Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model.

BACKGROUND: Pre-eclampsia is a leading cause of maternal deaths. These deaths mainly result from eclampsia, uncontrolled hypertension, or systemic inflammation. We developed and validated the fullPIERS model with the aim of identifying the risk of fatal or life-threatening complications in women with pre-eclampsia within 48 h of hospital admission for the disorder. METHODS: We developed and internally validated the fullPIERS model in a prospective, multicentre study in women who were admitted to tertiary obstetric centres with pre-eclampsia or who developed pre-eclampsia after admission. The outcome of interest was maternal mortality or other serious complications of pre-eclampsia. Routinely reported and informative variables were included in a stepwise backward elimination regression model to predict the adverse maternal outcome. We assessed performance using the area under the curve (AUC) of the receiver operating characteristic (ROC). Standard bootstrapping techniques were used to assess potential overfitting. FINDINGS: 261 of 2023 women with pre-eclampsia had adverse outcomes at any time after hospital admission (106 [5%] within 48 h of admission). Predictors of adverse maternal outcome included gestational age, chest pain or dyspnoea, oxygen saturation, platelet count, and creatinine and aspartate transaminase concentrations. The fullPIERS model predicted adverse maternal outcomes within 48 h of study eligibility (AUC ROC 0·88, 95% CI 0·84-0·92). There was no significant overfitting. fullPIERS performed well (AUC ROC >0·7) up to 7 days after eligibility. INTERPRETATION: The fullPIERS model identifies women at increased risk of adverse outcomes up to 7 days before complications arise and can thereby modify direct patient care (eg, timing of delivery, place of care), improve the design of clinical trials, and inform biomedical investigations related to pre-eclampsia. FUNDING: Canadian Institutes of Health Research; UNDP/UNFPA/WHO/World Bank Special Programme of Research, Development, and Research Training in Human Reproduction; Preeclampsia Foundation; International Federation of Obstetricians and Gynecologists; Michael Smith Foundation for Health Research; and Child and Family Research Institute

Probability Trends in the Assessment of Cardiovascular Autonomic Fluctuations during Cold Pressor Tests

Abstract Eighteen healthy volunteers between 23 and 53 years of age (mean age 34.9 ± 9.

Predictor variables for post-discharge mortality modelling in infants: a protocol development project

Background: Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs. Objectives: To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world. Methods: A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings. Results: In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables were retained. Conclusion: A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting

Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol

IntroductionIn low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility.MethodsThis prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5–10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values.DiscussionThe current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. Clinical trial registrationhttps://clinicaltrials.gov/, identifier (NCT05730387)

Real-time algorithm for changes detection in depth of anesthesia signals

This paper presents a real-time algorithm for changes detection in depth of anesthesia signals. A Page-Hinkley test (PHT) with a forgetting mechanism (PHT-FM) was developed. The samples are weighted according to their "age" so that more importance is given to recent samples. This enables the detection of the changes with less time delay than if no forgetting factor was used. The performance of the PHT-FM was evaluated in a two-fold approach. First, the algorithm was run offline in depth of anesthesia (DoA) signals previously collected during general anesthesia, allowing the adjustment of the forgetting mechanism. Second, the PHT-FM was embedded in a real-time software and its performance was validated online in the surgery room. This was performed by asking the clinician to classify in real-time the changes as true positives, false positives or false negatives. The results show that 69 % of the changes were classified as true positives, 26 % as false positives, and 5 % as false negatives. The true positives were also synchronized with changes in the hypnotic or analgesic rates made by the clinician. The contribution of this work has a high impact in the clinical practice since the PHT-FM alerts the clinician for changes in the anesthetic state of the patient, allowing a more prompt action. The results encourage the inclusion of the proposed PHT-FM in a real-time decision support system for routine use in the clinical practice. © 2012 Springer-Verlag

Total intravenous anesthesia and spontaneous respiration for airway endoscopy in children - a prospective evaluation

Summary Introduction: Inhalational anesthesia with spontaneous respiration is traditionally used to facilitate airway endoscopy in children. The potential difficulties in maintaining adequate depth of anesthesia using inhalational anesthesia and the anesthetic pollution of the surgical environment are significant disadvantages of this technique. We report our institutional experience using total intravenous anesthesia (TIVA) and spontaneous respiration. Methods: We prospectively studied 41 pediatric patients undergoing 52 airway endoscopies and airway surgeries. Following induction of anesthesia, a propofol infusion was titrated to a clinically adequate level of anesthesia, guided by the Bispectral Index (BIS), and a remifentanil infusion was titrated to respiratory rate. ECG, BP, pulse oximetry, BIS level, transcutaneous CO 2 (TcCO 2 ), respiratory rate, and drug infusion rates were recorded. Adverse events and the response to these events were also recorded. Results: Forty-one children underwent 52 airway procedures; 17 rigid bronchoscopies and 35 microlaryngobronchoscopies, including 18 LASER treatments, were performed. The mean (SD SD) age was 6.9 (5.8) years and weight 26.9 (21.2) kg. The mean induction time was 13 (6) min, and anesthesia duration was 49 (30) min. The mean highest TcCO 2 recorded during the procedures was 62.8 ± 15.3 mmHg. Coughing occurred in 14 (27%) patients, requiring additional topical anesthesia (3), a bolus of propofol (4) or remifentanil (1), or removal of the bronchoscope (1). Desaturation below 90% occurred in 10 (19%) cases; only three required intervention in the form of temporary assisted ventilation (2) or inhaled bronchodilators (1). No laryngospasm, stridor, or arrhythmias were observed. Conclusion: TIVA and spontaneous respiration is an effective technique to manage anesthesia for airway endoscopy and surgery in children

Transitioning from the “Three Delays” to a focus on continuity of care: a qualitative analysis of maternal deaths in rural Pakistan and Mozambique

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