Step-by-step causal analysis of EHRs to ground decision-making.
Causal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). The prevalence of such observational data and the difficulty for randomized controlled trials (RCT) to cover all population/treatment relationships make the...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-02-01
|
| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000721 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823864128091979776 |
|---|---|
| author | Matthieu Doutreligne Tristan Struja Judith Abecassis Claire Morgand Leo Anthony Celi Gaël Varoquaux |
| author_facet | Matthieu Doutreligne Tristan Struja Judith Abecassis Claire Morgand Leo Anthony Celi Gaël Varoquaux |
| author_sort | Matthieu Doutreligne |
| collection | DOAJ |
| description | Causal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). The prevalence of such observational data and the difficulty for randomized controlled trials (RCT) to cover all population/treatment relationships make these methods increasingly attractive for studying causal effects. However, researchers should be wary of many pitfalls. We propose and illustrate a framework for causal inference estimating the effect of albumin on mortality in sepsis using an Intensive Care database (MIMIC-IV) and comparing various sensitivity analyses to results from RCTs as gold-standard. The first step is study design, using the target trial concept and the PICOT framework: Population (patients with sepsis), Intervention (combination of crystalloids and albumin for fluid resuscitation), Control (crystalloids only), Outcome (28-day mortality), Time (intervention start within 24h of admission). We show that too large treatment-initiation times induce immortal time bias. The second step is selection of the confounding variables based on expert knowledge. Increasingly adding confounders enables to recover the RCT results from observational data. As the third step, we assess the influence of multiple models with varying assumptions, showing that a doubly robust estimator (AIPW) with random forests proved to be the most reliable estimator. Results show that these steps are all important for valid causal estimates. A valid causal model can then be used to individualize decision making: subgroup analyses showed that treatment efficacy of albumin was better for patients >60 years old, males, and patients with septic shock. Without causal thinking, machine learning is not enough for optimal clinical decision on an individual patient level. Our step-by-step analytic framework helps avoiding many pitfalls of applying machine learning to EHR data, building models that avoid shortcuts and extract the best decision-making evidence. |
| format | Article |
| id | doaj-art-ee120ab0a39242ff8fe0e0e7271abc7d |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-ee120ab0a39242ff8fe0e0e7271abc7d2025-02-09T05:30:53ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-02-0142e000072110.1371/journal.pdig.0000721Step-by-step causal analysis of EHRs to ground decision-making.Matthieu DoutreligneTristan StrujaJudith AbecassisClaire MorgandLeo Anthony CeliGaël VaroquauxCausal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). The prevalence of such observational data and the difficulty for randomized controlled trials (RCT) to cover all population/treatment relationships make these methods increasingly attractive for studying causal effects. However, researchers should be wary of many pitfalls. We propose and illustrate a framework for causal inference estimating the effect of albumin on mortality in sepsis using an Intensive Care database (MIMIC-IV) and comparing various sensitivity analyses to results from RCTs as gold-standard. The first step is study design, using the target trial concept and the PICOT framework: Population (patients with sepsis), Intervention (combination of crystalloids and albumin for fluid resuscitation), Control (crystalloids only), Outcome (28-day mortality), Time (intervention start within 24h of admission). We show that too large treatment-initiation times induce immortal time bias. The second step is selection of the confounding variables based on expert knowledge. Increasingly adding confounders enables to recover the RCT results from observational data. As the third step, we assess the influence of multiple models with varying assumptions, showing that a doubly robust estimator (AIPW) with random forests proved to be the most reliable estimator. Results show that these steps are all important for valid causal estimates. A valid causal model can then be used to individualize decision making: subgroup analyses showed that treatment efficacy of albumin was better for patients >60 years old, males, and patients with septic shock. Without causal thinking, machine learning is not enough for optimal clinical decision on an individual patient level. Our step-by-step analytic framework helps avoiding many pitfalls of applying machine learning to EHR data, building models that avoid shortcuts and extract the best decision-making evidence.https://doi.org/10.1371/journal.pdig.0000721 |
| spellingShingle | Matthieu Doutreligne Tristan Struja Judith Abecassis Claire Morgand Leo Anthony Celi Gaël Varoquaux Step-by-step causal analysis of EHRs to ground decision-making. PLOS Digital Health |
| title | Step-by-step causal analysis of EHRs to ground decision-making. |
| title_full | Step-by-step causal analysis of EHRs to ground decision-making. |
| title_fullStr | Step-by-step causal analysis of EHRs to ground decision-making. |
| title_full_unstemmed | Step-by-step causal analysis of EHRs to ground decision-making. |
| title_short | Step-by-step causal analysis of EHRs to ground decision-making. |
| title_sort | step by step causal analysis of ehrs to ground decision making |
| url | https://doi.org/10.1371/journal.pdig.0000721 |
| work_keys_str_mv | AT matthieudoutreligne stepbystepcausalanalysisofehrstogrounddecisionmaking AT tristanstruja stepbystepcausalanalysisofehrstogrounddecisionmaking AT judithabecassis stepbystepcausalanalysisofehrstogrounddecisionmaking AT clairemorgand stepbystepcausalanalysisofehrstogrounddecisionmaking AT leoanthonyceli stepbystepcausalanalysisofehrstogrounddecisionmaking AT gaelvaroquaux stepbystepcausalanalysisofehrstogrounddecisionmaking |