Affiliations
Department of Medicine, University of Michigan Health System
Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, Michigan
Given name(s)
Theodore J.
Family name
Iwashyna
Degrees
MD, PhD

Do Clinicians Understand Quality Metric Data?

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Do clinicians understand quality metric data? An evaluation in a Twitter-derived sample

Central line-associated bloodstream infections (CLABSIs) are common and serious occurrences across healthcare systems, with an attributable mortality of 12% to 25%.1,2 Given this burden,3–5 CLABSI is a focus for both high-profile public reporting and quality improvement interventions. An integral component of such interventions is audit and feedback via quality metrics. These measures are intended to allow decision makers to assess their own performance and appropriately allocate resources. Quality metrics present a substantial cost to health systems, with an estimated $15.4 billion dollars spent annually simply for reporting.6 Despite this toll, “audit and feedback” interventions have proven to be variably successful.7–9 The mechanisms that limit the effectiveness of these interventions remain
poorly understood.

One plausible explanation for limited efficacy of quality metrics is inadequate clinician numeracy—that is, “the ability to understand the quantitative aspects of clinical medicine, original research, quality improvement, and financial matters.”10 Indeed, clinicians are not consistently able to interpret probabilities and or clinical test characteristics. For example, Wegwarth et al. identified shortcomings in physician application of lead-time bias toward cancer screening.11 Additionally, studies have demonstrated systematic misinterpretations of probabilistic information in clinical settings, along with misconceptions regarding the impact of prevalence on post-test probabilities.12,13 Effective interpretation of rates may be a key—if unstated—requirement of many CLABSI quality improvement efforts.14–19 Our broader hypothesis is that clinicians who can more accurately interpret quality data, even if only from their own institution, are more likely to act on it appropriately and persistently than those who feel they must depend on a preprocessed interpretation of that same data by some other expert.

Therefore, we designed a survey to assess the numeracy of clinicians on CLABSI data presented in a prototypical feedback report. We studied 3 domains of comprehension: (1) basic numeracy: numerical tasks related to simple data; (2) risk-adjustment numeracy: numerical tasks related to risk-adjusted data; and (3) risk-adjustment interpretation: inferential tasks concerning risk-adjusted data. We hypothesized that clinician performance would vary substantially across domains, with the poorest performance in risk-
adjusted data.

METHODS

We conducted a cross-sectional survey of clinician numeracy regarding CLABSI feedback data. Respondents were also asked to provide demographic information and opinions regarding the reliability of quality metric data. Survey recruitment occurred on Twitter, a novel approach that leveraged social media to facilitate rapid recruitment of participants. The study instrument was administered using a web survey with randomized question order to preclude any possibility of order effects between questions. The study was deemed Institutional Review Board exempt by the University of Michigan: protocol HUM00106696.

Data Presentation Method

To determine the optimal mode of presenting data, we reviewed the literature on quality metric numeracy and presentation methods. Additionally, we evaluated quality metric presentation methods used by the Centers for Disease Control and Prevention (CDC), Centers for Medicare & Medicaid Services (CMS), and a tertiary academic medical center. After assessing the available literature and options, we adapted a CLABSI data presentation array from a study that had qualitatively validated the format using physician feedback (Appendix).20 We used hypothetical CLABSI data for our survey.

Survey Development

We developed a survey that included an 11-item test regarding CLABSI numeracy and data interpretation. Additional questions related to quality metric reliability and demographic information were included. No preexisting assessment tools existed for our areas of interest. Therefore, we developed a novel instrument using a broad, exploratory approach as others have employed.21 

First, we defined 3 conceptual categories related to CLABSI data. Within this conceptual framework, an iterative process of development and revision was used to assemble a question bank from which the survey would be constructed. A series of think-aloud sessions were held to evaluate each prompt for precision, clarity, and accuracy in assessing the conceptual categories. Correct and incorrect answers were defined based on literature review in conjunction with input from methodological and content experts (TJI and VC) (see Appendix for answer explanations). 

Within the conceptual categories related to CLABSI risk-adjustment, a key measure is the standardized infection ratio (SIR). This value is defined as the ratio of observed number of CLABSI over the expected number of CLABSIs.22 This is the primary measure to stratify hospital performance, and it was used in our assessment of risk-adjustment comprehension. In total, 54 question prompts were developed and subsequently narrowed to 11 study questions for the initial survey. 

The instrument was then pretested in a cohort of 8 hospitalists and intensivists to ensure appropriate comprehension, retrieval, and judgment processes.23 Questions were revised based on feedback from this cognitive testing to constitute the final instrument. During the survey, the data table was reshown on each page directly above each question and so was always on the same screen for the respondents.

Survey Sample

We innovated by using Twitter as an online platform for recruiting participants; we used Survey Monkey to host the electronic instrument. Two authors (TJI, VC) systematically sent out solicitation tweets to their followers. These tweets clearly indicated that the recruitment was for the purpose of a research study, and participants would receive no financial reward/incentive (Appendix). A link to the survey was provided in each tweet, and the period of recruitment was 30 days. To ensure respondents were clinicians, they needed to first answer a screening question recognizing that central lines were placed in the subclavian site but not the aorta, iliac, or radial sites.

To prevent systematic or anchoring biases, the order of questions was electronically randomized for each respondent. The primary outcome was the percentage correct of attempted questions.

Statistical Analysis

Descriptive statistics were calculated for all demographic variables. The primary outcome was evaluated as a dichotomous variable for each question (correct vs. incorrect response), and as a continuous variable when assessing mean percent correct on the overall survey. Demographic and conceptual associations were assessed via t-tests, chi-square, or Fisher exact tests. Point biserial correlations were calculated to assess for associations between response to a single question and overall performance on the survey. 

To evaluate the association between various respondent characteristics and responses, logistic regression analyses were performed. An ANOVA was performed to assess the association between self-reported reliability of quality metric data and the overall performance on attempted items. Analyses were conducted using STATA MP 14.0 (College Station, TX); P <0.05 was considered statistically significant.

RESULTS

A total of 97 respondents attempted at least 1 question on the survey, and 72 respondents attempted all 11 questions, yielding 939 unique responses for analysis. Seventy respondents (87%) identified as doctors or nurses, and 44 (55%) reported having 6 to 20 years of experience; the survey cohort also came from 6 nations (Table 1). All respondents answered the CLABSI knowledge filter question correctly.

Respondent Demographics
Table 1

Primary Outcome

The mean percent correct of attempted questions was 61% (standard deviation 21%, interquartile range 50%-75%) (Figure 1). Of those who answered all 11 CLABSI questions, the mean percent correct was 63% (95% CI, 59%-67%). Some questions were answered correctly more often than others—ranging from 17% to 95% (Table 2). Doctors answered 68% of questions correctly (95% CI, 63%-73%), while nurses and other respondents answered 57% of questions correctly (95% CI, 52%-62%) (P = 0.003). Other demographic variables—including self-reported involvement in a quality improvement committee and being from the United States versus elsewhere—were not associated with survey performance. The point biserial correlations for each individual question with overall performance were all more than 0.2 (range 0.24–0.62) and all statistically significant at P < 0.05.

Percent Correct of Attempted Questions
Figure 1

 

Concept-Specific Performance

Average percent correct declined across categories as numeracy requirements increased (P < 0.05 for all pairwise comparisons). In the area of basic numeracy, respondents’ mean percent correct was 82% (95% CI, 77%-87%) of attempted. This category had 4 questions, with a performance range of 77% to 90%. For example, on the question, “Which hospital has the lowest CLABSI rate?”, 80% of respondents answered correctly. For risk-adjustment numeracy, the mean percent correct was 70% (95% CI, 64%-76%); 2 items assessed this category. For “Which is better: a higher or lower SIR?”, 95% of the cohort answered correctly. However, on “If hospital B had its number of projected infection halved, what is its SIR?”, only 46% of those who attempted the question answered correctly.

Questions featuring risk-adjustment interpretation had an average percent correct of 43% (95% CI, 37%-49%). Five questions made up this category, with a percent correct range of 17% to 75%. For example, on the question, “Which hospital’s patients are the most predisposed to developing CLABSI?”, only 32% of respondents answered this correctly. In contrast, for the question “Which hospital is most effective at preventing CLABSI?”, 51% answered correctly. Figure 2 illustrates the cohort’s performance on each conceptual category while Table 2 displays question-by-question results.

Performance by Conceptual Category
Figure 2

CLABSI Numeracy and Interpretation Assessment
Table 2

Opinions Regarding CLABSI Data Reliability

Respondents were also asked about their opinion regarding the reliability of CLABSI quality metric data. Forty-three percent of respondents stated that such data were reliable at best 50% of the time. Notably, 10% of respondents indicated that CLABSI quality metric data were rarely or never reliable. There was no association between perceived reliability of quality metric data and survey performance (P = 0.87).

DISCUSSION

This Twitter-based study found wide variation in clinician interpretation of CLABSI quality data, with low overall performance. In particular, comprehension and interpretation of risk-adjusted data were substantially worse than unadjusted data. Although doctors performed somewhat better than nurses and other respondents, those involved in quality improvement initiatives performed no better than respondents who were not. Collectively, these findings suggest clinicians may not reliably comprehend quality metric data, potentially affecting their ability to utilize audit and feedback data. These results may have important implications for policy efforts that seek to leverage quality metric data to improve patient safety.

An integral component of many contemporary quality improvement initiatives is audit and feedback through metrics.6 Unfortunately, formal audit and feedback, along with other similar methods that benchmark data, have not consistently improved outcomes.24–27 A recent meta-analysis noted that audit and feedback interventions are not becoming more efficacious over time; the study further asserted that “new trials have provided little new knowledge regarding key effect modifiers.”9 Our findings suggest that numeracy and comprehension of quality metrics may be important candidate effect modifiers not previously considered. Simply put: we hypothesize that without intrinsic comprehension of data, impetus or insight to change practice might be diminished. In other words, clinicians may be more apt to act on insights they themselves derive from the data than when they are simply told what the data “mean.”

The present study further demonstrates that clinicians do not understand risk-adjusted data as well as raw data. Risk-adjustment has long been recognized as necessary to compare outcomes among hospitals.28,29 However, risk-adjustment is complex and, by its nature, difficult to understand. Although efforts have focused on improving the statistical reliability of quality metrics, this may represent but one half of the equation. Numeracy and interpretation of the data by decision makers are potentially equally important to effecting change. Because clinicians seem to have difficulty understanding risk-adjusted data, this deficit may be of growing importance as our risk-adjustment techniques become more sophisticated.

We note that clinicians expressed concerns regarding the reliability of quality metric feedback. These findings corroborate recent research that has reported reservations from hospital leaders concerning quality data.30,31 However, as shown in the context of patients and healthcare decisions, the aversion associated with quality metrics may be related to incomplete understanding of the data.32 Whether perceptions of unreliability drive lack of understanding or, conversely, whether lack of understanding fuels perceived unreliability is an important question that requires further study.

This study has several strengths. First, we used rigorous survey development techniques to evaluate the understudied issue of quality metric numeracy. Second, our sample size was sufficient to show statistically significant differences in numeracy and comprehension of CLABSI quality metric data. Third, we leveraged social media to rapidly acquire this sample. Finally, our results provided new insights that may have important implications in the area of quality metrics.

There were also limitations to our study. First, the Twitter-derived sample precludes the calculation of a response rate and may not be representative of individuals engaged in CLABSI prevention. However, respondents were solicited from the Twitter-followers of 2 health services researchers (TJI, VC) who are actively engaged in scholarly activities pertaining to critically ill patients and hospital-acquired complications. Thus, our sample likely represents a highly motivated subset that engages in these topics on a regular basis—potentially making them more numerate than average clinicians. Second, we did not ask whether the respondents had previously seen CLABSI data specifically, so we cannot stratify by exposure to such data. Third, this study assessed only CLABSI quality metric data; generalizations regarding numeracy with other metrics should be made with caution. However, as many such data are presented in similar formats, we suspect our findings are applicable to similar audit-and-feedback initiatives.

The findings of this study serve as a stimulus for further inquiry. Research of this nature needs to be carried out in samples drawn from specific, policy-relevant populations (eg, infection control practitioners, bedside nurses, intensive care unit directors). Such studies should include longitudinal assessments of numeracy that attempt to mechanistically examine its impact on CLABSI prevention efforts and outcomes. The latter is an important issue as the link between numeracy and behavioral response, while plausible, cannot be assumed, particularly given the complexity of issues related to behavioral modification.33 Additionally, whether alternate presentations of quality data affect numeracy, interpretation, and performance is worthy of further testing; indeed, this has been shown to be the case in other forms of communication.34–37 Until data from larger samples are available, it may be prudent for quality improvement leaders to assess the comprehension of local clinicians regarding feedback and whether lack of adequate comprehension is a barrier to deploying quality improvement interventions.

Quality measurement is a cornerstone of patient safety as it seeks to assess and improve the care delivered at the bedside. Rigorous metric development is important; however, ensuring that decision makers understand complex quality metrics may be equally fundamental. Given the cost of examining quality, elucidating the mechanisms of numeracy and interpretation as decision makers engage with quality metric data is necessary, along with whether improved comprehension leads to behavior change. Such inquiry may provide an evidence-base to shape alterations in quality metric deployment that will ensure maximal efficacy in driving practice change.

Disclosures

This work was supported by VA HSR&D IIR-13-079 (TJI). Dr. Chopra is supported by a career development award from the Agency of Healthcare Research and Quality (1-K08-HS022835-01). The views expressed here are the authors’ own and do not necessarily represent the view of the US Government or the Department of Veterans’ Affairs. The authors report no conflicts of interest.

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References

1. Scott RD II. The direct medical costs of healthcare-associated infections in us hospitals and the benefits of prevention. Centers for Disease Control and Prevention. Available at: http://www.cdc.gov/HAI/pdfs/hai/Scott_CostPaper.pdf. Published March 2009. Accessed November 8, 2016.

2. O’Grady NP, Alexander M, Burns LA, et al. Guidelines for the prevention of intravascular catheter-related infections. Am J Infect Control. 2011;39(4 suppl 1)::S1-S34. PubMed

3. Blot K, Bergs J, Vogelaers D, Blot S, Vandijck D. Prevention of central line-associated bloodstream infections through quality improvement interventions: a systematic review and meta-analysis. Clin Infect Dis. 2014;59(1):96-105. PubMed

4. Mermel LA. Prevention of intravascular catheter-related infections. Ann Intern Med. 2000;132(5):391-402. PubMed

5. Siempos II, Kopterides P, Tsangaris I, Dimopoulou I, Armaganidis AE. Impact of catheter-related bloodstream infections on the mortality of critically ill patients: a meta-analysis. Crit Care Med. 2009;37(7):2283-2289. PubMed

6. Casalino LP, Gans D, Weber R, et al. US physician practices spend more than $15.4 billion annually to report quality measures. Health Aff (Millwood). 2016;35(3):401-406. PubMed

7. Hysong SJ. Meta-analysis: audit and feedback features impact effectiveness on care quality. Med Care. 2009;47(3):356-363. PubMed

8. Ilgen DR, Fisher CD, Taylor MS. Consequences of individual feedback on behavior in organizations. J Appl Psychol. 1979;64:349-371. 

9. Ivers NM, Grimshaw JM, Jamtvedt G, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med. 2014;29(11):1534-1541. PubMed

10. Rao G. Physician numeracy: essential skills for practicing evidence-based medicine. Fam Med. 2008;40(5):354-358. PubMed

11. Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, Gigerenzer G. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Ann Intern Med. 2012;156(5):340-349. PubMed

12. Bramwell R, West H, Salmon P. Health professionals’ and service users’ interpretation of screening test results: experimental study. BMJ. 2006;333(7562):284. PubMed

13. Agoritsas T, Courvoisier DS, Combescure C, Deom M, Perneger TV. Does prevalence matter to physicians in estimating post-test probability of disease? A randomized trial. J Gen Intern Med. 2011;26(4):373-378. PubMed

14. Warren DK, Zack JE, Mayfield JL, et al. The effect of an education program on the incidence of central venous catheter-associated bloodstream infection in a medical ICU. Chest. 2004;126(5):1612-1618. PubMed

15. Rinke ML, Bundy DG, Chen AR, et al. Central line maintenance bundles and CLABSIs in ambulatory oncology patients. Pediatrics. 2013;132(5):e1403-e1412. PubMed

16. Pronovost P, Needham D, Berenholtz S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med. 2006;355(26):
2725-2732. PubMed

17. Rinke ML, Chen AR, Bundy DG, et al. Implementation of a central line maintenance care bundle in hospitalized pediatric oncology patients. Pediatrics. 2012;130(4):e996-e1004. PubMed

18. Sacks GD, Diggs BS, Hadjizacharia P, Green D, Salim A, Malinoski DJ. Reducing the rate of catheter-associated bloodstream infections in a surgical intensive care unit using the Institute for Healthcare Improvement Central Line Bundle. Am J Surg. 2014;207(6):817-823. PubMed

19. Berenholtz SM, Pronovost PJ, Lipsett PA, et al. Eliminating catheter-related bloodstream infections in the intensive care unit. Crit Care Med. 2004;32(10):2014-2020. PubMed

20. Rajwan YG, Barclay PW, Lee T, Sun IF, Passaretti C, Lehmann H. Visualizing central line-associated blood stream infection (CLABSI) outcome data for decision making by health care consumers and practitioners—an evaluation study. Online J Public Health Inform. 2013;5(2):218. PubMed

21. Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making 2007;27(5):672-680. PubMed

22. HAI progress report FAQ. 2016. Available at: http://www.cdc.gov/hai/surveillance/progress-report/faq.html. Last updated March 2, 2016. Accessed November 8, 2016.

23. Collins D. Pretesting survey instruments: an overview of cognitive methods. Qual Life Res. 2003;12(3):229-238. PubMed

24. Ivers N, Jamtvedt G, Flottorp S, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012;(6):CD000259. PubMed

25. Chatterjee P, Joynt KE. Do cardiology quality measures actually improve patient outcomes? J Am Heart Assoc. 2014;3(1):e000404. PubMed

26. Joynt KE, Blumenthal DM, Orav EJ, Resnic FS, Jha AK. Association of public reporting for percutaneous coronary intervention with utilization and outcomes among Medicare beneficiaries with acute myocardial infarction. JAMA. 2012;308(14):1460-1468. PubMed

27. Ryan AM, Nallamothu BK, Dimick JB. Medicare’s public reporting initiative on hospital quality had modest or no impact on mortality from three key conditions. Health Aff (Millwood). 2012;31(3):585-592. PubMed

28. Thomas JW. Risk adjustment for measuring health care outcomes, 3rd edition. Int J Qual Health Care. 2004;16(2):181-182. 

29. Iezzoni LI. Risk Adjustment for Measuring Health Care Outcomes. Ann Arbor, Michigan: Health Administration Press; 1994.

30. Goff SL, Lagu T, Pekow PS, et al. A qualitative analysis of hospital leaders’ opinions about publicly reported measures of health care quality. Jt Comm J Qual Patient Saf. 2015;41(4):169-176. PubMed

31. Lindenauer PK, Lagu T, Ross JS, et al. Attitudes of hospital leaders toward publicly reported measures of health care quality. JAMA Intern Med. 2014;174(12):
1904-1911. PubMed

32. Peters E, Hibbard J, Slovic P, Dieckmann N. Numeracy skill and the communication, comprehension, and use of risk-benefit information. Health Aff (Millwood). 2007;26(3):741-748. PubMed

33. Montano DE, Kasprzyk D. Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In: Glanz K, Rimer BK, Viswanath K, eds. Health Behavior and Health Education: Theory, Research and Practice. 5th ed. San Francisco, CA: Jossey-Bass; 2015:95–124.

34. Hamstra DA, Johnson SB, Daignault S, et al. The impact of numeracy on verbatim knowledge of the longitudinal risk for prostate cancer recurrence following radiation therapy. Med Decis Making. 2015;35(1):27-36. PubMed

35. Hawley ST, Zikmund-Fisher B, Ubel P, Jancovic A, Lucas T, Fagerlin A. The impact of the format of graphical presentation on health-related knowledge and treatment choices. Patient Educ Couns. 2008;73(3):448-455. PubMed

36. Zikmund-Fisher BJ, Witteman HO, Dickson M, et al. Blocks, ovals, or people? Icon type affects risk perceptions and recall of pictographs. Med Decis Making. 2014;34(4):443-453. PubMed

37. Korfage IJ, Fuhrel-Forbis A, Ubel PA, et al. Informed choice about breast cancer prevention: randomized controlled trial of an online decision aid intervention. Breast Cancer Res. 2013;15(5):R74. PubMed

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Central line-associated bloodstream infections (CLABSIs) are common and serious occurrences across healthcare systems, with an attributable mortality of 12% to 25%.1,2 Given this burden,3–5 CLABSI is a focus for both high-profile public reporting and quality improvement interventions. An integral component of such interventions is audit and feedback via quality metrics. These measures are intended to allow decision makers to assess their own performance and appropriately allocate resources. Quality metrics present a substantial cost to health systems, with an estimated $15.4 billion dollars spent annually simply for reporting.6 Despite this toll, “audit and feedback” interventions have proven to be variably successful.7–9 The mechanisms that limit the effectiveness of these interventions remain
poorly understood.

One plausible explanation for limited efficacy of quality metrics is inadequate clinician numeracy—that is, “the ability to understand the quantitative aspects of clinical medicine, original research, quality improvement, and financial matters.”10 Indeed, clinicians are not consistently able to interpret probabilities and or clinical test characteristics. For example, Wegwarth et al. identified shortcomings in physician application of lead-time bias toward cancer screening.11 Additionally, studies have demonstrated systematic misinterpretations of probabilistic information in clinical settings, along with misconceptions regarding the impact of prevalence on post-test probabilities.12,13 Effective interpretation of rates may be a key—if unstated—requirement of many CLABSI quality improvement efforts.14–19 Our broader hypothesis is that clinicians who can more accurately interpret quality data, even if only from their own institution, are more likely to act on it appropriately and persistently than those who feel they must depend on a preprocessed interpretation of that same data by some other expert.

Therefore, we designed a survey to assess the numeracy of clinicians on CLABSI data presented in a prototypical feedback report. We studied 3 domains of comprehension: (1) basic numeracy: numerical tasks related to simple data; (2) risk-adjustment numeracy: numerical tasks related to risk-adjusted data; and (3) risk-adjustment interpretation: inferential tasks concerning risk-adjusted data. We hypothesized that clinician performance would vary substantially across domains, with the poorest performance in risk-
adjusted data.

METHODS

We conducted a cross-sectional survey of clinician numeracy regarding CLABSI feedback data. Respondents were also asked to provide demographic information and opinions regarding the reliability of quality metric data. Survey recruitment occurred on Twitter, a novel approach that leveraged social media to facilitate rapid recruitment of participants. The study instrument was administered using a web survey with randomized question order to preclude any possibility of order effects between questions. The study was deemed Institutional Review Board exempt by the University of Michigan: protocol HUM00106696.

Data Presentation Method

To determine the optimal mode of presenting data, we reviewed the literature on quality metric numeracy and presentation methods. Additionally, we evaluated quality metric presentation methods used by the Centers for Disease Control and Prevention (CDC), Centers for Medicare & Medicaid Services (CMS), and a tertiary academic medical center. After assessing the available literature and options, we adapted a CLABSI data presentation array from a study that had qualitatively validated the format using physician feedback (Appendix).20 We used hypothetical CLABSI data for our survey.

Survey Development

We developed a survey that included an 11-item test regarding CLABSI numeracy and data interpretation. Additional questions related to quality metric reliability and demographic information were included. No preexisting assessment tools existed for our areas of interest. Therefore, we developed a novel instrument using a broad, exploratory approach as others have employed.21 

First, we defined 3 conceptual categories related to CLABSI data. Within this conceptual framework, an iterative process of development and revision was used to assemble a question bank from which the survey would be constructed. A series of think-aloud sessions were held to evaluate each prompt for precision, clarity, and accuracy in assessing the conceptual categories. Correct and incorrect answers were defined based on literature review in conjunction with input from methodological and content experts (TJI and VC) (see Appendix for answer explanations). 

Within the conceptual categories related to CLABSI risk-adjustment, a key measure is the standardized infection ratio (SIR). This value is defined as the ratio of observed number of CLABSI over the expected number of CLABSIs.22 This is the primary measure to stratify hospital performance, and it was used in our assessment of risk-adjustment comprehension. In total, 54 question prompts were developed and subsequently narrowed to 11 study questions for the initial survey. 

The instrument was then pretested in a cohort of 8 hospitalists and intensivists to ensure appropriate comprehension, retrieval, and judgment processes.23 Questions were revised based on feedback from this cognitive testing to constitute the final instrument. During the survey, the data table was reshown on each page directly above each question and so was always on the same screen for the respondents.

Survey Sample

We innovated by using Twitter as an online platform for recruiting participants; we used Survey Monkey to host the electronic instrument. Two authors (TJI, VC) systematically sent out solicitation tweets to their followers. These tweets clearly indicated that the recruitment was for the purpose of a research study, and participants would receive no financial reward/incentive (Appendix). A link to the survey was provided in each tweet, and the period of recruitment was 30 days. To ensure respondents were clinicians, they needed to first answer a screening question recognizing that central lines were placed in the subclavian site but not the aorta, iliac, or radial sites.

To prevent systematic or anchoring biases, the order of questions was electronically randomized for each respondent. The primary outcome was the percentage correct of attempted questions.

Statistical Analysis

Descriptive statistics were calculated for all demographic variables. The primary outcome was evaluated as a dichotomous variable for each question (correct vs. incorrect response), and as a continuous variable when assessing mean percent correct on the overall survey. Demographic and conceptual associations were assessed via t-tests, chi-square, or Fisher exact tests. Point biserial correlations were calculated to assess for associations between response to a single question and overall performance on the survey. 

To evaluate the association between various respondent characteristics and responses, logistic regression analyses were performed. An ANOVA was performed to assess the association between self-reported reliability of quality metric data and the overall performance on attempted items. Analyses were conducted using STATA MP 14.0 (College Station, TX); P <0.05 was considered statistically significant.

RESULTS

A total of 97 respondents attempted at least 1 question on the survey, and 72 respondents attempted all 11 questions, yielding 939 unique responses for analysis. Seventy respondents (87%) identified as doctors or nurses, and 44 (55%) reported having 6 to 20 years of experience; the survey cohort also came from 6 nations (Table 1). All respondents answered the CLABSI knowledge filter question correctly.

Respondent Demographics
Table 1

Primary Outcome

The mean percent correct of attempted questions was 61% (standard deviation 21%, interquartile range 50%-75%) (Figure 1). Of those who answered all 11 CLABSI questions, the mean percent correct was 63% (95% CI, 59%-67%). Some questions were answered correctly more often than others—ranging from 17% to 95% (Table 2). Doctors answered 68% of questions correctly (95% CI, 63%-73%), while nurses and other respondents answered 57% of questions correctly (95% CI, 52%-62%) (P = 0.003). Other demographic variables—including self-reported involvement in a quality improvement committee and being from the United States versus elsewhere—were not associated with survey performance. The point biserial correlations for each individual question with overall performance were all more than 0.2 (range 0.24–0.62) and all statistically significant at P < 0.05.

Percent Correct of Attempted Questions
Figure 1

 

Concept-Specific Performance

Average percent correct declined across categories as numeracy requirements increased (P < 0.05 for all pairwise comparisons). In the area of basic numeracy, respondents’ mean percent correct was 82% (95% CI, 77%-87%) of attempted. This category had 4 questions, with a performance range of 77% to 90%. For example, on the question, “Which hospital has the lowest CLABSI rate?”, 80% of respondents answered correctly. For risk-adjustment numeracy, the mean percent correct was 70% (95% CI, 64%-76%); 2 items assessed this category. For “Which is better: a higher or lower SIR?”, 95% of the cohort answered correctly. However, on “If hospital B had its number of projected infection halved, what is its SIR?”, only 46% of those who attempted the question answered correctly.

Questions featuring risk-adjustment interpretation had an average percent correct of 43% (95% CI, 37%-49%). Five questions made up this category, with a percent correct range of 17% to 75%. For example, on the question, “Which hospital’s patients are the most predisposed to developing CLABSI?”, only 32% of respondents answered this correctly. In contrast, for the question “Which hospital is most effective at preventing CLABSI?”, 51% answered correctly. Figure 2 illustrates the cohort’s performance on each conceptual category while Table 2 displays question-by-question results.

Performance by Conceptual Category
Figure 2

CLABSI Numeracy and Interpretation Assessment
Table 2

Opinions Regarding CLABSI Data Reliability

Respondents were also asked about their opinion regarding the reliability of CLABSI quality metric data. Forty-three percent of respondents stated that such data were reliable at best 50% of the time. Notably, 10% of respondents indicated that CLABSI quality metric data were rarely or never reliable. There was no association between perceived reliability of quality metric data and survey performance (P = 0.87).

DISCUSSION

This Twitter-based study found wide variation in clinician interpretation of CLABSI quality data, with low overall performance. In particular, comprehension and interpretation of risk-adjusted data were substantially worse than unadjusted data. Although doctors performed somewhat better than nurses and other respondents, those involved in quality improvement initiatives performed no better than respondents who were not. Collectively, these findings suggest clinicians may not reliably comprehend quality metric data, potentially affecting their ability to utilize audit and feedback data. These results may have important implications for policy efforts that seek to leverage quality metric data to improve patient safety.

An integral component of many contemporary quality improvement initiatives is audit and feedback through metrics.6 Unfortunately, formal audit and feedback, along with other similar methods that benchmark data, have not consistently improved outcomes.24–27 A recent meta-analysis noted that audit and feedback interventions are not becoming more efficacious over time; the study further asserted that “new trials have provided little new knowledge regarding key effect modifiers.”9 Our findings suggest that numeracy and comprehension of quality metrics may be important candidate effect modifiers not previously considered. Simply put: we hypothesize that without intrinsic comprehension of data, impetus or insight to change practice might be diminished. In other words, clinicians may be more apt to act on insights they themselves derive from the data than when they are simply told what the data “mean.”

The present study further demonstrates that clinicians do not understand risk-adjusted data as well as raw data. Risk-adjustment has long been recognized as necessary to compare outcomes among hospitals.28,29 However, risk-adjustment is complex and, by its nature, difficult to understand. Although efforts have focused on improving the statistical reliability of quality metrics, this may represent but one half of the equation. Numeracy and interpretation of the data by decision makers are potentially equally important to effecting change. Because clinicians seem to have difficulty understanding risk-adjusted data, this deficit may be of growing importance as our risk-adjustment techniques become more sophisticated.

We note that clinicians expressed concerns regarding the reliability of quality metric feedback. These findings corroborate recent research that has reported reservations from hospital leaders concerning quality data.30,31 However, as shown in the context of patients and healthcare decisions, the aversion associated with quality metrics may be related to incomplete understanding of the data.32 Whether perceptions of unreliability drive lack of understanding or, conversely, whether lack of understanding fuels perceived unreliability is an important question that requires further study.

This study has several strengths. First, we used rigorous survey development techniques to evaluate the understudied issue of quality metric numeracy. Second, our sample size was sufficient to show statistically significant differences in numeracy and comprehension of CLABSI quality metric data. Third, we leveraged social media to rapidly acquire this sample. Finally, our results provided new insights that may have important implications in the area of quality metrics.

There were also limitations to our study. First, the Twitter-derived sample precludes the calculation of a response rate and may not be representative of individuals engaged in CLABSI prevention. However, respondents were solicited from the Twitter-followers of 2 health services researchers (TJI, VC) who are actively engaged in scholarly activities pertaining to critically ill patients and hospital-acquired complications. Thus, our sample likely represents a highly motivated subset that engages in these topics on a regular basis—potentially making them more numerate than average clinicians. Second, we did not ask whether the respondents had previously seen CLABSI data specifically, so we cannot stratify by exposure to such data. Third, this study assessed only CLABSI quality metric data; generalizations regarding numeracy with other metrics should be made with caution. However, as many such data are presented in similar formats, we suspect our findings are applicable to similar audit-and-feedback initiatives.

The findings of this study serve as a stimulus for further inquiry. Research of this nature needs to be carried out in samples drawn from specific, policy-relevant populations (eg, infection control practitioners, bedside nurses, intensive care unit directors). Such studies should include longitudinal assessments of numeracy that attempt to mechanistically examine its impact on CLABSI prevention efforts and outcomes. The latter is an important issue as the link between numeracy and behavioral response, while plausible, cannot be assumed, particularly given the complexity of issues related to behavioral modification.33 Additionally, whether alternate presentations of quality data affect numeracy, interpretation, and performance is worthy of further testing; indeed, this has been shown to be the case in other forms of communication.34–37 Until data from larger samples are available, it may be prudent for quality improvement leaders to assess the comprehension of local clinicians regarding feedback and whether lack of adequate comprehension is a barrier to deploying quality improvement interventions.

Quality measurement is a cornerstone of patient safety as it seeks to assess and improve the care delivered at the bedside. Rigorous metric development is important; however, ensuring that decision makers understand complex quality metrics may be equally fundamental. Given the cost of examining quality, elucidating the mechanisms of numeracy and interpretation as decision makers engage with quality metric data is necessary, along with whether improved comprehension leads to behavior change. Such inquiry may provide an evidence-base to shape alterations in quality metric deployment that will ensure maximal efficacy in driving practice change.

Disclosures

This work was supported by VA HSR&D IIR-13-079 (TJI). Dr. Chopra is supported by a career development award from the Agency of Healthcare Research and Quality (1-K08-HS022835-01). The views expressed here are the authors’ own and do not necessarily represent the view of the US Government or the Department of Veterans’ Affairs. The authors report no conflicts of interest.

Central line-associated bloodstream infections (CLABSIs) are common and serious occurrences across healthcare systems, with an attributable mortality of 12% to 25%.1,2 Given this burden,3–5 CLABSI is a focus for both high-profile public reporting and quality improvement interventions. An integral component of such interventions is audit and feedback via quality metrics. These measures are intended to allow decision makers to assess their own performance and appropriately allocate resources. Quality metrics present a substantial cost to health systems, with an estimated $15.4 billion dollars spent annually simply for reporting.6 Despite this toll, “audit and feedback” interventions have proven to be variably successful.7–9 The mechanisms that limit the effectiveness of these interventions remain
poorly understood.

One plausible explanation for limited efficacy of quality metrics is inadequate clinician numeracy—that is, “the ability to understand the quantitative aspects of clinical medicine, original research, quality improvement, and financial matters.”10 Indeed, clinicians are not consistently able to interpret probabilities and or clinical test characteristics. For example, Wegwarth et al. identified shortcomings in physician application of lead-time bias toward cancer screening.11 Additionally, studies have demonstrated systematic misinterpretations of probabilistic information in clinical settings, along with misconceptions regarding the impact of prevalence on post-test probabilities.12,13 Effective interpretation of rates may be a key—if unstated—requirement of many CLABSI quality improvement efforts.14–19 Our broader hypothesis is that clinicians who can more accurately interpret quality data, even if only from their own institution, are more likely to act on it appropriately and persistently than those who feel they must depend on a preprocessed interpretation of that same data by some other expert.

Therefore, we designed a survey to assess the numeracy of clinicians on CLABSI data presented in a prototypical feedback report. We studied 3 domains of comprehension: (1) basic numeracy: numerical tasks related to simple data; (2) risk-adjustment numeracy: numerical tasks related to risk-adjusted data; and (3) risk-adjustment interpretation: inferential tasks concerning risk-adjusted data. We hypothesized that clinician performance would vary substantially across domains, with the poorest performance in risk-
adjusted data.

METHODS

We conducted a cross-sectional survey of clinician numeracy regarding CLABSI feedback data. Respondents were also asked to provide demographic information and opinions regarding the reliability of quality metric data. Survey recruitment occurred on Twitter, a novel approach that leveraged social media to facilitate rapid recruitment of participants. The study instrument was administered using a web survey with randomized question order to preclude any possibility of order effects between questions. The study was deemed Institutional Review Board exempt by the University of Michigan: protocol HUM00106696.

Data Presentation Method

To determine the optimal mode of presenting data, we reviewed the literature on quality metric numeracy and presentation methods. Additionally, we evaluated quality metric presentation methods used by the Centers for Disease Control and Prevention (CDC), Centers for Medicare & Medicaid Services (CMS), and a tertiary academic medical center. After assessing the available literature and options, we adapted a CLABSI data presentation array from a study that had qualitatively validated the format using physician feedback (Appendix).20 We used hypothetical CLABSI data for our survey.

Survey Development

We developed a survey that included an 11-item test regarding CLABSI numeracy and data interpretation. Additional questions related to quality metric reliability and demographic information were included. No preexisting assessment tools existed for our areas of interest. Therefore, we developed a novel instrument using a broad, exploratory approach as others have employed.21 

First, we defined 3 conceptual categories related to CLABSI data. Within this conceptual framework, an iterative process of development and revision was used to assemble a question bank from which the survey would be constructed. A series of think-aloud sessions were held to evaluate each prompt for precision, clarity, and accuracy in assessing the conceptual categories. Correct and incorrect answers were defined based on literature review in conjunction with input from methodological and content experts (TJI and VC) (see Appendix for answer explanations). 

Within the conceptual categories related to CLABSI risk-adjustment, a key measure is the standardized infection ratio (SIR). This value is defined as the ratio of observed number of CLABSI over the expected number of CLABSIs.22 This is the primary measure to stratify hospital performance, and it was used in our assessment of risk-adjustment comprehension. In total, 54 question prompts were developed and subsequently narrowed to 11 study questions for the initial survey. 

The instrument was then pretested in a cohort of 8 hospitalists and intensivists to ensure appropriate comprehension, retrieval, and judgment processes.23 Questions were revised based on feedback from this cognitive testing to constitute the final instrument. During the survey, the data table was reshown on each page directly above each question and so was always on the same screen for the respondents.

Survey Sample

We innovated by using Twitter as an online platform for recruiting participants; we used Survey Monkey to host the electronic instrument. Two authors (TJI, VC) systematically sent out solicitation tweets to their followers. These tweets clearly indicated that the recruitment was for the purpose of a research study, and participants would receive no financial reward/incentive (Appendix). A link to the survey was provided in each tweet, and the period of recruitment was 30 days. To ensure respondents were clinicians, they needed to first answer a screening question recognizing that central lines were placed in the subclavian site but not the aorta, iliac, or radial sites.

To prevent systematic or anchoring biases, the order of questions was electronically randomized for each respondent. The primary outcome was the percentage correct of attempted questions.

Statistical Analysis

Descriptive statistics were calculated for all demographic variables. The primary outcome was evaluated as a dichotomous variable for each question (correct vs. incorrect response), and as a continuous variable when assessing mean percent correct on the overall survey. Demographic and conceptual associations were assessed via t-tests, chi-square, or Fisher exact tests. Point biserial correlations were calculated to assess for associations between response to a single question and overall performance on the survey. 

To evaluate the association between various respondent characteristics and responses, logistic regression analyses were performed. An ANOVA was performed to assess the association between self-reported reliability of quality metric data and the overall performance on attempted items. Analyses were conducted using STATA MP 14.0 (College Station, TX); P <0.05 was considered statistically significant.

RESULTS

A total of 97 respondents attempted at least 1 question on the survey, and 72 respondents attempted all 11 questions, yielding 939 unique responses for analysis. Seventy respondents (87%) identified as doctors or nurses, and 44 (55%) reported having 6 to 20 years of experience; the survey cohort also came from 6 nations (Table 1). All respondents answered the CLABSI knowledge filter question correctly.

Respondent Demographics
Table 1

Primary Outcome

The mean percent correct of attempted questions was 61% (standard deviation 21%, interquartile range 50%-75%) (Figure 1). Of those who answered all 11 CLABSI questions, the mean percent correct was 63% (95% CI, 59%-67%). Some questions were answered correctly more often than others—ranging from 17% to 95% (Table 2). Doctors answered 68% of questions correctly (95% CI, 63%-73%), while nurses and other respondents answered 57% of questions correctly (95% CI, 52%-62%) (P = 0.003). Other demographic variables—including self-reported involvement in a quality improvement committee and being from the United States versus elsewhere—were not associated with survey performance. The point biserial correlations for each individual question with overall performance were all more than 0.2 (range 0.24–0.62) and all statistically significant at P < 0.05.

Percent Correct of Attempted Questions
Figure 1

 

Concept-Specific Performance

Average percent correct declined across categories as numeracy requirements increased (P < 0.05 for all pairwise comparisons). In the area of basic numeracy, respondents’ mean percent correct was 82% (95% CI, 77%-87%) of attempted. This category had 4 questions, with a performance range of 77% to 90%. For example, on the question, “Which hospital has the lowest CLABSI rate?”, 80% of respondents answered correctly. For risk-adjustment numeracy, the mean percent correct was 70% (95% CI, 64%-76%); 2 items assessed this category. For “Which is better: a higher or lower SIR?”, 95% of the cohort answered correctly. However, on “If hospital B had its number of projected infection halved, what is its SIR?”, only 46% of those who attempted the question answered correctly.

Questions featuring risk-adjustment interpretation had an average percent correct of 43% (95% CI, 37%-49%). Five questions made up this category, with a percent correct range of 17% to 75%. For example, on the question, “Which hospital’s patients are the most predisposed to developing CLABSI?”, only 32% of respondents answered this correctly. In contrast, for the question “Which hospital is most effective at preventing CLABSI?”, 51% answered correctly. Figure 2 illustrates the cohort’s performance on each conceptual category while Table 2 displays question-by-question results.

Performance by Conceptual Category
Figure 2

CLABSI Numeracy and Interpretation Assessment
Table 2

Opinions Regarding CLABSI Data Reliability

Respondents were also asked about their opinion regarding the reliability of CLABSI quality metric data. Forty-three percent of respondents stated that such data were reliable at best 50% of the time. Notably, 10% of respondents indicated that CLABSI quality metric data were rarely or never reliable. There was no association between perceived reliability of quality metric data and survey performance (P = 0.87).

DISCUSSION

This Twitter-based study found wide variation in clinician interpretation of CLABSI quality data, with low overall performance. In particular, comprehension and interpretation of risk-adjusted data were substantially worse than unadjusted data. Although doctors performed somewhat better than nurses and other respondents, those involved in quality improvement initiatives performed no better than respondents who were not. Collectively, these findings suggest clinicians may not reliably comprehend quality metric data, potentially affecting their ability to utilize audit and feedback data. These results may have important implications for policy efforts that seek to leverage quality metric data to improve patient safety.

An integral component of many contemporary quality improvement initiatives is audit and feedback through metrics.6 Unfortunately, formal audit and feedback, along with other similar methods that benchmark data, have not consistently improved outcomes.24–27 A recent meta-analysis noted that audit and feedback interventions are not becoming more efficacious over time; the study further asserted that “new trials have provided little new knowledge regarding key effect modifiers.”9 Our findings suggest that numeracy and comprehension of quality metrics may be important candidate effect modifiers not previously considered. Simply put: we hypothesize that without intrinsic comprehension of data, impetus or insight to change practice might be diminished. In other words, clinicians may be more apt to act on insights they themselves derive from the data than when they are simply told what the data “mean.”

The present study further demonstrates that clinicians do not understand risk-adjusted data as well as raw data. Risk-adjustment has long been recognized as necessary to compare outcomes among hospitals.28,29 However, risk-adjustment is complex and, by its nature, difficult to understand. Although efforts have focused on improving the statistical reliability of quality metrics, this may represent but one half of the equation. Numeracy and interpretation of the data by decision makers are potentially equally important to effecting change. Because clinicians seem to have difficulty understanding risk-adjusted data, this deficit may be of growing importance as our risk-adjustment techniques become more sophisticated.

We note that clinicians expressed concerns regarding the reliability of quality metric feedback. These findings corroborate recent research that has reported reservations from hospital leaders concerning quality data.30,31 However, as shown in the context of patients and healthcare decisions, the aversion associated with quality metrics may be related to incomplete understanding of the data.32 Whether perceptions of unreliability drive lack of understanding or, conversely, whether lack of understanding fuels perceived unreliability is an important question that requires further study.

This study has several strengths. First, we used rigorous survey development techniques to evaluate the understudied issue of quality metric numeracy. Second, our sample size was sufficient to show statistically significant differences in numeracy and comprehension of CLABSI quality metric data. Third, we leveraged social media to rapidly acquire this sample. Finally, our results provided new insights that may have important implications in the area of quality metrics.

There were also limitations to our study. First, the Twitter-derived sample precludes the calculation of a response rate and may not be representative of individuals engaged in CLABSI prevention. However, respondents were solicited from the Twitter-followers of 2 health services researchers (TJI, VC) who are actively engaged in scholarly activities pertaining to critically ill patients and hospital-acquired complications. Thus, our sample likely represents a highly motivated subset that engages in these topics on a regular basis—potentially making them more numerate than average clinicians. Second, we did not ask whether the respondents had previously seen CLABSI data specifically, so we cannot stratify by exposure to such data. Third, this study assessed only CLABSI quality metric data; generalizations regarding numeracy with other metrics should be made with caution. However, as many such data are presented in similar formats, we suspect our findings are applicable to similar audit-and-feedback initiatives.

The findings of this study serve as a stimulus for further inquiry. Research of this nature needs to be carried out in samples drawn from specific, policy-relevant populations (eg, infection control practitioners, bedside nurses, intensive care unit directors). Such studies should include longitudinal assessments of numeracy that attempt to mechanistically examine its impact on CLABSI prevention efforts and outcomes. The latter is an important issue as the link between numeracy and behavioral response, while plausible, cannot be assumed, particularly given the complexity of issues related to behavioral modification.33 Additionally, whether alternate presentations of quality data affect numeracy, interpretation, and performance is worthy of further testing; indeed, this has been shown to be the case in other forms of communication.34–37 Until data from larger samples are available, it may be prudent for quality improvement leaders to assess the comprehension of local clinicians regarding feedback and whether lack of adequate comprehension is a barrier to deploying quality improvement interventions.

Quality measurement is a cornerstone of patient safety as it seeks to assess and improve the care delivered at the bedside. Rigorous metric development is important; however, ensuring that decision makers understand complex quality metrics may be equally fundamental. Given the cost of examining quality, elucidating the mechanisms of numeracy and interpretation as decision makers engage with quality metric data is necessary, along with whether improved comprehension leads to behavior change. Such inquiry may provide an evidence-base to shape alterations in quality metric deployment that will ensure maximal efficacy in driving practice change.

Disclosures

This work was supported by VA HSR&D IIR-13-079 (TJI). Dr. Chopra is supported by a career development award from the Agency of Healthcare Research and Quality (1-K08-HS022835-01). The views expressed here are the authors’ own and do not necessarily represent the view of the US Government or the Department of Veterans’ Affairs. The authors report no conflicts of interest.

References

1. Scott RD II. The direct medical costs of healthcare-associated infections in us hospitals and the benefits of prevention. Centers for Disease Control and Prevention. Available at: http://www.cdc.gov/HAI/pdfs/hai/Scott_CostPaper.pdf. Published March 2009. Accessed November 8, 2016.

2. O’Grady NP, Alexander M, Burns LA, et al. Guidelines for the prevention of intravascular catheter-related infections. Am J Infect Control. 2011;39(4 suppl 1)::S1-S34. PubMed

3. Blot K, Bergs J, Vogelaers D, Blot S, Vandijck D. Prevention of central line-associated bloodstream infections through quality improvement interventions: a systematic review and meta-analysis. Clin Infect Dis. 2014;59(1):96-105. PubMed

4. Mermel LA. Prevention of intravascular catheter-related infections. Ann Intern Med. 2000;132(5):391-402. PubMed

5. Siempos II, Kopterides P, Tsangaris I, Dimopoulou I, Armaganidis AE. Impact of catheter-related bloodstream infections on the mortality of critically ill patients: a meta-analysis. Crit Care Med. 2009;37(7):2283-2289. PubMed

6. Casalino LP, Gans D, Weber R, et al. US physician practices spend more than $15.4 billion annually to report quality measures. Health Aff (Millwood). 2016;35(3):401-406. PubMed

7. Hysong SJ. Meta-analysis: audit and feedback features impact effectiveness on care quality. Med Care. 2009;47(3):356-363. PubMed

8. Ilgen DR, Fisher CD, Taylor MS. Consequences of individual feedback on behavior in organizations. J Appl Psychol. 1979;64:349-371. 

9. Ivers NM, Grimshaw JM, Jamtvedt G, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med. 2014;29(11):1534-1541. PubMed

10. Rao G. Physician numeracy: essential skills for practicing evidence-based medicine. Fam Med. 2008;40(5):354-358. PubMed

11. Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, Gigerenzer G. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Ann Intern Med. 2012;156(5):340-349. PubMed

12. Bramwell R, West H, Salmon P. Health professionals’ and service users’ interpretation of screening test results: experimental study. BMJ. 2006;333(7562):284. PubMed

13. Agoritsas T, Courvoisier DS, Combescure C, Deom M, Perneger TV. Does prevalence matter to physicians in estimating post-test probability of disease? A randomized trial. J Gen Intern Med. 2011;26(4):373-378. PubMed

14. Warren DK, Zack JE, Mayfield JL, et al. The effect of an education program on the incidence of central venous catheter-associated bloodstream infection in a medical ICU. Chest. 2004;126(5):1612-1618. PubMed

15. Rinke ML, Bundy DG, Chen AR, et al. Central line maintenance bundles and CLABSIs in ambulatory oncology patients. Pediatrics. 2013;132(5):e1403-e1412. PubMed

16. Pronovost P, Needham D, Berenholtz S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med. 2006;355(26):
2725-2732. PubMed

17. Rinke ML, Chen AR, Bundy DG, et al. Implementation of a central line maintenance care bundle in hospitalized pediatric oncology patients. Pediatrics. 2012;130(4):e996-e1004. PubMed

18. Sacks GD, Diggs BS, Hadjizacharia P, Green D, Salim A, Malinoski DJ. Reducing the rate of catheter-associated bloodstream infections in a surgical intensive care unit using the Institute for Healthcare Improvement Central Line Bundle. Am J Surg. 2014;207(6):817-823. PubMed

19. Berenholtz SM, Pronovost PJ, Lipsett PA, et al. Eliminating catheter-related bloodstream infections in the intensive care unit. Crit Care Med. 2004;32(10):2014-2020. PubMed

20. Rajwan YG, Barclay PW, Lee T, Sun IF, Passaretti C, Lehmann H. Visualizing central line-associated blood stream infection (CLABSI) outcome data for decision making by health care consumers and practitioners—an evaluation study. Online J Public Health Inform. 2013;5(2):218. PubMed

21. Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making 2007;27(5):672-680. PubMed

22. HAI progress report FAQ. 2016. Available at: http://www.cdc.gov/hai/surveillance/progress-report/faq.html. Last updated March 2, 2016. Accessed November 8, 2016.

23. Collins D. Pretesting survey instruments: an overview of cognitive methods. Qual Life Res. 2003;12(3):229-238. PubMed

24. Ivers N, Jamtvedt G, Flottorp S, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012;(6):CD000259. PubMed

25. Chatterjee P, Joynt KE. Do cardiology quality measures actually improve patient outcomes? J Am Heart Assoc. 2014;3(1):e000404. PubMed

26. Joynt KE, Blumenthal DM, Orav EJ, Resnic FS, Jha AK. Association of public reporting for percutaneous coronary intervention with utilization and outcomes among Medicare beneficiaries with acute myocardial infarction. JAMA. 2012;308(14):1460-1468. PubMed

27. Ryan AM, Nallamothu BK, Dimick JB. Medicare’s public reporting initiative on hospital quality had modest or no impact on mortality from three key conditions. Health Aff (Millwood). 2012;31(3):585-592. PubMed

28. Thomas JW. Risk adjustment for measuring health care outcomes, 3rd edition. Int J Qual Health Care. 2004;16(2):181-182. 

29. Iezzoni LI. Risk Adjustment for Measuring Health Care Outcomes. Ann Arbor, Michigan: Health Administration Press; 1994.

30. Goff SL, Lagu T, Pekow PS, et al. A qualitative analysis of hospital leaders’ opinions about publicly reported measures of health care quality. Jt Comm J Qual Patient Saf. 2015;41(4):169-176. PubMed

31. Lindenauer PK, Lagu T, Ross JS, et al. Attitudes of hospital leaders toward publicly reported measures of health care quality. JAMA Intern Med. 2014;174(12):
1904-1911. PubMed

32. Peters E, Hibbard J, Slovic P, Dieckmann N. Numeracy skill and the communication, comprehension, and use of risk-benefit information. Health Aff (Millwood). 2007;26(3):741-748. PubMed

33. Montano DE, Kasprzyk D. Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In: Glanz K, Rimer BK, Viswanath K, eds. Health Behavior and Health Education: Theory, Research and Practice. 5th ed. San Francisco, CA: Jossey-Bass; 2015:95–124.

34. Hamstra DA, Johnson SB, Daignault S, et al. The impact of numeracy on verbatim knowledge of the longitudinal risk for prostate cancer recurrence following radiation therapy. Med Decis Making. 2015;35(1):27-36. PubMed

35. Hawley ST, Zikmund-Fisher B, Ubel P, Jancovic A, Lucas T, Fagerlin A. The impact of the format of graphical presentation on health-related knowledge and treatment choices. Patient Educ Couns. 2008;73(3):448-455. PubMed

36. Zikmund-Fisher BJ, Witteman HO, Dickson M, et al. Blocks, ovals, or people? Icon type affects risk perceptions and recall of pictographs. Med Decis Making. 2014;34(4):443-453. PubMed

37. Korfage IJ, Fuhrel-Forbis A, Ubel PA, et al. Informed choice about breast cancer prevention: randomized controlled trial of an online decision aid intervention. Breast Cancer Res. 2013;15(5):R74. PubMed

References

1. Scott RD II. The direct medical costs of healthcare-associated infections in us hospitals and the benefits of prevention. Centers for Disease Control and Prevention. Available at: http://www.cdc.gov/HAI/pdfs/hai/Scott_CostPaper.pdf. Published March 2009. Accessed November 8, 2016.

2. O’Grady NP, Alexander M, Burns LA, et al. Guidelines for the prevention of intravascular catheter-related infections. Am J Infect Control. 2011;39(4 suppl 1)::S1-S34. PubMed

3. Blot K, Bergs J, Vogelaers D, Blot S, Vandijck D. Prevention of central line-associated bloodstream infections through quality improvement interventions: a systematic review and meta-analysis. Clin Infect Dis. 2014;59(1):96-105. PubMed

4. Mermel LA. Prevention of intravascular catheter-related infections. Ann Intern Med. 2000;132(5):391-402. PubMed

5. Siempos II, Kopterides P, Tsangaris I, Dimopoulou I, Armaganidis AE. Impact of catheter-related bloodstream infections on the mortality of critically ill patients: a meta-analysis. Crit Care Med. 2009;37(7):2283-2289. PubMed

6. Casalino LP, Gans D, Weber R, et al. US physician practices spend more than $15.4 billion annually to report quality measures. Health Aff (Millwood). 2016;35(3):401-406. PubMed

7. Hysong SJ. Meta-analysis: audit and feedback features impact effectiveness on care quality. Med Care. 2009;47(3):356-363. PubMed

8. Ilgen DR, Fisher CD, Taylor MS. Consequences of individual feedback on behavior in organizations. J Appl Psychol. 1979;64:349-371. 

9. Ivers NM, Grimshaw JM, Jamtvedt G, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med. 2014;29(11):1534-1541. PubMed

10. Rao G. Physician numeracy: essential skills for practicing evidence-based medicine. Fam Med. 2008;40(5):354-358. PubMed

11. Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, Gigerenzer G. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Ann Intern Med. 2012;156(5):340-349. PubMed

12. Bramwell R, West H, Salmon P. Health professionals’ and service users’ interpretation of screening test results: experimental study. BMJ. 2006;333(7562):284. PubMed

13. Agoritsas T, Courvoisier DS, Combescure C, Deom M, Perneger TV. Does prevalence matter to physicians in estimating post-test probability of disease? A randomized trial. J Gen Intern Med. 2011;26(4):373-378. PubMed

14. Warren DK, Zack JE, Mayfield JL, et al. The effect of an education program on the incidence of central venous catheter-associated bloodstream infection in a medical ICU. Chest. 2004;126(5):1612-1618. PubMed

15. Rinke ML, Bundy DG, Chen AR, et al. Central line maintenance bundles and CLABSIs in ambulatory oncology patients. Pediatrics. 2013;132(5):e1403-e1412. PubMed

16. Pronovost P, Needham D, Berenholtz S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med. 2006;355(26):
2725-2732. PubMed

17. Rinke ML, Chen AR, Bundy DG, et al. Implementation of a central line maintenance care bundle in hospitalized pediatric oncology patients. Pediatrics. 2012;130(4):e996-e1004. PubMed

18. Sacks GD, Diggs BS, Hadjizacharia P, Green D, Salim A, Malinoski DJ. Reducing the rate of catheter-associated bloodstream infections in a surgical intensive care unit using the Institute for Healthcare Improvement Central Line Bundle. Am J Surg. 2014;207(6):817-823. PubMed

19. Berenholtz SM, Pronovost PJ, Lipsett PA, et al. Eliminating catheter-related bloodstream infections in the intensive care unit. Crit Care Med. 2004;32(10):2014-2020. PubMed

20. Rajwan YG, Barclay PW, Lee T, Sun IF, Passaretti C, Lehmann H. Visualizing central line-associated blood stream infection (CLABSI) outcome data for decision making by health care consumers and practitioners—an evaluation study. Online J Public Health Inform. 2013;5(2):218. PubMed

21. Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the Subjective Numeracy Scale. Med Decis Making 2007;27(5):672-680. PubMed

22. HAI progress report FAQ. 2016. Available at: http://www.cdc.gov/hai/surveillance/progress-report/faq.html. Last updated March 2, 2016. Accessed November 8, 2016.

23. Collins D. Pretesting survey instruments: an overview of cognitive methods. Qual Life Res. 2003;12(3):229-238. PubMed

24. Ivers N, Jamtvedt G, Flottorp S, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012;(6):CD000259. PubMed

25. Chatterjee P, Joynt KE. Do cardiology quality measures actually improve patient outcomes? J Am Heart Assoc. 2014;3(1):e000404. PubMed

26. Joynt KE, Blumenthal DM, Orav EJ, Resnic FS, Jha AK. Association of public reporting for percutaneous coronary intervention with utilization and outcomes among Medicare beneficiaries with acute myocardial infarction. JAMA. 2012;308(14):1460-1468. PubMed

27. Ryan AM, Nallamothu BK, Dimick JB. Medicare’s public reporting initiative on hospital quality had modest or no impact on mortality from three key conditions. Health Aff (Millwood). 2012;31(3):585-592. PubMed

28. Thomas JW. Risk adjustment for measuring health care outcomes, 3rd edition. Int J Qual Health Care. 2004;16(2):181-182. 

29. Iezzoni LI. Risk Adjustment for Measuring Health Care Outcomes. Ann Arbor, Michigan: Health Administration Press; 1994.

30. Goff SL, Lagu T, Pekow PS, et al. A qualitative analysis of hospital leaders’ opinions about publicly reported measures of health care quality. Jt Comm J Qual Patient Saf. 2015;41(4):169-176. PubMed

31. Lindenauer PK, Lagu T, Ross JS, et al. Attitudes of hospital leaders toward publicly reported measures of health care quality. JAMA Intern Med. 2014;174(12):
1904-1911. PubMed

32. Peters E, Hibbard J, Slovic P, Dieckmann N. Numeracy skill and the communication, comprehension, and use of risk-benefit information. Health Aff (Millwood). 2007;26(3):741-748. PubMed

33. Montano DE, Kasprzyk D. Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In: Glanz K, Rimer BK, Viswanath K, eds. Health Behavior and Health Education: Theory, Research and Practice. 5th ed. San Francisco, CA: Jossey-Bass; 2015:95–124.

34. Hamstra DA, Johnson SB, Daignault S, et al. The impact of numeracy on verbatim knowledge of the longitudinal risk for prostate cancer recurrence following radiation therapy. Med Decis Making. 2015;35(1):27-36. PubMed

35. Hawley ST, Zikmund-Fisher B, Ubel P, Jancovic A, Lucas T, Fagerlin A. The impact of the format of graphical presentation on health-related knowledge and treatment choices. Patient Educ Couns. 2008;73(3):448-455. PubMed

36. Zikmund-Fisher BJ, Witteman HO, Dickson M, et al. Blocks, ovals, or people? Icon type affects risk perceptions and recall of pictographs. Med Decis Making. 2014;34(4):443-453. PubMed

37. Korfage IJ, Fuhrel-Forbis A, Ubel PA, et al. Informed choice about breast cancer prevention: randomized controlled trial of an online decision aid intervention. Breast Cancer Res. 2013;15(5):R74. PubMed

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Mobilization in severe sepsis: An integrative review

Severe sepsis, defined as an infection leading to systemic inflammatory response and acute organ dysfunction, is a significant cause of morbidity and mortality.[1, 2, 3] Although it has been a condition classically attributed to patients in the intensive care unit (ICU), accumulating data suggest that a substantial proportion of patients with severe sepsis are managed by hospitalists and floor teams in non‐ICU, general ward settings.[1, 4, 5] Although the incidence of severe sepsis continues to rise both in the United States and other developed nations,[2, 6, 7] advances in early recognition, management, and care of this condition have resulted in improved rates of survival.[8] The resultant increase in a severe sepsis survivor population[6] make the long‐term sequelae of this condition an important public health problem.[9]

In both the ICU and on general wards, severe sepsis survivors suffer from decreased functional status, worsened quality of life, increased cognitive dysfunction, and sarcopenia.[4, 6, 10, 11, 12, 13, 14] Not surprisingly, many such patients are discharged to long‐term care facilities for physical rehabilitation,[15] with escalating utilization of resources[16] and cost.[17, 18] Inexpensive interventions that improve outcomes following sepsis would thus be welcomed.

It is well known that physical therapy (PT) and early mobilization are beneficial in mitigating functional decline in a number of conditions.[19, 20, 21, 22] PT can improve outcomes in several ways: prevention of bed rest deconditioning, mitigation of mechanisms that lead to sarcopenia, increased pulmonary and tissue aerobic capacity, and improved sense of well‐being. Indeed, among the population cared for in ICU settings, early mobility and PT lead to more ventilator‐free days, better functional status at discharge, shorter duration of delirium, and even a potentially reduced risk of central line‐associated bloodstream infection (CLABSI).[23, 24] However, whether initiating early PT can improve outcomes in patients with severe sepsis treated by either intensivists or hospitalists/floor teams outside the ICU is unknown.

Therefore, to better understand this phenomenon, we systematically reviewed and integrated the literature regarding early mobilization and PT for severe sepsis outside the ICU. To be more inclusive, a secondary review including populations with any infectious etiology and severe sepsis treated within the ICU was also conducted. Our review begins by providing an overview of the pathophysiology behind functional decline in severe sepsis, along with existing evidence on early mobilization efficacy in other patient populations. We then proceed with a review of the extant literature on the aforementioned topic. We conclude with an evaluation of the current evidence on the subject, along with assertions regarding future research in the area.

PATHOPHYSIOLOGY OF DISABILITY FOLLOWING HOSPITALIZATION FOR SEVERE SEPSIS

The pathophysiology behind functional decline in patients hospitalized with severe sepsis is multifactorial (Figure 1). During hospitalization, it is well known that patients suffer from restricted mobility,[25] and that this impediment is linked to poor functional outcomes.[26] Described as far back as Hippocrates,[27] more recent studies have elucidated how prolonged bed rest leads to a multitude of physiological changes that promote deconditioning.[28] Specifically, skeletal muscle atrophy and decreased protein synthesis, independent of ongoing disease processes and acute illness, have been demonstrated in both animal and human models of prolonged inactivity.[29, 30] Additionally, bed rest leading to insensible fluid losses, a decline in stroke volume and effective cardiac output, bone loss, and decreased insulin sensitivity has been reported.[28, 31] There is little doubt that the aforementioned issues pertain to severe sepsis patients outside the ICU. In fact, nearly all of the acute mechanisms driving Creditor's hazards of hospitalization are noted among patients with severe sepsis.[32]

Figure 1
Sepsis and functional decline diagram. Abbreviations: IGF, insulin‐like growth factor; IL, interleukin; MTor, mammalian target of rapamycin; TNF, tumor necrosis factor.

Furthermore, several factors preceding hospitalization may increase risk of disability. For example, Covinsky et al. described a number of risk factors, such as comorbid conditions, cognitive impairment, and various psychosocial aspects such as depression and limited social support, as being associated with increased risk of functional decline.[33] Thus, both in‐hospital and prehospital factors likely combine within an individual patient's context to determine risk of physical decline.

On this backdrop and the inherent immobilization associated with hospitalization, sepsis and inflammation catalyze physiologic changes that further propagate deconditioning.[7] Implicated pathways and proteins for this process include the mammalian target of rapamycin, human growth hormone, insulin‐like growth factors, interleukin‐1, and tumor necrosis factor‐. Through several metabolic alterations, sepsis independently promotes skeletal muscle breakdown and impairs skeletal muscle synthesis.[34, 35, 36] Inflammation associated with sepsis also increases oxidant burden, further leading to muscle dysfunction and dysregulation.[7, 31, 37, 38]

EFFECTS OF PHYSICAL THERAPY AND MOBILIZATION ON CLINICAL OUTCOMES

In patients with nonsepsis conditions who are at risk for functional decline, the effectiveness of physical therapy has been studied in multiple settings with positive outcomes. For example, in hospitalized elderly patients with general deconditioning, PT‐based interventions have demonstrated reductions in length of hospital stay.[39] Additionally, exercise in healthy subjects who have been subjected to bed rest has been shown to attenuate physiological changes, and maintain plasma and red cell volume and work capacity.[40] Adequate safety and improved outcomes have also been demonstrated in the general population of critically ill patients who receive early PT and mobilization. Improved functional capacity at discharge, decreases in duration of delirium, increased ventilator‐free days, decreased risk for CLABSI, and a better general sense of well‐being following these interventions have been widely reported in the literature.[14, 19, 23, 24, 41, 42, 43, 44, 45] Interestingly, critically ill patients may have a dose‐ and time‐dependent response to PT; that is, high intensity and early onset mobility‐based interventions are often associated with more ventilator‐free time and improved functional outcomes, resulting in shorter ICU and hospital length of stay.[42, 46, 47, 48]

Moderate intensity exercise has also been shown to improve 6‐minute walking distance in patients convalescing from coronary artery bypass grafting surgery.[49] Furthermore, in the postoperative setting, patients suffering traumatic hip fractures are known to benefit from physical and occupational therapies with shorter time to ambulation and improved locomotion in the recovery period.[21, 50, 51] Among patients with stroke, PT and gait training has led to improvements in speed, gait, independence during walking, activities of daily living, and extended activities of daily living.[52, 53, 54] A recent meta‐analysis also suggested that extra PT compared to regular treatment in patients with acute and subacute conditions such as stroke and postoperative states improved mobility and quality of life, while reducing length of hospital stay.[22]

Although this evidence suggests potential benefits for PT and mobilization, it is important to note that the effect of these treatments in dissimilar populations is unknown and may not necessarily be positive. For example, a recent study examining PT and its impact on patients with hip osteoarthritis showed no clinical benefit.[55] Mobilizing patients in severe illness may be associated with important risks, including falls, worsening of their clinical status, or moral discouragement in the setting of limited capacity. Therefore, understanding which elements of mobilization efforts create the greatest impact in the context of delivery of the intervention is critical to assessing the risk, benefit, and efficacy of PT‐based interventions.

EARLY PHYSICAL THERAPY FOR SEVERE SEPSIS OUTSIDE THE ICU: LITERATURE REVIEW

Given the functional decline associated with severe sepsis and the evidence of PT efficacy in other populations, we reviewed the current literature for studies evaluating physical therapy in severe sepsis patients outside the ICU. With the assistance of medical reference librarians, we searched MEDLINE via PubMed (1950present), EMBASE (1946present), Cochrane CENTRAL Register of Controlled Trials, and the Cochrane Database of Reviews of Effectiveness (1960present via Ovid). The search was last updated in June 2014.

We searched for studies that (1) involved human patients 18 years of age, (2) included patients with a primary diagnosis of sepsis or severe sepsis being treated outside the ICU, (3) featured a primary intervention that included PT or an early mobilization‐based initiative, and (4) reported a primary clinical or functional outcome of interest. Early was defined based on the included studies' definition. To be fully inclusive, we also conducted a secondary review with inclusion criteria expanded to studies of either any infectious pathology or severe sepsis patient in the ICU that employed PT interventions.

Our electronic search retrieved 815 records (Figure 2). Despite this approach, no publications met our primary inclusion criteria as we found no study that implemented a mobility intervention directed toward patients with sepsis treated outside the ICU. Our expanded secondary review included patients with any infectious pathology or those with severe sepsis in the ICU treated with PT; in this review, 2 studies met eligibility criteria.[56] In a 2003 cluster‐randomized trial, Mundy and colleagues randomized patients admitted with pneumonia to receive early PT or usual care. The outcomes of interest were hospital length of stay, mortality, number of chest radiographs, emergency department visits, and readmissions at 30 and 90 days after hospital admission. Although the study has important limitations (including patient‐level difference between trial arms, subjective definition of early mobilization), the authors found a significant decrease in length of stay among patients with pneumonia who received early PT compared to controls (5.8 vs 6.9 days, absolute difference 1.1 days, 95% confidence interval: 02.2 days). The study also reported a substantial decrease in adjusted mean hospital charges for the early mobilization group versus the usual care group ($10,159 per patient vs. $12,868 per patient, P=0.05). In the second study, Sossdorf et al. retrospectively evaluated a cohort of 999 patients with severe sepsis and septic shock and assessed whether onset and frequency of PT‐based interventions was associated with clinical benefit. After multivariate analysis, the authors reported a small mortality benefit associated with the relative number of PT interventions (hazard ratio: 0.982, P<0.001).[45]

Figure 2
Systematic review flowchart. Abbreviations: CINAHL, Cumulative Index to Nursing and Allied Health Literature; ICU, intensive care unit; EM, early mobilization.

EXPLAINING THE VOID

Our integrative review of the current literature reveals a gap in our understanding of the role of early mobilization in severe sepsis both within and beyond the ICU. Given the promise of PT‐based interventions and the toll of severe sepsis, one must ask: why may this be so?

First, the understanding that severe sepsis leads to significant, long‐term consequences for survivors has only been identified recently. Thus, it is possible that the burden and consequences related to this condition have not been fully recognized in clinical settings, leading to a paucity of research and interventions. Although the association between sepsis and mortality has been known since the 1990s,[57] long‐term complications and enduring morbidity of this disease continue to be realized. Indeed, many studies delineating the longer‐term effects of sepsis have been only recently published.[6, 10, 11, 12, 13]

Second, it is likely that many clinicians ascribe to the viewpoint that severe sepsis is an ICU‐only condition, a myth that has been discounted by multiple studies.[1, 4, 5] Although our study shows a paucity of evidence in both ICU and nonICU‐based severe sepsis, almost half of severe sepsis occurs outside the ICU, carrying with it many of the same clinical implications. Additionally, increased morbidity, mortality, and resource utilization are known to be true in all patients with severe sepsis, irrespective of where they receive treatment in the hospital.[4, 5, 6] Recent evidence has also shown that severe sepsis treated on the floor may be clinically, epidemiologically, and even prognostically unique from its ICU counterpart.[5, 58, 59] Therefore, it appears that research domains with tailored interventions to both ICU and non‐ICU severe sepsis patients are important areas of inquiry for clinicians. Such research may serve the purpose of assessing impact of early mobilization and unmasking any treatment heterogeneity that may exist when dealing with severe sepsis. Though trials of PT in ICU‐based severe sepsis are underway,[60] it is prudent that these also extend beyond the ICU‐setting.

Third, variability in early mobility practices and billing documentation for severe sepsis patients may exist, adding barriers to performing high‐quality research on this topic. In fact, administrative billing records for PT may offer insufficient granularity about services provided or therapies administered, particularly in the ICU where variability in early mobilization practices have been shown despite common employment of physiotherapists.[61]

Finally, many hospitalists may believe that patients with severe sepsis are simply too sick for early mobilization or PT, possibly limiting their participation in clinical or research‐based interventions. This perception has been well described in ICU populations, where it has been well studied and shown to be false.[41, 42, 43] Nevertheless, if severe sepsis patients are viewed as relatively sick hospitalized patients, it is plausible that resistance against early mobilization interventions may exist.[62] Understanding these biases and being mindful of such barriers when conducting studies in this area would be important.

CONCLUSION AND FUTURE DIRECTIONS

The cost burdens of severe sepsis are substantial. Elixhauser et al. suggest that it is currently the single most expensive cause of acute hospitalization in the United States.[63] Importantly, a large proportion of patients with severe sepsis receive care from hospitalists and/or floor teams on the general wards. Our integrative review has demonstrated a knowledge gap when it comes to rigorous assessments of PT and mobilization treatments in patients with severe sepsis within and beyond the ICU. Existing evidence provides a strong rationale for why functional decline occurs in patients with severe sepsis. A reasonable argument for PT‐based interventions to mitigate functional decline in this subset exists, but rigorous evaluation of such interventions is necessary. Physical and mobilization‐based treatments are routinely available and efficacious in several other settings and populations. It could be rapidly deployed and potentially improve outcomes in those with severe sepsis. Research would be welcomed to establish optimal dosing, efficacy, and cost effectiveness of PT and early mobilization for severe sepsis, particularly in patients treated on the general wards by hospitalists and floor teams.

How may such a research agenda be launched? A balanced multipronged approach is necessary. First, large‐scale epidemiological data to understand variation in practice are needed. Focused studies carried out by community and academic hospitalists on septic patients treated outside the ICU are the call of the hour. These data, in turn, can help create registries that assess for risk factors, quality of treatment, and long‐term outcomes among survivors of this condition. Second, evaluation and improvement of the coding and precision of physical and occupational therapy billing records is necessary so that their added value can be assessed and tracked using administrative data. Third, targeted prospective studies and clinical trials to directly evaluate the effect of PT in well‐defined patient populations with sepsis outside the ICU are needed. In this arena, hospitalist expertise and trained physical therapists will be crucial. The focus of this work should be directed toward both short‐term and long‐term functional outcomes, as well as mortality and morbidity assessments. Fourth, these patient‐centered efforts should loop back and inform the foundational biology of severe sepsis, thus illuminating patient‐centered end points, from biomarker analysis to physiometric measurements in basic and translational research.

In conclusion, this review sheds light on the fact that interventions that may mitigate the functional and cognitive decline in survivors of severe sepsis appear underdeveloped. Although the precise benefit of such interventions remains unclear, the low‐cost, widespread availability and generalizability of PT‐based interventions make it a worthy candidate for future research. As the numbers of survivors of sepsis expand, an unmet public health need for interventions to improve the long‐term outcomes of this population exists. Hospitalists and intensivists caring for severe sepsis patients must rise to meet this need. Together, we can help improve the lives of patients afflicted with severe sepsis, wherever they may receive care in the hospital.

Acknowledgements

The authors acknowledge the efforts of medical research librarians Andy Hickner, MSI, and Marissa Conte, MSI, on this project.

Disclosures

This work was supported by the National Institutes of HealthK08, HL091249 (T.J.I.) and VA HSR&D IIR‐11109 (T.J.I.). The views expressed here are the authors' own and do not necessarily represent the views of the US government or the Department of Veterans' Affairs. The authors report no conflicts of interest.

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References
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  34. Breuille D, Voisin L, Contrepois M, et al. A sustained rat model for studying the long‐lasting catabolic state of sepsis. Infect Immun. 1999;67:10791085.
  35. Vary TC. Regulation of skeletal muscle protein turnover during sepsis. Curr Opin Clin Nutr. Metab Care. 1998;1:217224.
  36. Lang CH, Frost RA, Vary TC. Regulation of muscle protein synthesis during sepsis and inflammation. Am J Physiol Endocrinol Metab. 2007;293:E453E459.
  37. Couillard A, Prefaut C. From muscle disuse to myopathy in COPD: potential contribution of oxidative stress. Eur Respir J. 2005;26:703719.
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  45. Sossdorf M, Otto GP, Menge K, et al. Potential effect of physiotherapeutic treatment on mortality rate in patients with severe sepsis and septic shock: a retrospective cohort analysis. J Crit Care. 2013;28:954958.
  46. Chiang LL, Wang LY, Wu CP, et al. Effects of physical training on functional status in patients with prolonged mechanical ventilation. Phys Ther. 2006;86:12711281.
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Severe sepsis, defined as an infection leading to systemic inflammatory response and acute organ dysfunction, is a significant cause of morbidity and mortality.[1, 2, 3] Although it has been a condition classically attributed to patients in the intensive care unit (ICU), accumulating data suggest that a substantial proportion of patients with severe sepsis are managed by hospitalists and floor teams in non‐ICU, general ward settings.[1, 4, 5] Although the incidence of severe sepsis continues to rise both in the United States and other developed nations,[2, 6, 7] advances in early recognition, management, and care of this condition have resulted in improved rates of survival.[8] The resultant increase in a severe sepsis survivor population[6] make the long‐term sequelae of this condition an important public health problem.[9]

In both the ICU and on general wards, severe sepsis survivors suffer from decreased functional status, worsened quality of life, increased cognitive dysfunction, and sarcopenia.[4, 6, 10, 11, 12, 13, 14] Not surprisingly, many such patients are discharged to long‐term care facilities for physical rehabilitation,[15] with escalating utilization of resources[16] and cost.[17, 18] Inexpensive interventions that improve outcomes following sepsis would thus be welcomed.

It is well known that physical therapy (PT) and early mobilization are beneficial in mitigating functional decline in a number of conditions.[19, 20, 21, 22] PT can improve outcomes in several ways: prevention of bed rest deconditioning, mitigation of mechanisms that lead to sarcopenia, increased pulmonary and tissue aerobic capacity, and improved sense of well‐being. Indeed, among the population cared for in ICU settings, early mobility and PT lead to more ventilator‐free days, better functional status at discharge, shorter duration of delirium, and even a potentially reduced risk of central line‐associated bloodstream infection (CLABSI).[23, 24] However, whether initiating early PT can improve outcomes in patients with severe sepsis treated by either intensivists or hospitalists/floor teams outside the ICU is unknown.

Therefore, to better understand this phenomenon, we systematically reviewed and integrated the literature regarding early mobilization and PT for severe sepsis outside the ICU. To be more inclusive, a secondary review including populations with any infectious etiology and severe sepsis treated within the ICU was also conducted. Our review begins by providing an overview of the pathophysiology behind functional decline in severe sepsis, along with existing evidence on early mobilization efficacy in other patient populations. We then proceed with a review of the extant literature on the aforementioned topic. We conclude with an evaluation of the current evidence on the subject, along with assertions regarding future research in the area.

PATHOPHYSIOLOGY OF DISABILITY FOLLOWING HOSPITALIZATION FOR SEVERE SEPSIS

The pathophysiology behind functional decline in patients hospitalized with severe sepsis is multifactorial (Figure 1). During hospitalization, it is well known that patients suffer from restricted mobility,[25] and that this impediment is linked to poor functional outcomes.[26] Described as far back as Hippocrates,[27] more recent studies have elucidated how prolonged bed rest leads to a multitude of physiological changes that promote deconditioning.[28] Specifically, skeletal muscle atrophy and decreased protein synthesis, independent of ongoing disease processes and acute illness, have been demonstrated in both animal and human models of prolonged inactivity.[29, 30] Additionally, bed rest leading to insensible fluid losses, a decline in stroke volume and effective cardiac output, bone loss, and decreased insulin sensitivity has been reported.[28, 31] There is little doubt that the aforementioned issues pertain to severe sepsis patients outside the ICU. In fact, nearly all of the acute mechanisms driving Creditor's hazards of hospitalization are noted among patients with severe sepsis.[32]

Figure 1
Sepsis and functional decline diagram. Abbreviations: IGF, insulin‐like growth factor; IL, interleukin; MTor, mammalian target of rapamycin; TNF, tumor necrosis factor.

Furthermore, several factors preceding hospitalization may increase risk of disability. For example, Covinsky et al. described a number of risk factors, such as comorbid conditions, cognitive impairment, and various psychosocial aspects such as depression and limited social support, as being associated with increased risk of functional decline.[33] Thus, both in‐hospital and prehospital factors likely combine within an individual patient's context to determine risk of physical decline.

On this backdrop and the inherent immobilization associated with hospitalization, sepsis and inflammation catalyze physiologic changes that further propagate deconditioning.[7] Implicated pathways and proteins for this process include the mammalian target of rapamycin, human growth hormone, insulin‐like growth factors, interleukin‐1, and tumor necrosis factor‐. Through several metabolic alterations, sepsis independently promotes skeletal muscle breakdown and impairs skeletal muscle synthesis.[34, 35, 36] Inflammation associated with sepsis also increases oxidant burden, further leading to muscle dysfunction and dysregulation.[7, 31, 37, 38]

EFFECTS OF PHYSICAL THERAPY AND MOBILIZATION ON CLINICAL OUTCOMES

In patients with nonsepsis conditions who are at risk for functional decline, the effectiveness of physical therapy has been studied in multiple settings with positive outcomes. For example, in hospitalized elderly patients with general deconditioning, PT‐based interventions have demonstrated reductions in length of hospital stay.[39] Additionally, exercise in healthy subjects who have been subjected to bed rest has been shown to attenuate physiological changes, and maintain plasma and red cell volume and work capacity.[40] Adequate safety and improved outcomes have also been demonstrated in the general population of critically ill patients who receive early PT and mobilization. Improved functional capacity at discharge, decreases in duration of delirium, increased ventilator‐free days, decreased risk for CLABSI, and a better general sense of well‐being following these interventions have been widely reported in the literature.[14, 19, 23, 24, 41, 42, 43, 44, 45] Interestingly, critically ill patients may have a dose‐ and time‐dependent response to PT; that is, high intensity and early onset mobility‐based interventions are often associated with more ventilator‐free time and improved functional outcomes, resulting in shorter ICU and hospital length of stay.[42, 46, 47, 48]

Moderate intensity exercise has also been shown to improve 6‐minute walking distance in patients convalescing from coronary artery bypass grafting surgery.[49] Furthermore, in the postoperative setting, patients suffering traumatic hip fractures are known to benefit from physical and occupational therapies with shorter time to ambulation and improved locomotion in the recovery period.[21, 50, 51] Among patients with stroke, PT and gait training has led to improvements in speed, gait, independence during walking, activities of daily living, and extended activities of daily living.[52, 53, 54] A recent meta‐analysis also suggested that extra PT compared to regular treatment in patients with acute and subacute conditions such as stroke and postoperative states improved mobility and quality of life, while reducing length of hospital stay.[22]

Although this evidence suggests potential benefits for PT and mobilization, it is important to note that the effect of these treatments in dissimilar populations is unknown and may not necessarily be positive. For example, a recent study examining PT and its impact on patients with hip osteoarthritis showed no clinical benefit.[55] Mobilizing patients in severe illness may be associated with important risks, including falls, worsening of their clinical status, or moral discouragement in the setting of limited capacity. Therefore, understanding which elements of mobilization efforts create the greatest impact in the context of delivery of the intervention is critical to assessing the risk, benefit, and efficacy of PT‐based interventions.

EARLY PHYSICAL THERAPY FOR SEVERE SEPSIS OUTSIDE THE ICU: LITERATURE REVIEW

Given the functional decline associated with severe sepsis and the evidence of PT efficacy in other populations, we reviewed the current literature for studies evaluating physical therapy in severe sepsis patients outside the ICU. With the assistance of medical reference librarians, we searched MEDLINE via PubMed (1950present), EMBASE (1946present), Cochrane CENTRAL Register of Controlled Trials, and the Cochrane Database of Reviews of Effectiveness (1960present via Ovid). The search was last updated in June 2014.

We searched for studies that (1) involved human patients 18 years of age, (2) included patients with a primary diagnosis of sepsis or severe sepsis being treated outside the ICU, (3) featured a primary intervention that included PT or an early mobilization‐based initiative, and (4) reported a primary clinical or functional outcome of interest. Early was defined based on the included studies' definition. To be fully inclusive, we also conducted a secondary review with inclusion criteria expanded to studies of either any infectious pathology or severe sepsis patient in the ICU that employed PT interventions.

Our electronic search retrieved 815 records (Figure 2). Despite this approach, no publications met our primary inclusion criteria as we found no study that implemented a mobility intervention directed toward patients with sepsis treated outside the ICU. Our expanded secondary review included patients with any infectious pathology or those with severe sepsis in the ICU treated with PT; in this review, 2 studies met eligibility criteria.[56] In a 2003 cluster‐randomized trial, Mundy and colleagues randomized patients admitted with pneumonia to receive early PT or usual care. The outcomes of interest were hospital length of stay, mortality, number of chest radiographs, emergency department visits, and readmissions at 30 and 90 days after hospital admission. Although the study has important limitations (including patient‐level difference between trial arms, subjective definition of early mobilization), the authors found a significant decrease in length of stay among patients with pneumonia who received early PT compared to controls (5.8 vs 6.9 days, absolute difference 1.1 days, 95% confidence interval: 02.2 days). The study also reported a substantial decrease in adjusted mean hospital charges for the early mobilization group versus the usual care group ($10,159 per patient vs. $12,868 per patient, P=0.05). In the second study, Sossdorf et al. retrospectively evaluated a cohort of 999 patients with severe sepsis and septic shock and assessed whether onset and frequency of PT‐based interventions was associated with clinical benefit. After multivariate analysis, the authors reported a small mortality benefit associated with the relative number of PT interventions (hazard ratio: 0.982, P<0.001).[45]

Figure 2
Systematic review flowchart. Abbreviations: CINAHL, Cumulative Index to Nursing and Allied Health Literature; ICU, intensive care unit; EM, early mobilization.

EXPLAINING THE VOID

Our integrative review of the current literature reveals a gap in our understanding of the role of early mobilization in severe sepsis both within and beyond the ICU. Given the promise of PT‐based interventions and the toll of severe sepsis, one must ask: why may this be so?

First, the understanding that severe sepsis leads to significant, long‐term consequences for survivors has only been identified recently. Thus, it is possible that the burden and consequences related to this condition have not been fully recognized in clinical settings, leading to a paucity of research and interventions. Although the association between sepsis and mortality has been known since the 1990s,[57] long‐term complications and enduring morbidity of this disease continue to be realized. Indeed, many studies delineating the longer‐term effects of sepsis have been only recently published.[6, 10, 11, 12, 13]

Second, it is likely that many clinicians ascribe to the viewpoint that severe sepsis is an ICU‐only condition, a myth that has been discounted by multiple studies.[1, 4, 5] Although our study shows a paucity of evidence in both ICU and nonICU‐based severe sepsis, almost half of severe sepsis occurs outside the ICU, carrying with it many of the same clinical implications. Additionally, increased morbidity, mortality, and resource utilization are known to be true in all patients with severe sepsis, irrespective of where they receive treatment in the hospital.[4, 5, 6] Recent evidence has also shown that severe sepsis treated on the floor may be clinically, epidemiologically, and even prognostically unique from its ICU counterpart.[5, 58, 59] Therefore, it appears that research domains with tailored interventions to both ICU and non‐ICU severe sepsis patients are important areas of inquiry for clinicians. Such research may serve the purpose of assessing impact of early mobilization and unmasking any treatment heterogeneity that may exist when dealing with severe sepsis. Though trials of PT in ICU‐based severe sepsis are underway,[60] it is prudent that these also extend beyond the ICU‐setting.

Third, variability in early mobility practices and billing documentation for severe sepsis patients may exist, adding barriers to performing high‐quality research on this topic. In fact, administrative billing records for PT may offer insufficient granularity about services provided or therapies administered, particularly in the ICU where variability in early mobilization practices have been shown despite common employment of physiotherapists.[61]

Finally, many hospitalists may believe that patients with severe sepsis are simply too sick for early mobilization or PT, possibly limiting their participation in clinical or research‐based interventions. This perception has been well described in ICU populations, where it has been well studied and shown to be false.[41, 42, 43] Nevertheless, if severe sepsis patients are viewed as relatively sick hospitalized patients, it is plausible that resistance against early mobilization interventions may exist.[62] Understanding these biases and being mindful of such barriers when conducting studies in this area would be important.

CONCLUSION AND FUTURE DIRECTIONS

The cost burdens of severe sepsis are substantial. Elixhauser et al. suggest that it is currently the single most expensive cause of acute hospitalization in the United States.[63] Importantly, a large proportion of patients with severe sepsis receive care from hospitalists and/or floor teams on the general wards. Our integrative review has demonstrated a knowledge gap when it comes to rigorous assessments of PT and mobilization treatments in patients with severe sepsis within and beyond the ICU. Existing evidence provides a strong rationale for why functional decline occurs in patients with severe sepsis. A reasonable argument for PT‐based interventions to mitigate functional decline in this subset exists, but rigorous evaluation of such interventions is necessary. Physical and mobilization‐based treatments are routinely available and efficacious in several other settings and populations. It could be rapidly deployed and potentially improve outcomes in those with severe sepsis. Research would be welcomed to establish optimal dosing, efficacy, and cost effectiveness of PT and early mobilization for severe sepsis, particularly in patients treated on the general wards by hospitalists and floor teams.

How may such a research agenda be launched? A balanced multipronged approach is necessary. First, large‐scale epidemiological data to understand variation in practice are needed. Focused studies carried out by community and academic hospitalists on septic patients treated outside the ICU are the call of the hour. These data, in turn, can help create registries that assess for risk factors, quality of treatment, and long‐term outcomes among survivors of this condition. Second, evaluation and improvement of the coding and precision of physical and occupational therapy billing records is necessary so that their added value can be assessed and tracked using administrative data. Third, targeted prospective studies and clinical trials to directly evaluate the effect of PT in well‐defined patient populations with sepsis outside the ICU are needed. In this arena, hospitalist expertise and trained physical therapists will be crucial. The focus of this work should be directed toward both short‐term and long‐term functional outcomes, as well as mortality and morbidity assessments. Fourth, these patient‐centered efforts should loop back and inform the foundational biology of severe sepsis, thus illuminating patient‐centered end points, from biomarker analysis to physiometric measurements in basic and translational research.

In conclusion, this review sheds light on the fact that interventions that may mitigate the functional and cognitive decline in survivors of severe sepsis appear underdeveloped. Although the precise benefit of such interventions remains unclear, the low‐cost, widespread availability and generalizability of PT‐based interventions make it a worthy candidate for future research. As the numbers of survivors of sepsis expand, an unmet public health need for interventions to improve the long‐term outcomes of this population exists. Hospitalists and intensivists caring for severe sepsis patients must rise to meet this need. Together, we can help improve the lives of patients afflicted with severe sepsis, wherever they may receive care in the hospital.

Acknowledgements

The authors acknowledge the efforts of medical research librarians Andy Hickner, MSI, and Marissa Conte, MSI, on this project.

Disclosures

This work was supported by the National Institutes of HealthK08, HL091249 (T.J.I.) and VA HSR&D IIR‐11109 (T.J.I.). The views expressed here are the authors' own and do not necessarily represent the views of the US government or the Department of Veterans' Affairs. The authors report no conflicts of interest.

Severe sepsis, defined as an infection leading to systemic inflammatory response and acute organ dysfunction, is a significant cause of morbidity and mortality.[1, 2, 3] Although it has been a condition classically attributed to patients in the intensive care unit (ICU), accumulating data suggest that a substantial proportion of patients with severe sepsis are managed by hospitalists and floor teams in non‐ICU, general ward settings.[1, 4, 5] Although the incidence of severe sepsis continues to rise both in the United States and other developed nations,[2, 6, 7] advances in early recognition, management, and care of this condition have resulted in improved rates of survival.[8] The resultant increase in a severe sepsis survivor population[6] make the long‐term sequelae of this condition an important public health problem.[9]

In both the ICU and on general wards, severe sepsis survivors suffer from decreased functional status, worsened quality of life, increased cognitive dysfunction, and sarcopenia.[4, 6, 10, 11, 12, 13, 14] Not surprisingly, many such patients are discharged to long‐term care facilities for physical rehabilitation,[15] with escalating utilization of resources[16] and cost.[17, 18] Inexpensive interventions that improve outcomes following sepsis would thus be welcomed.

It is well known that physical therapy (PT) and early mobilization are beneficial in mitigating functional decline in a number of conditions.[19, 20, 21, 22] PT can improve outcomes in several ways: prevention of bed rest deconditioning, mitigation of mechanisms that lead to sarcopenia, increased pulmonary and tissue aerobic capacity, and improved sense of well‐being. Indeed, among the population cared for in ICU settings, early mobility and PT lead to more ventilator‐free days, better functional status at discharge, shorter duration of delirium, and even a potentially reduced risk of central line‐associated bloodstream infection (CLABSI).[23, 24] However, whether initiating early PT can improve outcomes in patients with severe sepsis treated by either intensivists or hospitalists/floor teams outside the ICU is unknown.

Therefore, to better understand this phenomenon, we systematically reviewed and integrated the literature regarding early mobilization and PT for severe sepsis outside the ICU. To be more inclusive, a secondary review including populations with any infectious etiology and severe sepsis treated within the ICU was also conducted. Our review begins by providing an overview of the pathophysiology behind functional decline in severe sepsis, along with existing evidence on early mobilization efficacy in other patient populations. We then proceed with a review of the extant literature on the aforementioned topic. We conclude with an evaluation of the current evidence on the subject, along with assertions regarding future research in the area.

PATHOPHYSIOLOGY OF DISABILITY FOLLOWING HOSPITALIZATION FOR SEVERE SEPSIS

The pathophysiology behind functional decline in patients hospitalized with severe sepsis is multifactorial (Figure 1). During hospitalization, it is well known that patients suffer from restricted mobility,[25] and that this impediment is linked to poor functional outcomes.[26] Described as far back as Hippocrates,[27] more recent studies have elucidated how prolonged bed rest leads to a multitude of physiological changes that promote deconditioning.[28] Specifically, skeletal muscle atrophy and decreased protein synthesis, independent of ongoing disease processes and acute illness, have been demonstrated in both animal and human models of prolonged inactivity.[29, 30] Additionally, bed rest leading to insensible fluid losses, a decline in stroke volume and effective cardiac output, bone loss, and decreased insulin sensitivity has been reported.[28, 31] There is little doubt that the aforementioned issues pertain to severe sepsis patients outside the ICU. In fact, nearly all of the acute mechanisms driving Creditor's hazards of hospitalization are noted among patients with severe sepsis.[32]

Figure 1
Sepsis and functional decline diagram. Abbreviations: IGF, insulin‐like growth factor; IL, interleukin; MTor, mammalian target of rapamycin; TNF, tumor necrosis factor.

Furthermore, several factors preceding hospitalization may increase risk of disability. For example, Covinsky et al. described a number of risk factors, such as comorbid conditions, cognitive impairment, and various psychosocial aspects such as depression and limited social support, as being associated with increased risk of functional decline.[33] Thus, both in‐hospital and prehospital factors likely combine within an individual patient's context to determine risk of physical decline.

On this backdrop and the inherent immobilization associated with hospitalization, sepsis and inflammation catalyze physiologic changes that further propagate deconditioning.[7] Implicated pathways and proteins for this process include the mammalian target of rapamycin, human growth hormone, insulin‐like growth factors, interleukin‐1, and tumor necrosis factor‐. Through several metabolic alterations, sepsis independently promotes skeletal muscle breakdown and impairs skeletal muscle synthesis.[34, 35, 36] Inflammation associated with sepsis also increases oxidant burden, further leading to muscle dysfunction and dysregulation.[7, 31, 37, 38]

EFFECTS OF PHYSICAL THERAPY AND MOBILIZATION ON CLINICAL OUTCOMES

In patients with nonsepsis conditions who are at risk for functional decline, the effectiveness of physical therapy has been studied in multiple settings with positive outcomes. For example, in hospitalized elderly patients with general deconditioning, PT‐based interventions have demonstrated reductions in length of hospital stay.[39] Additionally, exercise in healthy subjects who have been subjected to bed rest has been shown to attenuate physiological changes, and maintain plasma and red cell volume and work capacity.[40] Adequate safety and improved outcomes have also been demonstrated in the general population of critically ill patients who receive early PT and mobilization. Improved functional capacity at discharge, decreases in duration of delirium, increased ventilator‐free days, decreased risk for CLABSI, and a better general sense of well‐being following these interventions have been widely reported in the literature.[14, 19, 23, 24, 41, 42, 43, 44, 45] Interestingly, critically ill patients may have a dose‐ and time‐dependent response to PT; that is, high intensity and early onset mobility‐based interventions are often associated with more ventilator‐free time and improved functional outcomes, resulting in shorter ICU and hospital length of stay.[42, 46, 47, 48]

Moderate intensity exercise has also been shown to improve 6‐minute walking distance in patients convalescing from coronary artery bypass grafting surgery.[49] Furthermore, in the postoperative setting, patients suffering traumatic hip fractures are known to benefit from physical and occupational therapies with shorter time to ambulation and improved locomotion in the recovery period.[21, 50, 51] Among patients with stroke, PT and gait training has led to improvements in speed, gait, independence during walking, activities of daily living, and extended activities of daily living.[52, 53, 54] A recent meta‐analysis also suggested that extra PT compared to regular treatment in patients with acute and subacute conditions such as stroke and postoperative states improved mobility and quality of life, while reducing length of hospital stay.[22]

Although this evidence suggests potential benefits for PT and mobilization, it is important to note that the effect of these treatments in dissimilar populations is unknown and may not necessarily be positive. For example, a recent study examining PT and its impact on patients with hip osteoarthritis showed no clinical benefit.[55] Mobilizing patients in severe illness may be associated with important risks, including falls, worsening of their clinical status, or moral discouragement in the setting of limited capacity. Therefore, understanding which elements of mobilization efforts create the greatest impact in the context of delivery of the intervention is critical to assessing the risk, benefit, and efficacy of PT‐based interventions.

EARLY PHYSICAL THERAPY FOR SEVERE SEPSIS OUTSIDE THE ICU: LITERATURE REVIEW

Given the functional decline associated with severe sepsis and the evidence of PT efficacy in other populations, we reviewed the current literature for studies evaluating physical therapy in severe sepsis patients outside the ICU. With the assistance of medical reference librarians, we searched MEDLINE via PubMed (1950present), EMBASE (1946present), Cochrane CENTRAL Register of Controlled Trials, and the Cochrane Database of Reviews of Effectiveness (1960present via Ovid). The search was last updated in June 2014.

We searched for studies that (1) involved human patients 18 years of age, (2) included patients with a primary diagnosis of sepsis or severe sepsis being treated outside the ICU, (3) featured a primary intervention that included PT or an early mobilization‐based initiative, and (4) reported a primary clinical or functional outcome of interest. Early was defined based on the included studies' definition. To be fully inclusive, we also conducted a secondary review with inclusion criteria expanded to studies of either any infectious pathology or severe sepsis patient in the ICU that employed PT interventions.

Our electronic search retrieved 815 records (Figure 2). Despite this approach, no publications met our primary inclusion criteria as we found no study that implemented a mobility intervention directed toward patients with sepsis treated outside the ICU. Our expanded secondary review included patients with any infectious pathology or those with severe sepsis in the ICU treated with PT; in this review, 2 studies met eligibility criteria.[56] In a 2003 cluster‐randomized trial, Mundy and colleagues randomized patients admitted with pneumonia to receive early PT or usual care. The outcomes of interest were hospital length of stay, mortality, number of chest radiographs, emergency department visits, and readmissions at 30 and 90 days after hospital admission. Although the study has important limitations (including patient‐level difference between trial arms, subjective definition of early mobilization), the authors found a significant decrease in length of stay among patients with pneumonia who received early PT compared to controls (5.8 vs 6.9 days, absolute difference 1.1 days, 95% confidence interval: 02.2 days). The study also reported a substantial decrease in adjusted mean hospital charges for the early mobilization group versus the usual care group ($10,159 per patient vs. $12,868 per patient, P=0.05). In the second study, Sossdorf et al. retrospectively evaluated a cohort of 999 patients with severe sepsis and septic shock and assessed whether onset and frequency of PT‐based interventions was associated with clinical benefit. After multivariate analysis, the authors reported a small mortality benefit associated with the relative number of PT interventions (hazard ratio: 0.982, P<0.001).[45]

Figure 2
Systematic review flowchart. Abbreviations: CINAHL, Cumulative Index to Nursing and Allied Health Literature; ICU, intensive care unit; EM, early mobilization.

EXPLAINING THE VOID

Our integrative review of the current literature reveals a gap in our understanding of the role of early mobilization in severe sepsis both within and beyond the ICU. Given the promise of PT‐based interventions and the toll of severe sepsis, one must ask: why may this be so?

First, the understanding that severe sepsis leads to significant, long‐term consequences for survivors has only been identified recently. Thus, it is possible that the burden and consequences related to this condition have not been fully recognized in clinical settings, leading to a paucity of research and interventions. Although the association between sepsis and mortality has been known since the 1990s,[57] long‐term complications and enduring morbidity of this disease continue to be realized. Indeed, many studies delineating the longer‐term effects of sepsis have been only recently published.[6, 10, 11, 12, 13]

Second, it is likely that many clinicians ascribe to the viewpoint that severe sepsis is an ICU‐only condition, a myth that has been discounted by multiple studies.[1, 4, 5] Although our study shows a paucity of evidence in both ICU and nonICU‐based severe sepsis, almost half of severe sepsis occurs outside the ICU, carrying with it many of the same clinical implications. Additionally, increased morbidity, mortality, and resource utilization are known to be true in all patients with severe sepsis, irrespective of where they receive treatment in the hospital.[4, 5, 6] Recent evidence has also shown that severe sepsis treated on the floor may be clinically, epidemiologically, and even prognostically unique from its ICU counterpart.[5, 58, 59] Therefore, it appears that research domains with tailored interventions to both ICU and non‐ICU severe sepsis patients are important areas of inquiry for clinicians. Such research may serve the purpose of assessing impact of early mobilization and unmasking any treatment heterogeneity that may exist when dealing with severe sepsis. Though trials of PT in ICU‐based severe sepsis are underway,[60] it is prudent that these also extend beyond the ICU‐setting.

Third, variability in early mobility practices and billing documentation for severe sepsis patients may exist, adding barriers to performing high‐quality research on this topic. In fact, administrative billing records for PT may offer insufficient granularity about services provided or therapies administered, particularly in the ICU where variability in early mobilization practices have been shown despite common employment of physiotherapists.[61]

Finally, many hospitalists may believe that patients with severe sepsis are simply too sick for early mobilization or PT, possibly limiting their participation in clinical or research‐based interventions. This perception has been well described in ICU populations, where it has been well studied and shown to be false.[41, 42, 43] Nevertheless, if severe sepsis patients are viewed as relatively sick hospitalized patients, it is plausible that resistance against early mobilization interventions may exist.[62] Understanding these biases and being mindful of such barriers when conducting studies in this area would be important.

CONCLUSION AND FUTURE DIRECTIONS

The cost burdens of severe sepsis are substantial. Elixhauser et al. suggest that it is currently the single most expensive cause of acute hospitalization in the United States.[63] Importantly, a large proportion of patients with severe sepsis receive care from hospitalists and/or floor teams on the general wards. Our integrative review has demonstrated a knowledge gap when it comes to rigorous assessments of PT and mobilization treatments in patients with severe sepsis within and beyond the ICU. Existing evidence provides a strong rationale for why functional decline occurs in patients with severe sepsis. A reasonable argument for PT‐based interventions to mitigate functional decline in this subset exists, but rigorous evaluation of such interventions is necessary. Physical and mobilization‐based treatments are routinely available and efficacious in several other settings and populations. It could be rapidly deployed and potentially improve outcomes in those with severe sepsis. Research would be welcomed to establish optimal dosing, efficacy, and cost effectiveness of PT and early mobilization for severe sepsis, particularly in patients treated on the general wards by hospitalists and floor teams.

How may such a research agenda be launched? A balanced multipronged approach is necessary. First, large‐scale epidemiological data to understand variation in practice are needed. Focused studies carried out by community and academic hospitalists on septic patients treated outside the ICU are the call of the hour. These data, in turn, can help create registries that assess for risk factors, quality of treatment, and long‐term outcomes among survivors of this condition. Second, evaluation and improvement of the coding and precision of physical and occupational therapy billing records is necessary so that their added value can be assessed and tracked using administrative data. Third, targeted prospective studies and clinical trials to directly evaluate the effect of PT in well‐defined patient populations with sepsis outside the ICU are needed. In this arena, hospitalist expertise and trained physical therapists will be crucial. The focus of this work should be directed toward both short‐term and long‐term functional outcomes, as well as mortality and morbidity assessments. Fourth, these patient‐centered efforts should loop back and inform the foundational biology of severe sepsis, thus illuminating patient‐centered end points, from biomarker analysis to physiometric measurements in basic and translational research.

In conclusion, this review sheds light on the fact that interventions that may mitigate the functional and cognitive decline in survivors of severe sepsis appear underdeveloped. Although the precise benefit of such interventions remains unclear, the low‐cost, widespread availability and generalizability of PT‐based interventions make it a worthy candidate for future research. As the numbers of survivors of sepsis expand, an unmet public health need for interventions to improve the long‐term outcomes of this population exists. Hospitalists and intensivists caring for severe sepsis patients must rise to meet this need. Together, we can help improve the lives of patients afflicted with severe sepsis, wherever they may receive care in the hospital.

Acknowledgements

The authors acknowledge the efforts of medical research librarians Andy Hickner, MSI, and Marissa Conte, MSI, on this project.

Disclosures

This work was supported by the National Institutes of HealthK08, HL091249 (T.J.I.) and VA HSR&D IIR‐11109 (T.J.I.). The views expressed here are the authors' own and do not necessarily represent the views of the US government or the Department of Veterans' Affairs. The authors report no conflicts of interest.

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  43. Kress JP. Clinical trials of early mobilization of critically ill patients. Crit Care Med. 2009;37:S442S447.
  44. Needham DM. Mobilizing patients in the intensive care unit: improving neuromuscular weakness and physical function. JAMA. 2008;300:16851690.
  45. Sossdorf M, Otto GP, Menge K, et al. Potential effect of physiotherapeutic treatment on mortality rate in patients with severe sepsis and septic shock: a retrospective cohort analysis. J Crit Care. 2013;28:954958.
  46. Chiang LL, Wang LY, Wu CP, et al. Effects of physical training on functional status in patients with prolonged mechanical ventilation. Phys Ther. 2006;86:12711281.
  47. Martin UJ, Hincapie L, Nimchuk M, et al. Impact of whole‐body rehabilitation in patients receiving chronic mechanical ventilation. Crit Care Med. 2005;33:22592265.
  48. Nava S. Rehabilitation of patients admitted to a respiratory intensive care unit. Arch Phys Med Rehabil. 1998;79:849854.
  49. Hirschhorn AD, Richards D, Mungovan SF, et al. Supervised moderate intensity exercise improves distance walked at hospital discharge following coronary artery bypass graft surgery—a randomised controlled trial. Heart Lung Circ. 2008;17:129138.
  50. Chudyk AM, Jutai JW, Petrella RJ, et al. Systematic review of hip fracture rehabilitation practices in the elderly. Arch Phys Med Rehabil. 2009;90:246262.
  51. Penrod JD, Boockvar KS, Litke A, et al. Physical therapy and mobility 2 and 6 months after hip fracture. J Am Geriatr Soc. 2004;52:11141120.
  52. Brazzelli M, Saunders DH, Greig CA, et al. Physical fitness training for stroke patients. Cochrane Database Syst Rev. 2011;(11):CD003316.
  53. Veerbeek JM, Koolstra M, Ket JC, et al. Effects of augmented exercise therapy on outcome of gait and gait‐related activities in the first 6 months after stroke: a meta‐analysis. Stroke. 2011;42:33113315.
  54. Kwakkel G, Peppen R, Wagenaar RC, et al. Effects of augmented exercise therapy time after stroke: a meta‐analysis. Stroke. 2004;35:25292539.
  55. Bennell KL, Egerton T, Martin J, et al. Effect of physical therapy on pain and function in patients with hip osteoarthritis: a randomized clinical trial. JAMA. 2014;311:19871997.
  56. Mundy LM, Leet TL, Darst K, et al. Early mobilization of patients hospitalized with community‐acquired pneumonia. Chest. 2003;124:883889.
  57. Quartin AA, Schein RM, Kett DH, et al. Magnitude and duration of the effect of sepsis on survival. Department of Veterans Affairs Systemic Sepsis Cooperative Studies Group. JAMA. 1997;277:10581063.
  58. Sundararajan V, Macisaac CM, Presneill JJ, et al. Epidemiology of sepsis in Victoria, Australia. Crit Care Med. 2005;33:7180.
  59. Esteban A, Frutos‐Vivar F, Ferguson ND, et al. Sepsis incidence and outcome: contrasting the intensive care unit with the hospital ward. Crit Care Med. 2007;35:12841289.
  60. Kayambu G, Boots RJ, Paratz JD. Early rehabilitation in sepsis: a prospective randomised controlled trial investigating functional and physiological outcomes The i‐PERFORM Trial (Protocol Article). BMC Anesthesiol. 2011;11:21.
  61. Hodgson CL, Berney S, Bellomo R, et al. TEAM: a prospective multi‐centre cohort study of early activity and mobilisation in ICU. In: American Thoracic Society 2013 International Conference; May 17–22, 2013; Philadelphia, PA. Am J Respir Crit Care Med. 2013;187:A3625.
  62. Needham DM, Davidson J, Cohen H, et al. Improving long‐term outcomes after discharge from intensive care unit: report from a stakeholders' conference. Crit Care Med. 2012;40:502509.
  63. Elixhauser A, Friedman B, Stranges E. Septicemia in U.S. hospitals, 2009: statistical brief #122. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD; 2006.
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  16. Kahn JM, Benson NM, Appleby D, et al. Long‐term acute care hospital utilization after critical illness. JAMA. 2010;303:22532259.
  17. Dick A, Liu H, Zwanziger J, et al. Long‐term survival and healthcare utilization outcomes attributable to sepsis and pneumonia. BMC Health Serv Res. 2012;12:432.
  18. Weycker D, Akhras KS, Edelsberg J, et al. Long‐term mortality and medical care charges in patients with severe sepsis. Crit Care Med. 2003;31:23162323.
  19. Burtin C, Clerckx B, Robbeets C, et al. Early exercise in critically ill patients enhances short‐term functional recovery. Crit Care Med. 2009;37:24992505.
  20. Heran BS, Chen JM, Ebrahim S, et al. Exercise‐based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev. 2011;(7):CD001800.
  21. Hoenig H, Rubenstein LV, Sloane R, et al. What is the role of timing in the surgical and rehabilitative care of community‐dwelling older persons with acute hip fracture? Arch Intern Med. 1997;157:513520.
  22. Peiris CL, Taylor NF, Shields N. Extra physical therapy reduces patient length of stay and improves functional outcomes and quality of life in people with acute or subacute conditions: a systematic review. Arch Phys Med Rehabilil. 2011;92:14901500.
  23. Hunter A, Johnson L, Coustasse A. Reduction of intensive care unit length of stay: the case of early mobilization. Health Care Manag (Frederick). 2014;33:128135.
  24. Schweickert WD, Pohlman MC, Pohlman AS, et al. Early physical and occupational therapy in mechanically ventilated, critically ill patients: a randomised controlled trial. Lancet. 2009;373:18741882.
  25. Gill TM, Allore HG, Holford TR, et al. Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004;292:21152124.
  26. Zisberg A, Shadmi E, Sinoff G, et al. Low mobility during hospitalization and functional decline in older adults. J Am Geriatr Soc. 2011;59:266273.
  27. Chadwick J, Mann WN. The Medical Works of Hippocrates. Oxford, United Kingdom: Blackwell; 1950.
  28. Convertino VA, Bloomfield SA, Greenleaf JE. An overview of the issues: physiological effects of bed rest and restricted physical activity. Med Sci Sports Exerc. 1997;29:187190.
  29. Ferrando AA, Lane HW, Stuart CA, et al. Prolonged bed rest decreases skeletal muscle and whole body protein synthesis. Am J Physiol. 1996;270:E627E633.
  30. Stein TP, Wade CE. Metabolic consequences of muscle disuse atrophy. J Nutr. 2005;135:1824S1828S.
  31. Winkelman C. Inactivity and inflammation in the critically ill patient. Crit Care Clin. 2007;23:2134.
  32. Creditor MC. Hazards of hospitalization of the elderly. Ann Intern Med. 1993;118:219223.
  33. Covinsky KE, Pierluissi E, Johnston CB. Hospitalization‐associated disability: “She was probably able to ambulate, but I'm not sure”. JAMA. 2011;306:17821793.
  34. Breuille D, Voisin L, Contrepois M, et al. A sustained rat model for studying the long‐lasting catabolic state of sepsis. Infect Immun. 1999;67:10791085.
  35. Vary TC. Regulation of skeletal muscle protein turnover during sepsis. Curr Opin Clin Nutr. Metab Care. 1998;1:217224.
  36. Lang CH, Frost RA, Vary TC. Regulation of muscle protein synthesis during sepsis and inflammation. Am J Physiol Endocrinol Metab. 2007;293:E453E459.
  37. Couillard A, Prefaut C. From muscle disuse to myopathy in COPD: potential contribution of oxidative stress. Eur Respir J. 2005;26:703719.
  38. Macdonald J, Galley HF, Webster NR. Oxidative stress and gene expression in sepsis. Br J Anaesth. 2003;90:221232.
  39. Fisher SR, Kuo YF, Graham JE, et al. Early ambulation and length of stay in older adults hospitalized for acute illness. Arch Intern Med. 2010;170:19421943.
  40. Greenleaf JE. Intensive exercise training during bed rest attenuates deconditioning. Med Sci Sports Exerc. 1997;29:207215.
  41. Bailey P, Thomsen GE, Spuhler VJ, et al. Early activity is feasible and safe in respiratory failure patients. Crit Care Med. 2007;35:139145.
  42. Morris PE, Goad A, Thompson C, et al. Early intensive care unit mobility therapy in the treatment of acute respiratory failure. Crit Care Med. 2008;36:22382243.
  43. Kress JP. Clinical trials of early mobilization of critically ill patients. Crit Care Med. 2009;37:S442S447.
  44. Needham DM. Mobilizing patients in the intensive care unit: improving neuromuscular weakness and physical function. JAMA. 2008;300:16851690.
  45. Sossdorf M, Otto GP, Menge K, et al. Potential effect of physiotherapeutic treatment on mortality rate in patients with severe sepsis and septic shock: a retrospective cohort analysis. J Crit Care. 2013;28:954958.
  46. Chiang LL, Wang LY, Wu CP, et al. Effects of physical training on functional status in patients with prolonged mechanical ventilation. Phys Ther. 2006;86:12711281.
  47. Martin UJ, Hincapie L, Nimchuk M, et al. Impact of whole‐body rehabilitation in patients receiving chronic mechanical ventilation. Crit Care Med. 2005;33:22592265.
  48. Nava S. Rehabilitation of patients admitted to a respiratory intensive care unit. Arch Phys Med Rehabil. 1998;79:849854.
  49. Hirschhorn AD, Richards D, Mungovan SF, et al. Supervised moderate intensity exercise improves distance walked at hospital discharge following coronary artery bypass graft surgery—a randomised controlled trial. Heart Lung Circ. 2008;17:129138.
  50. Chudyk AM, Jutai JW, Petrella RJ, et al. Systematic review of hip fracture rehabilitation practices in the elderly. Arch Phys Med Rehabil. 2009;90:246262.
  51. Penrod JD, Boockvar KS, Litke A, et al. Physical therapy and mobility 2 and 6 months after hip fracture. J Am Geriatr Soc. 2004;52:11141120.
  52. Brazzelli M, Saunders DH, Greig CA, et al. Physical fitness training for stroke patients. Cochrane Database Syst Rev. 2011;(11):CD003316.
  53. Veerbeek JM, Koolstra M, Ket JC, et al. Effects of augmented exercise therapy on outcome of gait and gait‐related activities in the first 6 months after stroke: a meta‐analysis. Stroke. 2011;42:33113315.
  54. Kwakkel G, Peppen R, Wagenaar RC, et al. Effects of augmented exercise therapy time after stroke: a meta‐analysis. Stroke. 2004;35:25292539.
  55. Bennell KL, Egerton T, Martin J, et al. Effect of physical therapy on pain and function in patients with hip osteoarthritis: a randomized clinical trial. JAMA. 2014;311:19871997.
  56. Mundy LM, Leet TL, Darst K, et al. Early mobilization of patients hospitalized with community‐acquired pneumonia. Chest. 2003;124:883889.
  57. Quartin AA, Schein RM, Kett DH, et al. Magnitude and duration of the effect of sepsis on survival. Department of Veterans Affairs Systemic Sepsis Cooperative Studies Group. JAMA. 1997;277:10581063.
  58. Sundararajan V, Macisaac CM, Presneill JJ, et al. Epidemiology of sepsis in Victoria, Australia. Crit Care Med. 2005;33:7180.
  59. Esteban A, Frutos‐Vivar F, Ferguson ND, et al. Sepsis incidence and outcome: contrasting the intensive care unit with the hospital ward. Crit Care Med. 2007;35:12841289.
  60. Kayambu G, Boots RJ, Paratz JD. Early rehabilitation in sepsis: a prospective randomised controlled trial investigating functional and physiological outcomes The i‐PERFORM Trial (Protocol Article). BMC Anesthesiol. 2011;11:21.
  61. Hodgson CL, Berney S, Bellomo R, et al. TEAM: a prospective multi‐centre cohort study of early activity and mobilisation in ICU. In: American Thoracic Society 2013 International Conference; May 17–22, 2013; Philadelphia, PA. Am J Respir Crit Care Med. 2013;187:A3625.
  62. Needham DM, Davidson J, Cohen H, et al. Improving long‐term outcomes after discharge from intensive care unit: report from a stakeholders' conference. Crit Care Med. 2012;40:502509.
  63. Elixhauser A, Friedman B, Stranges E. Septicemia in U.S. hospitals, 2009: statistical brief #122. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD; 2006.
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Epidemiology of Organ System Dysfunction

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The epidemiology of acute organ system dysfunction from severe sepsis outside of the intensive care unit

The International Consensus Conference (ICC) for sepsis defines severe sepsis as an infection leading to acute organ dysfunction.[1, 2] Severe sepsis afflicts over 1 million patients each year in Medicare alone, and is substantially more common among older Americans than acute myocardial infarction.[3, 4, 5] Recently, the Agency for Healthcare Research and Quality identified severe sepsis as the single most expensive cause of hospitalization in the United States.[6] The incidence of severe sepsis continues to rise.[4, 5]

Severe sepsis is often mischaracterized as a diagnosis cared for primarily in the intensive care unit (ICU). Yet, studies indicate that only 32% to 50% of patients with severe sepsis require ICU care, leaving the majority on the general care wards.[7, 8] These studies also reveal mortality rates of 26% to 30% among patients with severe sepsis who are not admitted to an ICU compared to 11% to 33% in the ICU.[7, 8]

Although a number of epidemiologic and interventional studies have focused on severe sepsis in the ICU,[3, 9, 10] much less is known about patients cared for on the general medicine wards. Without this information, clinicians cannot make informed choices about important management decisions such as targeted diagnostic testing, empirical antimicrobials, and other therapies. To this end, we sought to further characterize the infectious etiologies and resultant organ system dysfunctions in the subset of patients with severe sepsis admitted to non‐ICU medical services at a tertiary academic medical center.

METHODS

Population/Setting

All hospitalizations of adult patients (18 years old) who were initially admitted to non‐ICU medical services at the University of Michigan Hospital during 2009 through 2010 were included. The University of Michigan Hospital has 610 general medical‐surgical beds, including telemetry beds, with closed ICUs comprised of 179 beds staffed by intensivists. Patients transferred from other hospitals and those admitted to non‐medical services were excluded.

Data Abstraction and Definitions

All International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) diagnosis codes for hospitalizations were screened using a previously published and validated algorithm for severe sepsis.[11] Following this screening, 3 randomly selected round‐numbered batches of hospitalizations were sampled with subsequent application of the exclusion criteria. Medical records including physicians' notes, consultants' notes, nurses' notes, physical therapy notes, discharge coordinators' notes, emergency room flow sheets, as well as ward flow sheets were reviewed in detail by 3 practicing hospitalists using a structured instrument closely aligned with the ICC definition of severe sepsis.[2] We also sampled a smaller number of patients whose ICD‐9‐CM diagnoses screened negative for severe sepsis. Sample size was selected as part of a project with multiple objectives, and reflected a pragmatic balance between the anticipated precision of the results and the resources available to conduct chart review.[11] All discrepancies were reconciled among the 3 reviewers.

Reviewers first assessed whether infection was present, then evaluated for evidence of each organ system dysfunction, and finally determined the extent to which those organ dysfunctions were a response to the infection. Infection was defined either as a patient with a microbiologic culture growing a pathologic organism in a normally sterile site or documentation of a suspected infection with other confirmatory evidence (radiological, physical exam finding) with resultant systemic inflammatory response and administration of antimicrobials. Community‐acquired and healthcare‐associated infections were not differentiated. Microbiologic data, confirmatory tests, and site of infection were abstracted in detail.

Organ dysfunction was defined as per the 2001 ICC criteria,[2] and was assessed for neurological, pulmonary, cardiovascular, renal, gastrointestinal, hematological, and hepatic system involvement in all patients. A summary of these clinical definitions is included in Table 1. Data on important comorbidities were also abstracted. Immunosuppression was defined as having any of the following: solid organ transplant, bone marrow/stem cell transplant, human immunodeficiency virus/acquired immunodeficiency syndrome, neutropenia (absolute neutrophil count <1000), hematologic malignancy, solid organ malignancy with chemotherapy within the past 12 months, or pharmacologic immunosuppression (prednisone >20 mg daily for >4 weeks, calcineurin inhibitor, methotrexate, tumor necrosis factor inhibitors, azathioprine, sulfasalazine, hydroxychloroquine). Last, each chart was evaluated for the presence of explicit documentation with the presence of the words or phrases: sepsis, septic shock, or severe sepsis, indicating that the clinical service recognized and fully documented that a patient had severe sepsis.

Organ System Dysfunction Parameters as Defined by the 2001 International Consensus Conference
Organ SystemParameters to Indicate Dysfunction
  • NOTE: Abbreviations: BiPAP, bilevel positive airway pressure, BP, blood pressure; dL, deciliter; FiO2, fraction of inspired oxygen; INR, international normalized ratio; LPM, liters per minute; MAP, mean arterial pressure; mg, milligram; PaO2, partial pressure of oxygen in arterial blood; PTT, partial thromboplastin time.

CardiovascularSystolic BP <90, elevated lactate, MAP <70, requiring pressors >2 hours, decrease in systolic BP of >40
RenalCreatinine increase >0.5 mg/dL, oliguria
NeurologicalAcute mental status changes
PulmonaryIntubation, BiPAP, supplemental oxygen >6 LPM or 40% face mask, PaO2/FiO2 <300
HematologicINR >1.5 or PTT >60 not on anticoagulation, platelets <100 or 50% of baseline
IleusDecreased bowel motility requiring a change in diet
HepaticBilirubin >4 mg/dL and >1.5 baseline

Data Analysis

Methods for assessment of reviewer concordance have been previously described and were summarized using the kappa statistic.[11] Initial data extraction was performed in SAS 9.1 (SAS Institute, Cary, NC) and all analyses were conducted in Stata 12 (StataCorp LP, College Station, TX). Binomial 95% confidence intervals (CIs) are presented. This project was approved by the University of Michigan Institutional Review Board.

RESULTS

Of 23,288 hospitalizations examined from 2009 through 2010, the ICD‐9based automated screen for severe sepsis was positive for 3,146 (14 %). A random sample of 111 medical records, of which 92 had screened positive for severe sepsis and 19 had screened negative, was reviewed in detail. After review by the hospitalists, 64 of these 111 hospitalizations were judged to have severe sepsis, 61 of the 92 screened positive cases (66%), and 3 of the 19 screened negative cases (16%). The 3 reviewers had a kappa of 0.70, indicating good agreement.

Characteristics of the 64 patients with severe sepsis are shown in Table 2. The mean age was 63 years old (standard deviation [SD]=17.7), and 41% were male. The mean length of stay was 13.7 days (SD=20.8). Thirty‐nine percent (95% CI, 27%‐52%) of patients (25/64) were immunosuppressed. Of patients initially admitted to the general medical ward, 25% (16/64; 95% CI, 15%‐37%) ultimately required ICU care during their admission. The overall in‐hospital mortality rate was 13% (8/64; 95% CI, 6%‐23%). Immunosuppressed patients had a mortality rate of 20% and nonimmunosuppressed patients had a mortality rate of 8%. Only 47% (30/64; 95% CI, 34%‐60%) of the medical records had explicit clinician documentation of severe sepsis.

Demographics and Characteristics of Patients With Severe Sepsis (N=64)
Age, mean (SD), y63 (18)
  • NOTE: Abbreviations: SD, standard deviation.

Male sex, no. (%)26 (41)
Preexisting conditions, no. (%) 
History of diabetes20 (31)
End stage renal disease on chronic dialysis2 (3)
Chronic obstructive pulmonary disease on oxygen3 (5)
History of cancer15 (23)
Liver cirrhosis5 (8)
Immunosuppression25 (39)
Median length of stay (days)7.5
Mean length of stay (SD)13.7 (20.8)

The most common site of infection was found to be the genitourinary system, occurring in 41% (26/64; 95% CI, 29%‐54%) of patients (Table 3). Pulmonary and intra‐abdominal sites were also common, accounting for 14% (95% CI, 6.6%‐25%) and 13% (95% CI, 5.6%‐23%) of sites, respectively. An infecting organism was identified by culture in 66% (42/64; 95% CI, 53%‐77%) of case patients with specific pathogens listed in Table 4. Among patients with positive culture results, the majority grew Gram‐negative organisms (57%; 95% CI, 41%‐72%). Non‐Clostridium difficile Gram‐positive organisms were also prominent and identified in 48% (95% CI, 32%‐64%) of positive cultures. Candida was less common (12%, 95% CI, 4.0%‐26%). Fourteen cases (22%, 95% CI, 10%‐30%) had 2 or more concomitant infectious pathogens.

Site of Infection (N=64)
SiteNo. (%)
  • NOTE: Abbreviations: GI, gastrointestinal.

Genitourinary26 (41)
Pulmonary9 (14)
Intra‐abdominal (not intraluminal)8 (13)
Bloodstream/cardiac5 (8)
Skin and soft tissue4 (6)
GI lumen4 (6)
Joint2 (3)
Multiple sites4 (6)
Unknown2 (2)
Microbiology
 Absolute Frequency, Total Positive Culture Results, N=64, No. (%)*?>aPatients With Cultures Growing at Least One of the Pathogens, N=42, No. (%)*?>a
  • Multiple responses per patient possible.

  • Other includes Citrobacter, Enterobacter, Proteus, Achromobacter xylosoxidans, and Fusobacterium.

Gram‐negative pathogens30 (47)24 (57)
Escherichia coli12 (19)12 (29)
Escherichia coli (multidrug resistant)2 (3)2 (5)
Klebsiella6 (9)5 (12)
Pseudomonas aeruginosa6 (9)4 (10)
Pseudomonas aeruginosa (multidrug resistant)2 (3)2 (5)
Otherb6 (9)6 (14)
Gram‐positive pathogens29 (45)25 (59)
Enterococcus14 (22)13 (31)
Vancomycin‐resistant Enterococcus species5 (8)4 (10)
Staphylococcus aureus7 (11)7 (17)
Methicillin‐resistant Staphylococcus aureus3 (5)3 (7)
Streptococcus pneumoniae2 (3)2 (5)
Coagulase‐negative staphylococci1 (2)1 (2)
Clostridium difficile5 (8)5 (12)
Fungi  
Candida species5 (8)5 (12)
Mycobacterium avium1 (2)1 (2)
Two organisms 9 (21)
Three or more organisms 5 (12)

All 64 patients had at least 1 organ dysfunction, as required by the ICC definition of severe sepsis. Organ dysfunction in 2 or more organ systems occurred in 77% (95% CI, 64%‐86%) of the cases (49/64). The incidence for each organ system dysfunction is presented in Table 5, as well as its relationship to both mortality and ICU admission. The most common organ system dysfunctions were found to be cardiovascular (hypotension) and renal dysfunction occurring in 66% and 64% of the cases, respectively. In this non‐ICU population, pulmonary dysfunction occurred in 30% of cases, but was frequently associated with transfer to the ICU, as 63% of the patients with pulmonary failure required ICU care. Patients with more organ systems affected were more likely to be transferred to the ICU and to die.

Incidence and Outcomes of Organ Dysfunction in Patients Admitted to Non‐ICU Services
 No. (%)ICU Transfer, No. (%)Mortality, No. (%)
  • NOTE: Abbreviations: GI, gastrointestinal; ICU, intensive care unit.

  • Multiple responses per patient possible.

  • Percentage of patients with each organ system dysfunction who needed ICU care while in the hospital.

  • Percentage of patients with organ system dysfunction who died while in the hospital.

Number of failed organs, N = 64
115 (23%)0 (0%)0 (0%)
225 (39%)2 (8%)0 (0%)
37 (11%)2 (29%)1 (14%)
410 (16%)6 (60%)3 (30%)
>47 (11%)6 (86%)4 (57%)
Types of organ system dysfunction, all patients, N = 64*?>a
Cardiovascular42 (66%)16 (38%)b8 (19%)c
Renal41 (64%)10 (24%)b5 (12%)c
Central nervous system35 (54%)14 (40%)b7 (18%)c
Pulmonary19 (30%)12 (63%)b8 (42%)c
Hematologic15 (23%)6 (40%)b6 (40%)c
GI (ileus)8 (13%)5 (63%)b1 (13%)c
Hepatic5 (8%)4 (80%)b2 (40%)c

DISCUSSION

Severe sepsis was common among patients admitted to the general medical ward in this tertiary care center. Our patient cohort differed in important ways from previously described typical cases of severe sepsis among ICU populations. Severe sepsis on the general medical wards was more commonly associated with Gram‐negative pathogens in the setting of genitourinary tract infections. This is in contrast to Gram‐positive organisms and respiratory tract infections, which are more common in the ICU.[3, 10] Renal and cardiac dysfunction were commonly observed organ failures, whereas in the ICU, severe sepsis has been reported to more likely involve respiratory failure. These results suggest that hospitalists seeking to provide evidence‐based care to prevent postsepsis morbidity and mortality for their non‐ICU patients need to heighten their index of suspicion when caring for an infected patient and appreciate that many severe sepsis patients may not fit neatly into traditional sepsis treatment algorithms.

Studies characterizing severe sepsis in the ICU setting indicate a predominance of pulmonary infections and respiratory failure with occurrence rates of 74% to 95% and 54% to 61%, respectively.[3, 12, 13] Given that either shock or pulmonary dysfunction is often required for admission to many ICUs, it is perhaps not surprising that these rates are dramatically different on the general medicine ward, with a relative scarcity of pulmonary infections (14%) and respiratory dysfunction (30%). Instead, genitourinary infections were noted in 41% (95% CI, 29%‐54%) of the cases, in contrast to the rates of genitourinary infections in ICU patients with severe sepsis, which have rates of 5.4% to 9.1%.[3, 10] Likely as a result of this, a Gram‐negative predominance is noted in the associated microbiology. Furthermore, our study indicates that C difficile and vancomycin‐resistant Enterococcus (VRE) species appear to represent an emerging cause of severe sepsis on the general medicine wards, as they have not been noted to be causative micro‐organisms in previous studies of sepsis. This is concordant with other studies showing increases in incidence and severity of disease for C difficile as well as VRE.[14, 15]

Previous epidemiologic studies of severe sepsis originating outside the ICU are lacking, but some work has been done. One study on the epidemiology of sepsis both with and without organ dysfunction aggregated all hospitalized patients and included those both admitted to the general medicine wards and directly to the ICU.[7] Similar to our study, this study also found a predominance of Gram‐negative causative organisms, as well as comparable in‐hospital mortality rates (12.8% vs 13%). Additionally, genitourinary infections were noted in 20% of the patients, notably higher than rates reported to have been found in patients with severe sepsis in the ICU, but not the magnitude found in our study, perhaps as a result of the combined ICU‐ward population studied. A similar high prevalence of genitourinary infections was also noted in a recent administrative data‐based study of emergency medical services‐transported patients with severe sepsis, half of whom required intensive care during their hospitalization.[16]

Our study is unique in that it focuses on severe sepsis in patients, commonly cared for by hospitalists, who were admitted to the general medical ward, and uses patient level data to elucidate more characteristics of the defining organ dysfunction. Furthermore, our results suggest that severe sepsis was poorly documented in this setting, indicating a potential impact on billing, coding, case mix index, and hospital mortality statistics that rely on very specific wording, as well as a possible need for increased awareness among hospitalists. Without this awareness, an opportunity may be missed for improved patient care via specific sepsis‐targeted measures,[13, 17, 18] including more aggressive resuscitative measures[19] or intensive physical and occupational therapy interventions aimed at impacting the cognitive and functional debilities[20] that result from severe sepsis. Highlighting this growing need to better assist clinicians assess the severity of septic patients and recognize these complex cases on the general medicine wards, 1 recent study evaluated the fitness of several clinical disease‐severity scoring systems for patients with sepsis in general internal medicine departments.[21] Perhaps with the help of tools such as these, which are being piloted in some hospitals, the care of this growing population can be enhanced.

Our study has a number of limitations that should be kept in mind. First, this is a single center study performed at an academic tertiary care center with a relatively high incidence of immunosuppression, which may influence the spectrum of infecting organisms. Our center also has a relatively large, closed‐model ICU, which often operates at near capacity, potentially affecting the severity of our non‐ICU population. Second, although we screened a large number of patients, as necessitated by our intensive and detailed review of clinical information, our sample size with hospitalist‐validated severe sepsis is relatively small. With this small sample size, less prevalent infections, patient characteristics, and organ dysfunctions may by chance have been under or over‐represented, and one could expect some variance in the occurrence rates of organ system dysfunction and infection rates by sampling error alone. Further larger scale studies are warranted to confirm these data and their generalizability. Third, the data necessary to calculate sequential organ failure assessment or multiple organ dysfunction score were not collected. This may limit the ability to directly compare the organ dysfunction noted in this study with others. Additionally, given the ICC definitions of organ dysfunction, some of the organ dysfunction noted, particularly for neurological dysfunction, was reliant on subjective clinical findings documented in the record. Finally, we relied on the lack of specific terminology to indicate a lack of documentation of sepsis, which does not necessarily indicate a lack of recognition or undertreatment of this condition. However, these limitations are offset by the strengths of this study, including the patient‐level medical record validation of severe sepsis by trained hospitalist physicians, high kappa statistic, and strict application of guideline‐based definitions.

This work has important implications for both clinicians and for future research on severe sepsis. The results suggest that severe sepsis may be quite common outside the ICU, and that patients presenting with this condition who are admitted to general medical wards are not routinely characterized by the profound hypoxemia and refractory shock of iconic cases. Certainly, further study looking at larger numbers of cases is needed to better understand the specifics and nuances of this important topic as well as to further evaluate clinicians' ability to recognize and treat such patients in this setting. Furthermore, future research on the treatment of severe sepsis, including both antimicrobials and disease‐modifying agents (eg, anti‐inflammatories) must continue to include and even focus on this large population of non‐ICU patients with severe sepsis, as the risk/benefit ratios of such potential treatments may vary with severity of illness.

In conclusion, severe sepsis was commonly found in patients admitted on the general medicine wards. The epidemiology of the infections and resultant organ dysfunction appears to differ from that found in the ICU. More studies are needed to provide a deeper understanding of this disease process, as this will enable clinicians to better recognize and treat patients thus afflicted, no matter the setting.

Acknowledgments

The authors thank Laetitia Shapiro, AM, for her programming assistance.

Disclosures: This work was supported in part by the US National Institutes of HealthK08, HL091249 (TJI) and the University of Michigan SpecialistHospitalist Allied Research Program (SHARP). This work was also supported in part by VA Ann Arbor Healthcare System, Geriatric Research Education and Clinical Center (GRECC).

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References
  1. Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):16441655.
  2. Levy MM, Fink MP, Marshall JC, et al. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med. 2003;31(4):12501256.
  3. Angus DC, Linde‐Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):13031310.
  4. Iwashyna TJ, Cooke CR, Wunsch H, Kahn JM. Population burden of long‐term survivorship after severe sepsis in older americans. J Am Geriatr Soc. 2012;60(6):10701077.
  5. Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348(16):15461554.
  6. Elixhauser A, Friedman B, Stranges E. Septicemia in U.S. hospitals, 2009: statistical brief #122. October 2011. In: Healthcare Cost and Utilization Project Statistical Briefs. Rockville, MD: Agency for Health Care Policy and Research; 2006. Available from: http://www.ncbi.nlm.nih.gov/books/NBK65391. Accessed June 2, 2012.
  7. Esteban A, Frutos‐Vivar F, Ferguson ND, et al. Sepsis incidence and outcome: contrasting the intensive care unit with the hospital ward. Crit Care Med. 2007;35(5):12841289.
  8. Sundararajan V, Macisaac CM, Presneill JJ, Cade JF, Visvanathan K. Epidemiology of sepsis in Victoria, Australia. Crit Care Med. 2005;33(1):7180.
  9. Brunkhorst FM, Oppert M, Marx G, et al. Effect of empirical treatment with moxifloxacin and meropenem vs meropenem on sepsis‐related organ dysfunction in patients with severe sepsis: a randomized trial. JAMA. 2012;307(22):23902399.
  10. Guidet B, Aegerter P, Gauzit R, Meshaka P, Dreyfuss D. Incidence and impact of organ dysfunctions associated with sepsis. Chest. 2005;127(3):942951.
  11. Iwashyna TJ, Odden A, Rohde JM, et al. Identifying patients with severe sepsis using administrative claims: patient‐level validation of the Angus Implementation of the International Consensus Conference definition of severe sepsis [published online ahead of print September 18, 2012]. Medical Care. doi: 10.1097/MLR.0b013e318268ac86.
  12. Annane D, Aegerter P, Jars‐Guincestre MC, Guidet B. Current epidemiology of septic shock: the CUB‐Rea Network. Am J Respir Crit Care Med. 2003;168(2):165172.
  13. Russell JA. Management of sepsis. N Engl J Med. 2006;355(16):16991713.
  14. Lessa FC, Gould CV, McDonald C. Current status of Clostridium difficile infection ipidemiology. Clin Infect Dis. 2012;55(suppl 2):S65S70.
  15. McGeer AJ, Low DE. Vancomycin‐resistant enterococci. Semin Respir Infect. 2000;15(4):314326.
  16. Seymour CW, Rea TD, Kahn JM, Walkey A, Yealy DM, Angus DC. Severe sepsis in prehospital emergency care: analysis of incidence, care, and outcome. Am J Respir Crit Care Med. 2012;186(12):12641271.
  17. Suffredini AF, Munford RS. Novel Therapies for Septic Shock Over the Past 4 Decades. JAMA. 2011;306(2):194199.
  18. Castellanos‐Ortega A, Suberviola B, Garcia‐Astudillo LA, et al. Impact of the Surviving Sepsis Campaign protocols on hospital length of stay and mortality in septic shock patients: results of a three‐year follow‐up quasi‐experimental study. Crit Care Med. 2010;38(4):10361043.
  19. Claessens YE, Dhainaut JF. Diagnosis and treatment of severe sepsis. Crit Care. 2007;11(suppl 5):S2.
  20. Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long‐term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010;304(16):17871794.
  21. Ghanem‐Zoubi NO, Vardi M, Laor A, Weber G, Bitterman H. Assessment of disease‐severity scoring systems for patients with sepsis in general internal medicine departments. Crit Care. 2011;15:R95.
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The International Consensus Conference (ICC) for sepsis defines severe sepsis as an infection leading to acute organ dysfunction.[1, 2] Severe sepsis afflicts over 1 million patients each year in Medicare alone, and is substantially more common among older Americans than acute myocardial infarction.[3, 4, 5] Recently, the Agency for Healthcare Research and Quality identified severe sepsis as the single most expensive cause of hospitalization in the United States.[6] The incidence of severe sepsis continues to rise.[4, 5]

Severe sepsis is often mischaracterized as a diagnosis cared for primarily in the intensive care unit (ICU). Yet, studies indicate that only 32% to 50% of patients with severe sepsis require ICU care, leaving the majority on the general care wards.[7, 8] These studies also reveal mortality rates of 26% to 30% among patients with severe sepsis who are not admitted to an ICU compared to 11% to 33% in the ICU.[7, 8]

Although a number of epidemiologic and interventional studies have focused on severe sepsis in the ICU,[3, 9, 10] much less is known about patients cared for on the general medicine wards. Without this information, clinicians cannot make informed choices about important management decisions such as targeted diagnostic testing, empirical antimicrobials, and other therapies. To this end, we sought to further characterize the infectious etiologies and resultant organ system dysfunctions in the subset of patients with severe sepsis admitted to non‐ICU medical services at a tertiary academic medical center.

METHODS

Population/Setting

All hospitalizations of adult patients (18 years old) who were initially admitted to non‐ICU medical services at the University of Michigan Hospital during 2009 through 2010 were included. The University of Michigan Hospital has 610 general medical‐surgical beds, including telemetry beds, with closed ICUs comprised of 179 beds staffed by intensivists. Patients transferred from other hospitals and those admitted to non‐medical services were excluded.

Data Abstraction and Definitions

All International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) diagnosis codes for hospitalizations were screened using a previously published and validated algorithm for severe sepsis.[11] Following this screening, 3 randomly selected round‐numbered batches of hospitalizations were sampled with subsequent application of the exclusion criteria. Medical records including physicians' notes, consultants' notes, nurses' notes, physical therapy notes, discharge coordinators' notes, emergency room flow sheets, as well as ward flow sheets were reviewed in detail by 3 practicing hospitalists using a structured instrument closely aligned with the ICC definition of severe sepsis.[2] We also sampled a smaller number of patients whose ICD‐9‐CM diagnoses screened negative for severe sepsis. Sample size was selected as part of a project with multiple objectives, and reflected a pragmatic balance between the anticipated precision of the results and the resources available to conduct chart review.[11] All discrepancies were reconciled among the 3 reviewers.

Reviewers first assessed whether infection was present, then evaluated for evidence of each organ system dysfunction, and finally determined the extent to which those organ dysfunctions were a response to the infection. Infection was defined either as a patient with a microbiologic culture growing a pathologic organism in a normally sterile site or documentation of a suspected infection with other confirmatory evidence (radiological, physical exam finding) with resultant systemic inflammatory response and administration of antimicrobials. Community‐acquired and healthcare‐associated infections were not differentiated. Microbiologic data, confirmatory tests, and site of infection were abstracted in detail.

Organ dysfunction was defined as per the 2001 ICC criteria,[2] and was assessed for neurological, pulmonary, cardiovascular, renal, gastrointestinal, hematological, and hepatic system involvement in all patients. A summary of these clinical definitions is included in Table 1. Data on important comorbidities were also abstracted. Immunosuppression was defined as having any of the following: solid organ transplant, bone marrow/stem cell transplant, human immunodeficiency virus/acquired immunodeficiency syndrome, neutropenia (absolute neutrophil count <1000), hematologic malignancy, solid organ malignancy with chemotherapy within the past 12 months, or pharmacologic immunosuppression (prednisone >20 mg daily for >4 weeks, calcineurin inhibitor, methotrexate, tumor necrosis factor inhibitors, azathioprine, sulfasalazine, hydroxychloroquine). Last, each chart was evaluated for the presence of explicit documentation with the presence of the words or phrases: sepsis, septic shock, or severe sepsis, indicating that the clinical service recognized and fully documented that a patient had severe sepsis.

Organ System Dysfunction Parameters as Defined by the 2001 International Consensus Conference
Organ SystemParameters to Indicate Dysfunction
  • NOTE: Abbreviations: BiPAP, bilevel positive airway pressure, BP, blood pressure; dL, deciliter; FiO2, fraction of inspired oxygen; INR, international normalized ratio; LPM, liters per minute; MAP, mean arterial pressure; mg, milligram; PaO2, partial pressure of oxygen in arterial blood; PTT, partial thromboplastin time.

CardiovascularSystolic BP <90, elevated lactate, MAP <70, requiring pressors >2 hours, decrease in systolic BP of >40
RenalCreatinine increase >0.5 mg/dL, oliguria
NeurologicalAcute mental status changes
PulmonaryIntubation, BiPAP, supplemental oxygen >6 LPM or 40% face mask, PaO2/FiO2 <300
HematologicINR >1.5 or PTT >60 not on anticoagulation, platelets <100 or 50% of baseline
IleusDecreased bowel motility requiring a change in diet
HepaticBilirubin >4 mg/dL and >1.5 baseline

Data Analysis

Methods for assessment of reviewer concordance have been previously described and were summarized using the kappa statistic.[11] Initial data extraction was performed in SAS 9.1 (SAS Institute, Cary, NC) and all analyses were conducted in Stata 12 (StataCorp LP, College Station, TX). Binomial 95% confidence intervals (CIs) are presented. This project was approved by the University of Michigan Institutional Review Board.

RESULTS

Of 23,288 hospitalizations examined from 2009 through 2010, the ICD‐9based automated screen for severe sepsis was positive for 3,146 (14 %). A random sample of 111 medical records, of which 92 had screened positive for severe sepsis and 19 had screened negative, was reviewed in detail. After review by the hospitalists, 64 of these 111 hospitalizations were judged to have severe sepsis, 61 of the 92 screened positive cases (66%), and 3 of the 19 screened negative cases (16%). The 3 reviewers had a kappa of 0.70, indicating good agreement.

Characteristics of the 64 patients with severe sepsis are shown in Table 2. The mean age was 63 years old (standard deviation [SD]=17.7), and 41% were male. The mean length of stay was 13.7 days (SD=20.8). Thirty‐nine percent (95% CI, 27%‐52%) of patients (25/64) were immunosuppressed. Of patients initially admitted to the general medical ward, 25% (16/64; 95% CI, 15%‐37%) ultimately required ICU care during their admission. The overall in‐hospital mortality rate was 13% (8/64; 95% CI, 6%‐23%). Immunosuppressed patients had a mortality rate of 20% and nonimmunosuppressed patients had a mortality rate of 8%. Only 47% (30/64; 95% CI, 34%‐60%) of the medical records had explicit clinician documentation of severe sepsis.

Demographics and Characteristics of Patients With Severe Sepsis (N=64)
Age, mean (SD), y63 (18)
  • NOTE: Abbreviations: SD, standard deviation.

Male sex, no. (%)26 (41)
Preexisting conditions, no. (%) 
History of diabetes20 (31)
End stage renal disease on chronic dialysis2 (3)
Chronic obstructive pulmonary disease on oxygen3 (5)
History of cancer15 (23)
Liver cirrhosis5 (8)
Immunosuppression25 (39)
Median length of stay (days)7.5
Mean length of stay (SD)13.7 (20.8)

The most common site of infection was found to be the genitourinary system, occurring in 41% (26/64; 95% CI, 29%‐54%) of patients (Table 3). Pulmonary and intra‐abdominal sites were also common, accounting for 14% (95% CI, 6.6%‐25%) and 13% (95% CI, 5.6%‐23%) of sites, respectively. An infecting organism was identified by culture in 66% (42/64; 95% CI, 53%‐77%) of case patients with specific pathogens listed in Table 4. Among patients with positive culture results, the majority grew Gram‐negative organisms (57%; 95% CI, 41%‐72%). Non‐Clostridium difficile Gram‐positive organisms were also prominent and identified in 48% (95% CI, 32%‐64%) of positive cultures. Candida was less common (12%, 95% CI, 4.0%‐26%). Fourteen cases (22%, 95% CI, 10%‐30%) had 2 or more concomitant infectious pathogens.

Site of Infection (N=64)
SiteNo. (%)
  • NOTE: Abbreviations: GI, gastrointestinal.

Genitourinary26 (41)
Pulmonary9 (14)
Intra‐abdominal (not intraluminal)8 (13)
Bloodstream/cardiac5 (8)
Skin and soft tissue4 (6)
GI lumen4 (6)
Joint2 (3)
Multiple sites4 (6)
Unknown2 (2)
Microbiology
 Absolute Frequency, Total Positive Culture Results, N=64, No. (%)*?>aPatients With Cultures Growing at Least One of the Pathogens, N=42, No. (%)*?>a
  • Multiple responses per patient possible.

  • Other includes Citrobacter, Enterobacter, Proteus, Achromobacter xylosoxidans, and Fusobacterium.

Gram‐negative pathogens30 (47)24 (57)
Escherichia coli12 (19)12 (29)
Escherichia coli (multidrug resistant)2 (3)2 (5)
Klebsiella6 (9)5 (12)
Pseudomonas aeruginosa6 (9)4 (10)
Pseudomonas aeruginosa (multidrug resistant)2 (3)2 (5)
Otherb6 (9)6 (14)
Gram‐positive pathogens29 (45)25 (59)
Enterococcus14 (22)13 (31)
Vancomycin‐resistant Enterococcus species5 (8)4 (10)
Staphylococcus aureus7 (11)7 (17)
Methicillin‐resistant Staphylococcus aureus3 (5)3 (7)
Streptococcus pneumoniae2 (3)2 (5)
Coagulase‐negative staphylococci1 (2)1 (2)
Clostridium difficile5 (8)5 (12)
Fungi  
Candida species5 (8)5 (12)
Mycobacterium avium1 (2)1 (2)
Two organisms 9 (21)
Three or more organisms 5 (12)

All 64 patients had at least 1 organ dysfunction, as required by the ICC definition of severe sepsis. Organ dysfunction in 2 or more organ systems occurred in 77% (95% CI, 64%‐86%) of the cases (49/64). The incidence for each organ system dysfunction is presented in Table 5, as well as its relationship to both mortality and ICU admission. The most common organ system dysfunctions were found to be cardiovascular (hypotension) and renal dysfunction occurring in 66% and 64% of the cases, respectively. In this non‐ICU population, pulmonary dysfunction occurred in 30% of cases, but was frequently associated with transfer to the ICU, as 63% of the patients with pulmonary failure required ICU care. Patients with more organ systems affected were more likely to be transferred to the ICU and to die.

Incidence and Outcomes of Organ Dysfunction in Patients Admitted to Non‐ICU Services
 No. (%)ICU Transfer, No. (%)Mortality, No. (%)
  • NOTE: Abbreviations: GI, gastrointestinal; ICU, intensive care unit.

  • Multiple responses per patient possible.

  • Percentage of patients with each organ system dysfunction who needed ICU care while in the hospital.

  • Percentage of patients with organ system dysfunction who died while in the hospital.

Number of failed organs, N = 64
115 (23%)0 (0%)0 (0%)
225 (39%)2 (8%)0 (0%)
37 (11%)2 (29%)1 (14%)
410 (16%)6 (60%)3 (30%)
>47 (11%)6 (86%)4 (57%)
Types of organ system dysfunction, all patients, N = 64*?>a
Cardiovascular42 (66%)16 (38%)b8 (19%)c
Renal41 (64%)10 (24%)b5 (12%)c
Central nervous system35 (54%)14 (40%)b7 (18%)c
Pulmonary19 (30%)12 (63%)b8 (42%)c
Hematologic15 (23%)6 (40%)b6 (40%)c
GI (ileus)8 (13%)5 (63%)b1 (13%)c
Hepatic5 (8%)4 (80%)b2 (40%)c

DISCUSSION

Severe sepsis was common among patients admitted to the general medical ward in this tertiary care center. Our patient cohort differed in important ways from previously described typical cases of severe sepsis among ICU populations. Severe sepsis on the general medical wards was more commonly associated with Gram‐negative pathogens in the setting of genitourinary tract infections. This is in contrast to Gram‐positive organisms and respiratory tract infections, which are more common in the ICU.[3, 10] Renal and cardiac dysfunction were commonly observed organ failures, whereas in the ICU, severe sepsis has been reported to more likely involve respiratory failure. These results suggest that hospitalists seeking to provide evidence‐based care to prevent postsepsis morbidity and mortality for their non‐ICU patients need to heighten their index of suspicion when caring for an infected patient and appreciate that many severe sepsis patients may not fit neatly into traditional sepsis treatment algorithms.

Studies characterizing severe sepsis in the ICU setting indicate a predominance of pulmonary infections and respiratory failure with occurrence rates of 74% to 95% and 54% to 61%, respectively.[3, 12, 13] Given that either shock or pulmonary dysfunction is often required for admission to many ICUs, it is perhaps not surprising that these rates are dramatically different on the general medicine ward, with a relative scarcity of pulmonary infections (14%) and respiratory dysfunction (30%). Instead, genitourinary infections were noted in 41% (95% CI, 29%‐54%) of the cases, in contrast to the rates of genitourinary infections in ICU patients with severe sepsis, which have rates of 5.4% to 9.1%.[3, 10] Likely as a result of this, a Gram‐negative predominance is noted in the associated microbiology. Furthermore, our study indicates that C difficile and vancomycin‐resistant Enterococcus (VRE) species appear to represent an emerging cause of severe sepsis on the general medicine wards, as they have not been noted to be causative micro‐organisms in previous studies of sepsis. This is concordant with other studies showing increases in incidence and severity of disease for C difficile as well as VRE.[14, 15]

Previous epidemiologic studies of severe sepsis originating outside the ICU are lacking, but some work has been done. One study on the epidemiology of sepsis both with and without organ dysfunction aggregated all hospitalized patients and included those both admitted to the general medicine wards and directly to the ICU.[7] Similar to our study, this study also found a predominance of Gram‐negative causative organisms, as well as comparable in‐hospital mortality rates (12.8% vs 13%). Additionally, genitourinary infections were noted in 20% of the patients, notably higher than rates reported to have been found in patients with severe sepsis in the ICU, but not the magnitude found in our study, perhaps as a result of the combined ICU‐ward population studied. A similar high prevalence of genitourinary infections was also noted in a recent administrative data‐based study of emergency medical services‐transported patients with severe sepsis, half of whom required intensive care during their hospitalization.[16]

Our study is unique in that it focuses on severe sepsis in patients, commonly cared for by hospitalists, who were admitted to the general medical ward, and uses patient level data to elucidate more characteristics of the defining organ dysfunction. Furthermore, our results suggest that severe sepsis was poorly documented in this setting, indicating a potential impact on billing, coding, case mix index, and hospital mortality statistics that rely on very specific wording, as well as a possible need for increased awareness among hospitalists. Without this awareness, an opportunity may be missed for improved patient care via specific sepsis‐targeted measures,[13, 17, 18] including more aggressive resuscitative measures[19] or intensive physical and occupational therapy interventions aimed at impacting the cognitive and functional debilities[20] that result from severe sepsis. Highlighting this growing need to better assist clinicians assess the severity of septic patients and recognize these complex cases on the general medicine wards, 1 recent study evaluated the fitness of several clinical disease‐severity scoring systems for patients with sepsis in general internal medicine departments.[21] Perhaps with the help of tools such as these, which are being piloted in some hospitals, the care of this growing population can be enhanced.

Our study has a number of limitations that should be kept in mind. First, this is a single center study performed at an academic tertiary care center with a relatively high incidence of immunosuppression, which may influence the spectrum of infecting organisms. Our center also has a relatively large, closed‐model ICU, which often operates at near capacity, potentially affecting the severity of our non‐ICU population. Second, although we screened a large number of patients, as necessitated by our intensive and detailed review of clinical information, our sample size with hospitalist‐validated severe sepsis is relatively small. With this small sample size, less prevalent infections, patient characteristics, and organ dysfunctions may by chance have been under or over‐represented, and one could expect some variance in the occurrence rates of organ system dysfunction and infection rates by sampling error alone. Further larger scale studies are warranted to confirm these data and their generalizability. Third, the data necessary to calculate sequential organ failure assessment or multiple organ dysfunction score were not collected. This may limit the ability to directly compare the organ dysfunction noted in this study with others. Additionally, given the ICC definitions of organ dysfunction, some of the organ dysfunction noted, particularly for neurological dysfunction, was reliant on subjective clinical findings documented in the record. Finally, we relied on the lack of specific terminology to indicate a lack of documentation of sepsis, which does not necessarily indicate a lack of recognition or undertreatment of this condition. However, these limitations are offset by the strengths of this study, including the patient‐level medical record validation of severe sepsis by trained hospitalist physicians, high kappa statistic, and strict application of guideline‐based definitions.

This work has important implications for both clinicians and for future research on severe sepsis. The results suggest that severe sepsis may be quite common outside the ICU, and that patients presenting with this condition who are admitted to general medical wards are not routinely characterized by the profound hypoxemia and refractory shock of iconic cases. Certainly, further study looking at larger numbers of cases is needed to better understand the specifics and nuances of this important topic as well as to further evaluate clinicians' ability to recognize and treat such patients in this setting. Furthermore, future research on the treatment of severe sepsis, including both antimicrobials and disease‐modifying agents (eg, anti‐inflammatories) must continue to include and even focus on this large population of non‐ICU patients with severe sepsis, as the risk/benefit ratios of such potential treatments may vary with severity of illness.

In conclusion, severe sepsis was commonly found in patients admitted on the general medicine wards. The epidemiology of the infections and resultant organ dysfunction appears to differ from that found in the ICU. More studies are needed to provide a deeper understanding of this disease process, as this will enable clinicians to better recognize and treat patients thus afflicted, no matter the setting.

Acknowledgments

The authors thank Laetitia Shapiro, AM, for her programming assistance.

Disclosures: This work was supported in part by the US National Institutes of HealthK08, HL091249 (TJI) and the University of Michigan SpecialistHospitalist Allied Research Program (SHARP). This work was also supported in part by VA Ann Arbor Healthcare System, Geriatric Research Education and Clinical Center (GRECC).

The International Consensus Conference (ICC) for sepsis defines severe sepsis as an infection leading to acute organ dysfunction.[1, 2] Severe sepsis afflicts over 1 million patients each year in Medicare alone, and is substantially more common among older Americans than acute myocardial infarction.[3, 4, 5] Recently, the Agency for Healthcare Research and Quality identified severe sepsis as the single most expensive cause of hospitalization in the United States.[6] The incidence of severe sepsis continues to rise.[4, 5]

Severe sepsis is often mischaracterized as a diagnosis cared for primarily in the intensive care unit (ICU). Yet, studies indicate that only 32% to 50% of patients with severe sepsis require ICU care, leaving the majority on the general care wards.[7, 8] These studies also reveal mortality rates of 26% to 30% among patients with severe sepsis who are not admitted to an ICU compared to 11% to 33% in the ICU.[7, 8]

Although a number of epidemiologic and interventional studies have focused on severe sepsis in the ICU,[3, 9, 10] much less is known about patients cared for on the general medicine wards. Without this information, clinicians cannot make informed choices about important management decisions such as targeted diagnostic testing, empirical antimicrobials, and other therapies. To this end, we sought to further characterize the infectious etiologies and resultant organ system dysfunctions in the subset of patients with severe sepsis admitted to non‐ICU medical services at a tertiary academic medical center.

METHODS

Population/Setting

All hospitalizations of adult patients (18 years old) who were initially admitted to non‐ICU medical services at the University of Michigan Hospital during 2009 through 2010 were included. The University of Michigan Hospital has 610 general medical‐surgical beds, including telemetry beds, with closed ICUs comprised of 179 beds staffed by intensivists. Patients transferred from other hospitals and those admitted to non‐medical services were excluded.

Data Abstraction and Definitions

All International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) diagnosis codes for hospitalizations were screened using a previously published and validated algorithm for severe sepsis.[11] Following this screening, 3 randomly selected round‐numbered batches of hospitalizations were sampled with subsequent application of the exclusion criteria. Medical records including physicians' notes, consultants' notes, nurses' notes, physical therapy notes, discharge coordinators' notes, emergency room flow sheets, as well as ward flow sheets were reviewed in detail by 3 practicing hospitalists using a structured instrument closely aligned with the ICC definition of severe sepsis.[2] We also sampled a smaller number of patients whose ICD‐9‐CM diagnoses screened negative for severe sepsis. Sample size was selected as part of a project with multiple objectives, and reflected a pragmatic balance between the anticipated precision of the results and the resources available to conduct chart review.[11] All discrepancies were reconciled among the 3 reviewers.

Reviewers first assessed whether infection was present, then evaluated for evidence of each organ system dysfunction, and finally determined the extent to which those organ dysfunctions were a response to the infection. Infection was defined either as a patient with a microbiologic culture growing a pathologic organism in a normally sterile site or documentation of a suspected infection with other confirmatory evidence (radiological, physical exam finding) with resultant systemic inflammatory response and administration of antimicrobials. Community‐acquired and healthcare‐associated infections were not differentiated. Microbiologic data, confirmatory tests, and site of infection were abstracted in detail.

Organ dysfunction was defined as per the 2001 ICC criteria,[2] and was assessed for neurological, pulmonary, cardiovascular, renal, gastrointestinal, hematological, and hepatic system involvement in all patients. A summary of these clinical definitions is included in Table 1. Data on important comorbidities were also abstracted. Immunosuppression was defined as having any of the following: solid organ transplant, bone marrow/stem cell transplant, human immunodeficiency virus/acquired immunodeficiency syndrome, neutropenia (absolute neutrophil count <1000), hematologic malignancy, solid organ malignancy with chemotherapy within the past 12 months, or pharmacologic immunosuppression (prednisone >20 mg daily for >4 weeks, calcineurin inhibitor, methotrexate, tumor necrosis factor inhibitors, azathioprine, sulfasalazine, hydroxychloroquine). Last, each chart was evaluated for the presence of explicit documentation with the presence of the words or phrases: sepsis, septic shock, or severe sepsis, indicating that the clinical service recognized and fully documented that a patient had severe sepsis.

Organ System Dysfunction Parameters as Defined by the 2001 International Consensus Conference
Organ SystemParameters to Indicate Dysfunction
  • NOTE: Abbreviations: BiPAP, bilevel positive airway pressure, BP, blood pressure; dL, deciliter; FiO2, fraction of inspired oxygen; INR, international normalized ratio; LPM, liters per minute; MAP, mean arterial pressure; mg, milligram; PaO2, partial pressure of oxygen in arterial blood; PTT, partial thromboplastin time.

CardiovascularSystolic BP <90, elevated lactate, MAP <70, requiring pressors >2 hours, decrease in systolic BP of >40
RenalCreatinine increase >0.5 mg/dL, oliguria
NeurologicalAcute mental status changes
PulmonaryIntubation, BiPAP, supplemental oxygen >6 LPM or 40% face mask, PaO2/FiO2 <300
HematologicINR >1.5 or PTT >60 not on anticoagulation, platelets <100 or 50% of baseline
IleusDecreased bowel motility requiring a change in diet
HepaticBilirubin >4 mg/dL and >1.5 baseline

Data Analysis

Methods for assessment of reviewer concordance have been previously described and were summarized using the kappa statistic.[11] Initial data extraction was performed in SAS 9.1 (SAS Institute, Cary, NC) and all analyses were conducted in Stata 12 (StataCorp LP, College Station, TX). Binomial 95% confidence intervals (CIs) are presented. This project was approved by the University of Michigan Institutional Review Board.

RESULTS

Of 23,288 hospitalizations examined from 2009 through 2010, the ICD‐9based automated screen for severe sepsis was positive for 3,146 (14 %). A random sample of 111 medical records, of which 92 had screened positive for severe sepsis and 19 had screened negative, was reviewed in detail. After review by the hospitalists, 64 of these 111 hospitalizations were judged to have severe sepsis, 61 of the 92 screened positive cases (66%), and 3 of the 19 screened negative cases (16%). The 3 reviewers had a kappa of 0.70, indicating good agreement.

Characteristics of the 64 patients with severe sepsis are shown in Table 2. The mean age was 63 years old (standard deviation [SD]=17.7), and 41% were male. The mean length of stay was 13.7 days (SD=20.8). Thirty‐nine percent (95% CI, 27%‐52%) of patients (25/64) were immunosuppressed. Of patients initially admitted to the general medical ward, 25% (16/64; 95% CI, 15%‐37%) ultimately required ICU care during their admission. The overall in‐hospital mortality rate was 13% (8/64; 95% CI, 6%‐23%). Immunosuppressed patients had a mortality rate of 20% and nonimmunosuppressed patients had a mortality rate of 8%. Only 47% (30/64; 95% CI, 34%‐60%) of the medical records had explicit clinician documentation of severe sepsis.

Demographics and Characteristics of Patients With Severe Sepsis (N=64)
Age, mean (SD), y63 (18)
  • NOTE: Abbreviations: SD, standard deviation.

Male sex, no. (%)26 (41)
Preexisting conditions, no. (%) 
History of diabetes20 (31)
End stage renal disease on chronic dialysis2 (3)
Chronic obstructive pulmonary disease on oxygen3 (5)
History of cancer15 (23)
Liver cirrhosis5 (8)
Immunosuppression25 (39)
Median length of stay (days)7.5
Mean length of stay (SD)13.7 (20.8)

The most common site of infection was found to be the genitourinary system, occurring in 41% (26/64; 95% CI, 29%‐54%) of patients (Table 3). Pulmonary and intra‐abdominal sites were also common, accounting for 14% (95% CI, 6.6%‐25%) and 13% (95% CI, 5.6%‐23%) of sites, respectively. An infecting organism was identified by culture in 66% (42/64; 95% CI, 53%‐77%) of case patients with specific pathogens listed in Table 4. Among patients with positive culture results, the majority grew Gram‐negative organisms (57%; 95% CI, 41%‐72%). Non‐Clostridium difficile Gram‐positive organisms were also prominent and identified in 48% (95% CI, 32%‐64%) of positive cultures. Candida was less common (12%, 95% CI, 4.0%‐26%). Fourteen cases (22%, 95% CI, 10%‐30%) had 2 or more concomitant infectious pathogens.

Site of Infection (N=64)
SiteNo. (%)
  • NOTE: Abbreviations: GI, gastrointestinal.

Genitourinary26 (41)
Pulmonary9 (14)
Intra‐abdominal (not intraluminal)8 (13)
Bloodstream/cardiac5 (8)
Skin and soft tissue4 (6)
GI lumen4 (6)
Joint2 (3)
Multiple sites4 (6)
Unknown2 (2)
Microbiology
 Absolute Frequency, Total Positive Culture Results, N=64, No. (%)*?>aPatients With Cultures Growing at Least One of the Pathogens, N=42, No. (%)*?>a
  • Multiple responses per patient possible.

  • Other includes Citrobacter, Enterobacter, Proteus, Achromobacter xylosoxidans, and Fusobacterium.

Gram‐negative pathogens30 (47)24 (57)
Escherichia coli12 (19)12 (29)
Escherichia coli (multidrug resistant)2 (3)2 (5)
Klebsiella6 (9)5 (12)
Pseudomonas aeruginosa6 (9)4 (10)
Pseudomonas aeruginosa (multidrug resistant)2 (3)2 (5)
Otherb6 (9)6 (14)
Gram‐positive pathogens29 (45)25 (59)
Enterococcus14 (22)13 (31)
Vancomycin‐resistant Enterococcus species5 (8)4 (10)
Staphylococcus aureus7 (11)7 (17)
Methicillin‐resistant Staphylococcus aureus3 (5)3 (7)
Streptococcus pneumoniae2 (3)2 (5)
Coagulase‐negative staphylococci1 (2)1 (2)
Clostridium difficile5 (8)5 (12)
Fungi  
Candida species5 (8)5 (12)
Mycobacterium avium1 (2)1 (2)
Two organisms 9 (21)
Three or more organisms 5 (12)

All 64 patients had at least 1 organ dysfunction, as required by the ICC definition of severe sepsis. Organ dysfunction in 2 or more organ systems occurred in 77% (95% CI, 64%‐86%) of the cases (49/64). The incidence for each organ system dysfunction is presented in Table 5, as well as its relationship to both mortality and ICU admission. The most common organ system dysfunctions were found to be cardiovascular (hypotension) and renal dysfunction occurring in 66% and 64% of the cases, respectively. In this non‐ICU population, pulmonary dysfunction occurred in 30% of cases, but was frequently associated with transfer to the ICU, as 63% of the patients with pulmonary failure required ICU care. Patients with more organ systems affected were more likely to be transferred to the ICU and to die.

Incidence and Outcomes of Organ Dysfunction in Patients Admitted to Non‐ICU Services
 No. (%)ICU Transfer, No. (%)Mortality, No. (%)
  • NOTE: Abbreviations: GI, gastrointestinal; ICU, intensive care unit.

  • Multiple responses per patient possible.

  • Percentage of patients with each organ system dysfunction who needed ICU care while in the hospital.

  • Percentage of patients with organ system dysfunction who died while in the hospital.

Number of failed organs, N = 64
115 (23%)0 (0%)0 (0%)
225 (39%)2 (8%)0 (0%)
37 (11%)2 (29%)1 (14%)
410 (16%)6 (60%)3 (30%)
>47 (11%)6 (86%)4 (57%)
Types of organ system dysfunction, all patients, N = 64*?>a
Cardiovascular42 (66%)16 (38%)b8 (19%)c
Renal41 (64%)10 (24%)b5 (12%)c
Central nervous system35 (54%)14 (40%)b7 (18%)c
Pulmonary19 (30%)12 (63%)b8 (42%)c
Hematologic15 (23%)6 (40%)b6 (40%)c
GI (ileus)8 (13%)5 (63%)b1 (13%)c
Hepatic5 (8%)4 (80%)b2 (40%)c

DISCUSSION

Severe sepsis was common among patients admitted to the general medical ward in this tertiary care center. Our patient cohort differed in important ways from previously described typical cases of severe sepsis among ICU populations. Severe sepsis on the general medical wards was more commonly associated with Gram‐negative pathogens in the setting of genitourinary tract infections. This is in contrast to Gram‐positive organisms and respiratory tract infections, which are more common in the ICU.[3, 10] Renal and cardiac dysfunction were commonly observed organ failures, whereas in the ICU, severe sepsis has been reported to more likely involve respiratory failure. These results suggest that hospitalists seeking to provide evidence‐based care to prevent postsepsis morbidity and mortality for their non‐ICU patients need to heighten their index of suspicion when caring for an infected patient and appreciate that many severe sepsis patients may not fit neatly into traditional sepsis treatment algorithms.

Studies characterizing severe sepsis in the ICU setting indicate a predominance of pulmonary infections and respiratory failure with occurrence rates of 74% to 95% and 54% to 61%, respectively.[3, 12, 13] Given that either shock or pulmonary dysfunction is often required for admission to many ICUs, it is perhaps not surprising that these rates are dramatically different on the general medicine ward, with a relative scarcity of pulmonary infections (14%) and respiratory dysfunction (30%). Instead, genitourinary infections were noted in 41% (95% CI, 29%‐54%) of the cases, in contrast to the rates of genitourinary infections in ICU patients with severe sepsis, which have rates of 5.4% to 9.1%.[3, 10] Likely as a result of this, a Gram‐negative predominance is noted in the associated microbiology. Furthermore, our study indicates that C difficile and vancomycin‐resistant Enterococcus (VRE) species appear to represent an emerging cause of severe sepsis on the general medicine wards, as they have not been noted to be causative micro‐organisms in previous studies of sepsis. This is concordant with other studies showing increases in incidence and severity of disease for C difficile as well as VRE.[14, 15]

Previous epidemiologic studies of severe sepsis originating outside the ICU are lacking, but some work has been done. One study on the epidemiology of sepsis both with and without organ dysfunction aggregated all hospitalized patients and included those both admitted to the general medicine wards and directly to the ICU.[7] Similar to our study, this study also found a predominance of Gram‐negative causative organisms, as well as comparable in‐hospital mortality rates (12.8% vs 13%). Additionally, genitourinary infections were noted in 20% of the patients, notably higher than rates reported to have been found in patients with severe sepsis in the ICU, but not the magnitude found in our study, perhaps as a result of the combined ICU‐ward population studied. A similar high prevalence of genitourinary infections was also noted in a recent administrative data‐based study of emergency medical services‐transported patients with severe sepsis, half of whom required intensive care during their hospitalization.[16]

Our study is unique in that it focuses on severe sepsis in patients, commonly cared for by hospitalists, who were admitted to the general medical ward, and uses patient level data to elucidate more characteristics of the defining organ dysfunction. Furthermore, our results suggest that severe sepsis was poorly documented in this setting, indicating a potential impact on billing, coding, case mix index, and hospital mortality statistics that rely on very specific wording, as well as a possible need for increased awareness among hospitalists. Without this awareness, an opportunity may be missed for improved patient care via specific sepsis‐targeted measures,[13, 17, 18] including more aggressive resuscitative measures[19] or intensive physical and occupational therapy interventions aimed at impacting the cognitive and functional debilities[20] that result from severe sepsis. Highlighting this growing need to better assist clinicians assess the severity of septic patients and recognize these complex cases on the general medicine wards, 1 recent study evaluated the fitness of several clinical disease‐severity scoring systems for patients with sepsis in general internal medicine departments.[21] Perhaps with the help of tools such as these, which are being piloted in some hospitals, the care of this growing population can be enhanced.

Our study has a number of limitations that should be kept in mind. First, this is a single center study performed at an academic tertiary care center with a relatively high incidence of immunosuppression, which may influence the spectrum of infecting organisms. Our center also has a relatively large, closed‐model ICU, which often operates at near capacity, potentially affecting the severity of our non‐ICU population. Second, although we screened a large number of patients, as necessitated by our intensive and detailed review of clinical information, our sample size with hospitalist‐validated severe sepsis is relatively small. With this small sample size, less prevalent infections, patient characteristics, and organ dysfunctions may by chance have been under or over‐represented, and one could expect some variance in the occurrence rates of organ system dysfunction and infection rates by sampling error alone. Further larger scale studies are warranted to confirm these data and their generalizability. Third, the data necessary to calculate sequential organ failure assessment or multiple organ dysfunction score were not collected. This may limit the ability to directly compare the organ dysfunction noted in this study with others. Additionally, given the ICC definitions of organ dysfunction, some of the organ dysfunction noted, particularly for neurological dysfunction, was reliant on subjective clinical findings documented in the record. Finally, we relied on the lack of specific terminology to indicate a lack of documentation of sepsis, which does not necessarily indicate a lack of recognition or undertreatment of this condition. However, these limitations are offset by the strengths of this study, including the patient‐level medical record validation of severe sepsis by trained hospitalist physicians, high kappa statistic, and strict application of guideline‐based definitions.

This work has important implications for both clinicians and for future research on severe sepsis. The results suggest that severe sepsis may be quite common outside the ICU, and that patients presenting with this condition who are admitted to general medical wards are not routinely characterized by the profound hypoxemia and refractory shock of iconic cases. Certainly, further study looking at larger numbers of cases is needed to better understand the specifics and nuances of this important topic as well as to further evaluate clinicians' ability to recognize and treat such patients in this setting. Furthermore, future research on the treatment of severe sepsis, including both antimicrobials and disease‐modifying agents (eg, anti‐inflammatories) must continue to include and even focus on this large population of non‐ICU patients with severe sepsis, as the risk/benefit ratios of such potential treatments may vary with severity of illness.

In conclusion, severe sepsis was commonly found in patients admitted on the general medicine wards. The epidemiology of the infections and resultant organ dysfunction appears to differ from that found in the ICU. More studies are needed to provide a deeper understanding of this disease process, as this will enable clinicians to better recognize and treat patients thus afflicted, no matter the setting.

Acknowledgments

The authors thank Laetitia Shapiro, AM, for her programming assistance.

Disclosures: This work was supported in part by the US National Institutes of HealthK08, HL091249 (TJI) and the University of Michigan SpecialistHospitalist Allied Research Program (SHARP). This work was also supported in part by VA Ann Arbor Healthcare System, Geriatric Research Education and Clinical Center (GRECC).

References
  1. Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):16441655.
  2. Levy MM, Fink MP, Marshall JC, et al. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med. 2003;31(4):12501256.
  3. Angus DC, Linde‐Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):13031310.
  4. Iwashyna TJ, Cooke CR, Wunsch H, Kahn JM. Population burden of long‐term survivorship after severe sepsis in older americans. J Am Geriatr Soc. 2012;60(6):10701077.
  5. Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348(16):15461554.
  6. Elixhauser A, Friedman B, Stranges E. Septicemia in U.S. hospitals, 2009: statistical brief #122. October 2011. In: Healthcare Cost and Utilization Project Statistical Briefs. Rockville, MD: Agency for Health Care Policy and Research; 2006. Available from: http://www.ncbi.nlm.nih.gov/books/NBK65391. Accessed June 2, 2012.
  7. Esteban A, Frutos‐Vivar F, Ferguson ND, et al. Sepsis incidence and outcome: contrasting the intensive care unit with the hospital ward. Crit Care Med. 2007;35(5):12841289.
  8. Sundararajan V, Macisaac CM, Presneill JJ, Cade JF, Visvanathan K. Epidemiology of sepsis in Victoria, Australia. Crit Care Med. 2005;33(1):7180.
  9. Brunkhorst FM, Oppert M, Marx G, et al. Effect of empirical treatment with moxifloxacin and meropenem vs meropenem on sepsis‐related organ dysfunction in patients with severe sepsis: a randomized trial. JAMA. 2012;307(22):23902399.
  10. Guidet B, Aegerter P, Gauzit R, Meshaka P, Dreyfuss D. Incidence and impact of organ dysfunctions associated with sepsis. Chest. 2005;127(3):942951.
  11. Iwashyna TJ, Odden A, Rohde JM, et al. Identifying patients with severe sepsis using administrative claims: patient‐level validation of the Angus Implementation of the International Consensus Conference definition of severe sepsis [published online ahead of print September 18, 2012]. Medical Care. doi: 10.1097/MLR.0b013e318268ac86.
  12. Annane D, Aegerter P, Jars‐Guincestre MC, Guidet B. Current epidemiology of septic shock: the CUB‐Rea Network. Am J Respir Crit Care Med. 2003;168(2):165172.
  13. Russell JA. Management of sepsis. N Engl J Med. 2006;355(16):16991713.
  14. Lessa FC, Gould CV, McDonald C. Current status of Clostridium difficile infection ipidemiology. Clin Infect Dis. 2012;55(suppl 2):S65S70.
  15. McGeer AJ, Low DE. Vancomycin‐resistant enterococci. Semin Respir Infect. 2000;15(4):314326.
  16. Seymour CW, Rea TD, Kahn JM, Walkey A, Yealy DM, Angus DC. Severe sepsis in prehospital emergency care: analysis of incidence, care, and outcome. Am J Respir Crit Care Med. 2012;186(12):12641271.
  17. Suffredini AF, Munford RS. Novel Therapies for Septic Shock Over the Past 4 Decades. JAMA. 2011;306(2):194199.
  18. Castellanos‐Ortega A, Suberviola B, Garcia‐Astudillo LA, et al. Impact of the Surviving Sepsis Campaign protocols on hospital length of stay and mortality in septic shock patients: results of a three‐year follow‐up quasi‐experimental study. Crit Care Med. 2010;38(4):10361043.
  19. Claessens YE, Dhainaut JF. Diagnosis and treatment of severe sepsis. Crit Care. 2007;11(suppl 5):S2.
  20. Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long‐term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010;304(16):17871794.
  21. Ghanem‐Zoubi NO, Vardi M, Laor A, Weber G, Bitterman H. Assessment of disease‐severity scoring systems for patients with sepsis in general internal medicine departments. Crit Care. 2011;15:R95.
References
  1. Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):16441655.
  2. Levy MM, Fink MP, Marshall JC, et al. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med. 2003;31(4):12501256.
  3. Angus DC, Linde‐Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29(7):13031310.
  4. Iwashyna TJ, Cooke CR, Wunsch H, Kahn JM. Population burden of long‐term survivorship after severe sepsis in older americans. J Am Geriatr Soc. 2012;60(6):10701077.
  5. Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med. 2003;348(16):15461554.
  6. Elixhauser A, Friedman B, Stranges E. Septicemia in U.S. hospitals, 2009: statistical brief #122. October 2011. In: Healthcare Cost and Utilization Project Statistical Briefs. Rockville, MD: Agency for Health Care Policy and Research; 2006. Available from: http://www.ncbi.nlm.nih.gov/books/NBK65391. Accessed June 2, 2012.
  7. Esteban A, Frutos‐Vivar F, Ferguson ND, et al. Sepsis incidence and outcome: contrasting the intensive care unit with the hospital ward. Crit Care Med. 2007;35(5):12841289.
  8. Sundararajan V, Macisaac CM, Presneill JJ, Cade JF, Visvanathan K. Epidemiology of sepsis in Victoria, Australia. Crit Care Med. 2005;33(1):7180.
  9. Brunkhorst FM, Oppert M, Marx G, et al. Effect of empirical treatment with moxifloxacin and meropenem vs meropenem on sepsis‐related organ dysfunction in patients with severe sepsis: a randomized trial. JAMA. 2012;307(22):23902399.
  10. Guidet B, Aegerter P, Gauzit R, Meshaka P, Dreyfuss D. Incidence and impact of organ dysfunctions associated with sepsis. Chest. 2005;127(3):942951.
  11. Iwashyna TJ, Odden A, Rohde JM, et al. Identifying patients with severe sepsis using administrative claims: patient‐level validation of the Angus Implementation of the International Consensus Conference definition of severe sepsis [published online ahead of print September 18, 2012]. Medical Care. doi: 10.1097/MLR.0b013e318268ac86.
  12. Annane D, Aegerter P, Jars‐Guincestre MC, Guidet B. Current epidemiology of septic shock: the CUB‐Rea Network. Am J Respir Crit Care Med. 2003;168(2):165172.
  13. Russell JA. Management of sepsis. N Engl J Med. 2006;355(16):16991713.
  14. Lessa FC, Gould CV, McDonald C. Current status of Clostridium difficile infection ipidemiology. Clin Infect Dis. 2012;55(suppl 2):S65S70.
  15. McGeer AJ, Low DE. Vancomycin‐resistant enterococci. Semin Respir Infect. 2000;15(4):314326.
  16. Seymour CW, Rea TD, Kahn JM, Walkey A, Yealy DM, Angus DC. Severe sepsis in prehospital emergency care: analysis of incidence, care, and outcome. Am J Respir Crit Care Med. 2012;186(12):12641271.
  17. Suffredini AF, Munford RS. Novel Therapies for Septic Shock Over the Past 4 Decades. JAMA. 2011;306(2):194199.
  18. Castellanos‐Ortega A, Suberviola B, Garcia‐Astudillo LA, et al. Impact of the Surviving Sepsis Campaign protocols on hospital length of stay and mortality in septic shock patients: results of a three‐year follow‐up quasi‐experimental study. Crit Care Med. 2010;38(4):10361043.
  19. Claessens YE, Dhainaut JF. Diagnosis and treatment of severe sepsis. Crit Care. 2007;11(suppl 5):S2.
  20. Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long‐term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010;304(16):17871794.
  21. Ghanem‐Zoubi NO, Vardi M, Laor A, Weber G, Bitterman H. Assessment of disease‐severity scoring systems for patients with sepsis in general internal medicine departments. Crit Care. 2011;15:R95.
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Journal of Hospital Medicine - 8(5)
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Journal of Hospital Medicine - 8(5)
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The epidemiology of acute organ system dysfunction from severe sepsis outside of the intensive care unit
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The epidemiology of acute organ system dysfunction from severe sepsis outside of the intensive care unit
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Address for correspondence and reprint requests: Jeffrey M. Rohde, MD, Department of Internal Medicine, University of Michigan Medical School, 3119 Taubman Center, 1500 E. Medical Center Dr., Ann Arbor, MI 48109‐5376; Telephone: 734‐647‐1599; Fax: 734‐233‐9343; E‐mail: [email protected]
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