Routine Versus On-Demand Blood Sampling in Critically Ill Patients: A Systematic Review* : Critical Care Medicine

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Routine Versus On-Demand Blood Sampling in Critically Ill Patients: A Systematic Review*

Hjortsø, Carl J. S. BSc1; Møller, Morten H. MD, PhD1; Perner, Anders MD, PhD1; Brøchner, Anne C. MD, PhD2,3

Author Information
Critical Care Medicine 51(6):p 717-730, June 2023. | DOI: 10.1097/CCM.0000000000005852



Question: What is the current evidence concerning routine and on-demand blood sampling in critically ill patients?

Findings: In this systematic review of routine and on-demand blood sampling in critically ill patients, exposure to routine blood sampling was common and varied between critical care settings. Characteristics associated with increased routine blood testing included institutional and therapeutic factors. Reductions in routine blood testing appeared to be associated with reduced costs without apparent adverse patient outcomes.

Meaning: This review found that routine blood sampling in critically ill patients is common and supported by uncertain evidence.

In critical care settings, blood sampling is frequent, reflecting changes inpatient conditions or as part of daily routines (1). Data indicates that as much as half of all blood tests may be done without a clinical indication (2).

In 2010, the Archives of Internal Medicine (now JAMA Internal Medicine) launched the Less is More series advocating to investigate the harms and benefits of low-value healthcare and its impact on patients-important outcomes (3). This effort later inspired the development of the Choosing Wisely Campaign. The first of the Choosing Wisely Top 5 List in Critical Care Medicine states, “do not order diagnostic tests at regular intervals (such as every day), but rather in response to specific clinical questions” (4).

Extensive monitoring without a specific clinical question may be problematic. First, there is a risk of false positive tests. Second, technical errors, interpretive errors, and information overload may affect decision-making (5). Finally, excess blood sampling has been proposed to be associated with injury during phlebotomy, increased rates of central line-associated bloodstream infections, anemia, increased need for transfusions, overdiagnosis, and potentially unnecessary medical interventions (6–9).

With the U.S. national healthcare expenditures reaching 17.6% of the Gross Domestic Product in 2019 and increasing critical care costs, cost reduction or containment has become a growing concern regarding critically ill patients (10,11). Furthermore, data have shown considerable variation in the resources used to manage critically ill patients and no apparent correlation between intensivists’ spending and patient outcomes (12). Thus, it appears possible to lower costs and resource use without worsening patient outcomes.

Accordingly, the aim of this systematic review was to provide an overview of the current body of evidence on routine versus on-demand blood sampling in critical care. We hypothesized that routine blood tests in critical care settings are frequent and not supported by high-quality evidence.


Protocol and Registration

We reported our systematic review in compliance with a prepublished protocol (13) and the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guideline (14). The filled-in PRISMA checklist and a list of deviations from the protocol are available in the Supplemental Digital Content (

Eligibility Criteria

We framed the following research questions:

  • What proportion of critically ill patients are exposed to daily routine blood sampling?
  • What are the most frequent blood analyses done per daily routine?
  • Are specific characteristics (patient or organizational) associated with higher frequencies of daily routine blood sampling?
  • What are the reported potential patient-related benefits and harms of routine blood sampling compared with on-demand blood sampling in critically ill patients?
  • What are the reported potential resource-related implications of routine blood sampling compared with on-demand blood sampling in critically ill patients?

We included all study designs. We excluded studies in neonates, noncritically ill patient populations, and studies assessing a specific test for a specific indication (e.g., blood glucose test for diabetic patients).

We used a population, intervention, comparator, and outcome-based approach to define eligibility criteria.

Population. We included studies examining blood sampling in critical care settings, including adult ICU and PICU, high-dependency units (HDUs), emergency rooms, and trauma settings.

Intervention. On-demand blood sampling.

Comparator. Routine blood sampling or none.

Outcomes. Any outcome reported in the following three categories: patient-centered outcomes, test-centered outcomes, and resource utilization-centered outcomes.

Information Sources and Search Strategy

We systematically searched the Cochrane Library, the Medical Literature Analysis and Retrieval System Online, and the Excerpta Medica Database for relevant literature from 1946 to 2022 without limitations on publication date and language. The search strategy was pilot tested and refined before the final search. The latest search was performed in September 2022. To ensure literature saturation, we manually scanned the reference lists of all relevant trial and review articles identified in the search. The search strategies for all three databases are available in the Supplemental Digital Content ( We contacted the authors of identified conference abstract matching our eligibility criteria to retrieve any potential unpublished material or data not presented in the abstract.

Selection of Sources of Evidence

We used Covidence (Veritas Health Innovation, Melbourne, VIC, Australia) for the study selection process. Two authors (C.J.S.H., A.C.B.) independently screened titles and abstracts identified in the literature search and assessed their eligibility according to the inclusion criteria. All relevant studies were evaluated in full text. We translated studies written in languages other than English or Scandinavian using Google translate. Any disagreements were discussed and resolved by consensus (C.J.S.H., A.C.B.).

Data Extraction

Two authors (C.J.S.H., A.C.B.) independently extracted data using a predefined data extraction form. We extracted data regarding study characteristics, patient characteristics, and outcome measures. The full data extraction template is presented in the Supplemental Digital Content ( For all abstracts and in case of missing data, we contacted the authors for additional data.

Outcome Measures

We assessed any outcome reported.

Quality of Evidence Assessment

Two authors (C.J.S.H., A.C.B.) independently assessed the overall certainty of the evidence. We used an adaption of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach and assessed the overall certainty of the evidence for each research question (15). We downgraded the certainty of the evidence for risk of bias, inconsistency, indirectness, imprecision, and publication bias. Accordingly, we rated the overall certainty of the evidence from “high” to “very low.” Any disagreements were discussed with coauthors (M.H.M., A.P.).

Synthesis of Results

Study characteristics and results are presented descriptively with median and interquartile ranges for numeric data and numbers and percentages for categorical data according to design and population.

We grouped test frequency data in aggregated test groups based on the available data in the included studies (all routine blood tests, mixed biochemistry, coagulations screens, hematology, or blood gases), and used sample size and reported mean frequency of blood tests per patient day to calculate the median and interquartile ranges. Frequencies of biochemistry, coagulation screens, hematology, and blood gases were assessed per the unit used by the included studies.

The reported currencies were inflated to 2021 prices using the Gross Domestic Product implicit price deflator and then converted to Euros and U.S. dollars using December 2021 exchange rates to provide comparative adjusted estimates of annual cost reductions (16–18). Resource utilization-centered outcomes were grouped by setting and presented as median and interquartile ranges of estimated annual cost reduction per bed in the given unit.


Selection of Sources of Evidence

We included a total of 81 records representing 70 studies: 50 nonrandomized interventional studies (19–68) and 20 observational studies (69–88) (Fig. 1). Twenty-two studies were only available as conference abstracts (26–30,33,50,51,55,58,65–69,72,73,75,78,81,82,87). The main reasons for full-text exclusions were ineligible topic (n = 81), ineligible population (n = 58), no original data (n = 55), and ineligible intervention (n = 23). We contacted 41 authors (22 abstracts, 19 published articles) for additional data and clarifications and had a 32% answer rate (Supplemental Table 1,

Figure 1.:
Preferred Reporting Items for Systematic Review and Meta-Analysis flowchart. Flowchart illustrates study selection process.

Characteristics of Studies

Details about the included studies are available in Supplemental Tables 2 and 3 ( and summarized in Table 1. In brief, the studies were published between 1985 and 2022 and were conducted on four different continents. Twenty-eight out of 50 nonrandomized interventional studies were uninterrupted time series, 17 were interrupted time series, three were controlled interrupted time series, and two were nonrandomized controlled trials. Nine of the 20 observational studies were retrospective cohorts, eight were prospective cohorts, and three were cross-sectional studies.

TABLE 1. - Study Characteristics
Study Characteristics Studies, n (%) (Total = 70)
Study design
 Prospective cohort study 8 (11.4)
 Retrospective cohort study 9 (12.9)
 Cross-sectional study 3 (4.3)
 Nonrandomized controlled trial 2 (2.9)
 Controlled interrupted times series 3 (4.3)
 Interrupted times series 17 (24.3)
 Uninterrupted time series 28 (40.0)
Publication year
 1980–1989 2 (2.9)
 1990–1999 9 (12.9)
 2000–2009 10 (14.3)
 2010–2019 42 (60.0)
 2020–2022 7 (10.0)
 Europe 22 (31.4)
 North America 39 (55.7)
 South America 3 (4.3)
 Australia 6 (8.6)
Hospital type
 Teaching hospital 64 (91.4)
 Nonteaching hospital 4 (5.7)
 Both teaching and nonteaching 2 (2.9)
 Medical ICU 10 (14.3)
 Surgical ICU 10 (14.3)
 Mixed ICU 24 (34.3)
 Neurologic ICU 1 (1.4)
 PICU 6 (8.6)
 Trauma setting 4 (5.7)
 Other high-dependency unit 3 (4.3)
 Multiple settings 9 (12.9)
 Not reported 3 (4.3)
General study-level characteristics for all included studies. Data are presented as n (%).

Twenty-six out of 50 nonrandomized controlled studies implemented a multimodal intervention aiming to reduce routine blood testing composed of education (n = 26), guidelines (n = 17), ordering process changes (n = 11), audit (n = 4), checklists (n = 3), feedback (n = 3), financial support (n = 2), supervision (n = 2), review (n = 1), and visual reminders (n = 1). Twenty-three nonrandomized controlled studies implemented a monomodal intervention utilizing ordering process changes (n = 9), guidelines (n = 9), or education (n = 5). One study did not report details regarding their intervention (Supplemental Tables 2 and 3,

Most studies included patients from mixed ICUs. The remaining studies included patients from medical ICUs, surgical ICUs, PICUs, neurologic ICUs, trauma settings, and HDUs. The majority of studies were conducted in teaching hospitals. We observed significant heterogeneities in populations regarding the severity of illness, length of stay, and mortality (Supplemental Tables 4 and 5,

Outcome Measures

Outcome measures varied substantially between studies. We categorized them as test-centered, patient-centered, resource utilization-centered, and other outcomes as detailed in Supplemental Table 6 ( Findings within each outcome category are summarized in Supplemental Table 7 ( Additionally, the results of the individual studies are presented in Supplemental Tables 8 and 9 ( and summarized in the following chapters.

Exposure to Daily Routine Blood Sampling

Six out of 70 studies (9%) reported the proportion of included patients exposed to routine blood sampling (22,31,35,53,74,79). The reported daily routine blood sampling exposure was 100% in medical ICUs and 52% to 97% in trauma settings.

The overall certainty of the evidence for exposure-centered outcomes was very low and downgraded due to serious risk of bias, indirectness, and imprecision (Supplemental Table 10,

Most Frequent Routine Blood Tests

Sixty-three out of 70 studies (90%) reported test frequencies or the total number of routine blood tests during the study period (19–27,29–34,36–64,66–68,69–71,73,75–78,80–84,86–88). There were substantial variations in targeted tests and counting measures between studies, as shown in Supplemental Table 11 (

Twenty-two out of 70 studies (31%) reported frequencies of specific routine tests as tests per patient day (20,23,25,30,34,37,42,43,45–48,50,59,61,62,64,68,70,75–77). Overall, the most frequently ordered group of laboratory tests in ICU settings was mixed biochemistry with a median of five tests per day (interquartile range, 2–10, counted as defined in the included studies). Frequencies of aggregated test groups per patient day grouped by ICU settings are presented in Figure 2.

Figure 2.:
Test frequencies grouped by type and setting. ♦ indicating median test frequency. Chem = mixed biochemistry, Coag = coagulation tests, Gas = blood gases, Hema = hematological tests.

The overall certainty of the evidence for blood test frequency-centered outcomes was very low and downgraded due to serious risk of bias, inconsistency, and indirectness (Supplemental Table 10,

Factors Associated With Higher Frequencies of Routine Blood Sampling

Twenty-five out of 70 studies (36%) reported frequencies of all routine blood tests measured as tests per patient day (20,23–25,29,33,34,36–38,46,48,50,52,53,59,62,64,68,75–78,80,88).

Patients in medical ICUs were more frequently subjected to daily routine blood testing (median, 18; interquartile range, 10–33) compared with surgical ICUs (median, 12; interquartile range, 8–19), mixed ICUs (median, 8; interquartile range, 5–15), and PICUs (median, 8; interquartile range, 8–12), as presented in Figure 3.

Figure 3.:
Test frequencies grouped by setting. ♦ indicating median test frequency, error bars indicating interquartile range.

In total, four studies investigated the association between patient and therapeutic factors and the frequency of blood testing (83–85,88). Overall, studies reported arterial lines and mechanical ventilation as essential determinants of the frequencies of routine blood sampling.

Two out of 70 studies (3%) assessed the difference in laboratory test frequencies between teaching and nonteaching hospitals, reporting that patients in teaching hospitals were subjected to significantly more blood tests than patients in nonteaching hospitals (85,88).

The overall certainty of the evidence for outcomes related to factors influencing blood test frequencies was very low and downgraded due to serious risk of bias, inconsistency, and indirectness (Supplemental Table 10,

Patient-Related Benefits and Harms Associated With Routine Blood Sampling Compared With On-Demand Sampling

Forty-one out of the 50 (82%) included nonrandomized interventional studies (20–25,27,28,30–32,34–47,49–54,56–58,60–64,67) and 15 out of the 20 (75%) included observational studies (69,71,73–76,78–82,84–87) reported patient-centered outcome measures. Findings are presented in Supplemental Table 7 ( and summarized as follows.

Mortality. Twenty-eight out of 50 (56%) nonrandomized interventional studies reported the differences in mortality associated with an intervention targeting reduced use of routine blood sampling; no statistically significant differences were observed in mortality between groups (20,22–25,32,34,36–40,42–44,46,47,50,52–54,57,58,60,62–64,66).

Length of Stay. Length of stay was addressed in 26 of 50 (52%) nonrandomized interventional studies (20–22,25,30,32,34,36,37,39,40,42–46,52–54,57,58,60,62–64,66). Six studies reported a decrease in ICU length of stay with an intervention targeted reduced use of routine blood sampling (20,40,45,46,63,66), whereas the remaining 20 studies found no statistically significant differences.

Transfusion Rates. Transfusion rates were assessed in 12 out of 50 (24%) interventional studies (20,24,25,36,38,42,44,53,56,63,64,66), six of which reported a reduction in the number of RBCs transfused per patient associated with an intervention targeting reduced use of routine blood testing (25,36,38,44,63,66).

Adverse Events. Nineteen out of 50 studies (38%), assessing an intervention targeting reduced use of blood tests, reported adverse events (21–24,27,30–32,35,36,40,44,45,49,52,53,60,66,67). The majority found no adverse events or no statistically significant difference in adverse events associated with an intervention targeting reduced use of routine blood sampling. One study reported two minor adverse effects resulting in delayed testing with no adverse patient outcomes (49).

The overall certainty of the evidence for patient-centered outcomes from nonrandomized interventional studies was low and downgraded due to serious risk of bias and indirectness, while the certainty of evidence from observational studies was very low and downgraded due to serious risk of bias (Supplemental Table 10,

Resource-Related Implications of Routine Blood Sampling Compared With On-Demand Blood Sampling in Critically Ill Patients

Thirty-seven out of the 50 (74%) included nonrandomized interventional studies (20,22–26,28,30,31,34,37–53,55,57–62,66,67) and 13 out of the 20 (65%) included observational studies (69–73,75–78,80,82,83,86) reported resource utilization-centered outcome measures. Estimated annual cost reductions were reported in 34 of 50 (68%) nonrandomized interventional studies (20,23–26,30,31,34,37–53,55,57–62,66,67); variations were substantial in the reported estimates. Some studies reported purely on laboratory-related savings, whereas others included indirect savings and cost reductions from reduced nurse workload and chest radiograph use. The reported cost reduction varied considerably between studies and is available in Supplemental Table 12 ( The median and interquartile ranges of all annual cost reductions per ICU bed are presented in Table 2.

TABLE 2. - Cost Reductions Associated With Reduced Routine Blood Testing
Population n Studies Adjusted Estimated Annual Cost Reduction per ICU Bed ($) Adjusted Estimated Annual Cost Reduction per ICU Bed (€)
Medical ICU 6 20,465 (11,793–49,414) 18,094 (10,427–43,691)
Mixed ICU 10 5,617 (3,737–8,464) 4,966 (3,304–7,484)
Surgical ICU 4 22,729 (10,523–33,413) 20,096 (9,304–23,580)
PICU 2 18,438 (10,207–26,669) 16,303 (9,025–23,580)
Median (interquartile range) of adjusted estimated annual direct and indirect cost reduction associated with an intervention aiming to reduce routine blood testing among critically ill patients grouped by setting.
Data and adjustments are available in Supplemental Table 12 (

The overall certainty of the evidence for resource utilization-related outcomes was very low and downgraded due to serious risk of bias, inconsistency, and indirectness (Supplemental Table 10,


In this systematic review, we found considerable variations in routine blood sampling practices in critically ill patients. We identified teaching hospital status, admission to medical ICU, mechanical ventilation, and the presence of an arterial line as associated with a higher frequency of routine blood sampling. Interventions targeting testing seemed effective in reducing blood test frequencies and associated costs without apparent adverse patient outcomes. However, the certainty of evidence was low or very low.

We observed large variations in test frequency measures between studies. These variations may have been due to broadly inconsistent routine blood testing strategies and a lack of proper distinction between routine and on-demand tests. Furthermore, variations in the types of tests targeted with intervention and differences in counting measures between studies most likely contributed to the inconsistency. Whether these dissimilarities are a result of different test strategies or study designs remain unclear, thus warranting further investigation of current bloodwork strategies in intensive care settings.

Interventions targeting reduced use of testing differed considerably, indicating little consensus on the best practice regarding how to reduce potentially unnecessary blood testing, including what tests to target. Furthermore, some studies implemented broad interventions targeting the use of chest radiographs, blood transfusion, and pharmacy, potentially affecting results. Education of the clinical staff was the predominant intervention in the included studies followed by guideline development and ordering process changes such as modifications of the electronic medical record. All three interventions could be adopted in a broader setting to combat excessive blood testing in critically ill patients. Furthermore, we found that all included studies with interventions targeting reduced use of routine blood testing appeared safe. This finding is consistent with an earlier systematic review of interventions to reduce pathology testing and chest radiographs in ICU settings (89). The efficacy of different strategies to reduce blood test use has been described in hospital patients but not specifically for critically ill patients (90,91).

This review included studies conducted in critically ill patients broadly, including studies in both newly admitted critical care patients and patients having been in a critical care setting for a longer period of time. With only four included studies conducted in an initial trauma care setting, the identified studies were predominantly conducted in ongoing critical care settings, for example, ICUs or other high-dependency settings. Diagnostic blood testing differs in its purpose between de novo diagnosing during initial care and ongoing intensive care naturally leading to a difference in blood test frequencies. However, patient-safe reductions in blood test frequencies seemed feasible in both acute and ongoing critical care settings, indicating a common potential redundancy in current bloodwork strategies. Furthermore, the causality in differences between types of intensive care, for example, general, surgical, and medical is uncertain as patient characteristics were reported with limited details in the included studies. A potential correlation between intensive care settings and routine test frequencies is at best hypothesis-generating. Other factors related to higher test frequencies included the presence of arterial lines and mechanical ventilation. Although both factors may serve as surrogates for severity of illness, observational data indicate a clear correlation between the presence of arterial lines and the development of anemia during intensive care stay when adjusting for severity of illness, suggesting a relationship between the presence of arterial lines and increased phlebotomy for diagnostic purposes (92). Although an increased risk of ICU-acquired anemia for patients with arterial lines may primarily originate in blood loss due to flushing of in-dwelling lines (93), the findings of this review suggest that an increased frequency of routine testing may also contribute.

Anemia is common in critically ill patients and is associated with increased transfusion rates and worsened patient outcomes (94). Blood loss from laboratory testing is substantial and significantly associated with RBC transfusions (95). Despite associations between phlebotomy volumes and transfusion rates, only a fifth of the identified studies assessed the association between an intervention targeting reduced use of blood testing and reduced transfusion rates, leaving room for further investigation of this matter. Through our systematic search, we identified numerous studies aiming to reduce the volume of blood drawn for diagnostic tests using alternate blood test equipment and thus reduce the incidence of anemia in ICU settings. We did not include these studies as the scope and interventions differed from our aim. Although strategies pertaining blood-sparing techniques and small-volume blood collection tubes may correlate better to reduced transfusion rates (96), this review indicated that a restrictive blood testing strategy may also have an impact on hemoglobin levels, suggesting that multiple lines of approach are warranted to reduce occurrence of anemia and transfusion of RBC during critical illness.

Data suggests that the economic impact of laboratory testing comprises 17% to 40% of the variable ICU patient costs, making up 10% of the total direct ICU patient costs and averaging $168 per patient day (97–100). Korenstein et al (101) proposed a conceptual map illustrating that overused medical services, for example, blood testing, directly or indirectly through downstream services may lead to short-term and long-term consequences. Six domains of adverse effects were presented, covering social, financial, and psychological consequences, physical harm, additional treatment burden, and dissatisfaction with healthcare. Such effects might further increase the cost associated with inappropriate blood testing, although this seems not to have been explicitly investigated in a critical care setting. With limited resources and the growing cost of critical care services, rationing resources in intensive care settings is essential (102).

As we collect increasingly more data on critically ill patients, excess blood test results add to the already growing information load to which physicians must relate (103). In a recent survey of English emergency departments, physicians reported a widespread agreement that information overload had an impact on their work and that the problem increased over time. Among others, impaired decision-making and imprecise clinical judgment were reported as consequences of such information overload (104). Potentially redundant blood test measures may add to this information overload, thus distorting medical decision-making and possibly worsening the quality of patient care.

The strengths of our systematic review include a broad systematic literature search with no restrictions on language or publication year, a prepublished protocol, adherence to the PRISMA statement, and assessment of the certainty of evidence according to an adaption of GRADE.

Our review also has limitations. First, because of heterogeneity in terms of definitions, interventions, and study design, we cannot be certain to have identified all available studies and quality improvement projects. Second, variations in units of frequency measures may have affected our results as not all test frequencies were itemized, and some measurements were unfit to be synthesized. Third, this review lies methodologically between a systematic and a scoping review, thus, no registration on the International Prospective Register of Systematic Reviews was possible, and we refrained from conducting any meta-analyses due to heterogeneity. Fourth, as protocolized, we did not assess the risk of bias in detail for all studies. Fifth, we applied an adaption of the GRADE methodology adhering to the GRADE domains for assessing the certainty of evidence for each research question. Finally, studies in languages other than English or Scandinavian were translated during the study selection process, which may have led to misinterpretations. However, there were no disagreements between authors during screening and data extraction in this context.


In this systematic review, we observed considerable variations in blood sampling practices in critically ill patients. The frequency of routine blood testing seemed to vary between critical care settings and may be associated with teaching hospital status, admission to medical ICU, mechanical ventilation, and the presence of an arterial line. A reduction in routine blood testing appeared to be associated with reduced transfusion rates and costs and no adverse events. However, the certainty of the evidence was very low/low. Therefore, high-quality randomized trials are warranted to assess the balance between the benefits and harms of different blood testing strategies in critically ill patients.


The investigators would like to thank all the authors of the primary research material, particularly those who provided clarifications and additional data on their work.


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