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Background Serious mental illnesses are associated with increased risk of cardiometabolic comorbidities. The objective of this study was to evaluate the prevalence of cardiometabolic comorbidity and its association with hospitalization outcomes and costs among inpatients with schizophrenia or bipolar disorder. Methods This retrospective database analysis reviewed patients with an inpatient diagnosis of schizophrenia or bipolar disorder from the Premier Perspective® Database (4/1/2010–6/30/2012). Patients were categorized into 4 cohorts based on the number of ICD-9-CM cardiometabolic comorbidities (i.e., 0, 1, 2, or 3+). Outcomes included length of stay, mortality during the index hospitalization, healthcare costs, and 30-day all-cause readmission rates. Results Of 57,506 patients with schizophrenia, 66.1% had at least one cardiometabolic comorbidity; 39.3% had two or more comorbidities. Of 124,803 patients with bipolar disorder, 60.5% had at least one cardiometabolic comorbidity; 33.4% had two or more. Average length of stay was 8.5 (for patients with schizophrenia) and 5.2 (for patients with bipolar disorder) days. Each additional cardiometabolic comorbidity was associated with an increase in length of stay for patients with bipolar disorder ( p  < .001) but not for patients with schizophrenia. Mortality rates during the index hospitalization were 1.2% (schizophrenia) and .7% (bipolar disorder). Each additional cardiometabolic comorbidity was associated with a significant increase in mortality for patients with bipolar disorder (OR 1.218, p  < .001), and a numerical increase in mortality for patients with schizophrenia (OR 1.014, p  = .727). Patients with more cardiometabolic comorbidities were more likely to have a 30-day readmission (schizophrenia = 9–13%; bipolar disorder = 7–12%), and to incur higher costs (schizophrenia = $10,606–15,355; bipolar disorder = $7126–13,523) (all p  < .01). Conclusions Over 60% of inpatients with schizophrenia or bipolar disorder had cardiometabolic comorbidities. Greater cardiometabolic comorbidity burden was associated with an increased likelihood of readmission and higher costs among patients with schizophrenia or bipolar disorder, and an increase in length of stay and mortality for patients with bipolar disorder.

Background Pulmonary hypertension due to interstitial lung disease (PH-ILD) is associated with high rates of respiratory failure and death. Healthcare resource utilization (HCRU) and cost data are needed to characterize PH-ILD disease burden. Methods A retrospective cohort analysis of the Truven Health MarketScan ® Commercial Claims and Encounters Database and Medicare Supplemental Database between June 2015 to June 2019 was conducted. Patients with ILD were identified and indexed based on their first claim with a PH diagnosis. Patients were required to be 18 years of age on the index date and continuously enrolled for 12-months pre- and post-index. Patients were excluded for having a PH diagnosis prior to ILD diagnosis or the presence of other non-ILD, PH-associated conditions. Treatment patterns, HCRU, and healthcare costs were compared between the 12 months pre- versus 12 months post-index date. Results In total, 122 patients with PH-ILD were included (mean [SD] age, 63.7 [16.6] years; female, 64.8%). The same medication classes were most frequently used both pre- and post-index (corticosteroids: pre-index 43.4%, post-index 53.5%; calcium channel blockers: 25.4%, 36.9%; oxygen: 12.3%, 25.4%). All-cause hospitalizations increased 2-fold, with 29.5% of patients hospitalized pre-index vs. 59.0% post-index ( P  < 0.0001). Intensive care unit (ICU) utilization increased from 6.6 to 17.2% ( P  = 0.0433). Mean inpatient visits increased from 0.5 (SD, 0.9) to 1.1 (1.3) ( P  < 0.0001); length of stay (days) increased from 5.4 (5.9) to 7.5 (11.6) ( P  < 0.0001); bed days from 2.5 (6.6) to 8.0 (16.3) ( P  < 0.0001); ICU days from 3.8 (2.3) to 7.0 (13.2) ( P  = 0.0362); and outpatient visits from 24.5 (16.8) to 32.9 (21.8) ( P  < 0.0001). Mean (SD) total all-cause healthcare costs increased from $43,201 ($98,604) pre-index to $108,387 ($190,673) post-index ( P  < 0.0001); this was largely driven by hospitalizations (which increased from a mean [SD] of $13,133 [$28,752] to $63,218 [$75,639] [ P  < 0.0001]) and outpatient costs ($16,150 [$75,639] to $25,604 [$93,964] [ P  < 0.0001]). Conclusion PH-ILD contributes to a high HCRU and cost burden. Timely identification, management, and treatment are needed to mitigate the clinical and economic consequences of PH-ILD development and progression.

Background Group 3 pulmonary hypertension (PH) describes a subpopulation of patients with PH due to chronic lung disease and/or hypoxia, with chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) being two large subgroups. Claims database studies provide insights into the real-world treatment patterns and outcomes among these patients. However, claims data do not provide sufficient detail to assign the clinical subtype of PH required for identifying these patients. Methods A panel of PH clinical experts and researchers was convened to discuss methodologies to identify patients with Group 3 PH associated with COPD or ILD in retrospective claims databases. To inform the discussion, a literature review was conducted to identify claims-based studies of Group 3 PH associated with COPD or ILD published from 2010 through June 2020. Results Targeted title and abstract review identified 11 claims-based studies and two conference abstracts (eight based in the United States [US] and five conducted outside the US) that met search criteria. Based on insights from the panel and literature review, the following components were detailed across studies in the identification of Group 3 PH associated with COPD and ILD: (a) COPD or ILD identification, (b) PH identification, (c) defining the sequence between COPD/ILD and PH, and (d) other PH Group and Group 3 PH exclusions. Conclusion This article provides recommended approaches and considerations for identifying and studying patients with Group 3 PH associated with COPD or ILD using administrative claims data that provide the foundation for future validation studies.

Background Given the improved convenience of oral prostacyclins, there is a shift toward their use in treating pulmonary arterial hypertension (PAH). Objectives Our objective was to compare patient characteristics, medication adherence, healthcare resource use (HCRU), and costs among patients receiving oral treprostinil or selexipag. Methods We used Truven Health MarketScan Commercial and Medicare databases to identify patients with PAH with a diagnosis code for pulmonary hypertension (PH) plus a prescription for oral treprostinil or selexipag from July 2013 to September 2017. Medication adherence, persistence, and all-cause and PAH-related HCRU and costs were compared between cohorts during the 6-month follow-up. Adjusted healthcare costs were obtained using recycled predictions and bootstrapped samples. Results A total of 256 (130 oral treprostinil, 126 selexipag) patients fulfilled the study criteria. The oral treprostinil cohort was more likely to be male, to have previously used parenteral prostacyclins, and to have higher outpatient costs at baseline than the selexipag cohort. During follow-up, both cohorts had similar proportions of patients who were adherent to and persistent with their respective therapies. All-cause and PAH-related medical utilization was generally similar between cohorts. The oral treprostinil cohort had 66.9% lower total PAH-related healthcare costs (mean difference − $75,183; 95% confidence interval [CI] − 102,584 to − 49,771) and 70.6% lower PAH-related pharmacy costs (mean difference − $76,439; 95% CI − 104,512 to − 51,458) than the selexipag cohort, with similar differences in all-cause healthcare and pharmacy costs. Conclusions Lower all-cause and PAH-related total healthcare and pharmacy costs were observed in patients receiving oral treprostinil compared with those receiving selexipag. It will be important to study longer-term costs and clinical outcomes.

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Introduction Empagliflozin has demonstrated lower rates of cardiovascular outcomes vs. standard of care among patients with type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD). However, the impact of empagliflozin compared to other branded antihyperglycemic agents (AHAs) on total cost of care has yet to be quantified. Methods and Results This retrospective cohort study evaluated the impact of empagliflozin ( n  = 441) on costs and healthcare resource utilization (HCRU) vs. other branded AHAs ( n  = 13,122) among patients with T2DM and CVD, using the IQVIA PharMetrics ® Plus Claims Database (1 August 2013–31 December 2017). Date of the first prescription (index date) for empagliflozin or other branded AHAs was used to classify patients into study cohorts. All-cause costs and HCRU were computed on a per patient per month (PPPM) basis and compared across study cohorts using outcome-appropriate statistical models. Overall, the empagliflozin cohort was younger and had a lower comorbidity burden. After covariate adjustment, the total all-cause costs (mean difference − $412 PPPM; 95% CI − $593, − $214) were significantly lower for the empagliflozin cohort. These cost differences were mainly driven by lower all-cause medical costs (mean difference − $400 PPPM; 95% CI − $577, − $196). For HCRU, the mean adjusted all-cause visits in the physician office and other outpatient settings were lower with empagliflozin vs. other branded AHAs ( p  < 0.001). Conclusions This study demonstrated that the all-cause healthcare costs and HCRU were significantly lower for patients with T2DM and CVD who initiated empagliflozin vs. other branded AHAs. Along with the positive clinical evidence base of empagliflozin, these results can guide healthcare decision makers during therapy selection.

The foundation of this dissertation is built upon the belief that treatment effects are often heterogeneous. Thus, different patients experience different outcomes on the same medication. The existence of such heterogeneity gives indication that the current clinical evidence may not be appropriate. This becomes increasingly evident when heterogeneous treatment effects (HTE) prove to be qualitative. Thus basing clinical decisions on averages could have implications on the patients’ well-being, the cost of healthcare to society, and the availability of medications in the marketplace. Hence, the Prognostic Propensity Score (PPS) method was developed with three goals in mind; (1) To identify if HTE are present, (2) To identify if HTE are quantitative or qualitative, and (3) To identify unique patient characteristics or tailoring variables when qualitative HTE are present. Accomplishing these goals will provide physicians and other decision makers with evidence that will allow them to treat patients more efficiently. Thus, we have created the prognostic propensity score (PPS) method. The PPS is defined as the expected outcome (on control) given the individual’s covariates. To calculate the PPS we regress the outcome of interest on the covariates for only those patients treated with the control (Drug A). Using the coefficients from this model and the patient characteristics, we then compute the PPS for each patient assuming that every patient is a member of the control group. We identify if treatment effects vary across subgroups by partitioning patients by PPS into strata and calculating the treatment effect within each stratum. We repeat this analysis using the alternative treatment (Drug B) as the control. Identifying and comparing the stratum that receives the optimal benefit from each treatment we determine which patient characteristics are uniquely associated with success for the individual treatments. To demonstrate the use of the PPS, we use a sample of California Medicaid beneficiaries diagnosed with schizophrenia. Results of this study indicate that the PPS can adequately identify HTE, sufficiently differentiate quantitative and qualitative HTE and has the potential to identify tailoring variables. Ultimately, this method will allow physicians to more accurately prescribe the most beneficial treatment for each and every patient.