CIPHER (Collaborative Integrated Pregnancy High-dependency Estimate of Risk)
Maternal Intensive Care Unit (ICU) admissions follow both obstetric and non-obstetric complications in pregnancy. 0.25-1.5% of hospital admissions during pregnancy will require admission to the ICU. About two-thirds of these admissions are due to obstetric causes (e.g. haemorrhage and pre-eclampsia) and one-third is due to maternal medical or surgical complications. Pregnant women with underlying medical conditions are over-represented in maternal mortality and severe morbidity statistics.
A number of critical care outcome prediction models exist in ICU to predict hospital mortality, by incorporating measures of physiologic derangement and co-morbidities. The most commonly used models are the Acute Physiology and Chronic Health Evaluation (APACHE) and the Simplified Acute Physiology Score (SAPS). Their use in obstetric populations admitted to ICU for obstetric reasons to predict hospital mortality has a tendency to overestimate mortality.
Pregnancy and the post-partum state have unique physiology. Maternal cardiac output, respiratory rate and heart rate increase in pregnancy. Equally, maternal “normal range” blood values are altered in pregnancy, with lower levels of creatinine, haematocrit and blood urea nitrogen for example. No outcome prediction models have been designed specifically for use in obstetric patients. We aim to build on the expertise and experience in clinical prediction modelling from PIERS and develop a clinical prediction model that is applicable to any cause of maternal morbidity or mortality.
The identification of variables that predict outcome in pregnant or recently delivered women admitted to the ICU is the first step in the development of a new clinical prediction model for obstetric patients in the ICU. Such a tool will assist in providing more appropriate management to those that require it most.
To develop and evaluate a predictor of risk of severe maternal morbidity or mortality for pregnant or recently delivered women in ICU: The Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER). This study is being conducted in a number of tertiary care hospitals worldwide, using at ICU data.
Secondary aims are to assess perinatal outcome and to compare performance of CIPHER against existing critical care prediction models.
The primary outcome for the CIPHER model are one/more of (i) maternal death, or (ii) prolonged duration of organ support (>7 days).
Perinatal outcome will be measured as a secondary outcome.
The final CIPHER model was chosen as the most generalisable model that would be relevant to both the population used to develop the model and other populations. The area under the ROC curve of the CIPHER model suggests excellent discrimination and future clinical utility. Developed specifically for obstetric patients from both HIC and LMIC, the CIPHER model contains fewer variables and has better discrimination in our obstetric population than APACHE 2.
The CIPHER model is a promising step towards development of a globally applicable tool for predicting adverse maternal outcome in ICU-admitted pregnant and postpartum women. Ultimately, we hope to apply CIPHER in worldwide settings to reduce the burden of pregnancy related
morbidity and mortality.
- Payne BA, Ryan H, Bone J, Magee LA, Aarvold A, Ansermino JM, et al.. Development and internal validation of the multivariable CIPHER (Collaborative Integrated Pregnancy High-dependency Estimate of Risk) clinical risk prediction model. Critical Care. 2018 October 30.
- Ryan HM, Sharma S, Magee LA, Ansermino JM, MacDonell K, Payne BA, Walley KR, von Dadelszen P. The Usefulness of the APACHE II Score in Obstetric Critical Care: A Structured Review. Journal of Obstetrics and Gynaecology Canada. 2016 Aug 30.
- Aarvold AB, Ryan HM, Magee LA, von Dadelszen P, Fjell C, Walley KR. Multiple Organ Dysfunction Score Is Superior to the Obstetric-Specific Sepsis in Obstetrics Score in Predicting Mortality in Septic Obstetric Patients. Critical Care Medicine. 2016 Sep 19.