Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class Analysis.

Schizophrenia does not present uniformly among patients and as a result this patient population is characterized by a diversity in the type and amount of healthcare supports needed for daily functioning. Despite this, little work has been completed to understand the heterogeneity that exists among these patients. In this work we used a data-driven approach to identify subgroups of high-cost patients with schizophrenia to identify potentially actionable interventions for the improvement of outcomes and to inform conversations on how to most efficiently allocate resources in an already strained system. Administrative health data was used to conduct a retrospective analysis of "high-cost" adult patients with schizophrenia residing in Alberta, Canada in 2017. Costs were derived from inpatient encounters, outpatient primary care and specialist encounters, emergency department encounters, and drug costs. Latent class analysis was used to group patients based on their unique clinical profiles. Latent class analysis of 1659 patients revealed the following patient groups: (1) young, high-needs males early in their disease course; (2) actively managed middle-aged patients; (3) elderly patients with multiple chronic conditions and polypharmacy; (4) unstably housed males with low treatment rates; (5) unstably housed females with high acute care use and low treatment rates. This taxonomy may be used to inform policy, including the identification of interventions most likely to improve care and reduce health spending for each subgroup.

Factors associated with high health care spending among patients with schizophrenia. / Facteurs associés aux dépenses élevées en soins de santé chez les patients atteints de schizophrénie.

INTRODUCTION: Understanding the reasons for the wide variation in health care spending among patients with schizophrenia may benefit the development of interventions aimed at improving patient outcomes and health care spending efficiency. The aim of our study was to determine factors associated with high health care spending in the patient population. METHODS: A serial cross-sectional study used the administrative health records of residents of Alberta, Canada between 1 January 2008 and 31 December 2017 and provincial costing methodologies to calculate total health care spending and sector-specific costs. Factors that modified the odds of being a high cost (i.e. 95th percentile or higher) patient with schizophrenia were estimated using generalized estimating equations. RESULTS: This study captured 242 818 person-years of observations among 38 177 unique patients with schizophrenia. Increased odds of being a high-cost patient were associated with younger age (18-29 years), male sex, unstable housing status and requiring care from multiple medical specialties. The strongest estimated associations between high cost status and comorbidity were for metastatic cancer (OR = 2.26) and cirrhosis (OR = 2.07). In contrast, polypharmacy was associated with a decreased odds of being high cost compared with untreated patients. CONCLUSION: Factors associated with being a high-cost patient are the result of complex interactions between individual, structural and treatment-related factors. Efforts to improve patient outcomes and address rising health care costs must consider the value of allocating resources towards early detection and support of patients with schizophrenia along with the prevention/management of comorbidity.

10-Year Trends in Healthcare Spending among Patients with Schizophrenia in Alberta, Canada.

OBJECTIVES: Schizophrenia is characterized by high levels of disability often resulting in increased healthcare utilization and spending. With expanding healthcare costs across all healthcare sectors, there is a need to understand how healthcare spending has changed over time. We conducted a population-based study using administrative health data from Alberta, Canada, to describe changes in medical complexity and direct healthcare spending among patients with schizophrenia over a 10-year period. METHODS: A serial cross-sectional study from January 1, 2008, to December 31, 2017, was conducted to determine changes in demographic characteristics, medical complexity, and costs among all adults (18 years or older) with schizophrenia. Total healthcare spending and sector-specific costs attributable to hospitalizations, emergency department visits, practitioner billings, and prescriptions were calculated and compared over time. RESULTS: Over the 10-year period the contact prevalence of patients with schizophrenia increased from 0.6% (n = 16,183) to 1.0% (n = 33,176) within the province. There was a marked change in medical complexity with the number of patients living with 3 or more comorbidities increasing from 33.0% to 47.3%. Direct annual healthcare costs increased 2-fold from 321 to 639 million CAD (493 million USD) with a 7-fold increase in medication expenditures over the 10-year time frame. As of 2017, spending on pharmaceutical treatment surpassed hospitalizations as the leading spending category in this population. CONCLUSIONS: Healthcare spending among patients with schizophrenia continues to increase and may be partially attributable to growing rates of multimorbidity within this population. Although promising second-generation antipsychotic medications have entered the market, this has resulted in considerable changes in the distribution of healthcare spending over time. These findings will inform policy discussions around resource allocation and efforts to curb health spending while also improving care for patients with schizophrenia.

Derivation and Internal Validation of a Clinical Risk Prediction Tool for Hyperkalemia-Related Emergency Department Encounters Among Hemodialysis Patients.

BACKGROUND: Approximately 10% of emergency department (ED) visits among dialysis patients are for conditions that could potentially be managed in outpatient settings, such as hyperkalemia. OBJECTIVE: Using population-based data, we derived and internally validated a risk score to identify hemodialysis patients at increased risk of hyperkalemia-related ED events. DESIGN: Retrospective cohort study. SETTING: Ten in-center hemodialysis sites in southern Alberta, Canada. PATIENTS: All maintenance hemodialysis patients (≥18 years) between March 2009 and March 2017. MEASUREMENTS: Predictors of hyperkalemia-related ED events included patient demographics, comorbidities, health-system use, laboratory measurements, and dialysis information. The outcome of interest (hyperkalemia-related ED events) was defined by International Classification of Diseases (10th Revision; ICD-10) codes and/or serum potassium [K+] ≥6 mmol/L. METHODS: Bootstrapped logistic regression was used to derive and internally validate a model of important predictors of hyperkalemia-related ED events. A point system was created based on regression coefficients. Model discrimination was assessed by an optimism-adjusted C-statistic and calibration by deciles of risk and calibration slope. RESULTS: Of the 1533 maintenance hemodialysis patients in our cohort, 331 (21.6%) presented to the ED with 615 hyperkalemia-related ED events. A 9-point scale for risk of a hyperkalemia-related ED event was created with points assigned to 5 strong predictors based on their regression coefficients: ≥1 laboratory measurement of serum K+ ≥6 mmol/L in the prior 6 months (3 points); ≥1 Hemoglobin A1C [HbA1C] measurement ≥8% in the prior 12 months (1 point); mean ultrafiltration of ≥10 mL/kg/h over the preceding 2 weeks (2 points); ≥25 hours of cumulative time dialyzing over the preceding 2 weeks (1 point); and dialysis vintage of ≥2 years (2 points). Model discrimination (C-statistic: 0.75) and calibration were good. LIMITATIONS: Measures related to health behaviors, social determinants of health, and residual kidney function were not available for inclusion as potential predictors. CONCLUSIONS: While this tool requires external validation, it may help identify high-risk patients and allow for preventative strategies to avoid unnecessary ED visits and improve patient quality of life. TRIAL REGISTRATION: Not applicable-observational study design.

CONTEXTE: Environ 10 % des visites aux urgences des patients hémodialysés concernent des affections qui pourraient être prises en charge en ambulatoire, notamment l'hyperkaliémie. OBJECTIF: À l'aide de données populationnelles, nous avons dérivé et validé en interne une cote de risque pour dépister les patients hémodialysés présentant un risque accru de visites aux urgences liées à l'hyperkaliémie. TYPE D'ÉTUDE: Étude de cohorte rétrospective. CADRE: Dix sites d'hémodialyse en center du sud de l'Alberta (Canada). SUJETS: Tous les adultes sous hémodialyse chronique entre mars 2009 et mars 2017. MESURES: Les prédicteurs d'une visite aux urgences liée à l'hyperkaliémie incluaient les données démographiques du patient, les maladies concomitantes, l'utilization du système de santé, les mesures de laboratoire et les informations sur la dialyze. Le résultat d'intérêt (nombre de visites aux urgences liées à l'hyperkaliémie) a été défini par les codes CIM-10 et/ou une kaliémie [K+] égale ou supérieure à 6 mmol/L. MÉTHODOLOGIE: La régression logistique de type « bootstrap ¼ a été utilisée pour dériver et valider en interne un modèle des principaux prédicteurs d'une visites aux urgences liée à l'hyperkaliémie. Un système de pointage a été créé à partir des coefficients de régression. La discrimination du modèle a été évaluée par une statistique C corrigée selon l'optimisme, et l'étalonnage par des déciles de risque et une courbe d'étalonnage. RÉSULTATS: Des 1 533 patients de notre cohorte, 331 (21,6 %) se sont présentés aux urgences pour un total de 615 événements liés à l'hyperkaliémie. Une échelle à neuf points mesurant le risque a été créée, où un pointage a été attribué à cinq puissants prédicteurs en fonction du coefficient de régression: i) au moins une mesure de K+ égale ou supérieure à 6 mmol/L dans les six mois précédents (3 points); ii) au moins une mesure de l'hémoglobine A1C [HbA1C] égale ou supérieure à 8 % dans les 12 mois précédents (1 point); iii) une ultrafiltration moyenne d'au moins 10 mL/kg/heure dans les deux semaines précédentes (2 points); iv) un cumulatif d'au moins 25 heures de dialyze dans les deux semaines précédentes (1 point); et v) le fait d'être en dialyze depuis au moins 2 ans (2 points). La discrimination du modèle (statistique C: 0,75) et l'étalonnage ont été jugés bons. LIMITES: Les mesures relatives aux comportements en matière de santé, aux déterminants sociaux de la santé et à la fonction rénale résiduelle n'étaient pas disponibles pour leur inclusion comme prédicteurs potentiels. CONCLUSION: Bien que cet outil doive être validé en externe, il peut aider à dépister les patients présentant un risque élevé de visiter les urgences pour une hyperkaliémie. Il pourrait également favoriser l'élaboration de stratégies préventives visant à réduire les visites inutiles et à améliorer la qualité de vie des patients. ENREGISTREMENT DE L'ESSAI: Sans objet ­ essai observationnel.

A Clinical Risk Prediction Tool for 6-Month Mortality After Dialysis Initiation Among Older Adults.

BACKGROUND: Information on an individual's risk for death following dialysis therapy initiation may inform the decision to initiate maintenance dialysis for older adults. We derived and validated a clinical risk prediction tool for all-cause mortality among older adults during the first 6 months of maintenance dialysis treatment. STUDY DESIGN: Prediction model using retrospective administrative and clinical data. SETTING & PARTICIPANTS: We linked administrative and clinical data to define a cohort of 2,199 older adults (age ≥ 65 years) in Alberta, Canada, who initiated maintenance dialysis therapy (excluding acute kidney injury) in May 2003 to March 2012. CANDIDATE PREDICTORS: Demographics, laboratory data, comorbid conditions, and measures of health system use. OUTCOMES: All-cause mortality within 6 months of dialysis therapy initiation. ANALYTICAL APPROACH: Predicted mortality by logistic regression with 10-fold cross-validation. RESULTS: 375 (17.1%) older adults died within 6 months. We developed a 19-point risk score for 6-month mortality that included age 80 years or older (2 points), glomerular filtration rate of 10 to 14.9mL/min/1.73m2 (1 point) or ≥15mL/min/1.73m2 (3 points), atrial fibrillation (2 points), lymphoma (5 points), congestive heart failure (2 points), hospitalization in the prior 6 months (2 points), and metastatic cancer (3 points). Model discrimination (C statistic = 0.72) and calibration (Hosmer-Lemeshow χ2=10.36; P=0.2) were reasonable. As examples, a score < 5 equated to <25% of individuals dying in 6 months, whereas a score > 12 predicted that more than half the individuals would die in the first 6 months. LIMITATIONS: The tool has not been externally validated; thus, generalizability cannot be assessed. CONCLUSIONS: We used readily available clinical information to derive and internally validate a 7-variable tool to predict early mortality among older adults after dialysis therapy initiation. Following successful external validation, the tool may be useful as a clinical decision tool to aid decision making for older adults with kidney failure.