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Shahn Z, Jung B, Talmor D, Kennedy EH, Lehman LWH, Baedorf-Kassis E. The impact of aggressive and conservative propensity for initiation of neuromuscular blockade in mechanically ventilated patients with hypoxemic respiratory failure. J Crit Care 2024; 82:154803. [PMID: 38552450 PMCID: PMC11139559 DOI: 10.1016/j.jcrc.2024.154803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 05/14/2024]
Abstract
INTRODUCTION Neuromuscular blockade (NMB) in ventilated patients may cause benefit or harm. We applied "incremental interventions" to determine the impact of altering NMB initiation aggressiveness. METHODS Retrospective cohort study of ventilated patients with PaO2/FiO2 ratio < 150 mmHg and PEEP≥ 8cmH2O from the Medical Information Mart of Intensive Care IV database (MIMIC-IV version 1.0) estimating the effect of incremental interventions on in-hospital mortality and ventilator-free days, modifying hourly propensity for NMB initiation to be aggressive or conservative relative to usual care, adjusting for confounding with inverse probability weighting. RESULTS 5221 patients were included (13.3% initiated on NMB). Incremental interventions estimated a strong effect on NMB usage: 5-fold higher hourly odds of initiation increased usage to 36.5% (CI = [34.3%,38.7%]) and 5-fold lower odds decreased usage to 3.8% (CI = [3.3%,4.3%]). Aggressive and conservative strategies demonstrated a U-shaped mortality relationship. 5-fold higher or lower propensity increased in-hospital mortality by 2.6% (0.95 CI = [1.5%,3.7%]) or 1.3% (0.95 CI = [0.1%,2.5%]) respectively. In secondary analysis of a healthier patient cohort, results were similar, however conservative strategies also improved ventilator-free days. INTERPRETATION Aggressive or conservative initiation of NMB may worsen mortality. In healthier populations, marginally conservative NMB initiation strategies may lead to increased ventilator free days with minimal impact on mortality.
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Affiliation(s)
- Zach Shahn
- MIT-IBM Watson AI Lab, Cambridge, MA, United States of America; CUNY Graduate School of Public Health and Health Policy, New York City, NY, United States of America
| | - Boris Jung
- Medical Intensive Care Unit, Lapeyronie Teaching Hospital, Montpellier University, Montpellier, France; Department of Anesthesia, Pain and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, United States of America; Division of Pulmonary and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, United States of America
| | - Daniel Talmor
- Department of Anesthesia, Pain and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, United States of America
| | - Edward H Kennedy
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States of America
| | - Li-Wei H Lehman
- MIT-IBM Watson AI Lab, Cambridge, MA, United States of America; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, 02142, United States of America
| | - Elias Baedorf-Kassis
- Department of Anesthesia, Pain and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, United States of America; Division of Pulmonary and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115, United States of America.
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McNamara L, Baedorf Kassis E. Optimal positive-end expiratory pressure weaning in acute respiratory distress syndrome patients. Curr Opin Crit Care 2024; 30:85-88. [PMID: 38085868 DOI: 10.1097/mcc.0000000000001122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
PURPOSE OF REVIEW Positive-end expiratory pressure (PEEP) is a tool in managing acute respiratory distress syndrome (ARDS). In this review, we discuss the various approaches to weaning PEEP after the acute phase of ARDS. RECENT FINDINGS There is a paucity of research specifically looking at the differences between PEEP weaning protocols. Data in some populations though, particularly those with elevated BMI, suggest that a physiologic approach to PEEP weaning may be helpful. Use of various tools to optimize PEEP prior to and during spontaneous breathing trials (SBTs) may allow for improved alveolar recruitment and respiratory outcomes. SUMMARY Although further prospective studies are warranted, we should consider using a physiologic approach to PEEP weaning in ARDS rather than a one size fits all model, which is currently the standard used in many clinical trials and throughout many ICUs.
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Affiliation(s)
| | - Elias Baedorf Kassis
- Division of Pulmonary and Critical Care
- Department of Anesthesia, Pain and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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Smit JM, Krijthe JH, Kant WMR, Labrecque JA, Komorowski M, Gommers DAMPJ, van Bommel J, Reinders MJT, van Genderen ME. Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice. NPJ Digit Med 2023; 6:221. [PMID: 38012221 PMCID: PMC10682453 DOI: 10.1038/s41746-023-00961-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/05/2023] [Indexed: 11/29/2023] Open
Abstract
This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions.
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Affiliation(s)
- J M Smit
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands.
- Pattern Recognition & Bioinformatics group, EEMCS, Delft University of Technology, Delft, The Netherlands.
| | - J H Krijthe
- Pattern Recognition & Bioinformatics group, EEMCS, Delft University of Technology, Delft, The Netherlands
| | - W M R Kant
- Data Science group, Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands
| | - J A Labrecque
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - M Komorowski
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
- Intensive Care Unit, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - D A M P J Gommers
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - J van Bommel
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M J T Reinders
- Pattern Recognition & Bioinformatics group, EEMCS, Delft University of Technology, Delft, The Netherlands
| | - M E van Genderen
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
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