This retrospective database study utilized patient data stored in IQVIA’s proprietary Hospital Charge Detail Master (CDM) database pertaining to the period between 1 September 2012 and 31 May 2017. The CDM database manages daily transactional patient charges from over 650 hospitals from 46 states in the USA, covering 7 million annual inpatient stays and 60 million annual outpatient visits. Patient-level data include healthcare services from hospital departments (inpatient, outpatient clinic, emergency department, pharmacy) and encounters associated with ICD-9-CM/ICD-10 diagnosis codes and Current Procedural Terminology codes. Information on drugs and devices dispensed are available and reported in text fields in the database. Detailed charges associated with each visit are also available, as well as patient demographics and admission/discharge characteristics. In this database study, all patient-level data were anonymized and de-identified in compliance with the Health Insurance Portability and Accountability Act (HIPAA). As a retrospective cohort analysis of HIPAA-compliant de-identified patient data, no Institutional Review Board (IRB) review or clinical trial registration was required for this study.
Patients with a hospitalization related to a cardiac, thoracic or upper abdominal surgical procedure between 1 September 2013 and 30 April 2017 (i.e. the selection window) were selected into the study; the index date was defined as the first date of hospitalization with surgery. Patients were required to be at least 18 years of age and to have at least one record in the CDM database within 12 months prior to the index date (i.e. the baseline) and at least one record after the discharge date of the index hospitalization. All patients were required to have evidence of IS use during the index hospitalization. Patients were excluded if they had a prior cardiac, thoracic or upper abdominal surgery within 30 days of the index date, had more than one type of surgery during the index hospitalization or had incomplete demographic data (i.e. missing age, gender, payer type or geographic region).
Two patient cohorts were then developed. The first included patients for whom there was evidence of the use of the Aerobika OPEP device during the index hospitalization. These patients were also required to have no evidence of use of the Aerobika OPEP device at any time before the index date and no evidence of use of any other PEP or OPEP device at any time during the study period. An IS (control) group was also selected that consisted of patients for whom there was no evidence of the use of any PEP or OPEP device at any time during the study period.
Once the selection of the Aerobika OPEP cohort was complete, these patients were propensity score (PS) matched at a 1:1 ratio to IS patients, using the greedy nearest neighbor matching technique. PS matching used a logit regression constructed from patient characteristics measured during the 12-month period before the index date and during the index hospitalization. This methodology is commonly used in observational studies since it mimics the selection process of randomized clinical trials and decreases bias in the estimation of treatment effects between comparison groups. The following variables were included in the PS model: age category, gender, region, payer type, positive airway pressure device use during index hospitalization, surgical procedure, index year, Charlson Comorbidity Index (CCI) category, comorbid conditions (acute respiratory tract infections, asthma, atrial fibrillation, bronchiectasis, cardiovascular disease, COPD, congestive heart failure, malignancy, obstructive sleep apnea, obesity, pulmonary hypertension, pulmonary fibrosis, peripheral artery disease), medication history and medication use during index hospitalization (antibiotics, proton pump inhibitors, long-acting β2-agonist [LABA], long-acting muscarinic antagonist [LAMA], short-acting β2-agonist [SABA], short-acting muscarinic antagonist [SAMA], inhaled corticosteroids [ICS], oral corticosteroids, ICS/LABA combination, SABA/SAMA combination). In addition, preliminary analyses suggested significant heterogeneity between the Aerobika OPEP group and the IS group in terms of frequency of PPCs during the index hospitalization and the cost and length of stay of the index hospitalization. This is reflective of how the Aerobika OPEP device is currently used in clinical practice as a reactive intervention when a complication develops after surgery. Therefore, as well as the length of stay and cost of index hospitalization, the presence of each of the following PPCs during the index hospitalization (yes/no) was included in the PS model: respiratory failure, atelectasis, hypoxemia, pulmonary edema, pulmonary embolism, pleural effusion, pneumothorax, pulmonary eosinophilia, pneumonia, other pulmonary infection and tracheobronchitis.
Outcomes occurring within 30 days post-discharge of the index hospitalization were evaluated. These outcomes included the number and proportion of patients with at least one all-cause hospital readmission, total length of stay (days) of the rehospitalization, time to first hospital readmission (days) among patients who had at least one hospital readmission (conditional mean), number and proportion of patients with at least one hospital readmission with the procedure code indicating chest X-ray (proxy for respiratory complication) and total all-cause costs of hospital-related events. CDM data consists of charges for healthcare services; therefore, a cost-to-charge ratio of 0.4770 was used to convert charges reported in the CDM database to estimated costs. The ratio was calculated based on the average cost-to-charge ratios published by the Healthcare Cost and Utilization Project for the 2014 National Inpatient Sample. Costs were inflation-adjusted to 2017 U.S. dollars using the Medical Care component of the U.S. Consumer Price Index for All Urban Consumers [19].
Baseline patient characteristics were reported using descriptive statistics. The p value was assessed to evaluate balance of variables between Aerobika OPEP patients and matched IS patients after PS matching. For study outcomes, pair-wise comparisons were made between the Aerobika OPEP group and the IS group for each study measure. The nonparametric Wilcoxon signed-rank test was used to compare continuous variables, and the nonparametric McNemar/Bowker test was used to compare categorical variables. A p value of < 0.05 was considered to be statistically significant for all study measures.
The additional effect of Aerobika OPEP (vs. IS alone) on total all-cause hospital costs post-discharge was evaluated using a multivariate generalized linear model (GLM) with a gamma distribution and a log link in the matched population. The following covariates were adjusted to control for remaining imbalance after PS matching: comorbid conditions (atrial fibrillation, diabetes, hypothyroidism, obesity, stroke or transient ischemic attack) and the log of total healthcare costs during index hospitalization. If two variables were highly correlated, the most clinically relevant variable was included in the model (e.g. oxygen use was excluded since CCI and PPC during the index hospitalization were included). All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).