BACKGROUND AND OBJECTIVES:

Inflammatory marker testing in children has been identified as a potential area of overuse. We sought to describe variation in early inflammatory marker (C-reactive protein and erythrocyte sedimentation rate) testing for infection-related hospitalizations across children’s hospitals and to determine its association with length of stay (LOS), 30-day readmission rate, and cost.

METHODS:

We conducted a cross-sectional study of children aged 0 to 17 years with infection-related hospitalizations using the Pediatric Health Information System. After adjusting for patient characteristics, we examined rates of inflammatory marker testing (C-reactive protein or erythrocyte sedimentation rate) during the first 2 days of hospitalization. We used k-means clustering to assign each hospital to 1 of 3 groups on the basis of similarities in adjusted diagnostic testing rates across 12 infectious conditions. Multivariable regression was used to examine the association between hospital testing group and outcomes.

RESULTS:

We included 55 771 hospitalizations from 48 hospitals. In 7945 (14.3%), there was inflammatory marker testing in the first 2 days of hospitalization. We observed wide variation in inflammatory marker testing rates across hospitals and infections. Group A hospitals tended to perform more tests than group B or C hospitals (37.4% vs 18.0% vs 10.4%; P < .001) and had the longest adjusted LOS (3.2 vs 2.9 vs 2.8 days; P = .01). There was no significant difference in adjusted 30-day readmission rates or costs.

CONCLUSIONS:

Inflammatory marker testing varied widely across hospitals. Hospitals with higher inflammatory testing for one infection tend to test more frequently for other infections and have longer LOS, suggesting opportunities for diagnostic stewardship.

An estimated 4 to 5 billion tests are performed in the United States annually, including many that are essential to the delivery of high-quality health care and a substantial proportion that are unnecessary.14  Routine diagnostic testing and nontargeted testing contribute to the ∼34% of all health care spending in the United States that is attributed to waste.5  The costs associated with unnecessary testing are not limited to the direct financial burden of the tests on the health care system, but extend to include the psychological costs (eg, anxiety, stress, pain) that patients and their families experience in association with phlebotomy as well as false-positive testing.68  Overuse of diagnostic testing may also lead to downstream consequences, including repeat or expanded testing, the use of unnecessary therapies, prolonged hospitalization, and increased out-of-pocket expenditures of patients and their families.9,10

Recently, efforts such as the Choosing Wisely initiative have been focused on improving diagnostic stewardship as a means of improving health care value and curbing health care expenditures. Inflammatory markers (including C-reactive protein [CRP] and erythrocyte sedimentation rate [ESR]) are obtained in the management of infections and other inflammatory conditions to guide the differentiation of viral from bacterial disease, to tailor antibiotic therapy (including transitions from parenteral to enteral antibiotic therapy), and to determine the length of therapy.1116  Although targeted inflammatory marker testing may have the benefit of reducing a proportion of antibiotic overuse and tailoring therapies, widespread nontargeted testing has the potential to promote diagnostic overuse with little added clinical benefit.17  This is particularly true for concomitant testing given the overlap in information provided between CRP and ESR, both revealing acute inflammation over different time courses. For this reason, the American Society of Clinical Pathology, in one of its Choosing Wisely recommendations, suggests CRP testing over ESR testing, especially when a diagnosis has not yet been established.18

Little is known about patterns of use of inflammatory markers and their impact on hospital outcomes across infectious diagnoses in children. Understanding patterns of inflammatory marker testing may identify opportunities to curb unnecessary diagnostic testing in children. Therefore, we sought to describe variation in early inflammatory marker (CRP and ESR) testing across children’s hospitals and to determine if variation in testing is associated with hospital resource use, including length of stay (LOS), 30-day readmission rate, and cost.

In this multicenter cross-sectional study of children with an infection-related hospitalization (inpatient and observation), we used the Pediatric Health Information System (PHIS). PHIS is an administrative and billing database of 51 freestanding tertiary care pediatric hospitals in the United States that are affiliated with the Children’s Hospital Association (Lenexa, KS). Patient data are deidentified in PHIS; encryption of patient identifiers, however, allows for tracking of individual patients across multiple hospital visits. The current study included data from a total of 48 hospitals, with 3 hospitals excluded for incomplete data. Because we used deidentified data, this study was not considered human subjects research by the policies of the local institutional review board.

Children 0 to 17 years of age with an index hospitalization for infection at a PHIS-participating hospital from January 1, 2016, to December 31, 2017, were eligible for inclusion. Hospitalizations for infection were identified by using All Patient Refined Diagnosis-Related Groups (APR-DRGs) version 32 (3M Corporation, St Paul, MN). APR-DRGs are a patient classification scheme that groups medical patients on the basis of the principal diagnosis and incorporates severity of illness and risk of mortality using demographics and comorbidities. Although originally designed for inpatient stays, the PHIS database uses the APR-DRG grouper for both inpatient and observation stays because dedicated observation units are infrequent in pediatrics and observation stays are defined retrospectively by payer. We examined the following APR-DRG categories: infections of the upper respiratory tract (APR-DRG 113); major respiratory infections and inflammations (APR-DRG 137); bronchiolitis and respiratory syncytial virus (RSV) pneumonia (APR-DRG 138); pneumonia (APR-DRG 139); major gastrointestinal and peritoneal infections (APR-DRG 248); nonbacterial gastroenteritis (APR-DRG 249); osteomyelitis, septic arthritis, and other musculoskeletal infections (APR-DRG 344); cellulitis and other bacterial skin infections (APR-DRG 383); kidney and urinary tract infections (APR-DRG 463); postoperative, posttraumatic, and other device infections (APR-DRG 721); viral illness (APR-DRG 723); and other infectious and parasitic diseases (APR-DRG 724).

We excluded transfers in because of the potential inability to capture diagnostic test use from the transferring hospital. Finally, we excluded patients with cancer and immunodeficiency using Feudtner’s complex chronic conditions (CCCs)19  as well as hospitalizations that included ICU stays because these children may have more complicated infections and because patterns of usage of inflammatory markers may differ from those seen on general inpatient teams. Children within other CCC categories were retained within analyses.

Inflammatory marker testing was defined as obtaining either a ESR or CRP test. We focused on early testing, defined as during the first 2 days of the hospitalization, because we sought to capture early (emergency department or inpatient) test use across a broad range of infections rather than examine the impact of late or repeat testing on hospitalization outcomes. We chose to examine early testing because a previous study of diagnostic testing revealed that overuse occurred more frequently during initial testing compared with repeat testing.4  Additionally, this mitigated the risk of identifying a misleading effect-cause relationship because it would be challenging to determine if children who stay longer have more opportunities to get testing versus testing having a causal role in prolonged LOS. We describe both overall (ie, all PHIS hospitals) and individual hospital testing rates. Concomitant testing was defined as receipt of ESR and CRP tests during the first 2 days of the hospitalization.

Outcomes included variation in rates of inflammatory marker testing across hospitals. We also examined hospital-level LOS measured in days, all-cause 30-day hospital readmissions, and cost in US dollars. The time frame of 30 days was chosen to measure subsequent visits associated with treatment failure, antibiotic-associated adverse effects, or invasive bacterial infection. Cost of the index hospitalization included use from the emergency department visit and hospitalization. In PHIS, costs are estimated from charges by using hospital year–specific cost-to-charge ratios.

We examined demographic characteristics, including age, sex, race and/or ethnicity, and primary payer. We examined patient characteristics, including the number and types of medical complexity using CCCs.

We calculated unadjusted hospital-level inflammatory marker testing rates for individual infection APR-DRGs. We then adjusted testing rates for age, presence of a CCC, and severity using generalized linear models, controlling for clustering of patients within a hospital using a random intercept for each hospital. For generation of the heat maps, we grouped each hospital by quartile on the basis of adjusted tested rates across each infection APR-DRG. To group hospitals with similar inflammatory marker testing rates, we used k-means clustering, assigning each hospital to 1 of 3 groups on the basis of similarities in adjusted diagnostic testing rates across infection APR-DRGs.20  Canonical discriminant analysis was used to determine the number of groups. We used descriptive statistics to describe patient and hospital characteristics overall and for each of the 3 testing groups. Comparisons in patient and hospital characteristics across hospital testing groups were conducted by using the Kruskal-Wallis test. Generalized linear mixed models were used to examine the association of hospital testing group and outcomes, with adjustment for age, presence of a CCC, and severity. Severity was defined by using hospitalization resource intensity scores for kids (H-RISKs),21  which was developed to quantify severity of illness among hospitalized children and used to assign relative weights to each APR-DRG and severity-of-illness level, facilitating comparison across APR-DRG groups. All statistical analyses were performed by using SAS version 9.4 (SAS Institute, Inc, Cary, NC), and P values <.05 were considered statistically significant.

We included 55 771 hospitalizations for infection from 48 hospitals (Table 1). In 7945 (14.2%), inflammatory marker testing was obtained in the first 2 days of hospitalization. The median age was 1 year (interquartile range [IQR] 0–6 years). A majority of patients were male and non-Hispanic white and had government insurance. Approximately 22% of patients had a comorbid CCC. CRP testing rates varied from 2.4% for bronchiolitis and RSV pneumonia to 57.7% for osteomyelitis, septic arthritis, and other musculoskeletal infections. ESR testing rates varied from 0.4% for bronchiolitis and RSV pneumonia to 50.4% for osteomyelitis, septic arthritis, and other musculoskeletal infections. Among patients with a CRP test, ∼45.9% also had an ESR test obtained (ie, concomitant testing of ESR and CRP), with the greatest rates of concomitant testing occurring for osteomyelitis, septic arthritis, and other musculoskeletal infections (84.4%), followed by major gastrointestinal and peritoneal infections (56.1%) (Supplemental Table 3). The median unadjusted LOS across hospitals was 2.0 (IQR 1.7–2.2) days, the median unadjusted 30-day all-cause readmission rate across hospitals was 7.3% (IQR 5.8%–8.8%), and the median unadjusted cost across hospitals was $2822 (IQR$1486–$5530). TABLE 1 Demographic and Clinical Characteristics Overall and by Hospital Testing Cluster OverallHospital Cluster GroupP ABC No. hospitals 48 13 29 — No. hospitalizations 55 771 3389 16 371 36 011 — CRP and/or ESR testing, n (%) CRP 7692 (13.8) 1177 (34.7) 2891 (17.7) 3624 (10.1) <.001 ESR 3780 (6.8) 634 (18.7) 1178 (7.2) 1968 (5.5) <.001 CRP or ESR 7945 (14.2) 1266 (37.4) 2950 (18.0) 3729 (10.4) <.001 CRP and ESR 3527 (6.3) 545 (16.1) 1119 (6.8) 1863 (5.2) <.001 Sex, n (%) Female 25 338 (45.4) 1553 (45.8) 7345 (44.9) 16 440 (45.7) .219 Male 30 433 (54.6) 1836 (54.2) 9026 (55.1) 19 571 (54.3) — Age, y, n (%) <1 18 995 (34.1) 1017 (30.0) 5392 (32.9) 12 586 (35.0) <.001 1–5 21 589 (38.7) 1231 (36.3) 6578 (40.2) 13 780 (38.3) — 6–12 9354 (16.8) 700 (20.7) 2716 (16.6) 5938 (16.5) — 13–17 5833 (10.5) 441 (13.0) 1685 (10.3) 3707 (10.3) — Race and/or ethnicity, n (%) Non-Hispanic white 27 407 (49.1) 1477 (43.6) 6483 (39.6) 19 447 (54.0) <0001 Non-Hispanic Black 7540 (13.5) 356 (10.5) 1694 (10.3) 5490 (15.2) — Hispanic 12 063 (21.6) 932 (27.5) 5303 (32.4) 5828 (16.2) — Asian 1095 (2.0) 79 (2.3) 410 (2.5) 606 (1.7) — Other 7666 (13.7) 545 (16.1) 2481 (15.2) 4640 (12.9) — Payer, n (%) Private 20 378 (36.5) 1284 (37.9) 5668 (34.6) 13 426 (37.3) <.001 Government 33 398 (59.9) 1947 (57.5) 10 291 (62.9) 21 160 (58.8) — Other 1995 (3.6) 158 (4.7) 412 (2.5) 1425 (4.0) — Chronic conditions, n (%) Any chronic condition 11 984 (21.5) 758 (22.4) 3368 (20.6) 7858 (21.8) .002 Cardiovascular 2555 (4.6) 143 (4.2) 748 (4.6) 1664 (4.6) .563 Neurologic and neuromuscular 2652 (4.8) 145 (4.3) 736 (4.5) 1771 (4.9) .044 Respiratory 1368 (2.5) 82 (2.4) 356 (2.2) 930 (2.6) .020 Renal and urologic 1917 (3.4) 125 (3.7) 470 (2.9) 1322 (3.7) <.001 Gastrointestinal 4700 (8.4) 285 (8.4) 1310 (8.0) 3105 (8.6) .060 Hematology and immunodeficiency 1610 (2.9) 113 (3.3) 465 (2.8) 1032 (2.9) .272 Metabolic 1530 (2.7) 80 (2.4) 442 (2.7) 1008 (2.8) .302 Other congenital or genetic defect 2221 (4) 133 (3.9) 664 (4.1) 1424 (4.0) .846 Neonatal 510 (0.9) 27 (0.8) 154 (0.9) 329 (0.9) .725 Technology dependency 4828 (8.7) 277 (8.2) 1327 (8.1) 3224 (9.0) .004 Transplantation 70 (0.1) 3 (0.1) 19 (0.1) 48 (0.1) .719 No. CCCs, n (%) No CCCs 43 787 (78.5) 2631 (77.6) 13 003 (79.4) 28 153 (78.2) .001 1 CCC 5880 (10.5) 402 (11.9) 1686 (10.3) 3792 (10.5) — 2 CCCs 2416 (4.3) 151 (4.5) 649 (4.0) 1616 (4.5) — 3+ CCCs 3688 (6.6) 205 (6.0) 1033 (6.3) 2450 (6.8) — Mean H-RISK (SD) 0.355 (0.276) 0.373 (0.284) 0.361 (0.291) 0.351 (0.269) <.001 OverallHospital Cluster GroupP ABC No. hospitals 48 13 29 — No. hospitalizations 55 771 3389 16 371 36 011 — CRP and/or ESR testing, n (%) CRP 7692 (13.8) 1177 (34.7) 2891 (17.7) 3624 (10.1) <.001 ESR 3780 (6.8) 634 (18.7) 1178 (7.2) 1968 (5.5) <.001 CRP or ESR 7945 (14.2) 1266 (37.4) 2950 (18.0) 3729 (10.4) <.001 CRP and ESR 3527 (6.3) 545 (16.1) 1119 (6.8) 1863 (5.2) <.001 Sex, n (%) Female 25 338 (45.4) 1553 (45.8) 7345 (44.9) 16 440 (45.7) .219 Male 30 433 (54.6) 1836 (54.2) 9026 (55.1) 19 571 (54.3) — Age, y, n (%) <1 18 995 (34.1) 1017 (30.0) 5392 (32.9) 12 586 (35.0) <.001 1–5 21 589 (38.7) 1231 (36.3) 6578 (40.2) 13 780 (38.3) — 6–12 9354 (16.8) 700 (20.7) 2716 (16.6) 5938 (16.5) — 13–17 5833 (10.5) 441 (13.0) 1685 (10.3) 3707 (10.3) — Race and/or ethnicity, n (%) Non-Hispanic white 27 407 (49.1) 1477 (43.6) 6483 (39.6) 19 447 (54.0) <0001 Non-Hispanic Black 7540 (13.5) 356 (10.5) 1694 (10.3) 5490 (15.2) — Hispanic 12 063 (21.6) 932 (27.5) 5303 (32.4) 5828 (16.2) — Asian 1095 (2.0) 79 (2.3) 410 (2.5) 606 (1.7) — Other 7666 (13.7) 545 (16.1) 2481 (15.2) 4640 (12.9) — Payer, n (%) Private 20 378 (36.5) 1284 (37.9) 5668 (34.6) 13 426 (37.3) <.001 Government 33 398 (59.9) 1947 (57.5) 10 291 (62.9) 21 160 (58.8) — Other 1995 (3.6) 158 (4.7) 412 (2.5) 1425 (4.0) — Chronic conditions, n (%) Any chronic condition 11 984 (21.5) 758 (22.4) 3368 (20.6) 7858 (21.8) .002 Cardiovascular 2555 (4.6) 143 (4.2) 748 (4.6) 1664 (4.6) .563 Neurologic and neuromuscular 2652 (4.8) 145 (4.3) 736 (4.5) 1771 (4.9) .044 Respiratory 1368 (2.5) 82 (2.4) 356 (2.2) 930 (2.6) .020 Renal and urologic 1917 (3.4) 125 (3.7) 470 (2.9) 1322 (3.7) <.001 Gastrointestinal 4700 (8.4) 285 (8.4) 1310 (8.0) 3105 (8.6) .060 Hematology and immunodeficiency 1610 (2.9) 113 (3.3) 465 (2.8) 1032 (2.9) .272 Metabolic 1530 (2.7) 80 (2.4) 442 (2.7) 1008 (2.8) .302 Other congenital or genetic defect 2221 (4) 133 (3.9) 664 (4.1) 1424 (4.0) .846 Neonatal 510 (0.9) 27 (0.8) 154 (0.9) 329 (0.9) .725 Technology dependency 4828 (8.7) 277 (8.2) 1327 (8.1) 3224 (9.0) .004 Transplantation 70 (0.1) 3 (0.1) 19 (0.1) 48 (0.1) .719 No. CCCs, n (%) No CCCs 43 787 (78.5) 2631 (77.6) 13 003 (79.4) 28 153 (78.2) .001 1 CCC 5880 (10.5) 402 (11.9) 1686 (10.3) 3792 (10.5) — 2 CCCs 2416 (4.3) 151 (4.5) 649 (4.0) 1616 (4.5) — 3+ CCCs 3688 (6.6) 205 (6.0) 1033 (6.3) 2450 (6.8) — Mean H-RISK (SD) 0.355 (0.276) 0.373 (0.284) 0.361 (0.291) 0.351 (0.269) <.001 Hospitals in group A are high-testing hospitals, those in group B are intermediate-testing hospitals, and those in group C are low-testing hospitals. —, not applicable. We observed substantial variation in diagnostic testing across hospitals (Fig 1). Hospitals that obtained inflammatory marker testing frequently for one infection appear to test more frequently for other infections. For example, 6 hospitals tested above the median in all 12 diagnosis groups, and 12 hospitals tested below the median in all 12 diagnosis groups. Hospitals that tested more often for CRP also tested more often for ESR across infection subtypes. We observed similar patterns when examining concomitant testing (ie, hospitals that obtained concomitant testing for one infection appeared to obtain concomitant testing for other infections) (Supplemental Fig 3). FIGURE 1 Heat map of adjusted inflammatory marker testing rate quartiles across hospitals. Within the heat map, each quartile was assigned a score of 1 to 4, with scores summed across infection types within a hospital. Data are sorted on the basis of the overall score for each hospital. Hospitals with lower overall scores are presented on the left, and those with higher overall scores are presented on the right. Data are adjusted for age, presence of CCCs, and severity by using H-RISK. FIGURE 1 Heat map of adjusted inflammatory marker testing rate quartiles across hospitals. Within the heat map, each quartile was assigned a score of 1 to 4, with scores summed across infection types within a hospital. Data are sorted on the basis of the overall score for each hospital. Hospitals with lower overall scores are presented on the left, and those with higher overall scores are presented on the right. Data are adjusted for age, presence of CCCs, and severity by using H-RISK. Close modal Using k-means clustering, we grouped hospitals into 1 of 3 groups (labeled A for the highest-testing hospitals, B for the moderate-testing hospitals, and C for low-testing hospitals) on the basis of similarities in adjusted diagnostic testing rates. The groups were composed of 6, 13, and 29 hospitals, respectively. Group A hospitals tended to perform more inflammatory marker (CRP or ESR) tests than hospitals in groups B and C (37.4% vs 18.0% vs 10.4%; P < .001) (Table 1, Fig 2). Although infants 0 to 1 month of age are frequently considered a distinct population regarding testing practices, differences in the proportions of inflammatory marker tests by group were similarly observed for these infants (Groups A–C: 19.4% vs 10.8% vs 5.9%; P < .001). We observed statistically significant but small differences across cluster groups in the distribution of patient demographic and clinical characteristics, such as age, race and/or ethnicity, payer, number and type of chronic conditions, and mean H-RISK (Table 1). Within our adjusted models, we observed a significant difference in mean LOS across testing groups. The hospitals in the highest-testing group (A) had longer adjusted LOS compared with hospitals in groups B and C (3.24 vs 2.85 vs 2.82 days; P < .01) (Table 2). There was no significant difference in adjusted mean 30-day readmission rates or costs. FIGURE 2 Box and whisker diagrams revealing unadjusted percentage of inflammatory testing and interquartile ranges within testing clusters. Hospitals in group A are high-testing hospitals, those in group B are intermediate-testing hospitals, and those in group C are low-testing hospitals. FIGURE 2 Box and whisker diagrams revealing unadjusted percentage of inflammatory testing and interquartile ranges within testing clusters. Hospitals in group A are high-testing hospitals, those in group B are intermediate-testing hospitals, and those in group C are low-testing hospitals. Close modal TABLE 2 Association of Hospital Testing Clusters and Outcomes (LOS, 30-Day Readmissions, and Cost) Hospital Cluster GroupP ABC LOS, d, mean (95% CI) 3.24 (2.97–3.54) 2.85 (2.68–3.02) 2.82 (2.71–2.93) .013 30-Day readmission rate, %, mean (95% CI) 10.8 (8.7–13.2) 9.3 (8.0–10.7) 10.1 (9.1–11.2) .426 Cost,$, mean (95% CI) 7766 (5693–10 594) 6270 (5077–7744) 5538 (4806–6381) .131
Hospital Cluster GroupP
ABC
LOS, d, mean (95% CI) 3.24 (2.97–3.54) 2.85 (2.68–3.02) 2.82 (2.71–2.93) .013
30-Day readmission rate, %, mean (95% CI) 10.8 (8.7–13.2) 9.3 (8.0–10.7) 10.1 (9.1–11.2) .426
Cost, \$, mean (95% CI) 7766 (5693–10 594) 6270 (5077–7744) 5538 (4806–6381) .131

Data are adjusted for age, presence of CCCs, and severity by using H-RISK. Hospitals in group A are high-testing hospitals, those in group B are intermediate-testing hospitals, and those in group C are low-testing hospitals. CI, confidence interval.

In this multicenter cross-sectional study of early inflammatory marker testing in children, we observed wide variation in inflammatory marker use in children hospitalized with infections. We identified 2 major findings: (1) hospitals that obtained early inflammatory marker testing frequently for one infection obtained early inflammatory marker testing more frequently for other infections, and (2) hospitals with greater early inflammatory marker testing had longer LOS without a significant effect in readmission rates. These findings suggest opportunities to promote diagnostic stewardship.

As expected in our current study, inflammatory marker testing varied widely across infections. Although inflammatory markers were obtained infrequently for bronchiolitis (consistent with guidelines22 ), they were obtained with greater frequency for other infections, such as osteomyelitis, septic arthritis, and other musculoskeletal infections, in which studies have clearly defined evidence-based recommendations for use of inflammatory markers in diagnosing (eg, Kocher criteria23 ) and guiding antibiotic therapy, including transitions to oral antibiotics. Inflammatory markers are used to assist in the diagnosis and management of infections and inflammatory conditions as well as to promote antimicrobial stewardship and reduce unnecessary exposure to antibiotics.14,24,25  However, because CRP and ESR testing lack sensitivity and specificity, their utility in assisting with diagnosis lies primarily in cases in which there is either high or low pretest probability of a bacterial infection. Although diagnostic testing may help clinicians to exclude bacterial illnesses (ie, reduce antibiotic prescriptions) or transition to oral antibiotics sooner (ie, reduce exposure to parenteral antibiotics), overreliance on routine inflammatory marker testing may contribute to increased health care use and costs. For example, Kainth and Gigliotti17  previously reported that concomitant ESR and CRP testing within a large academic center resulted in increased expenditures without substantial clinical benefit. In other studies, authors describe a cascade effect, with practitioners obtaining increased rates of consultations, tests, and referrals in the setting of false-positive inflammatory marker testing.26  Consequently, in future studies, researchers should seek to define best practices for inflammatory marker testing and investigate strategies that balance diagnostic stewardship with antimicrobial stewardship to tackle health care spending.

Our current study highlights how several hospitals with increased inflammatory marker testing for one infectious diagnosis tend to have increased inflammatory marker testing for other infectious diagnoses, including increased rates of concomitant testing. Although variation in testing across conditions is desirable (ie, variation based on differential evidence), substantial variation in adjusted testing rates across hospitals is perhaps more worrisome and highlights an opportunity to standardize aspects of care across institutions, especially where these differences are associated with differences in hospitalization outcomes (eg, increased LOS). The reasons behind increased intensity of testing at some hospitals are likely numerous and include both individual provider factors, such as provider experience, practicing defensive medicine, and low tolerance of diagnostic ambiguity, as well as systems factors.27  Systems factors, such as local testing culture, may impact diagnostic test use at an organizational level and contribute to patterns in overuse similar to that observed in our study. For example, local protocols and/or policies, driven by factors such as the training environment or the intensity of services hospital wide (eg, more oncology patients or patients with immunodeficiency with limited ability to mount a fever response), may influence testing patterns at individual institutions. These results highlight that in addition to addressing individual testing behaviors, future interventions to impact diagnostic test use should also address testing culture within health care systems.28

In our study, increased inflammatory marker testing was associated with increased LOS, without concomitant reductions in 30-day readmissions or association with costs. This finding is consistent with a growing body of literature revealing that variation in testing is associated with increased health care use.2931  Although differences in LOS in adjusted analyses were modest in our study (ie, <12 hours), one cannot underestimate the impact that improving LOS can have on patients, families, and health care systems. For example, reducing LOS may help alleviate some of the emotional and financial stress that patients and their families experience, even for hospitalizations for transient illnesses, while simultaneously reducing an individual patient’s risk of obtaining false-positive results that require additional follow-up.3238  Additionally, reducing LOS can improve hospital efficiency and may potentially lead to cost savings.39  Our observation of no statistical differences in readmissions and cost is not wholly unexpected. Readmissions are overall uncommon in children, and although costs did decrease in a relatively linear manner across high- to low-testing institutions, these differences were not statistically significant. Our findings for costs likely reflect the fact that LOS is the predominant driver of pediatric inpatient costs and that institutional intensity of testing and services is more likely to influence LOS compared with cost. Taken together, our findings further suggest that increased resource use is not synonymous with improved health care delivery and that there are opportunities to reduce inflammatory marker testing, including reducing rates of concomitant testing.

Efforts such as the Choosing Wisely campaign have led to enhanced recognition of diagnostic overuse and initiation of local and national quality improvement initiatives aimed at reducing overuse. Recent studies reveal that implementation of quality improvement methodology and clinical practice guidelines may encourage reductions in unnecessary diagnostic testing. For example, use of quality improvement initiatives, such as education and standardized communication, has led to reductions in electrolyte and complete blood cell count testing as well as reductions in chest radiography for asthma.4042  Similarly, use of clinical practice guidelines has effectively lead to reductions in unnecessary bronchodilator, antibiotic, and chest radiography use in pediatric bronchiolitis.43,44  Although future studies are needed to define best practices for inflammatory marker testing and to outline achievable benchmarks for testing, use of quality improvement methodology and clinical practice guidelines may be effective strategies to reduce unnecessary inflammatory marker testing.

Our study should be interpreted in the context of several limitations. First, the PHIS database is an administrative database and does not contain data pertaining to clinical decision-making surrounding diagnostic testing. Consequently, we are limited in our ability to evaluate the appropriateness of diagnostic testing (ie, to determine if diagnostic test obtainment truly impacted the decision to start an antibiotic or the decision of when to transition to oral therapy). Additionally, we are unable to examine how local protocols and/or policies influence testing patterns at individual institutions because these data are not collected by PHIS. We sought to broadly describe variation in inflammatory marker use; however, procalcitonin testing was obtained infrequently across PHIS-participating hospitals during our study period and was not included in the analyses despite its demonstrated improved sensitivity and specificity compared with CRP testing for identifying febrile infants.45,46  Although we accounted for factors such as age, presence of a complex chronic condition, and APR-DRG severity of illness in our analyses, unaccounted-for differences in patient characteristics could certainly have contributed to variation and observed differences in the relationship between test variation and hospitalization outcomes across groups. Our focus on variation in initial testing during the first 2 days of hospitalization was only one aspect of pediatric diagnostic test overuse, and in future evaluations, researchers should focus on examining the impact of repeat diagnostic test obtainment on hospitalization outcomes.

For children hospitalized with infection, inflammatory marker testing varies widely across infection types and hospitals. Hospitals with higher inflammatory testing for one infection tend to test more frequently across other infection diagnoses and have longer LOS, suggesting a culture of overuse at some hospitals. Our results highlight the need to define best practices for diagnostic test use and the need for future quality improvement initiatives centered on optimizing diagnostic stewardship processes to improve health care value.

Dr Markham conceptualized and designed the study, analyzed and interpreted the data, and drafted the initial manuscript; Drs Thurm and Hall were involved in the study design, supervised the data analysis and interpretation, and reviewed and revised the manuscript; Drs S.S. Shah, Quinonez, Tchou, Antoon, Genies, Parlar-Chun, Johnson, S.P. Shah, and Ittel were involved in the study design, participated in the interpretation of data, and reviewed and revised the manuscript; Dr Brady supervised the conceptualization and design of the study, participated in the interpretation of data, and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: No external funding.

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## Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.