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)

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

The Canadian Perinatal Network: A National Network Focused on Threatened Preterm Birth at 22 to 28 Weeks\u27 Gestation

Objective: The Canadian Perinatal Network (CPN) maintains an ongoing national database focused on threatened very preterm birth. The objective of the network is to facilitate between-hospital comparisons and other research that will lead to reductions in the burden of illness associated with very preterm birth. Methods: Women were included in the database if they were admitted to a participating tertiary perinatal unit at 22+0 to 28+6 weeks\u27 gestation with one or more conditions most commonly responsible for very preterm birth, including spontaneous preterm labour with contractions, incompetent cervix, prolapsing membranes, preterm prelabour rupture of membranes, gestational hypertension, intrauterine growth restriction, or antepartum hemorrhage. Data were collected by review of maternal and infant charts, entered directly into standardized electronic data forms and uploaded to the CPN via a secure network. Results: Between 2005 and 2009, the CPN enrolled 2524 women from 14 hospitals including those with preterm labour and contractions (27.4%), short cervix without contractions (16.3%), prolapsing membranes (9.4%), antepartum hemorrhage (26.0%), and preterm prelabour rupture of membranes (23 0%) The mean gestational age at enrolment was 25.9 ± 1.9 weeks and the mean gestation age at delivery was 29.9 ± 5.1 weeks; 57.0% delivered at \u3c 29 weeks and 75.4% at \u3c 34 weeks. Complication rates were high and included serious maternal complications (26 7%), stillbirth (8.2%), neonatal death (16.3%), neonatal intensive care unit admission (60 7%), and serious neonatal morbidity (35 0%). Conclusion: This national dataset contains detailed information about women at risk of very preterm birth. It is available to clinicians and researchers who are working with one or more CPN collaborators and who are interested in studies relating processes of care to maternal or perinatal outcomes

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

STRIDER (Sildenafil TheRapy in dismal prognosis early onset fetal growth restriction): An international consortium of randomised placebo-controlled trials

Background: Severe, early-onset fetal growth restriction due to placental insufficiency is associated with a high risk of perinatal mortality and morbidity with long-lasting sequelae. Placental insufficiency is the result of abnormal formation and function of the placenta with inadequate remodelling of the maternal spiral arteries. There is currently no effective therapy available. Some evidence suggests sildenafil citrate may improve uteroplacental blood flow, fetal growth, and meaningful infant outcomes. The objective of the Sildenafil TheRapy In Dismal prognosis Early onset fetal growth Restriction (STRIDER) collaboration is to evaluate the effectiveness of sildenafil versus placebo in achieving healthy perinatal survival through the conduct of randomised clinical trials and systematic review including individual patient data meta-analysis.  Methods: Five national/bi-national multicentre randomised placebo-controlled trials have been launched. Women with a singleton pregnancy between 18 and 30 weeks with severe fetal growth restriction of likely placental origin, and where the likelihood of perinatal death/severe morbidity is estimated to be significant are included. Participants will receive either sildenafil 25 mg or matching placebo tablets orally three times daily from recruitment to 32 weeks gestation.  Discussion: The STRIDER trials were conceived and designed through international collaboration. Although the individual trials have different primary outcomes for reasons of sample size and feasibility, all trials will collect a standard set of outcomes including survival without severe neonatal morbidity at time of hospital discharge. This is a summary of all the STRIDER trial protocols and provides an example of a prospectively planned international clinical research collaboration. All five individual trials will contribute to a pre-planned systematic review of the topic including individual patient data meta-analysis