SURGICAL INFECTIONS Volume 17, Number 1, 2016 ª Mary Ann Liebert, Inc. DOI: 10.1089/sur.2014.084
Surgical Infection Society Articles
CAUTIs and CLABSIs: Do Physicians REALLY Know What They Are? There`se M. Duane,1 Rajesh Ramanathan,2 Patricia Leavell,2 Catherine Mays,2 and Janis Ober 2
Abstract
Background: The incidences of hospital-acquired conditions, such as catheter-associated urinary tract infections (CAUTIs) and central line-associated blood stream infections (CLABSIs) are being used to compare quality at institutions and determine reimbursements. These data come from the University HealthSystem Consortium (UHC) administrative database that relies almost exclusively on physician documentation as opposed to objective U.S. Centers for Disease Control and Prevention (CDC) guidelines. We hypothesize that the UHC-identified rates of CAUTIs and CLABSIs are inaccurate compared with the CDC definitions for these infections. Methods: We performed a retrospective study from January 2012 through September 2013 comparing the incidences of CLABSIs and CAUTIs, as identified through our UHC database to those identified by the Department of Epidemiology using strict CDC guidelines. We performed subset analysis on those infections identified by UHC but not CDC to determine the causes for these discrepancies. Results: There were a total of 221 CAUTIs and 238 CLABSIs identified during this time frame. Of these, 16 CAUTIs (7.2%) and 44 (18.5%) CLABSIs were detected by both UHC and CDC. 72.4% (42/58) of the CAUTIs and 52.7% (49/93) of the CLABSIs identified by UHC were not identified by CDC. 91% (163/179) of the CAUTIs and 77% (145/189) of the CLABSIs identified by CDC were not identified by UHC. The cause of these differences in identification included lack of culture data, lack of positive cultures, and catheters present on admission. Conclusions: There is a major disconnect between identification of infections depending on what process is used. This can lead to inappropriate treatment and inaccurate institutional comparisons that impact reimbursements. Because UHC identification of infections are primarily based on physician documentation, educating providers should result in more accurate recognition of infections thereby ensuring appropriate use of therapy.
H
There is a major disconnect between the definitions of these infections based on whether the UHC method or the CDC criteria is used. This disparity can lead to inaccurate institutional comparisons, which impacts reimbursements. The most recent iteration from the CMS puts 1% of total hospital Medicare reimbursements at risk for ‘‘low performing institutions’’ through its HAC reduction program. Such a penalty could result in millions of lost dollars to an institution because of potentially inaccurate representations of HAC rates. Hence it is of vital importance to ensure that such HACs are appropriately defined. We hypothesize that the UHC defined rates of HACs are inaccurate compared with the CDC definition to determine these infections.
ospital acquired conditions (HACs), specifically central line-associated blood stream infections and catheter-associated urinary tract infections, are major problems for our healthcare system, substantially increasing morbidity and mortality for patients and overall costs for the institution [1]. Considered preventable, they are being used by the federal Agency for Healthcare Research and Quality (AHRQ) and Center for Medicare and Medicaid Services (CMS) to compare institutions and determine reimbursements. These data come from the University HealthSystem Consortium (UHC) administrative database that relies heavily on subjective provider documentation, as opposed to objective criteria defined by the U.S. Centers for Disease Control and Prevention (CDC) [2]. 1
John Peter Smith Health System, Ft. Worth, Texas. Virginia Commonwealth University Medical Center, Richmond, Virginia. Presented at the Thirty-fourth Annual Meeting of the Surgical Infection Society, Baltimore, Maryland, May 1–3, 2014.
2
13
14 Patients and Methods
We performed a retrospective study from January 2012 through September 2013 to investigate the incidences of two hospital-acquired conditions (HACs): Central line-associated blood stream infections and catheter-associated urinary tract infections. We compared the number of these infections identified through the UHC administrative database to the total identified by the Department of Epidemiology (CDC). Agreement between HACs identified by the Department of Epidemiology and UHC was quantified through calculation of the kappa statistic. HACs identified by the Department of Epidemiology were considered true disease and used as reference for calculating the diagnostic accuracy of HACs identified by UHC. We performed a subset analysis to evaluate those HACs identified by UHC but not CDC to determine the reasons for the misidentifications. This study was approved by the Virginia Commonwealth University IRB. The UHC data comes from diagnostic and procedural codes associated with HACs and patient safety indicators (PSI). These parameters are set by AHRQ and CMS. At our institution, medical coders review all medical documentation in a patient’s record and assign the appropriate diagnosis and procedure codes. In addition, the institutional 3M coding software has triggers in place to alert the coders to the presence of both HACs and PSIs. Once the coders complete the coding for a patient encounter, a clinical documentation nurse, coding manager, and clinical reviewer, prior to the encounter being submitted to UHC for analysis, then review the record. The Department of Epidemiology follows patient microbial culture data daily to identify hospital acquired infections. In addition, infection control nurses track urinary and vascular catheter use throughout the institution, and record timing of placement and removal. In the setting of positive cultures, further investigation is performed to determine if the infection was present on admission, based on positive cultures present within two 2 d of admission, or if the catheterassociated infection is secondary to an infection at another site. If these exclusionary criteria do not exist, designation of a catheter-associated infection may be made using strict infection site-specific CDC guidelines related to the clinical signs and laboratory data [2]. Results
There were a total of 459 infections identified between both UHC and CDC methods. Of these, 221 were CAUTIs and 238 were CLABSIs. Out of the 221 CAUTIs, 16 (7.2%) were detected by both UHC and CDC methods. Forty-four (18.5%) of the 238 CLABSIs were identified by both UHC and CDC approaches. Using the CDC definition, more than twice as many infections were documented than were found with the UHC method (368 vs. 151). The CDC method identified 179 CAUTIs and 189 CLABSIs. Of the 179 CDCidentified CAUTIs, 91% were not identified by UHC. Of the 189 CDC-identified CLABSIs, 77% were not identified by UHC. The kappa statistic for agreement between UHC and CDC methods was poor for CAUTIs (kappa: 0.13) and CLABSIs (kappa: 0.31) as indicated by kappa scores below 0.4. Table 1 shows the incidence of HACs based on the identification method used.
DUANE ET AL.
Table 1. Incidence of Infections Based on Identification Methods
CAUTI (n = 221) Identified on both reports Identified only CDC Identified only by UHC Total CLABSI (n = 238) Identified on both reports Identified only by CDC Identified only by UHC Total Total of both HACs
UHC
CDC
Percentage
16
16 163
7.2% 73.8% 19.0% 100.0%
42 58 44 49 93 151
179 44 145
18.5% 60.9% 20.6% 100 %
189 368
CAUTI = catheter-associated urinary tract infections; CDC = U.S. Centers for Disease Control; CLABSI = central line-associated blood stream infections; UHC = University HealthSystem Consortium; HACS = hospital acquired conditions.
Using CDC-identified HACs as the marker of true disease, the diagnostic accuracy of UHC identification of CAUTIs and CLABSIs was investigated. UHC-identification of CAUTIs and CLABSIs was associated with poor sensitivity (8.9% and 23.7% respectively) and high specificity (99.9% and 99.9% respectively). Table 2 displays the diagnostic accuracy of UHC identification. The UHC method identified a total of 151 infections: 58 CAUTIs and 93 CLABSIs. 72.4% (42/58) of the CAUTIs identified by UHC were not identified by CDC. 52.7% (49/ 93) of the CLABSIs identified by UHC were not identified by CDC. The reasons for infections identified by UHC but not CDC included lack of culture data obtained, lack of culturepositivity, and infections because of catheters that were present on admission. These findings are shown in Table 3.
Table 2. Diagnostic Accuracy of UHC Identification of CAUTI and CLABSI
Total sample Incidence (per thousand encounters) UHC+/CDC+ (True positive) UHC+/CDC(False positive) UHC-/CDC+ (False negative) UHC-/CDC(True negative) Sensitivity (95% CI) Specificity (95% CI) Positive predictive value (95% CI) Negative predictive value (95% CI) Disease prevalence (95% CI)
CAUTI
CLABSI
48,234 221 (4.6)
48,234 238 (4.9)
16
44
42
49
163
145
48013
47996
8.9% ( 5.2–14.1) 23.7% (17.8–30.4) 99.9% (99.9–99.9) 99.9% (99.9–99.9) 27.6% (16.7–40.9) 47.9% (37.5–58.4) 99.7% (99.6–99.7) 99.7% (99.7–99.8) 0.4% ( 0.3– 0.4)
0.4% ( 0.4– 0.5)
CAUTI = catheter-associated urinary tract infections; CDC = U.S. Centers for Disease Control; CLABSI = central line-associated blood stream infections; UHC = University HealthSystem Consortium; CI = confidence interval.
CAUTIS AND CLABSIS
15
Table 3. Reasons for UHC Misidentification of HACs
Catheter present on admission Cultures not drawn Negative cultures Did not fit criteria Correct identification
CAUTI (n = 42)
CLABSI (n = 49)
4 2 2 9+ 25
8 10 10 6* 15
*Contamination with coagulase-negative staphylococcus species + Insufficient CFU identified, >2 organisms identified, or culture >48 h after catheter removal CAUTI = catheter-associated urinary tract infections; CDC = U.S. Centers for Disease Control; CLABSI = central line-associated blood stream infections; UHC = University HealthSystem Consortium; HACs = hospital acquired conditions.
Discussion
Hospital acquired conditions, in particular catheterassociated infections of the blood and urine, have a major detrimental impact on the health care system [1]. Morbidity and mortality from such conditions directly compromise patient outcomes and their effects are well documented. As a result of such infections, hospital lengths of stay are prolonged, further laboratory and radiographic investigations are increased, and overall costs increase [3,4]. In an effort to characterize these implications, the Centers for Medicaid and Medicare now track closely the incidence of these diseases. Closer observation by regulatory and payer agencies began in 2008 when the concept of HACs were introduced. At that time, individual claims were reduced if the HAC resulted in assignment of a patient to a greater Diagnosis-Related Group (DRG). As the importance of HACs and additionally patient safety indicators (PSI) increased, the financial consequences became greater. In 2014, PSI 90, which combines HACs with other indicators, was added to the hospital value based purchasing program that places 1–2% of hospital Medicare payments at risk. As of 2015, the HAC reduction program will be fully implemented, after which Medicare will reduce hospital payments by 1% for those hospitals ranked among the lowest performing quartile with regard to hospital acquired conditions. Therefore, if physicians and other health care providers do not actually understand what the definitions of these infections are and document accurately, millions of dollars may be lost by the institution. On the basis of our results, there is a profound inconsistency between the more subjective documentation-based UHC identification and the more objective CDC criteriabased identification. The UHC definition relies on provider documentation such that simply stating in the medical record that a patient has a CAUTI or CLABSI will result in a HAC regardless of consideration of culture data, timing of catheter placement or duration between catheterization and infectiononset. Despite the rigor involved in optimizing the coding, the process is flawed as it begins with the subjective documentation. Consequently, there is great disparity in the overall number of infections identified, with many more identified through the CDC approach. Such disparities are found throughout the literature [5–7]. Our kappa statistic was below 0.4 for both CAUTIs and CLABSIs indicating poor
agreement between the two identification methods, mirroring other findings of discordance between administrative database extractions and gold-standard chart review [8]. Furthermore, Cass et al. noted a 0% and 21% sensitivity for administrative databases compared with CDC for CAUTI and CLABSI detection, respectively [9]. Our findings confirmed the low sensitivity and positive predictive value associated with UHC identification, thereby making it a poor screening test. The more concerning fact is that so few of the UHC identified patients correlated with those identified by the CDC method. This finding has serious ramifications. First, it may result in patients without true infections, as defined through culture positivity, being treated with antimicrobial agents that they do not need, and secondly, it would have a deleterious effect on the institution’s profile, thus increasing the likelihood of a financial penalty. As shown in the tables, 26% of the UHC-identified CAUTIs and 53% of the UHC-identified CLABSIs may not have required any antimicrobial therapy given that they either had no positive cultures, contaminants-only, or never had cultures drawn, thus making the diagnosis suspect. However, as a result of providers documenting these infections in the chart, a substantial amount of patients likely received unnecessary antimicrobial therapy. Strong antimicrobial stewardship programs embrace appropriate therapy and minimization of duration along with de-escalation to improve patient outcomes, and such findings would be inconsistent with this practice [10,11]. It is also important to note that almost 60% of the UHCidentified CAUTIs and 31% of the UHC-identified CLABSIs were considered true positives when the charts were reviewed. Upon further investigation, the majority of these were linked to location and timing of catheter placement, whereby many of the ‘missed infections’ were because of catheters placed in the operating room and thus not having the associated catheter-placement documentation in the medical record. Similarly for the CLABSIs, peripherally inserted central catheter (PICC) documentation at our institution varies and this may have contributed to false negatives in the CDC cohort. These findings emphasize the inherent challenges with the process of infection identification. Given the limitations of both of these approaches, it is vital for providers to have a clear understanding of what defines a hospital-acquired infection so as to appropriately treat patients and document accurately. As the HAC reduction program progresses, changes are being made to account for some of the inadequacies. Information to assist with comparison scores will be coming from two domains: One will rely primarily on the PSI 90 that combines a number of patient safety indicators as defined by UHC, and the other domain will utilize the CDC National Healthcare Safety Network (NHSN) to incorporate more objective data [12]. This development has future accuracy implications as surgical site infections will also be measured as part of the reduction program in 2016. This study is limited by the fact that we do not have data on why the CDC identified so many more infections than UHC. Moreover, we cannot be sure whether some of these patients were under-treated as well. Finally, improving accuracy in defining these infections would have resulted in a substantial increase in UHC HACs given the much greater number of CDC defined infections. This improvement, in turn, would lower our institutional rating and jeopardize more funds such
16
that it may not be to our institution’s financial benefit. However, our goal was not necessarily to improve our financial standing but to ensure that we, along with other institutions, are being measured fairly. In conclusion, health care providers need to have a better understanding of what defines hospital acquired conditions, as ignorance is certainly not bliss in this situation. The accuracy of identification and documentation has implications for direct patient care and long-term institutional financial viability. Investment in strong infection prevention and epidemiology expertise is critical to the success of these efforts. Ongoing education of providers to understand these definitions as well as document appropriately should improve this situation and prepare all of us for the changes that are on the horizon.
DUANE ET AL.
5.
6.
7.
8.
Acknowledgments
The authors would like to thank the Department of Epidemiology at the Virginia Commonwealth University for their assistance with the data collection. Author Disclosure Statement
No author has any commercial associations that would create a conflict of interest in the connection with this manuscript. References
1. Klevens RM, Edwards JR, Richards CL Jr, et al. Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122:160–166. 2. July 2013 CDC/NHSN Protocol Clarifications. 2014. Available from http://www.cdc.gov/nhsn/pdfs/validation/2013/ pscmanual_july2013.pdf Accessed 4/27/2014. 3. Yi SH, Baggs J, Gould CV, et al. Medicare reimbursement attributable to catheter-associated urinary tract infection in the inpatient setting: A retrospective cohort analysis. Med Care 2014;52:469–478. 4. Stevens V, Geiger K, Concannon C, et al. Inpatient costs, mortality and 30-day re-admission in patients with central-
9. 10. 11.
12.
line-associated bloodstream infections. Clin Microbiol Infect 2013;320:O318–O324. Sherman ER, Heydon KH, St John KH, et al. Administrative data fail to accurately identify cases of healthcareassociated infection. Infect Control Hosp Epidemiol 2006;27:332–337. Stevenson KB, Khan Y, Dickman J, et al. Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections. Am J Infect Control 2008;36:155–164. Wright SB, Huskins WC, Dokholyan RS, et al. Administrative databases provide inaccurate data for surveillance of long-term central venous catheter-associated infections. Infect Control Hosp Epidemiol 2003;24:946–949. Lawson EH, Louie R, Zingmond DS, et al. A comparison of clinical registry versus administrative claims data for reporting of 30-day surgical complications. Ann Surg 2012;256: 973–981. Cass AL, Kelly JW, Probst JC, et al. Identification of device-associated infections utilizing administrative data. Am J Infect Control 2013;41:1195–1199. Bartlett JG. A call to arms: The imperative for antimicrobial stewardship. Clin Infect Dis 2011;53:S4–S7. Dellit TH, Owens RC, McGowan JE Jr, et al. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis 2007;44:159–177. National Healthcare Safety Network (NHSN) of the Centers for Disease Control and Prevention. 2014. Available from http://www.cdc.gov/nhsn/enrolled-facilities/index.html Accessed 4/28/2014.
Address correspondence to: Dr. There`se M. Duane 1500 S. Main Street, OPC Suite 303 Ft. Worth, TX 76104 E-mail: [email protected]
Surgical Infection Society Articles
CAUTIs and CLABSIs: Do Physicians REALLY Know What They Are? There`se M. Duane,1 Rajesh Ramanathan,2 Patricia Leavell,2 Catherine Mays,2 and Janis Ober 2
Abstract
Background: The incidences of hospital-acquired conditions, such as catheter-associated urinary tract infections (CAUTIs) and central line-associated blood stream infections (CLABSIs) are being used to compare quality at institutions and determine reimbursements. These data come from the University HealthSystem Consortium (UHC) administrative database that relies almost exclusively on physician documentation as opposed to objective U.S. Centers for Disease Control and Prevention (CDC) guidelines. We hypothesize that the UHC-identified rates of CAUTIs and CLABSIs are inaccurate compared with the CDC definitions for these infections. Methods: We performed a retrospective study from January 2012 through September 2013 comparing the incidences of CLABSIs and CAUTIs, as identified through our UHC database to those identified by the Department of Epidemiology using strict CDC guidelines. We performed subset analysis on those infections identified by UHC but not CDC to determine the causes for these discrepancies. Results: There were a total of 221 CAUTIs and 238 CLABSIs identified during this time frame. Of these, 16 CAUTIs (7.2%) and 44 (18.5%) CLABSIs were detected by both UHC and CDC. 72.4% (42/58) of the CAUTIs and 52.7% (49/93) of the CLABSIs identified by UHC were not identified by CDC. 91% (163/179) of the CAUTIs and 77% (145/189) of the CLABSIs identified by CDC were not identified by UHC. The cause of these differences in identification included lack of culture data, lack of positive cultures, and catheters present on admission. Conclusions: There is a major disconnect between identification of infections depending on what process is used. This can lead to inappropriate treatment and inaccurate institutional comparisons that impact reimbursements. Because UHC identification of infections are primarily based on physician documentation, educating providers should result in more accurate recognition of infections thereby ensuring appropriate use of therapy.
H
There is a major disconnect between the definitions of these infections based on whether the UHC method or the CDC criteria is used. This disparity can lead to inaccurate institutional comparisons, which impacts reimbursements. The most recent iteration from the CMS puts 1% of total hospital Medicare reimbursements at risk for ‘‘low performing institutions’’ through its HAC reduction program. Such a penalty could result in millions of lost dollars to an institution because of potentially inaccurate representations of HAC rates. Hence it is of vital importance to ensure that such HACs are appropriately defined. We hypothesize that the UHC defined rates of HACs are inaccurate compared with the CDC definition to determine these infections.
ospital acquired conditions (HACs), specifically central line-associated blood stream infections and catheter-associated urinary tract infections, are major problems for our healthcare system, substantially increasing morbidity and mortality for patients and overall costs for the institution [1]. Considered preventable, they are being used by the federal Agency for Healthcare Research and Quality (AHRQ) and Center for Medicare and Medicaid Services (CMS) to compare institutions and determine reimbursements. These data come from the University HealthSystem Consortium (UHC) administrative database that relies heavily on subjective provider documentation, as opposed to objective criteria defined by the U.S. Centers for Disease Control and Prevention (CDC) [2]. 1
John Peter Smith Health System, Ft. Worth, Texas. Virginia Commonwealth University Medical Center, Richmond, Virginia. Presented at the Thirty-fourth Annual Meeting of the Surgical Infection Society, Baltimore, Maryland, May 1–3, 2014.
2
13
14 Patients and Methods
We performed a retrospective study from January 2012 through September 2013 to investigate the incidences of two hospital-acquired conditions (HACs): Central line-associated blood stream infections and catheter-associated urinary tract infections. We compared the number of these infections identified through the UHC administrative database to the total identified by the Department of Epidemiology (CDC). Agreement between HACs identified by the Department of Epidemiology and UHC was quantified through calculation of the kappa statistic. HACs identified by the Department of Epidemiology were considered true disease and used as reference for calculating the diagnostic accuracy of HACs identified by UHC. We performed a subset analysis to evaluate those HACs identified by UHC but not CDC to determine the reasons for the misidentifications. This study was approved by the Virginia Commonwealth University IRB. The UHC data comes from diagnostic and procedural codes associated with HACs and patient safety indicators (PSI). These parameters are set by AHRQ and CMS. At our institution, medical coders review all medical documentation in a patient’s record and assign the appropriate diagnosis and procedure codes. In addition, the institutional 3M coding software has triggers in place to alert the coders to the presence of both HACs and PSIs. Once the coders complete the coding for a patient encounter, a clinical documentation nurse, coding manager, and clinical reviewer, prior to the encounter being submitted to UHC for analysis, then review the record. The Department of Epidemiology follows patient microbial culture data daily to identify hospital acquired infections. In addition, infection control nurses track urinary and vascular catheter use throughout the institution, and record timing of placement and removal. In the setting of positive cultures, further investigation is performed to determine if the infection was present on admission, based on positive cultures present within two 2 d of admission, or if the catheterassociated infection is secondary to an infection at another site. If these exclusionary criteria do not exist, designation of a catheter-associated infection may be made using strict infection site-specific CDC guidelines related to the clinical signs and laboratory data [2]. Results
There were a total of 459 infections identified between both UHC and CDC methods. Of these, 221 were CAUTIs and 238 were CLABSIs. Out of the 221 CAUTIs, 16 (7.2%) were detected by both UHC and CDC methods. Forty-four (18.5%) of the 238 CLABSIs were identified by both UHC and CDC approaches. Using the CDC definition, more than twice as many infections were documented than were found with the UHC method (368 vs. 151). The CDC method identified 179 CAUTIs and 189 CLABSIs. Of the 179 CDCidentified CAUTIs, 91% were not identified by UHC. Of the 189 CDC-identified CLABSIs, 77% were not identified by UHC. The kappa statistic for agreement between UHC and CDC methods was poor for CAUTIs (kappa: 0.13) and CLABSIs (kappa: 0.31) as indicated by kappa scores below 0.4. Table 1 shows the incidence of HACs based on the identification method used.
DUANE ET AL.
Table 1. Incidence of Infections Based on Identification Methods
CAUTI (n = 221) Identified on both reports Identified only CDC Identified only by UHC Total CLABSI (n = 238) Identified on both reports Identified only by CDC Identified only by UHC Total Total of both HACs
UHC
CDC
Percentage
16
16 163
7.2% 73.8% 19.0% 100.0%
42 58 44 49 93 151
179 44 145
18.5% 60.9% 20.6% 100 %
189 368
CAUTI = catheter-associated urinary tract infections; CDC = U.S. Centers for Disease Control; CLABSI = central line-associated blood stream infections; UHC = University HealthSystem Consortium; HACS = hospital acquired conditions.
Using CDC-identified HACs as the marker of true disease, the diagnostic accuracy of UHC identification of CAUTIs and CLABSIs was investigated. UHC-identification of CAUTIs and CLABSIs was associated with poor sensitivity (8.9% and 23.7% respectively) and high specificity (99.9% and 99.9% respectively). Table 2 displays the diagnostic accuracy of UHC identification. The UHC method identified a total of 151 infections: 58 CAUTIs and 93 CLABSIs. 72.4% (42/58) of the CAUTIs identified by UHC were not identified by CDC. 52.7% (49/ 93) of the CLABSIs identified by UHC were not identified by CDC. The reasons for infections identified by UHC but not CDC included lack of culture data obtained, lack of culturepositivity, and infections because of catheters that were present on admission. These findings are shown in Table 3.
Table 2. Diagnostic Accuracy of UHC Identification of CAUTI and CLABSI
Total sample Incidence (per thousand encounters) UHC+/CDC+ (True positive) UHC+/CDC(False positive) UHC-/CDC+ (False negative) UHC-/CDC(True negative) Sensitivity (95% CI) Specificity (95% CI) Positive predictive value (95% CI) Negative predictive value (95% CI) Disease prevalence (95% CI)
CAUTI
CLABSI
48,234 221 (4.6)
48,234 238 (4.9)
16
44
42
49
163
145
48013
47996
8.9% ( 5.2–14.1) 23.7% (17.8–30.4) 99.9% (99.9–99.9) 99.9% (99.9–99.9) 27.6% (16.7–40.9) 47.9% (37.5–58.4) 99.7% (99.6–99.7) 99.7% (99.7–99.8) 0.4% ( 0.3– 0.4)
0.4% ( 0.4– 0.5)
CAUTI = catheter-associated urinary tract infections; CDC = U.S. Centers for Disease Control; CLABSI = central line-associated blood stream infections; UHC = University HealthSystem Consortium; CI = confidence interval.
CAUTIS AND CLABSIS
15
Table 3. Reasons for UHC Misidentification of HACs
Catheter present on admission Cultures not drawn Negative cultures Did not fit criteria Correct identification
CAUTI (n = 42)
CLABSI (n = 49)
4 2 2 9+ 25
8 10 10 6* 15
*Contamination with coagulase-negative staphylococcus species + Insufficient CFU identified, >2 organisms identified, or culture >48 h after catheter removal CAUTI = catheter-associated urinary tract infections; CDC = U.S. Centers for Disease Control; CLABSI = central line-associated blood stream infections; UHC = University HealthSystem Consortium; HACs = hospital acquired conditions.
Discussion
Hospital acquired conditions, in particular catheterassociated infections of the blood and urine, have a major detrimental impact on the health care system [1]. Morbidity and mortality from such conditions directly compromise patient outcomes and their effects are well documented. As a result of such infections, hospital lengths of stay are prolonged, further laboratory and radiographic investigations are increased, and overall costs increase [3,4]. In an effort to characterize these implications, the Centers for Medicaid and Medicare now track closely the incidence of these diseases. Closer observation by regulatory and payer agencies began in 2008 when the concept of HACs were introduced. At that time, individual claims were reduced if the HAC resulted in assignment of a patient to a greater Diagnosis-Related Group (DRG). As the importance of HACs and additionally patient safety indicators (PSI) increased, the financial consequences became greater. In 2014, PSI 90, which combines HACs with other indicators, was added to the hospital value based purchasing program that places 1–2% of hospital Medicare payments at risk. As of 2015, the HAC reduction program will be fully implemented, after which Medicare will reduce hospital payments by 1% for those hospitals ranked among the lowest performing quartile with regard to hospital acquired conditions. Therefore, if physicians and other health care providers do not actually understand what the definitions of these infections are and document accurately, millions of dollars may be lost by the institution. On the basis of our results, there is a profound inconsistency between the more subjective documentation-based UHC identification and the more objective CDC criteriabased identification. The UHC definition relies on provider documentation such that simply stating in the medical record that a patient has a CAUTI or CLABSI will result in a HAC regardless of consideration of culture data, timing of catheter placement or duration between catheterization and infectiononset. Despite the rigor involved in optimizing the coding, the process is flawed as it begins with the subjective documentation. Consequently, there is great disparity in the overall number of infections identified, with many more identified through the CDC approach. Such disparities are found throughout the literature [5–7]. Our kappa statistic was below 0.4 for both CAUTIs and CLABSIs indicating poor
agreement between the two identification methods, mirroring other findings of discordance between administrative database extractions and gold-standard chart review [8]. Furthermore, Cass et al. noted a 0% and 21% sensitivity for administrative databases compared with CDC for CAUTI and CLABSI detection, respectively [9]. Our findings confirmed the low sensitivity and positive predictive value associated with UHC identification, thereby making it a poor screening test. The more concerning fact is that so few of the UHC identified patients correlated with those identified by the CDC method. This finding has serious ramifications. First, it may result in patients without true infections, as defined through culture positivity, being treated with antimicrobial agents that they do not need, and secondly, it would have a deleterious effect on the institution’s profile, thus increasing the likelihood of a financial penalty. As shown in the tables, 26% of the UHC-identified CAUTIs and 53% of the UHC-identified CLABSIs may not have required any antimicrobial therapy given that they either had no positive cultures, contaminants-only, or never had cultures drawn, thus making the diagnosis suspect. However, as a result of providers documenting these infections in the chart, a substantial amount of patients likely received unnecessary antimicrobial therapy. Strong antimicrobial stewardship programs embrace appropriate therapy and minimization of duration along with de-escalation to improve patient outcomes, and such findings would be inconsistent with this practice [10,11]. It is also important to note that almost 60% of the UHCidentified CAUTIs and 31% of the UHC-identified CLABSIs were considered true positives when the charts were reviewed. Upon further investigation, the majority of these were linked to location and timing of catheter placement, whereby many of the ‘missed infections’ were because of catheters placed in the operating room and thus not having the associated catheter-placement documentation in the medical record. Similarly for the CLABSIs, peripherally inserted central catheter (PICC) documentation at our institution varies and this may have contributed to false negatives in the CDC cohort. These findings emphasize the inherent challenges with the process of infection identification. Given the limitations of both of these approaches, it is vital for providers to have a clear understanding of what defines a hospital-acquired infection so as to appropriately treat patients and document accurately. As the HAC reduction program progresses, changes are being made to account for some of the inadequacies. Information to assist with comparison scores will be coming from two domains: One will rely primarily on the PSI 90 that combines a number of patient safety indicators as defined by UHC, and the other domain will utilize the CDC National Healthcare Safety Network (NHSN) to incorporate more objective data [12]. This development has future accuracy implications as surgical site infections will also be measured as part of the reduction program in 2016. This study is limited by the fact that we do not have data on why the CDC identified so many more infections than UHC. Moreover, we cannot be sure whether some of these patients were under-treated as well. Finally, improving accuracy in defining these infections would have resulted in a substantial increase in UHC HACs given the much greater number of CDC defined infections. This improvement, in turn, would lower our institutional rating and jeopardize more funds such
16
that it may not be to our institution’s financial benefit. However, our goal was not necessarily to improve our financial standing but to ensure that we, along with other institutions, are being measured fairly. In conclusion, health care providers need to have a better understanding of what defines hospital acquired conditions, as ignorance is certainly not bliss in this situation. The accuracy of identification and documentation has implications for direct patient care and long-term institutional financial viability. Investment in strong infection prevention and epidemiology expertise is critical to the success of these efforts. Ongoing education of providers to understand these definitions as well as document appropriately should improve this situation and prepare all of us for the changes that are on the horizon.
DUANE ET AL.
5.
6.
7.
8.
Acknowledgments
The authors would like to thank the Department of Epidemiology at the Virginia Commonwealth University for their assistance with the data collection. Author Disclosure Statement
No author has any commercial associations that would create a conflict of interest in the connection with this manuscript. References
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