Anesthesia & Analgesia:
Patient Safety: Research Report
The Association of Preoperative Statin Use and Acute Kidney Injury After Noncardiac Surgery
Argalious, Maged Y. MD, MBA*; Dalton, Jarrod E. MA†‡; Sreenivasalu, Thilak MD§; O’Hara, Jerome MD*; Sessler, Daniel I. MD‡
From the *Department of General Anesthesia, §Anesthesiology Institute, and Departments of †Quantitative Health Science and ‡Outcomes Research, Cleveland Clinic, Cleveland, Ohio.
Accepted for publication November 21, 2012.
Published ahead of print January 25, 2013
Supported by internal funds only.
This report was previously presented, in part, at the ASA meeting in October 2010 in abstract format.
The authors declare no conflicts of interest.
Reprints will not be available from the authors.
Address correspondence to Maged Y. Argalious, MD, MBA, Department of General Anesthesia, Anesthesiology Institute, Cleveland Clinic, 9500 Euclid Ave./G30, Cleveland, OH 44195. Address e-mail to firstname.lastname@example.org.
BACKGROUND: Our objective was to examine the association between preoperative statin therapy and the incidence of postoperative acute kidney injury (AKI) in patients undergoing elective noncardiac surgery.
METHODS: We analyzed the electronic records of 57,246 patients who had elective noncardiac surgery at the Cleveland Clinic Main Campus between December 2004 and March 2010. Patients were divided into 2 groups depending on preoperative therapy with statin drugs. Our primary outcome was AKI, defined as “risk,” “injury,” or “failure” using the RIFLE (Risk, Injury, Failure, Loss, and End-stage Kidney) criteria. Secondary outcomes included postoperative dialysis and all-cause hospital mortality. Each statin user was matched to a nonuser based on propensity scores. The propensity scores were estimated using a multivariable logistic regression model, incorporating all available baseline potential confounders. After the propensity-matching procedure, we performed final analyses for the primary and secondary outcomes. For the primary analysis, we used a univariable logistic regression model to estimate the odds ratio (OR) (and 95% confidence intervals) for AKI, postoperative dialysis, and hospital mortality between matched statin users and nonusers.
RESULTS: Of the total group, 23,745 records were unusable because of missing data. Among the remaining 28,508 patients analyzed, the overall incidence of AKI was 6.1%. Three hundred sixty-one of 4805 statin users (7.5%) and 1377 of 23,703 nonusers (5.8%) experienced AKI. The incidence of postoperative dialysis was 0.05%. Six statin users (0.12%) and 8 nonusers (0.03%) required dialysis postoperatively. The incidence of hospital mortality was 0.62%. Mortality was observed for 47 patients (1.0%) and 130 patients (0.5%), respectively. Among 4172 matched pairs, the incidence (95% confidence interval) of AKI was 7.1% (6.2%, 8.1%) in the matched statin users and 8.0% (7.1%, 9.0%) in the nonusers, corresponding to an OR of 0.88 (0.75, 1.03), which was not statistically significant (P = 0.12, χ2 test). The secondary outcomes were also not significantly different in matched statin users and nonusers. Postoperative dialysis was required for 0.10% (0.02%, 0.33%) and 0.12% (0.04%, 0.37%) of patients in the respective groups (OR = 0.80 [0.16, 3.70]; P = 0.74). Hospital mortality occurred in 1.0% (0.7%, 1.5%) and 1.3% (0.9%, 1.8%) of patients, respectively (OR = 0.76 [0.47, 1.20]; P = 0.18).
CONCLUSIONS: Our data did not support the hypothesis that preoperative statin therapy in doses routinely used to treat hypercholesterolemia is associated with a change in the incidence of AKI, postoperative dialysis, or hospital mortality in patients undergoing noncardiac surgery.
Acute kidney injury (AKI) occurs in 1% to 5% of patients having noncardiac surgery1–3 and contributes to increased hospital mortality.1,4 The predominant mechanism of perioperative AKI is thought to be impaired perfusion; the initial insult seems to be hypoxic, followed by production of reactive oxygen species and activation of inflammatory mechanisms during reperfusion.5
Statins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors) provide effective primary and secondary prevention against cardiovascular events in hyperlipidemic subjects6,7 and in patients with chronic kidney disease not requiring dialysis,8,9 which are independent of lipid lowering.10,11 The vasodilatory, antiinflammatory, and antithrombotic effects of statins are mediated by increased endothelial nitric oxide synthase expression,12 reduced expression of cytokines, chemokines, and adhesion molecules, and decreased C-reactive protein concentrations.13
Statins reduce endothelin secretion and rapidly increase nitric oxide production, thereby increasing flow-mediated vasodilation and endothelial function.14 Statins also scavenge free radicals,15 are antiinflammatory by blocking the infiltration of inflammatory cells and down-regulating the expression of inflammatory mediators, such as interleukin-6 and C-reactive protein,16 and possess antithrombotic properties,17 all of which were likely to be renal protective. As might thus be expected, animal models show that giving statins before an ischemic event significantly reduces AKI.18–22
Compelling mechanistic evidence therefore suggests that statins might reduce postoperative kidney injury after noncardiac surgery. However, the extent to which statin use is associated with renal protection in noncardiac surgery remains unknown. We thus tested the hypothesis that preoperative statin use is independently associated with renal protection in noncardiac surgery patients. Secondarily, we sought to evaluate the relationship between statin use and new-onset postoperative dialysis and all-cause hospital mortality.
Data from 57,246 patients undergoing noncardiac surgery at the Cleveland Clinic between December 8, 2004 and March 8, 2010 (most recent operation per patient) were obtained from the Anesthesia Institute’s Perioperative Health Documentation System database. This database is approved for research by our IRB.
We included patients who had an ASA physical status ≤IV. Patients who had outpatient procedures or emergency surgery were excluded. We also excluded patients who had a previous kidney transplant, who already required renal replacement therapy, and patients with end-stage renal disease, which included those having arteriovenous fistula surgery, nephrectomy of a sole remaining kidney, or a baseline serum creatinine of ≥5.0 mg/dL.
Our primary outcome was AKI based on the RIFLE (Risk, Injury, Failure, Loss, and End-stage Kidney) criteria.23 The RIFLE criteria were used to categorize patients into 3 groups: (1) patients at risk of renal injury (as defined by a maximal postoperative plasma creatinine concentration ≥1.5 mg/dL but less than twice the preoperative concentration); (2) patients with renal injury (maximal postoperative plasma creatinine concentration ≥2 mg/dL but less than 3 times the preoperative concentration); and (3) patients with renal failure (maximal postoperative plasma creatinine concentration >4 mg/dL with an increase of at least 0.5 mg/dL, or maximal preoperative creatinine at least 3 times the preoperative concentration).
The primary exposure of interest was routine preoperative statin use, determined by querying patients’ active medication list in the database for the presence of any commercially available statin drugs. With the exception of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, patients at our institution are normally instructed to take routine medications, including statins, on the morning of surgery.
Propensity scores for statin use were estimated using a multivariable logistic regression model, incorporating all available baseline potential confounders (listed in the top panel of Table 1). Each statin user was matched to a nonuser based on the propensity scores; specifically, we used exact matching on propensity score to within 0.01 units. The data were randomly permuted before matching.
We adjusted for severity of surgery (in terms of risk of postoperative AKI) by estimating for each procedure type the overall incidence of AKI; the resulting measure of procedural risk for AKI was then used as a potential confounder during our propensity-matching analysis. Procedure codes were defined using the United States Agency for Healthcare Research and Quality’s single-level Clinical Classifications Software for International Classification of Diseases, Ninth Revision, Clinical Modification procedure code.a
Balance between statin users and nonusers on the available baseline potential confounders was assessed using standard univariable summary statistics (e.g., means and standard deviations, percentages, or medians and quartiles) and absolute standardized differences. The absolute standardized difference is a useful measure of balance between 2 groups on a covariate, and is equal to the absolute value of the difference in means, mean ranks, or proportions divided by a pooled measure of standard deviation. Thus, the absolute standardized difference approximately represents the number of standard deviations apart 2 groups are on a covariate. An absolute standardized difference value ≥0.1 generally suggests slight imbalance between the groups, which could be potentially confounding depending on the degree of association between the baseline variable in question and the outcome.
After the propensity-matching procedure, we performed final analyses for the primary outcome and the secondary outcomes. For the primary analysis, we used a univariable logistic regression model to estimate the odds ratio (OR) for AKI between matched statin users and nonusers and its associated 95% confidence interval. The null hypothesis of no relationship between statin use and AKI was evaluated using a standard χ2 test; the type I (false-positive) error rate for this primary hypothesis was fixed at 0.05. Similar analyses were performed for the secondary outcomes; however, for these analyses, we used the Bonferroni correction to control the overall type I error rate at 0.05 for the secondary outcomes.
R software version 2.12.1 for Windows (The R Foundation for Statistical Computing, Vienna, Austria) and SAS software version 9.2 for Windows (SAS Institute, Cary, NC) were used for all statistical analyses. Patients were propensity matched using the R package “Matching.”24
Among the 57,246 patients identified through the Perioperative Health Documentation System database, who had noncardiac surgery, 4993 (8.7%) did not meet our inclusion/exclusion criteria (Fig. 1). Statin use, baseline serum creatinine, and/or the AKI outcome were unavailable for 23,745 patients, generally because postoperative serum creatinine analysis was not clinically indicated; thus, 28,508 patients were analyzed. A comparison of baseline characteristics between patients included in the study and patients excluded because of unavailable data on the primary exposure and outcome is provided in Appendix 1.
Baseline serum albumin and body mass index were excluded from the model estimating propensity scores because many values were missing (43% and 30%, respectively). Furthermore, 881 patients with missing values for ≥1 of the other baseline variables included in the model estimating propensity scores were removed. Our propensity-matching procedure yielded successful matches for 4172 (87%) of the statin users; therefore, our analyzed dataset consisted of 8344 propensity-matched patients.
Before matching, statin users were generally older and sicker than nonusers in baseline characteristics (Table 1). After matching, none of the baseline potential confounders exhibited absolute standardized difference scores >0.2 (Fig. 2). Although serum albumin and body mass index were not considered in the matching, they were nonetheless well balanced among the matched patients with available measurements. Thus, the propensity-matching procedure was successful in balancing baseline potential confounders. Intraoperative characteristics such as case duration and fluid volumes, also not used for propensity matching, were nonetheless similar among the matched patients (Table 1).
The incidence (95% confidence interval) of AKI was 7.1% (6.2%, 8.1%) in the matched statin users and 8.0% (7.1%, 9.0%) in the nonusers, corresponding to an OR of 0.88 (0.75, 1.03), which was not statistically significant (P = 0.12, χ2 test).
The secondary outcomes were also not significantly different between matched statin users and nonusers. Postoperative dialysis was required for 0.10% (0.02%, 0.33%) and 0.12% (0.04%, 0.37%) of patients in the matched groups (OR = 0.80 [0.16, 3.70]; P = 0.74). In-hospital mortality occurred in 1.0% (0.7%, 1.5%) and 1.3% (0.9%, 1.8%) of the matched patients, respectively (OR = 0.76 [0.47, 1.20]; P = 0.18). A summary of the incidence of primary and secondary outcomes among statin users and nonusers (both before and after matching) is provided in Table 2.
Our principal finding is that preoperative statin therapy does not seem to be associated with a reduced risk of AKI in patients undergoing noncardiac surgery (although, as our confidence interval for the primary comparison of interest indicates, we could not exclude from possibility a reduction in the odds of AKI as large as 25%). This result is similar to that reported in patients having noncardiac25 and cardiac surgery.26 In contrast, Molnar et al.27 reported an association between preoperative statin use and a reduced risk of AKI in patients having major elective surgery. However, their study cohort was restricted to patients older than 65 years of age. Furthermore, they combined patients having cardiac and noncardiac surgery, although pathophysiologic mechanisms for AKI probably differ.5,28 Most importantly, however, Molnar et al. used AKI diagnostic codes based on the International Classification of Diseases, Ninth Revision classification system, which has only 28% sensitivity for AKI.29
We also found no evidence supporting the idea that preoperative statin use is associated with a reduced incidence of new-onset dialysis or hospital mortality. The same results were reported in a single-center, albeit smaller, retrospective study by Kor et al.25 in patients undergoing major vascular surgery. A recent study associating statin use with a lower incidence of new-onset dialysis also reported an absence of association between statin use and reduced dialysis dependence 90 to 120 days postoperatively, making a significant long-term renoprotective effect of statin drugs in current clinical doses highly unlikely.27
Our study results contrast with those reported by Lindenauer et al.,30 in a large retrospective study cohort based on administrative data from 328 hospitals. In their propensity-matched cohort, hospital mortality after major noncardiac surgery was 2.2% in patients treated with lipid-decreasing medications compared with 3.2% in patients who did not receive lipid-decreasing therapy or in whom treatment was initiated after the second day of hospitalization (P < 0.001). In contrast, this study is a retrospective, single tertiary care center study based on clinical and not administrative data and included only patients receiving statin therapy (and not other lipid-decreasing medications). In addition, all noncardiac procedures were included in this report (and not only major noncardiac surgery), which explains the decrease in hospital mortality reported in this study: 1.0% in the statin propensity-matched group and 1.3% in the no statin group; OR (95% confidence interval): 0.76 (0.47, 1.20) (P = 0.18).
A “healthy user effect” referring to the phenomenon in which patients who adhere to a medication schedule also engage in a healthy lifestyle, resulting in better overall health,31 can explain the reduction in hospital mortality reported by Lindenauer et al. in patients receiving a lipid-decreasing drug. Another explanation is that the sample size of this study was not large enough to detect a difference between the 2 groups. A post hoc sample size calculation suggests that approximately 27,000 patients per group would be necessary to exclude a type II error in assessing the effects of statin therapy on hospital mortality (assuming 90% power and an OR of ≤0.7).
Available evidence suggests that routine preoperative statin use has little if any renoprotective effect.25,26,32 In fact, there are currently no known effective and safe drugs that prevent ischemic renal injury.33–36 Maintaining adequate arterial blood pressure might thus be the best clinical approach at this time.37 It remains possible, however, that current doses of statins, mainly used to treat hypercholesterolemia, are insufficient in attenuating AKI. Consistent with this theory, Verma et al.38 demonstrated attenuation of cell injury with pravastatin, but only at high serum concentrations of the drug.11
Although statin drugs are restarted on the first postoperative day according to an institutional protocol, the possibility of late reinstitution of statin drugs in some patients (because of postoperative ileus or surgical team preference) cannot be excluded and may have potentially masked a protective effect of statins. “Statin withdrawal” can occur when the drugs are discontinued postoperatively, or reinstituted >48 hours after surgery. Statin withdrawal is thought to augment the risk of AKI, new-onset dialysis, and hospital mortality.25 Le Manach et al.39 also reported that postoperative statin withdrawal for at least 4 days is an independent predictor of postoperative cardiac myonecrosis.
Our registry does not include specific statin formulations, doses, or preoperative duration of use. Potency varies among statins and molecules that cause comparable lipid decreasing may differ considerably in their antiinflammatory characteristics and ability to prevent atrial fibrillation and provide other cardiac benefits.40 It thus remains plausible that adequate doses of certain statins could provide substantial protection against AKI, but this will be determined only by focused, prospective, randomized, controlled studies.
As with any observational study, our results are subject to potential selection bias, confounding, and measurement bias.31 In addition, 40% of our patients were excluded because of missing data (Fig. 1).We attempted to limit these sources of error by using propensity matching. Because the Perioperative Health Documentation System is mostly based on clinical (rather than administrative) records, it is fairly dense and includes many known predictors of AKI. Nonetheless, it remains likely that at least some unknown confounders impaired our analysis. Intraoperative hemodynamic variables may have differed between the 2 groups and could have influenced the incidence of AKI.36 We note, however, that important confounding factors mostly increase the likelihood of spurious associations, rather than cause lack of association as we found.
In summary, we did not identify an independent association between preoperative statin therapy at current clinical doses used for treatment of hypercholesterolemia and risk of AKI, postoperative dialysis, or hospital mortality in patients having noncardiac surgery.
Name: Maged Y. Argalious, MD, MBA.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Attestation: Maged Argalious has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.
Name: Jarrod E. Dalton, MA.
Contribution: This author helped analyze the data and write the manuscript.
Attestation: Jarrod Dalton has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Thilak Sreenivasalu, MD.
Contribution: This author helped conduct the study.
Attestation: Thilak Sreenivasalu has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Jerome O’Hara, MD.
Contribution: This author helped conduct the study.
Attestation: Jerome O’Hara has seen the original study data and approved the final manuscript.
Name: Daniel I. Sessler, MD.
Contribution: This author helped write the manuscript.
Attestation: Daniel I. Sessler has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
This manuscript was handled by: Sorin J. Brull, MD, FCARCSI (Hon).
a HCUP Clinical Classifications Software (CCS) for ICD-9-CM. Available at: http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed January 7, 2011. Cited Here...
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