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The Pipeline From Abstract Presentation to Publication in Pediatric Hospital Medicine
Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,1 with 8% of pediatric residency graduates each year entering the field.2 Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.9
Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.
We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.
METHODS
Study Design
Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.
Main Exposures
The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.
Main Outcome Measures
All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.21 Journals without an impact factor (eg, newer journals) were assigned a 0.
Data Analysis
All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule22 and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.
RESULTS
For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).
DISCUSSION
About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.
Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),11 the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.20 Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.23 A lack of time and fear of rejection after peer review are commonly cited explanations.18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.18 To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.
Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.16 Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.
Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.20 Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,12 with publication rates as high as 98% within 3 years of presentation.17 In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,12 authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.
The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.
This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.16 Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.
CONCLUSIONS
Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.
Disclosure
Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.
1. Stucky ER, Ottolini MC, Maniscalco J. Pediatric hospital medicine core competencies: development and methodology. J Hosp Med. 2010;5(6):339-343. PubMed
2. Freed GL, McGuinness GA, Althouse LA, Moran LM, Spera L. Long-term plans for those selecting hospital medicine as an initial career choice. Hosp Pediatr. 2015;5(4):169-174. PubMed
3. Rauch D. Pediatric Hospital Medicine Subspecialty. 2016; https://www.aap.org/en-us/about-the-aap/Committees-Councils-Sections/Section-on-Hospital-Medicine/Pages/Pediatric-Hospital-Medicine-Subspecialty.aspx. Accessed November 28, 2016.
4. Bekmezian A, Teufel RJ, Wilson KM. Research needs of pediatric hospitalists. Hosp Pediatr. 2011;1(1):38-44. PubMed
5. Teufel RJ, Bekmezian A, Wilson K. Pediatric hospitalist research productivity: predictors of success at presenting abstracts and publishing peer-reviewed manuscripts among pediatric hospitalists. Hosp Pediatr. 2012;2(3):149-160. PubMed
6. Wilson KM, Shah SS, Simon TD, Srivastava R, Tieder JS. The challenge of pediatric hospital medicine research. Hosp Pediatr. 2012;2(1):8-9. PubMed
7. Froom P, Froom J. Presentation Deficiencies in structured medical abstracts. J Clin Epidemiol. 1993;46(7):591-594. PubMed
8. Relman AS. News reports of medical meetings: how reliable are abstracts? N Engl J Med. 1980;303(5):277-278. PubMed
9. Soffer A. Beware the 200-word abstract! Arch Intern Med. 1976;136(11):1232-1233. PubMed
10. Bhandari M, Devereaux P, Guyatt GH, et al. An observational study of orthopaedic abstracts and subsequent full-text publications. J Bone Joint Surg Am. 2002;84(4):615-621. PubMed
11. Castagnetti M, Subramaniam R, El-Ghoneimi A. Abstracts presented at the European Society for Pediatric Urology (ESPU) meetings (2003–2010): Characteristics and outcome. J Pediatr Urol. 2014;10(2):355-360. PubMed
12. Halikman R, Scolnik D, Rimon A, Glatstein MM. Peer-Reviewed Journal Publication of Abstracts Presented at an International Emergency Medicine Scientific Meeting: Outcomes and Comparison With the Previous Meeting. Pediatr Emerg Care. 2016. PubMed
13. Relman AS. Peer review in scientific journals--what good is it? West J Med. 1990;153(5):520. PubMed
14. Riordan F. Do presenters to paediatric meetings get their work published? Arch Dis Child. 2000;83(6):524-526. PubMed
15. Scherer RW, Dickersin K, Langenberg P. Full publication of results initially presented in abstracts: a meta-analysis. JAMA. 1994;272(2):158-162. PubMed
16. Scherer RW, Langenberg P, Elm E. Full publication of results initially presented in abstracts. Cochrane Database Syst Rev. 2005. PubMed
17. Marx WF, Cloft HJ, Do HM, Kallmes DF. The fate of neuroradiologic abstracts presented at national meetings in 1993: rate of subsequent publication in peer-reviewed, indexed journals. Am J Neuroradiol. 1999;20(6):1173-1177. PubMed
18. Roy D, Sankar V, Hughes J, Jones A, Fenton J. Publication rates of scientific papers presented at the Otorhinolarygological Research Society meetings. Clin Otolaryngol Allied Sci. 2001;26(3):253-256. PubMed
19. McCormick MC, Holmes JH. Publication of research presented at the pediatric meetings: change in selection. Am J Dis Child. 1985;139(2):122-126. PubMed
20. Carroll AE, Sox CM, Tarini BA, Ringold S, Christakis DA. Does presentation format at the Pediatric Academic Societies’ annual meeting predict subsequent publication? Pediatrics. 2003;112(6):1238-1241. PubMed
21. Saha S, Saint S, Christakis DA. Impact factor: a valid measure of journal quality? J Med Libr Assoc. 2003;91(1):42. PubMed
22. Office for Human Research Protections. Code of Federal Regulations, Title 45 Public Welfare: Part 46, Protection of Human Subjects, §46.102(f ). http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html#46.102. Accessed October 21, 2016.
23. Weber EJ, Callaham ML, Wears RL, Barton C, Young G. Unpublished research from a medical specialty meeting: why investigators fail to publish. JAMA. 1998;280(3):257-259. PubMed
24. Timmer A, Hilsden RJ, Cole J, Hailey D, Sutherland LR. Publication bias in gastroenterological research–a retrospective cohort study based on abstracts submitted to a scientific meeting. BMC Med Res Methodol. 2002;2(1):1. PubMed
Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,1 with 8% of pediatric residency graduates each year entering the field.2 Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.9
Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.
We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.
METHODS
Study Design
Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.
Main Exposures
The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.
Main Outcome Measures
All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.21 Journals without an impact factor (eg, newer journals) were assigned a 0.
Data Analysis
All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule22 and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.
RESULTS
For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).
DISCUSSION
About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.
Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),11 the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.20 Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.23 A lack of time and fear of rejection after peer review are commonly cited explanations.18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.18 To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.
Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.16 Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.
Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.20 Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,12 with publication rates as high as 98% within 3 years of presentation.17 In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,12 authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.
The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.
This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.16 Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.
CONCLUSIONS
Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.
Disclosure
Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.
Pediatric hospital medicine (PHM) is one of the most rapidly growing disciplines in pediatrics,1 with 8% of pediatric residency graduates each year entering the field.2 Research plays an important role in advancing care in the field and is a critical component for board certification and fellowship accreditation.3-6 The annual PHM conference, which has been jointly sponsored by the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine, is an important venue for the dissemination of research findings. Abstract selection is determined by peer review; however, reviewers are provided with only a brief snapshot of the research, which may not contain sufficient information to fully evaluate the methodological quality of the work.7-10 Additionally, while instructions are provided, reviewers often lack formal training in abstract review. Consequently, scores may vary.9
Publication in a peer-reviewed journal is considered a measure of research success because it requires more rigorous peer review than the abstract selection process at scientific meetings.11-16 Rates of subsequent journal publication differ based on specialty and meeting, and they have been reported at 23% to 78%.10,12,14-18 In pediatrics, publication rates after presentation at scientific meetings range from 36% to 63%, with mean time to publication ranging from 20 to 26 months following the meeting.11,19,20 No studies have reviewed abstract submissions to the annual PHM meeting to determine if selection or presentation format is associated with subsequent publication in a peer-reviewed journal.
We sought to identify the publication rate of abstracts submitted to the 2014 PHM conference and determine whether presentation format was associated with the likelihood of subsequent journal publication or time to publication.
METHODS
Study Design
Data for this retrospective cohort study were obtained from a database of all abstracts submitted for presentation at the 2014 PHM conference in Lake Buena Vista, Florida.
Main Exposures
The main exposure was presentation format, which was categorized as not presented (ie, rejected), poster presentation, or oral presentation. PHM has a blinded abstract peer-review process; in 2014, an average of 10 reviewers scored each abstract. Reviewers graded abstracts on a scale of 1 (best in category) to 7 (unacceptable for presentation) according to the following criteria: originality, scientific importance, methodological rigor, and quality of presentation. Abstracts with the lowest average scores in each content area, usually less than or equal to 3, were accepted as oral presentations while most abstracts with scores greater than 5 were rejected. For this study, information collected from each abstract included authors, if the primary author was a trainee, title, content area, and presentation format. Content areas included clinical research, educational research, health services research (HSR) and/or epidemiology, practice management research, and quality improvement. Abstracts were then grouped by presentation format and content area for analysis. The Pediatric Academic Societies (PAS) annual meeting, another common venue for the presentation of pediatric research, precedes the PHM conference. Because acceptance for PAS presentation may represent more strongly developed abstract submissions for PHM, we identified which abstracts had also been presented at the PAS conference that same year by cross-referencing authors and abstract titles with the PAS 2014 program.
Main Outcome Measures
All submissions were assessed for subsequent publication in peer-reviewed journals through January 2017 (30 months following the July 2014 PHM conference). To identify abstracts that went on to full publication, 2 authors (JC and LEH) independently searched for the lead author’s name and the presentation title in PubMed, Google Scholar, and MedEdPORTAL in January 2017. PubMed was searched using both the general search box and an advanced search for author and title. Google Scholar was added to capture manuscripts that may not have been indexed in PubMed at the time of our search. MedEdPORTAL, a common venue for the publication of educational initiatives that are not currently indexed in PubMed, was searched by lead author name via the general search box. If a full manuscript was published discussing similar outcomes or results and was written by the same authors who had submitted a PHM conference abstract, it was considered to have been published. The journal, month, and year of publication were recorded. For journals published every 2 months, the date of publication was recorded as falling between the 2 months. For those journals with biannual publication in the spring and fall, the months of March and October were used, respectively. The impact factor of the publication journal was also recorded for the year preceding publication. A journal’s impact factor is frequently used as a quantitative measure of journal quality and reflects the frequency with which a journal’s articles are cited in the scientific literature.21 Journals without an impact factor (eg, newer journals) were assigned a 0.
Data Analysis
All abstracts submitted to the PHM conference were analyzed based on content area and presentation format. The proportion of all abstracts subsequently published was determined for each format type and content area, and the odds ratio (OR) for publication after abstract submission was calculated using logistic regression. We calculated an adjusted OR for subsequent publication controlling for PAS presentation and the trainee status of the primary author. The journals most frequently publishing abstracts submitted to the PHM conference were identified. Median time to publication was calculated using the number of months elapsed between the PHM conference and publication date and compared across all abstract formats using Cox proportional hazards models adjusted for PAS presentation and trainee status. Kaplan-Meier survival curves were also generated for each of the 3 formats and compared using log-rank tests. The median impact factor was determined for each abstract format and compared using Wilcoxon rank-sum tests. Median impact factor by content area was compared using a Kruskal-Wallis test. All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). P values < 0.05 were considered statistically significant. In accordance with the Common Rule22 and the policies of the Cincinnati Children’s Hospital Medical Center Institutional Review Board, this research was not considered human subjects research.
RESULTS
For the 2014 PHM meeting, 226 abstracts were submitted, of which 183 (81.0%) were selected for presentation, including 154 (68.0%) as poster presentations and 29 (12.8%) as oral presentations. Of all submitted abstracts, 82 (36.3%) were published within 30 months following the meeting. Eighty-one of these (98.8%) were identified via PubMed, and 1 was found only in MedEdPORTAL. No additional publications were found via Google Scholar. The presenting author for the PHM abstract was the first author for 87.8% (n = 72) of the publications. A trainee was the presenting author for only 2 of these abstracts. For the publications in which the first author was not the presenting author, the presenting author was the senior author in 2 of the publications and the second or third author on the remaining 8. Of the abstracts accepted for presentation, 70 (38.3%) were subsequently published. Abstracts accepted for oral presentation had almost 7-fold greater odds of subsequent publication than those that were rejected (Table 1; OR 6.8; 95% confidence interval [CI], 2.4-19.4). Differences in the odds of publication for rejected abstracts compared with those accepted for poster presentation were not statistically significant (OR 1.2; 95% CI, 0.5-2.5).
DISCUSSION
About one-third of abstracts submitted to the 2014 PHM conference were subsequently published in peer-reviewed journals within 30 months of the conference. Compared with rejected abstracts, the rate of publication was significantly higher for abstracts selected for oral presentation but not for those selected for poster presentation. For abstracts ultimately published in journals, selection for oral presentation was significantly associated with both a shorter time to publication and a higher median journal impact factor compared with rejected abstracts. Time to publication and median journal impact factor were similar between rejected abstracts and those accepted for poster presentation. Our findings suggest that abstract reviewers may be able to identify which abstracts will ultimately withstand more stringent peer review in the publication process when accepting abstracts for oral presentation. However, the selection for poster presentation versus rejection may not be indicative of future publication or the impact factor of the subsequent publication journal.
Previous studies have reviewed publication rates after meetings of the European Society for Pediatric Urology (publication rate of 47%),11 the Ambulatory Pediatric Association (now the Academic Pediatric Association; publication rate of 47%), the American Pediatric Society/Society for Pediatric Research (publication rate of 54%), and the PAS (publication rate of 45%).19,20 Our lower publication rate of 36.3% may be attributed to the shorter follow-up time in our study (30 months from the PHM conference), whereas prior studies monitored for publication up to 60 months after the PAS conference.20 Factors associated with subsequent publication include statistically significant results, a large sample size, and a randomized controlled trial study design.15,16 The primary reason for nonpublication for up to 80% of abstracts is failure to submit a manuscript for publication.23 A lack of time and fear of rejection after peer review are commonly cited explanations.18,23,24 Individuals may view acceptance for an oral presentation as positive reinforcement and be more motivated to pursue subsequent manuscript publication than individuals whose abstracts are offered poster presentations or are rejected. Trainees frequently present abstracts at scientific meetings, representing 40.3% of primary authors submitting abstracts to PHM in 2014, but may not have sufficient time or mentorship to develop a complete manuscript.18 To our knowledge, there have been no publications that assess the impact of trainee status on subsequent publication after conference submission.
Our study demonstrated that selection for oral presentation was associated with subsequent publication, shorter time to publication, and publication in journals with higher impact factors. A 2005 Cochrane review also demonstrated that selection for oral presentation was associated with subsequent journal publication.16 Abstracts accepted for oral publication may represent work further along in the research process, with more developed methodology and results. The shorter time to publication for abstracts accepted for oral presentation could also reflect feedback provided by conference attendees after the presentation, whereas poster sessions frequently lack a formalized process for critique.
Carroll et al. found no difference in time to publication between abstracts accepted for presentation at the PAS and rejected abstracts.20 Previous studies demonstrate that most abstracts presented at scientific meetings that are subsequently accepted for publication are published within 2 to 3 years of the meeting,12 with publication rates as high as 98% within 3 years of presentation.17 In contrast to Carroll et al., we found that abstracts accepted for oral presentation had a 4-fold greater likelihood of publication at each month than rejected abstracts. However, abstracts accepted for poster presentation did not have a significant difference in the proportional hazard ratio models for publication compared with rejected abstracts. Because space considerations limit the number of abstracts that can be accepted for presentation at a conference, some abstracts that are suitable for future publication may have been rejected due to a lack of space. Because researchers often use scientific meetings as a forum to receive peer feedback,12 authors who present at conferences may take more time to write a manuscript in order to incorporate this feedback.
The most common journal in which submitted abstracts were subsequently published was Hospital Pediatrics, representing twice as many published manuscripts as the second most frequent journal, Pediatrics. Hospital Pediatrics, which was first published in 2011, did not have an impact factor assigned during the study period. Yet, as a peer-reviewed journal dedicated to the field of PHM, it is well aligned with the research presented at the PHM meeting. It is unclear if Hospital Pediatrics is a journal to which pediatric hospitalists tend to submit manuscripts initially or if manuscripts are frequently submitted elsewhere prior to their publication in Hospital Pediatrics. Submission to other journals first likely extends the time to publication, especially for abstracts accepted for poster presentation, which may describe studies with less developed methods or results.
This study has several limitations. Previous studies have demonstrated mean time to publication of 12 to 32 months following abstract presentation with a median time of 19.6 months.16 Because we only have a 30-month follow-up, there may be abstracts still in the review process that are yet to be published, especially because the length of the review process varies by journal. We based our literature search on the first author of each PHM conference abstract submission, assuming that this presenting author would be one of the publishing authors even if not remaining first author; if this was not the case, we may have missed some abstracts that were subsequently published in full. Likewise, if a presenting author’s last name changed prior to the publication of a manuscript, a publication may have been missed. This limitation would cause us to underestimate the overall publication rate. It is not clear whether this would differentially affect the method of presentation. However, in this study, there was concordance between the presenting author and the publication’s first author in 87.8% of the abstracts subsequently published in full. Presenting authors who did not remain the first author on the published manuscript maintained authorship as either the senior author or second or third author, which may represent changes in the degree of involvement or a division of responsibilities for individuals working on a project together. While our search methods were comprehensive, there is a possibility that abstracts may have been published in a venue that was not searched. Additionally, we only reviewed abstracts submitted to PHM for 1 year. As the field matures and the number of fellowship programs increases, the quality of submitted abstracts may increase, leading to higher publication rates or shorter times to publication. It is also possible that the publication rate may not be reflective of PHM as a field because hospitalists may submit their work to conferences other than the PHM. Lastly, it may be more challenging to interpret any differences in impact factor because some journals, including Hospital Pediatrics (which represented a plurality of poster presentation abstracts that were subsequently published and is a relatively new journal), did not have an impact factor assigned during the study period. Assigning a 0 to journals without an impact factor may artificially lower the average impact factor reported. Furthermore, an impact factor, which is based on the frequency with which an individual journal’s articles are cited in scientific or medical publications, may not necessarily reflect a journal’s quality.
CONCLUSIONS
Of the 226 abstracts submitted to the 2014 PHM conference, approximately one-third were published in peer-reviewed journals within 30 months of the conference. Selection for oral presentation was found to be associated with subsequent publication as well as publication in journals with higher impact factors. The overall low publication rate may indicate a need for increased mentorship and resources for research development in this growing specialty. Improved mechanisms for author feedback at poster sessions may provide constructive suggestions for further development of these projects into full manuscripts or opportunities for trainees and early-career hospitalists to network with more experienced researchers in the field.
Disclosure
Drs. Herrmann, Hall, Kyler, Andrews, Williams, and Shah and Mr. Cochran have nothing to disclose. Dr. Wilson reports personal fees from the American Academy of Pediatrics during the conduct of the study. The authors have no financial relationships relevant to this article to disclose.
1. Stucky ER, Ottolini MC, Maniscalco J. Pediatric hospital medicine core competencies: development and methodology. J Hosp Med. 2010;5(6):339-343. PubMed
2. Freed GL, McGuinness GA, Althouse LA, Moran LM, Spera L. Long-term plans for those selecting hospital medicine as an initial career choice. Hosp Pediatr. 2015;5(4):169-174. PubMed
3. Rauch D. Pediatric Hospital Medicine Subspecialty. 2016; https://www.aap.org/en-us/about-the-aap/Committees-Councils-Sections/Section-on-Hospital-Medicine/Pages/Pediatric-Hospital-Medicine-Subspecialty.aspx. Accessed November 28, 2016.
4. Bekmezian A, Teufel RJ, Wilson KM. Research needs of pediatric hospitalists. Hosp Pediatr. 2011;1(1):38-44. PubMed
5. Teufel RJ, Bekmezian A, Wilson K. Pediatric hospitalist research productivity: predictors of success at presenting abstracts and publishing peer-reviewed manuscripts among pediatric hospitalists. Hosp Pediatr. 2012;2(3):149-160. PubMed
6. Wilson KM, Shah SS, Simon TD, Srivastava R, Tieder JS. The challenge of pediatric hospital medicine research. Hosp Pediatr. 2012;2(1):8-9. PubMed
7. Froom P, Froom J. Presentation Deficiencies in structured medical abstracts. J Clin Epidemiol. 1993;46(7):591-594. PubMed
8. Relman AS. News reports of medical meetings: how reliable are abstracts? N Engl J Med. 1980;303(5):277-278. PubMed
9. Soffer A. Beware the 200-word abstract! Arch Intern Med. 1976;136(11):1232-1233. PubMed
10. Bhandari M, Devereaux P, Guyatt GH, et al. An observational study of orthopaedic abstracts and subsequent full-text publications. J Bone Joint Surg Am. 2002;84(4):615-621. PubMed
11. Castagnetti M, Subramaniam R, El-Ghoneimi A. Abstracts presented at the European Society for Pediatric Urology (ESPU) meetings (2003–2010): Characteristics and outcome. J Pediatr Urol. 2014;10(2):355-360. PubMed
12. Halikman R, Scolnik D, Rimon A, Glatstein MM. Peer-Reviewed Journal Publication of Abstracts Presented at an International Emergency Medicine Scientific Meeting: Outcomes and Comparison With the Previous Meeting. Pediatr Emerg Care. 2016. PubMed
13. Relman AS. Peer review in scientific journals--what good is it? West J Med. 1990;153(5):520. PubMed
14. Riordan F. Do presenters to paediatric meetings get their work published? Arch Dis Child. 2000;83(6):524-526. PubMed
15. Scherer RW, Dickersin K, Langenberg P. Full publication of results initially presented in abstracts: a meta-analysis. JAMA. 1994;272(2):158-162. PubMed
16. Scherer RW, Langenberg P, Elm E. Full publication of results initially presented in abstracts. Cochrane Database Syst Rev. 2005. PubMed
17. Marx WF, Cloft HJ, Do HM, Kallmes DF. The fate of neuroradiologic abstracts presented at national meetings in 1993: rate of subsequent publication in peer-reviewed, indexed journals. Am J Neuroradiol. 1999;20(6):1173-1177. PubMed
18. Roy D, Sankar V, Hughes J, Jones A, Fenton J. Publication rates of scientific papers presented at the Otorhinolarygological Research Society meetings. Clin Otolaryngol Allied Sci. 2001;26(3):253-256. PubMed
19. McCormick MC, Holmes JH. Publication of research presented at the pediatric meetings: change in selection. Am J Dis Child. 1985;139(2):122-126. PubMed
20. Carroll AE, Sox CM, Tarini BA, Ringold S, Christakis DA. Does presentation format at the Pediatric Academic Societies’ annual meeting predict subsequent publication? Pediatrics. 2003;112(6):1238-1241. PubMed
21. Saha S, Saint S, Christakis DA. Impact factor: a valid measure of journal quality? J Med Libr Assoc. 2003;91(1):42. PubMed
22. Office for Human Research Protections. Code of Federal Regulations, Title 45 Public Welfare: Part 46, Protection of Human Subjects, §46.102(f ). http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html#46.102. Accessed October 21, 2016.
23. Weber EJ, Callaham ML, Wears RL, Barton C, Young G. Unpublished research from a medical specialty meeting: why investigators fail to publish. JAMA. 1998;280(3):257-259. PubMed
24. Timmer A, Hilsden RJ, Cole J, Hailey D, Sutherland LR. Publication bias in gastroenterological research–a retrospective cohort study based on abstracts submitted to a scientific meeting. BMC Med Res Methodol. 2002;2(1):1. PubMed
1. Stucky ER, Ottolini MC, Maniscalco J. Pediatric hospital medicine core competencies: development and methodology. J Hosp Med. 2010;5(6):339-343. PubMed
2. Freed GL, McGuinness GA, Althouse LA, Moran LM, Spera L. Long-term plans for those selecting hospital medicine as an initial career choice. Hosp Pediatr. 2015;5(4):169-174. PubMed
3. Rauch D. Pediatric Hospital Medicine Subspecialty. 2016; https://www.aap.org/en-us/about-the-aap/Committees-Councils-Sections/Section-on-Hospital-Medicine/Pages/Pediatric-Hospital-Medicine-Subspecialty.aspx. Accessed November 28, 2016.
4. Bekmezian A, Teufel RJ, Wilson KM. Research needs of pediatric hospitalists. Hosp Pediatr. 2011;1(1):38-44. PubMed
5. Teufel RJ, Bekmezian A, Wilson K. Pediatric hospitalist research productivity: predictors of success at presenting abstracts and publishing peer-reviewed manuscripts among pediatric hospitalists. Hosp Pediatr. 2012;2(3):149-160. PubMed
6. Wilson KM, Shah SS, Simon TD, Srivastava R, Tieder JS. The challenge of pediatric hospital medicine research. Hosp Pediatr. 2012;2(1):8-9. PubMed
7. Froom P, Froom J. Presentation Deficiencies in structured medical abstracts. J Clin Epidemiol. 1993;46(7):591-594. PubMed
8. Relman AS. News reports of medical meetings: how reliable are abstracts? N Engl J Med. 1980;303(5):277-278. PubMed
9. Soffer A. Beware the 200-word abstract! Arch Intern Med. 1976;136(11):1232-1233. PubMed
10. Bhandari M, Devereaux P, Guyatt GH, et al. An observational study of orthopaedic abstracts and subsequent full-text publications. J Bone Joint Surg Am. 2002;84(4):615-621. PubMed
11. Castagnetti M, Subramaniam R, El-Ghoneimi A. Abstracts presented at the European Society for Pediatric Urology (ESPU) meetings (2003–2010): Characteristics and outcome. J Pediatr Urol. 2014;10(2):355-360. PubMed
12. Halikman R, Scolnik D, Rimon A, Glatstein MM. Peer-Reviewed Journal Publication of Abstracts Presented at an International Emergency Medicine Scientific Meeting: Outcomes and Comparison With the Previous Meeting. Pediatr Emerg Care. 2016. PubMed
13. Relman AS. Peer review in scientific journals--what good is it? West J Med. 1990;153(5):520. PubMed
14. Riordan F. Do presenters to paediatric meetings get their work published? Arch Dis Child. 2000;83(6):524-526. PubMed
15. Scherer RW, Dickersin K, Langenberg P. Full publication of results initially presented in abstracts: a meta-analysis. JAMA. 1994;272(2):158-162. PubMed
16. Scherer RW, Langenberg P, Elm E. Full publication of results initially presented in abstracts. Cochrane Database Syst Rev. 2005. PubMed
17. Marx WF, Cloft HJ, Do HM, Kallmes DF. The fate of neuroradiologic abstracts presented at national meetings in 1993: rate of subsequent publication in peer-reviewed, indexed journals. Am J Neuroradiol. 1999;20(6):1173-1177. PubMed
18. Roy D, Sankar V, Hughes J, Jones A, Fenton J. Publication rates of scientific papers presented at the Otorhinolarygological Research Society meetings. Clin Otolaryngol Allied Sci. 2001;26(3):253-256. PubMed
19. McCormick MC, Holmes JH. Publication of research presented at the pediatric meetings: change in selection. Am J Dis Child. 1985;139(2):122-126. PubMed
20. Carroll AE, Sox CM, Tarini BA, Ringold S, Christakis DA. Does presentation format at the Pediatric Academic Societies’ annual meeting predict subsequent publication? Pediatrics. 2003;112(6):1238-1241. PubMed
21. Saha S, Saint S, Christakis DA. Impact factor: a valid measure of journal quality? J Med Libr Assoc. 2003;91(1):42. PubMed
22. Office for Human Research Protections. Code of Federal Regulations, Title 45 Public Welfare: Part 46, Protection of Human Subjects, §46.102(f ). http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html#46.102. Accessed October 21, 2016.
23. Weber EJ, Callaham ML, Wears RL, Barton C, Young G. Unpublished research from a medical specialty meeting: why investigators fail to publish. JAMA. 1998;280(3):257-259. PubMed
24. Timmer A, Hilsden RJ, Cole J, Hailey D, Sutherland LR. Publication bias in gastroenterological research–a retrospective cohort study based on abstracts submitted to a scientific meeting. BMC Med Res Methodol. 2002;2(1):1. PubMed
© 2018 Society of Hospital Medicine
LOS in Children With Medical Complexity
Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]
Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]
Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.
METHODS
Study Design and Data Source
This study is a multicenter, retrospective cohort analysis of the Pediatric Health Information System (PHIS). PHIS is an administrative database of 44 not for profit, tertiary care pediatric hospitals affiliated with the Children's Hospital Association (CHA) (Overland Park, KS). PHIS contains data regarding patient demographics, diagnoses, and procedures (with International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] codes), All‐Patient Refined Diagnostic Related Groups version 30 (APR‐DRGs) (3M Health Information Systems, Salt Lake City, UT), and service lines that aggregate the APR‐DRGs into 38 distinct groups. Data quality and reliability are assured through CHA and participating hospitals. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this study of deidentified data was not considered human subjects research.
Study Population
Inclusion Criteria
Children discharged following an observation or inpatient admission from a hospital participating in the PHIS database between January 1, 2013 and December 31, 2014 were eligible for inclusion if they were considered medically complex. Medical complexity was defined using Clinical Risk Groups (CRGs) version 1.8, developed by 3M Health Information Systems and the National Association of Children's Hospitals and Related Institutions. CRGs were used to assign each hospitalized patient to 1 of 9 mutually exclusive chronicity groups according to the presence, type, and severity of chronic conditions.[15, 16, 17, 18] Each patient's CRG designation was based on 2 years of previous hospital encounters.
As defined in prior studies and definitional frameworks of CMC,[1] patients belonging to CRG group 6 (significant chronic disease in 2 organ systems), CRG group 7 (dominant chronic disease in 3 organ systems), and CRG group 9 (catastrophic condition) were considered medically complex.[17, 19] Patients with malignancies (CRG group 8) were not included for analysis because they are a unique population with anticipated, long hospital stays. Patients with CRG group 5, representing those with chronic conditions affecting a single body system, were also not included because most do not have attributes consistent with medical complexity.
Exclusion Criteria
We used the APR‐DRG system, which leverages ICD‐9‐CM codes to identify the health problem most responsible for the hospitalization, to refine the study cohort. We excluded hospitalizations that were classified by the APR‐DRG system as neonatal, as we did not wish to focus on LOS in the neonatal intensive care unit (ICU) or for birth admissions. Similarly, hospitalizations for chemotherapy (APR‐DRG 693) or malignancy (identified with previously used ICD‐9‐CM codes)[20] were also excluded because long LOS is anticipated. We also excluded hospitalizations for medical rehabilitation (APR‐DRG 860).
Outcome Measures
The primary outcome measure was long LOS, defined as LOS 10 days. The cut point of LOS 10 days represents the 90th percentile of LOS for all children, with and without medical complexity, hospitalized during 2013 to 2014. LOS 10 days has previously been used as a threshold of long LOS.[21] For hospitalizations involving transfer at admission from another acute care facility, LOS was measured from the date of transfer. We also assessed hospitals' cost attributable to long LOS admissions.
Patient Demographics and Clinical Characteristics
We measured demographic characteristics including age, gender, race/ethnicity, insurance type, and distance traveled (the linear distance between the centroid of the patient's home ZIP code and the centroid of the hospital's ZIP code). Clinical characteristics included CRG classification, complex chronic condition (CCC), and dependence on medical technology. CCCs are defined as any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or 1 system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center.[20] Medical technology included devices used to optimize the health and functioning of the child (eg, gastrostomy, tracheostomy, cerebrospinal fluid shunt).[22]
Hospitalization Characteristics
Characteristics of the hospitalization included transfer from an outside facility, ICU admission, surgical procedure (using surgical APR‐DRGs), and discharge disposition (home, skilled nursing facility, home health services, death, other). Cost of the hospitalization was estimated in the PHIS from charges using hospital and year‐specific ratios of cost to charge.
Statistical Analysis
Continuous data (eg, distance from hospital to home residence) were described with median and interquartile ranges (IQR) because they were not normally distributed. Categorical data (eg, type of chronic condition) were described with counts and frequencies. In bivariate analyses, demographic, clinical, and hospitalization characteristics were stratified by LOS (long LOS vs LOS <10 days), and compared using 2 statistics or Wilcoxon rank sum tests as appropriate.
We modeled the likelihood of experiencing a long LOS using generalized linear mixed effects models with a random hospital intercept and discharge‐level fixed effects for age, gender, payor, CCC type, ICU utilization, transfer status, a medical/surgical admission indicator derived from the APR‐DRG, and CRG assigned to each hospitalization. To examine hospital‐to‐hospital variability, we generated hospital risk‐adjusted rates of long LOS from these models. Similar models and hospital risk‐adjusted rates were built for a post hoc correlational analysis of 30‐day all cause readmission, where hospitals' rates and percent of long LOS were compared with a Pearson correlation coefficient. Also, for our multivariable models, we performed a sensitivity analysis using an alternative definition of long LOS as 4 days (the 75th percentile of LOS for all children, with and without medical complexity, hospitalized during 20132014). All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), and P values <0.05 were considered statistically significant.
RESULTS
Study Population
There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).
| Characteristic | Overall (n = 954,018) | Length of Stay | |
|---|---|---|---|
| <10 Days (n = 811,936) | 10 Days (n = 142,082) | ||
| |||
| Age at admission, y, % | |||
| <1 | 14.6 | 12.7 | 25.7 |
| 14 | 27.1 | 27.9 | 22.4 |
| 59 | 20.1 | 21.0 | 14.9 |
| 1018 | 33.6 | 34.0 | 31.7 |
| 18+ | 4.6 | 4.4 | 5.4 |
| Gender, % | |||
| Female | 47.0 | 46.9 | 47.5 |
| Race/ethnicity, % | |||
| Non‐Hispanic white | 49.4 | 49.4 | 49.4 |
| Non‐Hispanic black | 23.1 | 23.8 | 19.3 |
| Hispanic | 18.2 | 17.8 | 20.4 |
| Asian | 2.0 | 1.9 | 2.3 |
| Other | 7.4 | 7.1 | 8.6 |
| Complex chronic condition, % | |||
| Any | 79.5 | 77.3 | 91.8 |
| Technology assistance | 37.1 | 34.1 | 54.2 |
| Gastrointestinal | 30.0 | 27.2 | 45.9 |
| Neuromuscular | 28.2 | 27.7 | 30.9 |
| Cardiovascular | 16.8 | 14.5 | 29.9 |
| Respiratory | 14.1 | 11.5 | 29.4 |
| Congenital/genetic defect | 17.2 | 16.7 | 20.2 |
| Metabolic | 9.9 | 8.9 | 15.4 |
| Renal | 10.1 | 9.5 | 13.8 |
| Hematology/emmmunodeficiency | 11.7 | 12.0 | 10.0 |
| Neonatal | 3.8 | 3.1 | 7.7 |
| Transplantation | 4.5 | 4.2 | 6.7 |
| Clinical risk group, % | |||
| Chronic condition in 2 systems | 68.4 | 71.2 | 53.9 |
| Catastrophic chronic condition | 31.4 | 28.8 | 46.1 |
| Distance from hospital to home residence in miles, median [IQR] | 16.2 [7.440.4] | 15.8 [7.338.7] | 19.1 [8.552.6] |
| Transferred from outside hospital (%) | 6.5 | 5.3 | 13.6 |
| Admitted for surgery, % | 23.4 | 20.7 | 38.7 |
| Use of intensive care, % | 19.6 | 14.9 | 46.5 |
| Discharge disposition, % | |||
| Home | 91.2 | 92.9 | 81.4 |
| Home healthcare | 4.5 | 3.5 | 9.9 |
| Other | 2.9 | 2.6 | 4.5 |
| Postacute care facility | 1.1 | 0.8 | 3.1 |
| Died | 0.4 | 0.3 | 1.1 |
| Payor, % | |||
| Government | 61.1 | 60.6 | 63.5 |
| Private | 33.2 | 33.6 | 30.9 |
| Other | 5.7 | 5.7 | 5.7 |
| Hospital resource use | |||
| Median length of stay [IQR] | 3 [16] | 2 [14] | 16 [1226] |
| Median hospital cost [IQR] | $8,144 [$4,122$18,447] | $6,689 [$3,685$12,395] | $49,207 [$29,444$95,738] |
| Total hospital cost, $, billions | $22.2 | $8.5 | $13.7 |
Demographics and Clinical Characteristics of Children With and Without Long LOS
Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).
Hospitalization Characteristics of Children With and Without Long LOS
Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).
The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.
| |
| Most common reason for admission* | |
| Cystic fibrosis | 10.7% |
| Respiratory system diagnosis with ventilator support 96+ hours | 5.5% |
| Malfunction, reaction, and complication of cardiac or vascular device or procedure | 2.8% |
| Craniotomy except for trauma | 2.6% |
| Major small and large bowel procedures | 2.3% |
| Most common complex chronic condition | |
| Gastrointestinal devices | 39.7% |
| Heart and great vessel malformations | 12.8% |
| Cystic fibrosis | 12.5% |
| Dysrhythmias | 11.0% |
| Respiratory devices | 10.7% |
Multivariable Analysis of Characteristics Associated With Long LOS
In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.
| Characteristic | Odds Ratio (95% CI) of LOS 10 Days | P Value |
|---|---|---|
| ||
| Use of intensive care | 3.5 (3.4‐3.5) | <0.001 |
| Transfer from another acute‐care hospital | 2.1 (2.0‐2.1) | <0.001 |
| Procedure/surgery | 1.8 (1.8‐1.9) | <0.001 |
| Complex chronic condition | ||
| Respiratory | 2.7 (2.6‐2.7) | <0.001 |
| Gastrointestinal | 1.8 (1.8‐1.8) | <0.001 |
| Metabolic | 1.7 (1.7‐1.7) | <0.001 |
| Cardiovascular | 1.6 (1.5‐1.6) | <0.001 |
| Neonatal | 1.5 (1.5‐1.5) | <0.001 |
| Renal | 1.4 (1.4‐1.4) | <0.001 |
| Transplant | 1.4 (1.4‐1.4) | <0.001 |
| Hematology and immunodeficiency | 1.3 (1.3‐1.3) | <0.001 |
| Technology assistance | 1.1 (1.1, 1.1) | <0.001 |
| Neuromuscular | 0.9 (0.9‐0.9) | <0.001 |
| Congenital or genetic defect | 0.8 (0.8‐0.8) | <0.001 |
| Age at admission, y | ||
| <1 | 1.2 (1.2‐1.3) | <0.001 |
| 14 | 0.5 (0.5‐0.5) | <0.001 |
| 59 | 0.6 (0.6‐0.6) | <0.001 |
| 1018 | 0.9 (0.9‐0.9) | <0.001 |
| 18+ | Reference | |
| Male | 0.9 (0.9‐0.9) | <0.001 |
| Race/ethnicity | ||
| Non‐Hispanic black | 0.9 (0.9‐0.9) | <0.001 |
| Hispanic | 1.1 (1.0‐1.1) | <0.001 |
| Asian | 1.0 (1.0‐1.1) | 0.3 |
| Other | 1.1 (1.1‐1.1) | <0.001 |
| Non‐Hispanic white | Reference | |
| Payor | ||
| Private | 0.9 (0.8 0.9) | <0.001 |
| Other | 1.0 (1.0‐1.0) | 0.4 |
| Government | Reference | |
| Season | ||
| Spring | 1.0 (1.0 1.0) | <0.001 |
| Summer | 0.9 (0.9‐0.9) | <0.001 |
| Fall | 1.0 (0.9‐1.0) | <0.001 |
| Winter | Reference | |
Variation in the Prevalence of Long LOS Across Children's Hospitals
After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.
DISCUSSION
The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.
Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.
Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.
The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]
This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.
PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.
Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.
- , , , et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi‐institutional study. JAMA Pediatr. 2013;167(2):170–177.
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- , , , et al. Effect of an enhanced medical home on serious illness and cost of care among high‐risk children with chronic illness: a randomized clinical trial. JAMA. 2014;312(24):2640–2648.
- , , , et al. Children with medical complexity and Medicaid: spending and cost savings. Health Aff Proj Hope. 2014;33(12):2199–2206.
- Children's Hospital Association. CARE Award. Available at: https://www.childrenshospitals.org/Programs‐and‐Services/Quality‐Improvement‐and‐Measurement/CARE‐Award. Accessed December 18, 2015.
- , , , et al. Hospital readmission and parent perceptions of their child's hospital discharge. Int J Qual Health Care. 2013;25(5):573–581.
- , , , et al. Weekend matters: Friday and Saturday admissions are associated with prolonged hospitalization of children. Clin Pediatr (Phila). 2013;52(9):875–878.
- , , , . Attributable cost and length of stay for central line‐associated bloodstream infections. Pediatrics. 2014;133(6):e1525–e1532.
- , , , et al. Effect of healthcare‐acquired infection on length of hospital stay and cost. Infect Control Hosp Epidemiol. 2007;28(3):280–292.
- , , , , . Hospital utilization and costs among children with influenza, 2003. Am J Prev Med. 2009;36(4):292–296.
- , , , . Charges and lengths of stay attributable to adverse patient‐care events using pediatric‐specific quality indicators: a multicenter study of freestanding children's hospitals. Pediatrics. 2008;121(6):e1653–e1659.
- , , , , . Variation in resource utilization for the management of uncomplicated community‐acquired pneumonia across community and children's hospitals. J Pediatr. 2014;165(3):585–591.
- , , , , . Variation and outcomes associated with direct hospital admission among children with pneumonia in the United States. JAMA Pediatr. 2014;168(9):829–836.
- , , , et al. Clinical Risk Groups (CRGs): a classification system for risk‐adjusted capitation‐based payment and health care management. Med Care. 2004;42(1):81–90.
- , , , et al. Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data. Acad Pediatr. 2010;10(6):417–423.
- , , , , . Profile of medical charges for children by health status group and severity level in a Washington State Health Plan. Health Serv Res. 2004;39(1):73–89.
- , , , . Using medical billing data to evaluate chronically ill children over time. J Ambulatory Care Manage. 2006;29(4):283–290.
- , , , , , . Medical complexity and pediatric emergency department and inpatient utilization. Pediatrics. 2013;131(2):e559–e565.
- , , , , . Pediatric complex chronic conditions classification system version 2: updated for ICD‐10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199.
- . Analyzing intensive care unit length of stay data: problems and possible solutions. Crit Care Med. 1997;25(9):1594–1600.
- , , , et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children's hospitals. JAMA. 2011;305(7):682–690.
- . Hospital readmissions and repeat emergency department visits among children with medical complexity: an integrative review. J Pediatr Nurs. 2013;28(4):316–339.
- , , , . Hospital readmission in children with complex chronic conditions discharged from subacute care. Hosp Pediatr. 2014;4(3):153–158.
- , , , et al. Preventing hospitalizations in children with medical complexity: a systematic review. Pediatrics. 2014;134(6):e1628–e1647.
- , , , . Hospital readmissions for newly discharged pediatric home mechanical ventilation patients. Pediatr Pulmonol. 2012;47(4):409–414.
- , , , et al. Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991–2008. JAMA. 2011;305(15):1560–1567.
- , , , et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303(21):2141–2147.
- U.S. Department of Health and Human Services. CMS Statistics 2013. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/CMS‐Statistics‐Reference‐Booklet/Downloads/CMS_Stats_2013_final.pdf. Published August 2013. Accessed October 6, 2015.
- Centers for Medicare and Medicaid Services. Evaluation of the premier hospital quality incentive demonstration. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/Premier_ExecSum_2010.pdf. Published March 3, 2009. Accessed September 18, 2015.
- , , , et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955–962; quiz 965–966.
- , , , , . Parent and provider perspectives on pediatric readmissions: what can we learn about readiness for discharge? Hosp Pediatr. 2015;5(11):559–565.
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- , , , et al. Bronchiolitis: clinical characteristics associated with hospitalization and length of stay. Pediatr Emerg Care. 2012;28(2):99–103.
- , , , , . The effect of transfers between health care facilities on costs and length of stay for pediatric burn patients. J Burn Care Res. 2015;36(1):178–183.
- , , , et al. Goal‐directed resuscitative interventions during pediatric interfacility transport. Crit Care Med. 2015;43(8):1692–1698.
Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]
Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]
Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.
METHODS
Study Design and Data Source
This study is a multicenter, retrospective cohort analysis of the Pediatric Health Information System (PHIS). PHIS is an administrative database of 44 not for profit, tertiary care pediatric hospitals affiliated with the Children's Hospital Association (CHA) (Overland Park, KS). PHIS contains data regarding patient demographics, diagnoses, and procedures (with International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] codes), All‐Patient Refined Diagnostic Related Groups version 30 (APR‐DRGs) (3M Health Information Systems, Salt Lake City, UT), and service lines that aggregate the APR‐DRGs into 38 distinct groups. Data quality and reliability are assured through CHA and participating hospitals. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this study of deidentified data was not considered human subjects research.
Study Population
Inclusion Criteria
Children discharged following an observation or inpatient admission from a hospital participating in the PHIS database between January 1, 2013 and December 31, 2014 were eligible for inclusion if they were considered medically complex. Medical complexity was defined using Clinical Risk Groups (CRGs) version 1.8, developed by 3M Health Information Systems and the National Association of Children's Hospitals and Related Institutions. CRGs were used to assign each hospitalized patient to 1 of 9 mutually exclusive chronicity groups according to the presence, type, and severity of chronic conditions.[15, 16, 17, 18] Each patient's CRG designation was based on 2 years of previous hospital encounters.
As defined in prior studies and definitional frameworks of CMC,[1] patients belonging to CRG group 6 (significant chronic disease in 2 organ systems), CRG group 7 (dominant chronic disease in 3 organ systems), and CRG group 9 (catastrophic condition) were considered medically complex.[17, 19] Patients with malignancies (CRG group 8) were not included for analysis because they are a unique population with anticipated, long hospital stays. Patients with CRG group 5, representing those with chronic conditions affecting a single body system, were also not included because most do not have attributes consistent with medical complexity.
Exclusion Criteria
We used the APR‐DRG system, which leverages ICD‐9‐CM codes to identify the health problem most responsible for the hospitalization, to refine the study cohort. We excluded hospitalizations that were classified by the APR‐DRG system as neonatal, as we did not wish to focus on LOS in the neonatal intensive care unit (ICU) or for birth admissions. Similarly, hospitalizations for chemotherapy (APR‐DRG 693) or malignancy (identified with previously used ICD‐9‐CM codes)[20] were also excluded because long LOS is anticipated. We also excluded hospitalizations for medical rehabilitation (APR‐DRG 860).
Outcome Measures
The primary outcome measure was long LOS, defined as LOS 10 days. The cut point of LOS 10 days represents the 90th percentile of LOS for all children, with and without medical complexity, hospitalized during 2013 to 2014. LOS 10 days has previously been used as a threshold of long LOS.[21] For hospitalizations involving transfer at admission from another acute care facility, LOS was measured from the date of transfer. We also assessed hospitals' cost attributable to long LOS admissions.
Patient Demographics and Clinical Characteristics
We measured demographic characteristics including age, gender, race/ethnicity, insurance type, and distance traveled (the linear distance between the centroid of the patient's home ZIP code and the centroid of the hospital's ZIP code). Clinical characteristics included CRG classification, complex chronic condition (CCC), and dependence on medical technology. CCCs are defined as any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or 1 system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center.[20] Medical technology included devices used to optimize the health and functioning of the child (eg, gastrostomy, tracheostomy, cerebrospinal fluid shunt).[22]
Hospitalization Characteristics
Characteristics of the hospitalization included transfer from an outside facility, ICU admission, surgical procedure (using surgical APR‐DRGs), and discharge disposition (home, skilled nursing facility, home health services, death, other). Cost of the hospitalization was estimated in the PHIS from charges using hospital and year‐specific ratios of cost to charge.
Statistical Analysis
Continuous data (eg, distance from hospital to home residence) were described with median and interquartile ranges (IQR) because they were not normally distributed. Categorical data (eg, type of chronic condition) were described with counts and frequencies. In bivariate analyses, demographic, clinical, and hospitalization characteristics were stratified by LOS (long LOS vs LOS <10 days), and compared using 2 statistics or Wilcoxon rank sum tests as appropriate.
We modeled the likelihood of experiencing a long LOS using generalized linear mixed effects models with a random hospital intercept and discharge‐level fixed effects for age, gender, payor, CCC type, ICU utilization, transfer status, a medical/surgical admission indicator derived from the APR‐DRG, and CRG assigned to each hospitalization. To examine hospital‐to‐hospital variability, we generated hospital risk‐adjusted rates of long LOS from these models. Similar models and hospital risk‐adjusted rates were built for a post hoc correlational analysis of 30‐day all cause readmission, where hospitals' rates and percent of long LOS were compared with a Pearson correlation coefficient. Also, for our multivariable models, we performed a sensitivity analysis using an alternative definition of long LOS as 4 days (the 75th percentile of LOS for all children, with and without medical complexity, hospitalized during 20132014). All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), and P values <0.05 were considered statistically significant.
RESULTS
Study Population
There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).
| Characteristic | Overall (n = 954,018) | Length of Stay | |
|---|---|---|---|
| <10 Days (n = 811,936) | 10 Days (n = 142,082) | ||
| |||
| Age at admission, y, % | |||
| <1 | 14.6 | 12.7 | 25.7 |
| 14 | 27.1 | 27.9 | 22.4 |
| 59 | 20.1 | 21.0 | 14.9 |
| 1018 | 33.6 | 34.0 | 31.7 |
| 18+ | 4.6 | 4.4 | 5.4 |
| Gender, % | |||
| Female | 47.0 | 46.9 | 47.5 |
| Race/ethnicity, % | |||
| Non‐Hispanic white | 49.4 | 49.4 | 49.4 |
| Non‐Hispanic black | 23.1 | 23.8 | 19.3 |
| Hispanic | 18.2 | 17.8 | 20.4 |
| Asian | 2.0 | 1.9 | 2.3 |
| Other | 7.4 | 7.1 | 8.6 |
| Complex chronic condition, % | |||
| Any | 79.5 | 77.3 | 91.8 |
| Technology assistance | 37.1 | 34.1 | 54.2 |
| Gastrointestinal | 30.0 | 27.2 | 45.9 |
| Neuromuscular | 28.2 | 27.7 | 30.9 |
| Cardiovascular | 16.8 | 14.5 | 29.9 |
| Respiratory | 14.1 | 11.5 | 29.4 |
| Congenital/genetic defect | 17.2 | 16.7 | 20.2 |
| Metabolic | 9.9 | 8.9 | 15.4 |
| Renal | 10.1 | 9.5 | 13.8 |
| Hematology/emmmunodeficiency | 11.7 | 12.0 | 10.0 |
| Neonatal | 3.8 | 3.1 | 7.7 |
| Transplantation | 4.5 | 4.2 | 6.7 |
| Clinical risk group, % | |||
| Chronic condition in 2 systems | 68.4 | 71.2 | 53.9 |
| Catastrophic chronic condition | 31.4 | 28.8 | 46.1 |
| Distance from hospital to home residence in miles, median [IQR] | 16.2 [7.440.4] | 15.8 [7.338.7] | 19.1 [8.552.6] |
| Transferred from outside hospital (%) | 6.5 | 5.3 | 13.6 |
| Admitted for surgery, % | 23.4 | 20.7 | 38.7 |
| Use of intensive care, % | 19.6 | 14.9 | 46.5 |
| Discharge disposition, % | |||
| Home | 91.2 | 92.9 | 81.4 |
| Home healthcare | 4.5 | 3.5 | 9.9 |
| Other | 2.9 | 2.6 | 4.5 |
| Postacute care facility | 1.1 | 0.8 | 3.1 |
| Died | 0.4 | 0.3 | 1.1 |
| Payor, % | |||
| Government | 61.1 | 60.6 | 63.5 |
| Private | 33.2 | 33.6 | 30.9 |
| Other | 5.7 | 5.7 | 5.7 |
| Hospital resource use | |||
| Median length of stay [IQR] | 3 [16] | 2 [14] | 16 [1226] |
| Median hospital cost [IQR] | $8,144 [$4,122$18,447] | $6,689 [$3,685$12,395] | $49,207 [$29,444$95,738] |
| Total hospital cost, $, billions | $22.2 | $8.5 | $13.7 |
Demographics and Clinical Characteristics of Children With and Without Long LOS
Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).
Hospitalization Characteristics of Children With and Without Long LOS
Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).
The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.
| |
| Most common reason for admission* | |
| Cystic fibrosis | 10.7% |
| Respiratory system diagnosis with ventilator support 96+ hours | 5.5% |
| Malfunction, reaction, and complication of cardiac or vascular device or procedure | 2.8% |
| Craniotomy except for trauma | 2.6% |
| Major small and large bowel procedures | 2.3% |
| Most common complex chronic condition | |
| Gastrointestinal devices | 39.7% |
| Heart and great vessel malformations | 12.8% |
| Cystic fibrosis | 12.5% |
| Dysrhythmias | 11.0% |
| Respiratory devices | 10.7% |
Multivariable Analysis of Characteristics Associated With Long LOS
In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.
| Characteristic | Odds Ratio (95% CI) of LOS 10 Days | P Value |
|---|---|---|
| ||
| Use of intensive care | 3.5 (3.4‐3.5) | <0.001 |
| Transfer from another acute‐care hospital | 2.1 (2.0‐2.1) | <0.001 |
| Procedure/surgery | 1.8 (1.8‐1.9) | <0.001 |
| Complex chronic condition | ||
| Respiratory | 2.7 (2.6‐2.7) | <0.001 |
| Gastrointestinal | 1.8 (1.8‐1.8) | <0.001 |
| Metabolic | 1.7 (1.7‐1.7) | <0.001 |
| Cardiovascular | 1.6 (1.5‐1.6) | <0.001 |
| Neonatal | 1.5 (1.5‐1.5) | <0.001 |
| Renal | 1.4 (1.4‐1.4) | <0.001 |
| Transplant | 1.4 (1.4‐1.4) | <0.001 |
| Hematology and immunodeficiency | 1.3 (1.3‐1.3) | <0.001 |
| Technology assistance | 1.1 (1.1, 1.1) | <0.001 |
| Neuromuscular | 0.9 (0.9‐0.9) | <0.001 |
| Congenital or genetic defect | 0.8 (0.8‐0.8) | <0.001 |
| Age at admission, y | ||
| <1 | 1.2 (1.2‐1.3) | <0.001 |
| 14 | 0.5 (0.5‐0.5) | <0.001 |
| 59 | 0.6 (0.6‐0.6) | <0.001 |
| 1018 | 0.9 (0.9‐0.9) | <0.001 |
| 18+ | Reference | |
| Male | 0.9 (0.9‐0.9) | <0.001 |
| Race/ethnicity | ||
| Non‐Hispanic black | 0.9 (0.9‐0.9) | <0.001 |
| Hispanic | 1.1 (1.0‐1.1) | <0.001 |
| Asian | 1.0 (1.0‐1.1) | 0.3 |
| Other | 1.1 (1.1‐1.1) | <0.001 |
| Non‐Hispanic white | Reference | |
| Payor | ||
| Private | 0.9 (0.8 0.9) | <0.001 |
| Other | 1.0 (1.0‐1.0) | 0.4 |
| Government | Reference | |
| Season | ||
| Spring | 1.0 (1.0 1.0) | <0.001 |
| Summer | 0.9 (0.9‐0.9) | <0.001 |
| Fall | 1.0 (0.9‐1.0) | <0.001 |
| Winter | Reference | |
Variation in the Prevalence of Long LOS Across Children's Hospitals
After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.
DISCUSSION
The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.
Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.
Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.
The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]
This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.
PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.
Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.
Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]
Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]
Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.
METHODS
Study Design and Data Source
This study is a multicenter, retrospective cohort analysis of the Pediatric Health Information System (PHIS). PHIS is an administrative database of 44 not for profit, tertiary care pediatric hospitals affiliated with the Children's Hospital Association (CHA) (Overland Park, KS). PHIS contains data regarding patient demographics, diagnoses, and procedures (with International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] codes), All‐Patient Refined Diagnostic Related Groups version 30 (APR‐DRGs) (3M Health Information Systems, Salt Lake City, UT), and service lines that aggregate the APR‐DRGs into 38 distinct groups. Data quality and reliability are assured through CHA and participating hospitals. In accordance with the policies of the Cincinnati Children's Hospital Medical Center Institutional Review Board, this study of deidentified data was not considered human subjects research.
Study Population
Inclusion Criteria
Children discharged following an observation or inpatient admission from a hospital participating in the PHIS database between January 1, 2013 and December 31, 2014 were eligible for inclusion if they were considered medically complex. Medical complexity was defined using Clinical Risk Groups (CRGs) version 1.8, developed by 3M Health Information Systems and the National Association of Children's Hospitals and Related Institutions. CRGs were used to assign each hospitalized patient to 1 of 9 mutually exclusive chronicity groups according to the presence, type, and severity of chronic conditions.[15, 16, 17, 18] Each patient's CRG designation was based on 2 years of previous hospital encounters.
As defined in prior studies and definitional frameworks of CMC,[1] patients belonging to CRG group 6 (significant chronic disease in 2 organ systems), CRG group 7 (dominant chronic disease in 3 organ systems), and CRG group 9 (catastrophic condition) were considered medically complex.[17, 19] Patients with malignancies (CRG group 8) were not included for analysis because they are a unique population with anticipated, long hospital stays. Patients with CRG group 5, representing those with chronic conditions affecting a single body system, were also not included because most do not have attributes consistent with medical complexity.
Exclusion Criteria
We used the APR‐DRG system, which leverages ICD‐9‐CM codes to identify the health problem most responsible for the hospitalization, to refine the study cohort. We excluded hospitalizations that were classified by the APR‐DRG system as neonatal, as we did not wish to focus on LOS in the neonatal intensive care unit (ICU) or for birth admissions. Similarly, hospitalizations for chemotherapy (APR‐DRG 693) or malignancy (identified with previously used ICD‐9‐CM codes)[20] were also excluded because long LOS is anticipated. We also excluded hospitalizations for medical rehabilitation (APR‐DRG 860).
Outcome Measures
The primary outcome measure was long LOS, defined as LOS 10 days. The cut point of LOS 10 days represents the 90th percentile of LOS for all children, with and without medical complexity, hospitalized during 2013 to 2014. LOS 10 days has previously been used as a threshold of long LOS.[21] For hospitalizations involving transfer at admission from another acute care facility, LOS was measured from the date of transfer. We also assessed hospitals' cost attributable to long LOS admissions.
Patient Demographics and Clinical Characteristics
We measured demographic characteristics including age, gender, race/ethnicity, insurance type, and distance traveled (the linear distance between the centroid of the patient's home ZIP code and the centroid of the hospital's ZIP code). Clinical characteristics included CRG classification, complex chronic condition (CCC), and dependence on medical technology. CCCs are defined as any medical condition that can be reasonably expected to last at least 12 months (unless death intervenes) and to involve either several different organ systems or 1 system severely enough to require specialty pediatric care and probably some period of hospitalization in a tertiary care center.[20] Medical technology included devices used to optimize the health and functioning of the child (eg, gastrostomy, tracheostomy, cerebrospinal fluid shunt).[22]
Hospitalization Characteristics
Characteristics of the hospitalization included transfer from an outside facility, ICU admission, surgical procedure (using surgical APR‐DRGs), and discharge disposition (home, skilled nursing facility, home health services, death, other). Cost of the hospitalization was estimated in the PHIS from charges using hospital and year‐specific ratios of cost to charge.
Statistical Analysis
Continuous data (eg, distance from hospital to home residence) were described with median and interquartile ranges (IQR) because they were not normally distributed. Categorical data (eg, type of chronic condition) were described with counts and frequencies. In bivariate analyses, demographic, clinical, and hospitalization characteristics were stratified by LOS (long LOS vs LOS <10 days), and compared using 2 statistics or Wilcoxon rank sum tests as appropriate.
We modeled the likelihood of experiencing a long LOS using generalized linear mixed effects models with a random hospital intercept and discharge‐level fixed effects for age, gender, payor, CCC type, ICU utilization, transfer status, a medical/surgical admission indicator derived from the APR‐DRG, and CRG assigned to each hospitalization. To examine hospital‐to‐hospital variability, we generated hospital risk‐adjusted rates of long LOS from these models. Similar models and hospital risk‐adjusted rates were built for a post hoc correlational analysis of 30‐day all cause readmission, where hospitals' rates and percent of long LOS were compared with a Pearson correlation coefficient. Also, for our multivariable models, we performed a sensitivity analysis using an alternative definition of long LOS as 4 days (the 75th percentile of LOS for all children, with and without medical complexity, hospitalized during 20132014). All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC), and P values <0.05 were considered statistically significant.
RESULTS
Study Population
There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).
| Characteristic | Overall (n = 954,018) | Length of Stay | |
|---|---|---|---|
| <10 Days (n = 811,936) | 10 Days (n = 142,082) | ||
| |||
| Age at admission, y, % | |||
| <1 | 14.6 | 12.7 | 25.7 |
| 14 | 27.1 | 27.9 | 22.4 |
| 59 | 20.1 | 21.0 | 14.9 |
| 1018 | 33.6 | 34.0 | 31.7 |
| 18+ | 4.6 | 4.4 | 5.4 |
| Gender, % | |||
| Female | 47.0 | 46.9 | 47.5 |
| Race/ethnicity, % | |||
| Non‐Hispanic white | 49.4 | 49.4 | 49.4 |
| Non‐Hispanic black | 23.1 | 23.8 | 19.3 |
| Hispanic | 18.2 | 17.8 | 20.4 |
| Asian | 2.0 | 1.9 | 2.3 |
| Other | 7.4 | 7.1 | 8.6 |
| Complex chronic condition, % | |||
| Any | 79.5 | 77.3 | 91.8 |
| Technology assistance | 37.1 | 34.1 | 54.2 |
| Gastrointestinal | 30.0 | 27.2 | 45.9 |
| Neuromuscular | 28.2 | 27.7 | 30.9 |
| Cardiovascular | 16.8 | 14.5 | 29.9 |
| Respiratory | 14.1 | 11.5 | 29.4 |
| Congenital/genetic defect | 17.2 | 16.7 | 20.2 |
| Metabolic | 9.9 | 8.9 | 15.4 |
| Renal | 10.1 | 9.5 | 13.8 |
| Hematology/emmmunodeficiency | 11.7 | 12.0 | 10.0 |
| Neonatal | 3.8 | 3.1 | 7.7 |
| Transplantation | 4.5 | 4.2 | 6.7 |
| Clinical risk group, % | |||
| Chronic condition in 2 systems | 68.4 | 71.2 | 53.9 |
| Catastrophic chronic condition | 31.4 | 28.8 | 46.1 |
| Distance from hospital to home residence in miles, median [IQR] | 16.2 [7.440.4] | 15.8 [7.338.7] | 19.1 [8.552.6] |
| Transferred from outside hospital (%) | 6.5 | 5.3 | 13.6 |
| Admitted for surgery, % | 23.4 | 20.7 | 38.7 |
| Use of intensive care, % | 19.6 | 14.9 | 46.5 |
| Discharge disposition, % | |||
| Home | 91.2 | 92.9 | 81.4 |
| Home healthcare | 4.5 | 3.5 | 9.9 |
| Other | 2.9 | 2.6 | 4.5 |
| Postacute care facility | 1.1 | 0.8 | 3.1 |
| Died | 0.4 | 0.3 | 1.1 |
| Payor, % | |||
| Government | 61.1 | 60.6 | 63.5 |
| Private | 33.2 | 33.6 | 30.9 |
| Other | 5.7 | 5.7 | 5.7 |
| Hospital resource use | |||
| Median length of stay [IQR] | 3 [16] | 2 [14] | 16 [1226] |
| Median hospital cost [IQR] | $8,144 [$4,122$18,447] | $6,689 [$3,685$12,395] | $49,207 [$29,444$95,738] |
| Total hospital cost, $, billions | $22.2 | $8.5 | $13.7 |
Demographics and Clinical Characteristics of Children With and Without Long LOS
Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).
Hospitalization Characteristics of Children With and Without Long LOS
Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).
The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.
| |
| Most common reason for admission* | |
| Cystic fibrosis | 10.7% |
| Respiratory system diagnosis with ventilator support 96+ hours | 5.5% |
| Malfunction, reaction, and complication of cardiac or vascular device or procedure | 2.8% |
| Craniotomy except for trauma | 2.6% |
| Major small and large bowel procedures | 2.3% |
| Most common complex chronic condition | |
| Gastrointestinal devices | 39.7% |
| Heart and great vessel malformations | 12.8% |
| Cystic fibrosis | 12.5% |
| Dysrhythmias | 11.0% |
| Respiratory devices | 10.7% |
Multivariable Analysis of Characteristics Associated With Long LOS
In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.
| Characteristic | Odds Ratio (95% CI) of LOS 10 Days | P Value |
|---|---|---|
| ||
| Use of intensive care | 3.5 (3.4‐3.5) | <0.001 |
| Transfer from another acute‐care hospital | 2.1 (2.0‐2.1) | <0.001 |
| Procedure/surgery | 1.8 (1.8‐1.9) | <0.001 |
| Complex chronic condition | ||
| Respiratory | 2.7 (2.6‐2.7) | <0.001 |
| Gastrointestinal | 1.8 (1.8‐1.8) | <0.001 |
| Metabolic | 1.7 (1.7‐1.7) | <0.001 |
| Cardiovascular | 1.6 (1.5‐1.6) | <0.001 |
| Neonatal | 1.5 (1.5‐1.5) | <0.001 |
| Renal | 1.4 (1.4‐1.4) | <0.001 |
| Transplant | 1.4 (1.4‐1.4) | <0.001 |
| Hematology and immunodeficiency | 1.3 (1.3‐1.3) | <0.001 |
| Technology assistance | 1.1 (1.1, 1.1) | <0.001 |
| Neuromuscular | 0.9 (0.9‐0.9) | <0.001 |
| Congenital or genetic defect | 0.8 (0.8‐0.8) | <0.001 |
| Age at admission, y | ||
| <1 | 1.2 (1.2‐1.3) | <0.001 |
| 14 | 0.5 (0.5‐0.5) | <0.001 |
| 59 | 0.6 (0.6‐0.6) | <0.001 |
| 1018 | 0.9 (0.9‐0.9) | <0.001 |
| 18+ | Reference | |
| Male | 0.9 (0.9‐0.9) | <0.001 |
| Race/ethnicity | ||
| Non‐Hispanic black | 0.9 (0.9‐0.9) | <0.001 |
| Hispanic | 1.1 (1.0‐1.1) | <0.001 |
| Asian | 1.0 (1.0‐1.1) | 0.3 |
| Other | 1.1 (1.1‐1.1) | <0.001 |
| Non‐Hispanic white | Reference | |
| Payor | ||
| Private | 0.9 (0.8 0.9) | <0.001 |
| Other | 1.0 (1.0‐1.0) | 0.4 |
| Government | Reference | |
| Season | ||
| Spring | 1.0 (1.0 1.0) | <0.001 |
| Summer | 0.9 (0.9‐0.9) | <0.001 |
| Fall | 1.0 (0.9‐1.0) | <0.001 |
| Winter | Reference | |
Variation in the Prevalence of Long LOS Across Children's Hospitals
After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.
DISCUSSION
The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.
Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.
Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.
The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]
This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.
PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.
Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.
- , , , et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi‐institutional study. JAMA Pediatr. 2013;167(2):170–177.
- , , , et al. Children with complex chronic conditions in inpatient hospital settings in the united states. Pediatrics. 2010;126(4):647–655.
- , . Meeting the health care needs of persons with disabilities. Milbank Q. 2002;80(2):381–391.
- , , , et al. Effect of an enhanced medical home on serious illness and cost of care among high‐risk children with chronic illness: a randomized clinical trial. JAMA. 2014;312(24):2640–2648.
- , , , et al. Children with medical complexity and Medicaid: spending and cost savings. Health Aff Proj Hope. 2014;33(12):2199–2206.
- Children's Hospital Association. CARE Award. Available at: https://www.childrenshospitals.org/Programs‐and‐Services/Quality‐Improvement‐and‐Measurement/CARE‐Award. Accessed December 18, 2015.
- , , , et al. Hospital readmission and parent perceptions of their child's hospital discharge. Int J Qual Health Care. 2013;25(5):573–581.
- , , , et al. Weekend matters: Friday and Saturday admissions are associated with prolonged hospitalization of children. Clin Pediatr (Phila). 2013;52(9):875–878.
- , , , . Attributable cost and length of stay for central line‐associated bloodstream infections. Pediatrics. 2014;133(6):e1525–e1532.
- , , , et al. Effect of healthcare‐acquired infection on length of hospital stay and cost. Infect Control Hosp Epidemiol. 2007;28(3):280–292.
- , , , , . Hospital utilization and costs among children with influenza, 2003. Am J Prev Med. 2009;36(4):292–296.
- , , , . Charges and lengths of stay attributable to adverse patient‐care events using pediatric‐specific quality indicators: a multicenter study of freestanding children's hospitals. Pediatrics. 2008;121(6):e1653–e1659.
- , , , , . Variation in resource utilization for the management of uncomplicated community‐acquired pneumonia across community and children's hospitals. J Pediatr. 2014;165(3):585–591.
- , , , , . Variation and outcomes associated with direct hospital admission among children with pneumonia in the United States. JAMA Pediatr. 2014;168(9):829–836.
- , , , et al. Clinical Risk Groups (CRGs): a classification system for risk‐adjusted capitation‐based payment and health care management. Med Care. 2004;42(1):81–90.
- , , , et al. Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data. Acad Pediatr. 2010;10(6):417–423.
- , , , , . Profile of medical charges for children by health status group and severity level in a Washington State Health Plan. Health Serv Res. 2004;39(1):73–89.
- , , , . Using medical billing data to evaluate chronically ill children over time. J Ambulatory Care Manage. 2006;29(4):283–290.
- , , , , , . Medical complexity and pediatric emergency department and inpatient utilization. Pediatrics. 2013;131(2):e559–e565.
- , , , , . Pediatric complex chronic conditions classification system version 2: updated for ICD‐10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199.
- . Analyzing intensive care unit length of stay data: problems and possible solutions. Crit Care Med. 1997;25(9):1594–1600.
- , , , et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children's hospitals. JAMA. 2011;305(7):682–690.
- . Hospital readmissions and repeat emergency department visits among children with medical complexity: an integrative review. J Pediatr Nurs. 2013;28(4):316–339.
- , , , . Hospital readmission in children with complex chronic conditions discharged from subacute care. Hosp Pediatr. 2014;4(3):153–158.
- , , , et al. Preventing hospitalizations in children with medical complexity: a systematic review. Pediatrics. 2014;134(6):e1628–e1647.
- , , , . Hospital readmissions for newly discharged pediatric home mechanical ventilation patients. Pediatr Pulmonol. 2012;47(4):409–414.
- , , , et al. Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991–2008. JAMA. 2011;305(15):1560–1567.
- , , , et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303(21):2141–2147.
- U.S. Department of Health and Human Services. CMS Statistics 2013. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/CMS‐Statistics‐Reference‐Booklet/Downloads/CMS_Stats_2013_final.pdf. Published August 2013. Accessed October 6, 2015.
- Centers for Medicare and Medicaid Services. Evaluation of the premier hospital quality incentive demonstration. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/Premier_ExecSum_2010.pdf. Published March 3, 2009. Accessed September 18, 2015.
- , , , et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955–962; quiz 965–966.
- , , , , . Parent and provider perspectives on pediatric readmissions: what can we learn about readiness for discharge? Hosp Pediatr. 2015;5(11):559–565.
- , . Preventing readmissions in children: how do we do that? Hosp Pediatr. 2015;5(11):602–604.
- , , . Pediatric post‐acute hospital care: striving for identity and value. Hosp Pediatr. 2015;5(10):548–551.
- , , , et al. Pediatric hospital discharges to home health and postacute facility care: a national study. JAMA Pediatr. 2016;170(4):326–333.
- , , , et al. Bronchiolitis: clinical characteristics associated with hospitalization and length of stay. Pediatr Emerg Care. 2012;28(2):99–103.
- , , , , . The effect of transfers between health care facilities on costs and length of stay for pediatric burn patients. J Burn Care Res. 2015;36(1):178–183.
- , , , et al. Goal‐directed resuscitative interventions during pediatric interfacility transport. Crit Care Med. 2015;43(8):1692–1698.
- , , , et al. Inpatient growth and resource use in 28 children's hospitals: a longitudinal, multi‐institutional study. JAMA Pediatr. 2013;167(2):170–177.
- , , , et al. Children with complex chronic conditions in inpatient hospital settings in the united states. Pediatrics. 2010;126(4):647–655.
- , . Meeting the health care needs of persons with disabilities. Milbank Q. 2002;80(2):381–391.
- , , , et al. Effect of an enhanced medical home on serious illness and cost of care among high‐risk children with chronic illness: a randomized clinical trial. JAMA. 2014;312(24):2640–2648.
- , , , et al. Children with medical complexity and Medicaid: spending and cost savings. Health Aff Proj Hope. 2014;33(12):2199–2206.
- Children's Hospital Association. CARE Award. Available at: https://www.childrenshospitals.org/Programs‐and‐Services/Quality‐Improvement‐and‐Measurement/CARE‐Award. Accessed December 18, 2015.
- , , , et al. Hospital readmission and parent perceptions of their child's hospital discharge. Int J Qual Health Care. 2013;25(5):573–581.
- , , , et al. Weekend matters: Friday and Saturday admissions are associated with prolonged hospitalization of children. Clin Pediatr (Phila). 2013;52(9):875–878.
- , , , . Attributable cost and length of stay for central line‐associated bloodstream infections. Pediatrics. 2014;133(6):e1525–e1532.
- , , , et al. Effect of healthcare‐acquired infection on length of hospital stay and cost. Infect Control Hosp Epidemiol. 2007;28(3):280–292.
- , , , , . Hospital utilization and costs among children with influenza, 2003. Am J Prev Med. 2009;36(4):292–296.
- , , , . Charges and lengths of stay attributable to adverse patient‐care events using pediatric‐specific quality indicators: a multicenter study of freestanding children's hospitals. Pediatrics. 2008;121(6):e1653–e1659.
- , , , , . Variation in resource utilization for the management of uncomplicated community‐acquired pneumonia across community and children's hospitals. J Pediatr. 2014;165(3):585–591.
- , , , , . Variation and outcomes associated with direct hospital admission among children with pneumonia in the United States. JAMA Pediatr. 2014;168(9):829–836.
- , , , et al. Clinical Risk Groups (CRGs): a classification system for risk‐adjusted capitation‐based payment and health care management. Med Care. 2004;42(1):81–90.
- , , , et al. Identifying children with lifelong chronic conditions for care coordination by using hospital discharge data. Acad Pediatr. 2010;10(6):417–423.
- , , , , . Profile of medical charges for children by health status group and severity level in a Washington State Health Plan. Health Serv Res. 2004;39(1):73–89.
- , , , . Using medical billing data to evaluate chronically ill children over time. J Ambulatory Care Manage. 2006;29(4):283–290.
- , , , , , . Medical complexity and pediatric emergency department and inpatient utilization. Pediatrics. 2013;131(2):e559–e565.
- , , , , . Pediatric complex chronic conditions classification system version 2: updated for ICD‐10 and complex medical technology dependence and transplantation. BMC Pediatr. 2014;14:199.
- . Analyzing intensive care unit length of stay data: problems and possible solutions. Crit Care Med. 1997;25(9):1594–1600.
- , , , et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children's hospitals. JAMA. 2011;305(7):682–690.
- . Hospital readmissions and repeat emergency department visits among children with medical complexity: an integrative review. J Pediatr Nurs. 2013;28(4):316–339.
- , , , . Hospital readmission in children with complex chronic conditions discharged from subacute care. Hosp Pediatr. 2014;4(3):153–158.
- , , , et al. Preventing hospitalizations in children with medical complexity: a systematic review. Pediatrics. 2014;134(6):e1628–e1647.
- , , , . Hospital readmissions for newly discharged pediatric home mechanical ventilation patients. Pediatr Pulmonol. 2012;47(4):409–414.
- , , , et al. Clinical characteristics and outcomes of Medicare patients undergoing total hip arthroplasty, 1991–2008. JAMA. 2011;305(15):1560–1567.
- , , , et al. Trends in length of stay and short‐term outcomes among Medicare patients hospitalized for heart failure, 1993–2006. JAMA. 2010;303(21):2141–2147.
- U.S. Department of Health and Human Services. CMS Statistics 2013. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/CMS‐Statistics‐Reference‐Booklet/Downloads/CMS_Stats_2013_final.pdf. Published August 2013. Accessed October 6, 2015.
- Centers for Medicare and Medicaid Services. Evaluation of the premier hospital quality incentive demonstration. Available at: https://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/Premier_ExecSum_2010.pdf. Published March 3, 2009. Accessed September 18, 2015.
- , , , et al. A framework of pediatric hospital discharge care informed by legislation, research, and practice. JAMA Pediatr. 2014;168(10):955–962; quiz 965–966.
- , , , , . Parent and provider perspectives on pediatric readmissions: what can we learn about readiness for discharge? Hosp Pediatr. 2015;5(11):559–565.
- , . Preventing readmissions in children: how do we do that? Hosp Pediatr. 2015;5(11):602–604.
- , , . Pediatric post‐acute hospital care: striving for identity and value. Hosp Pediatr. 2015;5(10):548–551.
- , , , et al. Pediatric hospital discharges to home health and postacute facility care: a national study. JAMA Pediatr. 2016;170(4):326–333.
- , , , et al. Bronchiolitis: clinical characteristics associated with hospitalization and length of stay. Pediatr Emerg Care. 2012;28(2):99–103.
- , , , , . The effect of transfers between health care facilities on costs and length of stay for pediatric burn patients. J Burn Care Res. 2015;36(1):178–183.
- , , , et al. Goal‐directed resuscitative interventions during pediatric interfacility transport. Crit Care Med. 2015;43(8):1692–1698.
