Delivery Characteristics and the Risk of Early-Onset Neonatal Sepsis

BACKGROUND AND OBJECTIVES: Multiple strategies are used to identify newborn infants at high risk of culture-confirmed early-onset sepsis (EOS). Delivery characteristics have been used to identify preterm infants at lowest risk of infection to guide initiation of empirical antibiotics. Our objectives were to identify term and preterm infants at lowest risk of EOS using delivery characteristics and to determine antibiotic use among them. METHODS: This was a retrospective cohort study of term and preterm infants born January 1, 2009 to December 31, 2014, with blood culture with or without cerebrospinal fluid culture obtained ≤72 hours after birth. Criteria for determining low EOS risk included: cesarean delivery, without labor or membrane rupture before delivery, and no antepartum concern for intraamniotic infection or nonreassuring fetal status. We determined the association between these characteristics, incidence of EOS, and antibiotic duration among infants without EOS. RESULTS: Among 53 575 births, 7549 infants (14.1%) were evaluated and 41 (0.5%) of those evaluated had EOS. Low-risk delivery characteristics were present for 1121 (14.8%) evaluated infants, and none had EOS. Whereas antibiotics were initiated in a lower proportion of these infants (80.4% vs 91.0%, P < .001), duration of antibiotics administered to infants born with and without low-risk characteristics was not different (adjusted difference 0.6 hours, 95% CI [−3.8, 5.1]). CONCLUSIONS: Risk of EOS among infants with low-risk delivery characteristics is extremely low. Despite this, a substantial proportion of these infants are administered antibiotics. Delivery characteristics should inform empirical antibiotic management decisions among infants born at all gestational ages.

How to Determine the Cost of Capital for a Commercial Forestry

This paper focuses on designing a methodological workflow to fill a knowledge gap for determining the cost of capital for commercial forestry projects. Upon reviewing the literature, a method to determine the cost of capital for profit-oriented forestry seems to be lacking. Accordingly, we selected and analyzed 42 companies that do businesses worldwide, are present on the stock exchange, and possess or lease forest land. Based on their business activities (growing forest, sawmilling, final production, paper production), these companies are classified into four subgroups. An algorithm has been devised using the concept of risk diversification and the capital asset pricing model for three groups of investors and four forestry subgroups. In doing so, the real risk-free rate (0.43%) is set as the difference between an average return on 10-year US government bonds (2.59% nominal) and the 10-year average US inflation rate (2.16%). The measure of forestry systematic risk (beta coefficient) varies between 0.83 and 1.41, while the equity (stock exchange market) risk premium is set to 6%. Unsystematic risk is determined using a process of mapping which takes into account all risk elements marked as relevant for the forestry sector. This approach provides results that reveal the cost of capital varying between 5.41% and 16.55% based on the current level of an investor's portfolio diversification and the risk characteristics of the forestry subgroup. Finally, the forestry companies meeting the investor's expectations are noted as preferable investment opportunities.

AI in spotting high-risk characteristics of medical imaging and molecular pathology

Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.

132 Clinical characteristics and outcomes of a contemporary, real-world, single-centre cohort of patients with advanced heart failure

Aims An increasing number of patients with heart failure (HF) progresses to an advanced stage, characterized by persistent and sever symptoms and worse prognosis. A detailed characterization of patients with advanced HF is needed to optimize clinical management and timely refer for heart transplant or left ventricular assist device implantation. Methods and results A retrospective analysis was performed on patients with HF who were admitted to hospital or performed an outpatient visit at our centre (Spedali Civili di Brescia, Brescia, Italy) from 1 January 2020 to 31 December 2020, and who had at least one of the following high-risk characteristics: (1) previous or ongoing requirement for inotropes; (2) persisting New York Heart Association (NYHA) class III or IV and/or persistently high natriuretic peptides (BNP or NT-proBNP); (3) end-organ dysfunction, defined as worsening renal or liver dysfunction in the setting of HF; (4) ejection fraction (EF) <20%; (5) recurrent appropriate defibrillator shocks; (6) more than 1 hospitalization for HF in the last year; (7) persisting fluid overload and/or increasing diuretic requirement; (8) consistently low blood pressure (systolic blood pressure <90–100 mmHg); and (9) inability to up-titrate or need to decrease/cease HF therapies, such as beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor-neprilysin inhibitors, or mineralocorticoid receptor antagonists. The updated 2018 Heart Failure Association (HFA)—European Society of Cardiology (ESC) criteria for defining advanced HF were evaluated. The primary endpoint was all-cause mortality; secondary endpoints were a composite of all-cause mortality or hospitalization for HF and a composite of all-cause mortality or hospitalization for any reason. Among 493 patients with HF who were hospitalized or performed an outpatient visit in 2020, 230 (46.7%) had at least one high risk criterion and were included in the study. Mean age was 75.5 ± 11.9 years, 156 patients (67.8%) were men, and 160 patients (69.6%) were hospitalized and included as inpatients. Median EF was 38% [interquartile range (IQR): 25–50%] and 117 patients (50.9%) had HF with reduced EF (<40%); median NT-proBNP was 4044 (IQR: 2262–7664) pg/mL. Among the included 230 patients, 38 (16.5%) had all four updated HFA-ESC criteria defining advanced HF, 53 (23.0%) had American College of Cardiology (ACC)/American Heart Association (AHA) stage D, 21 (9.1%) had INTERMACS profile 1–3. In-hospital mortality was 10.6% (among inpatients). After a median follow-up of 301 (214–442) days, a total of 62 patients died (27.0%), and the secondary endpoints of all-cause death or HF hospitalization and all-cause death or any hospitalization were observed in 107 (46.5%) and 139 (60.4%) patients, respectively. Patients fulfilling all four updated HFA-ESC criteria for advanced HF had a higher risk of all-cause mortality (unadjusted HR: 2.06; 95% CI: 1.18–3.60; P = 0.011), also after adjustment for covariates of interest (adjusted HR: 2.20; 95% CI: 1.03, 4.70; P = 0.041). Conclusions In our contemporary, real-world cohort of HF patients with high-risk characteristics, mid-term prognosis was poor, and the use of updated HFA-ESC criteria defining advanced HF identified a subset at increased risk of mortality.

547 Supernormal conduction of an accessory pathway: the case of a young sportsman

Aims Ventricular pre-excitation is defined by the presence of all of the following electrocardiographic criteria: PQ interval duration ≤120 ms, QRS duration ≥120 ms, and presence of δ wave (defined as initial ‘slurring’ of the QRS complex). Ventricular pre-excitation together with the presence of symptoms (orthodromic and/or antidromic atrioventricular reentry tachycardia, atrial fibrillation) defines Wolff–Parkinson–White (WPW) syndrome. The anatomical substrate of ventricular pre-excitation consists of an extranodal accessory atrio-ventricular connection: the so-called Kent bundle. Such pathways can have antegrade, retrograde, or mixed conductive properties. Accessory pathways endowed with anterograde conductive capability may be responsible for manifest, intermittent, or non-manifest ventricular pre-excitation depending on whether it is respectively always visible on the ECG, not always visible on the ECG and not visible on the ECG even though the pathway has the ability to antegrade conduction. The rare phenomenon of supernormal conduction of anomalous pathways is part of the manifest pre-excitation, which represents the topic of the case reported below. Methods and results We report the clinical case of a young not agonist sportsman undergoing an electrophysiological study (SEF) because of he is suffering from ventricular pre-excitation. The SEF did not showed the inducibility of arrhythmias and, at the same time, apparently it showed low risk characteristics of the pathway even during adrenergic stimulus. However, a careful study, performed with atrial stimulation with couplings up to refractoriness of the atrioventricular node revealed supernormal conduction properties of the Kent bundle which proved to have high risk characteristics according to current guidelines and, therefore, was effectively treated with catheter ablation. Conclusions This case invites us to careful studying of accessory pathways properties, especially since, although rare, they may possess supernormal conduction characteristics capable of determining high ventricular rates in the case of sustained atrial tachyarrhythmias, especially in conditions of adrenergic hyperactivity.

Development of an Impairment Point in Time Probability of Default Model for Revolving Retail Credit Products: South African Case Study

A new methodology to derive IFRS 9 PiT PDs is proposed. The methodology first derives a PiT term structure with accompanying segmented term structures. Secondly, the calibration of credit scores using the Lorenz curve approach is used to create account-specific PD term structures. The PiT term structures are derived by using empirical information based on the most recent default information and account risk characteristics prior to default. Different PiT PD term structures are developed to capture the structurally different default risk patterns for different pools of accounts using segmentation. To quantify what a materially different term structure constitutes, three tests are proposed. Account specific PiT PDs are derived through the Lorenz curve calibration using the latest default experience and credit scores. The proposed methodology is illustrated on an actual dataset, using a revolving retail credit portfolio from a South African bank. The main advantages of the proposed methodology include the use of well-understood methods (e.g., Lorenz curve calibration, scorecards, term structure modelling) in the banking industry. Further, the inclusion of re-default events in the proposed IFRS 9 PD methodology will simplify the development of the accompanying IFRS 9 LGD model due to the reduced complexity for the modelling of cure cases. Moreover, attrition effects are naturally included in the PD term structures and no longer require a separate model. Lastly, the PD term structure is based on months since observation, and therefore the arrears cycle could be investigated as a possible segmentation.

Factors predicting citizens’ risk tolerance regarding earthquakes

<p>People tolerate different levels of risk from different hazards in their day-to-day life. Perceptions of risks and the amount of risk mitigation people desire for different hazards vary. Previous research shows that the psychometric properties of different hazards predict the level of risk people tolerate for various hazards, but not for earthquakes. Risk tolerance is likely to also be affected by factors other than the psychometric properties of hazards. This research tested how earthquakes score on psychometric risk properties compared to five other hazards, and aimed to replicate previous research on the risk factors predicting risk tolerance. Secondly, the research aimed to test if other factors, namely framing effects, risk perception and fatalistic thinking predict risk tolerance for earthquakes. In Study 1, participants from Wellington, New Zealand (N = 139) rated six different hazards (nuclear power, smoking, alcohol, driving, flying and earthquakes) on several risk characteristics and measures of risk tolerance. The results showed that the different hazards were perceived differently in terms of risk tolerance and that participants thought different risk mitigation actions were appropriate for the six hazards. Factor analysis showed that factors derived from risk characteristics did not predict risk tolerance. Study 2 (N = 173) assessed the effects of framing messages, risk perception and fatalism on risk tolerance (judgments about the firmness of the legislation; willingness to pay tax) and judgments about who should pay. The frames had an effect on participants’ concern about the risk, but did not affect the other measures. Generally participants thought that the Government should pay for strengthening buildings, however, those participants who perceived damage as preventable (fatalism measure) thought that private owners should pay for strengthening.</p>

Factors predicting citizens’ risk tolerance regarding earthquakes

<p>People tolerate different levels of risk from different hazards in their day-to-day life. Perceptions of risks and the amount of risk mitigation people desire for different hazards vary. Previous research shows that the psychometric properties of different hazards predict the level of risk people tolerate for various hazards, but not for earthquakes. Risk tolerance is likely to also be affected by factors other than the psychometric properties of hazards. This research tested how earthquakes score on psychometric risk properties compared to five other hazards, and aimed to replicate previous research on the risk factors predicting risk tolerance. Secondly, the research aimed to test if other factors, namely framing effects, risk perception and fatalistic thinking predict risk tolerance for earthquakes. In Study 1, participants from Wellington, New Zealand (N = 139) rated six different hazards (nuclear power, smoking, alcohol, driving, flying and earthquakes) on several risk characteristics and measures of risk tolerance. The results showed that the different hazards were perceived differently in terms of risk tolerance and that participants thought different risk mitigation actions were appropriate for the six hazards. Factor analysis showed that factors derived from risk characteristics did not predict risk tolerance. Study 2 (N = 173) assessed the effects of framing messages, risk perception and fatalism on risk tolerance (judgments about the firmness of the legislation; willingness to pay tax) and judgments about who should pay. The frames had an effect on participants’ concern about the risk, but did not affect the other measures. Generally participants thought that the Government should pay for strengthening buildings, however, those participants who perceived damage as preventable (fatalism measure) thought that private owners should pay for strengthening.</p>