Pattern and Outcome of Obstetric Admissions into the Intensive Care Unit of a Southeast Nigerian Hospital

Risk Prediction Method of Obstetric Nursing Based on Data Mining

Obstetric nursing is not only complex but also prone to risks, which can have adverse effects on hospitals. Improper handling of existing risks in obstetric care can lead to enormous harm to patients and families. Therefore, it is necessary to pay attention to the risks of obstetric nursing, especially to predict the risks in a timely manner, and take effective measures to prevent them in time, so as to achieve the purpose of allowing patients to recover as soon as possible. Data mining has powerful forecasting function, so this paper proposes to combine the data-mining-based support vector machine method and XGBoost method into a forecasting model, which overcomes the shortcomings of unstable forecasting and low accuracy of a single forecasting model. The experimental results of this paper have shown that the prediction accuracy of the SVM-XGBoost combined prediction model has reached 100%, the accuracy of the single SVM prediction model is about 78%, and the accuracy of the single XGBoost prediction model is about 75%. Compared with the single SVM model and the XGBoost prediction model, the accuracy rate had increased by about 22% and 25%, and the precision rate and recall rate are also improved. Therefore, it is very suitable to use the SVM-XGBoost combined prediction model to predict the risk of obstetric nursing.

Prognosticating Fetomaternal ICU Outcomes

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.

Determinants of maternal mortality in a critical care unit: A prospective analysis

Introduction:

An admission of a pregnant woman to an intensive care unit (ICU) is considered as an objective marker of maternal near miss. Only a few studies from the Indian subcontinent have reported on the ability of ICU scoring systems in predicting the mortality in obstetric patients.

Methods:

A prospective analysis of all critically ill obstetric patients admitted to the critical care department was done.

Results:

In the period between April 2013 and September 2017, there were 101 obstetric admissions to the critical care ICU. Of these, 82 patients (81.2%) were discharged from the hospital, 18 patients (17.8%) died, and one left against medical advice. The common diagnoses seen in these patients were cardiac failure (n = 39; 38.6%); pregnancy-induced hypertension (n = 26; 25.7%); acute respiratory distress syndrome (n = 20; 19.8%); intra-abdominal sepsis (n = 19; 18.8%); tropical diseases (n = 19; 18.8%); and tuberculosis (n = 13; 12.9%). When we compared the survivors with the nonsurvivors, a higher severity of illness score and a low PaO₂/FiO₂ were found to increase the odds of death. The area of distribution under the receiver operator characteristic curve was 0.726 (95% confidence interval [CI] = 0.575–0.877), 0.890 (95% CI = 0.773–1.006), 0.867 (95% CI = 0.755–0.979), and 0.850 (95% CI = 0.720–0.980) for the PaO₂/FiO₂, Simplified Acute Physiology Score (SAPS) II, Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation (APACHE) II score, respectively, for predicting mortality. The standardized mortality ratio was better with SAPSII than with APACHE II.

Conclusions:

Cardiac dysfunction is a leading cause of ICU admission. Obstetric patients frequently require ventilatory support, intensive hemodynamic monitoring, and blood transfusion. The APACHE II score is a good index for assessing ICU outcomes.

A Study of Clinical Profile and Fetomaternal Outcome of Obstetric Patients Admitted to Intensive Care Unit: A Prospective Hospital-based Study

Aims and objectives

To study clinical profile of obstetric patients admitted to intensive care unit (ICU) and to analyze the relation of demographic factors such as age, parity, literacy level, socioeconomic status, acute physiology and chronic health evaluation II (APACHE II) score, and level of delay with fetomaternal outcome.

Design

It is a prospective cross-sectional observational study.

Materials and methods

After admission to ICU a detailed history, analysis of basic demographic variables along with level of delay was done. APACHE II score was calculated. These parameters were correlated with fetomaternal outcome. The Chi-squared test was used to compare categorical variables. The one-way analysis of variance was used to compare the continuous variables among the strata with Tukey's post hoc test.

Results

Incidence of obstetric ICU admission was 0.77%. Mean age was 26.03 years. Most common indication of ICU admission was obstetrical hemorrhage (37.1%) followed by hypertensive disorders of pregnancy (25.8%). Type I delay was the most common followed by type II delay. Mean APACHE II score was 14.77 ± 6.85. Observed mortality rate (30.6%) was found to be higher than predicted mortality rate (25%). APACHE II score was significantly high in the presence of level 1 (p = 0.003) and level 2 delays (p = 0.0001). Also, it was significantly increased with the duration of delays.

Conclusion

Unbooked and referred cases had high incidence of ICU admission. The presence of delay was associated with poor outcome.

How to cite this article

Miglani U, Pathak AP, Laul P, Sarangi S, Gandhi S, Miglani S, et al. A Study of Clinical Profile and Fetomaternal Outcome of Obstetric Patients Admitted to Intensive Care Unit: A Prospective Hospital-based Study. Indian J Crit Care Med 2020;24(11):1071-1076.

Epidemiology, Outcomes, and Risk Factors for Mortality in Critically Ill Women Admitted to an Obstetric High-Dependency Unit in Sierra Leone

A better understanding of the context-specific epidemiology, outcomes, and risk factors for death of critically ill parturients in resource-poor hospitals is needed to tackle the still alarming in-hospital maternal mortality in African countries. From October 2017 to October 2018, we performed a 1-year retrospective cohort study in a referral maternity hospital in Freetown, Sierra Leone. The primary endpoint was the association between risk factors and high-dependency unit (HDU) mortality. Five hundred twenty-three patients (median age 25 years, interquartile range [IQR]: 21-30 years) were admitted to the HDU for a median of 2 (IQR: 1-3) days. Among them, 65% were referred with a red obstetric early warning score (OEWS) code, representing 1.17 cases per HDU bed per week; 11% of patients died in HDU, mostly in the first 24 hours from admission. The factors independently associated with HDU mortality were ward rather than postoperative referrals (odds ratio [OR]: 3.21; 95% CI: 1.48-7.01; P = 0.003); admissions with red (high impairment of patients' vital signs) versus yellow (impairment of vital signs) or green (little or no impairment of patients' vital signs) OEWS (OR: 3.66; 95% CI: 1.15-16.96; P = 0.04); responsiveness to pain or unresponsiveness on the alert, voice, pain unresponsive scale (OR: 5.25; 95% CI: 2.64-10.94; P ≤ 0.0001); and use of vasopressors (OR: 3.24; 95% CI: 1.32-7.66; P = 0.008). Critically ill parturients were predominantly referred with a red OEWS code and usually required intermediate care for 48 hours. Despite the provided interventions, death in the HDU was frequent, affecting one of 10 critically ill parturients. Medical admission, a red OEWS code, and a poor neurological and hemodynamic status were independently associated with mortality, whereas adequate oxygenation was associated with survival.