[PPT] - Mortali lity Predic ictio ion in in Cancer Patie ient Popula PowerPoint Presentation

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A A Mach chin ine Le Learnin ing Alg lgorit ithm for Sh Short-Term Mortali lity Predic ictio ion in in Cancer Patie ient Popula latio ions

Jun June 25, 2017 Maximilian J. Pany

MD-PhD candidate, Harvard/MIT Harvard Medical School

Aymen A. Elfiky Ravi B. Parikh Ziad Obermeyer

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Outline

1. Background & Objective
2. Methods
3. Results
4. Conclusions

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Outline

1. Background & Objective
2. Methods
3. Results
4. Conclusions

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Chemotherapy is life-saving, but not costless

Chemotherapy is started too often, too late
Patients who die soon after starting chemotherapy incur the

costs of treatment without its benefits

Understanding the risks of chemotherapy is important
Informed consent to treatment
Finances, estate planning
Family, life events
Palliative care, advance care directives
Chemotherapy use near the end of life as a marker of poor

quality of care

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Our prognoses are only as accurate as our predictions, and predictions are hard

Side effects of chemotherapy are variable
Genetics, comorbidities
Cognitive biases lead to underestimated mortality risk
Optimism, avoidance, incentives, …
Currently, mortality estimates rely on RCT and SEER data

https://clinicaltrials.gov/; https://seer.cancer.gov

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Can a machine learning model accurately predict mortality at chemotherapy initiation?

Fundamental challenge
Rich clinical data from EHR
Structured and unstructured data elements
All patients in given clinical setting
Objective: To develop and validate a ML algorithm

predicting mortality for patients initiating chemotherapy regimens at a national cancer center

Critical event in the disease trajectory
‘Pause point’ to weigh difficult questions

and opportunity

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Outline

1. Background & Objective
2. Methods
3. Results
4. Conclusions

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Empirical approach

Track all ~27,000 patients from a national cancer center at

start of ~52,000 chemotherapy regimens

Predict mortality (Social Security data) at 30 days
Compare to

derivation validation 2004 2012 2014

https://pinterest.com; www.happyfamilyart.com

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RHS: 5,390 predictors chosen by cross-validation

Categories
Two time periods before chemo start
0-1 month (recent)
1-12 months (baseline)
Data up to day before included

Demographics Vital signs Care utilization Laboratory results Grouped ICD-9 codes (diagnoses, procedures) Natural language processing

f physician notes

Procedures Prescribed medications

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Outline

1. Background & Objective
2. Methods
3. Results
4. Conclusions

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Model predictors

Mean by risk decile Variable Type Top Median Bottom Model variance explained Demographics Age Mean 62.3 62.1 51.9 1.30% Female Mean 0.56 0.61 0.87 1.17% Diagnoses Comorbidity score Year, max 4.15 2.62 1.59 0.01% Ascites Year, max 0.31 0.07 0.01 0.39% Medications Corticosteroids Year, max 0.53 0.15% Vital signs Pulse Year, max 106.1 95.7 87.1 0.37% Weight (kg) Change

3.1
1

0.1 0.00% Labs WBC Year, max 13.9 12.4 9.8 0.03% C-reactive protein Year, max 93.9 65.6 2.2 0.19% Diagnostic testing Ejection fraction (%) Year, max 54.4 48 51.9 0.01%

Linear terms: 14% Non-linear terms and interactions: 86%

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Survival (validation set) by predicted risk

Observed survival (%) 25 50 75 100 q1 q2 q9 q10 mean 0d 90d 180d 30d

Palliative chemo 30-day mortality (AUC 0.94)

Highest risk decile: 22.6%
Lowest risk decile: 0.0%

Accurate for

All cancers, all stages

(AUC>0.90 for all)

New DFCI trial agents not

in derivation set (AUC 0.94)

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Regimen- specific mortality

Predicted mortality (%) Observed mortality (%) RCT ML

ML predictions vs RCT mean mortality by regimen

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Predictions vs SEER estimates

Predicted mortality (%) Observed mortality (%) SEER ML

Stage 4; Age, sex, race-specific estimates

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Outline

1. Background & Objective
2. Methods
3. Results
4. Conclusions

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Conclusions and next steps

A machine learning model accurately predicted short-

term mortality in patients initiating chemotherapy

The model performed better than commonly used

trial- and population-based estimates

Implications for care and financial planning
Further research, including prospective studies, is

necessary to determine this model’s generalizability

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Limitations

Single-institution study based on retrospective data
Applicability to novel oncologic therapies?
Model trained only on patients selected into

chemotherapy

Counter-factual: we don’t know what would have

happened without chemotherapy

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Acknowledgments

Co-authors: Drs. Aymen A. Elfiky, Ravi B. Parikh, and

Ziad Obermeyer

Comments: Drs. Jennifer Temel and Deborah Schrag
Funding:
Office of the Director of the National Institutes of Health

(DP5 OD012161)

National Institute of Aging (T32 AG51108)
Dana Farber Cancer Institute

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References (1/2)

Emanuel EJ, Young-Xu Y, Levinsky NG, Gazelle G, Saynina O, Ash AS. Chemotherapy use

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Aggressiveness of Cancer Care Near the End of Life. J Clin Oncol 2004;22(2):315–21.

Earle CC, Landrum MB, Souza JM, Neville BA, Weeks JC, Ayanian JZ. Aggressiveness of

Cancer Care Near the End of Life: Is It a Quality-of-Care Issue? J Clin Oncol 2008;26(23):3860–6.

Saito AM, Landrum MB, Neville BA, Ayanian JZ, Earle CC. The effect on survival of

continuing chemotherapy to near death. BMC Palliat Care 2011;10:14.

Prigerson HG, Bao Y, Shah MA, et al. Chemotherapy Use, Performance Status, and Quality
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Greer JA, Pirl WF, Jackson VA, et al. Effect of early palliative care on chemotherapy use and

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References (2/2)

Glare P, Virik K, Jones M, et al. A systematic review of physicians’ survival predictions in

terminally ill cancer patients. BMJ 2003;327(7408):195.

Stone PC, Lund S. Predicting prognosis in patients with advanced cancer. Ann Oncol

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Silvestri G, Pritchard R, Welch HG. Preferences for chemotherapy in patients with

advanced non-small cell lung cancer: descriptive study based on scripted interviews. BMJ 1998;317(7161):771–5.

Hirose T, Yamaoka T, Ohnishi T, et al. Patient willingness to undergo chemotherapy and

thoracic radiotherapy for locally advanced non-small cell lung cancer. Psychooncology 2009;18(5):483–9.

Keating NL, Landrum MB, Rogers SO, et al. Physician factors associated with discussions

about end-of-life care. Cancer 2010;116(4):998–1006.

Keating NL, Beth Landrum M, Arora NK, et al. Cancer patients’ roles in treatment

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Liu P-H, Landrum MB, Weeks JC, et al. Physicians’ propensity to discuss prognosis is

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Statistical Summaries - SEER Cancer Statistics [Internet]. Available from:

https://seer.cancer.gov/statistics/summaries.html