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

Prognostic scores for prediction of maternal near miss and maternal death after admission to an intensive care unit: A narrative review

Near miss morbidity and maternal death (defined as severe maternal outcomes - SMO) are the most important adverse outcomes in obstetric settings to assess delays and characteristics of health care management. Intensive care units (ICUs) represent an opportunity of adequate care for women who, in several cases, experienced earlier clinical delays in their maternal health care management. Some prognostic scores widely used in ICU have been useful in characterizing patients in terms of severity of illness in clinical studies, for evaluation of ICU performance, in quality improvement initiatives and for benchmark purposes. Prediction of SMO during the admission to the ICU could greatly improve obstetric care management. We reviewed the feasibility of the existing ICU clinical and obstetric prediction scores in predicting maternal near miss and maternal death.

Comparison of the CIPHER prognostic model with the existing scores in predicting severe maternal outcomes during intensive care unit admission

OBJECTIVE

To compare the performance of the Collaborative Integrated Pregnancy High-Dependency Estimate of Risk (CIPHER) model in predicting maternal death and near-miss morbidity (Severe Maternal Outcome [SMO]) with the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the Simplified Acute Physiology Score (SAPS) III scores.

METHODS

A retrospective and a prospective study was conducted at two centers in Brazil. For each score, area under curve (AUC) was used and score calibration was assessed using the Hosmer-Lemeshow statistic (H-L) test and the standardized mortality ratio (SMR).

RESULTS

A cohort of 590 women was analyzed. A SMO was observed in 216 (36.6%) women. Of these, 13 (2.2%) were maternal deaths and 203 (34.4%) met one or more maternal near-miss criteria. The CIPHER model did not show significant diagnostic ability (AUC 0.52) and consequently its calibration was poor (H-L P<0.05). The SAPS III had the best performance (AUC 0.77, H-L P>0.05 and SMR 0.85).

CONCLUSION

The performance of the CIPHER model was lower compared to the other scores. Since the CIPHER model is not ready for clinical use, the SAPS III score should be considered for the prediction of SMO.

Prognostic Value of an Estimate-of-Risk Model in Critically Ill Obstetric Patients in Brazil

The CIPHER (Collaborative Integrated Pregnancy High-Dependency Estimate of Risk) prognostic model was not predictive of risk of death, prolonged organ support, or lifesaving intervention among critically ill patients in Brazil.

To externally validate the CIPHER (Collaborative Integrated Pregnancy High-Dependency Estimate of Risk) prognostic model for pregnant and postpartum women admitted to the intensive care unit.

High mortality rate of obstetric critically ill women in Rwanda and its predictability

BACKGROUND

Reasons for admission to intensive care units (ICUs) for obstetric patients vary from one setting to another. Outcomes from ICU and prediction models are not well explored in Rwanda owing to lack of appropriate scores. This study aimed to assess reasons for admission and accuracy of prediction models for mortality of obstetric patients admitted to ICUs of two public tertiary hospitals in Rwanda.

METHODS

We prospectively collected data from all obstetric patients admitted to the ICUs of the two public tertiary hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admission, demographic and clinical characteristics, outcome including death and its predictability by both the Modified Early Obstetric Warning Score (MEOWS) and quick Sequential Organ Failure Assessment (qSOFA). We analysed the accuracy of mortality prediction models by MEOWS or qSOFA by using logistic regression adjusting for factors associated with mortality. Area under the Receiver Operating characteristic (AUROC) curves is used to show the predicting capacity for each individual tool.

RESULTS

Obstetric patients (n = 94) represented 12.8 % of all 747 ICU admissions which is 1.8 % of all 4.999 admitted women for pregnancy or labor. Sepsis (n = 30; 31.9 %) and obstetric haemorrhage (n = 24; 25.5 %) were the two commonest reasons for ICU admission. Overall ICU mortality for obstetric patients was 54.3 % (n = 51) with average length of stay of 6.6 ± 7.525 days. MEOWS score was an independent predictor of mortality (adjusted (a)OR 1.25; 95 % CI 1.07-1.46) and so was qSOFA score (aOR 2.81; 95 % CI 1.25-6.30) with an adjusted AUROC of 0.773 (95 % CI 0.67-0.88) and 0.764 (95 % CI 0.65-0.87), indicating fair accuracy for ICU mortality prediction in these settings of both MEOWS and qSOFA scores.

CONCLUSIONS

Sepsis and obstetric haemorrhage were the commonest reasons for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA scores could accurately predict ICU mortality of obstetric patients in resource-limited settings, but larger studies are needed before a recommendation for their use in routine practice in similar settings.

Maternal Risk Modeling in Critical Care-Development of a Multivariable Risk Prediction Model for Death and Prolonged Intensive Care

OBJECTIVES

We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care.

DESIGN

We used data from a high-quality prospectively collected national database, supported with literature review and expert opinion. We tested univariable associations between each risk factor and outcome. We then developed two separate multivariable logistic regression models for the outcomes of acute hospital mortality and death or prolonged ICU length of stay. We validated two parsimonious risk prediction models specific for a maternal population.

SETTING

The Intensive Care National Audit and Research Centre Case Mix Programme is the national clinical audit for adult critical care in England, Wales, and Northern Ireland.

PATIENTS

All female admissions to adult general critical care units, for the period January 1, 2007-December 31, 2016, 16-50 years old, and admitted either while pregnant or within 42 days of delivery-a cohort of 15,480 women.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. For the primary outcome of acute hospital mortality, our parsimonious risk model consisting of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-1.00); these variables are commonly available for all maternal admissions. For the secondary composite outcome of death or ICU length of stay greater than 48 hours, the risk model consisting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-0.83).

CONCLUSIONS

We developed risk prediction models specific to the maternal critical care population. The models compare favorably against general adult ICU risk prediction models in current use within this population.

Society for Maternal-Fetal Medicine Consult Series #54: Assessing the risk of maternal morbidity and mortality

The rates of maternal morbidity and mortality in the United States demand a comprehensive approach to assessing pregnancy-related risks. Numerous medical and nonmedical factors contribute to maternal morbidity and mortality. Reducing the number of women who experience pregnancy morbidity requires identifying which women are at greatest risk and initiating appropriate interventions early in the reproductive life course. The purpose of this Consult is to educate all healthcare practitioners about factors contributing to a high-risk pregnancy, strategies to assess maternal health risks due to pregnancy, and the importance of risk assessment across the reproductive spectrum in reducing maternal morbidity and mortality.

A novel id-iri score: development and internal validation of the multivariable community acquired sepsis clinical risk prediction model

We aimed to develop a scoring system for predicting in-hospital mortality of community-acquired (CA) sepsis patients. This was a prospective, observational multicenter study performed to analyze CA sepsis among adult patients through ID-IRI (Infectious Diseases International Research Initiative) at 32 centers in 10 countries between December 1, 2015, and May 15, 2016. After baseline evaluation, we used univariate analysis at the second and logistic regression analysis at the third phase. In this prospective observational study, data of 373 cases with CA sepsis or septic shock were submitted from 32 referral centers in 10 countries. The median age was 68 (51-77) years, and 174 (46,6%) of the patients were females. The median hospitalization time of the patients was 15 (10-21) days. Overall mortality rate due to CA sepsis was 17.7% (n = 66). The possible predictors which have strong correlation and the variables that cause collinearity are acute oliguria, altered consciousness, persistent hypotension, fever, serum creatinine, age, and serum total protein. CAS (%) is a new scoring system and works in accordance with the parameters in third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). The system has yielded successful results in terms of predicting mortality in CA sepsis patients.

Association of Vegetarian Diet with Chronic Kidney Disease

Chronic kidney disease (CKD) and its complications are major global public health issues. Vegetarian diets are associated with a more favorable profile of metabolic risk factors and lower blood pressure, but the protective effect in CKD is still unknown. We aim to assess the association between vegetarian diets and CKD. A cross-sectional study was based on subjects who received physical checkups at the Taipei Tzu Chi Hospital from 5 September 2005, to 31 December 2016. All subjects completed a questionnaire to assess their demographics, medical history, diet pattern, and lifestyles. The diet patterns were categorized into vegan, ovo-lacto vegetarian, or omnivore. CKD was defined as an estimated GFR <60 mL/min/1.73 m² or the presence of proteinuria. We evaluated the association between vegetarian diets and CKD prevalence by using multivariate analysis. Our study recruited 55,113 subjects. CKD was significantly less common in the vegan group compared with the omnivore group (vegan 14.8%, ovo-lacto vegetarians 20%, and omnivores 16.2%, P < 0.001). The multivariable logistic regression analysis revealed that vegetarian diets including vegan and ovo-lacto vegetarian diets were possible protective factors [odds ratios = 0.87 (0.77–0.99), P = 0.041; 0.84 (0.78–0.90), P < 0.001]. Our study showed a strong negative association between vegetarian diets and prevalence of CKD. If such associations are causal, vegetarian diets could be helpful in reducing the occurrence of CKD.

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

PURPOSE

Pregnancy-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.

METHODS

Search 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.

RESULTS

Thirty-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]).

CONCLUSIONS

Despite 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.