CAP Laboratory Improvement Programs. Delta Check Practices and Outcomes

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1 CAP Laoratory Improvement Programs Delta Check Practices and Outcomes A Q-Proes Study Involving 49 Health Care Facilities and 6541 Delta Check Alerts Ron B. Schifman, MD; Michael Talert, MD; Rhona J. Souers, MS Context. Delta checks serve as a patient-ased quality control tool to detect testing prolems. Ojective. To evaluate delta check practices and outcomes. Design. Q-Proes participants provided information aout delta check policies and procedures. Information aout investigations, prolems, and corrective actions was prospectively collected for up to 100 testing episodes involving delta check alerts. Results. Among 4505 testing episodes involving 6541 delta check alerts, the median frequencies of actions taken among 49 laoratories were clinical review, 38.0%; retest, 25.0%, or recheck, 20.2%; current specimen, nothing, 15.4%; analytical check, 5.0%; other; 2%; and retest or check previous specimen, 0%. Rates of any action taken y analyte ranged from 84 of 179 (46.9%) for glucose to 748 of 868 (86.2%) for hemogloin and potassium. Among 4505 testing episodes, nontesting prolems included physiologic causes (1472; 32.7%); treatment causes (1318; 19.2%); and transfusion causes (846; 9.9%). Testing prolems included 77 interference (1.7%), 62 contamination (1.4%), 51 clotting (1.1%), 27 other (0.6%), 12 mislaeling (0.3%), and 5 analytical (0.1%). Testing prolems y analyte ranged from 13 of 457 (2.8%) for lood urea nitrogen to 12 of 46 (26.1%) for mean corpuscular hemogloin concentration. Using more delta check analytes was associated with detecting more testing prolems (P ¼.04). More delta check alerts per testing episode resulted in more actions taken (P ¼.001) and more prolems identified (P,.001). The most common outcome among 4500 testing episodes was reporting results without modifications or comments in 2512 (55.8%); results were not reported in 136 (3.0%). Conclusions. Actions taken in response to delta check alerts varied widely, and most testing prolems detected were preanalytical. Using a higher numer of different analytes and evaluating previous specimens may improve delta check practices. (Arch Pathol La Med. 2017;141: ; doi: / arpa cp) The delta check is a measurement of the difference etween a patient s sequential test results. A largerthan-expected interval change in results may indicate a testing prolem associated with either the former or the current specimen and prompt an investigation efore results are reported. Delta checks are widely used in clinical laoratories as a patient-ased quality assessment tool to detect errors associated with either specimen collection, analysis, or reporting prolems and provides a safety net for identifying testing errors that might otherwise go unnoticed. 1,2 Delta checks are also an important component of autoverification procedures that improve laoratory efficiency. 3 However, delta checks have some limitations. A narrow group of analytes that have low physiological variation are typically selected to reduce the proportion of false alerts. Delta checks are also primarily restricted to only tests that tend to e frequently repeated during relatively short periods of time. As a result, delta checks are principally confined to hospital or other settings (eg, dialysis) in which patients are repeatedly tested. Investigation of potential testing errors triggered y a delta check alert requires extra work for laoratory staff and may delay reporting results. 4 Because a large proportion of delta check alerts are not associated with any identified error, 1,2,5 it is important to select analytes and set parameters that are as effective as possile for detecting testing prolems. Although several studies have investigated the use of delta checks for identifying mislaeled specimens, 1,4,6 information is lacking aout routine delta check practices such as the types of tests commonly used, calculation methods, and sensitivity for detecting prolems other than laeling errors. This Q-Proes study provides new information for understanding delta check practices, the effectiveness of delta check alerts for detecting prolems, and the effects of delta check alerts on testing outcomes among a diverse group of clinical laoratories. Accepted for pulication Septemer 15, Pulished as an Early Online Release April 12, From the Diagnostics Department, Southern Arizona VA Healthcare System, Tuscon (Dr Schifman); the Department of Pathology, University of Arizona, Tucson (Dr Schifman); the Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City (Dr Talert); and the Surveys Department, College of American Pathologists, Northfield, Illinois (Ms Souers). The authors have no relevant financial interest in the products or companies descried in this article. METHODS Reprints: Ron B. Schifman, MD, Southern Arizona VA Healthcare System, 3601 S 6th Ave, Tucson, AZ ( ronald. This oservational study was conducted in 2014 according to the schifman@va.gov). Q-Proes format as previously descried, which involved laora- Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al 813

2 Tale 1. Worksheet Categories for Actions, Prolems, and Outcomes Used y Participants for Collecting Information Aout Delta Check Alerts Actions taken None taken (current or previous specimens) Repeat testing (current or previous specimens) Testing repeated from primary tue Testing repeated from original aliquot Testing repeated from new aliquot Performed additional (nonordered) tests Checked for ABO discrepancy Specimen check (current or previous specimens) Check for hemolysis, lipemia, icterus Check for clot Check for primary tue mislaeling Check for aliquot mislaeling Analytical check (current specimen) Checked other results on same run Checked quality control Retested quality control Retested specimen after recaliration Retested after instrument trouleshooting Clinical review Checked lood transfusion history Reviewed medical record Contacted clinical personnel for more information Other actions Checked LIS or interface Rechecked calculations Checked for discrepancies with other laoratory sections Reviewed with technical supervisor Reviewed with pathologist/laoratory director Request new specimen Checked for other preanalytical prolem(s) Checked for other analytical prolem(s) Checked for other postanalytical prolem(s) Test prolem(s) suspected or confirmed (current or previous specimens) Prolem Analytical measurement error Calculation error Clotted specimen Data entry error Clerical error, other than data entry Interface/data transmission error Delay in processing Delay in transport Hemolysis, lipemia, icterus Improper handling (temperature, light) Improper processing (dilution, aliquot) Mislaeled aliquot Mislaeled specimen Specimen contamination (eg, IV fluid) Wrong specimen container Other prolem (descrie elow) No prolem identified Cause for delta check is unexplained Delta check caused y physiologic condition Delta check caused lood transfusion No testing prolem, other cause not listed for delta check Areviations: IV, intravenous; LIS, laoratory information system. tories that suscrie to and participate in the program. 7 Instructions and data collection materials were provided and were returned to the College of American Pathologists y a specified time. After data analysis, suscriers received an individual detailed report aout their performance on quality indicators enchmarked against other participants. The range of performance among participants, summary analysis of significant associations with demographics and practice variales, and a critique report were sent to each participating institution. Participants were instructed to prospectively collect delta check alerts for up to 60 days or 100 testing events, whichever occurred first. Delta checks on results from point-of-care tests or from ody fluid specimens were excluded. Delta checks used for purposes other than quality control or trouleshooting, such as reflex testing or duplicate test cancellations, were excluded. Assessment of discordant results that occurred within the same specimen (eg, discrepant hemogloin/hematocrit or lood urea nitrogen/creatinine values) that triggered trouleshooting or corrective actions was not included in this study. Participants were asked to provide information aout analytes that were eing used y their laoratories for delta checks. This information included the maximum time interval used for calculations, the type of calculation (asolute, percentage, or rate of change), the amount of change needed to trigger an alert, and whether all or a limited range of results were used for calculations. Participants used a worksheet (Tale 1) to record information aout specific investigations, prolems identified, and corrective measures taken in response to consecutive testing episodes that involved one or more delta check alerts. Participants provided general information aout delta check practices y completing a questionnaire. This included methods for developing delta check parameters, role of the laoratory director, use of delta checks in the laoratory s system for quality assurance, frequency of reviewing delta check criteria, general procedures for handling delta check alerts, use in autoverification processes, and overall opinion aout the value of delta checks. If a question was left unanswered, the participant was excluded from the analysis for that question. Finally, participants provided institutional demographic information that included occupied ed size, government affiliation, location, and type of institution. Data were taulated and analyzed y the iostatistics department of the College of American Pathologists. Statistical analyses were performed to determine factors significantly associated with demographics, delta check actions, testing prolems, and outcomes. Associations were analyzed using Kruskal-Wallis tests for discrete-valued independent variales and regression analysis for the continuous independent variale. Variales with significant associations (P,.1) were then included in a forward-selection multivariate regression model that also controlled for the numer of analytes with delta checks. A significance level of P,.05 was used for this final model. Action-specific rates were also tested for association with use of a checklist and having written criteria for handling delta checks using the Wilcoxon rank sum test. Analysis of actions taken and prolems identified per numer of delta checks involved with a single testing episode was performed y Mantel-Haenszel v 2 test. All analyses were performed with SAS 9.2 (SAS Institute, Cary, North Carolina). RESULTS A total of 49 facilities participated in this study. A summary of institutional demographics for 47 reporting facilities is shown in Tale 2. Results from the general questionnaire are shown in Tale 3. Among 46 participants, one or more methods used for determining delta check limits were reported, including the literature (n ¼ 37; 80.4%), laoratory director (n ¼ 34; 73.9%), technical experience (n ¼ 22; 47.8%), medical staff consultation (n ¼ 19; 41.3%), simulation or review of historical laoratory data (n ¼ 15; 32.6%), iological variation (n ¼ 11; 23.9%), reference range (n ¼ 7; 15.2%), and other (n ¼ 3; 6.5%). When asked if delta checks were less useful now than in the past ecause of improvements in laoratory instruments and specimen 814 Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al

3 Tale 2. Institutional Demographics Demographic No. % Institution type (N ¼ 47) Voluntary, nonprofit hospital Nongovernmental, university hospital Proprietary hospital Veterans hospital System/integrated delivery network Children s hospital County hospital Indian Health Service State acute hospital Other, governmental, federal Other, governmental, nonfederal Other, nongovernmental Governmental affiliation (N ¼ 48) Nongovernmental Governmental, federal Governmental, nonfederal Occupied ed size (N ¼ 46) Location (N ¼ 47) City % Suuran % Rural % Other 1 2.1% Teaching hospital (N ¼ 47) No % Yes % identification procedures, 18 (39.1%) and 9 (19.6%) of 46 study participants agreed, respectively. Analytes and Calculations Used for Delta Checks Among 46 participants, the median numer of different analytes used for delta checks was 15 and ranged from 6 to 32 for laoratories within the 10th to 90th percentile range. Tale 4 shows the distriution of analytes and delta check calculations if reported y 10 or more participants. Other analytes used for delta checks included osmolality (n ¼ 9 participants); red lood cell relative distriution width, anion gap, and lactate (n ¼ 8);creatinekinaseMBisoenzymeand prostate-specific antigen (n ¼ 7); free calcium (n ¼ 6); and D- dimer, hemogloin A 1c, high-density cholesterol, and reticulocyte count (n 5). Asolute differences and percentage change were used for delta check calculations; no participant used rate-of-change calculations for any analyte. Use of delta checks for some analytes was restricted within a specific range of results y a small numer of participants (Tale 4). Actions Taken in Response to Delta Checks A total of 4514 testing episodes were reported that involved 6541 delta check alerts. Most of these involved a single analyte, although 885 (19.6%) involved 2 analytes, 255 (5.6%) involved 3 analytes, 102 (2.3%) involved 4 analytes, and 63 (1.4%) involved 5 or more analytes. Among 4508 testing episodes involving one or more delta check alerts, at least one action was taken in 3411 (75.7%). Tale 5 shows the types of actions y specific analytes for which at least 20 evaluations, in aggregate, were undertaken in response to a delta check alert. Overall, a total of 5892 different investigations were reported y participants, of which the most common was clinical review, followed y inspection and repeat testing of the current specimen. In contrast, inspection and repeat testing of the initial specimen involved with a delta check alert was much less common (Tale 5). Distriution of actions for all analytes involving delta check alerts within each laoratory is shown in Tale 6. A median of 15 analytes were used for delta checks among 46 laoratories. Participants (n ¼ 17) who used a relatively higher numer of different analytes (.20) for delta checks reported taking one or more actions more frequently (median ¼ 87.0%; 10th 90th percentile ¼ 41.0% 100%) in response to a delta check alert compared with the median of actions taken 78.8% (10th 90th percentile range ¼ 19.6% 100%) of the time for 29 laoratories that used fewer than 20 analytes. This difference did not reach statistical significance, P ¼.18, Kruskal-Wallis test. However, there was a significant association etween the quantity of delta checks triggered per testing episode and the numer of different actions taken, P ¼.001, Mantel-Haenszel v 2 test (Figure 1). The median rate of any actions taken in response to a delta check alert was 92.4% in laoratories (n ¼ 8) that used a checklist compared with 84.3% of those (n ¼ 38) that did not, P ¼.52. The median rate of any action taken in response to a delta check alert was 92.4% in laoratories (n ¼ 34) that had written criteria for handling delta check alerts compared with 78.0% of those participants (n ¼ 11) that did not, P ¼.09, Kruskal-Wallis test. Prolems Identified y Delta Checks The distriution of rates of testing prolems per testing event y facility is shown in Tale 7. One or more testing prolems were identified in 217 of 4505 testing episodes involving delta check alerts (4.8%). Overall, 240 separate testing prolems were reported y participants, including 183 (76.3%) and 57 (23.8%) involving the current and previous specimens, respectively (Tale 8). Other potential prolems associated with delta check alerts listed on the study worksheet that were not reported for any testing event included clerical errors (not related to data entry), interface/data transmission errors, specimen transport delays, mislaeled aliquots, and wrong specimen contain- Tale 3. Study Participants Responses to Questionnaire Aout Delta Checks Responses, No. (%) Question Yes No Total The laoratory director is required to approve all new and changed delta checks 37 (82.2) 8 (17.8) 45 The frequency of delta check events is monitored as part of quality assurance or other assessment process 19 (41.3) 27 (58.7) 46 The laoratory has written criteria descriing specific actions required to handle delta check alerts 34 (75.6) 11 (24.4) 45 A checklist is used to handle delta check alerts 8 (17.4) 38 (82.6) 46 Delta checks are used in the autoverification process 34 (91.9) 3 (8.1) 37 Delta checks reviewed for potential revision within last 3 years 30 (65.2) 16 (34.8) 46 Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al 815

4 Analytes Tale 4. Delta Check Analytes and Criteria Used y Study Participants (N ¼ 46) Maximum Time Interval Asolute Change Percentage Change Limited Range of Results a Facilities, No. Median (25th 75th Percentile), d Facilities, No. ers. Nearly all testing prolems involved specimen collection issues (eg, hemolysis, contamination with intravenous fluids, or clotting). Only 12 mislaeled specimens were identified y delta check alerts. One case involved laeling errors on oth the previous and current specimens. Among 18 different delta check alerts involving mislaeling errors, the most frequent analyte was hemogloin (n ¼ 6), followed y mean corpuscular volume (MCV; n ¼ 4) and sodium (n ¼ 3). The frequency of analytical errors detected y delta check alerts was 1.1 per 1000 testing episodes. Tale 9 shows the frequency of testing prolems identified y individual analytes for which there were at least 25 delta check alerts. In a few cases, prolems were identified in oth previous and current specimens. Participants who used a relatively higher numer of different analytes (.20) for delta checks (n ¼ 17) uncovered a significantly larger numer of testing prolems (median ¼ 5.0%; 10th 90th percentile ¼ 1.0% 16.1%) compared with a median of 3.1% (10th 90th percentile range ¼ 0.0% 8.1%) for 29 laoratories that used fewer than 20 analytes, P ¼.04, Kruskal-Wallis test. In addition, the likelihood of identifying a testing prolem was Median change Facilities, No. Median Change, % Facilities, No. No. (%) Chemistry Sodium 36 3 (3 7) 22 9 meq/l (7.5) Calcium 36 3 (3 7) 19 2 mg/dl Potassium 35 3 (3 7) 26 1 meq/l Creatinine 34 4 (3 7) 16 1 mg/dl (16.7) BUN 29 3 (3 7) mg/dl (9.1) Alumin 29 5 (3 7) 17 1 g/dl (9.7) Protein 29 5 (3 7) 13 2 g/dl Total iliruin 27 4 (3 7) 16 2 mg/dl (16.7) Chloride 26 3 (3 7) meq/l 6 12 Phosphorus 22 5 (3 7) 9 2 mg/dl CO (3 7) 13 8 meq/l 9 20 Magnesium 20 4 (3 7) 14 1 mg/dl 5 25 Uric acid 18 7 (3 7) 10 2 mg/dl Direct iliruin 17 7 (3 7) 9 2 mg/dl (15.0) AST 16 7 (3 7) (10.5) Alkaline phosphatase 13 5 (3 7) 7 20 U/L (17.6) Glucose 14 6 (3 7) mg/dl 5 50 Lactate dehydrogenase 11 7 (5 14) c-glutamyl transferase 9 7 (5 7) Cholesterol 9 7 (7 7) 5 70 mg/dl (27.3) Hematology MCV 42 5 (3 10) 30 4 fl 15 6% 45 1 (2.2%) Hemogloin 37 5 (2 7) 29 2 g/dl 10 20% 38 2 (5.3%) Platelet count 32 5 (3 7) /lL 25 34% 34 1 (2.9%) WBC count 21 3 (2 7) /lL 14 35% 23 3 (13.0%) Hematocrit 21 7 (3 7) 14 6% 7 14% 22 1 (4.5%) MCH 7 3 (3 3) 8 4 pg 3 MCHC 7 3 (1 7) 8 2 g/l 2 Coagulation Prothromin time 14 7 (2 14) % 18 2 (11.1%) INR (1 14) % 13 1 (7.7%) Firinogen 8 2 (1 3) % Areviations: AST, aspartate aminotransferase; BUN, lood urea nitrogen; CO 2, caron dioxide; INR, international normalized ratio; MCH, mean corpuscular hemogloin; MCHC, mean corpuscular hemogloin concentration; MCV, mean corpuscular volume; WBC, white lood cell. SI conversion factors: To convert iliruin, BUN, calcium, cholesterol, creatinine, glucose, magnesium, and phosphorus to millimoles per liter, multiply y a Delta check calculation restricted to a specific range of results. Median change not reported for fewer than 6 reporting facilities. significantly associated with a higher numer of delta check alerts per testing episode, P,.001, Mantel-Haenszel v 2 test (Figure 2). The median rate of detecting testing prolems in response to a delta check alert was 3.7% in facilities (n ¼ 8) that used a checklist compared with 4.0% of those (n ¼ 38) that did not, P ¼.52. Further, the median rate of detecting testing prolems in response to a delta check alert was 3.1% in facilities (n ¼ 34) that had written criteria for handling delta check alerts compared with 5.0% of those participants (n ¼ 11) that did not, P ¼.46, Kruskal-Wallis test. Outcomes Associated With Delta Checks Among 4500 testing episodes involving one or more delta check alerts, participants reported 4652 outcomes (Tale 10). Most outcomes involved taking no additional action other than adding a comment to the laoratory report. This was reported in aout one-third of testing episodes. In addition, 369 results involving delta check alerts were reported as expected outcomes ased on various patient conditions. The remaining 265 outcomes (5.9%) involved various corrective actions in which a testing prolem was identified. In Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al

5 Analytes Used for Delta Checks Tale 5. Total Delta Checks, No. cases (3.0%), results were not reported. One or more delta check alerts persisted in 50 of 72 recollected specimens (69.4%). Tale 11 shows the frequency of outcomes involving corrective actions taken in response to testing prolems y specific analyte involved with delta check alerts. DISCUSSION The use of delta check rules in laoratory medicine as a patient-ased quality control method was introduced y Aggregate Actions Taken in Response to Delta Check Alerts y Analyte No. (%) With 1 Evaluation Specific Types of Actions Taken in Response to Delta Check Alert, No. (%) Retest or Other Testing Actions Analytical Clinical Other Current Previous Current Previous Check Review Actions Chemistry Potassium (86.2) 260 (43.1) 9 (1.5) 314 (52.1) 13 (2.2) 137 (22.7) 209 (34.7) 74 (12.3) BUN (70.0) 102 (22.4) 3 (0.7) 88 (19.3) 4 (0.9) 83 (18.2) 175 (38.4) 44 (9.6) Creatinine (78.4) 137 (31.1) 4 (0.9) 152 (34.5) 6 (1.4) 70 (15.9) 240 (54.5) 35 (8.0) Calcium (83.1) 135 (48.6) 1 (0.4) 65 (23.4) 1 (0.4) 37 (13.3) 158 (56.8) 24 (8.6) Sodium (86.0) 108 (46.0) 2 (0.9) 79 (33.6) 5 (2.1) 39 (16.6) 90 (38.3) 41 (17.4) Chloride (72.3) 36 (17.8) 1 (0.5) 42 (20.8) 3 (1.5) 20 (9.9) 100 (49.5) 13 (6.4) Glucose (46.9) 44 (24.6) 1 (0.6) 18 (10.1) 0 (0.0) 16 (8.9) 33 (18.4) 7 (3.9) Total iliruin (59.6) 29 (17.5) 1 (0.6) 41 (24.7) 3 (1.8) 23 (13.9) 46 (27.7) 11 (6.6) CO (68.0) 61 (40.7) 0 (0.0) 58 (38.7) 1 (0.7) 57 (38.0) 23 (15.3) 10 (6.7) Alumin (73.0) 39 (28.5) 1 (0.7) 49 (35.8) 0 (0.0) 38 (27.7) 49 (35.8) 24 (17.5) Protein (80.6) 44 (34.1) 2 (1.6) 35 (37.1) 4 (3.1) 31 (24.0) 71 (55.0) 22 (17.1) Phosphorus (72.9) 34 (26.4) 3 (2.3) 28 (21.7) 3 (2.3) 33 (25.6) 29 (22.5) 18 (14.0) Magnesium (71.6) 32 (33.7) 0 (0.0) 21 (22.1) 1 (1.1) 12 (12.6) 36 (37.9) 11 (11.6) AST (69.1) 25 (26.6) 1 (1.1) 22 (23.4) 2 (2.1) 14 (14.9) 35 (37.2) 5 (5.3) Uric acid (57.7) 5 (19.2) 0 (0.0) 2 (7.7) 0 (0.0) 2 (7.7) 7 (26.9) 2 (7.7) Hematology Hemogloin (86.2) 391 (45.0) 9 (1.0) 203 (23.4) 8 (0.9) 89 (10.3) 488 (56.2) 118 (13.6) Platelet count (79.9) 183 (36.0) 3 (0.6) 213 (41.9) 3 (0.6) 63 (12.4) 211 (41.5) 65 (12.8) MCV (84.7) 153 (40.3) 4 (1.1) 97 (25.5) 3 (0.8) 42 (11.1) 223 (58.7) 39 (10.3) WBC count (69.1) 111 (33.6) 1 (0.3) 68 (20.6) 2 (0.6) 34 (10.3) 116 (35.2) 32 (9.7) Hematocrit (80.7) 99 (31.3) 5 (1.6) 57 (18.0) 3 (0.9) 41 (13.0) 172 (54.4) 32 (10.1) RDW (81.5) 30 (55.6) 1 (1.9) 10 (18.5) 0 (0.0) 3 (5.6) 20 (37.0) 3 (5.6) MCHC (69.6) 13 (28.3) 0 (0.0) 17 (37.0) 0 (0.0) 8 (17.4) 10 (21.7) 7 (15.2) Coagulation PT (74.4) 44 (35.2) 1 (0.8) 41 (32.8) 2 (1.6) 9 (7.2) 45 (36.0) 7 (5.6) INR (65.3) 39 (39.8) 1 (1.0) 40 (40.8) 2 (2.0) 7 (7.1) 19 (19.4) 6 (6.1) Other (64.9) 118 (31.1) 1 (0.3) 96 (25.3) 7 (1.8) 81 (21.4) 117 (30.9) 23 (6.1) Areviations: AST, aspartate aminotransferase; BUN, lood urea nitrogen; CO 2, caron dioxide; INR, international normalized ratio; MCHC, mean corpuscular hemogloin concentration; MCV, mean corpuscular volume; PT, prothromin time; RDW, red lood cell relative distriution width; WBC, white lood cell. Tale 6. Distriution of Specific Actions Taken Within Each Laoratory as Percentage of All Delta Check Alerts Specific Type of Investigation a All Laoratory Percentiles, %(N¼ 49) 10th 25th Median 75th 90th Clinical review Retest current specimen Check current specimen None Analytical check Other Retest previous specimen Check previous specimen Linderg in 1967 as a new concept related to emerging technology in laoratory informatics. 8 Application of this tool ecame practical in the late 1970s with the development of laoratory information systems that were capale of handling delta check calculations and alerting testing personnel aout exceptions efore results were reported Besides quality assessment, delta checks have also een designed to detect conditions that may require immediate medical attention, such as a rapid rise in serum creatinine with acute kidney injury. 12 However, use of delta checks for this purpose was not examined in this study. Delta checks are now widely used y laoratories as a quality control technique. For example, among 37 study participants, 34 (91.9%) reported using delta checks as a part of their autoverification procedures. However, there is limited information aout the effectiveness of delta check procedures for detecting prolems other than specimen identification errors. Furthermore, the value of this quality control tool has een questioned. 5 For example, among 46 participants responding to the study questionnaire, 18 (39%) elieved delta checks were less useful than in the past Tale 7. Distriution of the Percentage of Delta Check Alerts That Involved a Testing Prolem Among Each Facility (N ¼ 49) All Laoratory Percentiles, % 5th 10th 25th Median 75th 90th 95th a Percentage of actions taken per testing episode involving one or more delta checks. Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al 817

6 Figure 1. Association etween numer of delta check alerts per testing episode and actions taken. ecause of improvements in laoratory instrumentation, although only 9 (20%) thought that delta checks had ecome less useful for detecting misidentified specimens. Concerns aout the use of delta checks may e warranted ecause guidance for selecting the most suitale analytes, criteria for actionale delta check thresholds, and guidance aout procedures most effective for detecting prolems is limited. 13 Studies primarily involve recommendations ased on theoretical assessments and statistical models using estimates of iological variation and reference ranges or simulations involving proailities of detecting specimen mislaeling or mix-up errors In spite of sparse information availale aout selecting delta check parameters, the most common method for setting delta check limits reported y participants was from the literature. This Q-Proes study was undertaken to help participants etter understand delta check practices and identify potential improvements. Results from this study also have wider applicaility and further expand knowledge aout the use of delta checks, including the types of analytes and calculations used in routine practice, the frequency and types of investigations performed, and types of prolems identified in response to delta check alerts. Analytes and Calculations Used for Delta Checks The median facility used 15 different analytes for delta checks, ut this numer varied widely among facilities. An Tale 8. Testing Prolem Investigations (N ¼ 4505 Testing Episodes) Aggregate Testing Prolems Associated With Delta Check Alerts Current Specimen (No.) Previous Specimen (No.) Total, No. (%) Interference (hemolysis, lipemia, icterus) (1.7) Specimen contamination (eg, IV fluid) (1.4) Clotted specimen (1.1) Other testing prolem (0.6) Mislaeled specimen (0.3) Analytical measurement error (0.1) Improper processing (dilution, aliquot, etc) (,0.1) Delay in processing (,0.1) Calculation error (,0.1) Improper handling (temperature, light) (,0.1) Data entry error (,0.1) No testing prolem identified Physiologic condition 1472 (32.7) No testing prolem, other cause 1318 (29.3) Treatment (eg, dialysis) 864 (19.2) No explanation for delta check 446 (9.9) Blood transfusion 332 (7.4) Areviation: IV, intravenous. 818 Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al

7 Tale 9. Analyte Aggregate Testing Prolems Suspected or Identified With Delta Check Alerts y Analyte Among All Facilities Total Delta Check Alerts, No. Previous Specimen, No. (%) Any Testing Prolem(s) Detected Current Specimen, No. (%) Total, No. (%) a Chemistry Potassium (2.5) 73 (12.2) 84 (14.0) BUN (0.4) 11 (2.4) 13 (2.8) Creatinine (0.7) 8 (1.8) 11 (2.5) Calcium (0.4) 20 (7.2) 21 (7.5) Sodium (1.7) 24 (10.2) 28 (11.9) Chloride (0.5) 11 (5.4) 12 (5.9) Glucose (1.1) 8 (4.4) 10 (5.6) Total iliruin (0.0) 9 (5.4) 9 (5.4) Caron dioxide (0.0) 11 (7.3) 11 (7.3) Alumin (1.5) 7 (5.1) 9 (6.6) Protein, total (1.6) 3 (2.3) 5 (3.9) Phosphorus (0.0) 7 (5.4) 7 (5.4) Magnesium 96 0 (0.0) 10 (10.4) 10 (10.4) AST 94 0 (0.0) 7 (7.4) 7 (7.4) Uric acid 26 0 (0.0) 1 (3.8) 1 (3.8) Hematology Hemogloin (0.7) 30 (3.5) 36 (4.2) Platelet count (1.6) 25 (5.0) 33 (6.5) MCV (1.6) 18 (4.7) 23 (6.1) WBC (1.5) 15 (4.5) 20 (6.0) Hematocrit (0.3) 20 (6.3) 21 (6.6) RDW 54 0 (0.0) 9 (16.7) 9 (16.7) MCHC 46 2 (4.3) 11 (23.9) 12 (26.1) Coagulation PT (0.0) 5 (4.0) 5 (4.0) INR 98 7 (7.1) 4 (4.1) 11 (11.2) Other (1.1) 8 (2.1) 12 (3.2) Areviations: AST, aspartate aminotransferase; BUN, lood urea nitrogen; INR, international normalized ratio; MCHC, mean corpuscular hemogloin concentration; MCV, mean corpuscular volume; PT, prothromin time; RDW, red lood cell relative distriution width; WBC, white lood cells. a Some testing prolems were identified in oth previous and current specimens. important finding oserved in this study was that the use of a higher numer of different delta check analytes detected significantly more prolems ut did not affect the frequency of actions taken. This may have een caused, in part, y a single prolem triggering delta checks on multiple tests, which would have involved the same amount of time and effort to investigate as a single delta check alert. Aout 30% of testing episodes triggering a delta check involved multiple Figure 2. Association etween numer of delta check alerts per testing episode and testing prolems identified. Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al 819

8 Tale 10. Aggregate Outcomes Resulting From Delta Check Alerts Among All Facilities Outcomes (N ¼ 4500 Testing Episodes) No. (%) a No prolem identified or suspected No prolem identified, standard report 2512 (55.8) issued No prolem identified, report issued with 1506 (33.5) comment Expected delta check alert ased on patient 369 (8.2) condition Suspected or identified prolem Result not reported, specimen recollected 126 (2.8) Recollected specimen also showed delta 50 (1.1) check Recollected specimen did not show delta 22 (0.5) Result not reported, specimen not 10 (0.2) recollected Corrected report issued on previous 8 (0.2) specimen Other action 49 (1.1) a Includes multiple responses per testing episode. analytes. A higher numer of different analytes used for delta checks would e expected to increase the sensitivity of detecting prolems, although the quantity of false alerts might also e higher. As expected, the most common analytes used for delta checks involved tests that were used for monitoring and that were frequently repeated, such as those in electrolyte and hematology panels. Other analytes such as cholesterol, prostate-specific antigen, or hemogloin A 1c, which are infrequently retested during short (,7 days) periods of time, have less application for delta checks and were understandaly used y only a few participants. Although the use of delta check calculations involving rate of change or multivariate methods has een descried, 6,19,20 all study participants used only asolute and percentage change calculations. The median changes reported y participants for asolute and percentage changes were similar to criteria recommend y Kim et al, 21 except these investigators suggested using rate of change for enzymes and renal function tests (creatinine and lood urea nitrogen). Studies that have recommended maximum time intervals for calculating delta checks have ranged from 2 to 5 days 21 to 5 to 7 days, 22 which is comparale to the median range of times (3 7 days) used y study participants for various analytes. More complex delta check calculations may not have een used y participants ecause of insufficient support y laoratory information systems, added difficulty, or unproven value. Lacking guidelines or strong supportive evidence, variation in preferences for delta check calculations was expected. 19 Actions The likelihood of uncovering a testing prolem or another reason that triggers a delta check alert depends, in part, on thoroughness of the investigation, which may require a significant amount of time and effort. It also delays reporting results. A worksheet for documenting evaluations was provided to study participants, which may have iased results toward more rigorous evaluations. Nevertheless, there was sustantial variation in the extent and types of actions taken in response to a delta check alert. Surprisingly, nearly a quarter of all testing episodes involving a delta Tale 11. Delta Check Alerts Leading to Corrective Action for Suspected or Identified Testing Prolems y Specific Analyte Analytes Total Delta Check Alerts (No.) Actions, No. (%) Chemistry Potassium (9.9) BUN (2.0) Creatinine (1.6) Calcium (8.2) Sodium (12.1) Chloride (7.0) Glucose (4.4) Total iliruin (0.0) Caron dioxide (6.0) Alumin (5.1) Protein, total (2.3) Phosphorus (2.3) Magnesium 96 7 (7.3) AST 94 3 (3.2) Uric acid 26 0 (0.0) Hematology Hemogloin (8.0) Platelet count (9.7) MCV (6.6) WBC count (6.9) Hematocrit (5.7) RDW (18.5) MCHC (23.9) Coagulation PT (6.4) INR (13.3) Other analytes (2.9) Areviations: AST, aspartate aminotransferase; BUN, lood urea nitrogen; INR, international normalized ratio; MCHC, mean corpuscular hemogloin concentration; MCV, mean corpuscular volume; PT, prothromin time; RDW, red lood cell relative distriution width; WBC, white lood cell. check alert led to no investigation at all, although this was highly variale among laoratories, ranging from taking some action nearly all the time y some laoratories to checking less than 20% of testing events y others. Use of standardized procedures, a checklist, or a flow chart would e expected to improve standardization and promote thoroughness of investigations. Some participants used a checklist and many reported having written procedures for handling delta check alerts, ut this was not associated with frequency of investigations or prolems identified. This could e due to study sample size, compliance with procedures, or their effectiveness. These oservations suggest that additional guidance and standardization for handling investigations to detect testing errors associated with delta check alerts is needed. 23 The quantity of different analytes that triggered delta check alerts during any testing episode was significantly associated with the total numer of actions taken and testing prolems identified (Figures 1 and 2). These results support using higher numers of different analytes for delta checks. It is also consistent with previous studies that show added value y use of multivariate delta check methods. 6 However, these types of calculations were not used y any of the participants in this study, and may therefore have less practical value than expected, perhaps ecause of limited capailities of current laoratory information systems. Nevertheless, innovative methods, using complex formulas 820 Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al

9 on multiple analytes, show promise for improving the value of delta checks, especially for detection of mislaeling prolems. 24 The most frequent action taken in response to a delta check alert involved clinical review. This finding was expected ecause the most common prolems associated with delta check alerts involved clinical conditions rather than laoratory testing errors. The second most frequent action was retesting the current specimen. Studies involving repeat testing to confirm critical value results show that this method rarely identifies analytical prolems. For example, the likelihood of a significantly different result upon retesting for a critical serum potassium value is 1.9 per 1000 tests. 25 Similarly, repeat testing to detect analytical errors in response to a delta check alert also has low productivity, as shown in another study in which the analytical error rate was 2.8 per 1000 repeat chemistry tests involving delta check alerts. 4 This is similar to the analytical error rate of 1.1 per 1000 testing events (not tests) reported in this study under less controlled conditions. However, analytical errors were proaly underestimated ecause current specimens were retested only 25% of the time and previous specimens much less often. Relative to other types of evaluations, retesting specimens detected very few testing errors. Checking the specimen for laeling or other prolems (eg, hemolysis) was the third most common action in response to delta check alerts and had more impact on detecting errors ecause these were more common prolems than analytical measurement errors, which are primarily identified y retesting. Because a prolem might e equally likely in either specimen involved with a delta check alert, evaluating oth should e done whenever possile. Nevertheless, retesting the current specimen was done 42 times more often than retesting the prior specimen, and the current specimen was checked for laeling or other prolems 31 times more often. This occurred in spite of a worksheet provided to study participants that included a separate area to document actions taken on the previous specimen. The reasons why previous specimens were less often evaluated for errors are unknown. This could have een due, in part, from known prolems with reliaility of retesting due to specimen staility, storage, or length of retention conditions, especially for certain analytes such as caron dioxide. Other factors, such as inconvenience of finding archival specimens or lack of standardized protocols for investigating delta check alerts, may have also played a role. In spite of the disproportionate attention given to evaluating only the most current specimen involved with a delta check alert, 23.8% of all testing prolems were associated with the prior specimen. In a study 4 involving 9831 specimens retested ecause of delta check alerts, only the current specimen was evaluated for analytical errors. It is therefore likely that testing prolems detected y delta check alerts are underestimated y this and previous studies. These results also suggest that in practice, some testing errors go undetected ecause of not evaluating the initial specimen, which lessens the effectiveness of delta checks. This oservation indicates an opportunity to improve laoratory practices y developing protocols that include, whenever possile, assessment of oth specimens involved with a delta check alert. Prolem Detection Previous studies have shown that a testing prolem is uncovered in aout 0.4% to 6.0% of testing episodes involving a delta check alert. 9,21 This is comparale to the aggregate (4.8%) and median (4.0%) laoratory rates oserved in this study and falls within the 80th central percentile range (0.0% and 9.0%) reported y all facilities. Some variation is likely related to laoratory practices. For example, aout one-quarter of participants did not have written criteria for handling delta check alerts. Prolem detection rates also varied widely among analytes used for delta checks. Among chemistry tests, certain electrolytes (sodium, potassium, and magnesium) showed the highest prolem detection rates. In contrast, tests for renal function (creatinine, lood urea nitrogen) were relatively insensitive, presumaly ecause of higher relative frequency of effects on these tests from treatment (eg, dialysis). Among the hematology analytes, delta check alerts involving red lood cell relative distriution width, mean corpuscular hemogloin concentration, and mean corpuscular hemogloin were more effective than those involving hemogloin or platelet counts. Calculated mean corpuscular hemogloin and mean corpuscular hemogloin concentration indices on automated cell counters can e falsely elevated and trigger a delta check alert ecause of their heightened sensitivity from interference in hemogloin measurements caused y lipemia or y falsely low red lood cell counts caused y cold agglutinins. 13 An interesting finding among coagulation tests showed that prolem detection rates associated with delta check alerts were much higher for the international normalized ratio compared with prothromin time, from which the international normalized ratio is derived. International normalized ratio results are especially prone to e falsely elevated ecause of specimen collection errors, which would more likely trigger delta checks. 26 Further, international normalized ratio was the only delta check analyte associated with more identified testing prolems in previously collected specimens compared with current ones, even though there was a much higher rate of checking current specimens. The variation in sensitivity among different analytes to detect testing errors y delta checks might involve several factors. These include differences in time interval and cutoff thresholds used, intensity of investigations, inherent properties (iological staility, analytical precision), and relative frequency of nontesting causes (eg, treatment). This information has practical value for selecting analytes to use for delta checks ecause some were oserved to e more effective than others for identifying testing prolems. Nearly all testing errors detected y delta check alerts in this study were in the preanalytical phase of testing, including interference y hemolysis, lipemia, or icterus; contamination y intravenous fluids; and effects of clotting. Only 5 of 240 testing prolems (2.1%) or 0.04% of all 4514 testing episodes that triggered a delta check alert involved an analytical testing prolem. Furthermore, only 2 of 240 testing prolems (0.8%) involved a postanalytical calculation or data entry error. Previous studies have shown that the majority (53% 75%) of testing errors identified y any method occur in the preanalytical stage of testing. 27 However, the relative frequency of the remaining analytical (16% 31%) and postanalytical causes for testing errors (9% 30%) are much higher than those oserved for the types of prolems detected y delta checks. Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al 821

10 The highest frequency of prolems identified y delta check alerts were patient-related issues that were not related to testing. These included physiological changes or treatment (lood transfusion or dialysis), which may e due, in part, to the population (inpatients) for which delta checks are most applicale. Large physiological changes that are unrelated to the testing process could nevertheless affect interpretation of test results if the cause is not recognized. Use of delta checks is considered an important method for identifying specimen mislaeling errors. 28 In this study, misidentification errors were detected y delta check alerts in 12 of 4505 testing episodes (0.3%). This is within the range estimated from studies involving simulated specimen mix-ups in an inpatient population. 15,18 Another study 29 using lael image recognition technology detected laeling errors in only 0.01% of cases in an outpatient population, for which delta checks are not eneficial. Detection of misidentified specimens is highly dependent on prevalence of mislaeling and numer and type of delta checks in use, as well as extent of investigations. Nevertheless, laeling issues are a highly documented cause (10.1% 16.3%) of laoratory errors as measured y incident reports, 30,31 and some may only e found y use of delta checks. Outcomes and Quality Management The most common outcome resulting from a delta check alert was issuing a standard report without modifications. The next more frequent outcome was to place an additional comment onto the test report even though no testing prolem was identified. Although information aout the content of these comments was not solicited from participants, providing specific information aout the proale cause of a delta check alert (eg, lood transfusion, dialysis) may e helpful for interpreting results and should e considered a est practice. Fewer ut more clinically significant outcomes occurred when delta check alerts identified testing errors that led to specimen recollection and prevented reporting possily incorrect results. Very few cases entailed issuing a corrective report, although a higher numer would have likely occurred if previously tested specimens were more often evaluated. Of note is that a large proportion of delta check alerts persisted upon retesting. This could have een due to an unrecognized prolem with the initial test that was not investigated, an unexplained preanalytical event, or an unidentified change in the patient s condition that affected test results. Only 19 of 46 participants (41.3%) reported monitoring prolems identified y delta check alerts as a quality management tool. Although various other quality systems may e used to track specimen collection errors, some prolems, such as contamination y intravenous fluids, might e detected only y a delta check alert. Monitoring delta check alert trends and causes in laoratory quality assessment programs would e expected to strengthen performance improvement, especially for assessing prolems involving preanalytical errors that occur in the inpatient setting. This study also showed lack of involvement y the laoratory director in selecting and approving changes in delta check parameters y 8 of 45 study participants (17.8%). This might e explained if delta checks are not viewed as part of the laoratory s quality management program or if oversight was delegated y the laoratory director who was then considered not involved. Laoratory directors should e engaged with how delta checks are eing selected and used ecause of their impact on laoratory operations and quality. Summary In summary, several important and practical findings were revealed from this study that could improve the use of delta checks. First, using a larger numer of different analytes for delta checks appears to have more value than using only a few. Results from this study may also assist laoratories in reviewing or revising analytes and criteria used for their delta check procedures. In addition, use of standardized and documented methods such as checklists for investigating delta check alerts that are customized y analyte for prioritizing actions is recommended. These improvements are expected to increase the numer of prolems detected while streamlining the evaluation process. Current practices should e changed to include examination of the previous specimen involved with a delta check for potential prolems whenever possile. Because of the low rate of analytical errors detected y delta check alerts, the value of automatically retesting specimens, as commonly performed y study participants, should e reexamined, especially for chemistry tests, and discontinued if warranted. Finally, consideration should e given to monitoring causes and outcomes of delta check alerts as part of the laoratory s overall performance improvement program. References 1. Wheeler LA, Sheiner LB. A clinical evaluation of various delta check methods. Clin Chem. 1981;27(1): Nosanchuk JS, Gottmann AW. CUMS and delta checks: a systematic approach to quality control. Am J Clin Pathol. 1974;62: Krasowski MD, Davis SR, Drees D, et al. Autoverification in a core clinical chemistry laoratory at an academic medical center. J Pathol Inform. 2014;5:13. doi: / Deetz CO, Nolan DK, Scott MG. An examination of the usefulness of repeat testing practices in a large hospital clinical chemistry laoratory. Am J Clin Pathol. 2012;137(1): Ovens K, Naugler C. How useful are delta checks in the 21st century?: a stochastic-dynamic model of specimen mix-up and detection. J Pathol Inform. 2012;3:5. 6. Iizuka Y, Kume H, Kitamura M. Multivariate delta check method for detecting specimen mix-up. Clin Chem. 1982;28(11): Howanitz PJ. Quality assurance measurements in departments of pathology and laoratory medicine. Arch Pathol La Med. 1990;114: Linderg, D. A. Collection, evaluation, and transmission of hospital laoratory data. Methods Inf Med. 1967;6(3): Ladenson JH. Patients as their own controls: use of the computer to identify laoratory error. Clin Chem. 1975;21(11): Sher PP. An evaluation of the detection capacity of a computer-assisted real-time delta check system. Clin Chem. 1979;25(6): Whitehurst P, Di Silvio TV, Boyadjian G. Evaluation of discrepancies in patients results an aspect of computer-assisted quality control. Clin Chem. 1975;21: Flynn N, Dawnay A. A simple electronic alert for acute kidney injury. Ann Clin Biochem. 2015;52(pt 2): Barnes PW, McFadden SL, Machin SJ, Simson E. The international consensus group for hematology review: suggested criteria for action following automated CBC and WBC differential analysis. La Hematol. 2005;11(2): Park SH, Kim SY, Lee W, Chun S, Min WK. New decision criteria for selecting delta check methods ased on the ratio of the delta difference to the width of the reference range can e generally applicale for each clinical chemistry test item. Ann La Med. 2012;32(5): Yamashita T, Ichihara K, Miyamoto A. A novel weighted cumulative deltacheck method for highly sensitive detection of specimen mix-up in the clinical laoratory. Clin Chem La Med. 2013;51(4): Zhang P, Tang H, Chen K, Chen Y, Xu D. Biological variations of hematologic parameters determined y UniCel DxH 800 hematology analyzer. Arch Pathol La Med. 2013;137(8): Sheiner LB, Wheeler LA, Moore JK. The performance of delta check methods. Clin Chem. 1979;25(12): Strathmann FG, Baird GS, Hoffman NG. Simulations of delta check rule performance to detect specimen mislaeling using historical laoratory data. Clin Chim Acta. 2011;412(21 22): Lacher DA, Connelly DP. Rate and delta checks compared for selected chemistry tests. Clin Chem. 1988;34(10): Arch Pathol La Med Vol 141, June 2017 Delta Check Practices and Outcomes Schifman et al