Risk of hospitalization from drug-drug interactions in the Elderly: real-world evidence in a large administrative database

Sensitivity analyses

When we adjusted the models for prevalent user status, the results were virtually coincident with those of the primary analysis (Supplementary Table 4); the combination of ACEIs/ARBs or diuretics and glucocorticoids was significantly associated with an increased risk of hospitalization (past use: adj. OR 1.36, 95% CI 1.12-1.64, P 0.002; current use: adj. OR 2.35, 95% CI 1.93-2.86, P <0.001).

When we examined whether DDIs were associated with an increased risk of either hospitalization or specialist examination/consultation, whichever occurred first, results were not fully consistent with those of the primary analysis (Table 5). The directions of the odds (risks) changed for analysis #1 (ACEIs/ARBs plus NSAIDs), #3 (diuretics plus NSAIDs), #5 (vitamin K antagonists plus NSAIDs), #6 (NOACs plus NSAIDs), #7 (vitamin K antagonists plus antibiotics/antimycotics) and #10 (SSRIs plus ASA). However, the increased risk associated with taking ACEIs/ARBs or diuretics plus glucocorticoids (adj. OR 1.95; 95% CI 1.72-2.20; P <0.001) and antidiabetics plus fluoroquinolones (adj. OR 1.52; 95% CI 1.23-1.89; P <0.001) was confirmed.

Considering patients who had been hospitalized for specific conditions as exposed to NSAIDs even in the absence of recent reimbursed prescriptions, results did not change appreciably and, again, the increased risk associated with concurrent use of SSRIs and vitamin K antagonists (analyses #4, #5) failed to achieve statistical significance (Table 6).

Lastly, to account for immeasurable time bias, we restricted the analyses to cases and controls who spent <50% of their matched follow-up periods in the hospital. The results of this sensitivity analysis were virtually coincident with those of the primary analysis (Supplementary Table 6).

ACE-inhibitors (or diuretics) plus glucocorticoids

The increased risk of cardiovascular diseases in patients taking glucocorticoids is well known (at least 25%), as well as its dose-dependence [10–15]. It should first be recognized that the activity of inflammatory disease in these patients (e.g. with rheumatoid arthritis) can increase per se the cardiovascular risk, thereby representing a confounder by indication [10]. Glucocorticoid mechanism of action is complex and involves elevated angiotensinogen synthesis, increased sympathetic nervous system activation, and mineralocorticoid-like action. Also the weight gain and the android fat distribution due to glucocorticoids seem to contribute [16].

As for DDI with ACEIs/ARBs or diuretics, glucocorticoids can antagonize their effect and cause a loss of blood pressure control, thus secondary hypertension and pseudo-resistant hypertension [17]. Patients with a history of essential hypertension are certainly at greater risk of developing secondary hypertension due to from glucocorticoids and relevant cardiovascular diseases [16], and in fact the risk ratio found in our study was 2.36. The cumulative effect of glucocorticoids could also explain the still detectable, although lower, risk in patients with past exposure to glucocorticoids. As regards the mean age of our cases (82 years), it is in line with the higher risk of older patients to developing glucocorticoid-induced hypertension [10–18]. Patients taking glucocorticoids are even more likely to develop other well-known cardiovascular risk factors, such as diabetes mellitus and hypercholesterolemia [16]. The direct effects of the glucocorticoid receptor on heart and blood vessels affect vascular function, remodeling and atherogenesis, and therefore also contribute to cardiovascular diseases [19].

It is not possible to completely avoid glucocorticoids in patients with ACEIs/ARBs or diuretic therapy, since they are highly effective and sometimes crucial for diseases that require immediate suppression of inflammation and immune activity, such as chronic obstructive pulmonary disease or rheumatoid arthritis. However, taking into account the results of this study, blood pressure monitoring is strongly recommended during glucocorticoid therapy, together with reduction in the length of glucocorticoid cycles.

Antidiabetics plus fluoroquinolones

Our results are in line with previous studies that show an association between the use of fluoroquinolones and dysglycemia (hypo- or hyperglycemia) compared to other antibacterial (macrolides). Gatifloxacin was even withdrawn from the market, while levofloxacin, ciprofloxacin and moxifloxacin are maintained on the market despite association with dysglycemia, as their benefit-risk profile remained favorable [20–22]. Fluoroquinolones are more frequently associated with hyperglycemia [23], although hypoglycemia can also lead to severe cases and even fatal outcomes [24]. We must acknowledge that infections in patients with diabetes are per se a common cause of dysglycemia. The high risk-ratio of 4.4 in the current study could be partly due to this effect, since we studied the effect of fluoroquinolones compared to absence of fluoroquinolones in patients taking antidiabetic drugs. On the other hand, the interaction with antidiabetic drugs can independently contribute to dysglycemia.

A recent analysis of the FDA Adverse Event Reporting System (FAERS) on reports of hypoglycemia for various antimicrobial therapies also found an increased reporting of hypoglycemia in patients using fluoroquinolones (reporting odds ratio=1.6).

Inhibition of potassium-ATP(K-ATP) channels in pancreatic B-cells, with consequent insulin secretion increase [25], and inhibition of antidiabetic CYP metabolism by fluoroquinolones [26] have been postulated as mechanisms of hypoglycemia. Instead, mechanism of the more common fluoroquinolone-induced hyperglycemia is to date unknown.

When antibiotic therapy is advised in patients with antidiabetic treatment, it is recommended to consider not only the antimicrobial potency of the antibiotics, but also the risk for potentially serious adverse effects. Albeit rare, fluoroquinolone-induced dysglycemia may be serious, so these medications are not recommended for this group of patients.

SSRIs plus NSAIDs

Increased bleeding risk for SSRIs plus NSAIDs is well described in the literature [27–30]. They both have antiplatelet activity, thereby they have a synergistic effect on hemostatic function. In the current study, we failed to achieve a statistically significant association between bleeding and use of NSAIDs in patients under SSRIs, although the risk ratio was about 3.

Instead, in patients with a previous episode of bleeding, we found a significant 5.6-fold risk ratio of bleeding in patients currently using NSAIDs concomitantly with SSRIs, leading us to conclude that patients with SSRI therapy and previous bleeding have a higher risk of recurrent bleeding when using NSAID. These results are in line with a previous retrospective study [31] and strongly suggest the importance of stringent assessment of both benefit-risk profile and appropriateness for each individual patient before prescribing SSRIs and NSAIDs.

History of comorbidities

Usually, previous comorbidities increase the risk of adverse events. However, in this study we only found an increased risk ratio for ACE-inhibitors/diuretics plus glucocorticoids and for SSRIs plus NSAIDs. This inconsistency could be due to the prescribing habits of physicians, as recently showed by Nash et al. [32], who found fewer NSAID prescriptions among patients with kidney injury or heart failure that receive long-term custodial care. However, it should be acknowledged that some interaction analyses had limited statistical power.

On the other hand, these findings might indicate that having a history of diseases does not increase the risk. Recent studies and systematic reviews have shown a similar risk for cardiovascular or renal diseases as a result of NSAID use in high-risk patients [33–35]. However, these studies only analyzed the use of NSAID, regardless of combination with other drugs, and the increased risk attributable to NSAIDs alone, if any, is probably too low to be detected in a cohort study.

Strengths and limitations

We conducted a large population-based study, which to our knowledge is the first real-world evidence study on 10 different potential DDISs that also takes into account the role of high-risk comorbidities. Methodological strengths include the study design, in which cases and controls are matched by follow-up duration, thereby preventing time-related bias [36]. In addition, drug exposure was collected from the OPD, avoiding possible recall bias [37].

As for chronic drugs of interest, we used the threshold of 80% in the MPR as a proxy of continuous use. We chose to lower this threshold for SSRIs and NOAC to 66% due to very high variability in average daily doses and above all probable lower dose than the DDD in older populations. This approach could have influenced the results, by overestimating the number of patients exposed to DDIs in both current- and past-user groups. Given the average time of follow up of 2.5 months, no strong impact is expected.

In addition, data from dispensing databases is subject to measurement error. A dispensed package does not indicate that the full package is consumed, which is mostly common for short-cycle drugs or drugs taken on an as-needed basis. Furthermore, the prescribed daily doses could differ from the DDDs, especially for glucocorticoids, and this might have affected the validity of our results. Unfortunately, administrative data do not allow differentiating between high- and low-dosage or between long- and short-term medication use.

The use of NSAIDs could be particularly underestimated by only considering recent reimbursed prescriptions; notably patients could use over-the-counter (OTC) NSAIDs or NSAIDs prescribed in the past.

By adding other diagnoses, specialist visits and imaging as a proxy for disease, or including patients with conditions indicating use of NSAID to the sensitivity analyses, we have tried to minimize the information bias of OTC NSAIDs and the possible underestimation of incidence of the outcome. previous research showed that OTC drugs likely contribute to a small amount of bias [38].

Lastly, we did not consider a wide range of high-risk comorbidities, and had no data on other patient characteristics that could influence the study outcome (e.g., imaging with contrast, smoking habits, lifestyle, blood pressure, indication for drugs or severity of diseases).

Setting and study population

The study population comprised residents of the Local Healthcare Authority of Bologna in Northern Italy (≈876,000 inhabitants), aged ≥65, who were prescribed one of the chronic drugs of interest between January 2017 and June 2017 (see left side of Table 7 for the detailed list of chronic drug therapies with ATC codes). For each subject, cohort entry was the date of a first dispensed prescription of a chronic drug over the 6-month recruitment period. All patients were followed up to 6 months after the cohort entry.

Data were retrieved from the Regional Health Authority Outpatient Pharmaceutical Database (OPD), which contains information on patients (unique identification number, sex and age), prescriptions (substance name, ATC code (WHO Collaborating Centre for Drug Statistics Methodology, ATC classification index with DDDs, 2020. Oslo, Norway 2019), brand name, date of prescription filling, number of unit doses and number of packages) and prescribers; it does not include the actual prescribed daily dose of the drug. The OPD includes drugs reimbursed by the healthcare system that are prescribed by the primary care physician or the specialist, or directly dispensed by the hospital pharmacies [39].

Exposure to drug-drug interactions

The 10 DDIs considered in our study derive from the Italian experience on prevalence of potentially inappropriate prescriptions (see the right side of Table 7 for the detailed list of interacting drugs with ATC codes) [39–41]. Dispensed prescriptions of interacting drugs were retrieved from the OPD.

Study outcomes

The outcome of this study was represented by hospital admission due to a condition potentially induced by DDI as the principal diagnosis (see Supplementary Table 1 for the detailed list of DDI-related hospital admissions with ICD-9-CM codes for each interaction analysis). Data were retrieved from the Hospital Discharge Records (HDRs) Database, which can be linked to the OPD using the unique patient identifier.

All-cause deaths within 6 months of cohort entry were considered as censored events (source: Regional Mortality Register Database).

Potential confounders

Aiming to keep our study fully generalizable to older populations, we did not exclude patients with a history of high-risk comorbidities (e.g., patients taking angiotensin converting enzyme inhibitors [ACEIs] or angiotensin receptor blockers [ARBs] with previous hospital admission for kidney failure). Instead, we adjusted all analyses for the presence of previous hospitalizations for these conditions (see Supplementary Table 3) over a lookback period of 3 years before the cohort entry (source: HDRs).

Other variables we analyzed to reduce the potential source of confounding were:

• Sex;

• Age;

• Degree of urbanization of the municipality where the patient lived, classified as city, towns/suburbs or rural, using the Eurostat’s Degree of Urbanization (DEGURBA) classification system (revised definition, 2014);

• Use of antidiabetic drug therapies in the 6 months prior to cohort entry (as proxy of diabetes, representing a major cardiovascular risk factor); Elixhauser comorbidity score based on hospitalizations in the previous 3 years [42];

• Number of concurrent medications during follow-up (>4 dispensations of different chemical subgroups– IV level ATC codes);

Use of interfering medications during follow-up that are known to be associated with the outcomes (see Supplementary Table 2).

Statistical analysis

Numerical variables were summarized as mean ± standard deviation; categorical variables were summarized as frequencies and percentages.

Association between exposure (taking interacting drugs) and outcomes (DDI-related hospitalizations) was assessed using a nested case-control design. In each interaction analysis, patients hospitalized during follow-up were defined as cases, and up to 10 controls were randomly selected and matched to each case by follow-up duration, age (5-year groups), sex and history of high-risk conditions (see Supplementary Table 3). An illustrative example of this technique, which is called “incidence density sampling”, is provided in Supplementary Figure 11. We chose this approach to ensure an equal time window for measuring DDI exposure in cases and controls.

To focus the analyses on adherent users, cases and matched controls were excluded if the proportion of days covered by chronic medications between cohort entry and the matching date was <80% (<66% for SSRIs [analyses #4, #10] and vitamin K antagonists [#5, #7]). The proportion of days covered was estimated by using the medication possession ratio (MPR), which was based on the defined daily doses (DDDs; WHO Collaborating Centre for Drug Statistics Methodology, ATC classification index with DDDs, 2020. Oslo, Norway 2019). The DDD represents the average adult dose used for the main indication of the drug and thereby allows approximate quantification of days supplied. We set a MPR of 80% as a proxy for chronic therapy of the drugs of interest, because the DDDs may be different from the prescribed daily doses, possibly leading to an underestimation of adherence.

Cases and controls were then classified into 3 mutually exclusive groups: (i) current exposure, (ii) past exposure and (iii) no exposure to DDI. A subject was considered currently exposed if the prescription of the interacting drug was detected in the 30-day period prior to matching date. The ‘past-exposed group’ included persons with the last prescription over 30 days before the matching date. Subjects who were not prescribed the interacting drug during the matched follow-up period were considered as non-exposed to DDI. Dispensed prescriptions of interaction drugs were also collected in the 2-month period prior to cohort entry: if a prescription was present and, on the basis of the number of DDDs contained in the packages, relevant doses also covered the days after the cohort entry, the patient was considered as exposed to DDI. We did this to mitigate possible underestimation in the number of prescriptions among cases and controls with short matched follow-up periods.

In a secondary analysis, we stratified cases and matched controls by the presence of previous hospitalizations for high-risk conditions (see Supplementary Table 3) to assess the effect of this variable in the main models (this was a matching variable). The association between DDI and outcomes was estimated using conditional logistic regression, which is appropriate for a time-matched nested case-control study. Results were expressed as odds ratios that, for the incidence density sampling used in this study, provide unbiased estimates of the relative risks (rate ratios) in the underlying cohort [43]. Regression models included all the potential confounders described above.

All analyses were carried out by using Stata software, version 15 (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LP). The significance level was set at 0.05.

Sensitivity analyses

We conducted some sensitivity analyses to test the robustness of the findings from the primary analysis. First, we adjusted analyses by prevalent versus incident user status (presence versus absence of other dispensed prescriptions for the chronic drug of interest in the 6 months prior to cohort entry) to assess the potential confounding effect of being a prevalent or incident medication user. Second, we added information from the Outpatient Care Database to the outcomes to additionally assess the effect of including information on specialist visits and (non-)invasive imaging (see the codes in italics in Supplementary Table 1). Third, in an attempt to capture over-the-counter medications, we considered as NSAID users (either past or current) those patients who had been hospitalized for diseases that indicate NSAID use (Supplementary Table 5). Last, we excluded patients who spent >50% of their individual follow-up in the hospital, because drugs dispensed during inpatient treatment cannot be retrieved from the OPD, possibly leading to immeasurable time bias [44].

Ethics statement

Ethical approval was granted from the Comitato Etico di Area Vasta Emilia Centro (Submission Number 611/2019/OSS/AUSLBO). This retrospective study was carried out in conformity with the regulations on data management with the Italian law on privacy (Legislation Decree 196/2003 amended by Legislation Decree 101/2018).

Impact statement

We certify that this work is original research. It identified some specific drug-drug interactions with actual increased risk of hospitalization: ACE-inhibitors (or diuretics) plus glucocorticoids, antidiabetics plus fluoroquinolones, SSRIs plus NSAIDs in patients with previous bleeding episodes. Many other potential drug-drug interactions with high prevalence (for instance, ACE-inhibitors plus NSAIDs) did not seem to have an impact on adverse clinical outcomes.