower bounds). Consensus was considered 75% agreement. Sixteen experts participated in the study; the experts were mostly male (75%) and evenly divided between emergency and family medicine. After 2 rounds, consensus was achieved on 8 of 9 characteristics (89%). Four characteristics were determined as useful to classify potentially redirectable emergency department visits: age (81%), triage acuity (100%), specialist consult in the emergency department (94%) and emergency department visit outcome (81%). Specifications of each characteristic were refined as follows: young and middle-aged adults with a non-emergent triage acuity, did not receive a specialist physician consult in the emergency department and discharged from the emergency department. Strong consensus was achieved to specify a classification system for potentially redirectable emergency department visits. These results will be combined with knowledge of which subacute care centres could conduct the main physician interventions to retrospectively identify emergency department visits that could have been suitable for paramedic redirection for further research. ID ISRCTN22901977.

Scaling up a decentralized offline patient ID generation and matching algorithm to accelerate universal health coverage: Insights from a literature review and health facility survey in Nigeria.

BackgroundQuality of health service delivery data remains sub-optimal in many Low and middle-income countries (LMICs) despite over a decade of progress in digitization and Health Management Information Systems (HMIS) improvements. Identifying everyone residing in a country utilizing universal civil registration and/or national unique identification number systems especially for vulnerable patients seeking care within the care continuum is an essential part of pursuing universal health coverage (UHC). Many different strategies or candidate digital technologies exist for uniquely identifying and tracking patients within a health system, and the different strategies also have their advantages and trade-offs. The recent approval of Decentralized identifier (DID) core specification by World Wide Web Consortium (W3C) heralds the search for consensus on standard interoperable DID methods.ObjectiveThis paper aims to: (1) assess how candidate Patient Identification Systems fit the digital Patient ID desirable attributes framework in literature; and (2) use insights from Nigeria to propose the scale-up of an offline, interoperable decentralized Patient ID generation and a matching model for addressing network reliability challenges of centralized electronic registries in LMICs.MethodsWe combined: (i) systematic review of the literature to identify the characteristics of leading candidates for Patient ID systems, with (ii) review of policies and (iii) quantitative survey of 14 general hospitals in Nigeria's Federal Capital Territory to understand the model(s) of patient ID strategies currently implemented by public hospitals.ResultsEvidence from the literature review and quantitative survey showed that no current Patient ID strategy in Nigeria simultaneously meets the six attributes of uniqueness, unchanging, uncontroversial, inexpensive, ubiquitous, and uncomplicated required for ensuring the reliability of unique patient identification systems and of the HMIS more generally.ConclusionsThe findings are used to propose a model of algorithms for universal-offline Patient ID generation and matching models that is cost effective and can be easily scaled-up throughout Nigeria. The prototype has promise for generating and validating a universally unique Patient ID given a set of patient characteristics without a central rigid authority. The model can also help to fast-track the implementation of a Master Patient Index (MPI) and interoperability of existing digital health platforms in LMICs.

Radiomics model based on features of axillary lymphatic nodes to predict axillary lymphatic node metastasis in breast cancer.

Breast cancer (BC) is among the most common cancers worldwide. Machine learning-based radiomics model could predict axillary lymph node metastasis (ALNM) of BC accurately. The purpose is to develop a machine learning model to predict ALNM of BC by focusing on the radiomics features of axillary lymphatic node (ALN). A group of 398 BC patients with 800 ALNs were retrospectively collected. A set of patient characteristics were obtained to form clinical factors. Three hundred and twenty-six radiomics features were extracted from each region of interest for ALN in contrast-enhanced computed tomography (CECT) image. A framework composed of four feature selection methods and 14 machine learning classification algorithms was systematically applied. A clinical model, a radiomics model, and a combined model were developed using a cross-validation approach and compared. Metrics of the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the performance of these models in the prediction of ALNM in BC. Among the 800 cases of ALNs, there were 388 cases of positive metastasis (48.50%) and 412 cases of negative metastasis (51.50%). The baseline clinical model achieved the performance with an AUC = 0.8998 (95% CI [0.8540, 0.9457]). The radiomics model achieved an AUC = 0.9081 (95% CI [0.8640, 0.9523]). The combined model using the clinical factors and radiomics features achieved the best results with an AUC = 0.9305 (95% CI [0.8928, 0.9682]). Combinations of feature selection methods and machine learning-based classification algorithms can develop promising predictive models to predict ALNM in BC using CECT features. The combined model of clinical factors and radiomics features outperforms both the clinical model and the radiomic model.

Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach.

Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses reinforcement learning (RL) algorithms to recommend patient-tailored electrolyte replacement policies for ICU patients. We used electronic health records (EHR) data that originated from two institutions (UPHS; MIMIC-IV). The tool uses a set of patient characteristics, such as their physiological and pharmacological state, a pre-defined set of possible repletion actions, and a set of clinical goals to present clinicians with a recommendation for the route and dose of an electrolyte. RL-driven electrolyte repletion substantially reduces the frequency of magnesium and potassium replacements (up to 60%), adjusts the timing of interventions in all three electrolytes considered (potassium, magnesium, and phosphate), and shifts them towards orally administered repletion over intravenous replacement. This shift in recommended treatment limits risk of the potentially harmful effects of over-repletion and implies monetary savings. Overall, the RL-driven electrolyte repletion recommendations reduce excess electrolyte replacements and improve the safety, precision, efficacy, and cost of each electrolyte repletion event, while showing robust performance across patient cohorts and hospital systems.

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

BackgroundSeveral models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.ObjectiveWe aimed to develop a machine learning–based score—the Piacenza score—for 30-day mortality prediction in patients with COVID-19 pneumonia.MethodsThe study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients’ medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori.ResultsThe Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.ConclusionsOur findings demonstrated that a customizable machine learning–based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

Hospital volumes and the likelihood of stent implantation among patients undergoing catheterization of the left heart: more is more?

Abstract Background Percutaneous coronary intervention (PCI) is an established procedure, but recent studies analyzing the indication for stenting are going to change clinical practice. Previous studies suggested that hospital volume is inversely related to in-hospital mortality but its impact on likelihood of stent implantation and the number of stents remains unclear. Purpose There is a conflict of objectives between nationwide care including short transfer and intervention times and a few large centers with maximum technology and experience. We examine the effect of hospital volume on in-hospital mortality, likelihood of stent implantation, number of stents, length of hospital stay, and reimbursement in a recent nationwide cohort from Germany. Methods Using German national electronic health records, all patients who underwent coronary angiography with a documented coronary artery disease were identified by ICD and OPS codes. Risk-adjustment was applied using a predefined set of patient characteristics to account for differences in the risk factor composition of the patient populations between centers. Results In 2017, a total of 528,188 patients with a documented coronary artery disease underwent coronary angiography in Germany. Mean age was 69.8 years and 29.3% of patients were female. 55% of all patients received PCI, with a mean number of 1.01 stents implanted per patient. In-hospital mortality was 2.9%, length of hospital stay was 6.5 days and mean reimbursement was €5,531. Multivariable regression analyses showed a positive linear association between hospital volumes and the likelihood of stent implantation (p=0.003) as well as the number of implanted stents (p=0.020). No association was found between hospital volumes and in-hospital mortality (p=0.105), length of hospital stay (p=0.201) or reimbursement (p=0.108). Inspection of the non-linear impact of procedure volumes on stent implantation practices indicates a ceiling effect in the volume-outcome relationship: implantation likelihood and number of stents per patient are lowest in centers with less than 100 procedures per year (34.4% and 0.62, respectively). Then, implantation likelihood and number of stents constantly increase until the volume category of 500 procedures per year and center. For centers with >500 procedures per year, the likelihood of stent implantation and the number of implanted stents remained relatively constant (about 60% and 1.07, respectively). Conclusion Patients undergoing coronary angiography in low-volume centers are less frequently subject to PCI but at comparable risk for in-hospital mortality. Furthermore, the data suggest that more complex cases are treated in high volume centers with consistent mortality rates and thus constant safety is ensured in high volume hospitals. Thresholds are discussed. Impact of hospital volumes on PCI Funding Acknowledgement Type of funding source: None

Advanced cardiometabolic & inflammatory markers for prediction of cardiovascular disease and cancer

Abstract The two leading causes of mortality worldwide are cardiovascular disease (CVD) and cancer. The annual total cost of CVD and cancer is an estimated $844.4 billion in the US and is projected to double by 2030. Thus, there has been an increased shift to preventive medicine to improve health outcomes and development of risk scores, which allow early identification of individuals at risk to target personalised interventions and prevent disease. Our aim was to define a Risk Score R(x) which, given the baseline characteristics of a given individual, outputs the relative risk for composite CVD, cancer incidence and all-cause mortality. A non-linear model was used to calculate risk scores based on the participants of the UK Biobank (= 502548). The model used parameters including patient characteristics (age, sex, ethnicity), baseline conditions, lifestyle factors of diet and physical activity, blood pressure, metabolic markers and advanced lipid variables, including ApoA and ApoB and lipoprotein(a), as input. The risk score was defined by normalising the risk function by a fixed value, the average risk of the training set. To fit the non-linear model >400,000 participants were used as training set and >45,000 participants were used as test set for validation. The exponent of risk function was represented as a multilayer neural network. This allowed capturing interdependent behaviour of covariates, training a single model for all outcomes, and preserving heterogeneity of the groups, which is in contrast to CoxPH models which are traditionally used in risk scores and require homogeneous groups. The model was trained over 60 epochs and predictive performance was determined by the C-index with standard errors and confidence intervals estimated with bootstrap sampling. By inputing the variables described, one can obtain personalised hazard ratios for 3 major outcomes of CVD, cancer and all-cause mortality. Therefore, an individual with a risk Score of e.g. 1.5, at any time he/she has 50% more chances than average of experiencing the corresponding event. The proposed model showed the following discrimination, for risk of CVD (C-index = 0.8006), cancer incidence (C-index = 0.6907), and all-cause mortality (C-index = 0.7770) on the validation set. The CVD model is particularly strong (C-index >0.8) and is an improvement on a previous CVD risk prediction model also based on classical risk factors with total cholesterol and HDL-c on the UK Biobank data (C-index = 0.7444) published last year (Welsh et al. 2019). Unlike classically-used CoxPH models, our model considers correlation of variables as shown by the table of the values of correlation in Figure 1. This is an accurate model that is based on the most comprehensive set of patient characteristics and biomarkers, allowing clinicians to identify multiple targets for improvement and practice active preventive cardiology in the era of precision medicine. Figure 1. Correlation of variables in the R(x) Funding Acknowledgement Type of funding source: None

Initiating opioid agonist treatment for opioid use disorder nationally in the Veterans Health Administration: Who gets what?

Background: Despite substantial benefits associated with opioid agonist treatment (OAT) with buprenorphine and methadone for opioid use disorder (OUD), only a small proportion of patients with OUD initiate OAT. There is a lack of studies addressing the correlates of OAT initiation among patients with OUD. Methods: Using Veterans Health Administration (VHA) national administrative data, we identified veterans with OUD who started OAT with either buprenorphine or methadone maintenance treatment (MMT) in fiscal year (FY) 2012 (first prescription of buprenorphine or first methadone clinic visit after the first 60 days of FY) and those who received no OAT that year. Multivariate logistic regression models including sociodemographic characteristics, diagnoses, and service and psychotropic drug use variables were used to identify independent predictors of OAT initiation. Results: Greater age (10-year increments; odds ratio [OR]: 0.96, 95% confidence interval [CI]: 0.0.9–0.97) and black race (OR: 0.46, 95% CI: 0.38–0.55) were associated with lower odds of being started on buprenorphine compared with no OAT, but not with MMT initiation. Veterans with cocaine and anxiolytic-sedative hypnotic use disorders had higher odds of being started on both buprenorphine and methadone compared with no OAT. Receipt of any mental health inpatient treatment was associated with higher odds of being started on buprenorphine but not methadone. Overall, we were unable to identify a robust set of patient characteristics associated with initiation of OAT. Conclusion: This study points out the stark reality that in the middle of an opioid crisis, we have very little insight into which patients with OUD initiate OAT.

Patient Companionship in a Movement Disorders Clinic: Outside Assistance Inside the Office.

Objective: We (1) report whether a companion (i.e., spouse, relative, aide) accompanied our consecutive outpatients with a range of movement disorders, (2) identified the set of patient characteristics that was associated with the need for a visit companion, and (3) characterized the role(s) of these companions during the visit. Our overarching goals were to further understand patient needs and the extent of their support networks, and to enrich the clinician-patient interface.Methods: Two-hundred consecutive patients were enrolled from the Movement Disorders Clinic at Yale School of Medicine. We noted whether patients were accompanied by another person during the visit and documented the role of the visit companion during the encounter.Results: One-hundred-twenty-eight of 200 patients (64.0%) brought a companion, with these being spouses (44.8%), adult children (24.1%) or an aide, nurse or social worker (14.5%). Patients who were unemployed (odds ratio [OR] = 5.32, p = 0.019), had a diagnosis of Parkinson's disease or other Parkinsonian syndromes (OR = 10.61, p = 0.001), or were dependent in any instrumental activities of daily living (iADLs) (OR = 4.99, p = 0.005) or basic activities of daily living (bADLs) (OR = 5.81, p = 0.02), had increased odds of presenting to the clinical visit with a visit companion. Visit companions' main roles involved communication (86.7%) and transportation (84.4%).Conclusion: Visit companions were commonly present during movement disorders outpatient visits–two-thirds of patients were accompanied. A number of factors increased the odds of requiring such a companion by 4- or 5-fold.

Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space.

Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al., 2009), though recent studies suggest that this is tailing off, e.g., Holden et al. (2013). Around 70% of children demonstrate a clinically significant treatment response to stimulant medication (Spencer et al., 1996; Schachter et al., 2001; Swanson et al., 2001; Barbaresi et al., 2006). However, it is unclear which patient characteristics may moderate treatment effectiveness. As such, most existing research has focused on investigating univariate or multivariate correlations between a set of patient characteristics and the treatment outcome, with respect to dosage of one or several types of medication. The results of such studies are often contradictory and inconclusive due to a combination of small sample sizes, low-quality data, or a lack of available information on covariates. In this paper, feature extraction techniques such as latent trait analysis were applied to reduce the dimension of on a large dataset of patient characteristics, including the responses to symptom-based questionnaires, developmental health factors, demographic variables such as age and gender, and socioeconomic factors such as parental income. We introduce a Bayesian modeling approach in a “learning in the model space” framework that combines existing knowledge in the literature on factors that may potentially affect treatment response, with constraints imposed by a treatment response model. The model is personalized such that the variability among subjects is accounted for by a set of subject-specific parameters. For remission classification, this approach compares favorably with conventional methods such as support vector machines and mixed effect models on a range of performance measures. For instance, the proposed approach achieved an area under receiver operator characteristic curve of 82–84%, compared to 75–77% obtained from conventional regression or machine learning (“learning in the data space”) methods.

Abstract PR622

Background & Objectives: A patient’s health status often determines whether surgery is recommended for cancer therapy. The risk of complications may outweigh the perceived benefit. Consequently, measures that predict surgical outcomes may be helpful in determining whether or not to operate. Materials & Methods: We retrospectively apply four mortality predictors to 62,763 adult surgical cancer patients from the University of Texas MD Anderson Cancer Center during January 2007 - March 2014. We use the first surgery for each patient that is over 60 minutes in duration and compare the following indexes: Charlson Comorbidity Index1 as implemented by Deyo et al.2; Dalton’s Risk Quantification Index (RQI)3; Sessler’s Risk Stratification Index (RSI)4 and the Surgical Apgar Score5. We implement each metric as described by the authors and evaluate each score’s ability to predict 30-day mortality using logistic regression, randomly selecting two-thirds of the cases for learning the model and using the remaining cases for evaluation. We report the C-Statistic for each metric in isolation and in combination with the other measures. Results: We obtain individual C-Statistics from 0.639 to 0.784 and a combined C-Statistic of 0.817. The least predictive index is the RQI and the most predictive is the RSI.Conclusion: Despite not being designed specifically for cancer patients, or even necessarily for 30-day mortality, these metrics provide accurate predictions for our patient body. Furthermore, by combining the scores together, we are able to improve predictions, as each index is based on a different set of patient characteristics. RQI was the least useful metric, contributing only 0.01 to the combined C-Statistic. These indexes can be used to calculate and communicate the likelihood of 30-day mortality and may aid in clinical decision-making.

G166 Risk of death or emergency readmission following discharge after infant cardiac intervention for congenital heart disease

Aims 1) To identify risk factors for death or unplanned readmission within one year following hospital discharge after cardiac intervention for congenital heart disease. 2) To characterise patient groups at highest risk who would benefit from targeted intervention. Methods Records in the national congenital cardiac surgical audit (NICOR) pertaining to UK infants who had a cardiac surgery or intervention aged under 12 months between 01/01/2005 and 31/12/2010 were matched with intensive care admission records in Paediatric Intensive Care Audit Network (PICANET); linked records with known life-status were obtained for 7634 infants. Outcome measures were: Outcome 1 – death within 1-year following discharge; Outcome 2 – Outcome 1 or emergency readmission to PICU within 1-year following discharge. Potential risk factors available from either dataset were pre-specified and univariate and multivariate logistic regression used to investigate the effects of these on each outcome. Classification and regression tree (CART) analysis was used to identify distinct patient groups differentiated by risk of Outcome 2, each defined by a set of patient characteristics. Results 3.2% (246/7643) and 6.7% (514/7643) of infants experienced Outcome 1 and 2 respectively. Fitted multivariate models for both outcomes were robust in risk factor selection (Outcome 1 - ROC AUC = 0.78, 95% CI [0.75, 0.82]; Outcome 2 - ROC AUC = 0.78 [0.75, 0.80]). Risk factors significant in the multivariate Outcome 2 model were: age at procedure, weight z-score, cardiac procedure, cardiac diagnosis, non-cardiac congenital anomaly, neurodevelopmental condition, prematurity ( 1 month [24% risk]; (4) No congenital anomalies and LOS > 1 month [9% risk]. Conclusions Understanding patient risk groups should inform recommendations for improving services, support development of interventions to mitigate each profile of risk and facilitate evaluation of the priority and feasibility of targeting each group.

Conditional transformation models for survivor function estimation.

In survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patient's risk than summary statistics, such as median survival time. Nevertheless, standard methods for analysing survival data seldom estimate the survivor function directly. Therefore, we propose the application of conditional transformation models (CTMs) for the estimation of the conditional distribution function of survival times given a set of patient characteristics. We used the inverse probability of censoring weighting approach to account for right-censored observations. Our proposed modelling approach allows the prediction of patient-specific survivor functions. In addition, CTMs constitute a flexible model class that is able to deal with proportional as well as non-proportional hazards. The well-known Cox model is included in the class of CTMs as a special case. We investigated the performance of CTMs in survival data analysis in a simulation that included proportional and non-proportional hazard settings and different scenarios of explanatory variables. Furthermore, we re-analysed the survival times of patients suffering from chronic myelogenous leukaemia and studied the impact of the proportional hazards assumption on previously published results.

Using G-Computation to Estimate the Effect of Regionalization of Surgical Services on the Absolute Reduction in the Occurrence of Adverse Patient Outcomes

Numerous studies have found that increased hospital or surgeon operative volumes, as measured by the number of procedures performed, are associated with improved patient outcomes after surgery. These findings have been used to support important health policy decisions about regionalization of surgical services, in which provision of specific surgical services is restricted to hospitals that maintain operative volumes above a specified threshold. The most common statistical approach in volume-outcome studies is to regress patient outcomes on a set of patient characteristics and a variable denoting provider volume. When outcomes are binary, such as operative mortality, logistic regression is used, resulting in the odds ratio being the reported measure of association. However, the odds ratio is a relative measure of effect and does not allow policy makers to estimate the absolute benefit of regionalization. To describe how G-computation can be used to estimate the expected number of lives saved due to regionalization of surgical services. Retrospective cohort design of patients undergoing 1 of 3 different surgical procedures in Ontario, Canada. Regionalization of colorectal cancer surgery, esophagectomy, or pancreaticoduodenectomy in Ontario could reduce the average annual number of perioperative deaths by 20.2, 2.0, and 3.6, for the 3 procedures, respectively. The absolute reduction in number of operative deaths due to regionalization of surgical procedures can be calculated. This can help inform health policy debate about benefits of regionalization.

THU0592 Longitudinal Measurement of Fatigue in Rheumatoid Arthritis: Which Factors Predict Fatigue?

BackgroundIn cross-sectional studies it was found that psychosocial factors, pain and daily functioning, rather than inflammation, are associated with fatigue severity in rheumatoid arthritis (RA). However, it is unknown which...

Examining Predictors of the Physical and Mental Components of Quality of Life in the TOP MS Study (P07.103)

Objective: To assess the most effective predictors of the Physical Component Score (PCS) and Mental Component Score (MCS) of the SF-12 used to assess quality of life in the Treatment Optimization in MS (TOP MS) Study. Background Several MS patient characteristics may be associated with the PCS and MCS including patient age, disability status and levels of fatigue, perceived cognitive deficits and depression. Design/Methods: Potential participants for the TOP MS study were identified at specialty pharmacies with medication therapy management programs. Signed informed consent forms were returned to the pharmacies and study enrollment produced log-on instructions for the study website. At baseline and at regular intervals over 24 months, enrolled participants receive reminders to respond to surveys, including the SF-12 Health Survey, self-reported EDSS, Fatigue Severity Scale (FSS), Perceived Deficits Questionnaire (PDQ), and Patient Health Questionnaire (PHQ-depression). Self-reported responses are entered directly into the study database. Multiple regression analyses were used to identify predictors of the PCS and MCS. Results: The array of patient characteristics that was highly effective at predicting the PCS included patient age, self-reported EDSS, and scores on the FSS, PDQ, and PHQ (adjusted R 2 = .620, p 2 = .467, p Conclusions: While the same set of patient characteristics was highly effective at predicting the PCS and MCS, depression was a unique and mediating variable between disability and MCS while the PCS had two independent predictors: disability and fatigue. Supported by: Teva Pharmaceuticals. Disclosure: Dr. Oleen-Burkey has received personal compensation for activities with Teva Neuroscience. Dr. Oleen-Burkey holds stock and/or stock options in Pfizer, Inc and Teva Neuroscience. Dr. Oleen-Burkey has received research support from Teva Neuroscience. Dr. Zwibel has received personal compensation for activities with Serono, Inc., and Teva Neuroscience. Dr. Zwibel has received research support from Teva Neuroscience. Dr. Coyle has received personal compensation for activities with Acorda Therapeutics, Avanir Pharmaceuticals, Bayer Pharmaceuticals Corporation, Biogen Idec, Genzyme Corporation, Novartis, Questcor, Roche Diagnostics Corporation, Sanofi-Aventis Pharmaceuticals, Inc., and Teva Neuroscience. Dr. Coyle has received personal compensation in an editorial capacity for NEURA. Dr. Coyle has received research support from Serono, Inc., Novartis, and Sanofi-Aventis Pharmaceuticals, Inc. Dr. Denney has received personal compensation for activities with Teva Neuroscience Inc. as a consultant.Dr. Denney has received research support from Serono Inc.

Treatment Intensity in Child and Adolescent Mental Health Services and Health Care Reform in Norway, 1998–2006

Treatment Intensity in Child and Adolescent Mental Health Services and Health Care Reform in Norway, 1998–2006

Dimensions of manifest depression

DEPRESSION is a complex psychiatric concept. The term is used to refer to subjective mood as well as to symptom syndromes associated with psychopathologic states called depression. As a psychiatric syndrome, depression is manifest in a number of signs and symptoms which distinguish the depressed patient from the normal. As in the case of other recognized syndromes, not all depressed patients evidence all symptoms, at least not to the same degree of intensity. An important question concerns characteristics of individual difference in depressed patients. If depressed patients vary in the nature and intensity of symptoms presented, what primary symptom dimensions are most useful in characterizing these individual differences ? Must variation in the symptomatology of depressed patients be accounted for in terms of a unique set of factors which are not recognized dimensions of psychiatric symptomatology in other patient populations, or is there a common set of factors which exist at different levels in various types of psychiatric patients including cases of depression? The methods of factor analysis were employed in the present investigation in an effort to provide answers to these questions. The factor analysis model assumes that observed behavior is complex and that it is determined by a parsimonious set of underlying factors, traits, or processes. Any given bit of observed behavior, such as rateable psychiatric symptomatology, may be determined by more than a single basic factor, and certainly several different measureable patient characteristics may result from the same underlying factor. To the extent that several measureable patient characteristics reflect a single underlying factor which exists at different levels in different individuals, these characteristics will tend to co-vary (or to be correlated). That is, an individual who is high on the underlying factor will tend to have high scores on the several characteristics reflecting that factor, and individuals who are low on the factor will have low scores on these same characteristics. It is through identification of sets of patient characteristics which tend to co-vary that factor analysis leads to inferences concerning the nature of primary sources of variance responsible for this co-variation. Thus, factor analysis is a methodology for the study of individual differences. The co-variation or correlation of symptom measures depends upon the fact that they reflect underlying factors which exist at different levels in different individuals.