Experimental research protocols enable us to gather preclinical, quantitative, objective data on the accuracy of bone-preparation gestures, while taking into account the influencing factors, which include the surgeon’s experience, local difficulties associated with the anatomical site, and the technologies that may be used by the surgeons. When seeking to illustrate a method for investigating accuracy in preclinical settings, bone-tumor surgery represents a good educational example for several reasons. This surgery is difficult, and it requires multiple 3D bone cuts around the tumor to resect it with safe margins59. Its high potential for inaccuracy has motivated the development of assistive bone-cutting technologies60. A large number of experiments have been carried out on synthetic bone models to investigate the effect of assistive technologies on bone-cutting accuracy56,61,62. The first clinical cases involving optical navigation that were published describe tumor resection and graft-reconstruction gestures that were performed with the assistance of a direct navigation system for cutting with an oscillating saw63,64. A publication on the integration of 3D-printing PSI technology in clinical practice is, to our knowledge, the first clinical study of patients (11 in total) with bone tumors who underwent operations in which PSI was used35. Figure 2 describes the clinical use of a patient-specific surgical-guide technology in a case of pelvic bone tumor. In this clinical study, histopathological analysis of the resected specimens showed tumor-free bone-resection margins in all of the cases. Bone-cutting accuracy was computed by registering postoperative and preoperative computed tomography (CT) scans, and averaged 2.5 and –0.8 mm, respectively.
The experimental protocol that was set up to investigate bone-cutting accuracy in preclinical settings consists of 5 steps, which are illustrated in the context of bone tumors61,62. First, the surgical target has a 10-mm safe margin (i.e., the desired surgical gesture is a bone cut located 10 mm from the tumor border). Second, success and failure are defined as a safe resection margin (bone cut made 10 mm from the tumor border) and an intralesional resection (bone cut made within the volume of the tumor), respectively. Third, the accuracy of the actual surgical procedure is defined as the error in millimeters with respect to the safe margin. For example, an accuracy of 5 mm means that the bone cut was made 15 mm from the tumor border, an accuracy of −5 mm means that the bone cut was made 5 mm from the tumor border, and an accuracy of <−10 mm means that the bone cut was made within the tumor volume. Fourth, the factors included in the research are the experience of the surgeons (10 senior and 13 junior surgeons), the cutting technologies that were used (conventional freehand technique, navigation, and PSI), and any surgical difficulties (e.g., complete resection of a simulated tumor on the acetabulum using 4 cuts, including 2 iliac cuts, 1 cut in the pubis, and 1 cut in the ischium). The data set comprises 23 surgeons, each of whom carried out a tumor resection by means of 4 cuts using 3 different cutting technologies, for a total of 276 cut planes. Fifth, measurement of all of the cut planes was carried out mechanically using a mechanical measurement arm with micrometer resolution.
The implementation of this experimental protocol enabled us to gather and analyze 276 measurements of bone-cutting error in relation to the desired 10-mm safe margin (Fig. 3). Statistical analyses show that “assistive technology” was the main factor that influenced the order of magnitude of the error in relation to the surgical target. The “surgeon’s experience” factor did not influence the order of magnitude of the error in relation to the surgical target (i.e., senior and junior surgeons carried out their bone cuts to the same performance standard for each of the 3 cutting techniques that were investigated).
The effect of the assistive technologies on the accuracy of bone-cutting in terms of trueness and precision is shown in Figure 3. The level of trueness is the same because the mean of the observed data is identical for the 3 cutting techniques (freehand, navigation, and PSI). Moreover, this level of trueness is correct because it corresponds to the surgical target (i.e., the 10-mm safe margin). The level of precision, however, is better with navigation technology, and better still when PSI is used. In fact, the variability in the observed data is substantially reduced with navigation and PSI techniques when compared with the freehand technique. This suggests that assistive technologies allow surgeons to better control the sources of variability and inaccuracy when making bone cuts.
Even if improvements in accuracy in terms of precision are observed experimentally when using navigation and PSI technologies, is this enough to have confidence in the use of these technologies in clinical routine? The clinical relevance of improvements in accuracy observed experimentally may be questioned by analyzing the effect on the risk of failure as defined in the experimental protocol (i.e., the risk of intralesional resection). With the freehand-cutting technique, 5 intralesional resections were observed in 92 cases, yielding a failure rate of about 5%. This failure rate, although observed experimentally in synthetic bone models under ideal conditions (perfect tumor accessibility, no bleeding, no soft tissues, etc.), is sufficient to conclude that the risk of intralesional resection in clinical practice is certainly not negligible. With use of the navigation and PSI cutting techniques, however, no intralesional resections have been observed. The observed failure rate is therefore 0%. Consequently, this result allows us to draw relevant conclusions regarding the risk of intralesional resection when using navigation and PSI technologies in clinical practice.
The surgical target (the desired safe margin) is defined by the surgeon based on his or her experience and on the specific characteristics of the tumor that will be resected (e.g., type, volume, and site). These characteristics define the accepted tolerance in relation to the surgical target (e.g., the safety zone around the surgical target). For bone-tumor surgery, the safety zone can be defined as shown in Figure 4. The surgical target is the safe margin of 10 mm. The safety zone may be defined as extending from 5 to 15 mm, considering that the tumor is below 5 mm and there are vessels, nerves, organs, and other structures above 15 mm.
Therefore, a bone cut made at the desired safe margin of 10 mm is regarded as a success. When a bone cut is made outside of the tolerance range (i.e., outside of the safety zone), it is considered a failure. If a bone cut is made within the safety zone, it is not considered a failure; however, the farther away it is from the surgical target, the less successful it is considered to be.
The clinically relevant question in the case of bone-tumor surgery is, “What would happen if the surgical target (the desired safe margin) was smaller, and therefore had a narrower tolerance (safety zone) because it was closer to the tumor border?” If there was a smaller desired safe margin, would there still be no observed intralesional resections when using navigation and PSI cutting techniques?
The risk of failure is a clinically relevant concept that is linked to the concepts of “accuracy” and “surgical target.” This naturally leads clinicians and researchers to ask themselves several questions when studying the accuracy of a surgical gesture. What is the accepted risk of failure? What is the surgical target? What is the accepted tolerance in relation to the surgical target? In other words, what will happen if the surgical gesture is carried out farther from or nearer to the target? Also, what will happen if the surgical target is modified?
The 4 key observations regarding ISO methodology in preclinical settings (illustrated in Fig. 6) are listed below.
1. Accuracy is multifactorial. It is crucial to take into account all of the factors that may influence the accuracy of a surgical gesture, including the surgeon’s experience, assistive technologies, and local difficulties associated with the anatomical site.
2. For in vitro investigations, accuracy should be linked to the concepts of trueness (distance from the surgical target) and precision (variability in relation to the surgical target).
3. The clinical relevance of improvements in accuracy that have been observed experimentally may be evaluated by analyzing the impact on the estimated risk of failure and by taking into account the level of tolerance with respect to the surgical target (the extent of the safety zone).
4. The ISO methodology enables preclinical testing of new assistive technologies to quantify improvements in accuracy when using assistive technologies and to assess the benefits in terms of reducing the risk of failure and achieving surgical targets with tighter tolerances.
The ISO methodology described above presents some limitations for use in clinical settings. First, the ISO methodology requires that an accepted reference (target) value be defined. The assessment of improvements in accuracy when using navigation systems, robots, and PSI can be limited when no agreed-upon clinically adequate target has been defined. For example, with knee and hip arthroplasty, achieving neutral mechanical alignment when implanting a knee prosthesis or observing a safety zone for acetabular cup orientation recently have been shown to not correlate with clinical success. Second, clinical outcomes are multifactorial. The quality of a surgical procedure cannot be solely limited to the accuracy of the bone-preparation gestures. Many additional factors also may influence the quality of a surgical treatment, including patient demographics and comorbidities, preoperative and postoperative-care protocols, and individual adherence to these protocols. Third, the definition of success and failure of a surgical treatment is not binary and cannot rely exclusively on the execution of the bone-preparation gestures. The success of a surgical treatment also relies on the patient’s satisfaction and functional outcomes after surgery. Finally, the definition of the safety zone is intrinsic to the clinical situation. The safety zone in other types of procedures (e.g., knee and hip arthroplasty) might differ considerably from the safety zone of bone-tumor resection as described above. In general, the safety zone is related to the concept of uncertainty (i.e., not knowing the target precisely) and is independent of the concept of accuracy of the bone-preparation gestures.
In conclusion, evaluation techniques to determine accuracy will need to consider the variety of technologies and systems that can be used during or after the intervention, not to execute but to measure the accuracy of the executed bone-preparation gestures (e.g., bone-cutting and the positioning of implants and prostheses). Some image-based measurement techniques could use radiographs or CT to perform image registration and bone segmentation. However, measurements based on radiographs are known to be subject to errors of magnification as well as rotation, and if CT-based measurements are performed, we need to deal carefully with acquisition parameters such as pixel size, slice thickness, and interslice distance, among others, which could influence image quality. Additional measurement techniques could use navigation systems and robots to perform intraoperative real-time tracking and tool localization. From an engineering perspective, a commonly accepted recommendation is to minimize the measuring errors by using measurement procedures and systems with an accuracy at least an order of magnitude greater than the errors expected during the execution of the bone-preparation gestures.
Investigation performed at the Computer Assisted and Robotic Surgery, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
1. Siston RA, Giori NJ, Goodman SB, Delp SL. Surgical navigation for total knee arthroplasty: a perspective. J Biomech. 2007;40(4):728–35.
2. Jenny JY. The current status of computer-assisted high tibial osteotomy, unicompartmental knee replacement, and revision total knee replacement. Instr Course Lect. 2008;57:721–6.
3. Taylor RH, Stoianovici D. Medical robotics in computer-integrated surgery. IEEE Trans Robot Autom. 2003;19(5):765–81.
4. Koulalis D, O’Loughlin PF, Plaskos C, Kendoff D, Cross MB, Pearle AD. Sequential versus automated cutting guides in computer-assisted total knee arthroplasty. Knee. 2011 ;18(6):436–42. Epub 2010 Sep 15.
5. Roche M, O’Loughlin PF, Kendoff D, Musahl V, Pearle AD. Robotic arm-assisted unicompartmental knee arthroplasty: preoperative planning and surgical technique. Am J Orthop (Belle Mead NJ). 2009 ;38(2)(Suppl):10–5.
6. Dutton AQ, Yeo SJ, Yang KY, Lo NN, Chia KU, Chong HC. Computer-assisted minimally invasive total knee arthroplasty compared with standard total knee arthroplasty. A prospective, randomized study. J Bone Joint Surg Am. 2008 ;90(1):2–9.
7. Cobb J, Henckel J, Gomes P, Harris S, Jakopec M, Rodriguez F, Barrett A, Davies B. Hands-on robotic unicompartmental knee replacement: a prospective, randomised controlled study of the Acrobot system. J Bone Joint Surg Br. 2006 ;88(2):188–97.
8. Wolf A, Jaramaz B, Lisien B, DiGioia AM. MBARS: mini bone-attached robotic system for joint arthroplasty. Int J Med Robot. 2005 ;1(2):101–21.
9. Plaskos C, Cinquin P, Lavallée S, Hodgson AJ. Praxiteles: a miniature bone-mounted robot for minimal access total knee arthroplasty. Int J Med Robot. 2005 ;1(4):67–79.
10. Hafez MA, Chelule KL, Seedhom BB, Sherman KP. Computer-assisted total knee arthroplasty using patient-specific templating. Clin Orthop Relat Res. 2006 ;444:184–92.
11. Ng VY, DeClaire JH, Berend KR, Gulick BC, Lombardi AV Jr. Improved accuracy of alignment with patient-specific positioning guides compared with manual instrumentation in TKA. Clin Orthop Relat Res. 2012 ;470(1):99–107.
12. Olsen M, Chiu M, Gamble P, Boyle RA, Tumia N, Schemitsch EH. A comparison of conventional guidewire alignment jigs with imageless computer navigation in hip resurfacing arthroplasty. J Bone Joint Surg Am. 2010 ;92(9):1834–41.
13. Huo MH, Parvizi J, Bal BS, Mont MA; Council of Musculoskeletal Specialty Societies (COMSS) of the American Academy of Orthopaedic Surgeons. What’s new in total hip arthroplasty. J Bone Joint Surg Am. 2008 ;90(9):2043–55.
14. Honl M, Schwieger K, Salineros M, Jacobs J, Morlock M, Wimmer M. Orientation of the acetabular component. A comparison of five navigation systems with conventional surgical technique. J Bone Joint Surg Br. 2006 ;88(10):1401–5.
15. Barrett AR, Davies BL, Gomes MP, Harris SJ, Henckel J, Jakopec M, Kannan V, Rodriguez y Baena FM, Cobb JP. Computer-assisted hip resurfacing surgery using the Acrobot navigation system. Proc Inst Mech Eng H. 2007 ;221(7):773–85.
16. Zhang YZ, Chen B, Lu S, Yang Y, Zhao JM, Liu R, Li YB, Pei GX. Preliminary application of computer-assisted patient-specific acetabular navigational template for total hip arthroplasty in adult single development dysplasia of the hip. Int J Med Robot. 2011 ;7(4):469–74. Epub 2011 Oct 7.
17. Sakai T, Hanada T, Murase T, Kitada M, Hamada H, Yoshikawa H, Sugano N. Validation of patient specific surgical guides in total hip arthroplasty. Int J Med Robot Comp. 2014;10(1):113–20.
18. Gebhard F, Scheiderer B, Richter P, Riepl C. Multidepartmental use of a fixed 3D navigation system. In: Haaker R, Konermann W, editors. Computer and template assisted orthopedic surgery. Berlin: Springer; 2013. p 147–52.
19. Silbermann J, Riese F, Allam Y, Reichert T, Koeppert H, Gutberlet M. Computer tomography assessment of pedicle screw placement in lumbar and sacral spine: comparison between free-hand and O-arm based navigation techniques. Eur Spine J. 2011 ;20(6):875–81. Epub 2011 Jan 21.
20. Gelalis ID, Paschos NK, Pakos EE, Politis AN, Arnaoutoglou CM, Karageorgos AC, Ploumis A, Xenakis TA. Accuracy of pedicle screw placement: a systematic review of prospective in vivo studies comparing free hand, fluoroscopy guidance and navigation techniques. Eur Spine J. 2012 ;21(2):247–55. Epub 2011 Sep 7.
21. Lieberman IH, Hardenbrook MA, Wang JC, Guyer RD. Assessment of pedicle screw placement accuracy, procedure time, and radiation exposure using a miniature robotic guidance system. J Spinal Disord Tech. 2012 ;25(5):241–8.
22. Lu S, Xu YQ, Zhang YZ, Li YB, Xie L, Shi JH, Guo H, Chen GP, Chen YB. A novel computer-assisted drill guide template for lumbar pedicle screw placement: a cadaveric and clinical study. Int J Med Robot. 2009 ;5(2):184–91.
23. Ferrari V, Parchi P, Condino S, Carbone M, Baluganti A, Ferrari M, Mosca F, Lisanti M. An optimal design for patient-specific templates for pedicle spine screws placement. Int J Med Robot. 2013 ;9(3):298–304. Epub 2012 May 15.
24. Hankemeier S, Hüfner T, Wang G, Kendoff D, Zeichen J, Zheng G, Krettek C. Navigated open-wedge high tibial osteotomy: advantages and disadvantages compared to the conventional technique in a cadaver study. Knee Surg Sports Traumatol Arthrosc. 2006 ;14(10):917–21. Epub 2006 Feb 24.
25. Saragaglia D, Roberts J. Navigated osteotomies around the knee in 170 patients with osteoarthritis secondary to genu varum. Orthopedics. 2005 ;28(10)(Suppl):s1269–74.
26. Wang G, Zheng G, Keppler P, Gebhard F, Staubli A, Mueller U, Schmucki D, Fluetsch S, Nolte LP. Implementation, accuracy evaluation, and preliminary clinical trial of a CT-free navigation system for high tibial opening wedge osteotomy. Comput Aided Surg. 2005 ;10(2):73–85.
27. Dobbe JG, Vroemen JC, Strackee SD, Streekstra GJ. Patient-tailored plate for bone fixation and accurate 3D positioning in corrective osteotomy. Med Biol Eng Comput. 2013 ;51(1-2):19–27. Epub 2012 Oct 10.
28. Victor J, Premanathan A. Virtual 3D planning and patient specific surgical guides for osteotomies around the knee: a feasibility and proof-of-concept study. Bone Joint J. 2013 ;95-B(11)(Suppl A):153–8.
29. Oka K, Murase T, Moritomo H, Yoshikawa H. Corrective osteotomy for malunited both bones fractures of the forearm with radial head dislocations using a custom-made surgical guide: two case reports. J Shoulder Elbow Surg. 2012 ;21(10):e1–8. Epub 2012 Jun 27.
30. Jeys L, Matharu GS, Nandra RS, Grimer RJ. Can computer navigation-assisted surgery reduce the risk of an intralesional margin and reduce the rate of local recurrence in patients with a tumour of the pelvis or sacrum? Bone Joint J. 2013 ;95-B(10):1417–24.
31. Wong KC, Kumta SM, Sze KY, Wong CM. Use of a patient-specific CAD/CAM surgical jig in extremity bone tumor resection and custom prosthetic reconstruction. Comput Aided Surg. 2012;17(6):284–93. Epub 2012 Oct 3.
32. Khan FA, Lipman JD, Pearle AD, Boland PJ, Healey JH. Surgical technique: Computer-generated custom jigs improve accuracy of wide resection of bone tumors. Clin Orthop Relat Res. 2013 ;471(6):2007–16. Epub 2013 Jan 5.
33. Cho HS, Oh JH, Han I, Kim HS. The outcomes of navigation-assisted bone tumour surgery: minimum three-year follow-up. J Bone Joint Surg Br. 2012 ;94(10):1414–20.
34. Wong KC, Kumta SM. Computer-assisted tumor surgery in malignant bone tumors. Clin Orthop Relat Res. 2013 ;471(3):750–61.
35. Gouin F, Paul L, Odri GA, Cartiaux O. Computer-assisted planning and patient-specific instruments for bone tumor resection within the pelvis: a series of 11 patients. Sarcoma. 2014;2014:842709. Epub 2014 Jul 2.
36. Yau WP, Chiu KY, Zuo JL, Tang WM, Ng TP. Computer navigation did not improve alignment in a lower-volume total knee practice. Clin Orthop Relat Res. 2008 ;466(4):935–45. Epub 2008 Feb 8.
37. Bonutti PM, Dethmers DA, McGrath MS, Ulrich SD, Mont MA. Navigation did not improve the precision of minimally invasive knee arthroplasty. Clin Orthop Relat Res. 2008 ;466(11):2730–5. Epub 2008 Jul 10.
38. Molfetta L, Caldo D. Computer navigation versus conventional implantation for varus knee total arthroplasty: a case-control study at 5 years follow-up. Knee. 2008 ;15(2):75–9. Epub 2008 Jan 30.
39. Kim YH, Kim JS, Choi Y, Kwon OR. Computer-assisted surgical navigation does not improve the alignment and orientation of the components in total knee arthroplasty. J Bone Joint Surg Am. 2009 ;91(1):14–9.
40. White D, Chelule KL, Seedhom BB. Accuracy of MRI vs CT imaging with particular reference to patient specific templates for total knee replacement surgery. Int J Med Robot. 2008 ;4(3):224–31.
41. Nunley RM, Ellison BS, Zhu J, Ruh EL, Howell SM, Barrack RL. Do patient-specific guides improve coronal alignment in total knee arthroplasty? Clin Orthop Relat Res. 2012 ;470(3):895–902. Epub 2011 Dec 20.
42. Victor J, Van Doninck D, Labey L, Innocenti B, Parizel PM, Bellemans J. How precise can bony landmarks be determined on a CT scan of the knee? Knee. 2009 ;16(5):358–65. Epub 2009 Feb 05.
43. Thienpont E, Schwab PE, Fennema P. A systematic review and meta-analysis of patient-specific instrumentation for improving alignment of the components in total knee replacement. Bone Joint J. 2014 ;96-B(8):1052–61.
44. Stiehl JB, Bach J, Heck DA. Validation and metrology in CAOS. In: Stiehl JB, Konermann WH, Haaker RG, DiGioia AM III, editors. Navigation and MIS in orthopedic surgery. Heidelberg: Springer; 2007. p 68–78.
45. ASTM Standard F2554-10, Standard practice for measurement of positional accuracy of computer assisted surgical systems. West Conshohocken, PA: ASTM International; 2010. https://www.astm.org/Standards/F2554.htm
46. ASTM Standard E177-08. Standard practice for use of the terms precision and bias in ASTM test methods. West Conshohocken, PA: ASTM International; 2008. https://www.astm.org/Standards/E177.htm
47. Clarke JV, Deakin AH, Nicol AC, Picard F. Measuring the positional accuracy of computer assisted surgical tracking systems. Comput Aided Surg. 2010;15(1-3):13–8.
48. Haidegger T, Kazanzides P, Rudas I, Benyó B, Benyó Z. The importance of accuracy measurement standards for computer-integrated interventional systems. Read at EURON GEM Sig Workshop on the Role of Experiments in Robotics Research at ICRA 2010; 2010 May 3; Anchorage, Alaska.
51. Barrera OA, Haider H, Garvin KL. Towards a standard in assessment of bone cutting for total knee replacement. Proc Inst Mech Eng H. 2008 ;222(1):63–74.
52. Ritacco LE, Milano FE, Farfalli GL, Ayerza MA, Muscolo DL, Aponte-Tinao LA. Accuracy of 3-D planning and navigation in bone tumor resection. Orthopedics. 2013 ;36(7):e942–50.
53. Pearle AD, Kendoff D, Musahl V. Perspectives on computer-assisted orthopaedic surgery: movement toward quantitative orthopaedic surgery. J Bone Joint Surg Am. 2009 ;91(Suppl 1):7–12.
54. Rivkin G, Liebergall M. Challenges of technology integration and computer-assisted surgery. J Bone Joint Surg Am. 2009 ;91(Suppl 1):13–6.
55. Cartiaux O, Paul L, Docquier PL, Francq BG, Raucent B, Dombre E, Banse X. Accuracy in planar cutting of bones: an ISO-based evaluation. Int J Med Robot. 2009 ;5(1):77–84.
56. Cartiaux O, Paul L, Docquier PL, Raucent B, Dombre E, Banse X. Computer-assisted and robot-assisted technologies to improve bone-cutting accuracy when integrated with a freehand process using an oscillating saw. J Bone Joint Surg Am. 2010 ;92(11):2076–82.
57. Dobbe JGG, Kievit AJ, Schafroth MU, Blankevoort L, Streekstra GJ. Evaluation of a CT-based technique to measure the transfer accuracy of a virtually planned osteotomy. Med Eng Phys. 2014 ;36(8):1081–7. Epub 2014 Jun 6.
58. Khan F, Pearle A, Lightcap C, Boland PJ, Healey JH. Haptic robot-assisted surgery improves accuracy of wide resection of bone tumors: a pilot study. Clin Orthop Relat Res. 2013 ;471(3):851–9.
59. Delloye C, Banse X, Brichard B, Docquier PL, Cornu O. Pelvic reconstruction with a structural pelvic allograft after resection of a malignant bone tumor. J Bone Joint Surg Am. 2007 ;89(3):579–87.
60. Cartiaux O, Docquier PL, Paul L, Francq BG, Cornu OH, Delloye C, Raucent B, Dehez B, Banse X. Surgical inaccuracy of tumor resection and reconstruction within the pelvis: an experimental study. Acta Orthop. 2008 ;79(5):695–702.
61. Cartiaux O, Banse X, Paul L, Francq BG, Aubin CE, Docquier PL. Computer-assisted planning and navigation improves cutting accuracy during simulated bone tumor surgery of the pelvis. Comput Aided Surg. 2013;18(1-2):19–26. Epub 2012 Nov 26.
62. Cartiaux O, Paul L, Francq BG, Banse X, Docquier PL. Improved accuracy with 3D planning and patient-specific instruments during simulated pelvic bone tumor surgery. Ann Biomed Eng. 2014 ;42(1):205–13. Epub 2013 Aug 21.
63. Docquier PL, Paul L, Cartiaux O, Delloye C, Banse X. Computer-assisted resection and reconstruction of pelvic tumor sarcoma. Sarcoma. 2010;2010:125162. Epub 2010 Nov 28.
64. Docquier PL, Cartiaux O, Paul L, Delloye C, Banse X. Computer navigated bone cutting in the resection of a pelvic bone tumor and reconstruction with a massive bone allograft. JBJS Essential Surg Tech. 2011;1:1–13.
65. Francq BG, Cartiaux O. Delta method and bootstrap in linear mixed models to estimate a proportion when no event is observed: application to intralesional resection in bone tumor surgery. Stat Med. 2016 ;35(20):3563–82. Epub 2016 Mar 15.