Ureteroscopic treatment of renal stones:

Dusting vs Extraction

Khurshid Ghani Dept of Urology

Will Meurer Depts of Emergency Medicine and Neurology

University of Michigan

Background

• Use of flexible ureteroscopy (URS) for treating patients with kidney stones has increased compared with other methods

• Technique: Stones are broken down with a laser fiber and are either (a) broken into small fragments (dust) which are left in place to drain spontaneously or (b) broken down and then extracted using baskets and devices.

• Technique (b) costs more money and takes longer time

• There is no consensus among urologists as to whether

Knowledge Gap

• There is no consensus among urologists about which technique results in a better stone-free rate.

• Studies looking at this show equivalent results, yet many urologists continue to extract (this can have complications)

RCT between dusting vs extraction

• Hypothesis: stone-free rate after extraction is 70%, and that after dusting is 50%.

• Maybe there are specific stone sizes that are most suited to extraction or dusting?

• Maybe there are specific stone hardness that are more suited to one method or the other?

• Can an adaptive RCT help us answer this question?

Outcome Measure

• Primary: CT scanning : Stone Free Rate = SFR

• Secondary: How many procedures required/cost?

Main covariate

• Stone Size

• Probably range 5mm – 20 mm

• Smaller <5 – everyone would “dust”

• Larger > 20 – everyone would use multi-stage procedure

Trial Requirements

• Approximately 100-200 subjects

• Primary outcome assessments radiological (could be blinded)

• Comparative effectiveness (both treatments reasonable

Main question to answer

• Who should get what?

• Where is transition between small enough to “dust” and “clearly do two-step procedure”

• Can you find non-inferiority of “dust” (the cheaper way) under some threshold?

• Can you find superiority of “two-step” over some threshold?

• Might you find some middle space where you clearly won’t separate (enrich?)

Proposed Adaptive Design

• Enroll N1 patients 5-20mm stones, equally randomized • Perform logistic regression fitting

Pr(Success) ~ Stone size*Group • Superiority: For each stone size calculate Pr(Sup)=Pr(Pr(Success | 2step) > Pr(Success | Dusting))

• Non-inferiority: For each stone size calculate Pr(NI)=Pr(Pr(Success | Dusting) – Pr(Success | 2step) > -0.10) • Stop enrolling sizes for which Pr(2 step Sup)>0.95 or Pr(Dusting NI) > 0.95 • Enroll N2 more patients with stone sizes for which

superiority and non-inferiority isn’t known.

5 10 15 20

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Example Trial, N1=N2=100

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Ex1: Interim Analysis after N1=100

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Ex1: Interim Analysis after N1=100

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Ex1: Interim Analysis after N1=100

Ex1: Interim Analysis after N1=100

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Ex1: Interim Superiority Analysis

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0.56 0.59 0.62 0.67 0.71 0.77 0.84 0.9 0.94 0.96 0.97 0.98 0.98 0.97 0.96 0.945 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Ex1: Interim Superiority Analysis

Stop Enrolling Patients With Stones > 14mm

Treat them all with 2-step

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Ex1: Interim Non-inferiority Analysis

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First 100 Patients Enroll 100 more with stones 5-13

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First 100 Patients Enroll 100 more with stones 5-13

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Ex1: Final Analysis after N=200

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0.45 0.52 0.6 0.7 0.78 0.87 0.93 0.97 0.98 0.98 0.99 0.99 0.99 0.99 0.98 0.985 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Ex1: Final Sup & NI Analysis, N=200

Operating Characteristics, Case 1; N=200

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Pr(

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N p

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Sup Power =0.76

Stone Size

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5 10 15 20

NI Power =0.14

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No

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5 10 15 20

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20

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N p

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Sup Power =0.86

Stone Size

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5 10 15 20

NI Power =0.17

Stone Size

No

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Operating Characteristics, Case 1; N=300

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N p

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Sup Power =0.4

Stone Size

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NI Power =0.19

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Operating Characteristics, Case 2; N=200

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5 6 7 8 9 11 13 15 17 19

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N p

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Sup Power =0.44

Stone Size

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5 10 15 20

NI Power =0.22

Stone Size

No

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5 10 15 20

Operating Characteristics, Case 2; N=300

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Pr(

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020

40

60

80

Stone Size

N p

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Sup Power =0.14

Stone Size

Su

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Cla

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5 10 15 20

NI Power =0.35

Stone Size

No

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Operating Characteristics, Case 3; N=200

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N p

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Sup Power =0.18

Stone Size

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5 10 15 20

NI Power =0.42

Stone Size

No

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Operating Characteristics, Case 3; N=300

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Sup Power =0.18

Stone Size

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5 10 15 20

NI Power =0.37

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No

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Operating Characteristics, Case 4; N=200

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N p

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Sup Power =0.59

Stone Size

Su

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5 10 15 20

NI Power =0.12

Stone Size

No

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5 10 15 20

Operating Characteristics, Case 5; N=200

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N p

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Sup Power =0.74

Stone Size

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5 10 15 20

NI Power =0.1

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5 10 15 20

Operating Characteristics, Case 5; N=300

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N p

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Sup Power =0.05

Stone Size

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5 10 15 20

NI Power =0.4

Stone Size

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5 10 15 20

Control Type I error, .95 .99/.98

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N p

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Sup Power =0.76

Stone Size

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5 10 15 20

NI Power =0.23

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Control Type I error, .95 .99/.98

Was 86% at 95/95

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N p

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Sup Power =0.76

Stone Size

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5 10 15 20

NI Power =0.23

Stone Size

No

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5 10 15 20

Control Type I error, .95 .99/.98

Power for n=300 Fixed Trial just comparing groups = 60%

Summary • Enrichment trial increases power & leads to personalized

medicine • Need to control Type I error via use of extensive simulation • May need to formalize hypothesis test

– Learn in first N1 patients – Choose NI region < C1, Sup region > C2

– Confirm in second N2 patients – Hypothesis test NI in <C1 & Sup in >C2

• Clearly state whether all patients or just second set to be used in final analysis

• Adjust for multiplicity if the former

– Explore choice of N1 & N2 to optimize learn vs. confirm

• Consider how operational bias may effect trial – Ask lots of people lots of questions