Original Research

Annals of Internal Medicine

Atypical Antipsychotic Drugs and the Risk for Acute Kidney Injury and Other Adverse Outcomes in Older Adults A Population-Based Cohort Study Y. Joseph Hwang, MSc; Stephanie N. Dixon, PhD; Jeffrey P. Reiss, MD, MSc; Ron Wald, MD, MPH; Chirag R. Parikh, MD, PhD; Sonja Gandhi, BSc; Salimah Z. Shariff, PhD; Neesh Pannu, MD, SM; Danielle M. Nash, MSc; Faisal Rehman, MD; and Amit X. Garg, MD, PhD

Objective: To investigate the risk for AKI and other adverse outcomes associated with use of atypical antipsychotic drugs versus nonuse.

Results: Atypical antipsychotic drug use versus nonuse was associated with a higher risk for hospitalization with AKI (relative risk [RR], 1.73 [95% CI, 1.55 to 1.92]). This association was consistent when AKI was assessed in a subpopulation for which information on serum creatinine levels was available (5.46% vs. 3.34%; RR, 1.70 [CI, 1.22 to 2.38]; absolute risk increase, 2.12% [CI, 0.80% to 3.43%]). Drug use was also associated with hypotension (RR, 1.91 [CI, 1.60 to 2.28]), acute urinary retention (RR, 1.98 [CI, 1.63 to 2.40]), and all-cause mortality (RR, 2.39 [CI, 2.28 to 2.50]).

Design: Population-based cohort study.

Limitation: Only older adults were included in the study.

Setting: Ontario, Canada, from 2003 to 2012.

Conclusion: Atypical antipsychotic drug use is associated with an increased risk for AKI and other adverse outcomes that may explain the observed association with AKI. The findings support current safety concerns about the use of these drugs in older adults.

Background: Several adverse outcomes attributed to atypical antipsychotic drugs, specifically quetiapine, risperidone, and olanzapine, are known to cause acute kidney injury (AKI). Such outcomes include hypotension, acute urinary retention, and the neuroleptic malignant syndrome or rhabdomyolysis.

Patients: Adults aged 65 years or older who received a new outpatient prescription for an oral atypical antipsychotic drug (n ⫽ 97 777) matched 1:1 with those who did not receive such a prescription. Measurements: The primary outcome was hospitalization with AKI (assessed by using a hospital diagnosis code and, in a subpopulation, serum creatinine levels) within 90 days of prescription for atypical antipsychotic drugs.

E

ach year, millions of older adults worldwide are prescribed atypical antipsychotic drugs (quetiapine, risperidone, and olanzapine). These drugs are frequently used to manage behavioral symptoms of dementia, which is not an approved indication, and such use has raised safety concerns (1, 2). These drugs antagonize ␣-adrenergic, muscarinic, serotonin, and dopamine receptors (3). Acute kidney injury (AKI) (defined as a sudden loss of kidney function) from atypical antipsychotic drugs is described in several case reports (4 – 8). Adverse outcomes potentially attributable to these drugs, such as hypotension, acute urinary retention, and the neuroleptic malignant syndrome or rhabdomyolysis, are known to cause AKI (4 –11). Moreover, pneumonia, acute myocardial infarction, and ventricular arrhythmia have been associated with these drugs in previous population-based studies and AKI may also cooccur with these events (12–14). However, no clinical or epidemiologic studies have quantified the risk for AKI from atypical antipsychotic drugs and information on outcomes of hypotension, acute urinary retention, and the

See also: Web-Only Supplement 242 © 2014 American College of Physicians

Primary Funding Source: Academic Medical Organization of Southwestern Ontario. Ann Intern Med. 2014;161:242-248. doi:10.7326/M13-2796 For author affiliations, see end of text.

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neuroleptic malignant syndrome or rhabdomyolysis is limited. Such information would contribute to growing knowledge of potential adverse events from this drug class. The U.S. Food and Drug Administration warns of an increased risk for death in older patients treated with these drugs based on analyses of randomized, placebo-controlled trials (averaging 10 weeks in duration) (1). For these reasons, we did this population-based study of older adults to investigate the 90-day risk for hospitalization with AKI and other adverse outcomes from new use of an oral atypical antipsychotic drug initiated in the nonhospital setting.

METHODS Design and Setting

We conducted this study at the Institute for Clinical Evaluative Sciences according to a prespecified protocol that was approved by the research ethics board at Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. Patient informed consent was not required. We did a population-based, retrospective cohort study of older adults using linked health care databases in Ontario, Canada. Ontario residents have universal access to hospital care and physician services, and those aged 65 years or older have universal prescription drug coverage. The reporting of this study followed guidelines for obser-

Atypical Antipsychotic Drugs and the Risk for Acute Kidney Injury

vational studies (Table 1 of the Supplement, available at www.annals.org) (15). Data Sources

We ascertained patient characteristics, drug use, covariate information, and outcome data using records from 5 databases. We obtained vital statistics from the Registered Persons Database of Ontario, which contains demographic information on all Ontario residents who have ever been issued a health card. We used the Ontario Drug Benefit database to identify prescription drug use. This database contains highly accurate records—the error rate is less than 1%— of all outpatient prescriptions dispensed to patients aged 65 years or older (16). We identified diagnostic and procedural information on all hospitalizations from the Canadian Institute for Health Information Discharge Abstract Database. We obtained covariate information from the Ontario Health Insurance Plan database, which includes health claims for inpatient and outpatient physician services. We identified diagnostic information on all admissions to adult mental health beds from the Ontario Mental Health Reporting System. We have used these databases to research adverse drug reactions and health outcomes (including AKI) (17–22). The databases were complete for all variables used in this study, except for prescriber information (which was missing for 10.8% of patients in the cohort). Codes from the International Classification of Diseases, Ninth Revision (before 2002), and Tenth Revision (after 2002), were used to assess baseline comorbid conditions in the 5 years before cohort entry (Table 2 of the Supplement). Codes used to ascertain outcomes are detailed in Table 3 of the Supplement, which lists only codes from the Tenth Revision because all events would have occurred after implementation of that coding system. A subpopulation in southwestern Ontario had information on outpatient serum creatinine levels available before cohort entry; this group was in the catchment area of 12 hospitals in which linked laboratory values were also available (23). Patients

We established a cohort of older adults with evidence of a new outpatient prescription for an oral atypical antipsychotic drug (quetiapine, risperidone, or olanzapine) between June 2003 and December 2011. The date of this prescription served as the index date (cohort entry date) for the drug recipients. We matched a group of drug nonrecipients similar in health status to the recipients. We randomly assigned an index date to the entire Ontario population according to the index date of the drug recipients. For example, if more recipients had an index date between 2003 and 2005, a greater proportion of the population would have been randomly assigned an index date between 2003 and 2005. From these adults, after applying our exclusions to both groups, we matched a drug nonrecipient to each recipient on the following 11 characteristics: age (within 2 years); sex; residential status www.annals.org

Original Research

Context Acute kidney injury (AKI) is reportedly associated with atypical antipsychotic drugs, although the risk has not been quantified.

Contribution This population-based cohort study found that persons who had received a prescription for any of 3 atypical antipsychotic drugs in the previous 90 days had an elevated risk for hospitalization with AKI. These drugs were also associated with increased risk for hypotension, acute urinary retention, and death.

Caution Only older adults and 3 antipsychotic agents were studied.

Implication An association with specific adverse events may explain the increased risk for AKI observed with certain atypical antipsychotic drugs. —The Editors

(community-dwelling or long-term care); evidence of comorbid conditions (dementia, schizophrenia or other psychotic disorder, bipolar disorder, major depression or anxiety disorder, Parkinson disease, and chronic kidney disease); constituency in the subpopulation with available information on serum creatinine levels; and the logit of the propensity score for the predicted probability of newly receiving an atypical antipsychotic drug (within a caliper of ⫾0.2 SDs). We derived this propensity score from a logistic regression model and selected 91 variables for inclusion in the score on the basis of their potential association with the study outcomes or atypical antipsychotic drug initiation (variables listed in Table 4 of the Supplement) (24). One of the variables was the Johns Hopkins Adjusted Clinical Group Aggregated Diagnosis Groups (a validated measure of the complexity of comorbid conditions based on groups of diagnoses) (25, 26). Before matching, we excluded the following patients from both groups: those with prescriptions for any antipsychotic drug in the 180 days before their index date to ensure that the drug was newly prescribed (or had the potential to be newly prescribed in the case of the nonrecipients); those who were discharged from a hospital in the 2 days before their index date to ensure that drug use was newly initiated in the nonhospitalized setting (as in Ontario, patients continuing atypical antipsychotic drug treatment initiated in a hospital would have their oral outpatient prescription dispensed the day of or the day after hospital discharge); and those with evidence of end-stage renal disease before their index date (because the development of AKI is no longer relevant). Among the drug recipients, those who received a prescription for more than 1 type of antipsychotic drug (for example, a prescription for 19 August 2014 Annals of Internal Medicine Volume 161 • Number 4 243

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Atypical Antipsychotic Drugs and the Risk for Acute Kidney Injury

quetiapine and olanzapine) on their index date were excluded to compare mutually exclusive groups in subgroup analyses. Among the nonrecipients, those who did not have at least 1 outpatient medication dispensed in the 90 days before their index date were excluded to ensure that such persons were able to receive a prescription. Each drug recipient and nonrecipient could be selected only once for cohort entry. Outcomes

We followed patients for 90 days after the index date to assess the prespecified outcomes. We chose 90 days to focus on acute adverse events, avoid potential crossovers between the 2 groups that might occur with longer followup, and mimic the duration of follow-up described in clinical trials of atypical antipsychotic drugs in older patients (1, 2, 27). The primary outcome was hospitalization with AKI. The secondary adverse outcomes were known causes of AKI (hospitalization with hypotension, acute urinary retention, the neuroleptic malignant syndrome or rhabdomyolysis, pneumonia, acute myocardial infarction, and ventricular arrhythmia) and all-cause mortality. The diagnosis codes used to identify the outcomes and information on their accuracy are presented in Table 3 of the Supplement (28 –30). For hospitalization records, up to 25 diagnosis codes can be assigned per hospitalization (for example, codes for AKI or rhabdomyolysis). Therefore, patients with codes for multiple study outcomes were accounted for in the assessment of each outcome. We previously examined the validity of the database code for hospitalization with AKI used in the current study. In this previous validation study (30), the database code for AKI identified a median increase in serum creatinine level of 98 ␮mol/L (1.11 mg/dL) (interquartile range [IQR], 43 to 200 ␮mol/L [0.49 to 2.26 mg/dL]) at the time of hospital presentation from the most recent value before hospitalization. The absence of such a code represented no statistically significant change in serum creatinine level (6 ␮mol/L [0.07 mg/dL]; IQR, ⫺4 to 20 ␮mol/L [⫺0.05 to 0.23 mg/dL]) (30). Although specificity was greater than 95%, the sensitivity of the hospital diagnosis was limited for milder forms of the condition. Particularly, the incidence of AKI as defined by the diagnosis code can be underestimated up to 5-fold when compared with definitions using serum creatinine measurements. For this reason, we examined a subpopulation with linked hospital laboratory values and defined hospitalization with AKI by evidence of an absolute increase in serum creatinine level of 27 ␮mol/L (0.31 mg/dL) or more from baseline or a relative increase of 50% or more (31).

expressed the risk for an outcome in relative and absolute terms. We estimated odds ratios and 95% CIs using conditional logistic regression, which accounted for matching. The conditional logistic regression model was adjusted for local health integration network, which refers to 14 geographically defined health authorities in Ontario that are responsible for regional administration of different health care services (including regional physicians’ offices, hospitals, community mental health and addiction centers, community health centers, and long-term care facilities) (33). Odds ratios can be interpreted as relative risks (RRs), and such an interpretation was appropriate given the odds ratios observed. Absolute risk increase for the outcomes diagnosed in the hospital is underestimated because the codes used to identify the conditions are insensitive. Using tests for interaction, we analyzed the primary outcome of AKI in the following 4 prespecified subgroups: antipsychotic drug type (quetiapine, risperidone, or olanzapine); antipsychotic drug dose (high or low [high dose was defined by a higher-than-median starting daily dose for the study cohort [⬎25 mg/d for quetiapine, ⬎0.5 mg/d for risperidone, and ⬎2.5 mg/d for olanzapine]); evidence of chronic kidney disease; and residential status (community-dwelling or long-term care). In Ontario, the validated algorithm for chronic kidney disease identifies older adults with a median estimated glomerular filtration rate of 38 mL/min/1.73 m2 (IQR, 27 to 52 mL/min/ 1.73 m2), whereas its absence identifies those with a median estimated glomerular filtration rate of 69 mL/min/ 1.73 m2 (IQR, 56 to 82 mL/min/1.73 m2) (34). To assess the temporality and robustness of our primary findings, we reapplied the exclusion criteria to our existing cohort on the day that preceded the index date by 180 days. After reapplying exclusions, we followed the retained matched pairs for the 90-day outcomes and estimated odds ratios and 95% CIs using conditional logistic regression. Because there was no plausible reason why the 2 groups would differ in outcomes before the initiation of an atypical antipsychotic drug, we reasoned that null associations would enhance assertions that the 2 groups were similar in baseline risk for the study outcomes. We did all analyses with SAS, version 9.3 (SAS Institute). In all outcome analyses, we interpreted 2-tailed P values less than 0.05 as statistically significant. Role of the Funding Source

The study design and conduct, opinions, results, and conclusions in this article are those of the authors and are independent of the funding sources.

Statistical Analysis

RESULTS

We compared baseline characteristics between atypical antipsychotic drug recipients and nonrecipients using standardized differences (32). This metric describes differences between group means relative to pooled SD and, when greater than 10%, indicates a meaningful difference. We

Cohort selection is presented in the Appendix Figure (available at www.annals.org), and baseline characteristics are presented in Table 5 of the Supplement. There were 122 610 atypical antipsychotic drug recipients and 1 204 613 nonrecipients before matching. The recipients

244 19 August 2014 Annals of Internal Medicine Volume 161 • Number 4

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Atypical Antipsychotic Drugs and the Risk for Acute Kidney Injury

Original Research

Table 1. 90-Day Risk for Hospitalization With AKI and Other Adverse Outcomes and All-Cause Mortality in Atypical Antipsychotic Drug Recipients and Nonrecipients After the Index Date Events, n (%)*

Variable

Drug Recipients (n ⴝ 97 777) AKI Other adverse outcomes Hypotension Acute urinary retention The neuroleptic malignant syndrome or rhabdomyolysis Pneumonia Acute myocardial infarction Ventricular arrhythmia All-cause mortality

Relative Risk (95% CI)†

Absolute Risk Increase (95% CI), %

Nonrecipients (n ⴝ 97 777)

1002 (1.02)

602 (0.62)

1.73 (1.55–1.92)

0.41 (0.33–0.49)

384 (0.39) 329 (0.34) 99 (0.10) 1692 (1.73) 652 (0.67) 214 (0.22) 6666 (6.82)

215 (0.22) 170 (0.17) 69 (0.07) 1137 (1.16) 492 (0.50) 151 (0.15) 2985 (3.05)

1.91 (1.60–2.28) 1.98 (1.63–2.40) 1.36 (0.96–1.92) 1.50 (1.39–1.62) 1.36 (1.20–1.53) 1.47 (1.18–1.82) 2.39 (2.28–2.50)

0.17 (0.12–0.22) 0.16 (0.12–0.20) 0.03 (0.00–0.06) 0.57 (0.46–0.67) 0.16 (0.10–0.23) 0.06 (0.03–0.10) 3.76 (3.58–3.95)

AKI ⫽ acute kidney injury. * Events (and the proportion of patients with an event) were assessed by using hospital diagnosis codes. For some outcomes, such as the neuroleptic malignant syndrome or rhabdomyolysis, this underestimates the true event rate because these codes have high specificity but low sensitivity. † The conditional logistic regression model was adjusted for local health integration network, which refers to 14 geographically defined health authorities in Ontario responsible for regional administration of different health care services (including regional physicians’ offices, hospitals, community mental health and addiction centers, community health centers, and long-term care facilities).

were older than the nonrecipients and were more likely to reside in a long-term care facility. The recipients were more likely to be diagnosed with dementia, psychiatric diseases, Parkinson disease, and cardiovascular diseases. A total of 97 777 drug recipients were successfully matched to 97 777 nonrecipients. The 2 groups were well-balanced and showed no meaningful differences in the 91 measured baseline characteristics (Table 5 of the Supplement) (demographics, comorbid conditions, concurrent medication use, and health care contacts and use). The mean age was 80.7 years, 23.8% of patients resided in a long-term care facility, and 53.8% had a diagnosis of dementia. The most frequently prescribed atypical antipsychotic drug was risperidone (n ⫽ 44 707; 45.7%), followed by quetiapine (n ⫽ 34 498; 35.3%) and olanzapine (n ⫽ 18 572; 19.0%). The median starting daily dose for quetiapine was 25 mg/d (IQR, 25 to 50 mg/d); for risperidone, 0.5 mg/d (IQR, 0.3 to 0.6 mg/d); and for olanzapine, 2.5 mg/d (IQR, 2.5 to 5.0 mg/d). When prescriber information was available (n ⫽ 87 228; 89.2%), the most frequent prescribers were family physicians (n ⫽ 71 714; 82.2%) followed by psychiatrists (n ⫽ 5925; 6.8%) and geriatricians (n ⫽ 4104; 4.7%). Baseline distribution of the region of Ontario (local health integration network) was well-balanced in matched recipients and nonrecipients (Table 5 of the Supplement). Baseline characteristics were similar in a subpopulation of patients with available serum creatinine levels (1796 matched pairs of drug recipients and nonrecipients) (Table 6 of the Supplement). The primary outcome was 90-day hospitalization with AKI assessed by using a hospital diagnosis code (Table 1). Atypical antipsychotic drug use versus nonuse was associated with a higher risk for hospitalization with AKI when assessed by using a hospital diagnosis code (1002 of 97 777 recipients [1.02%] vs. 602 of 97 777 nonrecipients [0.62%]; RR, 1.73 [95% CI, 1.55 to 1.92]; absolute risk www.annals.org

increase, 0.41% [CI, 0.33% to 0.49%]). In the subpopulation with available information on serum creatinine levels, atypical antipsychotic drug use versus nonuse was associated with a higher risk for hospitalization with AKI (98 of 1796 recipients [5.46%] vs. 60 of 1796 nonrecipients [3.34%]; RR, 1.70 [CI, 1.22 to 2.38]; absolute risk increase, 2.12% [CI, 0.80% to 3.43%]). The secondary outcomes assessed using hospital diagnosis codes are presented in Table 1. Atypical antipsychotic drug use versus nonuse was associated with a higher 90-day risk for hospitalization with hypotension (RR, 1.91 [CI, 1.60 to 2.28]), acute urinary retention (RR, 1.98 [CI, 1.63 to 2.40]), pneumonia (RR, 1.50 [CI, 1.39 to 1.62]), acute myocardial infarction (RR, 1.36 [CI, 1.20 to 1.53]), and ventricular arrhythmia (RR, 1.47 [CI, 1.18 to 1.82]). The relative risk for the neuroleptic malignant syndrome or rhabdomyolysis was not significant (RR, 1.36 [CI, 0.96 to 1.92]). Atypical antipsychotic drug use versus nonuse was also associated with a higher 90-day risk for all-cause mortality (RR, 2.39 [CI, 2.28 to 2.50]). Subgroup analyses are presented in Table 2. Antipsychotic drug type and dose did not influence the association between atypical antipsychotic drug use and hospitalization with AKI (interaction P ⫽ 0.10 and 0.59, respectively). Similarly, the presence of chronic kidney disease did not influence the observed association (interaction P ⫽ 0.16). The absolute increase in the incidence of AKI associated with atypical antipsychotic drug use versus nonuse was greater in patients with chronic kidney disease (absolute risk increase, 1.28% [CI, 0.72% to 1.84%]) than in those without chronic kidney disease (absolute risk increase, 0.34% [CI, 0.26% to 0.41%]). The association between drug use and AKI was higher in community dwellers (RR, 1.90 [CI, 1.67 to 2.16]) than in long-term care residents (RR, 1.46 [CI, 1.14 to 1.71]) (interaction P ⬍ 0.01). 19 August 2014 Annals of Internal Medicine Volume 161 • Number 4 245

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Atypical Antipsychotic Drugs and the Risk for Acute Kidney Injury

Table 2. The Association Between Atypical Antipsychotic Drug Use and Hospitalization With AKI* Variable

Events/At Risk, n/N (%)†

Relative Risk (95% CI)‡

Interaction P Value

Absolute Risk Increase (95% CI), %

Drug Recipients

Nonrecipients

Antipsychotic drug type Quetiapine Risperidone Olanzapine

372/34 498 (1.08) 457/44 707 (1.02) 173/18 572 (0.93)

194/34 498 (0.56) 305/44 707 (0.68) 103/18 572 (0.55)

2.00 (1.66–2.41) 1.59 (1.36–1.86) 1.76 (1.35–2.30)

0.10 0.10 0.10

0.52 (0.38–0.65) 0.34 (0.22–0.46) 0.38 (0.20–0.55)

Antipsychotic drug dose§ High Low

372/34 644 (1.07) 630/63 133 (1.00)

214/34 644 (0.62) 388/63 133 (0.61)

1.80 (1.51–2.15) 1.73 (1.51–1.97)

0.59 0.59

0.46 (0.32–0.59) 0.38 (0.29–0.48)

Chronic kidney disease㛳 Yes No

305/7656 (3.98) 697/90 121 (0.77)

207/7656 (2.70) 395/90 121 (0.44)

1.61 (1.33–1.96) 1.82 (1.60–2.07)

0.16 0.16

1.28 (0.72–1.84) 0.34 (0.26–0.41)

Residential status Community-dwelling Long-term care

733/74 468 (0.98) 269/23 309 (1.15)

400/74 468 (0.54) 202/23 309 (0.87)

1.90 (1.67–2.16) 1.46 (1.14–1.71)

⬍0.01 ⬍0.01

0.45 (0.36–0.53) 0.29 (0.11–0.47)

AKI ⫽ acute kidney injury. * Assessed in subgroups defined by antipsychotic drug type, antipsychotic drug dose, evidence of chronic kidney disease, and residential status. Sets of drug recipients and nonrecipients were matched on the presence of chronic kidney disease and residential status. For antipsychotic drug type and dose, matched sets were categorized according to this characteristic in drug recipients. † AKI (and the proportion of patients with the event) was assessed by using a hospital diagnosis code. The true event rate of AKI is underestimated for some outcomes because the code for AKI has high specificity but low sensitivity. ‡ The conditional logistic regression model was adjusted for local health integration network, which refers to 14 geographically defined health authorities in Ontario responsible for regional administration of different health care services (including regional physicians’ offices, hospitals, community mental health and addiction centers, community health centers, and long-term care facilities). § High dose was defined as ⬎25 mg/d for quetiapine, ⬎0.5 mg/d for risperidone, and ⬎2.5 mg/d for olanzapine. Low dose was defined as ⱕ25 mg/d for quetiapine, ⱕ0.5 mg/d for risperidone, and ⱕ2.5 mg/d for olanzapine. 㛳 Chronic kidney disease was identified by using an algorithm of hospital diagnosis codes validated for older adults in the study region (34). The algorithm identified patients with a median estimated glomerular filtration rate of 38 mL/min/1.73 m2 (interquartile range, 27–52 mL/min/1.73 m2), whereas its absence identified patients with a median estimated glomerular filtration rate of 69 mL/min/1.73 m2 (interquartile range, 56 – 82 mL/min/1.73 m2).

When we repeated the analysis by following retained eligible matched pairs from the day that preceded the index date (cohort entry date) by 180 days, there was no observed association with 90-day risk for study outcomes, except for pneumonia (RR, 0.87 [CI, 0.79 to 0.97]) (Table 7 of the Supplement).

DISCUSSION In this population-based cohort study of older adults, we observed that new use of an atypical antipsychotic drug was common in routine care and associated with a higher 90-day risk for hospitalization with AKI. Drug use was also associated with an increased risk for other adverse outcomes, including hypotension, acute urinary retention, pneumonia, and acute cardiac events. These outcomes are known potential causes of AKI. We also observed a higher 90-day risk for all-cause mortality after new use of an atypical antipsychotic drug (6.8% in recipients vs. 3.1% in nonrecipients). This finding is similar to the results from randomized trials. In 2005, the U.S. Food and Drug Administration issued a black-box warning based on the analyses of 17 randomized, placebo-controlled trials (averaging 10 weeks in duration) that showed an approximate 1.6- to 1.7-times greater risk for death in older patients with dementia treated with atypical antipsychotic drugs versus placebo (incidence of death, 4.5% vs. 2.6%) (1). A meta-analysis of randomized, 246 19 August 2014 Annals of Internal Medicine Volume 161 • Number 4

placebo-controlled trials (10 to 12 weeks in duration) also provided supporting evidence for the warning (27). The current available evidence calls for a careful reevaluation of prescribing atypical antipsychotic drugs in older adults, especially for the unapproved indication of managing behavioral symptoms of dementia (35). The drugs should be used only after other approaches have been exhausted; when prescribed, patients must be warned about potential adverse effects. Proactive clinical monitoring shortly after initiation seems reasonable (for example, serum creatinine and blood pressure measurement, and if readily available, a bladder scan to detect urinary retention). When patients present with AKI, atypical antipsychotic drugs should be considered a potential cause and be promptly discontinued if feasible. In our study, although the incidence of AKI was slightly increased in patients prescribed a high versus a low initial dose of the atypical antipsychotic drug, the observed difference in risk by dose was not statistically significant. Although an association between drug use and AKI was seen in both community dwellers and long-term care residents, it was more pronounced in community dwellers. Older adults residing in the community may have less follow-up surveillance than those living in a long-term care facility. Further, long-term care residents may be relatively more predisposed to AKI than community dwellers, such www.annals.org

Atypical Antipsychotic Drugs and the Risk for Acute Kidney Injury

that additional risk posed by the atypical antipsychotic drug use is not as pronounced. Our study has several strengths. To our knowledge, this is the first population-based study of AKI resulting from antipsychotic drugs. Population-based studies complement information generated from clinical trials by providing the opportunity to study uncommon but important adverse drug reactions with adequate statistical power, inclusive of vulnerable groups who are not enrolled in clinical trials, and allowing effects to be studied in routine practice, where treatments and monitoring are less regimented than in trials (13, 14, 36, 37). We found that use of these drugs in routine care was common, which provided good precision for estimates of associated effects that are generalizable. Several limitations need to be considered. As with all observational studies, our study is subject to confounding by unmeasured health characteristics that may have differed between patients who did and did not receive an antipsychotic drug. The very impetus for an atypical antipsychotic drug prescription (such as severe behavioral challenges that may compromise oral intake) may have predisposed recipients to AKI and other adverse outcomes. However, such confounding probably does not explain the entire observed association. First, we did not detect a difference in 90-day risk for the study outcomes between the 2 groups when the cohort was examined 180 days before an atypical antipsychotic drug was initiated. This suggests the recipients and nonrecipients had a similar baseline risk for the study outcomes. Second, the association is supported by many case reports and the known biological effects of these drugs (4 –14). Last, we used the “new user design” and followed study patients for a short duration (90 days) to show a temporal association with the risk for acute adverse outcomes soon after initiation of an atypical antipsychotic drug (38). The elevated risk for AKI after initiation of an atypical antipsychotic drug may have been underestimated because the diagnosis code for AKI is insensitive (30). To address this concern, we supplemented our findings in a subpopulation with available serum creatinine levels and observed consistent results. It should be noted that patients who had AKI without hospitalization were not considered in this study. In addition, we generalize our findings only to older adults because we could not reliably study younger patients with our data sources. Finally, we can apply our findings only to quetiapine, risperidone, and olanzapine—the most commonly used atypical antipsychotic drugs in our region. Nonetheless, similar caution should extend to aripiprazole, ziprasidone, paliperidone, and other atypical antipsychotic drugs because the U.S. Food and Drug Administration warning for mortality risk in older patients with dementia extends to the entire class of atypical antipsychotic drugs (1, 2). In conclusion, atypical antipsychotic drugs are associated with an increased risk for AKI and other adverse outwww.annals.org

Original Research

comes that may explain the observed association with AKI. The findings support current safety concerns about the use of these drugs in older adults. From Case Western Reserve University School of Medicine, Cleveland, Ohio; Western University, London, Ontario, Canada; Institute for Clinical Evaluative Sciences, Keenan Research Centre in the Li Ka Shing Knowledge Institute of St Michael’s Hospital, and University of Toronto, Toronto, Ontario, Canada; Yale University School of Medicine, New Haven, Connecticut; and University of Alberta, Edmonton, Alberta, Canada. Grant Support: This study was supported by the Institute for Clinical Evaluative Sciences (ICES) Western site and conducted by members of the provincial ICES Kidney, Dialysis and Transplantation Program. ICES is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care, and ICES Western is funded by an operating grant from the Academic Medical Organization of Southwestern Ontario, the Schulich School of Medicine & Dentistry at Western University, and the Lawson Health Research Institute. Disclosures: Disclosures can be viewed at www.acponline.org/authors /icmje/ConflictOfInterestForms.do?msNum⫽M13-2796. Reproducible Research Statement: Study protocol and statistical code:

Portions are available to approved individuals through written agreements with Dr. Garg (e-mail, [email protected]). Data set: Not available. Requests for Single Reprints: Amit X. Garg, MD, PhD, London Kidney Clinical Research Unit, Room ELL-101, Westminster, London Health Sciences Centre, 800 Commissioners Road East, London, Ontario N6A 4G5, Canada; e-mail, [email protected].

Current author addresses and author contributions are available at www .annals.org.

References 1. U.S. Food and Drug Administration. Public health advisory: deaths with antipsychotics in elderly patients with behavioral disturbances. Silver Spring, MD: U.S. Food and Drug Administration; 2005. Accessed at www.fda.gov /Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders /DrugSafetyInformationforHeathcareProfessionals/PublicHealthAdvisories /ucm053171.htm on 25 April 2013. 2. Health Canada. Increased mortality associated with the use of atypical antipsychotic drugs in elderly patients with dementia. Ottawa, Ontario, Canada: Health Canada; 2005. Accessed at www.healthycanadians.gc.ca/recall-alert-rappel -avis/hc-sc/2005/14307a-eng.php on 25 April 2013. 3. Finkel S. Pharmacology of antipsychotics in the elderly: a focus on atypicals. J Am Geriatr Soc. 2004;52:S258-65. [PMID: 15541166] 4. Cohen R, Wilkins KM, Ostroff R, Tampi RR. Olanzapine and acute urinary retention in two geriatric patients. Am J Geriatr Pharmacother. 2007;5:241-6. [PMID: 17996664] 5. Raitasuo V, Vataja R, Elomaa E. Risperidone-induced neuroleptic malignant syndrome in young patient [Letter]. Lancet. 1994;344:1705. [PMID: 7527886] 6. Ahuja N, Palanichamy N, Mackin P, Lloyd A. Olanzapine-induced hyperglycaemic coma and neuroleptic malignant syndrome: case report and review of literature. J Psychopharmacol. 2010;24:125-30. [PMID: 18801826] doi: 10.1177/0269881108096901 7. Duggal HS, Singh I. Neuroleptic malignant syndrome presenting with acute renal failure [Letter]. Prog Neuropsychopharmacol Biol Psychiatry. 2008;32: 1074-5. [PMID: 18281139] doi:10.1016/j.pnpbp.2008.01.010 19 August 2014 Annals of Internal Medicine Volume 161 • Number 4 247

Original Research

Atypical Antipsychotic Drugs and the Risk for Acute Kidney Injury

8. Khan I, Vasudevan V, Arjomand F, Ali R, Shahzad S. Quetiapine induced fatal neuroleptic malignant syndrome (NMS) and hyperosmolar hyperglycemic nonketotic coma (HHNC). Chest. 2011;140:113A. doi:10.1378/chest.1114380 9. Sajatovic M, Calabrese JR, Mullen J. Quetiapine for the treatment of bipolar mania in older adults. Bipolar Disord. 2008;10:662-71. [PMID: 18837860] doi: 10.1111/j.1399-5618.2008.00614.x 10. Ritchie CW, Chiu E, Harrigan S, MacFarlane S, Mastwyk M, Halliday G, et al. A comparison of the efficacy and safety of olanzapine and risperidone in the treatment of elderly patients with schizophrenia: an open study of six months duration. Int J Geriatr Psychiatry. 2006;21:171-9. [PMID: 16416458] 11. Sokolski KN, Brown BJ, Melden M. Urinary retention following repeated high-dose quetiapine [Letter]. Ann Pharmacother. 2004;38:899-900. [PMID: 15039480] 12. Knol W, van Marum RJ, Jansen PA, Souverein PC, Schobben AF, Egberts AC. Antipsychotic drug use and risk of pneumonia in elderly people. J Am Geriatr Soc. 2008;56:661-6. [PMID: 18266664] doi:10.1111/j.1532-5415.2007 .01625.x 13. Pariente A, Fourrier-Re´glat A, Ducruet T, Farrington P, Be´land SG, Dartigues JF, et al. Antipsychotic use and myocardial infarction in older patients with treated dementia. Arch Intern Med. 2012;172:648-53. [PMID: 22450214] doi: 10.1001/archinternmed.2012.28 14. Ray WA, Chung CP, Murray KT, Hall K, Stein CM. Atypical antipsychotic drugs and the risk of sudden cardiac death. N Engl J Med. 2009;360:225-35. [PMID: 19144938] doi:10.1056/NEJMoa0806994 15. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147:573-7. [PMID: 17938396] 16. Levy AR, O’Brien BJ, Sellors C, Grootendorst P, Willison D. Coding accuracy of administrative drug claims in the Ontario Drug Benefit database. Can J Clin Pharmacol. 2003;10:67-71. [PMID: 12879144] 17. Patel AM, Shariff S, Bailey DG, Juurlink DN, Gandhi S, Mamdani M, et al. Statin toxicity from macrolide antibiotic coprescription: a population-based cohort study. Ann Intern Med. 2013;158:869-76. [PMID: 23778904] doi: 10.7326/0003-4819-158-12-201306180-00004 18. Lam NN, Weir MA, Yao Z, Blake PG, Beyea MM, Gomes T, et al. Risk of acute kidney injury from oral acyclovir: a population-based study. Am J Kidney Dis. 2013;61:723-9. [PMID: 23312723] doi:10.1053/j.ajkd.2012.12.008 19. Zhao YY, Weir MA, Manno M, Cordy P, Gomes T, Hackam DG, et al. New fibrate use and acute renal outcomes in elderly adults: a population-based study. Ann Intern Med. 2012;156:560-9. [PMID: 22508733] doi:10.7326/ 0003-4819-156-8-201204170-00003 20. Shih AW, Weir MA, Clemens KK, Yao Z, Gomes T, Mamdani MM, et al. Oral bisphosphonate use in the elderly is not associated with acute kidney injury. Kidney Int. 2012;82:903-8. [PMID: 22695327] doi:10.1038/ki.2012.227 21. Gandhi S, Fleet JL, Bailey DG, McArthur E, Wald R, Rehman F, et al. Calcium-channel blocker-clarithromycin drug interactions and acute kidney injury. JAMA. 2013;310:2544-53. [PMID: 24346990] doi:10.1001/jama.2013 .282426 22. Weir MA, Beyea MM, Gomes T, Juurlink DN, Mamdani M, Blake PG, et al. Orlistat and acute kidney injury: an analysis of 953 patients [Letter]. Arch Intern Med. 2011;171:703-4. [PMID: 21482850] doi:10.1001 /archinternmed.2011.103

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23. Gandhi S, Shariff SZ, Beyea MM, Weir MA, Hands T, Kearns G, et al. Identifying geographical regions serviced by hospitals to assess laboratory-based outcomes. BMJ Open. 2013;3. [PMID: 23293246] doi:10.1136/bmjopen-2012 -001921 24. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46: 399-424. [PMID: 21818162] 25. Johns Hopkins ACG System. Baltimore: Johns Hopkins Univ; 2013. Accessed at http://acg.jhsph.org/index.php/the-acg-system-advantage/acgs on 15 January 2014. 26. Reid RJ, MacWilliam L, Verhulst L, Roos N, Atkinson M. Performance of the ACG case-mix system in two Canadian provinces. Med Care. 2001;39:86-99. [PMID: 11176546] 27. Schneider LS, Dagerman KS, Insel P. Risk of death with atypical antipsychotic drug treatment for dementia: meta-analysis of randomized placebocontrolled trials. JAMA. 2005;294:1934-43. [PMID: 16234500] 28. Juurlink D, Preyra C, Croxford R, Chong A, Austin P, Tu J, et al. Canadian Institute for Health Information Discharge Abstract Database: a validation study. Toronto, Ontario, Canada: Institute for Clinical Evaluative Sciences; 2006. Accessed at www.ices.on.ca/flip-publication/canadian-istitute-for-health -information-discharge/index.html#41/z on 23 June 2014. 29. Jha P, Deboer D, Sykora K, Naylor CD. Characteristics and mortality outcomes of thrombolysis trial participants and nonparticipants: a populationbased comparison. J Am Coll Cardiol. 1996;27:1335-42. [PMID: 8626941] 30. Hwang YJ, Shariff SZ, Gandhi S, Wald R, Clark E, Fleet JL, et al. Validity of the International Classification of Diseases, Tenth Revision code for acute kidney injury in elderly patients at presentation to the emergency department and at hospital admission. BMJ Open. 2012;2. [PMID: 23204077] doi:10.1136/ bmjopen-2012-001821 31. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney Int Suppl. 2012;2:1-138. Accessed at www.kdigo.org/clinical _practice_guidelines/pdf/KDIGO%20AKI%20Guideline.pdf on 23 June 2014. 32. Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Siml Comput. 2009;38:1228-34. 33. Ontario’s Local Health Integration Networks. Accessed at www.lhins.on.ca /aboutlhin.aspx on 20 April 2014. 34. Fleet JL, Dixon SN, Shariff SZ, Quinn RR, Nash DM, Harel Z, et al. Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes. BMC Nephrol. 2013;14:81. [PMID: 23560464] doi:10.1186/1471-2369-14-81 35. Rabins PV, Lyketsos CG. Antipsychotic drugs in dementia: what should be made of the risks? [Editorial]. JAMA. 2005;294:1963-5. [PMID: 16234504] 36. Hilmer SN, Gnjidic D, Abernethy DR. Pharmacoepidemiology in the postmarketing assessment of the safety and efficacy of drugs in older adults. J Gerontol A Biol Sci Med Sci. 2012;67:181-8. [PMID: 21653991] doi:10.1093/gerona/ glr066 37. Stu¨rmer T, Jonsson Funk M, Poole C, Brookhart MA. Nonexperimental comparative effectiveness research using linked healthcare databases. Epidemiology. 2011;22:298-301. [PMID: 21464649] doi:10.1097/EDE.0b013 e318212640c 38. Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158:915-20. [PMID: 14585769]

www.annals.org

Annals of Internal Medicine Current Author Addresses: Mr. Hwang; Drs. Dixon, Shariff, and Garg; Ms. Gandhi; and Ms. Nash: London Kidney Clinical Research Unit, Room ELL-101, Westminster, London Health Sciences Centre, 800 Commissioners Road East, London, Ontario N6A 4G5, Canada. Dr. Reiss: London Health Sciences Centre, Room B8-142, 800 Commissioners Road East, London, Ontario N6A 5W9, Canada. Dr. Wald: Division of Nephrology, St. Michael’s Hospital, 61 Queen Street East, Room 9-140, Toronto, Ontario M5C 2T2, Canada. Dr. Parikh: Yale University and Veterans Affairs Medical Center, 60 Temple Street, Suite 6C, New Haven, CT 06510. Dr. Pannu: Division of Nephrology and Critical Care, University of Alberta, 11-107 CSB, 8440 112 Street, Edmonton, Alberta T6G 2G3, Canada. Dr. Rehman: University Hospital, Room ALL-139, 339 Windermere Road, London, Ontario N6A 5A5, Canada.

Author Contributions: Conception and design: Y.J. Hwang, S.N.

Dixon, J.P. Reiss, C.R. Parikh, S. Gandhi, S.Z. Shariff, A.X. Garg. Analysis and interpretation of the data: Y.J. Hwang, S.N. Dixon, J.P. Reiss, C.R. Parikh, S. Gandhi, S.Z. Shariff, N. Pannu, F. Rehman, A.X. Garg. Drafting of the article: Y.J. Hwang, J.P. Reiss, C.R. Parikh, F. Rehman, A.X. Garg. Critical revision of the article for important intellectual content: Y.J. Hwang, S.N. Dixon, J.P. Reiss, R. Wald, C.R. Parikh, S. Gandhi, S.Z. Shariff, N. Pannu, D.M. Nash, F. Rehman, A.X. Garg. Final approval of the article: Y.J. Hwang, S.N. Dixon, J.P. Reiss, R. Wald, C.R. Parikh, S. Gandhi, S.Z. Shariff, N. Pannu, D.M. Nash, F. Rehman, A.X. Garg. Provision of study materials or patients: A.X. Garg. Statistical expertise: Y.J. Hwang, S.N. Dixon, S.Z. Shariff, A.X. Garg. Obtaining of funding: A.X. Garg. Administrative, technical, or logistic support: Y.J. Hwang, J.P. Reiss, A.X. Garg. Collection and assembly of data: S.N. Dixon, S.Z. Shariff, A.X. Garg.

Appendix Figure. Study flow diagram.

Ontario residents aged 66 y or older who received a new oral outpatient prescription for an atypical antipsychotic drug (quetiapine, risperidone, or olanzapine) between June 2003 and December 2011 (n = 215 543)

Excluded from the atypical antipsychotic drug recipient group (n = 92 933) Prescriptions for any antipsychotic drug in the 180 d before the index date: 58 212 Discharged from a hospital in the 2 d before the index date: 28 802 Evidence of end-stage renal disease before the index date: 1065 A prescription for more than 1 type of antipsychotic drug on the index date: 4854

Atypical antipsychotic drug recipients (n = 122 610)

Unmatched (n = 24 833)

Atypical antipsychotic drug recipients (n = 97 777)

www.annals.org

Ontario residents aged 66 y or older who did not receive a new oral outpatient prescription for any antipsychotic drug between June 2003 and December 2011 (n = 1 726 930)

Excluded from the atypical antipsychotic drug nonrecipient group (n = 522 317) Prescriptions for any antipsychotic drug in the 180 d before the index date: 34 232 Discharged from a hospital in the 2 d before the index date: 13 298 Evidence of end-stage renal disease before the index date: 6047 Did not receive at least 1 outpatient medication in the 90 d before the index date: 468 740

Atypical antipsychotic drug nonrecipients (n = 1 204 613)

Unmatched (n = 1 106 836)

Atypical antipsychotic drug nonrecipients (n = 97 777)

19 August 2014 Annals of Internal Medicine Volume 161 • Number 4

Supplementary Material Table 1. STROBE checklist Item No Title and abstract

Introduction Background/rationale

1

2

Recommendation

Reported

(a) Indicate the study’s design with a commonly used term in the title or the abstract (b) Provide in the abstract an informative and balanced summary of what was done and what was found

Abstract

Explain the scientific background and rationale for the investigation being reported State specific objectives, including any pre-specified hypotheses

Introduction

Abstract

Objectives

3

Introduction

Methods Study design

4

Present key elements of study design early in the paper

Methods

Setting

5

Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection

Methods

Participants

6

(a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up

Methods, Appendix Figure

(b) For matched studies, give matching criteria and number of exposed and unexposed

Methods, Table 4 of the Supplement

Variables

7

Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable

Methods; Tables 2, 3, and 4 of the Supplement

Data sources/measurement

8

For each variable of interest, give sources of data and details of methods of assessment (measurement) Describe comparability of assessment methods if there is more than one group

Methods; Tables 2, 3, and 4 of the Supplement

Bias

9

Describe any efforts to address potential sources of bias

Discussion

Study size

10

Explain how the study size was arrived at

Methods; based on availability of the data

Quantitative variables

11

Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why

Methods

Statistical methods

Results Participants

12

13

(a) Describe all statistical methods, including those used to control for confounding (b) Describe any methods used to examine subgroups and interactions

Methods

(c) Explain how missing data were addressed

Methods

(d) If applicable, explain how loss to follow-up was addressed

Not applicable

(e) Describe any sensitivity analyses

Methods

(a) Report numbers of individuals at each stage of study—e.g. numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analyzed (b) Give reasons for non-participation at each stage

Methods, Results, Appendix Figure

(c) Consider use of a flow diagram Descriptive data

14

Methods

Methods, Appendix Figure Appendix Figure

(a) Give characteristics of study participants (e.g. demographic, clinical, social) and information on exposures and potential confounders

Results; Tables 5 and 6 of the Supplement

(b) Indicate number of participants with missing data for each variable of interest

Methods

(c) Summarize follow-up time (e.g. average and total amount)

Results, Table 1

Outcome data

15

Report numbers of outcome events or summary measures over time

Results, Table 1

Main results

16

(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g. 95% confidence interval). Make clear which confounders were adjusted for and why they were included

Results, Table 1

(b) Report category boundaries when continuous variables were categorized

Methods

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Results, Table 1

17

Report other analyses done—e.g. analyses of subgroups and interactions, and sensitivity analyses

Results; Tables 2 and 7 of the Supplement

18

Summarize key results with reference to study objectives

Discussion

Other analyses Discussion Key results

Limitations

19

Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias

Discussion

Interpretation

20

Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence

Discussion

Generalizability

21

Discuss the generalizability (external validity) of the study results

Discussion

Other information Funding

22

Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based

Methods

Table 2. Coding definitions for comorbid conditions Variable Dementia

Database CIHI-DAD

OMHRS OHIP Schizophrenia or other psychotic disorder

CIHI-DAD

OMHRS

Bipolar disorder

OHIP CIHI-DAD

OMHRS

Major depression and/or anxiety disorder

OHIP CIHI-DAD

OMHRS

Parkinson’s disease

OHIP CIHI-DAD

Alcoholism

OHIP CIHI-DAD

Seizure

OHIP CIHI-DAD

Chronic kidney disease

CIHI-DAD

OHIP

Code ICD-9 2900, 2901, 2903, 2904, 2908, 2909, 2948, 2949, 3310, 3311, 3312, 2941, 797 ICD-10 F065, F066, F068, F069, F09, F00, F01, F02, F03, F051, G30, G31, R54 DSM-IV 29040, 29041, 29042, 29043, 29120, 29282, 29410, 29411, 29480, 78090 290, 331, 797 ICD-9 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2970, 2971, 2972, 2973, 2978, 2979, 2980, 2981, 2983, 2984, 2988, 2989 ICD-10 F060, F062, F105, F107, F115, F117, F125, F127, F135, F137, F145, F147, F155, F157, F165, F167, F175, F177, F185, F187, F195, F197, F200, F201, F202, F203, F204, F205, F206, F208, F209, F220, F228, F229, F230, F231, F232, F233, F238, F239, F24, F250, F251, F252, F258, F259, F28, F29 DSM-IV 29130, 29150, 29211, 29212, 29381, 29382, 29510, 29520, 29530, 29540, 29560, 29570, 29590, 29710, 29730, 29880, 29890 291, 292, 295, 297, 298, Q021 ICD-9 2960, 2961, 2964, 2965, 2966, 2967, 2968 ICD-10 F300, F301, F302, F308, F309, F310, F311, F312, F313, F314, F315, F316, F317, F318, F319 DSM-IV 29600, 29601, 29602, 29603, 29604, 29605, 29606, 29640, 29641, 29642, 29643, 29644, 29645, 29646, 29650, 29651, 29652, 29653, 29654, 29655, 29656, 29660, 29661, 29662, 29663, 29664, 29665, 29666, 29670, 29680, 29689 296, Q020 ICD-9 2962, 2963, 3000, 3002, 3003, 3004, 3091, 311 ICD-10 F063, F064, F320, F321, F322, F323, F328, F329, F330, F331, F332, F333, F334, F338, F339, F341, F400, F401, F402, F408, F409, F410, F411, F412, F413, F418, F419, F420, F421, F422, F428, F429, F430, F431 DSM-IV 29189, 29284, 29289, 29383, 29384, 29620, 29621, 29622, 29623, 29624, 29625, 29626, 29630, 29631, 29632, 29633, 29634, 29635, 29636, 30000, 30001, 30002, 30021, 30022, 30023, 30029, 30030, 30040, 30113 311 ICD-9 332 ICD-10 G20, F023 332 ICD-9 303, 3050 ICD-10 E244, E512, F10, G312, G621, G721, I426, K292, K70, K860, T510, X45, X65, Y15, Y573, Z502, Z714, Z721 303 ICD-9 345, 7803 ICD-10 G40, G41, R560, R568 ICD-9 4030, 4031, 4039, 4040, 4041, 4049, 582, 583, 580, 581, 584, 585, 586, 587, 5880, 5888, 5889, 5937 ICD-10 E102, E112, E132, E142, I12, I13, N08, N18, N19 403, 585

Haemorrhagic stroke

CIHI-DAD

Ischemic stroke

CIHI-DAD

Transient ischemic attack

CIHI-DAD

Chronic liver disease

CIHI-DAD

Chronic lung disease

OHIP CIHI-DAD

OHIP Congestive heart failure

CIHI-DAD

Coronary artery disease

OHIP CIHI-DAD

OHIP Angina

CIHI-DAD

Atrial fibrillation/flutter

OHIP CIHI-DAD

Cancer

CIHI-DAD

Diabetes mellitus Hypertension Peripheral vascular disease

OHIP ODB ODB CIHI-DAD

OHIP

ICD-9 430, 431 ICD-10 I600, I601, I602, I603, I604, I605, I606, I607, I609, I61 ICD-9 436, 4340, 4341, 4349, 3623 ICD-10 I630, I631, I632, I633, I634, I635, I638, I639, I64, H341 ICD-9 435 ICD-10 G450, G451, G452, G453, G458, G459, H340 ICD-9 4561, 4562, 070, 5722, 5723, 5724, 5728, 573, 7824, V026, 2750, 2751, 7891, 7895, 571 ICD-10 B16, B17, B18, B19, I85, R17, R18, R160, R162, B942, Z225, E831, E830, K70, K713, K714, K715, K717, K721, K729, K73, K74, K753, K754, K758, K759, K76, K77 571, 573, 070, Z551, Z554 ICD-9 491, 492, 493, 494, 495, 496, 500, 501, 502, 503, 504, 505, 5064, 5069, 5081, 515, 516, 517, 5185, 5188, 5198, 5199, 4168, 4169 ICD-10 I272, I278, I279, J40, J41, J42, J43, J44, J45, J47, J60, J61, J62, J63, J64, J65, J66, J67, J68, J701, J703, J704, J708, J709, J82, J84, J92, J941, J949, J953, J961, J969, J984, J988, J989, J99 491, 492, 493, 494, 496, 501, 502, 515, 518, 519 J689, J889 ICD-9 425, 5184, 514, 428 ICD-10 I500, I501, I509, I255, J81 CCP 4961, 4962, 4963, 4964 CCI 1HP53, 1HP55, 1HZ53GRFR, 1HZ53LAFR, 1HZ53SYFR 428, R701, R702, Z429 ICD-9 410 412, 414, 4292, 4295, 4296, 4297 ICD-10 I21, I22, I23, I24, I25, Z955, Z958, Z959, R931, T822 CCP 4801, 4802, 4803, 4804, 4805, 481, 482, 483 CCI 1IJ26, 1IJ27, 1IJ54, 1IJ57, 1IJ50, 1IJ76 410, 412, R741, R742, R743, G298, E646, E651, E652, E654, E655, G262, Z434, Z448 ICD-9 413 ICD-10 I20 413 ICD-9 4273 ICD-10 I48 ICD-9 150, 154, 155, 157, 162, 174, 175, 185, 203, 204, 205, 206, 207, 208 ICD-10 971, 980, 982, 984, 985, 986, 987, 988, 989, 990, 991, 993, C15, C18, C19, C20, C22, C25, C34, C50, C56, C61, C82, C83, C85, C91, C92, C93, C94, C95, C00, D05 203, 204, 205, 206, 207, 208, 150, 154, 155, 157, 162, 174, 175, 183, 185

ICD-9 4402, 4408, 4409, 5571, 4439, 444 ICD-10 I700, I702, I708, I709, I731, I738, I739, K551 CCP 5125, 5129, 5014, 5016, 5018, 5028, 5038 CCI 1KA76, 1KA50, 1KE76, 1KG26, 1KG50, 1KG57, 1KG76MI, 1KG87 R787, R780, R797, R804, R809, R875, R815, R936, R783, R784,R785, E626, R814, R786, R937, R860, R861, R855, R856, R933, R934, R791, E672, R794, R813, R867, E649

Prostatic hyperplasia

CIHI-DAD

Prostatitis

OHIP CIHI-DAD OHIP

ICD-9 600 ICD-10 N40 600 ICD-9 6010, 6011, 6012 ICD-10 N410, N411, N412 601

CCI = Canadian Classification of Health Interventions; CCP = Canadian Classification of Diagnostic, Therapeutic and Surgical Procedures; CIHI-DAD = Canadian Institute for Health Information Discharge Abstract Database; ICD-9 = International Classification of Diseases, Ninth Revision; ICD-10 = International Classification of Diseases, Tenth Revision; Ontario Drug Benefit database = ODB; OHIP = Ontario Health Insurance Plan database; OMHRS = Ontario Mental Health Reporting System database; RPDB = Ontario’s Registered Persons Database.

Table 3. Coding definitions for hospitalized outcomes Outcome Primary outcome Acute kidney injury* Secondary outcomes Hypotension† Acute urinary retention† Neuroleptic malignant syndrome/rhabdomyolysis Pneumonia‡ Acute myocardial infarction§ Ventricular arrhythmia All-cause mortality∥

Database

Code

CIHI-DAD

ICD-10 N17

CIHI-DAD CIHI-DAD CIHI-DAD

ICD-10 I95 ICD-10 R33 ICD-10 G210, M628, T796

CIHI-DAD CIHI-DAD CIHI-DAD

ICD-10 J12, J13, J14, J15, J16, J17, J18, P23 ICD-10 I21, I22 ICD-10 I460, I469, I470, I472, I4900, I4901, CCI 1HZ09JAFS, 1HZ09JAJF, 1HZ30JN, 1HZ30JY Vital status field

RPDB

CCI = Canadian Classification of Health Interventions; CIHI-DAD = Canadian Institute for Health Information Discharge Abstract Database; ICD-10 = International Classification of Diseases, Tenth Revision; RPDB = Ontario’s Registered Persons Database. *Validation of the code for acute kidney injury was performed on approximately 39 000 hospitalizations with linked laboratory measurements for serum creatinine. See Methods section for a description of the validation.(30) †Using reabstracted information written in a patient’s chart as the reference standard, the code for hypotension and acute urinary retention has a sensitivity of 72% and 86%, and positive predictive value of 39% and 48%, respectively.(28) This is a poor reference standard compared to patient blood pressure measurements and post-void residual urine volumes. ‡Code J18 (most responsible diagnosis) has a sensitivity of 80% and positive predictive value of 69%.(28) §Code I21 (most responsible diagnosis) has a sensitivity of 89% and positive predictive value of 87%.(28) ∥All-cause mortality has a sensitivity of 97.8% and specificity of 100%.(29)

Table 4. Variables included in propensity score model Demographics Income Index date Residential status (communitydwelling or long-term care) Comorbid conditions

Concurrent medication use

Number of healthcare contacts

Healthcare use

Age, sex

Charlson comorbidity index, Johns Hopkins ACG System Aggregated Diagnosis Groups, dementia, schizophrenia or other psychotic disorder, bipolar disorder, major depression and/or anxiety disorder, Parkinson’s disease, alcoholism, seizure, chronic kidney disease, hemorrhagic stroke, ischemic stroke, transient ischemic attack, chronic liver disease, chronic lung disease, congestive heart failure, coronary artery disease, angina, atrial fibrillation/flutter, cancer, diabetes mellitus, hypertension, peripheral vascular disease, prostatic hyperplasia, prostatitis Number of unique drug product, anticonvulsant, antidepressant, antiparkinson drug, benzodiazepine, cholinesterase inhibitor, lithium, ACE inhibitor or ARB, beta-adrenergic antagonist, calcium channel blocker, NSAID (excluding aspirin), potassium sparing diuretic, non-potassium sparing diuretic, statin, antiplatelet, warfarin, digoxin, overactive bladder medication, acetylcholine inhaler, corticosteroid inhaler, beta-agonist inhaler, antibiotic, antineoplastic Hospitalization, emergency department visit, family physician visit, psychiatrist visit, geriatrician visit, neurologist visit, nephrologist visit, cardiologist visit, urologist visit, obstetrician/gynecologist visit, ophthalmologist visit Carotid ultrasound, cardiac catheterization, echocardiography, Holter monitoring, cardiac stress test, colorectal cancer screening, cervical cancer screening, prostate-specific antigen test, mammography, flu shot, bone mineral density test, hearing test, cystoscopy, transurethral resection of the prostate, cataract surgery, computed tomography of the head, computed tomography of the neck, computed tomography of the thorax, computed tomography of the abdomen, computed tomography of the pelvis, computed tomography of the spine, computed tomography of the extremities, chest x-ray, pulmonary function test, electroencephalography, at-home physician service

ACE inhibitor = angiotensin-converting enzyme inhibitor; ACG = adjusted clinical groups; ARB = angiotensin II receptor blocker; NSAID = non-steroidal anti-inflammatory drug.

Table 5. Baseline characteristics of atypical antipsychotic drug recipients and non-recipients*

Demographics Age, mean (SD), years Women Income quintile 1 (low) 2 3 (middle) 4 5 (high) Year of cohort entry 2003-2004 2005-2006 2007-2008 2009-2010 2011 Rural residence Long-term care Local health integration network‡ Network 1 Network 2 Network 3 Network 4 Network 5 Network 6 Network 7 Network 8 Network 9 Network 10 Network 11 Network 12 Network 13 Network 14 Comorbid conditions§ Charlson comorbidity index, mean (SD) Johns Hopkins ACG System Aggregated Diagnosis Groups, mean (SD) Dementia Schizophrenia or other psychotic disorder Bipolar disorder Major depression and/or anxiety disorder Parkinson’s disease Alcoholism Seizure Chronic kidney disease Hemorrhagic stroke Ischemic stroke

Recipients (n = 122 610)

Unmatched Non-recipients (n = 1 204 613)

Standardized Difference†

Recipients (n = 97 777)

Matched Non-Recipients (n = 97 777)

Standardized Difference†

80.8 (8.0) 77 508 (63.2)

75.6 (7.0) 687 831 (57.1)

74% 12%

80.7 (8.0) 62 673 (64.1)

80.6 (8.1) 62 673 (64.1)

1% 0%

27 282 (22.3) 25 465 (20.8) 24 514 (20.0) 23 106 (18.8) 22 243 (18.1)

234 967 (19.5) 253 463 (21.0) 239 272 (19.9) 235 555 (19.6) 241 356 (20.0)

7% 0% 0% 2% 5%

21 540 (22.0) 20 350 (20.8) 19 559 (20.0) 18 378 (18.8) 17 950 (18.4)

22 092 (22.6%) 20 468 (20.9%) 19 105 (19.5%) 18 340 (18.8%) 17 772 (18.2%)

1% 0% 1% 0% 0%

26 712 (21.8) 29 640 (24.2) 25 286 (20.6) 26 795 (21.9) 14 177 (11.6) 15 811 (12.9) 37 598 (30.7)

229 290 (19.0) 272 475 (22.6) 251 863 (20.9) 289 588 (24.0) 161 397 (13.4) 172 157 (14.3) 32 457 (2.7)

7% 4% 1% 5% 5% 4% 81%

20 629 (21.1) 23 311 (23.8) 20 262 (20.7) 21 878 (22.4) 11 697 (12.0) 12 791 (13.1) 23 309 (23.8)

20 224 (20.7) 22 667 (23.2) 19 827 (20.3) 22 494 (23.0) 12 565 (12.9) 13 063 (13.4) 23 309 (23.8)

1% 2% 1% 2% 3% 1% 0%

7959 (6.5) 9704 (7.9) 6304 (5.1) 15 858 (12.9) 5310 (4.3) 8297 (6.8) 11 370 (9.3) 13 765 (11.2) 13 478 (11.0) 6148 (5.0) 11 549 (9.4) 5066 (4.1) 5644 (4.6) 2157 (1.8)

66 980 (5.6) 100 382 (8.3) 59 227 (4.9) 155 740 (12.9) 51 552 (4.3) 80 528 (6.7) 97 719 (8.1) 140 593 (11.7) 145 641 (12.1) 58 535 (4.9) 110 933 (9.2) 46 040 (3.8) 66 777 (5.5) 23 966 (2.0)

4% 2% 1% 0% 0% 0% 4% 1% 3% 1% 1% 2% 4% 2%

6125 (6.3) 6974 (7.1) 5067 (5.2) 12 827 (13.1) 4314 (4.4) 6592 (6.7) 8934 (9.1) 11 100 (11.4) 10 991 (11.2) 5244 (5.4) 9127 (9.3) 4237 (4.3) 4552 (4.7) 1693 (1.7)

5210 (5.3) 7413 (7.6) 4558 (4.7) 13 025 (13.3) 3516 (3.6) 6127 (6.3) 9364 (9.6) 11 067 (11.3) 11 520 (11.8) 4373 (4.5) 10 639 (10.9) 3693 (3.8) 5234 (5.4) 2038 (2.1)

4% 2% 2% 1% 4% 2% 2% 0% 2% 4% 5% 3% 3% 3%

1.5 (1.8) 7.0 (3.8) 73 839 (60.2) 15 263 (12.4) 9673 (7.9) 27 250 (22.2) 8545 (7.0) 4066 (3.3) 2087 (1.7) 10 341 (8.4) 937 (0.8) 6981 (5.7)

0.9 (1.4) 5.3 (3.0) 90 115 (7.5) 14 095 (1.2) 11 413 (0.9) 74 960 (6.2) 18 901 (1.6) 14 783 (1.2) 5 725 (0.5) 70 440 (5.8) 2316 (0.2) 20 871 (1.7)

42% 54% 181% 79% 56% 61% 38% 18% 16% 11% 12% 28%

1.4 (1.8) 6.7 (3.7) 52 621 (53.8) 8187 (8.4) 5655 (5.8) 19 235 (19.7) 5683 (5.8) 2752 (2.8) 1444 (1.5) 7656 (7.8) 640 (0.7) 4920 (5.0)

1.4 (1.8) 6.7 (3.7) 52 621 (53.8) 8187 (8.4) 5655 (5.8) 19 235 (19.7) 5683 (5.8) 3030 (3.1) 1645 (1.7) 7656 (7.8) 689 (0.7) 5493 (5.6)

1% 1% 0% 0% 0% 0% 0% 2% 2% 0% 1% 3%

9

Transient ischemic attack Chronic liver disease Chronic lung disease Congestive heart failure Coronary artery disease∥ Angina Atrial fibrillation/flutter Cancer Diabetes mellitus¶ Hypertension** Peripheral vascular disease Prostatic hyperplasia Prostatitis Concurrent medication use†† Number of unique drug product, mean (SD) Anticonvulsant Antidepressant Antiparkinson drug Benzodiazepine Cholinesterase inhibitor Lithium ACE inhibitor or ARB Beta-adrenergic antagonist Calcium channel blocker NSAID (excluding aspirin) Potassium sparing diuretic Non-potassium sparing diuretic Statin Antiplatelet Warfarin Digoxin Overactive bladder medication Acetylcholine inhaler Corticosteroid inhaler Beta-agonist inhaler Antibiotic Antineoplastic Number of healthcare contacts, mean (SD)‡‡ Hospitalization Emergency department visit Family physician visit Psychiatrist visit Geriatrician visit Neurologist visit Nephrologist visit Cardiologist visit Urologist visit Obstetrician/gynecologist visit Ophthalmologist visit Healthcare use§§

2161 (1.8) 4020 (3.3) 35 655 (29.1) 24 999 (20.4) 41 440 (33.8)

8975 (0.7) 36 398 (3.0) 294 319 (24.4) 134 486 (11.2) 341 079 (28.3)

11% 1% 11% 28% 12%

1682 (1.7) 3114 (3.2) 28 180 (28.8) 19 291 (19.7) 32 521 (33.3)

1769 (1.8) 3363 (3.4) 29 699 (30.4) 20 337 (20.8) 33 596 (34.4)

1% 1% 3% 3% 2%

27 280 (22.2) 12 629 (10.3) 16 818 (13.7) 20 435 (16.7) 84 209 (68.7) 2587 (2.1) 15 299 (12.5) 5220 (4.3)

239 650 (19.9) 66 969 (5.6) 156 300 (13.0) 208 112 (17.3) 883 364 (73.3) 17 790 (1.5) 159 181 (13.2) 56 251 (4.7)

6% 20% 2% 2% 10% 5% 2% 2%

21 636 (22.1) 9558 (9.8) 13 338 (13.6) 16 260 (16.6) 67 900 (69.4) 1989 (2.0) 11 849 (12.1) 4073 (4.2)

22 169 (22.7) 10 187 (10.4) 13 8880 (14.2) 16 974 (17.4) 68 060 (69.6) 2198 (2.3) 12 036 (12.3) 4125 (4.2)

1% 2% 2% 2% 0% 1% 1% 0%

13.2 (8.3) 14 457 (11.8) 31 278 (25.5) 7711 (6.3) 49 212 (40.1) 36 347 (29.6) 1324 (1.1) 54 674 (44.6) 37 391 (30.5) 33 166 (27.0) 20 720 (16.9) 7998 (6.5) 41 896 (34.2) 41 975 (34.2) 9105 (7.4) 13 624 (11.1) 8448 (6.9) 6890 (5.6) 9728 (7.9) 8087 (6.6) 17 950 (14.6) 48 795 (39.8) 4653 (3.8)

9.2 (6.1) 45 907 (3.8) 85 243 (7.1) 16 993 (1.4) 198 801 (16.5) 25 683 (2.1) 1317 (0.1) 617 052 (51.2) 365 976 (30.4) 344 285 (28.6) 220 395 (18.3) 69 486 (5.8) 355 874 (29.5) 539 520 (44.8) 57 845 (4.8) 98 653 (8.2) 45 448 (3.8) 33 738 (2.8) 63 131 (5.2) 66 619 (5.5) 137 687 (11.4) 362 129 (30.1) 42 085 (3.5)

63% 39% 66% 36% 62% 141% 22% 13% 0% 3% 4% 3% 10% 21% 12% 11% 16% 16% 12% 5% 10% 21% 2%

12.6 (8.1) 10 242 (10.5) 21 312 (21.8) 5187 (5.3) 35 779 (36.6) 23 258 (23.8) 725 (0.7) 44 484 (45.5) 29 908 (30.6) 26 899 (27.5) 16 774 (17.2) 6403 (6.6) 33 195 (34.0) 34 409 (35.2) 7093 (7.3) 10 644 (10.9) 6575 (6.7) 5194 (5.3) 7513 (7.7) 6433 (6.6) 14 119 (14.4) 37 933 (38.8) 3681 (3.8)

13.1 (7.8) 11 243 (11.5) 21 328 (21.8) 5435 (5.6) 37 003 (37.8) 20 766 (21.2) 717 (0.7) 44 806 (45.8) 30 126 (30.8) 27 330 (28.0) 16 994 (17.4) 6612 (6.8) 34 196 (35.0) 34 656 (35.4) 7672 (7.9) 11 411 (11.7) 6988 (7.2) 5496 (5.6) 8228 (8.4) 6861(7.0) 15 079 (15.4) 38 610 (39.5) 3883 (4.0)

6% 3% 0% 1% 3% 3% 0% 1% 0% 1% 1% 1% 1% 1% 2% 2% 2% 1% 3% 2% 3% 1% 1%

1.8 (1.3) 2.3 (2.2) 22.7 (22.4) 1.0 (4.8) 0.9 (4.1) 0.6 (2.7) 0.2 (1.5) 2.7 (6.7) 0.6 (2.5) 0.1 (0.9) 1.3 (3.7)

1.6 (1.1) 1.8 (1.7) 12.8 (12.8) 0.1 (1.31) 0.1 (1.3) 0.2 (1.1) 0.2 (1.1) 2.5 (6.3) 0.5 (2.3) 0.2 (1.0) 1.8 (4.5)

14% 25% 71% 47% 44% 29% 5% 2% 1% 4% 12%

1.8 (1.3) 2.2 (2.1) 20.9 (21.0) 0.7 (3.7) 0.7 (3.4) 0.5 (2.4) 0.2 (1.4) 2.7 (6.7) 0.5 (2.5) 0.1 (0.9) 1.4 (3.8)

1.8 (1.3) 2.2 (2.0) 20.8 (19.7) 0.6 (3.2) 0.7 (3.0) 0.5 (2.0) 0.2 (1.6) 2.8 (6.8) 0.6 (2.5) 0.1 (0.9) 1.4 (4.0)

1% 2% 0% 3% 0% 1% 1% 2% 0% 0% 1%

10

Carotid ultrasound Cardiac catheterization Echocardiography Holter monitoring Cardiac stress test Colorectal cancer screening Cervical cancer screening Prostate-specific antigen test Mammography Flu shot Bone mineral density test Hearing test Cystoscopy Transurethral resection of the prostate Cataract surgery Computed tomography of the head Computed tomography of the neck Computed tomography of the thorax Computed tomography of the abdomen Computed tomography of the pelvis Computed tomography of the spine Computed tomography of the extremities Chest x-ray Pulmonary function test Electroencephalography At-home physician service

21 312 (17.4) 5069 (4.1) 44 657 (36.4) 22 565 (18.4) 30 982 (25.3) 33 848 (27.6) 8793 (7.2) 5186 (4.2) 20 847 (17.0) 96 080 (78.4) 35 876 (29.3) 21 985 (17.9) 16 694 (13.6) 2946 (2.4) 23 290 (19.0) 66 118 (53.9) 2729 (2.2) 16 698 (13.6) 25 875 (21.1) 23 171 (18.9) 9532 (7.8) 2472 (2.0) 98,250 (80.1) 22,265 (18.2) 5,932 (4.8) 26,338 (21.5)

174 468 (14.5) 77 445 (6.4) 449 763 (37.3) 207 193 (17.2) 386 858 (32.1) 393 931 (32.7) 168 741 (14.0) 114 893 (9.5) 287 167 (23.8) 949 968 (78.9) 467 315 (38.8) 195 972 (16.3) 143 992 (12.0) 22 277 (1.8) 230 493 (19.1) 249 210 (20.7) 21 424 (1.8) 128 683 (10.7) 200 107 (16.6) 176 224 (14.6) 79 656 (6.6) 17 077 (1.4) 828 470 (68.8) 254 347 (21.1) 20 948 (1.7) 68 663 (5.7)

8% 1% 2% 3% 15% 11% 20% 19% 16% 1% 20% 4% 5% 4% 0% 80% 3% 9% 12% 12% 5% 5% 25% 7% 22% 62%

16 923 (17.3) 4275 (4.4) 36 047 (36.9) 18 108 (18.5) 25 502 (26.1) 27 327 (28.0) 7553 (7.7) 4546 (4.7) 17 284 (17.7) 76 654 (78.4) 29 761 (30.4) 17 589 (18.0) 12 862 (13.2) 2151 (2.2) 18 890 (19.3) 48 094 (49.2) 2163 (2.2) 13 163 (13.5) 20 249 (20.7) 18 120 (18.5) 7489 (7.7) 1926 (2.0) 77 036 (78.8) 18 249 (18.7) 4233 (4.3) 18 981 (19.4)

17 747 (18.2) 4451 (4.6) 37 290 (38.1) 19 027 (19.5) 26 027 (26.6) 27 711 (28.3) 7474 (7.6) 4464 (4.6) 17 244 (17.6) 76 763 (78.5) 29 925 (30.6) 17 772 (18.2) 13 508 (13.8) 2300 (2.4) 19 084 (19.5) 49 695 (50.8) 2362 (2.4) 14 241 (14.6) 21 477 (22.0) 19 204 (19.6) 8038 (8.2) 2018 (2.1) 78 200 (80.0) 19 118 (19.6) 4400 (4.5) 18 694 (19.1)

2% 1% 3% 2% 1% 1% 0% 0% 0% 0% 0% 0% 2% 1% 1% 3% 1% 3% 3% 3% 2% 1% 3% 2% 1% 1%

ACE inhibitor = angiotensin-converting enzyme inhibitor; ACG = adjusted clinical groups; ARB = angiotensin II receptor blocker; IQR = interquartile range; NSAID = non-steroidal antiinflammatory drug; SD = standard deviation. *Data are presented as the number (percentage) of patients, unless otherwise reported. †Standardized differences are less sensitive to sample size than traditional hypothesis tests. They provide a measure of the difference between groups with respect to the pooled standard deviation; a standardized difference greater than 10% was considered as a meaningful difference between the groups. ‡Local health integration network are 14 geographically defined health authorities in Ontario responsible for regional administration of different healthcare services (including regional physicians, hospitals, community mental health and addictions, community health centers, and long-term care). §Comorbid conditions in the five years preceding the index date were considered. ∥Coronary artery disease includes hospitalization with myocardial infarction and receipt of coronary artery bypass graft surgery and percutaneous coronary intervention. ¶Diabetes mellitus and were defined by use of any diabetic medication in the previous 6 months preceding the index date. **Hypertension was defined by use of any antihypertensive medication in the previous 6 months preceding the index date. ††Concurrent medication use in the six months preceding the index date were considered. ‡‡Healthcare contacts in the year preceding the index date were considered. §§Healthcare use in the year preceding the index date was considered.

11

Table 6. Baseline characteristics of atypical antipsychotic drug recipients and matched non-recipients in the subpopulation with available serum creatinine values*

Demographics Age, mean (SD), y Women Income quintile 1 (low) 2 3 (middle) 4 5 (high) Year of cohort entry 2003-2004 2005-2006 2007-2008 2009-2010 2011 Rural residence Long-term care Comorbid conditions‡ Charlson comorbidity index, mean (SD) Johns Hopkins ACG System Aggregated Diagnosis Groups, mean (SD) Dementia Schizophrenia or other psychotic disorder Bipolar disorder Major depression and/or anxiety disorder Parkinson’s disease Alcoholism Seizure Chronic kidney disease Hemorrhagic stroke Ischemic stroke Transient ischemic attack Chronic liver disease Chronic lung disease Congestive heart failure Coronary artery disease§ Angina Atrial fibrillation/flutter Cancer Diabetes mellitus∥ Hypertension¶ Peripheral vascular disease Prostatic hyperplasia Prostatitis Concurrent medication use**

Recipients (n = 1796)

Non-Recipients (n = 1796)

Standardized Difference†

80.2 (7.7) 1164 (64.8)

80.2 (7.7) 1164 (64.8)

0% 0%

415 (23.1) 321 (17.9) 419 (23.3) 279 (15.5) 362 (20.2)

430 (23.9) 286 (15.9) 403 (22.4) 284 (15.8) 393 (21.9)

2% 5% 2% 1% 4%

259 (14.4) 401 (22.3) 407 (22.7) 460 (25.6) 269 (15.0) 201 (11.2) 321 (17.9)

227 (12.6) 373 (20.8) 442 (24.6) 489 (27.2) 265 (14.8) 189 (10.5) 321 (17.9)

5% 4% 5% 4% 1% 2% 0%

1.4 (1.7) 7.6 (3.4)

1.4 (1.7) 7.7 (3.4)

2% 4%

883 (49.2) 107 (6.0) 26 (1.5) 384 (21.4) 49 (2.7) 53 (3.0) 30 (1.7) 91 (5.1) 11 (0.6) 98 (5.5) 44 (2.5) 77 (4.3) 542 (30.2) 383 (21.3) 579 (32.2) 391 (21.8) 197 (11.0) 287 (16.0) 337 (18.8)

883 (49.2) 107 (6.0) 26 (1.5) 384 (21.4) 49 (2.7) 41 (2.3) 36 (2.0) 91 (5.1) 17 (1.0) 108 (6.0) 37 (2.1) 57 (3.2) 537 (29.9) 402 (22.4) 656 (36.5) 389 (21.7) 228 (12.7) 316 (17.6) 344 (19.2)

0% 0% 0% 0% 0% 4% 2% 0% 4% 2% 3% 6% 1% 3% 9% 0% 5% 4% 1%

1347 (75.0) 38 (2.1) 184 (10.2) 57 (3.2)

1341 (74.7) 21 (1.2) 197 (11.0) 67 (3.7)

1% 7% 2% 3%

12

Number of unique drug product, mean (SD) Anticonvulsant Antidepressant Antiparkinson drug Benzodiazepine Cholinesterase inhibitor Lithium ACE inhibitor or ARB Beta-adrenergic antagonist Calcium channel blocker NSAID (excluding aspirin) Potassium sparing diuretic Non-potassium sparing diuretic Statin Antiplatelet Warfarin Digoxin Overactive bladder medication Acetylcholine inhaler Corticosteroid inhaler Beta-agonist inhaler Antibiotic Antineoplastic Number of healthcare contacts, mean (SD)†† Hospitalization Emergency department visit Family physician visit Psychiatrist visit Geriatrician visit Neurologist visit Nephrologist visit Cardiologist visit Urologist visit Obstetrician/Gynecologist visit Ophthalmologist visit Healthcare use‡‡ Carotid ultrasound Cardiac catheterization Echocardiography Holter monitoring Cardiac stress test Colorectal cancer screening Cervical cancer screening Prostate-specific antigen test Mammography Flu shot Bone mineral density test Hearing test Cystoscopy Transurethral resection of the prostate

13.6 (7.7) 160 (8.9) 544 (30.3) 49 (2.7) 696 (38.8) 384 (21.4) 17 (1.0) 881 (49.1) 617 (34.4) 541 (30.1) 299 (16.7) 154 (8.6) 677 (37.7) 710 (39.5) 143 (8.0) 234 (13.0) 118 (6.6) 104 (5.8) 183 (10.2) 95 (5.3) 290 (16.2) 772 (43.0) 87 (4.8)

14.4 (7.9) 208 (11.6) 549 (30.6) 79 (4.4) 726 (40.4) 341 (19.0) 9 (0.5) 882 (49.1) 661 (36.8) 557 (31.0) 305 (17.0) 164 (9.1) 707 (39.4) 733 (40.8) 139 (7.7) 273 (15.2) 130 (7.2) 112 (6.2) 193 (10.8) 111 (6.2) 269 (15.0) 797 (44.4) 90 (5.0)

11% 9% 1% 9% 3% 6% 5% 0% 5% 2% 1% 2% 3% 3% 1% 6% 3% 2% 2% 4% 3% 3% 1%

1.7 (1.1) 2.3 (2.4) 21.7 (18.6) 0.6 (4.1) 1.1 (5.0) 0.8 (4.4) 0.1 (0.9) 3.2 (7.3) 0.6 (2.5) 0.1 (0.9) 1.5 (4.0)

1.7 (1.1) 2.2 (1.8) 21.3 (17.3) 0.3 (1.7) 1.1 (5.2) 0.7 (3.5) 0.1 (1.0) 3.2 (6.7) 0.7 (2.8) 0.3 (1.5) 1.4 (3.7)

2% 7% 2% 10% 0% 3% 0% 0% 5% 9% 1%

337 (18.8) 95 (5.3) 628 (35.0) 367 (20.4) 525 (29.2) 653 (36.4) 151 (8.4) 147 (8.2) 285 (15.9) 1525 (84.9) 511 (28.5) 390 (21.7) 196 (10.9) 30 (1.7)

354 (19.7) 107 (6.0) 650 (36.2) 373 (20.8) 517 (28.8) 641 (35.7) 137 (7.6) 147 (8.2) 266 (14.8) 1504 (83.7) 557 (31.0) 388 (21.6) 234 (13.0) 36 (2.0)

2% 3% 3% 1% 1% 1% 3% 0% 3% 3% 6% 0% 7% 2%

13

Cataract surgery Computed tomography of the head Computed tomography of the neck Computed tomography of the thorax Computed tomography of the abdomen Computed tomography of the pelvis Computed tomography of the spine Computed tomography of the extremities Chest x-ray Pulmonary function test Electroencephalography At-home physician service Renal function§§ Baseline serum creatinine concentration, median (IQR) µmol/L mg/dL eGFR, median (IQR), mL/min/1.73 m2 ∥∥ eGFR ≥60 mL/min per 1.73 m2 45-59 mL/min per 1.73 m2 30-44 mL/min per 1.73 m2