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Variation in Printed Handoff Documents
Handoffs among hospital providers are highly error prone and can result in serious morbidity and mortality. Best practices for verbal handoffs have been described[1, 2, 3, 4] and include conducting verbal handoffs face to face, providing opportunities for questions, having the receiver perform a readback, as well as specific content recommendations including action items. Far less research has focused on best practices for printed handoff documents,[5, 6] despite the routine use of written handoff tools as a reference by on‐call physicians.[7, 8] Erroneous or outdated information on the written handoff can mislead on‐call providers, potentially leading to serious medical errors.
In their most basic form, printed handoff documents list patients for whom a provider is responsible. Typically, they also contain demographic information, reason for hospital admission, and a task list for each patient. They may also contain more detailed information on patient history, hospital course, and/or care plan, and may vary among specialties.[9] They come in various forms, ranging from index cards with handwritten notes, to word‐processor or spreadsheet documents, to printed documents that are autopopulated from the electronic health record (EHR).[2] Importantly, printed handoff documents supplement the verbal handoff by allowing receivers to follow along as patients are presented. The concurrent use of written and verbal handoffs may improve retention of clinical information as compared with either alone.[10, 11]
The Joint Commission requires an institutional approach to patient handoffs.[12] The requirements state that handoff communication solutions should take a standardized form, but they do not provide details regarding what data elements should be included in printed or verbal handoffs. Accreditation Council for Graduate Medical Education Common Program Requirements likewise require that residents must become competent in patient handoffs[13] but do not provide specific details or measurement tools. Absent widely accepted guidelines, decisions regarding which elements to include in printed handoff documents are currently made at an individual or institutional level.
The I‐PASS study is a federally funded multi‐institutional project that demonstrated a decrease in medical errors and preventable adverse events after implementation of a standardized resident handoff bundle.[14, 15] The I‐PASS Study Group developed a bundle of handoff interventions, beginning with a handoff and teamwork training program (based in part on TeamSTEPPS [Team Strategies and Tools to Enhance Performance and Patient Safety]),[16] a novel verbal mnemonic, I‐PASS (Illness Severity, Patient Summary, Action List, Situation Awareness and Contingency Planning, and Synthesis by Receiver),[17] and changes to the verbal handoff process, in addition to several other elements.
We hypothesized that developing a standardized printed handoff template would reinforce the handoff training and enhance the value of the verbal handoff process changes. Given the paucity of data on best printed handoff practices, however, we first conducted a needs assessment to identify which data elements were currently contained in printed handoffs across sites, and to allow an expert panel to make recommendations for best practices.
METHODS
I‐PASS Study sites included 9 pediatric residency programs at academic medical centers from across North America. Programs were identified through professional networks and invited to participate. The nonintensive care unit hospitalist services at these medical centers are primarily staffed by residents and medical students with attending supervision. At 1 site, nurse practitioners also participate in care. Additional details about study sites can be found in the study descriptions previously published.[14, 15] All sites received local institutional review board approval.
We began by inviting members of the I‐PASS Education Executive Committee (EEC)[14] to build a collective, comprehensive list of possible data elements for printed handoff documents. This committee included pediatric residency program directors, pediatric hospitalists, education researchers, health services researchers, and patient safety experts. We obtained sample handoff documents from pediatric hospitalist services at each of 9 institutions in the United States and Canada (with protected health information redacted). We reviewed these sample handoff documents to characterize their format and to determine what discrete data elements appeared in each site's printed handoff document. Presence or absence of each data element across sites was tabulated. We also queried sites to determine the feasibility of including elements that were not presently included.
Subsequently, I‐PASS site investigators led structured group interviews at participating sites to gather additional information about handoff practices at each site. These structured group interviews included diverse representation from residents, faculty, and residency program leadership, as well as hospitalists and medical students, to ensure the comprehensive acquisition of information regarding site‐specific characteristics. Each group provided answers to a standardized set of open‐ended questions that addressed current practices, handoff education, simulation use, team structure, and the nature of current written handoff tools, if applicable, at each site. One member of the structured group interview served as a scribe and created a document that summarized the content of the structured group interview meeting and answers to the standardized questions.
Consensus on Content
The initial data collection also included a multivote process[18] of the full I‐PASS EEC to help prioritize data elements. Committee members brainstormed a list of all possible data elements for a printed handoff document. Each member (n=14) was given 10 votes to distribute among the elements. Committee members could assign more than 1 vote to an element to emphasize its importance.
The results of this process as well as the current data elements included in each printed handoff tool were reviewed by a subgroup of the I‐PASS EEC. These expert panel members participated in a series of conference calls during which they tabulated categorical information, reviewed narrative comments, discussed existing evidence, and conducted simple content analysis to identify areas of concordance or discordance. Areas of discordance were discussed by the committee. Disagreements were resolved with group consensus with attention to published evidence or best practices, if available.
Elements were divided into those that were essential (unanimous consensus, no conflicting literature) and those that were recommended (majority supported inclusion of element, no conflicting literature). Ratings were assigned using the American College of Cardiology/American Heart Association framework for practice guidelines,[19] in which each element is assigned a classification (I=effective, II=conflicting evidence/opinion, III=not effective) and a level of evidence to support that classification (A=multiple large randomized controlled trials, B=single randomized trial, or nonrandomized studies, C=expert consensus).
The expert panel reached consensus, through active discussion, on a list of data elements that should be included in an ideal printed handoff document. Elements were chosen based on perceived importance, with attention to published best practices[1, 16] and the multivoting results. In making recommendations, consideration was given to whether data elements could be electronically imported into the printed handoff document from the EHR, or whether they would be entered manually. The potential for serious medical errors due to possible errors in manual entry of data was an important aspect of recommendations made. The list of candidate elements was then reviewed by a larger group of investigators from the I‐PASS Education Executive Committee and Coordinating Council for additional input.
The panel asked site investigators from each participating hospital to gather data on the feasibility of redesigning the printed handoff at that hospital to include each recommended element. Site investigators reported whether each element was already included, possible to include but not included currently, or not currently possible to include within that site's printed handoff tool. Site investigators also reported how data elements were populated in their handoff documents, with options including: (1) autopopulated from administrative data (eg, pharmacy‐entered medication list, demographic data entered by admitting office), (2) autoimported from physicians' free‐text entries elsewhere in the EHR (eg, progress notes), (3) free text entered specifically for the printed handoff, or (4) not applicable (element cannot be included).
RESULTS
Nine programs (100%) provided data on the structure and contents of their printed handoff documents. We found wide variation in structure across the 9 sites. Three sites used a word‐processorbased document that required manual entry of all data elements. The other 6 institutions had a direct link with the EHR to enable autopopulation of between 10 and 20 elements on the printed handoff document.
The content of written handoff documents, as well as the sources of data included in them (present or future), likewise varied substantially across sites (Table 1). Only 4 data elements (name, age, weight, and a list of medications) were universally included at all 9 sites. Among the 6 institutions that linked the printed handoff to the EHR, there was also substantial variation in which elements were autoimported. Only 7 elements were universally autoimported at these 6 sites: patient name, medical record number, room number, weight, date of birth, age, and date of admission. Two elements from the original brainstorming were not presently included in any sites' documents (emergency contact and primary language).
| Data Elements | Sites With Data Element Included at Initial Needs Assessment (Out of Nine Sites) | Data Source (Current or Anticipated) | ||
|---|---|---|---|---|
| Autoimported* | Manually Entered | Not Applicable | ||
| ||||
| Name | 9 | 6 | 3 | 0 |
| Medical record number | 8 | 6 | 3 | 0 |
| Room number | 8 | 6 | 3 | 0 |
| Allergies | 6 | 4 | 5 | 0 |
| Weight | 9 | 6 | 3 | 0 |
| Age | 9 | 6 | 3 | 0 |
| Date of birth | 6 | 6 | 3 | 0 |
| Admission date | 8 | 6 | 3 | 0 |
| Attending name | 5 | 4 | 5 | 0 |
| Team/service | 7 | 4 | 5 | 0 |
| Illness severity | 1 | 0 | 9 | 0 |
| Patient summary | 8 | 0 | 9 | 0 |
| Action items | 8 | 0 | 9 | 0 |
| Situation monitoring/contingency plan | 5 | 0 | 9 | 0 |
| Medication name | 9 | 4 | 5 | 0 |
| Medication name and dose/route/frequency | 4 | 4 | 5 | 0 |
| Code status | 2 | 2 | 7 | 0 |
| Labs | 6 | 5 | 4 | 0 |
| Access | 2 | 2 | 7 | 0 |
| Ins/outs | 2 | 4 | 4 | 1 |
| Primary language | 0 | 3 | 6 | 0 |
| Vital signs | 3 | 4 | 4 | 1 |
| Emergency contact | 0 | 2 | 7 | 0 |
| Primary care provider | 4 | 4 | 5 | 0 |
Nine institutions (100%) conducted structured group interviews, ranging in size from 4 to 27 individuals with a median of 5 participants. The documents containing information from each site were provided to the authors. The authors then tabulated categorical information, reviewed narrative comments to understand current institutional practices, and conducted simple content analysis to identify areas of concordance or discordance, particularly with respect to data elements and EHR usage. Based on the results of the printed handoff document review and structured group interviews, with additional perspectives provided by the I‐PASS EEC, the expert panel came to consensus on a list of 23 elements that should be included in printed handoff documents, including 15 essential data elements and 8 additional recommended elements (Table 2).
|
| Essential Elements |
| Patient identifiers |
| Patient name (class I, level of evidence C) |
| Medical record number (class I, level of evidence C) |
| Date of birth (class I, level of evidence C) |
| Hospital service identifiers |
| Attending name (class I, level of evidence C) |
| Team/service (class I, level of evidence C) |
| Room number (class I, level of evidence C) |
| Admission date (class I, level of evidence C) |
| Age (class I, level of evidence C) |
| Weight (class I, level of evidence C) |
| Illness severity (class I, level of evidence B)[20, 21] |
| Patient summary (class I, level of evidence B)[21, 22] |
| Action items (class I, level of evidence B) [21, 22] |
| Situation awareness/contingency planning (class I, level of evidence B) [21, 22] |
| Allergies (class I, level of evidence C) |
| Medications |
| Autopopulation of medications (class I, level of evidence B)[22, 23, 24] |
| Free‐text entry of medications (class IIa, level of evidence C) |
| Recommended elements |
| Primary language (class IIa, level of evidence C) |
| Emergency contact (class IIa, level of evidence C) |
| Primary care provider (class IIa, level of evidence C) |
| Code status (class IIb, level of evidence C) |
| Labs (class IIa, level of evidence C) |
| Access (class IIa, level of evidence C) |
| Ins/outs (class IIa, level of evidence C) |
| Vital signs (class IIa, level of evidence C) |
Evidence ratings[19] of these elements are included. Several elements are classified as I‐B (effective, nonrandomized studies) based on either studies of individual elements, or greater than 1 study of bundled elements that could reasonably be extrapolated. These include Illness severity,[20, 21] patient summary,[21, 22] action items[21, 22] (to do lists), situation awareness and contingency plan,[21, 22] and medications[22, 23, 24] with attention to importing from the EHR. Medications entered as free text were classified as IIa‐C because of risk and potential significance of errors; in particular there was concern that transcription errors, errors of omission, or errors of commission could potentially lead to patient harms. The remaining essential elements are classified as I‐C (effective, expert consensus). Of note, date of birth was specifically included as a patient identifier, distinct from age, which was felt to be useful as a descriptor (often within a one‐liner or as part of the patient summary).
The 8 recommended elements were elements for which there was not unanimous agreement on inclusion, but the majority of the panel felt they should be included. These elements were classified as IIa‐C, with 1 exception. Code status generated significant controversy among the group. After extensive discussion among the group and consideration of safety, supervision, educational, and pediatric‐specific considerations, all members of the group agreed on the categorization as a recommended element; it is classified as IIb‐C.
All members of the group agreed that data elements should be directly imported from the EHR whenever possible. Finally, members agreed that the elements that make up the I‐PASS mnemonic (illness severity, patient summary, action items, situation awareness/contingency planning) should be listed in that order whenever possible. A sample I‐PASS‐compliant printed handoff document is shown Figure 1.
DISCUSSION
We identified substantial variability in the structure and content of printed handoff documents used by 9 pediatric hospitalist teaching services, reflective of a lack of standardization. We found that institutional printed handoff documents shared some demographic elements (eg, name, room, medical record number) but also varied in clinical content (eg, vital signs, lab tests, code status). Our expert panel developed a list of 15 essential and 8 recommended data elements for printed handoff documents. Although this is a large number of fields, the majority of the essential fields were already included by most sites, and many are basic demographic identifiers. Illness severity is the 1 essential field that was not routinely included; however, including this type of overview is consistently recommended[2, 4] and supported by evidence,[20, 21] and contributes to building a shared mental model.[16] We recommend the categories of stable/watcher/unstable.[17]
Several prior single‐center studies have found that introducing a printed handoff document can lead to improvements in workflow, communication, and patient safety. In an early study, Petersen et al.[25] showed an association between use of a computerized sign‐out program and reduced odds of preventable adverse events during periods of cross‐coverage. Wayne et al.[26] reported fewer perceived inaccuracies in handoff documents as well as improved clarity at the time of transfer, supporting the role for standardization. Van Eaton et al.[27] demonstrated rapid uptake and desirability of a computerized handoff document, which combined autoimportation of information from an EHR with resident‐entered patient details, reflecting the importance of both data sources. In addition, they demonstrated improvements in both the rounding and sign‐out processes.[28]
Two studies specifically reported the increased use of specific fields after implementation. Payne et al. implemented a Web‐based handoff tool and documented significant increases in the number of handoffs containing problem lists, medication lists, and code status, accompanied by perceived improvements in quality of handoffs and fewer near‐miss events.[24] Starmer et al. found that introduction of a resident handoff bundle that included a printed handoff tool led to reduction in medical errors and adverse events.[22] The study group using the tool populated 11 data elements more often after implementation, and introduction of this printed handoff tool in particular was associated with reductions in written handoff miscommunications. Neither of these studies included subanalysis to indicate which data elements may have been most important.
In contrast to previous single‐institution studies, our recommendations for a printed handoff template come from evaluations of tools and discussions with front line providers across 9 institutions. We had substantial overlap with data elements recommended by Van Eaton et al.[27] However, there were several areas in which we did not have overlap with published templates including weight, ins/outs, primary language, emergency contact information, or primary care provider. Other published handoff tools have been highly specialized (eg, for cardiac intensive care) or included many fewer data elements than our group felt were essential. These differences may reflect the unique aspects of caring for pediatric patients (eg, need for weights) and the absence of defined protocols for many pediatric conditions. In addition, the level of detail needed for contingency planning may vary between teaching and nonteaching services.
Resident physicians may provide valuable information in the development of standardized handoff documents. Clark et al.,[29] at Virginia Mason University, utilized resident‐driven continuous quality improvement processes including real‐time feedback to implement an electronic template. They found that engagement of both senior leaders and front‐line users was an important component of their success in uptake. Our study utilized residents as essential members of structured group interviews to ensure that front‐line users' needs were represented as recommendations for a printed handoff tool template were developed.
As previously described,[17] our study group had identified several key data elements that should be included in verbal handoffs: illness severity, a patient summary, a discrete action list, situation awareness/contingency planning, and a synthesis by receiver. With consideration of the multivoting results as well as known best practices,[1, 4, 12] the expert panel for this study agreed that each of these elements should also be highlighted in the printed template to ensure consistency between the printed document and the verbal handoff, and to have each reinforce the other. On the printed handoff tool, the final S in the I‐PASS mnemonic (synthesis by receiver) cannot be prepopulated, but considering the importance of this step,[16, 30, 31, 32] it should be printed as synthesis by receiver to serve as a text‐reminder to both givers and receivers.
The panel also felt, however, that the printed handoff document should provide additional background information not routinely included in a verbal handoff. It should serve as a reference tool both at the time of verbal handoff and throughout the day and night, and therefore should include more comprehensive information than is necessary or appropriate to convey during the verbal handoff. We identified 10 data elements that are essential in a printed handoff document in addition to the I‐PASS elements (Table 2).
Patient demographic data elements, as well as team assignments and attending physician, were uniformly supported for inclusion. The medication list was viewed as essential; however, the panel also recognized the potential for medical errors due to inaccuracies in the medication list. In particular, there was concern that including all fields of a medication order (drug, dose, route, frequency) would result in handoffs containing a high proportion of inaccurate information, particularly for complex patients whose medication regimens may vary over the course of hospitalization. Therefore, the panel agreed that if medication lists were entered manually, then only the medication name should be included as they did not wish to perpetuate inaccurate or potentially harmful information. If medication lists were autoimported from an EHR, then they should include drug name, dose, route, and frequency if possible.
In the I‐PASS study,[15] all institutions implemented printed handoff documents that included fields for the essential data elements. After implementation, there was a significant increase in completion of all essential fields. Although there is limited evidence to support any individual data element, increased usage of these elements was associated with the overall study finding of decreased rates of medical errors and preventable adverse events.
EHRs have the potential to help standardize printed handoff documents[5, 6, 33, 34, 35]; all participants in our study agreed that printed handoff documents should ideally be linked with the EHR and should autoimport data wherever appropriate. Manually populated (eg, word processor‐ or spreadsheet‐based) handoff tools have important limitations, particularly related to the potential for typographical errors as well as accidental omission of data fields, and lead to unnecessary duplication of work (eg, re‐entering data already included in a progress note) that can waste providers' time. It was also acknowledged that word processor‐ or spreadsheet‐based documents may have flexibility that is lacking in EHR‐based handoff documents. For example, formatting can more easily be adjusted to increase the number of patients per printed page. As technology advances, printed documents may be phased out in favor of EHR‐based on‐screen reports, which by their nature would be more accurate due to real‐time autoupdates.
In making recommendations about essential versus recommended items for inclusion in the printed handoff template, the only data element that generated controversy among our experts was code status. Some felt that it should be included as an essential element, whereas others did not. We believe that this was unique to our practice in pediatric hospital ward settings, as codes in most pediatric ward settings are rare. Among the concerns expressed with including code status for all patients were that residents might assume patients were full‐code without verifying. The potential inaccuracy created by this might have severe implications. Alternatively, residents might feel obligated to have code discussions with all patients regardless of severity of illness, which may be inappropriate in a pediatric population. Several educators expressed concerns about trainees having unsupervised code‐status conversations with families of pediatric patients. Conversely, although codes are rare in pediatric ward settings, concerns were raised that not including code status could be problematic during these rare but critically important events. Other fields, such as weight, might have less relevance for an adult population in which emergency drug doses are standardized.
Limitations
Our study has several limitations. We only collected data from hospitalist services at pediatric sites. It is likely that providers in other specialties would have specific data elements they felt were essential (eg, postoperative day, code status). Our methodology was expert consensus based, driven by data collection from sites that were already participating in the I‐PASS study. Although the I‐PASS study demonstrated decreased rates of medical errors and preventable adverse events with inclusion of these data elements as part of a bundle, future research will be required to evaluate whether some of these items are more important than others in improving written communication and ultimately patient safety. In spite of these limitations, our work represents an important starting point for the development of standards for written handoff documents that should be used in patient handoffs, particularly those generated from EHRs.
CONCLUSIONS
In this article we describe the results of a needs assessment that informed expert consensus‐based recommendations for data elements to include in a printed handoff document. We recommend that pediatric programs include the elements identified as part of a standardized written handoff tool. Although many of these elements are also applicable to other specialties, future work should be conducted to adapt the printed handoff document elements described here for use in other specialties and settings. Future studies should work to validate the importance of these elements, studying the manner in which their inclusion affects the quality of written handoffs, and ultimately patient safety.
Acknowledgements
Members of the I‐PASS Study Education Executive Committee who contributed to this manuscript include: Boston Children's Hospital/Harvard Medical School (primary site) (Christopher P. Landrigan, MD, MPH, Elizabeth L. Noble, BA. Theodore C. Sectish, MD. Lisa L. Tse, BA). Cincinnati Children's Hospital Medical Center/University of Cincinnati College of Medicine (Jennifer K. O'Toole, MD, MEd). Doernbecher Children's Hospital/Oregon Health and Science University (Amy J. Starmer, MD, MPH). Hospital for Sick Children/University of Toronto (Zia Bismilla, MD. Maitreya Coffey, MD). Lucile Packard Children's Hospital/Stanford University (Lauren A. Destino, MD. Jennifer L. Everhart, MD. Shilpa J. Patel, MD [currently at Kapi'olani Children's Hospital/University of Hawai'i School of Medicine]). National Capital Consortium (Jennifer H. Hepps, MD. Joseph O. Lopreiato, MD, MPH. Clifton E. Yu, MD). Primary Children's Medical Center/University of Utah (James F. Bale, Jr., MD. Adam T. Stevenson, MD). St. Louis Children's Hospital/Washington University (F. Sessions Cole, MD). St. Christopher's Hospital for Children/Drexel University College of Medicine (Sharon Calaman, MD. Nancy D. Spector, MD). Benioff Children's Hospital/University of California San Francisco School of Medicine (Glenn Rosenbluth, MD. Daniel C. West, MD).
Additional I‐PASS Study Group members who contributed to this manuscript include April D. Allen, MPA, MA (Heller School for Social Policy and Management, Brandeis University, previously affiliated with Boston Children's Hospital), Madelyn D. Kahana, MD (The Children's Hospital at Montefiore/Albert Einstein College of Medicine, previously affiliated with Lucile Packard Children's Hospital/Stanford University), Robert S. McGregor, MD (Akron Children's Hospital/Northeast Ohio Medical University, previously affiliated with St. Christopher's Hospital for Children/Drexel University), and John S. Webster, MD, MBA, MS (Webster Healthcare Consulting Inc., formerly of the Department of Defense).
Members of the I‐PASS Study Group include individuals from the institutions listed below as follows: Boston Children's Hospital/Harvard Medical School (primary site): April D. Allen, MPA, MA (currently at Heller School for Social Policy and Management, Brandeis University), Angela M. Feraco, MD, Christopher P. Landrigan, MD, MPH, Elizabeth L. Noble, BA, Theodore C. Sectish, MD, Lisa L. Tse, BA. Brigham and Women's Hospital (data coordinating center): Anuj K. Dalal, MD, Carol A. Keohane, BSN, RN, Stuart Lipsitz, PhD, Jeffrey M. Rothschild, MD, MPH, Matt F. Wien, BS, Catherine S. Yoon, MS, Katherine R. Zigmont, BSN, RN. Cincinnati Children's Hospital Medical Center/University of Cincinnati College of Medicine: Javier Gonzalez del Rey, MD, MEd, Jennifer K. O'Toole, MD, MEd, Lauren G. Solan, MD. Doernbecher Children's Hospital/Oregon Health and Science University: Megan E. Aylor, MD, Amy J. Starmer, MD, MPH, Windy Stevenson, MD, Tamara Wagner, MD. Hospital for Sick Children/University of Toronto: Zia Bismilla, MD, Maitreya Coffey, MD, Sanjay Mahant, MD, MSc. Lucile Packard Children's Hospital/Stanford University: Rebecca L. Blankenburg, MD, MPH, Lauren A. Destino, MD, Jennifer L. Everhart, MD, Madelyn Kahana, MD, Shilpa J. Patel, MD (currently at Kapi'olani Children's Hospital/University of Hawaii School of Medicine). National Capital Consortium: Jennifer H. Hepps, MD, Joseph O. Lopreiato, MD, MPH, Clifton E. Yu, MD. Primary Children's Hospital/University of Utah: James F. Bale, Jr., MD, Jaime Blank Spackman, MSHS, CCRP, Rajendu Srivastava, MD, FRCP(C), MPH, Adam Stevenson, MD. St. Louis Children's Hospital/Washington University: Kevin Barton, MD, Kathleen Berchelmann, MD, F. Sessions Cole, MD, Christine Hrach, MD, Kyle S. Schultz, MD, Michael P. Turmelle, MD, Andrew J. White, MD. St. Christopher's Hospital for Children/Drexel University: Sharon Calaman, MD, Bronwyn D. Carlson, MD, Robert S. McGregor, MD (currently at Akron Children's Hospital/Northeast Ohio Medical University), Vahideh Nilforoshan, MD, Nancy D. Spector, MD. and Benioff Children's Hospital/University of California San Francisco School of Medicine: Glenn Rosenbluth, MD, Daniel C. West, MD. Dorene Balmer, PhD, RD, Carol L. Carraccio, MD, MA, Laura Degnon, CAE, and David McDonald, and Alan Schwartz PhD serve the I‐PASS Study Group as part of the IIPE. Karen M. Wilson, MD, MPH serves the I‐PASS Study Group as part of the advisory board from the PRIS Executive Council. John Webster served the I‐PASS Study Group and Education Executive Committee as a representative from TeamSTEPPS.
Disclosures: The I‐PASS Study was primarily supported by the US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation (1R18AE000029‐01). The opinions and conclusions expressed herein are solely those of the author(s) and should not be constructed as representing the opinions or policy of any agency of the federal government. Developed with input from the Initiative for Innovation in Pediatric Education and the Pediatric Research in Inpatient Settings Network (supported by the Children's Hospital Association, the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine). A. J. S. was supported by the Agency for Healthcare Research and Quality/Oregon Comparative Effectiveness Research K12 Program (1K12HS019456‐01). Additional funding for the I‐PASS Study was provided by the Medical Research Foundation of Oregon, Physician Services Incorporated Foundation (Ontario, Canada), and Pfizer (unrestricted medical education grant to N.D.S.). C.P.L, A.J.S. were supported by the Oregon Comparative Effectiveness Research K12 Program (1K12HS019456 from the Agency for Healthcare Research and Quality). A.J.S. was also supported by the Medical Research Foundation of Oregon. The authors report no conflicts of interest.
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- , , , , . Content overlap in nurse and physician handoff artifacts and the potential role of electronic health records: a systematic review. J Biomed Inform. 2011;44(4):704–712.
- , , , . Clinical summarization capabilities of commercially‐available and internally‐developed electronic health records. Appl Clin Inform. 2012;3(1):80–93.
- , . An analysis and recommendations for multidisciplinary computerized handoff applications in hospitals. AMIA Annu Symp Proc. 2011;2011:588–597.
Handoffs among hospital providers are highly error prone and can result in serious morbidity and mortality. Best practices for verbal handoffs have been described[1, 2, 3, 4] and include conducting verbal handoffs face to face, providing opportunities for questions, having the receiver perform a readback, as well as specific content recommendations including action items. Far less research has focused on best practices for printed handoff documents,[5, 6] despite the routine use of written handoff tools as a reference by on‐call physicians.[7, 8] Erroneous or outdated information on the written handoff can mislead on‐call providers, potentially leading to serious medical errors.
In their most basic form, printed handoff documents list patients for whom a provider is responsible. Typically, they also contain demographic information, reason for hospital admission, and a task list for each patient. They may also contain more detailed information on patient history, hospital course, and/or care plan, and may vary among specialties.[9] They come in various forms, ranging from index cards with handwritten notes, to word‐processor or spreadsheet documents, to printed documents that are autopopulated from the electronic health record (EHR).[2] Importantly, printed handoff documents supplement the verbal handoff by allowing receivers to follow along as patients are presented. The concurrent use of written and verbal handoffs may improve retention of clinical information as compared with either alone.[10, 11]
The Joint Commission requires an institutional approach to patient handoffs.[12] The requirements state that handoff communication solutions should take a standardized form, but they do not provide details regarding what data elements should be included in printed or verbal handoffs. Accreditation Council for Graduate Medical Education Common Program Requirements likewise require that residents must become competent in patient handoffs[13] but do not provide specific details or measurement tools. Absent widely accepted guidelines, decisions regarding which elements to include in printed handoff documents are currently made at an individual or institutional level.
The I‐PASS study is a federally funded multi‐institutional project that demonstrated a decrease in medical errors and preventable adverse events after implementation of a standardized resident handoff bundle.[14, 15] The I‐PASS Study Group developed a bundle of handoff interventions, beginning with a handoff and teamwork training program (based in part on TeamSTEPPS [Team Strategies and Tools to Enhance Performance and Patient Safety]),[16] a novel verbal mnemonic, I‐PASS (Illness Severity, Patient Summary, Action List, Situation Awareness and Contingency Planning, and Synthesis by Receiver),[17] and changes to the verbal handoff process, in addition to several other elements.
We hypothesized that developing a standardized printed handoff template would reinforce the handoff training and enhance the value of the verbal handoff process changes. Given the paucity of data on best printed handoff practices, however, we first conducted a needs assessment to identify which data elements were currently contained in printed handoffs across sites, and to allow an expert panel to make recommendations for best practices.
METHODS
I‐PASS Study sites included 9 pediatric residency programs at academic medical centers from across North America. Programs were identified through professional networks and invited to participate. The nonintensive care unit hospitalist services at these medical centers are primarily staffed by residents and medical students with attending supervision. At 1 site, nurse practitioners also participate in care. Additional details about study sites can be found in the study descriptions previously published.[14, 15] All sites received local institutional review board approval.
We began by inviting members of the I‐PASS Education Executive Committee (EEC)[14] to build a collective, comprehensive list of possible data elements for printed handoff documents. This committee included pediatric residency program directors, pediatric hospitalists, education researchers, health services researchers, and patient safety experts. We obtained sample handoff documents from pediatric hospitalist services at each of 9 institutions in the United States and Canada (with protected health information redacted). We reviewed these sample handoff documents to characterize their format and to determine what discrete data elements appeared in each site's printed handoff document. Presence or absence of each data element across sites was tabulated. We also queried sites to determine the feasibility of including elements that were not presently included.
Subsequently, I‐PASS site investigators led structured group interviews at participating sites to gather additional information about handoff practices at each site. These structured group interviews included diverse representation from residents, faculty, and residency program leadership, as well as hospitalists and medical students, to ensure the comprehensive acquisition of information regarding site‐specific characteristics. Each group provided answers to a standardized set of open‐ended questions that addressed current practices, handoff education, simulation use, team structure, and the nature of current written handoff tools, if applicable, at each site. One member of the structured group interview served as a scribe and created a document that summarized the content of the structured group interview meeting and answers to the standardized questions.
Consensus on Content
The initial data collection also included a multivote process[18] of the full I‐PASS EEC to help prioritize data elements. Committee members brainstormed a list of all possible data elements for a printed handoff document. Each member (n=14) was given 10 votes to distribute among the elements. Committee members could assign more than 1 vote to an element to emphasize its importance.
The results of this process as well as the current data elements included in each printed handoff tool were reviewed by a subgroup of the I‐PASS EEC. These expert panel members participated in a series of conference calls during which they tabulated categorical information, reviewed narrative comments, discussed existing evidence, and conducted simple content analysis to identify areas of concordance or discordance. Areas of discordance were discussed by the committee. Disagreements were resolved with group consensus with attention to published evidence or best practices, if available.
Elements were divided into those that were essential (unanimous consensus, no conflicting literature) and those that were recommended (majority supported inclusion of element, no conflicting literature). Ratings were assigned using the American College of Cardiology/American Heart Association framework for practice guidelines,[19] in which each element is assigned a classification (I=effective, II=conflicting evidence/opinion, III=not effective) and a level of evidence to support that classification (A=multiple large randomized controlled trials, B=single randomized trial, or nonrandomized studies, C=expert consensus).
The expert panel reached consensus, through active discussion, on a list of data elements that should be included in an ideal printed handoff document. Elements were chosen based on perceived importance, with attention to published best practices[1, 16] and the multivoting results. In making recommendations, consideration was given to whether data elements could be electronically imported into the printed handoff document from the EHR, or whether they would be entered manually. The potential for serious medical errors due to possible errors in manual entry of data was an important aspect of recommendations made. The list of candidate elements was then reviewed by a larger group of investigators from the I‐PASS Education Executive Committee and Coordinating Council for additional input.
The panel asked site investigators from each participating hospital to gather data on the feasibility of redesigning the printed handoff at that hospital to include each recommended element. Site investigators reported whether each element was already included, possible to include but not included currently, or not currently possible to include within that site's printed handoff tool. Site investigators also reported how data elements were populated in their handoff documents, with options including: (1) autopopulated from administrative data (eg, pharmacy‐entered medication list, demographic data entered by admitting office), (2) autoimported from physicians' free‐text entries elsewhere in the EHR (eg, progress notes), (3) free text entered specifically for the printed handoff, or (4) not applicable (element cannot be included).
RESULTS
Nine programs (100%) provided data on the structure and contents of their printed handoff documents. We found wide variation in structure across the 9 sites. Three sites used a word‐processorbased document that required manual entry of all data elements. The other 6 institutions had a direct link with the EHR to enable autopopulation of between 10 and 20 elements on the printed handoff document.
The content of written handoff documents, as well as the sources of data included in them (present or future), likewise varied substantially across sites (Table 1). Only 4 data elements (name, age, weight, and a list of medications) were universally included at all 9 sites. Among the 6 institutions that linked the printed handoff to the EHR, there was also substantial variation in which elements were autoimported. Only 7 elements were universally autoimported at these 6 sites: patient name, medical record number, room number, weight, date of birth, age, and date of admission. Two elements from the original brainstorming were not presently included in any sites' documents (emergency contact and primary language).
| Data Elements | Sites With Data Element Included at Initial Needs Assessment (Out of Nine Sites) | Data Source (Current or Anticipated) | ||
|---|---|---|---|---|
| Autoimported* | Manually Entered | Not Applicable | ||
| ||||
| Name | 9 | 6 | 3 | 0 |
| Medical record number | 8 | 6 | 3 | 0 |
| Room number | 8 | 6 | 3 | 0 |
| Allergies | 6 | 4 | 5 | 0 |
| Weight | 9 | 6 | 3 | 0 |
| Age | 9 | 6 | 3 | 0 |
| Date of birth | 6 | 6 | 3 | 0 |
| Admission date | 8 | 6 | 3 | 0 |
| Attending name | 5 | 4 | 5 | 0 |
| Team/service | 7 | 4 | 5 | 0 |
| Illness severity | 1 | 0 | 9 | 0 |
| Patient summary | 8 | 0 | 9 | 0 |
| Action items | 8 | 0 | 9 | 0 |
| Situation monitoring/contingency plan | 5 | 0 | 9 | 0 |
| Medication name | 9 | 4 | 5 | 0 |
| Medication name and dose/route/frequency | 4 | 4 | 5 | 0 |
| Code status | 2 | 2 | 7 | 0 |
| Labs | 6 | 5 | 4 | 0 |
| Access | 2 | 2 | 7 | 0 |
| Ins/outs | 2 | 4 | 4 | 1 |
| Primary language | 0 | 3 | 6 | 0 |
| Vital signs | 3 | 4 | 4 | 1 |
| Emergency contact | 0 | 2 | 7 | 0 |
| Primary care provider | 4 | 4 | 5 | 0 |
Nine institutions (100%) conducted structured group interviews, ranging in size from 4 to 27 individuals with a median of 5 participants. The documents containing information from each site were provided to the authors. The authors then tabulated categorical information, reviewed narrative comments to understand current institutional practices, and conducted simple content analysis to identify areas of concordance or discordance, particularly with respect to data elements and EHR usage. Based on the results of the printed handoff document review and structured group interviews, with additional perspectives provided by the I‐PASS EEC, the expert panel came to consensus on a list of 23 elements that should be included in printed handoff documents, including 15 essential data elements and 8 additional recommended elements (Table 2).
|
| Essential Elements |
| Patient identifiers |
| Patient name (class I, level of evidence C) |
| Medical record number (class I, level of evidence C) |
| Date of birth (class I, level of evidence C) |
| Hospital service identifiers |
| Attending name (class I, level of evidence C) |
| Team/service (class I, level of evidence C) |
| Room number (class I, level of evidence C) |
| Admission date (class I, level of evidence C) |
| Age (class I, level of evidence C) |
| Weight (class I, level of evidence C) |
| Illness severity (class I, level of evidence B)[20, 21] |
| Patient summary (class I, level of evidence B)[21, 22] |
| Action items (class I, level of evidence B) [21, 22] |
| Situation awareness/contingency planning (class I, level of evidence B) [21, 22] |
| Allergies (class I, level of evidence C) |
| Medications |
| Autopopulation of medications (class I, level of evidence B)[22, 23, 24] |
| Free‐text entry of medications (class IIa, level of evidence C) |
| Recommended elements |
| Primary language (class IIa, level of evidence C) |
| Emergency contact (class IIa, level of evidence C) |
| Primary care provider (class IIa, level of evidence C) |
| Code status (class IIb, level of evidence C) |
| Labs (class IIa, level of evidence C) |
| Access (class IIa, level of evidence C) |
| Ins/outs (class IIa, level of evidence C) |
| Vital signs (class IIa, level of evidence C) |
Evidence ratings[19] of these elements are included. Several elements are classified as I‐B (effective, nonrandomized studies) based on either studies of individual elements, or greater than 1 study of bundled elements that could reasonably be extrapolated. These include Illness severity,[20, 21] patient summary,[21, 22] action items[21, 22] (to do lists), situation awareness and contingency plan,[21, 22] and medications[22, 23, 24] with attention to importing from the EHR. Medications entered as free text were classified as IIa‐C because of risk and potential significance of errors; in particular there was concern that transcription errors, errors of omission, or errors of commission could potentially lead to patient harms. The remaining essential elements are classified as I‐C (effective, expert consensus). Of note, date of birth was specifically included as a patient identifier, distinct from age, which was felt to be useful as a descriptor (often within a one‐liner or as part of the patient summary).
The 8 recommended elements were elements for which there was not unanimous agreement on inclusion, but the majority of the panel felt they should be included. These elements were classified as IIa‐C, with 1 exception. Code status generated significant controversy among the group. After extensive discussion among the group and consideration of safety, supervision, educational, and pediatric‐specific considerations, all members of the group agreed on the categorization as a recommended element; it is classified as IIb‐C.
All members of the group agreed that data elements should be directly imported from the EHR whenever possible. Finally, members agreed that the elements that make up the I‐PASS mnemonic (illness severity, patient summary, action items, situation awareness/contingency planning) should be listed in that order whenever possible. A sample I‐PASS‐compliant printed handoff document is shown Figure 1.
DISCUSSION
We identified substantial variability in the structure and content of printed handoff documents used by 9 pediatric hospitalist teaching services, reflective of a lack of standardization. We found that institutional printed handoff documents shared some demographic elements (eg, name, room, medical record number) but also varied in clinical content (eg, vital signs, lab tests, code status). Our expert panel developed a list of 15 essential and 8 recommended data elements for printed handoff documents. Although this is a large number of fields, the majority of the essential fields were already included by most sites, and many are basic demographic identifiers. Illness severity is the 1 essential field that was not routinely included; however, including this type of overview is consistently recommended[2, 4] and supported by evidence,[20, 21] and contributes to building a shared mental model.[16] We recommend the categories of stable/watcher/unstable.[17]
Several prior single‐center studies have found that introducing a printed handoff document can lead to improvements in workflow, communication, and patient safety. In an early study, Petersen et al.[25] showed an association between use of a computerized sign‐out program and reduced odds of preventable adverse events during periods of cross‐coverage. Wayne et al.[26] reported fewer perceived inaccuracies in handoff documents as well as improved clarity at the time of transfer, supporting the role for standardization. Van Eaton et al.[27] demonstrated rapid uptake and desirability of a computerized handoff document, which combined autoimportation of information from an EHR with resident‐entered patient details, reflecting the importance of both data sources. In addition, they demonstrated improvements in both the rounding and sign‐out processes.[28]
Two studies specifically reported the increased use of specific fields after implementation. Payne et al. implemented a Web‐based handoff tool and documented significant increases in the number of handoffs containing problem lists, medication lists, and code status, accompanied by perceived improvements in quality of handoffs and fewer near‐miss events.[24] Starmer et al. found that introduction of a resident handoff bundle that included a printed handoff tool led to reduction in medical errors and adverse events.[22] The study group using the tool populated 11 data elements more often after implementation, and introduction of this printed handoff tool in particular was associated with reductions in written handoff miscommunications. Neither of these studies included subanalysis to indicate which data elements may have been most important.
In contrast to previous single‐institution studies, our recommendations for a printed handoff template come from evaluations of tools and discussions with front line providers across 9 institutions. We had substantial overlap with data elements recommended by Van Eaton et al.[27] However, there were several areas in which we did not have overlap with published templates including weight, ins/outs, primary language, emergency contact information, or primary care provider. Other published handoff tools have been highly specialized (eg, for cardiac intensive care) or included many fewer data elements than our group felt were essential. These differences may reflect the unique aspects of caring for pediatric patients (eg, need for weights) and the absence of defined protocols for many pediatric conditions. In addition, the level of detail needed for contingency planning may vary between teaching and nonteaching services.
Resident physicians may provide valuable information in the development of standardized handoff documents. Clark et al.,[29] at Virginia Mason University, utilized resident‐driven continuous quality improvement processes including real‐time feedback to implement an electronic template. They found that engagement of both senior leaders and front‐line users was an important component of their success in uptake. Our study utilized residents as essential members of structured group interviews to ensure that front‐line users' needs were represented as recommendations for a printed handoff tool template were developed.
As previously described,[17] our study group had identified several key data elements that should be included in verbal handoffs: illness severity, a patient summary, a discrete action list, situation awareness/contingency planning, and a synthesis by receiver. With consideration of the multivoting results as well as known best practices,[1, 4, 12] the expert panel for this study agreed that each of these elements should also be highlighted in the printed template to ensure consistency between the printed document and the verbal handoff, and to have each reinforce the other. On the printed handoff tool, the final S in the I‐PASS mnemonic (synthesis by receiver) cannot be prepopulated, but considering the importance of this step,[16, 30, 31, 32] it should be printed as synthesis by receiver to serve as a text‐reminder to both givers and receivers.
The panel also felt, however, that the printed handoff document should provide additional background information not routinely included in a verbal handoff. It should serve as a reference tool both at the time of verbal handoff and throughout the day and night, and therefore should include more comprehensive information than is necessary or appropriate to convey during the verbal handoff. We identified 10 data elements that are essential in a printed handoff document in addition to the I‐PASS elements (Table 2).
Patient demographic data elements, as well as team assignments and attending physician, were uniformly supported for inclusion. The medication list was viewed as essential; however, the panel also recognized the potential for medical errors due to inaccuracies in the medication list. In particular, there was concern that including all fields of a medication order (drug, dose, route, frequency) would result in handoffs containing a high proportion of inaccurate information, particularly for complex patients whose medication regimens may vary over the course of hospitalization. Therefore, the panel agreed that if medication lists were entered manually, then only the medication name should be included as they did not wish to perpetuate inaccurate or potentially harmful information. If medication lists were autoimported from an EHR, then they should include drug name, dose, route, and frequency if possible.
In the I‐PASS study,[15] all institutions implemented printed handoff documents that included fields for the essential data elements. After implementation, there was a significant increase in completion of all essential fields. Although there is limited evidence to support any individual data element, increased usage of these elements was associated with the overall study finding of decreased rates of medical errors and preventable adverse events.
EHRs have the potential to help standardize printed handoff documents[5, 6, 33, 34, 35]; all participants in our study agreed that printed handoff documents should ideally be linked with the EHR and should autoimport data wherever appropriate. Manually populated (eg, word processor‐ or spreadsheet‐based) handoff tools have important limitations, particularly related to the potential for typographical errors as well as accidental omission of data fields, and lead to unnecessary duplication of work (eg, re‐entering data already included in a progress note) that can waste providers' time. It was also acknowledged that word processor‐ or spreadsheet‐based documents may have flexibility that is lacking in EHR‐based handoff documents. For example, formatting can more easily be adjusted to increase the number of patients per printed page. As technology advances, printed documents may be phased out in favor of EHR‐based on‐screen reports, which by their nature would be more accurate due to real‐time autoupdates.
In making recommendations about essential versus recommended items for inclusion in the printed handoff template, the only data element that generated controversy among our experts was code status. Some felt that it should be included as an essential element, whereas others did not. We believe that this was unique to our practice in pediatric hospital ward settings, as codes in most pediatric ward settings are rare. Among the concerns expressed with including code status for all patients were that residents might assume patients were full‐code without verifying. The potential inaccuracy created by this might have severe implications. Alternatively, residents might feel obligated to have code discussions with all patients regardless of severity of illness, which may be inappropriate in a pediatric population. Several educators expressed concerns about trainees having unsupervised code‐status conversations with families of pediatric patients. Conversely, although codes are rare in pediatric ward settings, concerns were raised that not including code status could be problematic during these rare but critically important events. Other fields, such as weight, might have less relevance for an adult population in which emergency drug doses are standardized.
Limitations
Our study has several limitations. We only collected data from hospitalist services at pediatric sites. It is likely that providers in other specialties would have specific data elements they felt were essential (eg, postoperative day, code status). Our methodology was expert consensus based, driven by data collection from sites that were already participating in the I‐PASS study. Although the I‐PASS study demonstrated decreased rates of medical errors and preventable adverse events with inclusion of these data elements as part of a bundle, future research will be required to evaluate whether some of these items are more important than others in improving written communication and ultimately patient safety. In spite of these limitations, our work represents an important starting point for the development of standards for written handoff documents that should be used in patient handoffs, particularly those generated from EHRs.
CONCLUSIONS
In this article we describe the results of a needs assessment that informed expert consensus‐based recommendations for data elements to include in a printed handoff document. We recommend that pediatric programs include the elements identified as part of a standardized written handoff tool. Although many of these elements are also applicable to other specialties, future work should be conducted to adapt the printed handoff document elements described here for use in other specialties and settings. Future studies should work to validate the importance of these elements, studying the manner in which their inclusion affects the quality of written handoffs, and ultimately patient safety.
Acknowledgements
Members of the I‐PASS Study Education Executive Committee who contributed to this manuscript include: Boston Children's Hospital/Harvard Medical School (primary site) (Christopher P. Landrigan, MD, MPH, Elizabeth L. Noble, BA. Theodore C. Sectish, MD. Lisa L. Tse, BA). Cincinnati Children's Hospital Medical Center/University of Cincinnati College of Medicine (Jennifer K. O'Toole, MD, MEd). Doernbecher Children's Hospital/Oregon Health and Science University (Amy J. Starmer, MD, MPH). Hospital for Sick Children/University of Toronto (Zia Bismilla, MD. Maitreya Coffey, MD). Lucile Packard Children's Hospital/Stanford University (Lauren A. Destino, MD. Jennifer L. Everhart, MD. Shilpa J. Patel, MD [currently at Kapi'olani Children's Hospital/University of Hawai'i School of Medicine]). National Capital Consortium (Jennifer H. Hepps, MD. Joseph O. Lopreiato, MD, MPH. Clifton E. Yu, MD). Primary Children's Medical Center/University of Utah (James F. Bale, Jr., MD. Adam T. Stevenson, MD). St. Louis Children's Hospital/Washington University (F. Sessions Cole, MD). St. Christopher's Hospital for Children/Drexel University College of Medicine (Sharon Calaman, MD. Nancy D. Spector, MD). Benioff Children's Hospital/University of California San Francisco School of Medicine (Glenn Rosenbluth, MD. Daniel C. West, MD).
Additional I‐PASS Study Group members who contributed to this manuscript include April D. Allen, MPA, MA (Heller School for Social Policy and Management, Brandeis University, previously affiliated with Boston Children's Hospital), Madelyn D. Kahana, MD (The Children's Hospital at Montefiore/Albert Einstein College of Medicine, previously affiliated with Lucile Packard Children's Hospital/Stanford University), Robert S. McGregor, MD (Akron Children's Hospital/Northeast Ohio Medical University, previously affiliated with St. Christopher's Hospital for Children/Drexel University), and John S. Webster, MD, MBA, MS (Webster Healthcare Consulting Inc., formerly of the Department of Defense).
Members of the I‐PASS Study Group include individuals from the institutions listed below as follows: Boston Children's Hospital/Harvard Medical School (primary site): April D. Allen, MPA, MA (currently at Heller School for Social Policy and Management, Brandeis University), Angela M. Feraco, MD, Christopher P. Landrigan, MD, MPH, Elizabeth L. Noble, BA, Theodore C. Sectish, MD, Lisa L. Tse, BA. Brigham and Women's Hospital (data coordinating center): Anuj K. Dalal, MD, Carol A. Keohane, BSN, RN, Stuart Lipsitz, PhD, Jeffrey M. Rothschild, MD, MPH, Matt F. Wien, BS, Catherine S. Yoon, MS, Katherine R. Zigmont, BSN, RN. Cincinnati Children's Hospital Medical Center/University of Cincinnati College of Medicine: Javier Gonzalez del Rey, MD, MEd, Jennifer K. O'Toole, MD, MEd, Lauren G. Solan, MD. Doernbecher Children's Hospital/Oregon Health and Science University: Megan E. Aylor, MD, Amy J. Starmer, MD, MPH, Windy Stevenson, MD, Tamara Wagner, MD. Hospital for Sick Children/University of Toronto: Zia Bismilla, MD, Maitreya Coffey, MD, Sanjay Mahant, MD, MSc. Lucile Packard Children's Hospital/Stanford University: Rebecca L. Blankenburg, MD, MPH, Lauren A. Destino, MD, Jennifer L. Everhart, MD, Madelyn Kahana, MD, Shilpa J. Patel, MD (currently at Kapi'olani Children's Hospital/University of Hawaii School of Medicine). National Capital Consortium: Jennifer H. Hepps, MD, Joseph O. Lopreiato, MD, MPH, Clifton E. Yu, MD. Primary Children's Hospital/University of Utah: James F. Bale, Jr., MD, Jaime Blank Spackman, MSHS, CCRP, Rajendu Srivastava, MD, FRCP(C), MPH, Adam Stevenson, MD. St. Louis Children's Hospital/Washington University: Kevin Barton, MD, Kathleen Berchelmann, MD, F. Sessions Cole, MD, Christine Hrach, MD, Kyle S. Schultz, MD, Michael P. Turmelle, MD, Andrew J. White, MD. St. Christopher's Hospital for Children/Drexel University: Sharon Calaman, MD, Bronwyn D. Carlson, MD, Robert S. McGregor, MD (currently at Akron Children's Hospital/Northeast Ohio Medical University), Vahideh Nilforoshan, MD, Nancy D. Spector, MD. and Benioff Children's Hospital/University of California San Francisco School of Medicine: Glenn Rosenbluth, MD, Daniel C. West, MD. Dorene Balmer, PhD, RD, Carol L. Carraccio, MD, MA, Laura Degnon, CAE, and David McDonald, and Alan Schwartz PhD serve the I‐PASS Study Group as part of the IIPE. Karen M. Wilson, MD, MPH serves the I‐PASS Study Group as part of the advisory board from the PRIS Executive Council. John Webster served the I‐PASS Study Group and Education Executive Committee as a representative from TeamSTEPPS.
Disclosures: The I‐PASS Study was primarily supported by the US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation (1R18AE000029‐01). The opinions and conclusions expressed herein are solely those of the author(s) and should not be constructed as representing the opinions or policy of any agency of the federal government. Developed with input from the Initiative for Innovation in Pediatric Education and the Pediatric Research in Inpatient Settings Network (supported by the Children's Hospital Association, the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine). A. J. S. was supported by the Agency for Healthcare Research and Quality/Oregon Comparative Effectiveness Research K12 Program (1K12HS019456‐01). Additional funding for the I‐PASS Study was provided by the Medical Research Foundation of Oregon, Physician Services Incorporated Foundation (Ontario, Canada), and Pfizer (unrestricted medical education grant to N.D.S.). C.P.L, A.J.S. were supported by the Oregon Comparative Effectiveness Research K12 Program (1K12HS019456 from the Agency for Healthcare Research and Quality). A.J.S. was also supported by the Medical Research Foundation of Oregon. The authors report no conflicts of interest.
Handoffs among hospital providers are highly error prone and can result in serious morbidity and mortality. Best practices for verbal handoffs have been described[1, 2, 3, 4] and include conducting verbal handoffs face to face, providing opportunities for questions, having the receiver perform a readback, as well as specific content recommendations including action items. Far less research has focused on best practices for printed handoff documents,[5, 6] despite the routine use of written handoff tools as a reference by on‐call physicians.[7, 8] Erroneous or outdated information on the written handoff can mislead on‐call providers, potentially leading to serious medical errors.
In their most basic form, printed handoff documents list patients for whom a provider is responsible. Typically, they also contain demographic information, reason for hospital admission, and a task list for each patient. They may also contain more detailed information on patient history, hospital course, and/or care plan, and may vary among specialties.[9] They come in various forms, ranging from index cards with handwritten notes, to word‐processor or spreadsheet documents, to printed documents that are autopopulated from the electronic health record (EHR).[2] Importantly, printed handoff documents supplement the verbal handoff by allowing receivers to follow along as patients are presented. The concurrent use of written and verbal handoffs may improve retention of clinical information as compared with either alone.[10, 11]
The Joint Commission requires an institutional approach to patient handoffs.[12] The requirements state that handoff communication solutions should take a standardized form, but they do not provide details regarding what data elements should be included in printed or verbal handoffs. Accreditation Council for Graduate Medical Education Common Program Requirements likewise require that residents must become competent in patient handoffs[13] but do not provide specific details or measurement tools. Absent widely accepted guidelines, decisions regarding which elements to include in printed handoff documents are currently made at an individual or institutional level.
The I‐PASS study is a federally funded multi‐institutional project that demonstrated a decrease in medical errors and preventable adverse events after implementation of a standardized resident handoff bundle.[14, 15] The I‐PASS Study Group developed a bundle of handoff interventions, beginning with a handoff and teamwork training program (based in part on TeamSTEPPS [Team Strategies and Tools to Enhance Performance and Patient Safety]),[16] a novel verbal mnemonic, I‐PASS (Illness Severity, Patient Summary, Action List, Situation Awareness and Contingency Planning, and Synthesis by Receiver),[17] and changes to the verbal handoff process, in addition to several other elements.
We hypothesized that developing a standardized printed handoff template would reinforce the handoff training and enhance the value of the verbal handoff process changes. Given the paucity of data on best printed handoff practices, however, we first conducted a needs assessment to identify which data elements were currently contained in printed handoffs across sites, and to allow an expert panel to make recommendations for best practices.
METHODS
I‐PASS Study sites included 9 pediatric residency programs at academic medical centers from across North America. Programs were identified through professional networks and invited to participate. The nonintensive care unit hospitalist services at these medical centers are primarily staffed by residents and medical students with attending supervision. At 1 site, nurse practitioners also participate in care. Additional details about study sites can be found in the study descriptions previously published.[14, 15] All sites received local institutional review board approval.
We began by inviting members of the I‐PASS Education Executive Committee (EEC)[14] to build a collective, comprehensive list of possible data elements for printed handoff documents. This committee included pediatric residency program directors, pediatric hospitalists, education researchers, health services researchers, and patient safety experts. We obtained sample handoff documents from pediatric hospitalist services at each of 9 institutions in the United States and Canada (with protected health information redacted). We reviewed these sample handoff documents to characterize their format and to determine what discrete data elements appeared in each site's printed handoff document. Presence or absence of each data element across sites was tabulated. We also queried sites to determine the feasibility of including elements that were not presently included.
Subsequently, I‐PASS site investigators led structured group interviews at participating sites to gather additional information about handoff practices at each site. These structured group interviews included diverse representation from residents, faculty, and residency program leadership, as well as hospitalists and medical students, to ensure the comprehensive acquisition of information regarding site‐specific characteristics. Each group provided answers to a standardized set of open‐ended questions that addressed current practices, handoff education, simulation use, team structure, and the nature of current written handoff tools, if applicable, at each site. One member of the structured group interview served as a scribe and created a document that summarized the content of the structured group interview meeting and answers to the standardized questions.
Consensus on Content
The initial data collection also included a multivote process[18] of the full I‐PASS EEC to help prioritize data elements. Committee members brainstormed a list of all possible data elements for a printed handoff document. Each member (n=14) was given 10 votes to distribute among the elements. Committee members could assign more than 1 vote to an element to emphasize its importance.
The results of this process as well as the current data elements included in each printed handoff tool were reviewed by a subgroup of the I‐PASS EEC. These expert panel members participated in a series of conference calls during which they tabulated categorical information, reviewed narrative comments, discussed existing evidence, and conducted simple content analysis to identify areas of concordance or discordance. Areas of discordance were discussed by the committee. Disagreements were resolved with group consensus with attention to published evidence or best practices, if available.
Elements were divided into those that were essential (unanimous consensus, no conflicting literature) and those that were recommended (majority supported inclusion of element, no conflicting literature). Ratings were assigned using the American College of Cardiology/American Heart Association framework for practice guidelines,[19] in which each element is assigned a classification (I=effective, II=conflicting evidence/opinion, III=not effective) and a level of evidence to support that classification (A=multiple large randomized controlled trials, B=single randomized trial, or nonrandomized studies, C=expert consensus).
The expert panel reached consensus, through active discussion, on a list of data elements that should be included in an ideal printed handoff document. Elements were chosen based on perceived importance, with attention to published best practices[1, 16] and the multivoting results. In making recommendations, consideration was given to whether data elements could be electronically imported into the printed handoff document from the EHR, or whether they would be entered manually. The potential for serious medical errors due to possible errors in manual entry of data was an important aspect of recommendations made. The list of candidate elements was then reviewed by a larger group of investigators from the I‐PASS Education Executive Committee and Coordinating Council for additional input.
The panel asked site investigators from each participating hospital to gather data on the feasibility of redesigning the printed handoff at that hospital to include each recommended element. Site investigators reported whether each element was already included, possible to include but not included currently, or not currently possible to include within that site's printed handoff tool. Site investigators also reported how data elements were populated in their handoff documents, with options including: (1) autopopulated from administrative data (eg, pharmacy‐entered medication list, demographic data entered by admitting office), (2) autoimported from physicians' free‐text entries elsewhere in the EHR (eg, progress notes), (3) free text entered specifically for the printed handoff, or (4) not applicable (element cannot be included).
RESULTS
Nine programs (100%) provided data on the structure and contents of their printed handoff documents. We found wide variation in structure across the 9 sites. Three sites used a word‐processorbased document that required manual entry of all data elements. The other 6 institutions had a direct link with the EHR to enable autopopulation of between 10 and 20 elements on the printed handoff document.
The content of written handoff documents, as well as the sources of data included in them (present or future), likewise varied substantially across sites (Table 1). Only 4 data elements (name, age, weight, and a list of medications) were universally included at all 9 sites. Among the 6 institutions that linked the printed handoff to the EHR, there was also substantial variation in which elements were autoimported. Only 7 elements were universally autoimported at these 6 sites: patient name, medical record number, room number, weight, date of birth, age, and date of admission. Two elements from the original brainstorming were not presently included in any sites' documents (emergency contact and primary language).
| Data Elements | Sites With Data Element Included at Initial Needs Assessment (Out of Nine Sites) | Data Source (Current or Anticipated) | ||
|---|---|---|---|---|
| Autoimported* | Manually Entered | Not Applicable | ||
| ||||
| Name | 9 | 6 | 3 | 0 |
| Medical record number | 8 | 6 | 3 | 0 |
| Room number | 8 | 6 | 3 | 0 |
| Allergies | 6 | 4 | 5 | 0 |
| Weight | 9 | 6 | 3 | 0 |
| Age | 9 | 6 | 3 | 0 |
| Date of birth | 6 | 6 | 3 | 0 |
| Admission date | 8 | 6 | 3 | 0 |
| Attending name | 5 | 4 | 5 | 0 |
| Team/service | 7 | 4 | 5 | 0 |
| Illness severity | 1 | 0 | 9 | 0 |
| Patient summary | 8 | 0 | 9 | 0 |
| Action items | 8 | 0 | 9 | 0 |
| Situation monitoring/contingency plan | 5 | 0 | 9 | 0 |
| Medication name | 9 | 4 | 5 | 0 |
| Medication name and dose/route/frequency | 4 | 4 | 5 | 0 |
| Code status | 2 | 2 | 7 | 0 |
| Labs | 6 | 5 | 4 | 0 |
| Access | 2 | 2 | 7 | 0 |
| Ins/outs | 2 | 4 | 4 | 1 |
| Primary language | 0 | 3 | 6 | 0 |
| Vital signs | 3 | 4 | 4 | 1 |
| Emergency contact | 0 | 2 | 7 | 0 |
| Primary care provider | 4 | 4 | 5 | 0 |
Nine institutions (100%) conducted structured group interviews, ranging in size from 4 to 27 individuals with a median of 5 participants. The documents containing information from each site were provided to the authors. The authors then tabulated categorical information, reviewed narrative comments to understand current institutional practices, and conducted simple content analysis to identify areas of concordance or discordance, particularly with respect to data elements and EHR usage. Based on the results of the printed handoff document review and structured group interviews, with additional perspectives provided by the I‐PASS EEC, the expert panel came to consensus on a list of 23 elements that should be included in printed handoff documents, including 15 essential data elements and 8 additional recommended elements (Table 2).
|
| Essential Elements |
| Patient identifiers |
| Patient name (class I, level of evidence C) |
| Medical record number (class I, level of evidence C) |
| Date of birth (class I, level of evidence C) |
| Hospital service identifiers |
| Attending name (class I, level of evidence C) |
| Team/service (class I, level of evidence C) |
| Room number (class I, level of evidence C) |
| Admission date (class I, level of evidence C) |
| Age (class I, level of evidence C) |
| Weight (class I, level of evidence C) |
| Illness severity (class I, level of evidence B)[20, 21] |
| Patient summary (class I, level of evidence B)[21, 22] |
| Action items (class I, level of evidence B) [21, 22] |
| Situation awareness/contingency planning (class I, level of evidence B) [21, 22] |
| Allergies (class I, level of evidence C) |
| Medications |
| Autopopulation of medications (class I, level of evidence B)[22, 23, 24] |
| Free‐text entry of medications (class IIa, level of evidence C) |
| Recommended elements |
| Primary language (class IIa, level of evidence C) |
| Emergency contact (class IIa, level of evidence C) |
| Primary care provider (class IIa, level of evidence C) |
| Code status (class IIb, level of evidence C) |
| Labs (class IIa, level of evidence C) |
| Access (class IIa, level of evidence C) |
| Ins/outs (class IIa, level of evidence C) |
| Vital signs (class IIa, level of evidence C) |
Evidence ratings[19] of these elements are included. Several elements are classified as I‐B (effective, nonrandomized studies) based on either studies of individual elements, or greater than 1 study of bundled elements that could reasonably be extrapolated. These include Illness severity,[20, 21] patient summary,[21, 22] action items[21, 22] (to do lists), situation awareness and contingency plan,[21, 22] and medications[22, 23, 24] with attention to importing from the EHR. Medications entered as free text were classified as IIa‐C because of risk and potential significance of errors; in particular there was concern that transcription errors, errors of omission, or errors of commission could potentially lead to patient harms. The remaining essential elements are classified as I‐C (effective, expert consensus). Of note, date of birth was specifically included as a patient identifier, distinct from age, which was felt to be useful as a descriptor (often within a one‐liner or as part of the patient summary).
The 8 recommended elements were elements for which there was not unanimous agreement on inclusion, but the majority of the panel felt they should be included. These elements were classified as IIa‐C, with 1 exception. Code status generated significant controversy among the group. After extensive discussion among the group and consideration of safety, supervision, educational, and pediatric‐specific considerations, all members of the group agreed on the categorization as a recommended element; it is classified as IIb‐C.
All members of the group agreed that data elements should be directly imported from the EHR whenever possible. Finally, members agreed that the elements that make up the I‐PASS mnemonic (illness severity, patient summary, action items, situation awareness/contingency planning) should be listed in that order whenever possible. A sample I‐PASS‐compliant printed handoff document is shown Figure 1.
DISCUSSION
We identified substantial variability in the structure and content of printed handoff documents used by 9 pediatric hospitalist teaching services, reflective of a lack of standardization. We found that institutional printed handoff documents shared some demographic elements (eg, name, room, medical record number) but also varied in clinical content (eg, vital signs, lab tests, code status). Our expert panel developed a list of 15 essential and 8 recommended data elements for printed handoff documents. Although this is a large number of fields, the majority of the essential fields were already included by most sites, and many are basic demographic identifiers. Illness severity is the 1 essential field that was not routinely included; however, including this type of overview is consistently recommended[2, 4] and supported by evidence,[20, 21] and contributes to building a shared mental model.[16] We recommend the categories of stable/watcher/unstable.[17]
Several prior single‐center studies have found that introducing a printed handoff document can lead to improvements in workflow, communication, and patient safety. In an early study, Petersen et al.[25] showed an association between use of a computerized sign‐out program and reduced odds of preventable adverse events during periods of cross‐coverage. Wayne et al.[26] reported fewer perceived inaccuracies in handoff documents as well as improved clarity at the time of transfer, supporting the role for standardization. Van Eaton et al.[27] demonstrated rapid uptake and desirability of a computerized handoff document, which combined autoimportation of information from an EHR with resident‐entered patient details, reflecting the importance of both data sources. In addition, they demonstrated improvements in both the rounding and sign‐out processes.[28]
Two studies specifically reported the increased use of specific fields after implementation. Payne et al. implemented a Web‐based handoff tool and documented significant increases in the number of handoffs containing problem lists, medication lists, and code status, accompanied by perceived improvements in quality of handoffs and fewer near‐miss events.[24] Starmer et al. found that introduction of a resident handoff bundle that included a printed handoff tool led to reduction in medical errors and adverse events.[22] The study group using the tool populated 11 data elements more often after implementation, and introduction of this printed handoff tool in particular was associated with reductions in written handoff miscommunications. Neither of these studies included subanalysis to indicate which data elements may have been most important.
In contrast to previous single‐institution studies, our recommendations for a printed handoff template come from evaluations of tools and discussions with front line providers across 9 institutions. We had substantial overlap with data elements recommended by Van Eaton et al.[27] However, there were several areas in which we did not have overlap with published templates including weight, ins/outs, primary language, emergency contact information, or primary care provider. Other published handoff tools have been highly specialized (eg, for cardiac intensive care) or included many fewer data elements than our group felt were essential. These differences may reflect the unique aspects of caring for pediatric patients (eg, need for weights) and the absence of defined protocols for many pediatric conditions. In addition, the level of detail needed for contingency planning may vary between teaching and nonteaching services.
Resident physicians may provide valuable information in the development of standardized handoff documents. Clark et al.,[29] at Virginia Mason University, utilized resident‐driven continuous quality improvement processes including real‐time feedback to implement an electronic template. They found that engagement of both senior leaders and front‐line users was an important component of their success in uptake. Our study utilized residents as essential members of structured group interviews to ensure that front‐line users' needs were represented as recommendations for a printed handoff tool template were developed.
As previously described,[17] our study group had identified several key data elements that should be included in verbal handoffs: illness severity, a patient summary, a discrete action list, situation awareness/contingency planning, and a synthesis by receiver. With consideration of the multivoting results as well as known best practices,[1, 4, 12] the expert panel for this study agreed that each of these elements should also be highlighted in the printed template to ensure consistency between the printed document and the verbal handoff, and to have each reinforce the other. On the printed handoff tool, the final S in the I‐PASS mnemonic (synthesis by receiver) cannot be prepopulated, but considering the importance of this step,[16, 30, 31, 32] it should be printed as synthesis by receiver to serve as a text‐reminder to both givers and receivers.
The panel also felt, however, that the printed handoff document should provide additional background information not routinely included in a verbal handoff. It should serve as a reference tool both at the time of verbal handoff and throughout the day and night, and therefore should include more comprehensive information than is necessary or appropriate to convey during the verbal handoff. We identified 10 data elements that are essential in a printed handoff document in addition to the I‐PASS elements (Table 2).
Patient demographic data elements, as well as team assignments and attending physician, were uniformly supported for inclusion. The medication list was viewed as essential; however, the panel also recognized the potential for medical errors due to inaccuracies in the medication list. In particular, there was concern that including all fields of a medication order (drug, dose, route, frequency) would result in handoffs containing a high proportion of inaccurate information, particularly for complex patients whose medication regimens may vary over the course of hospitalization. Therefore, the panel agreed that if medication lists were entered manually, then only the medication name should be included as they did not wish to perpetuate inaccurate or potentially harmful information. If medication lists were autoimported from an EHR, then they should include drug name, dose, route, and frequency if possible.
In the I‐PASS study,[15] all institutions implemented printed handoff documents that included fields for the essential data elements. After implementation, there was a significant increase in completion of all essential fields. Although there is limited evidence to support any individual data element, increased usage of these elements was associated with the overall study finding of decreased rates of medical errors and preventable adverse events.
EHRs have the potential to help standardize printed handoff documents[5, 6, 33, 34, 35]; all participants in our study agreed that printed handoff documents should ideally be linked with the EHR and should autoimport data wherever appropriate. Manually populated (eg, word processor‐ or spreadsheet‐based) handoff tools have important limitations, particularly related to the potential for typographical errors as well as accidental omission of data fields, and lead to unnecessary duplication of work (eg, re‐entering data already included in a progress note) that can waste providers' time. It was also acknowledged that word processor‐ or spreadsheet‐based documents may have flexibility that is lacking in EHR‐based handoff documents. For example, formatting can more easily be adjusted to increase the number of patients per printed page. As technology advances, printed documents may be phased out in favor of EHR‐based on‐screen reports, which by their nature would be more accurate due to real‐time autoupdates.
In making recommendations about essential versus recommended items for inclusion in the printed handoff template, the only data element that generated controversy among our experts was code status. Some felt that it should be included as an essential element, whereas others did not. We believe that this was unique to our practice in pediatric hospital ward settings, as codes in most pediatric ward settings are rare. Among the concerns expressed with including code status for all patients were that residents might assume patients were full‐code without verifying. The potential inaccuracy created by this might have severe implications. Alternatively, residents might feel obligated to have code discussions with all patients regardless of severity of illness, which may be inappropriate in a pediatric population. Several educators expressed concerns about trainees having unsupervised code‐status conversations with families of pediatric patients. Conversely, although codes are rare in pediatric ward settings, concerns were raised that not including code status could be problematic during these rare but critically important events. Other fields, such as weight, might have less relevance for an adult population in which emergency drug doses are standardized.
Limitations
Our study has several limitations. We only collected data from hospitalist services at pediatric sites. It is likely that providers in other specialties would have specific data elements they felt were essential (eg, postoperative day, code status). Our methodology was expert consensus based, driven by data collection from sites that were already participating in the I‐PASS study. Although the I‐PASS study demonstrated decreased rates of medical errors and preventable adverse events with inclusion of these data elements as part of a bundle, future research will be required to evaluate whether some of these items are more important than others in improving written communication and ultimately patient safety. In spite of these limitations, our work represents an important starting point for the development of standards for written handoff documents that should be used in patient handoffs, particularly those generated from EHRs.
CONCLUSIONS
In this article we describe the results of a needs assessment that informed expert consensus‐based recommendations for data elements to include in a printed handoff document. We recommend that pediatric programs include the elements identified as part of a standardized written handoff tool. Although many of these elements are also applicable to other specialties, future work should be conducted to adapt the printed handoff document elements described here for use in other specialties and settings. Future studies should work to validate the importance of these elements, studying the manner in which their inclusion affects the quality of written handoffs, and ultimately patient safety.
Acknowledgements
Members of the I‐PASS Study Education Executive Committee who contributed to this manuscript include: Boston Children's Hospital/Harvard Medical School (primary site) (Christopher P. Landrigan, MD, MPH, Elizabeth L. Noble, BA. Theodore C. Sectish, MD. Lisa L. Tse, BA). Cincinnati Children's Hospital Medical Center/University of Cincinnati College of Medicine (Jennifer K. O'Toole, MD, MEd). Doernbecher Children's Hospital/Oregon Health and Science University (Amy J. Starmer, MD, MPH). Hospital for Sick Children/University of Toronto (Zia Bismilla, MD. Maitreya Coffey, MD). Lucile Packard Children's Hospital/Stanford University (Lauren A. Destino, MD. Jennifer L. Everhart, MD. Shilpa J. Patel, MD [currently at Kapi'olani Children's Hospital/University of Hawai'i School of Medicine]). National Capital Consortium (Jennifer H. Hepps, MD. Joseph O. Lopreiato, MD, MPH. Clifton E. Yu, MD). Primary Children's Medical Center/University of Utah (James F. Bale, Jr., MD. Adam T. Stevenson, MD). St. Louis Children's Hospital/Washington University (F. Sessions Cole, MD). St. Christopher's Hospital for Children/Drexel University College of Medicine (Sharon Calaman, MD. Nancy D. Spector, MD). Benioff Children's Hospital/University of California San Francisco School of Medicine (Glenn Rosenbluth, MD. Daniel C. West, MD).
Additional I‐PASS Study Group members who contributed to this manuscript include April D. Allen, MPA, MA (Heller School for Social Policy and Management, Brandeis University, previously affiliated with Boston Children's Hospital), Madelyn D. Kahana, MD (The Children's Hospital at Montefiore/Albert Einstein College of Medicine, previously affiliated with Lucile Packard Children's Hospital/Stanford University), Robert S. McGregor, MD (Akron Children's Hospital/Northeast Ohio Medical University, previously affiliated with St. Christopher's Hospital for Children/Drexel University), and John S. Webster, MD, MBA, MS (Webster Healthcare Consulting Inc., formerly of the Department of Defense).
Members of the I‐PASS Study Group include individuals from the institutions listed below as follows: Boston Children's Hospital/Harvard Medical School (primary site): April D. Allen, MPA, MA (currently at Heller School for Social Policy and Management, Brandeis University), Angela M. Feraco, MD, Christopher P. Landrigan, MD, MPH, Elizabeth L. Noble, BA, Theodore C. Sectish, MD, Lisa L. Tse, BA. Brigham and Women's Hospital (data coordinating center): Anuj K. Dalal, MD, Carol A. Keohane, BSN, RN, Stuart Lipsitz, PhD, Jeffrey M. Rothschild, MD, MPH, Matt F. Wien, BS, Catherine S. Yoon, MS, Katherine R. Zigmont, BSN, RN. Cincinnati Children's Hospital Medical Center/University of Cincinnati College of Medicine: Javier Gonzalez del Rey, MD, MEd, Jennifer K. O'Toole, MD, MEd, Lauren G. Solan, MD. Doernbecher Children's Hospital/Oregon Health and Science University: Megan E. Aylor, MD, Amy J. Starmer, MD, MPH, Windy Stevenson, MD, Tamara Wagner, MD. Hospital for Sick Children/University of Toronto: Zia Bismilla, MD, Maitreya Coffey, MD, Sanjay Mahant, MD, MSc. Lucile Packard Children's Hospital/Stanford University: Rebecca L. Blankenburg, MD, MPH, Lauren A. Destino, MD, Jennifer L. Everhart, MD, Madelyn Kahana, MD, Shilpa J. Patel, MD (currently at Kapi'olani Children's Hospital/University of Hawaii School of Medicine). National Capital Consortium: Jennifer H. Hepps, MD, Joseph O. Lopreiato, MD, MPH, Clifton E. Yu, MD. Primary Children's Hospital/University of Utah: James F. Bale, Jr., MD, Jaime Blank Spackman, MSHS, CCRP, Rajendu Srivastava, MD, FRCP(C), MPH, Adam Stevenson, MD. St. Louis Children's Hospital/Washington University: Kevin Barton, MD, Kathleen Berchelmann, MD, F. Sessions Cole, MD, Christine Hrach, MD, Kyle S. Schultz, MD, Michael P. Turmelle, MD, Andrew J. White, MD. St. Christopher's Hospital for Children/Drexel University: Sharon Calaman, MD, Bronwyn D. Carlson, MD, Robert S. McGregor, MD (currently at Akron Children's Hospital/Northeast Ohio Medical University), Vahideh Nilforoshan, MD, Nancy D. Spector, MD. and Benioff Children's Hospital/University of California San Francisco School of Medicine: Glenn Rosenbluth, MD, Daniel C. West, MD. Dorene Balmer, PhD, RD, Carol L. Carraccio, MD, MA, Laura Degnon, CAE, and David McDonald, and Alan Schwartz PhD serve the I‐PASS Study Group as part of the IIPE. Karen M. Wilson, MD, MPH serves the I‐PASS Study Group as part of the advisory board from the PRIS Executive Council. John Webster served the I‐PASS Study Group and Education Executive Committee as a representative from TeamSTEPPS.
Disclosures: The I‐PASS Study was primarily supported by the US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation (1R18AE000029‐01). The opinions and conclusions expressed herein are solely those of the author(s) and should not be constructed as representing the opinions or policy of any agency of the federal government. Developed with input from the Initiative for Innovation in Pediatric Education and the Pediatric Research in Inpatient Settings Network (supported by the Children's Hospital Association, the Academic Pediatric Association, the American Academy of Pediatrics, and the Society of Hospital Medicine). A. J. S. was supported by the Agency for Healthcare Research and Quality/Oregon Comparative Effectiveness Research K12 Program (1K12HS019456‐01). Additional funding for the I‐PASS Study was provided by the Medical Research Foundation of Oregon, Physician Services Incorporated Foundation (Ontario, Canada), and Pfizer (unrestricted medical education grant to N.D.S.). C.P.L, A.J.S. were supported by the Oregon Comparative Effectiveness Research K12 Program (1K12HS019456 from the Agency for Healthcare Research and Quality). A.J.S. was also supported by the Medical Research Foundation of Oregon. The authors report no conflicts of interest.
- , , , , . Handoff strategies in settings with high consequences for failure: lessons for health care operations. Int J Qual Health Care. 2004;16(2):125–132.
- , , , , . Managing discontinuity in academic medical centers: strategies for a safe and effective resident sign‐out. J Hosp Med. 2006;1(4):257–266.
- , , . Development and implementation of an oral sign‐out skills curriculum. J Gen Intern Med. 2007;22(10):1470–1474.
- , , , , , . Hospitalist handoffs: a systematic review and task force recommendations. J Hosp Med. 2009;4(7):433–440.
- , , . A systematic review of the literature on the evaluation of handoff tools: implications for research and practice. J Am Med Inform Assoc. 2014;21(1):154–162.
- , , , , . Review of computerized physician handoff tools for improving the quality of patient care. J Hosp Med. 2013;8(8):456–463.
- , , , , . Answering questions on call: pediatric resident physicians' use of handoffs and other resources. J Hosp Med. 2013;8(6):328–333.
- , , , . Effectiveness of written hospitalist sign‐outs in answering overnight inquiries. J Hosp Med. 2013;8(11):609–614.
- , , . Sign‐out snapshot: cross‐sectional evaluation of written sign‐outs among specialties. BMJ Qual Saf. 2014;23(1):66–72.
- , , , . An experimental comparison of handover methods. Ann R Coll Surg Engl. 2007;89(3):298–300.
- , , , . Pilot study to show the loss of important data in nursing handover. Br J Nurs. 2005;14(20):1090–1093.
- The Joint Commission. Hospital Accreditation Standards 2015: Joint Commission Resources; 2015:PC.02.02.01.
- Accreditation Council for Graduate Medical Education. Common Program Requirements. 2013; http://acgme.org/acgmeweb/tabid/429/ProgramandInstitutionalAccreditation/CommonProgramRequirements.aspx. Accessed May 11, 2015.
- , , , . Establishing a multisite education and research project requires leadership, expertise, collaboration, and an important aim. Pediatrics. 2010;126(4):619–622.
- , , , et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
- US Department of Health and Human Services. Agency for Healthcare Research and Quality. TeamSTEPPS website. Available at: http://teamstepps.ahrq.gov/. Accessed July 12, 2013.
- , , , , , . I‐PASS, a mnemonic to standardize verbal handoffs. Pediatrics. 2012;129(2):201–204.
- , , . The Team Handbook. 3rd ed. Middleton, WI: Oriel STAT A MATRIX; 2010.
- ACC/AHA Task Force on Practice Guidelines. Methodology Manual and Policies From the ACCF/AHA Task Force on Practice Guidelines. Available at: http://my.americanheart.org/idc/groups/ahamah‐public/@wcm/@sop/documents/downloadable/ucm_319826.pdf. Published June 2010. Accessed January 11, 2015.
- , , , et al. Effect of illness severity and comorbidity on patient safety and adverse events. Am J Med Qual. 2012;27(1):48–57.
- , , , , . Consequences of inadequate sign‐out for patient care. Arch Intern Med. 2008;168(16):1755–1760.
- , , , et al. Rates of medical errors and preventable adverse events among hospitalized children following implementation of a resident handoff bundle. JAMA. 2013;310(21):2262–2270.
- , , , , . Medication discrepancies in resident sign‐outs and their potential to harm. J Gen Intern Med. 2007;22(12):1751–1755.
- , , , . Avoiding handover fumbles: a controlled trial of a structured handover tool versus traditional handover methods. BMJ Qual Saf. 2012;21(11):925–932.
- , , , , . Using a computerized sign‐out program to improve continuity of inpatient care and prevent adverse events. Jt Comm J Qual Improv. 1998;24(2):77–87.
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- , , , , . Interns overestimate the effectiveness of their hand‐off communication. Pediatrics. 2010;125(3):491–496.
- , , , , , . Improving patient safety by repeating (read‐back) telephone reports of critical information. Am J Clin Pathol. 2004;121(6):801–803.
- , , , , . Content overlap in nurse and physician handoff artifacts and the potential role of electronic health records: a systematic review. J Biomed Inform. 2011;44(4):704–712.
- , , , . Clinical summarization capabilities of commercially‐available and internally‐developed electronic health records. Appl Clin Inform. 2012;3(1):80–93.
- , . An analysis and recommendations for multidisciplinary computerized handoff applications in hospitals. AMIA Annu Symp Proc. 2011;2011:588–597.
- , , , , . Handoff strategies in settings with high consequences for failure: lessons for health care operations. Int J Qual Health Care. 2004;16(2):125–132.
- , , , , . Managing discontinuity in academic medical centers: strategies for a safe and effective resident sign‐out. J Hosp Med. 2006;1(4):257–266.
- , , . Development and implementation of an oral sign‐out skills curriculum. J Gen Intern Med. 2007;22(10):1470–1474.
- , , , , , . Hospitalist handoffs: a systematic review and task force recommendations. J Hosp Med. 2009;4(7):433–440.
- , , . A systematic review of the literature on the evaluation of handoff tools: implications for research and practice. J Am Med Inform Assoc. 2014;21(1):154–162.
- , , , , . Review of computerized physician handoff tools for improving the quality of patient care. J Hosp Med. 2013;8(8):456–463.
- , , , , . Answering questions on call: pediatric resident physicians' use of handoffs and other resources. J Hosp Med. 2013;8(6):328–333.
- , , , . Effectiveness of written hospitalist sign‐outs in answering overnight inquiries. J Hosp Med. 2013;8(11):609–614.
- , , . Sign‐out snapshot: cross‐sectional evaluation of written sign‐outs among specialties. BMJ Qual Saf. 2014;23(1):66–72.
- , , , . An experimental comparison of handover methods. Ann R Coll Surg Engl. 2007;89(3):298–300.
- , , , . Pilot study to show the loss of important data in nursing handover. Br J Nurs. 2005;14(20):1090–1093.
- The Joint Commission. Hospital Accreditation Standards 2015: Joint Commission Resources; 2015:PC.02.02.01.
- Accreditation Council for Graduate Medical Education. Common Program Requirements. 2013; http://acgme.org/acgmeweb/tabid/429/ProgramandInstitutionalAccreditation/CommonProgramRequirements.aspx. Accessed May 11, 2015.
- , , , . Establishing a multisite education and research project requires leadership, expertise, collaboration, and an important aim. Pediatrics. 2010;126(4):619–622.
- , , , et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
- US Department of Health and Human Services. Agency for Healthcare Research and Quality. TeamSTEPPS website. Available at: http://teamstepps.ahrq.gov/. Accessed July 12, 2013.
- , , , , , . I‐PASS, a mnemonic to standardize verbal handoffs. Pediatrics. 2012;129(2):201–204.
- , , . The Team Handbook. 3rd ed. Middleton, WI: Oriel STAT A MATRIX; 2010.
- ACC/AHA Task Force on Practice Guidelines. Methodology Manual and Policies From the ACCF/AHA Task Force on Practice Guidelines. Available at: http://my.americanheart.org/idc/groups/ahamah‐public/@wcm/@sop/documents/downloadable/ucm_319826.pdf. Published June 2010. Accessed January 11, 2015.
- , , , et al. Effect of illness severity and comorbidity on patient safety and adverse events. Am J Med Qual. 2012;27(1):48–57.
- , , , , . Consequences of inadequate sign‐out for patient care. Arch Intern Med. 2008;168(16):1755–1760.
- , , , et al. Rates of medical errors and preventable adverse events among hospitalized children following implementation of a resident handoff bundle. JAMA. 2013;310(21):2262–2270.
- , , , , . Medication discrepancies in resident sign‐outs and their potential to harm. J Gen Intern Med. 2007;22(12):1751–1755.
- , , , . Avoiding handover fumbles: a controlled trial of a structured handover tool versus traditional handover methods. BMJ Qual Saf. 2012;21(11):925–932.
- , , , , . Using a computerized sign‐out program to improve continuity of inpatient care and prevent adverse events. Jt Comm J Qual Improv. 1998;24(2):77–87.
- , , , et al. Simple standardized patient handoff system that increases accuracy and completeness. J Surg Educ. 2008;65(6):476–485.
- , , , . Organizing the transfer of patient care information: the development of a computerized resident sign‐out system. Surgery. 2004;136(1):5–13.
- , , , , . A randomized, controlled trial evaluating the impact of a computerized rounding and sign‐out system on continuity of care and resident work hours. J Am Coll Surg. 2005;200(4):538–545.
- , , . Template for success: using a resident‐designed sign‐out template in the handover of patient care. J Surg Educ. 2011;68(1):52–57.
- , , , , , . Read‐back improves information transfer in simulated clinical crises. BMJ Qual Saf. 2014;23(12):989–993.
- , , , , . Interns overestimate the effectiveness of their hand‐off communication. Pediatrics. 2010;125(3):491–496.
- , , , , , . Improving patient safety by repeating (read‐back) telephone reports of critical information. Am J Clin Pathol. 2004;121(6):801–803.
- , , , , . Content overlap in nurse and physician handoff artifacts and the potential role of electronic health records: a systematic review. J Biomed Inform. 2011;44(4):704–712.
- , , , . Clinical summarization capabilities of commercially‐available and internally‐developed electronic health records. Appl Clin Inform. 2012;3(1):80–93.
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© 2015 Society of Hospital Medicine
Herbs reduce fatigue in cancer patients
Photo by Alexander Baxevanis
An herbal mixture used in traditional Chinese medicine can reduce fatigue in cancer patients, results of a phase 1/2 study suggest.
The mixture, Ren Shen Yangrong Tang (RSYRT), is a soup containing 12 herbs.
In the study, cancer patients suffering from moderate to severe fatigue reported significantly less fatigue after taking RSYRT for 2 to 3 weeks.
Researchers reported these results in the Journal of Alternative and Complementary Medicine.
Yichen Xu, MD, of Beijing Cancer Hospital & Institute in China, and colleagues evaluated RSYRT in 33 patients who had completed cancer treatment. The patients had stable disease and no anemia.
Eleven patients had moderate fatigue (a score of 4-6 on a 0-10 scale), and 22 had severe fatigue (a score of 7-10). All patients had experienced fatigue for at least 4 months.
Patients took RSYRT twice a day for 6 weeks and experienced a significant decrease in fatigue severity. The mean fatigue score decreased from 7.06 at baseline to 3.30 at the 6-week mark (P<0.001).
The fatigue category also changed significantly (P=0.024). Among the 22 patients who had severe fatigue before RSYRT, half had mild fatigue after therapy, and half had moderate fatigue.
Among the 11 patients who had moderate fatigue at baseline, only 1 still had moderate fatigue after receiving RSYRT. The rest had mild fatigue.
All of the patients said they felt better after taking RSYRT for 4 weeks.
There were no “uncomfortable events” related to RSYRT, such as gastrointestinal upset, insomnia, headache, or rash. None of the patients required a dose reduction or dose interruption.
None of the patients had blood chemistry abnormalities or abnormal liver/kidney function. Two patients who had a change in ST segment before RSYRT had normal electrocardiogram results after treatment. 
Photo by Alexander Baxevanis
An herbal mixture used in traditional Chinese medicine can reduce fatigue in cancer patients, results of a phase 1/2 study suggest.
The mixture, Ren Shen Yangrong Tang (RSYRT), is a soup containing 12 herbs.
In the study, cancer patients suffering from moderate to severe fatigue reported significantly less fatigue after taking RSYRT for 2 to 3 weeks.
Researchers reported these results in the Journal of Alternative and Complementary Medicine.
Yichen Xu, MD, of Beijing Cancer Hospital & Institute in China, and colleagues evaluated RSYRT in 33 patients who had completed cancer treatment. The patients had stable disease and no anemia.
Eleven patients had moderate fatigue (a score of 4-6 on a 0-10 scale), and 22 had severe fatigue (a score of 7-10). All patients had experienced fatigue for at least 4 months.
Patients took RSYRT twice a day for 6 weeks and experienced a significant decrease in fatigue severity. The mean fatigue score decreased from 7.06 at baseline to 3.30 at the 6-week mark (P<0.001).
The fatigue category also changed significantly (P=0.024). Among the 22 patients who had severe fatigue before RSYRT, half had mild fatigue after therapy, and half had moderate fatigue.
Among the 11 patients who had moderate fatigue at baseline, only 1 still had moderate fatigue after receiving RSYRT. The rest had mild fatigue.
All of the patients said they felt better after taking RSYRT for 4 weeks.
There were no “uncomfortable events” related to RSYRT, such as gastrointestinal upset, insomnia, headache, or rash. None of the patients required a dose reduction or dose interruption.
None of the patients had blood chemistry abnormalities or abnormal liver/kidney function. Two patients who had a change in ST segment before RSYRT had normal electrocardiogram results after treatment.
Photo by Alexander Baxevanis
An herbal mixture used in traditional Chinese medicine can reduce fatigue in cancer patients, results of a phase 1/2 study suggest.
The mixture, Ren Shen Yangrong Tang (RSYRT), is a soup containing 12 herbs.
In the study, cancer patients suffering from moderate to severe fatigue reported significantly less fatigue after taking RSYRT for 2 to 3 weeks.
Researchers reported these results in the Journal of Alternative and Complementary Medicine.
Yichen Xu, MD, of Beijing Cancer Hospital & Institute in China, and colleagues evaluated RSYRT in 33 patients who had completed cancer treatment. The patients had stable disease and no anemia.
Eleven patients had moderate fatigue (a score of 4-6 on a 0-10 scale), and 22 had severe fatigue (a score of 7-10). All patients had experienced fatigue for at least 4 months.
Patients took RSYRT twice a day for 6 weeks and experienced a significant decrease in fatigue severity. The mean fatigue score decreased from 7.06 at baseline to 3.30 at the 6-week mark (P<0.001).
The fatigue category also changed significantly (P=0.024). Among the 22 patients who had severe fatigue before RSYRT, half had mild fatigue after therapy, and half had moderate fatigue.
Among the 11 patients who had moderate fatigue at baseline, only 1 still had moderate fatigue after receiving RSYRT. The rest had mild fatigue.
All of the patients said they felt better after taking RSYRT for 4 weeks.
There were no “uncomfortable events” related to RSYRT, such as gastrointestinal upset, insomnia, headache, or rash. None of the patients required a dose reduction or dose interruption.
None of the patients had blood chemistry abnormalities or abnormal liver/kidney function. Two patients who had a change in ST segment before RSYRT had normal electrocardiogram results after treatment.
Inhibitor promotes chemosensitization in CLL
PHILADELPHIA—A DNA-dependent protein kinase (DNA-PK) inhibitor can sensitize chronic lymphocytic leukemia (CLL) cells to chemotherapy, according to
preclinical research.
The inhibitor, NDD0004, sensitized CLL cells—even those from patients with high-risk cytogenetics—to treatment with mitoxantrone.
However, not all CLL samples were sensitive to treatment, so researchers are now trying to determine which patients might derive benefit from DNA-PK inhibitors.
Gesa Junge, a PhD student at Newcastle University in the UK, and her colleagues conducted this research and presented the results at the AACR Annual Meeting 2015 (abstract 3624*). The work was supported by AstraZeneca.
The researchers’ goal was to validate that DNA-PK inhibition is a valid approach to chemosensitization in CLL. So the team tested NU7441—a compound that inhibits DNA-PK and PI3 kinase—and NDD0004—a more selective DNA-PK inhibitor.
The team isolated CLL cells from patients’ peripheral blood, cultured the cells, and treated them with mitoxantrone and/or 1μM of NDD0004 or 1μM of NU7441.
Junge and her colleagues found that NDD0004 sensitized cells to mitoxantrone more effectively than NU7441. Sensitization was 202-fold higher with NDD004 plus mitoxantrone than with mitoxantrone alone and 69-fold higher with NU7441 plus mitoxantrone than with mitoxantrone alone (P=0.02).
However, sensitization varied between CLL samples, and the researchers have yet to determine why. Their experiments showed that variability was not a result of DNA-PK levels.
Still, the team found that CLL cells from patients with poor prognostic markers were sensitive to DNA-PK inhibition.
Sensitization with NU7441 plus mitoxantrone was 69-fold higher than mitoxantrone alone in CLL samples with del(13q), 25-fold higher in samples with del(11q), 12-fold higher in samples with TP53 mutation, and 16-fold higher in samples with ATM dysfunction.
Sensitization with NDD0004 plus mitoxantrone was 201-fold higher than mitoxantrone alone in CLL samples with del(13q), 314-fold higher in samples with del(11q), 27-fold higher in samples with TP53 mutation, and 18-fold higher in samples with ATM dysfunction.
To confirm that sensitization was a result of DNA-PK inhibition, Junge and her colleagues tested NDD0004 in an isogenic pair of DNA-PK-deficient and DNA-PK-proficient HCT116 cells. They found that HCT116 cells lacking DNA-PK were not sensitive to NDD0004, but cells with DNA-PK were sensitive.
The researchers also investigated the mechanism of NDD0004. Their results suggest the drug works by inhibiting the repair of DNA double-strand breaks.
“What we think is happening is that we are inducing DNA damage with mitoxantrone, and that gets repaired by 24 hours,” Junge said. “But if the DNA-PK inhibitor is there, the damage persists, and that seems to translate quite nicely into an apoptosis response.”
To further this research, Junge and her colleagues are hoping to identify biomarkers that can help them determine which CLL patients are likely to respond to DNA-PK inhibitors.
*Information in the abstract differs from that presented at the meeting.
PHILADELPHIA—A DNA-dependent protein kinase (DNA-PK) inhibitor can sensitize chronic lymphocytic leukemia (CLL) cells to chemotherapy, according to
preclinical research.
The inhibitor, NDD0004, sensitized CLL cells—even those from patients with high-risk cytogenetics—to treatment with mitoxantrone.
However, not all CLL samples were sensitive to treatment, so researchers are now trying to determine which patients might derive benefit from DNA-PK inhibitors.
Gesa Junge, a PhD student at Newcastle University in the UK, and her colleagues conducted this research and presented the results at the AACR Annual Meeting 2015 (abstract 3624*). The work was supported by AstraZeneca.
The researchers’ goal was to validate that DNA-PK inhibition is a valid approach to chemosensitization in CLL. So the team tested NU7441—a compound that inhibits DNA-PK and PI3 kinase—and NDD0004—a more selective DNA-PK inhibitor.
The team isolated CLL cells from patients’ peripheral blood, cultured the cells, and treated them with mitoxantrone and/or 1μM of NDD0004 or 1μM of NU7441.
Junge and her colleagues found that NDD0004 sensitized cells to mitoxantrone more effectively than NU7441. Sensitization was 202-fold higher with NDD004 plus mitoxantrone than with mitoxantrone alone and 69-fold higher with NU7441 plus mitoxantrone than with mitoxantrone alone (P=0.02).
However, sensitization varied between CLL samples, and the researchers have yet to determine why. Their experiments showed that variability was not a result of DNA-PK levels.
Still, the team found that CLL cells from patients with poor prognostic markers were sensitive to DNA-PK inhibition.
Sensitization with NU7441 plus mitoxantrone was 69-fold higher than mitoxantrone alone in CLL samples with del(13q), 25-fold higher in samples with del(11q), 12-fold higher in samples with TP53 mutation, and 16-fold higher in samples with ATM dysfunction.
Sensitization with NDD0004 plus mitoxantrone was 201-fold higher than mitoxantrone alone in CLL samples with del(13q), 314-fold higher in samples with del(11q), 27-fold higher in samples with TP53 mutation, and 18-fold higher in samples with ATM dysfunction.
To confirm that sensitization was a result of DNA-PK inhibition, Junge and her colleagues tested NDD0004 in an isogenic pair of DNA-PK-deficient and DNA-PK-proficient HCT116 cells. They found that HCT116 cells lacking DNA-PK were not sensitive to NDD0004, but cells with DNA-PK were sensitive.
The researchers also investigated the mechanism of NDD0004. Their results suggest the drug works by inhibiting the repair of DNA double-strand breaks.
“What we think is happening is that we are inducing DNA damage with mitoxantrone, and that gets repaired by 24 hours,” Junge said. “But if the DNA-PK inhibitor is there, the damage persists, and that seems to translate quite nicely into an apoptosis response.”
To further this research, Junge and her colleagues are hoping to identify biomarkers that can help them determine which CLL patients are likely to respond to DNA-PK inhibitors.
*Information in the abstract differs from that presented at the meeting.
PHILADELPHIA—A DNA-dependent protein kinase (DNA-PK) inhibitor can sensitize chronic lymphocytic leukemia (CLL) cells to chemotherapy, according to
preclinical research.
The inhibitor, NDD0004, sensitized CLL cells—even those from patients with high-risk cytogenetics—to treatment with mitoxantrone.
However, not all CLL samples were sensitive to treatment, so researchers are now trying to determine which patients might derive benefit from DNA-PK inhibitors.
Gesa Junge, a PhD student at Newcastle University in the UK, and her colleagues conducted this research and presented the results at the AACR Annual Meeting 2015 (abstract 3624*). The work was supported by AstraZeneca.
The researchers’ goal was to validate that DNA-PK inhibition is a valid approach to chemosensitization in CLL. So the team tested NU7441—a compound that inhibits DNA-PK and PI3 kinase—and NDD0004—a more selective DNA-PK inhibitor.
The team isolated CLL cells from patients’ peripheral blood, cultured the cells, and treated them with mitoxantrone and/or 1μM of NDD0004 or 1μM of NU7441.
Junge and her colleagues found that NDD0004 sensitized cells to mitoxantrone more effectively than NU7441. Sensitization was 202-fold higher with NDD004 plus mitoxantrone than with mitoxantrone alone and 69-fold higher with NU7441 plus mitoxantrone than with mitoxantrone alone (P=0.02).
However, sensitization varied between CLL samples, and the researchers have yet to determine why. Their experiments showed that variability was not a result of DNA-PK levels.
Still, the team found that CLL cells from patients with poor prognostic markers were sensitive to DNA-PK inhibition.
Sensitization with NU7441 plus mitoxantrone was 69-fold higher than mitoxantrone alone in CLL samples with del(13q), 25-fold higher in samples with del(11q), 12-fold higher in samples with TP53 mutation, and 16-fold higher in samples with ATM dysfunction.
Sensitization with NDD0004 plus mitoxantrone was 201-fold higher than mitoxantrone alone in CLL samples with del(13q), 314-fold higher in samples with del(11q), 27-fold higher in samples with TP53 mutation, and 18-fold higher in samples with ATM dysfunction.
To confirm that sensitization was a result of DNA-PK inhibition, Junge and her colleagues tested NDD0004 in an isogenic pair of DNA-PK-deficient and DNA-PK-proficient HCT116 cells. They found that HCT116 cells lacking DNA-PK were not sensitive to NDD0004, but cells with DNA-PK were sensitive.
The researchers also investigated the mechanism of NDD0004. Their results suggest the drug works by inhibiting the repair of DNA double-strand breaks.
“What we think is happening is that we are inducing DNA damage with mitoxantrone, and that gets repaired by 24 hours,” Junge said. “But if the DNA-PK inhibitor is there, the damage persists, and that seems to translate quite nicely into an apoptosis response.”
To further this research, Junge and her colleagues are hoping to identify biomarkers that can help them determine which CLL patients are likely to respond to DNA-PK inhibitors.
*Information in the abstract differs from that presented at the meeting.
CHMP recommends drug for WM
The European Medicines Agency’s Committee for Medicinal Products for Human Use (CHMP) is recommending that ibrutinib (Imbruvica) be approved to treat Waldenström’s macroglobulinemia (WM).
The CHMP is recommending the drug for use in WM patients who have received at least 1 prior therapy as well as previously untreated WM patients who are not suitable candidates for chemo-immunotherapy.
The European Commission will review this recommendation and should make a decision later this year.
Ibrutinib is already approved to treat WM in the US. The drug is also approved in the European Union, the US, and other countries to treat chronic lymphocytic leukemia and mantle cell lymphoma.
Janssen-Cilag International NV (Janssen) holds the marketing authorization for ibrutinib in Europe, and its affiliates market the drug in Europe and the rest of the world. In the US, ibrutinib is under joint development by Pharmacyclics and Janssen Biotech, Inc.
Phase 2 study
The CHMP’s recommendation for ibrutinib was based on a multicenter, phase 2 study in which researchers tested the drug in 63 patients with previously treated WM. Initial data showed an overall response rate of 87.3% in patients who received the drug for a median of 11.7 months.
Updated results from the study were published in NEJM in April. After a median treatment duration of 19.1 months, the overall response rate was 91%.
At 24 months, the estimated rate of progression-free survival was 69%, and the estimated rate of overall survival was 95%.
The most common grade 2-4 adverse events were neutropenia (22%) and thrombocytopenia (14%). Ibrutinib-related neutropenia and thrombocytopenia were reversible but required a dose reduction in 3 patients and treatment discontinuation in 4 patients.
Grade 2 or higher bleeding events occurred in 4 patients, and there were 15 infections considered possibly related to ibrutinib.
Treatment-related atrial fibrillation (AFib) occurred in 3 patients, all of whom had a prior history of paroxysmal AFib. AFib resolved when treatment was withheld, and all 3 patients were able to continue on therapy per protocol without an additional event.
The European Medicines Agency’s Committee for Medicinal Products for Human Use (CHMP) is recommending that ibrutinib (Imbruvica) be approved to treat Waldenström’s macroglobulinemia (WM).
The CHMP is recommending the drug for use in WM patients who have received at least 1 prior therapy as well as previously untreated WM patients who are not suitable candidates for chemo-immunotherapy.
The European Commission will review this recommendation and should make a decision later this year.
Ibrutinib is already approved to treat WM in the US. The drug is also approved in the European Union, the US, and other countries to treat chronic lymphocytic leukemia and mantle cell lymphoma.
Janssen-Cilag International NV (Janssen) holds the marketing authorization for ibrutinib in Europe, and its affiliates market the drug in Europe and the rest of the world. In the US, ibrutinib is under joint development by Pharmacyclics and Janssen Biotech, Inc.
Phase 2 study
The CHMP’s recommendation for ibrutinib was based on a multicenter, phase 2 study in which researchers tested the drug in 63 patients with previously treated WM. Initial data showed an overall response rate of 87.3% in patients who received the drug for a median of 11.7 months.
Updated results from the study were published in NEJM in April. After a median treatment duration of 19.1 months, the overall response rate was 91%.
At 24 months, the estimated rate of progression-free survival was 69%, and the estimated rate of overall survival was 95%.
The most common grade 2-4 adverse events were neutropenia (22%) and thrombocytopenia (14%). Ibrutinib-related neutropenia and thrombocytopenia were reversible but required a dose reduction in 3 patients and treatment discontinuation in 4 patients.
Grade 2 or higher bleeding events occurred in 4 patients, and there were 15 infections considered possibly related to ibrutinib.
Treatment-related atrial fibrillation (AFib) occurred in 3 patients, all of whom had a prior history of paroxysmal AFib. AFib resolved when treatment was withheld, and all 3 patients were able to continue on therapy per protocol without an additional event.
The European Medicines Agency’s Committee for Medicinal Products for Human Use (CHMP) is recommending that ibrutinib (Imbruvica) be approved to treat Waldenström’s macroglobulinemia (WM).
The CHMP is recommending the drug for use in WM patients who have received at least 1 prior therapy as well as previously untreated WM patients who are not suitable candidates for chemo-immunotherapy.
The European Commission will review this recommendation and should make a decision later this year.
Ibrutinib is already approved to treat WM in the US. The drug is also approved in the European Union, the US, and other countries to treat chronic lymphocytic leukemia and mantle cell lymphoma.
Janssen-Cilag International NV (Janssen) holds the marketing authorization for ibrutinib in Europe, and its affiliates market the drug in Europe and the rest of the world. In the US, ibrutinib is under joint development by Pharmacyclics and Janssen Biotech, Inc.
Phase 2 study
The CHMP’s recommendation for ibrutinib was based on a multicenter, phase 2 study in which researchers tested the drug in 63 patients with previously treated WM. Initial data showed an overall response rate of 87.3% in patients who received the drug for a median of 11.7 months.
Updated results from the study were published in NEJM in April. After a median treatment duration of 19.1 months, the overall response rate was 91%.
At 24 months, the estimated rate of progression-free survival was 69%, and the estimated rate of overall survival was 95%.
The most common grade 2-4 adverse events were neutropenia (22%) and thrombocytopenia (14%). Ibrutinib-related neutropenia and thrombocytopenia were reversible but required a dose reduction in 3 patients and treatment discontinuation in 4 patients.
Grade 2 or higher bleeding events occurred in 4 patients, and there were 15 infections considered possibly related to ibrutinib.
Treatment-related atrial fibrillation (AFib) occurred in 3 patients, all of whom had a prior history of paroxysmal AFib. AFib resolved when treatment was withheld, and all 3 patients were able to continue on therapy per protocol without an additional event.
APA: Predictive analytics and big data hold promise in mood disorders
TORONTO – “What if we could detect a mood episode before it happened?” It was with this question that Dr. Andrew A. Nierenberg began his talk on new advances in mood disorders research at the annual meeting of the American Psychiatric Association.
From predictive analytics to big data collaboration to therapeutic apps, Dr. Nierenberg led the audience through a tour of the now and near future.
One company in this space, Ginger.io, uses behavioral analytics to better understand patients’ changing social, mental, and physical health status. The data can then be fed quickly back to clinicians when intervention is warranted. The company’s app collects passive sensor data from patients’ smartphones about their movement, communication, and sleep patterns. Sophisticated analytical methods detect changes in behavior and predict people’s moods and actions.
“It’s a little creepy in some ways, but maybe not,” he said. “If you think about it, when people come to us in distress, it’s not at the very edge or beginning of a mood episode, but they’re deep into it [and that is] when we tend to intervene.”
When a patient is evaluated, he explained, the strength of the evaluation is dependent on accurate self-observation, and accurate storage and recall of the patient’s observations about their emotional states.
“Those are all problems for people with mood disorders,” Dr. Nierenberg said. “So, when we ask someone how they have been in the past week, we’re really getting a window into the past 3-6 hours. What these predictive analytics allow is real time data to look at what is actually happening with people.”
The question really being asked here, said Dr. Nierenberg, is whether it’s possible to see objective changes that are not among the information people are likely to report to their clinicians, that can predict a mood episode.
Harnessing technology
Big data also has come to mood disorders care in a big way. Large registries are being compiled for research purposes, and patient communities are growing that help patients cope with their conditions and help researchers collect huge amounts of data. Based on cognitive-behavioral therapy combined with relaxation and wellness techniques, we believe in holistic daily tools aimed at breaking the anxiety cycle. We’re not about quick fixes or false promises. We are about real progress, a day at a time.
According to its website, Big White Wall is an online community of people “who are anxious, down, or not coping who support and help each other by sharing what’s troubling them, guided by trained professionals.”
Other examples of these tech-based solutions are therapeutic apps and websites. Dr. Nierenberg mentioned just three: MoodGYM, Now Matters Now, and Pacifica, all of which are “cutting edge and evidence-based” and help patients manage their conditions.
• MoodGym is a free, interactive self-help program that provides cognitive-behavior therapy (CBT) training to help users prevent and cope with depression and anxiety.
• Now Matters Now is an online video-based program that uses “real” people, including suicide prevention researchers and clinicians, to teach coping skills such as mindfulness, paced breathing, and opposite action to individuals having suicidal thoughts. The skills taught are part of dialectical behavior therapy, or DBT, proven to be helpful for people considering suicide. Dr. Nierenberg called this community “quite extraordinary” and uniquely valuable, “because the majority of people who are having suicidal thoughts don’t have them when they’re in your office …”
• Pacifica is a self-help app for anxiety that uses CBT combined with relaxation and wellness techniques aimed at “breaking the anxiety cycle,” the company says.
‘A game changer’
The Patient Centered Outcomes Research Network (PCORnet.org) is “a game changer,” said Dr. Nierenberg. It is part of the Patient-Centered Outcomes Research Institute (PCORI), which is part of the Affordable Care Act, funded at about $500 million a year. One part of PCORnet.org is the Patient-Powered Research Networks, including a mood-focused network, moodnetwork.org.
“It allows the patients to choose how they want to be monitored, through self-report, but also gives them a voice in prioritizing research and research questions.” A goal is to transform research and mood disorder care by creating an infrastructure for both research and clinicians wanting to follow their patients and through prospective comparative effectiveness trials embedded within routine care.
The organizers hope to gather 50,000 patients in the network, a “wild and audacious goal,” admitted Dr. Nierenberg, who is the principal investigator of moodnetwork.org. PCORnet.org ultimately might cover 90 million people and truly be able to answer real-world questions in a way that most research today does not address, he added.
Dr. Nierenberg is a top researcher and educator from Massachusetts General Hospital and Harvard Medical School, Boston. In 2013, he won the prestigious Colvin Prize given by the Brain & Behavior Research Foundation for Outstanding Achievement in Mood Disorders Research.
Dr. Nierenberg reported working with several pharmaceutical companies in drug development.
TORONTO – “What if we could detect a mood episode before it happened?” It was with this question that Dr. Andrew A. Nierenberg began his talk on new advances in mood disorders research at the annual meeting of the American Psychiatric Association.
From predictive analytics to big data collaboration to therapeutic apps, Dr. Nierenberg led the audience through a tour of the now and near future.
One company in this space, Ginger.io, uses behavioral analytics to better understand patients’ changing social, mental, and physical health status. The data can then be fed quickly back to clinicians when intervention is warranted. The company’s app collects passive sensor data from patients’ smartphones about their movement, communication, and sleep patterns. Sophisticated analytical methods detect changes in behavior and predict people’s moods and actions.
“It’s a little creepy in some ways, but maybe not,” he said. “If you think about it, when people come to us in distress, it’s not at the very edge or beginning of a mood episode, but they’re deep into it [and that is] when we tend to intervene.”
When a patient is evaluated, he explained, the strength of the evaluation is dependent on accurate self-observation, and accurate storage and recall of the patient’s observations about their emotional states.
“Those are all problems for people with mood disorders,” Dr. Nierenberg said. “So, when we ask someone how they have been in the past week, we’re really getting a window into the past 3-6 hours. What these predictive analytics allow is real time data to look at what is actually happening with people.”
The question really being asked here, said Dr. Nierenberg, is whether it’s possible to see objective changes that are not among the information people are likely to report to their clinicians, that can predict a mood episode.
Harnessing technology
Big data also has come to mood disorders care in a big way. Large registries are being compiled for research purposes, and patient communities are growing that help patients cope with their conditions and help researchers collect huge amounts of data. Based on cognitive-behavioral therapy combined with relaxation and wellness techniques, we believe in holistic daily tools aimed at breaking the anxiety cycle. We’re not about quick fixes or false promises. We are about real progress, a day at a time.
According to its website, Big White Wall is an online community of people “who are anxious, down, or not coping who support and help each other by sharing what’s troubling them, guided by trained professionals.”
Other examples of these tech-based solutions are therapeutic apps and websites. Dr. Nierenberg mentioned just three: MoodGYM, Now Matters Now, and Pacifica, all of which are “cutting edge and evidence-based” and help patients manage their conditions.
• MoodGym is a free, interactive self-help program that provides cognitive-behavior therapy (CBT) training to help users prevent and cope with depression and anxiety.
• Now Matters Now is an online video-based program that uses “real” people, including suicide prevention researchers and clinicians, to teach coping skills such as mindfulness, paced breathing, and opposite action to individuals having suicidal thoughts. The skills taught are part of dialectical behavior therapy, or DBT, proven to be helpful for people considering suicide. Dr. Nierenberg called this community “quite extraordinary” and uniquely valuable, “because the majority of people who are having suicidal thoughts don’t have them when they’re in your office …”
• Pacifica is a self-help app for anxiety that uses CBT combined with relaxation and wellness techniques aimed at “breaking the anxiety cycle,” the company says.
‘A game changer’
The Patient Centered Outcomes Research Network (PCORnet.org) is “a game changer,” said Dr. Nierenberg. It is part of the Patient-Centered Outcomes Research Institute (PCORI), which is part of the Affordable Care Act, funded at about $500 million a year. One part of PCORnet.org is the Patient-Powered Research Networks, including a mood-focused network, moodnetwork.org.
“It allows the patients to choose how they want to be monitored, through self-report, but also gives them a voice in prioritizing research and research questions.” A goal is to transform research and mood disorder care by creating an infrastructure for both research and clinicians wanting to follow their patients and through prospective comparative effectiveness trials embedded within routine care.
The organizers hope to gather 50,000 patients in the network, a “wild and audacious goal,” admitted Dr. Nierenberg, who is the principal investigator of moodnetwork.org. PCORnet.org ultimately might cover 90 million people and truly be able to answer real-world questions in a way that most research today does not address, he added.
Dr. Nierenberg is a top researcher and educator from Massachusetts General Hospital and Harvard Medical School, Boston. In 2013, he won the prestigious Colvin Prize given by the Brain & Behavior Research Foundation for Outstanding Achievement in Mood Disorders Research.
Dr. Nierenberg reported working with several pharmaceutical companies in drug development.
TORONTO – “What if we could detect a mood episode before it happened?” It was with this question that Dr. Andrew A. Nierenberg began his talk on new advances in mood disorders research at the annual meeting of the American Psychiatric Association.
From predictive analytics to big data collaboration to therapeutic apps, Dr. Nierenberg led the audience through a tour of the now and near future.
One company in this space, Ginger.io, uses behavioral analytics to better understand patients’ changing social, mental, and physical health status. The data can then be fed quickly back to clinicians when intervention is warranted. The company’s app collects passive sensor data from patients’ smartphones about their movement, communication, and sleep patterns. Sophisticated analytical methods detect changes in behavior and predict people’s moods and actions.
“It’s a little creepy in some ways, but maybe not,” he said. “If you think about it, when people come to us in distress, it’s not at the very edge or beginning of a mood episode, but they’re deep into it [and that is] when we tend to intervene.”
When a patient is evaluated, he explained, the strength of the evaluation is dependent on accurate self-observation, and accurate storage and recall of the patient’s observations about their emotional states.
“Those are all problems for people with mood disorders,” Dr. Nierenberg said. “So, when we ask someone how they have been in the past week, we’re really getting a window into the past 3-6 hours. What these predictive analytics allow is real time data to look at what is actually happening with people.”
The question really being asked here, said Dr. Nierenberg, is whether it’s possible to see objective changes that are not among the information people are likely to report to their clinicians, that can predict a mood episode.
Harnessing technology
Big data also has come to mood disorders care in a big way. Large registries are being compiled for research purposes, and patient communities are growing that help patients cope with their conditions and help researchers collect huge amounts of data. Based on cognitive-behavioral therapy combined with relaxation and wellness techniques, we believe in holistic daily tools aimed at breaking the anxiety cycle. We’re not about quick fixes or false promises. We are about real progress, a day at a time.
According to its website, Big White Wall is an online community of people “who are anxious, down, or not coping who support and help each other by sharing what’s troubling them, guided by trained professionals.”
Other examples of these tech-based solutions are therapeutic apps and websites. Dr. Nierenberg mentioned just three: MoodGYM, Now Matters Now, and Pacifica, all of which are “cutting edge and evidence-based” and help patients manage their conditions.
• MoodGym is a free, interactive self-help program that provides cognitive-behavior therapy (CBT) training to help users prevent and cope with depression and anxiety.
• Now Matters Now is an online video-based program that uses “real” people, including suicide prevention researchers and clinicians, to teach coping skills such as mindfulness, paced breathing, and opposite action to individuals having suicidal thoughts. The skills taught are part of dialectical behavior therapy, or DBT, proven to be helpful for people considering suicide. Dr. Nierenberg called this community “quite extraordinary” and uniquely valuable, “because the majority of people who are having suicidal thoughts don’t have them when they’re in your office …”
• Pacifica is a self-help app for anxiety that uses CBT combined with relaxation and wellness techniques aimed at “breaking the anxiety cycle,” the company says.
‘A game changer’
The Patient Centered Outcomes Research Network (PCORnet.org) is “a game changer,” said Dr. Nierenberg. It is part of the Patient-Centered Outcomes Research Institute (PCORI), which is part of the Affordable Care Act, funded at about $500 million a year. One part of PCORnet.org is the Patient-Powered Research Networks, including a mood-focused network, moodnetwork.org.
“It allows the patients to choose how they want to be monitored, through self-report, but also gives them a voice in prioritizing research and research questions.” A goal is to transform research and mood disorder care by creating an infrastructure for both research and clinicians wanting to follow their patients and through prospective comparative effectiveness trials embedded within routine care.
The organizers hope to gather 50,000 patients in the network, a “wild and audacious goal,” admitted Dr. Nierenberg, who is the principal investigator of moodnetwork.org. PCORnet.org ultimately might cover 90 million people and truly be able to answer real-world questions in a way that most research today does not address, he added.
Dr. Nierenberg is a top researcher and educator from Massachusetts General Hospital and Harvard Medical School, Boston. In 2013, he won the prestigious Colvin Prize given by the Brain & Behavior Research Foundation for Outstanding Achievement in Mood Disorders Research.
Dr. Nierenberg reported working with several pharmaceutical companies in drug development.
EXPERT ANALYSIS FROM THE APA ANNUAL MEETING
Patient satisfaction doesn’t equal better hospital care
What happens when you give children everything they ask for? They get spoiled, of course. Any parent can tell you that.
The problem is that you’re trying to raise children to (eventually) be responsible adults. Part of this is teaching them that you can’t always win, you should always share, and you can’t always get what you want.
Most kids don’t like it. (I know I didn’t.) They only see that the candy or toy they want is being refused and don’t grasp the long-term plan of growing up to be a decent person. Across a thousand human cultures, any parent would agree.
But the same principle doesn’t seem to apply in modern health care. What would you think is more important in a hospital: competent staff or having a beverage offered to you after being checked into the emergency department?
Sadly, things like the latter seem to be winning because of the recent emphasis on patient satisfaction scores. In today’s world, 30% of a hospital’s Medicare reimbursement is based on these scores. That’s a lot of money.
Unfortunately, quality of care doesn’t necessarily have the same meaning between doctors and patients. The former will say it means you left the hospital with a good outcome. The latter will agree but also will throw in things like whether they got enough pain meds or their call light answered fast enough. If you’re having chest pain or severe dyspnea, getting that call light answered quickly is pretty important. But if all you want is a soda or for someone to hand you the TV remote … not so much.
The problem is that the patient satisfaction surveys (and yes, speed of call-light response is on there) don’t take that key point into account. What might make some patients happy isn’t necessarily in their best interest. The post-CABG patient who wants a double cheeseburger won’t be thrilled if he gets a salad instead. Another patient in for detox won’t be pleased if she doesn’t get Dilaudid on demand. A third will be angry that he’s not allowed to smoke. Those refusals are an integral part of their successful treatment and recovery plan, but they may not see it that way. And they’ll be sure to mark it on the survey.
As a result, the hospital gets penalized in spite of the fact that they’re doing their best to provide quality care. And the business-minded CEOs, who generally have no medical background, only care about this part of it.
Measuring what counts is important. But the idea that hospital care should be held to the same standards as Burger King and Walmart is fundamentally flawed. The things that are done in hospitals – cut people open, draw blood, biopsy bone marrow, put in endotracheal and feeding tubes – aren’t intended as recreational experiences. We try to make them as painless as possible, but in health care “do no harm” often means doing some harm in order to prevent a catastrophe.
The side effects of chemotherapy are (hopefully) offset by the successful treatment of cancer. But that doesn’t mean hair loss, nausea, vomiting, diarrhea, and other toxic symptoms are part of “customer satisfaction.” One study even found that the most satisfied patients had the highest mortality.
We owe patients the very best care we can give them, but they also need to understand that “best care” doesn’t always mean what they want in the short term. We’re focused on a goal that’s beyond the immediate horizon.
Dr. Block has a solo neurology practice in Scottsdale, Ariz.
What happens when you give children everything they ask for? They get spoiled, of course. Any parent can tell you that.
The problem is that you’re trying to raise children to (eventually) be responsible adults. Part of this is teaching them that you can’t always win, you should always share, and you can’t always get what you want.
Most kids don’t like it. (I know I didn’t.) They only see that the candy or toy they want is being refused and don’t grasp the long-term plan of growing up to be a decent person. Across a thousand human cultures, any parent would agree.
But the same principle doesn’t seem to apply in modern health care. What would you think is more important in a hospital: competent staff or having a beverage offered to you after being checked into the emergency department?
Sadly, things like the latter seem to be winning because of the recent emphasis on patient satisfaction scores. In today’s world, 30% of a hospital’s Medicare reimbursement is based on these scores. That’s a lot of money.
Unfortunately, quality of care doesn’t necessarily have the same meaning between doctors and patients. The former will say it means you left the hospital with a good outcome. The latter will agree but also will throw in things like whether they got enough pain meds or their call light answered fast enough. If you’re having chest pain or severe dyspnea, getting that call light answered quickly is pretty important. But if all you want is a soda or for someone to hand you the TV remote … not so much.
The problem is that the patient satisfaction surveys (and yes, speed of call-light response is on there) don’t take that key point into account. What might make some patients happy isn’t necessarily in their best interest. The post-CABG patient who wants a double cheeseburger won’t be thrilled if he gets a salad instead. Another patient in for detox won’t be pleased if she doesn’t get Dilaudid on demand. A third will be angry that he’s not allowed to smoke. Those refusals are an integral part of their successful treatment and recovery plan, but they may not see it that way. And they’ll be sure to mark it on the survey.
As a result, the hospital gets penalized in spite of the fact that they’re doing their best to provide quality care. And the business-minded CEOs, who generally have no medical background, only care about this part of it.
Measuring what counts is important. But the idea that hospital care should be held to the same standards as Burger King and Walmart is fundamentally flawed. The things that are done in hospitals – cut people open, draw blood, biopsy bone marrow, put in endotracheal and feeding tubes – aren’t intended as recreational experiences. We try to make them as painless as possible, but in health care “do no harm” often means doing some harm in order to prevent a catastrophe.
The side effects of chemotherapy are (hopefully) offset by the successful treatment of cancer. But that doesn’t mean hair loss, nausea, vomiting, diarrhea, and other toxic symptoms are part of “customer satisfaction.” One study even found that the most satisfied patients had the highest mortality.
We owe patients the very best care we can give them, but they also need to understand that “best care” doesn’t always mean what they want in the short term. We’re focused on a goal that’s beyond the immediate horizon.
Dr. Block has a solo neurology practice in Scottsdale, Ariz.
What happens when you give children everything they ask for? They get spoiled, of course. Any parent can tell you that.
The problem is that you’re trying to raise children to (eventually) be responsible adults. Part of this is teaching them that you can’t always win, you should always share, and you can’t always get what you want.
Most kids don’t like it. (I know I didn’t.) They only see that the candy or toy they want is being refused and don’t grasp the long-term plan of growing up to be a decent person. Across a thousand human cultures, any parent would agree.
But the same principle doesn’t seem to apply in modern health care. What would you think is more important in a hospital: competent staff or having a beverage offered to you after being checked into the emergency department?
Sadly, things like the latter seem to be winning because of the recent emphasis on patient satisfaction scores. In today’s world, 30% of a hospital’s Medicare reimbursement is based on these scores. That’s a lot of money.
Unfortunately, quality of care doesn’t necessarily have the same meaning between doctors and patients. The former will say it means you left the hospital with a good outcome. The latter will agree but also will throw in things like whether they got enough pain meds or their call light answered fast enough. If you’re having chest pain or severe dyspnea, getting that call light answered quickly is pretty important. But if all you want is a soda or for someone to hand you the TV remote … not so much.
The problem is that the patient satisfaction surveys (and yes, speed of call-light response is on there) don’t take that key point into account. What might make some patients happy isn’t necessarily in their best interest. The post-CABG patient who wants a double cheeseburger won’t be thrilled if he gets a salad instead. Another patient in for detox won’t be pleased if she doesn’t get Dilaudid on demand. A third will be angry that he’s not allowed to smoke. Those refusals are an integral part of their successful treatment and recovery plan, but they may not see it that way. And they’ll be sure to mark it on the survey.
As a result, the hospital gets penalized in spite of the fact that they’re doing their best to provide quality care. And the business-minded CEOs, who generally have no medical background, only care about this part of it.
Measuring what counts is important. But the idea that hospital care should be held to the same standards as Burger King and Walmart is fundamentally flawed. The things that are done in hospitals – cut people open, draw blood, biopsy bone marrow, put in endotracheal and feeding tubes – aren’t intended as recreational experiences. We try to make them as painless as possible, but in health care “do no harm” often means doing some harm in order to prevent a catastrophe.
The side effects of chemotherapy are (hopefully) offset by the successful treatment of cancer. But that doesn’t mean hair loss, nausea, vomiting, diarrhea, and other toxic symptoms are part of “customer satisfaction.” One study even found that the most satisfied patients had the highest mortality.
We owe patients the very best care we can give them, but they also need to understand that “best care” doesn’t always mean what they want in the short term. We’re focused on a goal that’s beyond the immediate horizon.
Dr. Block has a solo neurology practice in Scottsdale, Ariz.
Improving targeted therapy for leukemia, other diseases
Photo by Sam Ogden
A chemical strategy may allow researchers to target “undruggable” proteins and overcome resistance to current targeted therapies, according to a report published in Science.
The strategy uses tumor cells’ own protein-elimination system to break down and dispose of the proteins that drive cancer growth.
When tested in vitro and in vivo, the approach caused leukemia cells to die more quickly than they do with conventional targeted
therapies.
“One of the reasons [treatment] resistance occurs is that cancer-related proteins often have multiple functions within the cell, and conventional targeted therapies inhibit just one or a few of those functions,” said study author James Bradner, MD, of the Dana-Farber Cancer Institute in Boston, Massachusetts.
“Conventional drugs allow the targeted protein to adapt to the drug, and the cell finds alternate routes for its growth signals. We began designing approaches that cause the target protein to disintegrate, rather than merely be inhibited. It would be very powerful if we could chemically convert an inhibitor drug into a degrader drug.”
With this in mind, Dr Bradner’s team designed a chemical adapter that attaches to a targeted drug molecule. The adapter enables the drug to tow the cell’s protein-degradation machinery directly to the protein of interest. Once bound to the protein, the combination drug-and-protein-degrader essentially demolishes it.
The investigators tested the technology in leukemia cells. They built an adapter out of phthalimide, a chemical derivative of the drug thalidomide, and attached it to the BRD4 inhibitor JQ1. The phthalimide was designed to “hijack” the cereblon E3 ubiquitin ligase complex.
When the researchers treated the leukemia cells with a JQ1-phthalimide conjugate called dBET1, the BRD4 protein within the cells was degraded in less than an hour. The team said such rapid and extensive degradation suggests conjugates may be able to prevent or hinder cancer cells from developing resistance to targeted therapies.
“The potency, selectivity, and rapidity of this approach—namely, the ability to home in specifically on BRD4—are unprecedented in clinical approaches to protein degradation,” Dr Bradner said.
To determine how selective dBET1 actually is, the investigators measured the levels of all proteins in leukemia cells at 1 hour and 2 hours after treatment.
“We were stunned to find that only 3 proteins of more than 7000 in the entire cell were degraded: BRD2, 3, and 4, an exceptional degree of selectivity guided by the intended targets of JQ1,” Dr Bradner said. “It’s as though dBET1 is laser-guided to deliver protein-degrading machinery to targeted proteins.”
The researchers then tested dBET1 in mice bearing leukemia. As in the cell samples, there was a rapid degradation of BRD4 in the tumor cells and a potent anti-leukemic effect, with few noticeable side effects.
To see if compounds other than JQ1 can be used as a guidance system for a conjugate, the investigators created a set of molecules that lock the protein-degradation machinery onto a compound called SLF, which targets the protein FKBP12.
When they treated cancer cells with SLF, the team found it degraded the vast majority of FKBP12 in the cells within a few hours.
Buoyed by these results, the researchers are working to create a derivative of dBET1 that can be used as a drug in humans and to extend the conjugate strategy for the treatment of other diseases.
“The dBET1 and the dFKBP12 compounds are presently in a late stage of lead optimization for therapeutic development in both cancer and non-malignant diseases,” said Prem Das, PhD, chief research business development officer at Dana-Farber.
“Composition-of-matter and method-of-use patent applications have been filed on these and other additional targeted agents, as well as on the chemistry platform. They will be licensed for commercialization to an appropriate company according to standard Dana-Farber practice.”
Photo by Sam Ogden
A chemical strategy may allow researchers to target “undruggable” proteins and overcome resistance to current targeted therapies, according to a report published in Science.
The strategy uses tumor cells’ own protein-elimination system to break down and dispose of the proteins that drive cancer growth.
When tested in vitro and in vivo, the approach caused leukemia cells to die more quickly than they do with conventional targeted
therapies.
“One of the reasons [treatment] resistance occurs is that cancer-related proteins often have multiple functions within the cell, and conventional targeted therapies inhibit just one or a few of those functions,” said study author James Bradner, MD, of the Dana-Farber Cancer Institute in Boston, Massachusetts.
“Conventional drugs allow the targeted protein to adapt to the drug, and the cell finds alternate routes for its growth signals. We began designing approaches that cause the target protein to disintegrate, rather than merely be inhibited. It would be very powerful if we could chemically convert an inhibitor drug into a degrader drug.”
With this in mind, Dr Bradner’s team designed a chemical adapter that attaches to a targeted drug molecule. The adapter enables the drug to tow the cell’s protein-degradation machinery directly to the protein of interest. Once bound to the protein, the combination drug-and-protein-degrader essentially demolishes it.
The investigators tested the technology in leukemia cells. They built an adapter out of phthalimide, a chemical derivative of the drug thalidomide, and attached it to the BRD4 inhibitor JQ1. The phthalimide was designed to “hijack” the cereblon E3 ubiquitin ligase complex.
When the researchers treated the leukemia cells with a JQ1-phthalimide conjugate called dBET1, the BRD4 protein within the cells was degraded in less than an hour. The team said such rapid and extensive degradation suggests conjugates may be able to prevent or hinder cancer cells from developing resistance to targeted therapies.
“The potency, selectivity, and rapidity of this approach—namely, the ability to home in specifically on BRD4—are unprecedented in clinical approaches to protein degradation,” Dr Bradner said.
To determine how selective dBET1 actually is, the investigators measured the levels of all proteins in leukemia cells at 1 hour and 2 hours after treatment.
“We were stunned to find that only 3 proteins of more than 7000 in the entire cell were degraded: BRD2, 3, and 4, an exceptional degree of selectivity guided by the intended targets of JQ1,” Dr Bradner said. “It’s as though dBET1 is laser-guided to deliver protein-degrading machinery to targeted proteins.”
The researchers then tested dBET1 in mice bearing leukemia. As in the cell samples, there was a rapid degradation of BRD4 in the tumor cells and a potent anti-leukemic effect, with few noticeable side effects.
To see if compounds other than JQ1 can be used as a guidance system for a conjugate, the investigators created a set of molecules that lock the protein-degradation machinery onto a compound called SLF, which targets the protein FKBP12.
When they treated cancer cells with SLF, the team found it degraded the vast majority of FKBP12 in the cells within a few hours.
Buoyed by these results, the researchers are working to create a derivative of dBET1 that can be used as a drug in humans and to extend the conjugate strategy for the treatment of other diseases.
“The dBET1 and the dFKBP12 compounds are presently in a late stage of lead optimization for therapeutic development in both cancer and non-malignant diseases,” said Prem Das, PhD, chief research business development officer at Dana-Farber.
“Composition-of-matter and method-of-use patent applications have been filed on these and other additional targeted agents, as well as on the chemistry platform. They will be licensed for commercialization to an appropriate company according to standard Dana-Farber practice.”
Photo by Sam Ogden
A chemical strategy may allow researchers to target “undruggable” proteins and overcome resistance to current targeted therapies, according to a report published in Science.
The strategy uses tumor cells’ own protein-elimination system to break down and dispose of the proteins that drive cancer growth.
When tested in vitro and in vivo, the approach caused leukemia cells to die more quickly than they do with conventional targeted
therapies.
“One of the reasons [treatment] resistance occurs is that cancer-related proteins often have multiple functions within the cell, and conventional targeted therapies inhibit just one or a few of those functions,” said study author James Bradner, MD, of the Dana-Farber Cancer Institute in Boston, Massachusetts.
“Conventional drugs allow the targeted protein to adapt to the drug, and the cell finds alternate routes for its growth signals. We began designing approaches that cause the target protein to disintegrate, rather than merely be inhibited. It would be very powerful if we could chemically convert an inhibitor drug into a degrader drug.”
With this in mind, Dr Bradner’s team designed a chemical adapter that attaches to a targeted drug molecule. The adapter enables the drug to tow the cell’s protein-degradation machinery directly to the protein of interest. Once bound to the protein, the combination drug-and-protein-degrader essentially demolishes it.
The investigators tested the technology in leukemia cells. They built an adapter out of phthalimide, a chemical derivative of the drug thalidomide, and attached it to the BRD4 inhibitor JQ1. The phthalimide was designed to “hijack” the cereblon E3 ubiquitin ligase complex.
When the researchers treated the leukemia cells with a JQ1-phthalimide conjugate called dBET1, the BRD4 protein within the cells was degraded in less than an hour. The team said such rapid and extensive degradation suggests conjugates may be able to prevent or hinder cancer cells from developing resistance to targeted therapies.
“The potency, selectivity, and rapidity of this approach—namely, the ability to home in specifically on BRD4—are unprecedented in clinical approaches to protein degradation,” Dr Bradner said.
To determine how selective dBET1 actually is, the investigators measured the levels of all proteins in leukemia cells at 1 hour and 2 hours after treatment.
“We were stunned to find that only 3 proteins of more than 7000 in the entire cell were degraded: BRD2, 3, and 4, an exceptional degree of selectivity guided by the intended targets of JQ1,” Dr Bradner said. “It’s as though dBET1 is laser-guided to deliver protein-degrading machinery to targeted proteins.”
The researchers then tested dBET1 in mice bearing leukemia. As in the cell samples, there was a rapid degradation of BRD4 in the tumor cells and a potent anti-leukemic effect, with few noticeable side effects.
To see if compounds other than JQ1 can be used as a guidance system for a conjugate, the investigators created a set of molecules that lock the protein-degradation machinery onto a compound called SLF, which targets the protein FKBP12.
When they treated cancer cells with SLF, the team found it degraded the vast majority of FKBP12 in the cells within a few hours.
Buoyed by these results, the researchers are working to create a derivative of dBET1 that can be used as a drug in humans and to extend the conjugate strategy for the treatment of other diseases.
“The dBET1 and the dFKBP12 compounds are presently in a late stage of lead optimization for therapeutic development in both cancer and non-malignant diseases,” said Prem Das, PhD, chief research business development officer at Dana-Farber.
“Composition-of-matter and method-of-use patent applications have been filed on these and other additional targeted agents, as well as on the chemistry platform. They will be licensed for commercialization to an appropriate company according to standard Dana-Farber practice.”
Anticoagulant type doesn’t affect stent thrombosis risk
PARIS—New research suggests that patients who have undergone primary percutaneous coronary intervention (PCI) have a low risk of stent thrombosis, regardless of the anticoagulant therapy they receive.
In a large, registry-based study, stent thrombosis occurred in less than 1% of patients, regardless of whether they received bivalirudin with or without heparin, heparin alone, or a GP IIb/IIIa inhibitor (GPI) with or without heparin.
The study also showed that patients who experienced stent thrombosis between days 2 and 30, regardless of drug regimen, were more likely to die within a year than patients who developed stent thrombosis within the first 24 hours of their procedure.
Per Grimfjard, of Vasteras Hospital/Uppsala University in Sweden, presented these findings at EuroPCR 2015.
A number of recent studies have raised concerns that bivalirudin may increase the risk of stent thrombosis compared with heparin. But rates of stent thrombosis have differed substantially between studies.
So Dr Grimfjard and his colleagues decided to review stent thrombosis rates by drug choice among more than 30,000 patients who were treated with primary PCI for ST-elevation myocardial infarction (STEMI) between January 2007 and July 2014 in the Swedish Coronary Angiography and Angioplasty Register (SCAAR).
The researchers divided patients into 3 treatment groups: bivalirudin, heparin, and GPI. However, 77% of patients in the bivalirudin group also received heparin, and 3.6% received a GPI prior to or during the PCI procedure. In the GPI group, 77% of patients also received heparin.
The rates of stent thrombosis were low in all 3 groups—0.84% in the bivalirudin group, 0.94% in the heparin group, and 0.83% in the GPI group.
For all 3 drugs, mortality at 1 year was numerically higher if the stent thrombosis occurred between 2 and 30 days, as compared with day 0 to 1 post-PCI.
“[A] possible explanation is that a stent thrombosis that happens once the patient has left the hospital is likely to cause a more substantial infarction, the reason being longer delay from symptoms to revascularization,” Dr Grimfjard said.
He added that a more substantial myocardial infarction typically leads to more heart failure and arrhythmia long-term. Unfortunately, the findings regarding the timing of stent thrombosis do not offer any guidance for choosing optimal antithrombotic treatment.
He and his colleagues are currently enrolling patients in a 6000-patient, registry-based, randomized clinical trial called SWEDEHART-Validate. The team will compare heparin alone to bivalirudin and optional low-dose heparin in STEMI and non-STEMI patients undergoing PCI.
“Hopefully, this large, randomized trial will bring clarity to the choice of antithrombotic treatment strategy in these patients,” Dr Grimfjard said.
PARIS—New research suggests that patients who have undergone primary percutaneous coronary intervention (PCI) have a low risk of stent thrombosis, regardless of the anticoagulant therapy they receive.
In a large, registry-based study, stent thrombosis occurred in less than 1% of patients, regardless of whether they received bivalirudin with or without heparin, heparin alone, or a GP IIb/IIIa inhibitor (GPI) with or without heparin.
The study also showed that patients who experienced stent thrombosis between days 2 and 30, regardless of drug regimen, were more likely to die within a year than patients who developed stent thrombosis within the first 24 hours of their procedure.
Per Grimfjard, of Vasteras Hospital/Uppsala University in Sweden, presented these findings at EuroPCR 2015.
A number of recent studies have raised concerns that bivalirudin may increase the risk of stent thrombosis compared with heparin. But rates of stent thrombosis have differed substantially between studies.
So Dr Grimfjard and his colleagues decided to review stent thrombosis rates by drug choice among more than 30,000 patients who were treated with primary PCI for ST-elevation myocardial infarction (STEMI) between January 2007 and July 2014 in the Swedish Coronary Angiography and Angioplasty Register (SCAAR).
The researchers divided patients into 3 treatment groups: bivalirudin, heparin, and GPI. However, 77% of patients in the bivalirudin group also received heparin, and 3.6% received a GPI prior to or during the PCI procedure. In the GPI group, 77% of patients also received heparin.
The rates of stent thrombosis were low in all 3 groups—0.84% in the bivalirudin group, 0.94% in the heparin group, and 0.83% in the GPI group.
For all 3 drugs, mortality at 1 year was numerically higher if the stent thrombosis occurred between 2 and 30 days, as compared with day 0 to 1 post-PCI.
“[A] possible explanation is that a stent thrombosis that happens once the patient has left the hospital is likely to cause a more substantial infarction, the reason being longer delay from symptoms to revascularization,” Dr Grimfjard said.
He added that a more substantial myocardial infarction typically leads to more heart failure and arrhythmia long-term. Unfortunately, the findings regarding the timing of stent thrombosis do not offer any guidance for choosing optimal antithrombotic treatment.
He and his colleagues are currently enrolling patients in a 6000-patient, registry-based, randomized clinical trial called SWEDEHART-Validate. The team will compare heparin alone to bivalirudin and optional low-dose heparin in STEMI and non-STEMI patients undergoing PCI.
“Hopefully, this large, randomized trial will bring clarity to the choice of antithrombotic treatment strategy in these patients,” Dr Grimfjard said.
PARIS—New research suggests that patients who have undergone primary percutaneous coronary intervention (PCI) have a low risk of stent thrombosis, regardless of the anticoagulant therapy they receive.
In a large, registry-based study, stent thrombosis occurred in less than 1% of patients, regardless of whether they received bivalirudin with or without heparin, heparin alone, or a GP IIb/IIIa inhibitor (GPI) with or without heparin.
The study also showed that patients who experienced stent thrombosis between days 2 and 30, regardless of drug regimen, were more likely to die within a year than patients who developed stent thrombosis within the first 24 hours of their procedure.
Per Grimfjard, of Vasteras Hospital/Uppsala University in Sweden, presented these findings at EuroPCR 2015.
A number of recent studies have raised concerns that bivalirudin may increase the risk of stent thrombosis compared with heparin. But rates of stent thrombosis have differed substantially between studies.
So Dr Grimfjard and his colleagues decided to review stent thrombosis rates by drug choice among more than 30,000 patients who were treated with primary PCI for ST-elevation myocardial infarction (STEMI) between January 2007 and July 2014 in the Swedish Coronary Angiography and Angioplasty Register (SCAAR).
The researchers divided patients into 3 treatment groups: bivalirudin, heparin, and GPI. However, 77% of patients in the bivalirudin group also received heparin, and 3.6% received a GPI prior to or during the PCI procedure. In the GPI group, 77% of patients also received heparin.
The rates of stent thrombosis were low in all 3 groups—0.84% in the bivalirudin group, 0.94% in the heparin group, and 0.83% in the GPI group.
For all 3 drugs, mortality at 1 year was numerically higher if the stent thrombosis occurred between 2 and 30 days, as compared with day 0 to 1 post-PCI.
“[A] possible explanation is that a stent thrombosis that happens once the patient has left the hospital is likely to cause a more substantial infarction, the reason being longer delay from symptoms to revascularization,” Dr Grimfjard said.
He added that a more substantial myocardial infarction typically leads to more heart failure and arrhythmia long-term. Unfortunately, the findings regarding the timing of stent thrombosis do not offer any guidance for choosing optimal antithrombotic treatment.
He and his colleagues are currently enrolling patients in a 6000-patient, registry-based, randomized clinical trial called SWEDEHART-Validate. The team will compare heparin alone to bivalirudin and optional low-dose heparin in STEMI and non-STEMI patients undergoing PCI.
“Hopefully, this large, randomized trial will bring clarity to the choice of antithrombotic treatment strategy in these patients,” Dr Grimfjard said.
Team reports new method to identify immune cells
Photo by Graham Colm
A new method for identifying immune cells could pave the way for rapid detection of hematologic malignancies from a small blood sample, according to researchers.
The team found they could use wavelength modulated Raman spectroscopy (WMRS) to identify subsets of T cells, natural killer cells, and dendritic cells.
Traditional methods of identifying these cells usually involve labeling them with fluorescent or magnetically labeled antibodies.
Using WMRS, the researchers were able to identify immune cells with no labeling at all, thus permitting rapid identification and further analysis to take place with no potential alteration to the cells.
Simon Powis, PhD, of the University of St Andrews in Fife, Scotland, and his colleagues described this work in PLOS ONE.
Raman scattering refers to light scattering from molecules in a sample where the light energy can be shifted up or down and recorded as a “molecular fingerprint” that can be used for identification. Normally, this process is very weak and further hampered by other background light (eg, fluorescence).
WMRS subtly changes the incident laser light that, in turn, results in a modulation of the Raman signal, allowing it to be extracted from any (stationary) interfering signal.
Using WMRS, Dr Powis and his colleagues found they could identify CD4+ T cells, CD8+ T cells, CD56+ natural killer cells, CD303+ lymphoid/plasmacytoid dendritic cells, and CD1c+ myeloid dendritic cells.
“Under a normal light microscope, these immune cells essentially all look identical,” Dr Powis said. “With this new method, we can identify key cell types without any labeling.”
“Our next goal is to make a full catalogue of all the normal cell types of the immune system that can be detected in the bloodstream. Once we have this completed, we can then collaborate with our clinical colleagues to start identifying when these immune cells are altered, in conditions such as leukemia and lymphoma, potentially providing a rapid detection system from just a small blood sample.”
Photo by Graham Colm
A new method for identifying immune cells could pave the way for rapid detection of hematologic malignancies from a small blood sample, according to researchers.
The team found they could use wavelength modulated Raman spectroscopy (WMRS) to identify subsets of T cells, natural killer cells, and dendritic cells.
Traditional methods of identifying these cells usually involve labeling them with fluorescent or magnetically labeled antibodies.
Using WMRS, the researchers were able to identify immune cells with no labeling at all, thus permitting rapid identification and further analysis to take place with no potential alteration to the cells.
Simon Powis, PhD, of the University of St Andrews in Fife, Scotland, and his colleagues described this work in PLOS ONE.
Raman scattering refers to light scattering from molecules in a sample where the light energy can be shifted up or down and recorded as a “molecular fingerprint” that can be used for identification. Normally, this process is very weak and further hampered by other background light (eg, fluorescence).
WMRS subtly changes the incident laser light that, in turn, results in a modulation of the Raman signal, allowing it to be extracted from any (stationary) interfering signal.
Using WMRS, Dr Powis and his colleagues found they could identify CD4+ T cells, CD8+ T cells, CD56+ natural killer cells, CD303+ lymphoid/plasmacytoid dendritic cells, and CD1c+ myeloid dendritic cells.
“Under a normal light microscope, these immune cells essentially all look identical,” Dr Powis said. “With this new method, we can identify key cell types without any labeling.”
“Our next goal is to make a full catalogue of all the normal cell types of the immune system that can be detected in the bloodstream. Once we have this completed, we can then collaborate with our clinical colleagues to start identifying when these immune cells are altered, in conditions such as leukemia and lymphoma, potentially providing a rapid detection system from just a small blood sample.”
Photo by Graham Colm
A new method for identifying immune cells could pave the way for rapid detection of hematologic malignancies from a small blood sample, according to researchers.
The team found they could use wavelength modulated Raman spectroscopy (WMRS) to identify subsets of T cells, natural killer cells, and dendritic cells.
Traditional methods of identifying these cells usually involve labeling them with fluorescent or magnetically labeled antibodies.
Using WMRS, the researchers were able to identify immune cells with no labeling at all, thus permitting rapid identification and further analysis to take place with no potential alteration to the cells.
Simon Powis, PhD, of the University of St Andrews in Fife, Scotland, and his colleagues described this work in PLOS ONE.
Raman scattering refers to light scattering from molecules in a sample where the light energy can be shifted up or down and recorded as a “molecular fingerprint” that can be used for identification. Normally, this process is very weak and further hampered by other background light (eg, fluorescence).
WMRS subtly changes the incident laser light that, in turn, results in a modulation of the Raman signal, allowing it to be extracted from any (stationary) interfering signal.
Using WMRS, Dr Powis and his colleagues found they could identify CD4+ T cells, CD8+ T cells, CD56+ natural killer cells, CD303+ lymphoid/plasmacytoid dendritic cells, and CD1c+ myeloid dendritic cells.
“Under a normal light microscope, these immune cells essentially all look identical,” Dr Powis said. “With this new method, we can identify key cell types without any labeling.”
“Our next goal is to make a full catalogue of all the normal cell types of the immune system that can be detected in the bloodstream. Once we have this completed, we can then collaborate with our clinical colleagues to start identifying when these immune cells are altered, in conditions such as leukemia and lymphoma, potentially providing a rapid detection system from just a small blood sample.”
Co-infection may boost malaria mortality
Co-infection with malaria and a virus closely related to the Epstein-Barr virus (EBV) may make the malaria lethal, according to preclinical research published in PLOS Pathogens.
Children in sub-Saharan Africa become infected with EBV in infancy.
Within the same time period, they become susceptible to malaria parasite infection because protective antibodies from their mothers fade away.
“Where we think kids get into trouble is when both infections are happening at the same time, because case reports show EBV can produce a weeks-long suppression of the immune system,” said Tracey Lamb, PhD, of Emory University School of Medicine in Atlanta, Georgia.
Dr Lamb and her colleagues studied mice infected by the malaria parasite Plasmodium yoelii, which is usually non-lethal because the mice develop antibodies that control the parasites.
The researchers found that co-infection with murine gammaherpesvirus 68 (MHV68), a close relative of EBV that infects mice, made P yoelii lethal.
However, mice that had entered the chronic phase of MHV68 infection (several weeks to months after primary infection) were not affected.
The experiments indicated that MHV68 infection hinders the immune system in developing antibodies against P yoelii.
“These results are part of a pattern of evidence suggesting that clinicians treating severe malaria should check for acute EBV co-infection, and that ongoing malaria studies should include EBV as a potential risk factor for more severe forms of the disease,” said Caline Matar, a graduate student at Emory University School of Medicine.
“This phenomenon may not be unique to EBV,” added Sam Speck, PhD, also of Emory University School of Medicine.
“[I]nfections with other pathogens may also exacerbate malarial disease, since many pathogens have the capacity to suppress various components of the host immune response.”
Co-infection with malaria and a virus closely related to the Epstein-Barr virus (EBV) may make the malaria lethal, according to preclinical research published in PLOS Pathogens.
Children in sub-Saharan Africa become infected with EBV in infancy.
Within the same time period, they become susceptible to malaria parasite infection because protective antibodies from their mothers fade away.
“Where we think kids get into trouble is when both infections are happening at the same time, because case reports show EBV can produce a weeks-long suppression of the immune system,” said Tracey Lamb, PhD, of Emory University School of Medicine in Atlanta, Georgia.
Dr Lamb and her colleagues studied mice infected by the malaria parasite Plasmodium yoelii, which is usually non-lethal because the mice develop antibodies that control the parasites.
The researchers found that co-infection with murine gammaherpesvirus 68 (MHV68), a close relative of EBV that infects mice, made P yoelii lethal.
However, mice that had entered the chronic phase of MHV68 infection (several weeks to months after primary infection) were not affected.
The experiments indicated that MHV68 infection hinders the immune system in developing antibodies against P yoelii.
“These results are part of a pattern of evidence suggesting that clinicians treating severe malaria should check for acute EBV co-infection, and that ongoing malaria studies should include EBV as a potential risk factor for more severe forms of the disease,” said Caline Matar, a graduate student at Emory University School of Medicine.
“This phenomenon may not be unique to EBV,” added Sam Speck, PhD, also of Emory University School of Medicine.
“[I]nfections with other pathogens may also exacerbate malarial disease, since many pathogens have the capacity to suppress various components of the host immune response.”
Co-infection with malaria and a virus closely related to the Epstein-Barr virus (EBV) may make the malaria lethal, according to preclinical research published in PLOS Pathogens.
Children in sub-Saharan Africa become infected with EBV in infancy.
Within the same time period, they become susceptible to malaria parasite infection because protective antibodies from their mothers fade away.
“Where we think kids get into trouble is when both infections are happening at the same time, because case reports show EBV can produce a weeks-long suppression of the immune system,” said Tracey Lamb, PhD, of Emory University School of Medicine in Atlanta, Georgia.
Dr Lamb and her colleagues studied mice infected by the malaria parasite Plasmodium yoelii, which is usually non-lethal because the mice develop antibodies that control the parasites.
The researchers found that co-infection with murine gammaherpesvirus 68 (MHV68), a close relative of EBV that infects mice, made P yoelii lethal.
However, mice that had entered the chronic phase of MHV68 infection (several weeks to months after primary infection) were not affected.
The experiments indicated that MHV68 infection hinders the immune system in developing antibodies against P yoelii.
“These results are part of a pattern of evidence suggesting that clinicians treating severe malaria should check for acute EBV co-infection, and that ongoing malaria studies should include EBV as a potential risk factor for more severe forms of the disease,” said Caline Matar, a graduate student at Emory University School of Medicine.
“This phenomenon may not be unique to EBV,” added Sam Speck, PhD, also of Emory University School of Medicine.
“[I]nfections with other pathogens may also exacerbate malarial disease, since many pathogens have the capacity to suppress various components of the host immune response.”