Development and Validation of a Clinical Decision Score to Maximize Benefit and Minimize Harm from Intensive Blood Pressure Treatment

Transcription

1 Development and Validation of a Clinical Decision Score to Maximize Benefit and Minimize Harm from Intensive Blood Pressure Treatment Sanjay Basu, MD, PhD basus@stanford.edu

2 Disclosures and conflicts of interest None

3 Background Targeting a systolic blood pressure of less than 120 mm Hg, as compared with less than 140 mm Hg, did not reduce the rate of a composite outcome of fatal and nonfatal major cardiovascular events. - New Engl J Med, 2010 Targeting a systolic blood pressure of less than 120 mm Hg, as compared with less than 140 mm Hg, resulted in lower rates of fatal and nonfatal major cardiovascular events. New Engl J Med, 2015

4 Background Some severe adverse effects higher in intensive Rx group hypotension, syncope, electrolyte abnormalities, and acute kidney injury/failure Severe adverse events: fatal or life-threatening resulted in clinically significant or persistent disability required or prolonged a hospitalization or judged by investigators to represent a clinically significant hazard/harm

5 Research question Which patients have the most potential for benefit, and least potential for harm?

6 Analytic approach Cox models derived separately for: (1) benefit (reduced risk of CVD events/deaths) (2) harm (increased risk of severe adverse events) Candidate variables: study arm w/ interactions demographics tobacco use pre-randomization lab values, medication use and systolic/diastolic blood pressure levels

7 Analytic approach (cont d) Elastic net approach w/ repeat cross-validation To potentially reduce false positives Given collinear variables Separate out variation Net benefit = benefit (of intensive) harm (of intensive) Tertiles scored as 3, 2, 1: high benefit/low harm (3) vs low benefit/high harm (1) Externally validated on ACCORD-BP

8 Results Clinically-accessible variables were included in a benefit/harm score: Age, sex, race, systolic BP, # of BP meds, aspirin/statin use, tobacco use, serum Creatinine, urine microalbumin/creatinine, total/hdl cholesterol SPRINT participants with higher decision scores were: more likely to benefit from intensive treatment less likely to experience harm, than lower-scoring participants (P <0.001 for trend in both outcomes, by stratified log-rank test)

9 Figure 1a: Cumulative hazard of benefit and harm by treatment group for highest-score quartile A) Treatment benefit from intensive Rx Risk score = 3 (highest score tertile of SPRINT participants) (B) Treatment harm from intensive Rx P <0.001 (log rank test) P = 0.2 (log rank test)

10 Figure 1b: Cumulative hazard of benefit and harm by treatment group for lowest-score quartile Risk score = 1 (lowest score tertile of SPRINT participants) (C) Treatment benefit from intensive Rx (D) Treatment harm from intensive Rx P = 0.7 (log rank test) P = 0.01 (log rank test)

11 Table 1: Observed benefit and harm data for model predicted net benefit quartiles in SPRINT and ACCORD-BP (external validation); all results are percentage points of change in absolute risk SPRINT ACCORD-BP Benefit (absolute risk reduction in CVD) Harm (absolute risk increase in serious adverse events) Benefit (absolute risk reduction in CVD) Harm (absolute risk increase in serious adverse events) Tertile 3 (most benefit, least harm) Tertile 2 Tertile 1 (least benefit, most harm)

12 Table 1: Observed benefit and harm data for model predicted net benefit quartiles in SPRINT and ACCORD-BP (external validation); all results are percentage points of change in absolute risk SPRINT ACCORD-BP Benefit (absolute risk reduction in CVD) Harm (absolute risk increase in serious adverse events) Benefit (absolute risk reduction in CVD) Harm (absolute risk increase in serious adverse events) Tertile 3 (most benefit, least harm) -4.2 (-6.6, -1.8) -0.1 (-4.3, 3.0) Tertile (-2.6, 0.01) 2.5 (1.2, 3.8) Tertile 1 (least benefit, most harm) -0.6 (-1.8, 0.01) 3.8 (1.4, 6.1)

13 Table 1: Observed benefit and harm data for model predicted net benefit quartiles in SPRINT and ACCORD-BP (external validation); all results are percentage points of change in absolute risk SPRINT ACCORD-BP Benefit (absolute risk reduction in CVD) Harm (absolute risk increase in serious adverse events) Benefit (absolute risk reduction in CVD) Harm (absolute risk increase in serious adverse events) Tertile 3 (most benefit, least harm) -4.2 (-6.6, -1.8) -0.1 (-4.3, 3.0) -8.1 (-11.5, -4.6) 2.3 (-4.7, 9.3) Tertile (-2.6, 0.01) 2.5 (1.2, 3.8) 1.3 (-2.1, 4.7) 4.6 (2.0, 7.3) Tertile 1 (least benefit, most harm) -0.6 (-1.8, 0.01) 3.8 (1.4, 6.1) 2.1 (-1.6, 5.8) 9.7 (7.1, 12.3)

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16 Key Findings Though SPRINT s average effect was positive (better outcomes from intensive Rx) and ACCORD s average effect was neutral/negative (no better outcomes from intensive Rx) We found a subset of ACCORD patients (N = 994, or 21% of the sample) who benefited from therapy And the score derived from SPRINT correctly predicted that most ACCORD patients would not benefit Consideration of a number of factors in combination, rather than any single factor, was required to explain the important variations Average trial results can often hide clinically profound heterogeneities in treatment effects

17 Notable limitations Limitations: Short-term outcomes (censored at 5 years) due to early stoppage of the SPRINT trial couldn t include congestive heart failure outcome, because of differences between SPRINT and ACCORD study definitions Some may weigh benefit and harm differently in the calculation of net benefit = benefit harm We are exploring other approaches to weighing benefit and harm rather than treating them equally

18 Ongoing work What are the best metrics for HTE models? Replacing the calibration curve of expected vs observed risk with expected vs observed change in risk? Can we do a sensitivity analysis (ala Rosenbaum) How much hidden confounding must exist to produce false HTE? Direct comparisons across machine learning approaches Logistic/Cox vs. tree-based vs. neural networks Weighting net benefit calculations by individual patient preferences?

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20 Notes and acknowledgements Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e Code for replication (in R): SPRINT_POP and ACCORD research materials obtained from the NHLBI BioLINCC Study team: Jeremy Sussman, Joseph Rigdon, Lauren Steimle, Brian Denton, Rod Hayward, Sung Eun Choi, Seth Berkowitz, John Yudkin, Huseyin Naci Funding from: NIH DP2MD010478, U54MD010724, K08HL121056, P60DK20572, VA IIR and CDA Does not necessarily reflect the opinions or views of the SPRINT_POP, the ACCORD, or the NIH, NHLBI, NIDDK, or VA

21 Extra slides

22 Fig 1. Flow of SPRINT trial participants (derivation cohort) and ACCORD-BP participants (validation cohort) into the current study. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

23 Fig 2. Calibration plots for models fit by elastic net regularization versus traditional backwards selection. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

24 Fig 4. Predicted versus observed absolute risk differences in benefit and harm among SPRINT and ACCORD- BP trial participant subgroups, using predictions from the elastic net regularization model. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

25 Fig 5. Predicted versus observed absolute risk differences in benefit and harm among SPRINT and ACCORD- BP trial participant subgroups, using predictions from the traditional backwards selection model. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

26 Table 1. Baseline characteristics of the SPRINT trial participants included for model derivation (N = 9,069) and ACCORD-BP trial participants included for model validation (N = 4,498). Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

27 Table 2. Risk score for benefit from intensive blood pressure treatment, developed from the SPRINT trial. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

28 Table 3. Risk score for harm from intensive blood pressure treatment, developed from the SPRINT trial. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

29 Table 4. Observed outcomes by treatment arm and by the SPRINT trial population s predicted benefit/harm (derivation cohort). Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

30 Table 5. Observed outcomes by treatment arm and by the ACCORD-BP trial population s predicted benefit/harm (validation cohort). Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

31 Table 6. Coefficients for the CVD and severe adverse event models fit by traditional backwards selection. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e

32 Table 7. Comparison of discrimination and calibration for models fit by elastic net regularization versus traditional backwards selection. Basu S, Sussman JB, Rigdon J, Steimle L, Denton BT, et al. (2017) Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials. PLOS Medicine 14(10): e