Intensive Care Admissions in Pregnancy: Analysis of a Level of Support Scoring System

Thakur, Mili; Gonik, Bernard; Gill, Navleen; Awonuga, Awoniyi O.; Rocha, Frederico G.; González, Juan M.

doi:10.1007/s10995-015-1808-9

Cited by 9 publications

(16 citation statements)

References 29 publications

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“…It has been seen that an early and elective transfer of patients to the ICU by identifying the risk factors can help us in achieving the goal of decreasing maternal mortality. [ 6 ]…”

Section: Discussionmentioning

confidence: 99%

Obstetric admissions to tertiary level intensive care unit – Prevalence, clinical characteristics and outcomes

Joseph¹,

Bhatia²,

Abraham³

et al. 2018

Indian J Anaesth

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“…It has been seen that an early and elective transfer of patients to the ICU by identifying the risk factors can help us in achieving the goal of decreasing maternal mortality. [ 6 ]…”

Section: Discussionmentioning

confidence: 99%

Obstetric admissions to tertiary level intensive care unit – Prevalence, clinical characteristics and outcomes

Joseph¹,

Bhatia²,

Abraham³

et al. 2018

Indian J Anaesth

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“…An additional 11 articles were identified through the reference lists and citation tracking of 316 relevant articles with use of Web of Science. Finally, 38 studies of 12 prediction models met inclusion criteria (Tables 1 and 2, Figs 3 and 4, and S1 Table) [4,17,36–45,28,46–55,29,56–63,30–35]. The TRIPOD checklist of all 38 eligible articles is available on request (S2 File).…”

Section: Resultsmentioning

confidence: 99%

Risk prediction models for maternal mortality: A systematic review and meta-analysis

et al. 2018

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PurposePregnancy-related critical illness leads to death for 3–14% of affected women. Although identifying patients at risk could facilitate preventive strategies, guide therapy, and help in clinical research, no prior systematic review of this literature exploring the validity of risk prediction models for maternal mortality exists. Therefore, we have systematically reviewed and meta-analyzed risk prediction models for maternal mortality.MethodsSearch strategy: MEDLINE, EMBASE and Scopus, from inception to May 2017.Selection criteria: Trials or observational studies evaluating risk prediction models for maternal mortality.Data collection and analysis: Two reviewers independently assessed studies for eligibility and methodological quality, and extracted data on prediction performance.ResultsThirty-eight studies that evaluated 12 different mortality prediction models were included. Mortality varied across the studies, with an average rate 10.4%, ranging from 0 to 41.7%. The Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER) model and the Maternal Severity Index had the best performance, were developed and validated from studies of obstetric population with a low risk of bias. The CIPHER applies to critically ill obstetric patients (discrimination: area under the receiver operating characteristic curve (AUC) 0.823 (0.811–0.835), calibration: graphic plot [intercept—0.09, slope 0.92]). The Maternal Severity Index applies to hospitalized obstetric patients (discrimination: AUC 0.826 [0.802–0.851], calibration: standardized mortality ratio 1.02 [0.86–1.20]).ConclusionsDespite the high heterogeneity of the study populations and the limited number of studies validating the finally eligible prediction models, the CIPHER and the Maternal Severity Index are recommended for use among critically ill and hospitalized pregnant and postpartum women for risk adjustment in clinical research and quality improvement studies. Neither index has sufficient discrimination to be applicable for clinical decision making at the individual patient level.

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“… 11 However, Vasquez et al and Thakur et al have reported higher incidences of nonobstetric ICU admissions in their study. 12 , 13 Interestingly, women admitted for nonobstetric causes may have higher disease severity scores on admission and a higher need for mechanical ventilation and other ICU interventions. 11 , 14 – 16 Table 1 lists the obstetric and nonobstetric causes of ICU admissions by various authors in the last decade.…”

Section: Ethodsmentioning

confidence: 99%

“…However, it was observed that the general scores were not able to accurately predict the outcomes of pregnant women in the critical care due to the physiological changes in pregnancy which have a bearing on the variables of these models; another reason being that many critical conditions improved remarkably after delivery of the fetus and placenta, leading to over prediction of mortality by the general scores. 12 , 13 , 16 , 18 – 24 Thus, the need for customization of the scores for obstetric patients was perceived. This led to development of specific ObPMs.…”

Section: Ethodsmentioning

confidence: 99%

Prognosticating Fetomaternal ICU Outcomes

Suri¹,

Khanam²

2022

Indian Journal of Critical Care Medicine

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A bstract Although no scoring system is as yet fully validated for predicting maternal outcomes in critically ill obstetric patients, prognostication may be done objectively using severity predicting models. General critical care scoring systems which have been studied in obstetric patients are outcome prediction models (Acute Physiology and Chronic Health Evaluation [APACHE] I-IV, Simplified Acute Physiology Score [SAPS] I-III, Mortality Probability Model [MPM] I-IV) and organ dysfunction scores (Multiple Organ Dysfunction Score [MODS], Logistic Organ Dysfunction Score [LODS], Sequential Organ Failure Assessment [SOFA]). General critical care scoring systems may overpredict mortality rates in obstetric patients secondary to an altered physiology of organ systems during pregnancy. Obstetric prediction models were developed keeping in mind the physiological characteristics of obstetric population. They are Modified Early Obstetric Warning System (MEOWS), Obstetric Early Warning Score (OEWS), Maternal Early Warning Trigger (MEWT), and disease-specific obstetric scoring systems. The APACHE II model and MPM II are most often used scoring systems for predicting maternal mortality. The SOFA model is the best predictive model for sepsis in obstetrics. APACHE II and SAPS are more useful for nonobstetric population. Recent studies have also underscored the applicability of the OEWS in intensive care unit (ICU) settings with results comparable to the more elaborate APACHE II and SOFA scores. The Early Warning System helps in identifying acutely deteriorating pregnant and postpartum women in non-ICU settings who may require critical care. Fetal outcomes are largely dependent upon maternal outcomes. Prognostic systems applied to mothers may help in estimation of perinatal mortality and morbidity. How to cite this article Suri J, Khanam Z. Prognosticating Fetomaternal ICU Outcomes. Indian J Crit Care Med 2021;25(Suppl 3):S206–S222.

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Intensive Care Admissions in Pregnancy: Analysis of a Level of Support Scoring System

Cited by 9 publications

References 29 publications

Obstetric admissions to tertiary level intensive care unit – Prevalence, clinical characteristics and outcomes

Obstetric admissions to tertiary level intensive care unit – Prevalence, clinical characteristics and outcomes

Risk prediction models for maternal mortality: A systematic review and meta-analysis

Prognosticating Fetomaternal ICU Outcomes

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