Development and internal validation of the multivariable CIPHER (Collaborative Integrated Pregnancy High-dependency Estimate of Risk) clinical risk prediction model

Beth A. Payne* (Corresponding Author), Helen Ryan, Jeffrey Bone, Laura A. Magee, Alice B. Aarvold, J. Mark Ansermino, Zulfiqar A. Bhutta, Mary Bowen, J. Guilherme Cecatti, Cynthia Chazotte, Tim Crozier, Anne Cornélie J.M. De Pont, Oktay Demirkiran, Tao Duan, Marlot Kallen, Wessel Ganzevoort, Michael Geary, Dena Goffman, Jennifer A. Hutcheon, K. S. JosephStephen E. Lapinsky, Isam Lataifeh, Jing Li, Sarka Liskonova, Emily M. Hamel, Fionnuala M. McAuliffe, Colm O'Herlihy, Ben W.J. Mol, P. Gareth R. Seaward, Ramzy Tadros, Turkan Togal, Rahat Qureshi, U. Vivian Ukah, Daniela Vasquez, Euan Wallace, Paul Yong, Vivian Zhou, Keith R. Walley, Peter Von Dadelszen

*Corresponding author for this work

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Abstract

Background: Intensive care unit (ICU) outcome prediction models, such as Acute Physiology And Chronic Health Evaluation (APACHE), were designed in general critical care populations and their use in obstetric populations is contentious. The aim of the CIPHER (Collaborative Integrated Pregnancy High-dependency Estimate of Risk) study was to develop and internally validate a multivariable prognostic model calibrated specifically for pregnant or recently delivered women admitted for critical care. Methods: A retrospective observational cohort was created for this study from 13 tertiary facilities across five high-income and six low- or middle-income countries. Women admitted to an ICU for more than 24 h during pregnancy or less than 6 weeks post-partum from 2000 to 2012 were included in the cohort. A composite primary outcome was defined as maternal death or need for organ support for more than 7 days or acute life-saving intervention. Model development involved selection of candidate predictor variables based on prior evidence of effect, availability across study sites, and use of LASSO (Least Absolute Shrinkage and Selection Operator) model building after multiple imputation using chained equations to address missing data for variable selection. The final model was estimated using multivariable logistic regression. Internal validation was completed using bootstrapping to correct for optimism in model performance measures of discrimination and calibration. Results: Overall, 127 out of 769 (16.5%) women experienced an adverse outcome. Predictors included in the final CIPHER model were maternal age, surgery in the preceding 24 h, systolic blood pressure, Glasgow Coma Scale score, serum sodium, serum potassium, activated partial thromboplastin time, arterial blood gas (ABG) pH, serum creatinine, and serum bilirubin. After internal validation, the model maintained excellent discrimination (area under the curve of the receiver operating characteristic (AUROC) 0.82, 95% confidence interval (CI) 0.81 to 0.84) and good calibration (slope of 0.92, 95% CI 0.91 to 0.92 and intercept of -0.11, 95% CI -0.13 to -0.08). Conclusions: The CIPHER model has the potential to be a pragmatic risk prediction tool. CIPHER can identify critically ill pregnant women at highest risk for adverse outcomes, inform counseling of patients about risk, and facilitate bench-marking of outcomes between centers by adjusting for baseline risk.

Original languageEnglish
Article number278
JournalCritical Care
Volume22
Issue number1
DOIs
Publication statusPublished - 30 Oct 2018

Bibliographical note

Funding
This project was funded the Canadian Institutes for Health Research catalyst grant: Maternal health: from Pre-conception to Empty Nest grant on 1/9/2010 # H10–00654.

Most data were collected and shared without funding using internal resources.

Acknowledgements
CIPHER Group: Joni Kooy, Brittany Tarras, Nancy Liu, Rebecca Gordon, Shannon Lockhart, Annie Tran, Run shan Felar Yu, Yisa Yen, Andy Dhaliwal, Chris Lim, Nelson Luk, Saba Marzara and Navdeep Dha (Vancouver), Niamh Barrett (Monash, Melbourne), Lucia Haritgan (National Maternity Hospital, Dublin), Evan Lambe, Aoife Doyle, Aisling McMahon and Richard Katz (Rotunda Hospital), Andrea Das Neves and Vanina Aphalo (Argentina), Marlot Kallen (AMC, the Netherlands), Colleen Lee (Montefiore, New York), Katey Austin, Mary Mahler, Dinusha Sen and Alina Blazer (Mount Sinai, Toronto), Xiaotian Ni (Shanghai, China), Sheikh Irfan and Azra Amerjee (AKU, Pakistan), Antonio F. Oliveira Neto, Mary Angela Parpinelli, Maria Laura Costa, Thais Giovarotti, and Etienne Cordeiro (Campinas, Brazil).

Data Availability Statement

This project brought together datasets from 13 sites from around the world. The data are available for other investigators use. Please contact PvD with access requests.

Keywords

  • Critical care
  • High-risk pregnancy
  • Maternal morbidity
  • Maternal mortality
  • Risk prediction model

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