Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

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Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

Ahmad, Tariq (author): Yale Univ, CT USA; Yale Univ, CT USA

Lund, Lars H. (author): Karolinska Institutet,Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden

Rao, Pooja (author): Qure Ai, India

Ghosh, Rohit (author): Qure Ai, India

Warier, Prashant (author): Qure Ai, India

Vaccaro, Benjamin (author): Yale Univ, CT USA; Yale Univ, CT USA

Dahlström, Ulf (author): Linköpings universitet,Avdelningen för kardiovaskulär medicin,Medicinska fakulteten,Region Östergötland, Kardiologiska kliniken US

OConnor, Christopher M. (author): Duke Univ, NC USA

Felker, G. Michael (author): Duke Univ, NC USA

Desai, Nihar R. (author): Yale Univ, CT USA; Yale Univ, CT USA

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WILEY, 2018
2018
English.
In: Journal of the American Heart Association. - : WILEY. - 2047-9980. ; 7:8

Related links:: https://liu.diva-por... (primary) (Raw object); show more...; https://urn.kb.se/re...; https://doi.org/10.1...; http://kipublication...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

Background-Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response. Methods and Results-The Swedish Heart Failure Registry is a nationwide registry collecting detailed demographic, clinical, laboratory, and medication data and linked to databases with outcome information. We applied random forest modeling to identify predictors of 1-year survival. Cluster analysis was performed and validated using serial bootstrapping. Association between clusters and survival was assessed with Cox proportional hazards modeling and interaction testing was performed to assess for heterogeneity in response to HF pharmacotherapy across propensity-matched clusters. Our study included 44 886 HF patients enrolled in the Swedish Heart Failure Registry between 2000 and 2012. Random forest modeling demonstrated excellent calibration and discrimination for survival (C-statistic=0.83) whereas left ventricular ejection fraction did not (C-statistic=0.52): there were no meaningful differences per strata of left ventricular ejection fraction (1-year survival: 80%, 81%, 83%, and 84%). Cluster analysis using the 8 highest predictive variables identified 4 clinically relevant subgroups of HF with marked differences in 1-year survival. There were significant interactions between propensity-matched clusters (across age, sex, and left ventricular ejection fraction and the following medications: diuretics, angiotensin-converting enzyme inhibitors, )i-blockers, and nitrates, Pamp;lt;0.001, all). Conclusions-Machine learning algorithms accurately predicted outcomes in a large data set of HF patients. Cluster analysis identified 4 distinct phenotypes that differed significantly in outcomes and in response to therapeutics. Use of these novel analytic approaches has the potential to enhance effectiveness of current therapies and transform future HF clinical trials.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

Keyword

heart failure; machine learning; outcomes research

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By the author/editor: Ahmad, Tariq; Lund, Lars H.; Rao, Pooja; Ghosh, Rohit; Warier, Prashant; Vaccaro, Benjami ...; show more...; Dahlström, Ulf; OConnor, Christo ...; Felker, G. Micha ...; Desai, Nihar R.; show less...

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MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Cardiac and Card ...

Articles in the publication: Journal of the A ...

By the university: Linköping University; Karolinska Institutet

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