Cost effectiveness of adherence to IDSA/ATS guidelines in elderly patients hospitalized for Community-Aquired Pneumonia

This study is a retrospective study utilizing patient and outcome data from the Community Acquired Pneumonia Organization (CAPO) International Study Cohort, which is an international, multicenter observational study of patients hospitalized with CAP [15]. Data were abstracted from the database from June 1, 2001 to January 1, 2007 for all patients age 65 years or older and included data from 43 centers in 12 countries including North America, South America, Europe, Africa, and Southeast Asia (see Arnold et al. [8] for details concerning the patient cohort used in this study and the ‘Availability of Data and Materials’ section). The study was approved by the Human Subject Protection Program Institutional Review Board at the University of Louisville. Additional approval was obtained from the local internal review board for each participating hospital. Patient consent was waived due to the retrospective and observational study design. The initial antibiotic regimen for each patient was evaluated and categorized as adherent, over-treated, or under-treated according to the 2007 IDSA/ATS guidelines as previously described [5, 8]. Patients were stratified into ICU or ward admission status based on their initial admission orders. A patient was considered clinically stable on the day they were switched to oral antibiotics from intravenous antibiotics; ATS criteria for switching to oral therapy were used [16]. A small number of patients were admitted with oral antibiotics as their initial therapy; these patients were considered clinically stable upon admission. Baseline characteristics of the cohorts of interest were compared using chi-square or Fisher exact for categorical data, as appropriate, medians were compared with Kruska-Wallis test, and probabilities compared using Gray’s test for difference between cumulative incidence curves [17].

A 14-cycle Markov model was constructed modeling the four possible states in which a patient could exist during the 14 day observation period (Fig. 1). Each cycle is a 24 h period during the patient’s hospitalization. Patients admitted to the hospital (state 1) remained in this initial state until they either reached clinical stability (state 2), were discharged (state 3), or died in the hospital (state 4). Patients who reached clinical stability remained in that state until they were subsequently discharged. Patients initially admitted to the ICU were assumed to remain in the ICU until they died or reached clinically stability, after which they were transferred to the ward. Prior to reaching clinically stability patients were on intravenous medication, after which they were placed on oral medications.

Conditional transition probabilities for each possible transition from state i to state j (see Fig. 1) at time t ∈ [1, 14] were estimated based on the cause-specific transition hazards α _ij (t). Non-parametric estimates of the transition hazards were based on the number of individuals making the i → j transition at time t divided by the number of individuals in state i just prior to time t (i.e., the number ‘at-risk’ of making the i → j transition). Non-parametric estimates of the conditional transition probabilities were then obtained using the Aalen-Johansen estimator with the R package msSurv [18, 19]. Parametric estimates were obtained by first fitting time-dependent Cox regression models to each of the possible transition hazards outlined in Fig. 1. These estimates of the transition hazards were then used to obtain estimated transition probabilities for an individual with a given covariate profile using the R package mstate [20]. The covariates (in addition to adherence to antibiotic guidelines) used in the parametric models were disease severity (pneumonia severity index (PSI) risk class ≥4 vs. <4), presence of multilobar pneumonia, pleural effusion, altered mental status, tachypnea (respiratory rate > 30 breaths/min), hypotension (systolic blood pressure < 90 mm Hg), receipt of antimicrobials within 8 h of admission, pneumococcal vaccination, blood cultures obtained within 24 h of admission, and whether oxygen assessment was done [21]. All covariates were dichotomous, and the parametric transition probabilities were based on the most frequently occurring value for each covariate. Daily transition probabilities (the probability of making an i → j transition from time t to t + 1) were obtained from both parametric and non-parametric estimates and compared to ensure validity. The daily transition probabilities from the parametric multi-state model were modeled as continuous beta distributions in the Markov model based on the estimates and standard errors from the parametric model.

The antibiotic decision making was modeled using three comparators: Adherent, Over-treated, and Under-treated [8]. The multi-state Markov process followed the decision making nodes. ICU admissions and ward admissions were analyzed in two separate models. Cost-effectiveness analyses, probabilistic sensitivity analyses, and a Monte Carlo microsimulation with 10,000 patients were performed. For conciseness only results from transition probability estimates based on the parametric models are reported, however substantive conclusions based on the non-parametric estimates did not differ. “Adherent” was considered the base case comparator, against which the over- and under-treated groups were compared.

Our model required cost and utility estimations for the following unique states: ICU admission status (over-treated, adherent, and under-treated), ward admission status (over-treated, adherent, and under-treated), ward clinically stable (over-treated, adherent, and under-treated), discharge status, and death. Costs were adjusted according the US consumer price index to 2013 US dollars. Average daily hospital costs for a day in the ICU were dynamically modeled, with mean daily costs ranging from $5132 on ICU day one to $3825 for day eight and thereafter (c.f. medical costs reported in Table 4 in Dasta et al. [22], adjusted to 2013 US dollars). The daily ICU costs were modeled with a gamma probability distribution with parameters κ and θ estimated from reported standard deviations of cost data by Dasta et al. [22] Mean daily ward costs for treatment of CAP were estimated to be a static cost of $1060 per day (c.f. Table 3, column ‘Simple Pneumonia’ in Kaplan et al. [23]). Daily ward costs, like ICU costs, were modeled with a gamma probability distribution with parameters based on standard deviation estimates reported by Kaplan et al. [23] The initial antibiotic regimen of each cohort was retrieved from the CAPO database. Daily antibiotic costs were estimated based on average wholesale prices (year 2013 value) of each antibiotic obtained from the University of Louisville Hospital (Additional file 1). The daily cost of each respective antibiotic regimen in our database was calculated and averaged for our unique states described above. Admission status antibiotic regimens were intravenous, while patients in clinical stability by definition were on oral medications. Patients initially admitted to the ICU were assumed to transfer to the ward upon reaching clinical stability. The cost associated with each state was the sum of the hospital charges for the location of treatment (ICU or ward) and daily estimated antibiotic costs.

Utility estimations were obtained based on a survey (Additional file 2) administered to an expert panel survey of six CAPO investigators. Each expert was polled independently. While the executive director of CAPO assisted in convening the expert panel, he was not involved in the eliciting of utilities and was blinded to specific utility assessments. The demographics of the expert panel survey were as follows: 50 % male; one Fellow, four Assistant Professors, and one Professor; mean age 43.5; mean years of clinical practice experience was 18. All respondents were currently practicing in the US. Each survey respondent was asked to estimate the utility associated with the following states in the context of a standard gamble: ICU admission, ward admission, and ward clinically stable. Utility was defined as an estimate of relative quality of life on a scale of 0 to 1, with 0 being death and 1 being at home. It was assumed that the utility of each state would be independent of the antibiotic adherence classification. The utility estimates were modeled with a beta distribution, with shape parameters based on the standard deviation of the expert panel survey results.

Probabilistic sensitivity analyses were performed for the hospitals costs and utility estimates, since they were modeled as probability distributions. Sensitivity analyses of the fixed daily drug cost estimates were estimated in a 3-way analysis across ranges $1 to $100 in 20 intervals. Strategy dominance was evaluated across willingness to pay ranges from $0 to $1000, in 20 intervals. This range was based on the generally used threshold of willingness to pay of $100,000/QALY. Since our model cycles corresponded to days, rather than years, a willingness to pay range of 0 to $1000 per quality-adjusted life day corresponds to 0 to $365,000 per QALY [24].

TreeAge Pro 2015 (Cambridge, MA) software was used for all cost-effectiveness analyses.